research papers author order

• Affiliate Program

• UNITED STATES
• 台灣 (TAIWAN)
• TÜRKIYE (TURKEY)
• Academic Editing Services
• - Research Paper
• - Journal Manuscript
• - Dissertation
• - College & University Assignments
• Admissions Editing Services
• - Application Essay
• - Personal Statement
• - Recommendation Letter
• - Cover Letter
• - CV/Resume
• Business Editing Services
• - Business Documents
• - Report & Brochure
• - Website & Blog
• Writer Editing Services
• - Script & Screenplay
• Our Editors
• Client Reviews
• Editing & Proofreading Prices
• Wordvice Points
• Partner Discount
• Plagiarism Checker
• APA Citation Generator
• MLA Citation Generator
• Chicago Citation Generator
• Vancouver Citation Generator
• - APA Style
• - MLA Style
• - Chicago Style
• - Vancouver Style
• Writing & Editing Guide
• Academic Resources
• Admissions Resources

How to Order Authors in Scientific Papers

It’s rare that an article is authored by only one or two people anymore. In fact, the average original research paper has five authors these days. The growing list of collaborative research projects raises important questions regarding the author order for research manuscripts and the impact an author list has on readers’ perceptions.

With a handful of authors, a group might be inclined to create an author name list based on the amount of work contributed. What happens, though, when you have a long list of authors? It would be impractical to rank the authors by their relative contributions. Additionally, what if the authors contribute relatively equal amounts of work? Similarly, if a study was interdisciplinary (and many are these days), how can one individual’s contribution be deemed more significant than another’s?

Why does author order matter?

Although an author list should only reflect those who have made substantial contributions to a research project and its draft manuscript (see, for example, the authorship guidelines of the International Committee of Medical Journal Editors ), we’d be remiss to say that author order doesn’t matter. In theory, everyone on the list should be credited equally since it takes a team to successfully complete a project; however, due to industry customs and other practical limitations, some authors will always be more visible than others.

The following are some notable implications regarding author order.

• The “first author” is a coveted position because of its increased visibility. This author is the first name readers will see, and because of various citation rules, publications are usually referred to by the name of the first author only. In-text or bibliographic referencing rules, for example, often reduce all other named authors to “et al.” Since employers use first-authorship to evaluate academic personnel for employment, promotion, and tenure, and since graduate students often need a number of first-author publications to earn their degree, being the lead author on a manuscript is crucial for many researchers, especially early in their career.
• The last author position is traditionally reserved for the supervisor or principal investigator. As such, this person receives much of the credit when the research goes well and the flak when things go wrong. The last author may also be the corresponding author, the person who is the primary contact for journal editors (the first author could, however, fill this role as well, especially if they contributed most to the work).
• Given that there is no uniform rule about author order, readers may find it difficult to assess the nature of an author’s contribution to a research project. To address this issue, some journals, particularly medical ones, nowadays insist on detailed author contribution notes (make sure you check the target journal guidelines before submission to find out how the journal you are planning to submit to handles this). Nevertheless, even this does little to counter how strongly citation rules have enhanced the attention first-named authors receive.

Common Methods for Listing Authors

The following are some common methods for establishing author order lists.

• Relative contribution. As mentioned above, the most common way authors are listed is by relative contribution. The author who made the most substantial contribution to the work described in an article and did most of the underlying research should be listed as the first author. The others are ranked in descending order of contribution. However, in many disciplines, such as the life sciences, the last author in a group is the principal investigator or “senior author”—the person who often provides ideas based on their earlier research and supervised the current work.
• Alphabetical list . Certain fields, particularly those involving large group projects, employ other methods . For example, high-energy particle physics teams list authors alphabetically.
• Multiple “first” authors . Additional “first” authors (so-called “co-first authors”) can be noted by an asterisk or other symbols accompanied by an explanatory note. This practice is common in interdisciplinary studies; however, as we explained above, the first name listed on a paper will still enjoy more visibility than any other “first” author.
• Multiple “last” authors . Similar to recognizing several first authors, multiple last authors can be recognized via typographical symbols and footnotes. This practice arose as some journals wanted to increase accountability by requiring senior lab members to review all data and interpretations produced in their labs instead of being awarded automatic last-authorship on every publication by someone in their group.
• Negotiated order . If you were thinking you could avoid politics by drowning yourself in research, you’re sorely mistaken. While there are relatively clear guidelines and practices for designating first and last authors, there’s no overriding convention for the middle authors. The list can be decided by negotiation, so sharpen those persuasive argument skills!

As you can see, choosing the right author order can be quite complicated. Therefore, we urge researchers to consider these factors early in the research process and to confirm this order during the English proofreading process, whether you self-edit or received manuscript editing or paper editing services , all of which should be done before submission to a journal. Don’t wait until the manuscript is drafted before you decide on the author order in your paper. All the parties involved will need to agree on the author list before submission, and no one will want to delay submission because of a disagreement about who should be included on the author list, and in what order (along with other journal manuscript authorship issues).

On top of that, journals sometimes have clear rules about changing authors or even authorship order during the review process, might not encourage it, and might require detailed statements explaining the specific contribution of every new/old author, official statements of agreement of all authors, and/or a corrigendum to be submitted, all of which can further delay the publication process. We recommend periodically revisiting the named author issue during the drafting stage to make sure that everyone is on the same page and that the list is updated to appropriately reflect changes in team composition or contributions to a research project.

• Translators
• Graphic Designers

Please enter the email address you used for your account. Your sign in information will be sent to your email address after it has been verified.

How to Order and Format Author Names in Scientific Papers

As the world becomes more interconnected, the production of knowledge increasingly relies on collaboration. Scientific papers, the primary medium through which researchers communicate their findings, often feature multiple authors. However, authorship isn't merely a reflection of those who contributed to a study but often denotes prestige, recognition, and responsibility. In academic papers, the order of authors is not arbitrary. It can symbolize the level of contribution and the role played by each author in the research process. Deciding on the author order can sometimes be a complex and sensitive issue, making it crucial to understand the different roles and conventions of authorship in scientific research. This article will explore the various types of authors found in scientific papers, guide you on how to correctly order and format author names, and offer insights to help you navigate this critical aspect of academic publishing.

The first author

The first author listed in a scientific paper is typically the person who has made the most substantial intellectual contribution to the work. This role is often filled by a junior researcher such as a Ph.D. student or postdoctoral fellow, who has been intimately involved in almost every aspect of the project.

The first author usually plays a pivotal role in designing and implementing the research, including the formation of hypotheses, experimental design, data collection, data analysis, and interpretation of the findings. They also commonly take the lead in manuscript preparation, writing substantial portions of the paper, including the often-challenging task of turning raw data into a compelling narrative.

In academia, first authorship is a significant achievement, a clear demonstration of a researcher's capabilities and dedication. It indicates that the researcher possesses the skills and tenacity to carry a project from inception to completion. This position can dramatically impact a researcher's career trajectory, playing a critical role in evaluations for promotions, grants, and future academic positions.

However, being the first author is not just about prestige or professional advancement. It carries a weight of responsibility. The first author is generally expected to ensure the integrity and accuracy of the data presented in the paper. They are often the person who responds to reviewers' comments during the peer-review process and makes necessary revisions to the manuscript.

Also, as the first author, it is typically their duty to address any questions or critiques that may arise post-publication, often having to defend the work publicly, even years after publication.

Thus, first authorship is a role that offers significant rewards but also requires a strong commitment to uphold the principles of scientific integrity and transparency. While it's a coveted position that can be a steppingstone to career progression, the associated responsibilities and expectations mean that it should not be undertaken lightly.

The middle authors

The middle authors listed on a scientific paper occupy an essential, albeit sometimes ambiguous, role in the research project. They are typically those who have made significant contributions to the project, but not to the extent of the first author. This group often includes a mix of junior and senior researchers who have provided key input, assistance, or resources to the project.

The roles of middle authors can be quite diverse. Some might be involved in specific aspects of data collection or analysis. Others may bring specialized knowledge or technical skills essential to the project, providing expertise in a particular methodology, statistical analysis, or experimental technique. There might also be middle authors who have contributed vital resources to the project, such as unique reagents or access to a particular patient population.

In some fields, the order of middle authors reflects the degree of their contribution. The closer a middle author is to the first position, the greater their involvement, with the second author often having made the next largest contribution after the first author. This order may be negotiated among the authors, requiring clear communication and consensus.

However, in other disciplines, particularly those where large collaborative projects are common, the order of middle authors may not necessarily reflect their level of contribution. In such cases, authors might be listed alphabetically, or by some other agreed-upon convention. Therefore, it's crucial to be aware of the norms in your specific field when deciding the order of middle authors.

Being a middle author in a scientific paper carries less prestige and responsibility than being a first or last author, but it is by no means a minor role. Middle authors play a crucial part in the scientific endeavor, contributing essential expertise and resources. They are integral members of the research team whose collective efforts underpin the progress and achievements of the project. Without their diverse contributions, the scope and impact of scientific research would be significantly diminished.

The last author

In the listing of authors on a scientific paper, the final position carries a unique significance. It is typically occupied by the senior researcher, often the head of the laboratory or the principal investigator who has supervised the project. While they might not be involved in the day-to-day aspects of the work, they provide overarching guidance, mentorship, and often the resources necessary for the project's fruition.

The last author's role is multidimensional, often balancing the responsibilities of project management, funding acquisition, and mentorship. They guide the research's direction, help troubleshoot problems, and provide intellectual input to the project's design and interpretation of results. Additionally, they usually play a key role in the drafting and revision of the manuscript, providing critical feedback and shaping the narrative.

In academia, the last author position is a symbol of leadership and scientific maturity. It indicates that the researcher has progressed from being a hands-on contributor to someone who can guide a team, secure funding, and deliver significant research projects. Being the last author can have substantial implications for a researcher's career, signaling their ability to oversee successful projects and mentor the next generation of scientists.

However, along with prestige comes significant responsibility. The last author is often seen as the guarantor of the work. They are held accountable for the overall integrity of the study, and in cases where errors or issues arise, they are expected to take the lead in addressing them.

The convention of the last author as the senior researcher is common in many scientific disciplines, especially in the life and biomedical sciences. However, it's important to note that this is not a universal standard. In some fields, authors may be listed purely in the order of contribution or alphabetically. Therefore, an understanding of the specific norms and expectations of your scientific field is essential when considering author order.

In sum, the position of the last author, much like that of the first author, holds both honor and responsibility, reflecting a leadership role that goes beyond mere intellectual contribution to include mentorship, management, and accountability.

Formatting author names

When it comes to scientific publishing, details matter, and one such detail is the correct formatting of author names. While it may seem like a minor concern compared to the intellectual challenges of research, the proper formatting of author names is crucial for several reasons. It ensures correct attribution of work, facilitates accurate citation, and helps avoid confusion among researchers in the same field. This section will delve deeper into the conventions for formatting author names, offering guidance to ensure clarity and consistency in your scientific papers.

Typically, each author's full first name, middle initial(s), and last name are listed. It's crucial that the author's name is presented consistently across all their publications to ensure their work is correctly attributed and easily discoverable.

Here is a basic example following a common convention:

• Standard convention: John D. Smith

However, conventions can vary depending on cultural naming practices. In many Western cultures, the first name is the given name, followed by the middle initial(s), and then the family name. On the other hand, in many East Asian cultures, the family name is listed first.

Here is an example following this convention:

• Asian convention: Wang Xiao Long

When there are multiple authors, their names are separated by commas. The word "and" usually precedes the final author's name.

Here's how this would look:

• John D. Smith, Jane A. Doe, and Richard K. Jones

However, author name formatting can differ among journals. Some may require initials instead of full first names, or they might have specific guidelines for handling hyphenated surnames or surnames with particles (e.g., "de," "van," "bin"). Therefore, it's always important to check the specific submission guidelines of the journal to which you're submitting your paper.

Moreover, the formatting should respect each author's preferred presentation of their name, especially if it deviates from conventional Western naming patterns. As the scientific community becomes increasingly diverse and global, it's essential to ensure that each author's identity is accurately represented.

In conclusion, the proper formatting of author names is a vital detail in scientific publishing, ensuring correct attribution and respect for each author's identity. It may seem a minor point in the grand scheme of a research project, but getting it right is an essential part of good academic practice.

The concept of authorship in scientific papers goes well beyond just listing the names of those involved in a research project. It carries critical implications for recognition, responsibility, and career progression, reflecting a complex nexus of contribution, collaboration, and intellectual leadership. Understanding the different roles, correctly ordering the authors, and appropriately formatting the names are essential elements of academic practice that ensure the rightful attribution of credit and uphold the integrity of scientific research.

Navigating the terrain of authorship involves managing both objective and subjective elements, spanning from the universally acknowledged conventions to the nuances particular to different scientific disciplines. Whether it's acknowledging the pivotal role of the first author who carried the project from the ground up, recognizing the valuable contributions of middle authors who provided key expertise, or highlighting the mentorship and leadership role of the last author, each position is an integral piece in the mosaic of scientific authorship.

Furthermore, beyond the order of authors, the meticulous task of correctly formatting the author names should not be underestimated. This practice is an exercise in precision, respect for individual identity, and acknowledgement of cultural diversity, reflecting the global and inclusive nature of contemporary scientific research.

As scientific exploration continues to move forward as a collective endeavor, clear and equitable authorship practices will remain crucial. These practices serve not only to ensure that credit is assigned where it's due but also to foster an environment of respect and transparency. Therefore, each member of the scientific community, from fledgling researchers to seasoned scientists, would do well to master the art and science of authorship in academic publishing. After all, it is through this collective recognition and collaboration that we continue to expand the frontiers of knowledge.

Header image by Jon Tyson .

Related Posts

Perfecting Your Thesis Statement

How to Cite a Website

• Academic Writing Advice
• All Blog Posts
• Writing Advice
• Admissions Writing Advice
• Book Writing Advice
• Short Story Advice
• Employment Writing Advice
• Business Writing Advice
• Web Content Advice
• Article Writing Advice
• Magazine Writing Advice
• Grammar Advice
• Dialect Advice
• Editing Advice
• Freelance Advice
• Legal Writing Advice
• Poetry Advice
• Graphic Design Advice
• Logo Design Advice
• Translation Advice
• Blog Reviews
• Short Story Award Winners
• Scholarship Winners

Advance your scientific manuscript with expert editing

When you choose to publish with PLOS, your research makes an impact. Make your work accessible to all, without restrictions, and accelerate scientific discovery with options like preprints and published peer review that make your work more Open.

• PLOS Biology
• PLOS Climate
• PLOS Complex Systems
• PLOS Computational Biology
• PLOS Digital Health
• PLOS Genetics
• PLOS Global Public Health
• PLOS Medicine
• PLOS Mental Health
• PLOS Neglected Tropical Diseases
• PLOS Pathogens
• PLOS Sustainability and Transformation
• PLOS Collections
• About This Blog
• Official PLOS Blog
• EveryONE Blog
• Speaking of Medicine
• PLOS Biologue
• Absolutely Maybe
• DNA Science
• PLOS ECR Community
• All Models Are Wrong
• About PLOS Blogs

What author order can (and cannot) tell us: Understanding contributorship

Written by Lindsay Morton

Welcome back to the second in our three-part series on academic credit. In this post, we focus on identifying researchers’ specific contributions to a research project, and explore how those contributions are reflected on a published paper. Authorship is central to the reward system of science and directly impacts each researchers’ career prospects. Yet standards for allocating authorship are variable, and often opaque. What types of contributions merit inclusion on an author list? How are we to understand the contributions of each researcher who is included on the list?

Identifying specific author contributions

In the biological and medical sciences, the degree of public credit a researcher receives for a publication is based on their position within the author list. While the significance of author order varies across disciplines and cultures, traditionally, there are two highly valued and much-coveted positions: first author, credited with conceptualizing and executing the central parts of the study, and last author, occupying the most senior, supervisory position. That can be problematic, because it does not provide a consistent and fair way to acknowledge the essential contributions of midlist authors. An average author list cannot communicate, for example, who developed critical methods, collected the data, ran the analysis, or wrote the first draft. In some cases, an author list may also include honorary authors, either as an expression of esteem, in an attempt to leverage a famous name, or because the honorary author has asked to be included in all publications within their sphere.

The inadequacy of the author list as a vehicle for expressing author contribution is also evident in team science. As research becomes increasingly cross-disciplinary and complex, in many cases, it’s no longer possible for one person to lead and execute all aspects of a study. In team science, instead of organizing themselves hierarchically, researchers work together, with two or more equal partners taking on the responsibilities of a senior researcher within their specific areas of expertise, for example data collection and stewardship, statistics and design, coding, or methodological development. Our systems for allocating and representing academic credit have not kept pace with the ways researchers work today.

The importance and yet the ambiguity of the author list creates, at the very least, inaccurate and unfair perceptions about the contributions and capabilities of the researchers involved. It can also conceal bias and work to keep researchers from under-represented groups in midlist, junior roles. Because the allocation of credit is so central to how a research scientist is perceived, and to the future of their career, a fair and accurate representation of each author’s contribution is vital.

Solution: Tracking all author contributions with CRediT

CRediT (Contributor Roles Taxonomy) is a universal, community-developed open classification system that uses 14 different roles to describe the aspects of scientific authorship, from conceptualization to review and editing. Each author listed on a manuscript is assigned one or more taxonomic roles. Role assignments appear on the final published research article and are encoded into article meta-data where they can be harvested by databases and indexers.

The granularity of the CRediT taxonomy diminishes the importance of author order. For example, tenure applicants need not be evaluated on how many times they were listed as first author, but on their specific contributions to each work. The CRediT taxonomy also reinforces the author qualification guidelines by clearly highlighting instances of honorary authorship to authors at submission, giving them the opportunity to pause and consider the composition of their author list.

The CRediT taxonomy distinguishes itself from other, publisher- or discipline-specific author taxonomies in that it is both broadly applicable within the sciences and widely accepted, enabling it to establish norms and shared understanding across publishers, funders, and universities.

PLOS was part of the working group that originally developed and tested the CRediT taxonomy. When the system was finalized , PLOS transitioned from our previous, publisher-specific taxonomy to the new tool.

A meta-analysis of the CRediT taxonomy

CRediT has opened new avenues for meta-research, enabling scientists to better understand not only how each other contributed to the work, but to begin to identify and interpret patterns in contributorship that expose larger truths about the way science operates, and can point the way toward more efficient, robust and inclusive scientific practices. In the video below, we chat with Dr Cassidy Sugimoto and Dr Vincent Larivière about their recent study “Investigating the division of scientific labor using the Contributor Roles Taxonomy (CRediT)” and discuss ideas for future studies.

Investigating the division of scientific labor using the Contributor Roles Taxonomy (CRediT)  Vincent Larivière, David Pontille, Cassidy R. Sugimoto 

More opportunities for authorship

As the CRediT taxonomy helps to illustrate, writing articles is just one small part of conducting research. In addition to properly allocating credit for traditional research articles and peer review, Open Science also offers new opportunities to surface, share, and receive credit for more of the research process, including both open data and open methods, such as Registered Reports, Lab and Study Protocols, Methods Research Articles, and linked code. Sharing these research outputs as stand-alone resources allows them to accumulate citations in their own right, independent of the main research article, and increases discoverability by creating more points of entry. At the same time, making research artifacts public enhances trust in related research articles. Over time, a pattern of openness can help to build a reputation for high-quality research, collaborative sharing, and leadership.

In the next post in this series, we’ll discuss the importance of peer review, and how we can better acknowledge and reward the contributions peer reviewers make to published research. 

Written by Lindsay Morton In the sciences, credit counts. As a research scientist, your personal record directly determines your future opportunities in…

Written by Lindsay Morton In this third and final entry in our three-part series on academic credit, we turn our attention to…

Written by Veronique Kiermer and Iain Hrynaszkiewicz Earlier this month the Open Science Monitoring Initiative shared a draft of Open Science monitoring…

The Ethics of Manuscript Authorship: Best Practices for Attribution

The International Committee of Medical Journal Editors (ICMJE), has established four criteria that each author of a paper should meet. This article and our free white paper, Credit Where Credit Is Due, detail and explore these criteria.

Updated on July 25, 2013

Authorship is becoming an increasingly complicated issue as research collaborations proliferate, the importance of citations for tenure and grants persists, and no consensus on a definition is reached. This issue is fraught with ethical implications because clearly conveying who is responsible for published work is integral to scientific integrity.

Many journals currently adhere to the guidelines of the International Committee of Medical Journal Editors (ICMJE), which has established four criteria that each author of a paper should meet:

• Significant involvement in study conception/design, data collection, or data analysis/interpretation;
• Involvement in drafting or revising manuscript;
• Approval of final version of manuscript for publication; and
• Responsibility for accuracy and integrity of all aspects of research.

Download our free white paper on authorship for a copy of these criteria and our suggestions for choosing authors appropriately.

Moreover, by the ICMJE definition, authors “should be able to identify which co-authors are responsible for specific other parts of the work...[and] have confidence in the integrity of the contributions of their co-authors.” Based on this description and the fourth criterion, authorship implies not only past individual contribution to a research project but also ongoing joint accountability for that project. As a result, authors may share fame or infamy, depending on the validity of the work.

The ICMJE also notes that an author must have made “substantive intellectual contributions” to the manuscript. Creative input is thus more eligible for authorship than purely mechanical work. A technician merely acquiring data, a senior researcher only obtaining funding or providing supervision, a collaborator solely providing a new reagent or samples, and other research-related but non-creative tasks do not merit authorship on their own. These individuals and their contributions could be cited in an acknowledgments section instead.

Despite this clearly outlined definition, numerous issues (including ethical concerns) have arisen regarding authorship attribution. These issues have emerged partly because many journals continue to adhere to their own guidelines or to various modified versions of the ICMJE criteria (see, for example, Table 2 in this EMBO reports article ) and partly because the ICMJE guidelines may be insufficient, as argued at the 2012 International Workshop on Contributorship and Scholarly Attribution . A selection of topics that is specifically pertinent to academia is as follows:

Contribution ambiguity

The specific roles of individual authors in a research project are not always clear, especially when a manuscript is attributed to a large group. To address this problem, several journals (such as PNAS ) require public disclosure of the specific contributions of each author. Some have also suggested the establishment of a database or the use of existing research community networks (such as ResearchGate ) to track contributions. This tracking is particularly relevant because scholarly output is increasingly defined by metrics beyond paper citations (also known as altmetrics ). To further clarify the roles of authors and encourage integrity, certain journals require a public guarantor for each article, or an author who takes responsibility for the entire research project, including conception, data acquisition and analysis, and publication. Ambiguity surrounding authorship may also arise from the publication of papers by researchers with the same name but could be minimized by the use of an ORCID identifier .

Authorship order

The meaning of the list order of authors on a paper varies between fields. In certain areas, the list is alphabetical, whereas in others, the convention includes citing every person who contributed in some way to the project (which may conflict with the ICMJE guidelines). In many disciplines, the author order indicates the magnitude of contribution, with the first author adding the most value and the last author representing the most senior, predominantly supervisory role. In this model, disputes may arise regarding who merits sole or shared first authorship. The Committee on Publication Ethics recommends that researchers discuss authorship order from project initiation to manuscript submission, revising as necessary, and record each decision in writing. Furthermore, contributions could be quantified, such as based on a points system (subscription required) , to facilitate authorship decisions.

Honorary authorship

Honorary authorship is given to an individual despite a lack of substantial contributions to a research project. One form, gift authorship , is bestowed out of respect for or gratitude to an individual. For example, in Asian cultures, departmental heads or senior researchers may be added to a paper regardless of their involvement in the research. Another form, guest authorship , may be used for multiple purposes, including to increase the apparent quality of a paper by adding a well-known name or to conceal a paper's industry ties by including an academic author. Additional issues regarding honorary authorship are the inclusion of an author on a manuscript without his or her permission (which is often prevented by journal guidelines that require the consent of all authors) and coercive authorship , which typically consists of a senior researcher (such as a dissertation advisor) forcing a junior researcher (such as a graduate student) to include a gift or guest author.

Honorary authorship is a major ethical issue in scholarly publication, as this dishonest practice was found in approximately 18% of articles in six medical journals in 2008. From the standpoint of journals, lists of specific contributions may help to minimize this practice, as could reminders that all authors are accountable for the integrity of a published work. The institution of double-blind peer review could also decrease the influence of authors' prominence in the field on journal acceptance. At research institutions, guidelines could equate honorary authorship with research misconduct. Additionally, the donation of resources to a project without the expectation of automatic authorship could be encouraged by the use of contributions, including those listed in acknowledgments sections, as a measure of output, as discussed above.

In all cases described here, more universal standards for manuscript authorship will be critical for fostering good practices. As you write and review manuscripts, remember the best practices found in this white paper , and consider ways to bring authorship credit and accountability to the attention of your colleagues and readers.

Michaela Panter, PhD

See our "Privacy Policy"

Logo Left Content

Logo Right Content

Stanford University School of Medicine blog

Who’s on first? Duking out scientific paper authorship order

It's been over 80 years, but Abbott and Costello's famous comedic skit " Who's on First" lives on in our collective memories. Their increasingly ridiculous conversation about baseball and the name of the player on first base can still reliably produce a giggle in many circles.

But in the lab , questions about order can be anything but a laughing matter -- particularly when it comes to the list of authors on a scientific paper. Many nonscientists don't realize that, traditionally, the most important places on the roster are the first -- indicating the person who conceived of and performed most of the research discussed in the paper -- and the last -- a hallowed place reserved for the senior scientist in whose laboratory the research was conducted.

In the biomedical research world, having many "first authorship" papers is largely seen as an indication of a scientist's skill and tenacity; researchers with many "senior authorship" papers often garner a reputation of strong leadership and high productivity.

But as the National Institutes of Health and other funders increasingly reward collaborative research, and scientific projects grow more complex, determining authorship order is becoming less clear. Some are even venturing outside the lab to do so.

Authorship smash down

Recently Stanford researcher Garry Nolan , PhD, tweeted about an unconventional way two researchers in his laboratory who had each contributed equally to a study decided who should be listed first on the print version of the paper.

The researchers, graduate students Bokai Zhu and Yunhao Bai , played three games of Mario Kart's Super Smash Bros. ; the winner, Bai, was awarded top billing, and was permitted to list himself as the first author on his resume (called a curriculum vitae , or CV, in science circles). A footnote to the authorship list notes that Zhu and Bai contributed equally to the paper's contents and can consider themselves co-first authors on their CVs.

"All the important results are already in the paper itself . We thought, why not use this opportunity to have some fun?" Zhu said, in a recent conversation with my colleague Lisa Kim for her new video series " 90 seconds with Lisa Kim ."

"As science has become more multidisciplinary and collaborative, it becomes more difficult to determine who should receive credit for a group's findings," Nolan said. "It's not unusual for a scientific paper to have a dozen or more authors from multiple labs or institutions, and assigning authorship order becomes increasingly difficult."

In response, scientists like Zhu and Bai are becoming more creative. As on their paper, footnotes are increasingly used in print or online versions of a study to indicate authors (both first and last) who contributed equally to the paper's findings. "There's also a movement toward agreeing that each co-first or co-last author may list themselves as first or last author on their own CV," Nolan said.

Agreeing to ... agree

But as long as the "first or last" rubric remains, researchers are going to have to come to ways to agree. Much hinges on the ability of the authors to collaboratively decide whose careers could benefit the most from the extra boost. Sometimes that might mean that a lab leader cedes last authorship to a senior lab member who will soon be launching a job hunt, or for a postdoctoral researcher to allow a soon-to-graduate PhD student to list themselves first.

"To me, a key purpose of an academic institution is to advance the careers of your students, teach them the ways of science, and hopefully impart some wisdom while also doing important scientific work," Nolan said. "If a funding institution is going to demand cooperation and collaboration, we as scientists need to adapt. Right now, it depends on people being gracious."

Or, perhaps, a friendly video game smackdown? Maybe next time they'll play Mario Super Sluggers , instead!

Photo by  Ryan Quintal

Popular posts

Mental health and menopause: There are connections and solutions

Could the avian flu be our next pandemic threat?

Office of the Provost

Guidance on authorship in scholarly or scientific publications, general principles.

The public’s trust in and benefit from academic research and scholarship relies upon all those involved in the scholarly endeavor adhering to the highest ethical standards, including standards related to publication and dissemination of findings and conclusions.

Accordingly, all scholarly or scientific publications involving faculty, staff, students and/or trainees arising from academic activities performed under the auspices of Yale University must include appropriate attribution of authorship and disclosure of relevant affiliations of those involved in the work, as described below.

These publications, which, for the purposes of this guidance, include articles, abstracts, manuscripts submitted for publication, presentations at professional meetings, and applications for funding, must appropriately acknowledge contributions of colleagues involved in the design, conduct or dissemination of the work by neither overly attributing contribution nor ignoring meaningful contributions.

Financial and other supporting relationships of those involved in the scholarly work must be transparent and disclosed in publications arising from the work.

Authorship Standards

Authorship of a scientific or scholarly paper should be limited to those individuals who have contributed in a meaningful and substantive way to its intellectual content. All authors are responsible for fairly evaluating their roles in the project as well as the roles of their co-authors to ensure that authorship is attributed according to these standards in all publications for which they will be listed as an author.

Requirement for Attribution of Authorship

Each author should have participated sufficiently in the work to take public responsibility for its content. All co-authors should have been directly involved in all three of the following:

• planning and contribution to some component (conception, design, conduct, analysis, or interpretation) of the work which led to the paper or interpreting at least a portion of the results;
• writing a draft of the article or revising it for intellectual content; and
• final approval of the version to be published.  All authors should review and approve the manuscript before it is submitted for publication, at least as it pertains to their roles in the project.

Some diversity exists across academic disciplines regarding acceptable standards for substantive contributions that would lead to attribution of authorship. This guidance is intended to allow for such variation to disciplinary best practices while ensuring authorship is not inappropriately assigned.

Lead Author

The first author is usually the person who has performed the central experiments of the project. Often, this individual is also the person who has prepared the first draft of the manuscript. The lead author is ultimately responsible for ensuring that all other authors meet the requirements for authorship as well as ensuring the integrity of the work itself. The lead author will usually serve as the corresponding author.

Co-Author(s)

Each co-author is responsible for considering his or her role in the project and whether that role merits attribution of authorship. Co-authors should review and approve the manuscript, at least as it pertains to their roles in the project.

External Collaborators, Including Sponsor or Industry Representatives

Individuals who meet the criteria for authorship should be included as authors irrespective of their institutional affiliations. In general, the use of “ghostwriters” is prohibited, i.e., individuals who have contributed significant portions of the text should be named as authors or acknowledged in the final publication. Industry representatives or others retained by industry who contribute to an article and meet the requirements for authorship or acknowledgement must be appropriately listed as contributors or authors on the article and their industry affiliation must be disclosed in the published article.

Acknowledgements

Individuals who do not meet the requirements for authorship but who have provided a valuable contribution to the work should be acknowledged for their contributing role as appropriate to the publication.

Courtesy or Gift Authorship

Individuals do not satisfy the criteria for authorship merely because they have made possible the conduct of the research and/or the preparation of the manuscript. Under no circumstance should individuals be added as co-authors based on the individual’s stature as an attempt to increase the likelihood of publication or credibility of the work. For example, heading a laboratory, research program, section, or department where the research takes place does not, by itself, warrant co-authorship of a scholarly paper. Nor should “gift” co-authorship be conferred on those whose only contributions have been to provide, for example, routine technical services, to refer patients or participants for a study, to provide a valuable reagent, to assist with data collection and assembly, or to review a completed manuscript for suggestions. Although not qualifying as co-authors, individuals who assist the research effort may warrant appropriate acknowledgement in the completed paper.

Senior faculty members should be named as co-authors on work independently generated by their junior colleagues only if they have made substantial intellectual contributions to the experimental design, interpretation of findings and manuscript preparation.

Authorship Disputes

Determinations of authorship roles are often complex, delicate and potentially controversial. To avoid confusion and conflict, discussion of attribution should be initiated early in the development of any collaborative publication. For disputes that cannot be resolved amicably, individuals may seek the guidance of the dean of their school or the cognizant deputy provost in the Faculty of Arts & Sciences.

Disclosure of Research Funding and Other Support

In all scientific and scholarly publications and all manuscripts submitted for publication, authors should acknowledge the sources of support for all activities leading to and facilitating preparation of the publication or manuscript, including, but not limited to:

• grant, contract, and gift support;
• salary support if other than institutional funds. Note that salary support that is provided to the University by an external entity does not constitute institutional funds by virtue of being distributed by the University; and
• technical or other support if substantive and meaningful to the completion of the project.

Disclosure of Financial Interests and External Activities

Authors should fully disclose related financial interests and outside activities in publications (including articles, abstracts, manuscripts submitted for publication), presentations at professional meetings, and applications for funding.

In addition, authors should comply with the disclosure requirements of the University’s Committee on Conflict of Interest.

ORI  Introduction  to RCR: Chapter 9. Authorship and Publication

• was intimately involved in the conception and design of the research,
• assumed responsibility for data collection and interpretation,
• participated in drafting the publication, and
• approved the final version of the publication.
• the accuracy of the data,
• the names listed as authors (all deserve authorship and no one has been neglected),
• approval of the final draft by all authors, and
• handling all correspondence and responding to inquiries.

ICJME Statement on Authorship

Email Updates

View the latest institution tables

View the latest country/territory tables

The A to Z of paper authorship

It's bad news for Z but A is AOK for authors listed alphabetically. 

Gemma Conroy

Credit: PhotoAlto sas / Alamy Stock Photo

It's bad news for Z but A is AOK for authors listed alphabetically.

21 August 2018

PhotoAlto sas / Alamy Stock Photo

To keep authorship fair, journals in all fields should list authors based on their contribution rather than in alphabetical order.

That’s the conclusion of Matthias Weber, an economist at the University of St. Gallen , who looked at 10 different studies examining the effects of alphabetical authorship in a literature review published in Research Evaluation .

Matthias Weber

The alphabetical ordering of names has been examined for its effect on career prospects, happiness and even life expectancy. In academia, name-ordering in authorship has long been a contentious issue. Weber’s analysis suggests that in fields which continue to follow the convention of listing authors by alphabetical order, researchers with surnames beginning with letters early in the alphabet continue to derive advantage. And those whose names appear late in the alphabet may shy away from collaborations due to the lack of prominence their work is likely to receive.

Weber says that recognition and visibility play an important role in academic hiring decisions and promotion opportunities. If authors are listed based on their surname instead of their contribution, this can lead to alphabetical discrimination.

“This is unfair,” says Weber. “As a society, we expect the best candidates to fill research positions, not people with a particular surname.”

Oblivion in et.al. .

In papers with more than two authors, those with A-surnames are more visible because they are listed first, while authors whose surnames start late in the alphabet, listed last, can disappear when the first author is named and the rest are relegated to the abbreviation ‘et al.’.

"Studies show that scholars behave differently under an alphabetical norm than under a contribution-based norm. For instance, scholars late in the alphabet write papers on their best ideas alone more often than scholars earlier in the alphabet in fields where an alphabetical norm prevails. Scholars late in the alphabet are also less likely to collaborate with multiple others under an alphabetical norm," writes Weber on the LSE Impact Blog .

In most disciplines, the order of authors in journal articles is determined by their contribution to the research. But in fields such as high energy physics, where there are often over a thousand authors on a research paper , alphabetical listing is the norm.

According to Kevin Varvell, a high energy particle physicist at the University of Sydney , this alphabetical name ordering can create barriers for researchers when they apply for grants or jobs outside their field. In these scenarios, researchers are often evaluated by people in other disciplines which use contribution-based authorship.

“Evaluators may apply metrics or prejudices from their own fields,” says Varvell, who is a collaborator on the ATLAS Experiment at CERN . He says that when researchers in physics or maths don’t get funding, they may wonder whether the evaluators have misunderstood the extent of their contributions due to the alphabetical authorship convention.

A is for advantage

Alphabetical discrimination is particularly rife in economics where alphabetical author listing is the norm, Weber notes. A 2008 study investigating the link between surnames and faculty positions at top economics departments in the United States showed that full professors with A-surnames were 20% more likely to work in a top-ranked department than those with a Z-surname.

The study also revealed that A-surname authors receive more abstract views and paper downloads than Z-surname authors. Thanks to the high visibility conferred by their name, A-authors also publish more papers and are more likely to be recognised as ‘experts’ in their field.

Weber points out that the visibility advantage can also fuel discrimination against women, early-career researchers and authors from diverse ethnic backgrounds. Many Chinese surnames, for example, begin with letters late in the alphabet.

But Weber found researchers are reacting in a number of ways to overcome the hurdles associated with having surnames late in the alphabet. Some may shy away from working in large teams to avoid their name being lost in ‘et al.’. Others resort to manipulating their surnames to move closer to the front.

A fix for a prefix

This name tweaking is particularly apparent when considering surnames with prefixes, such as ‘de’ and ‘van’. A 2008 study found that authors with prefixes starting with ‘V’ are less likely to use their full surname in journal articles than authors with D-prefixes.

The same study found that in surnames using Greek letters, which can be transcribed into more than one letter in the English alphabet, authors were more likely to transcribe their surname so that it’s closer to the beginning of the alphabet.

To prevent alphabetical discrimination, Weber thinks that authors in all disciplines should be ordered according to their relative contribution. In this style, the main contributor is listed first and the most senior author in the team is listed last.

But Varvell says that this system could be difficult to implement in fields with thousands of collaborators taking part in a single experiment. The ATLAS Experiment at CERN, for example, involves 3,000 researchers and is one of the largest scientific collaborations ever undertaken. Trying to single out researchers that have contributed the most could spark rivalry within teams and leave out those who have spent years working to get the experiment off the ground, Varvell warns.

“The alphabetical system at least acknowledges the efforts of everyone involved,” he says.

Data analysis by Willem Sijp

Defining the Role of Authors and Contributors

Page Contents

• Why Authorship Matters
• Who Is an Author?
• Non-Author Contributors
• Artificial Intelligence (AI)-Assisted Technology

1. Why Authorship Matters

Authorship confers credit and has important academic, social, and financial implications. Authorship also implies responsibility and accountability for published work. The following recommendations are intended to ensure that contributors who have made substantive intellectual contributions to a paper are given credit as authors, but also that contributors credited as authors understand their role in taking responsibility and being accountable for what is published.

Editors should be aware of the practice of excluding local researchers from low-income and middle-income countries (LMICs) from authorship when data are from LMICs. Inclusion of local authors adds to fairness, context, and implications of the research. Lack of inclusion of local investigators as authors should prompt questioning and may lead to rejection.

Because authorship does not communicate what contributions qualified an individual to be an author, some journals now request and publish information about the contributions of each person named as having participated in a submitted study, at least for original research. Editors are strongly encouraged to develop and implement a contributorship policy. Such policies remove much of the ambiguity surrounding contributions, but leave unresolved the question of the quantity and quality of contribution that qualify an individual for authorship. The ICMJE has thus developed criteria for authorship that can be used by all journals, including those that distinguish authors from other contributors.

2. Who Is an Author?

The ICMJE recommends that authorship be based on the following 4 criteria:

• Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND
• Drafting the work or reviewing it critically for important intellectual content; AND
• Final approval of the version to be published; AND
• Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

In addition to being accountable for the parts of the work done, an author should be able to identify which co-authors are responsible for specific other parts of the work. In addition, authors should have confidence in the integrity of the contributions of their co-authors.

All those designated as authors should meet all four criteria for authorship, and all who meet the four criteria should be identified as authors. Those who do not meet all four criteria should be acknowledged—see Section II.A.3 below. These authorship criteria are intended to reserve the status of authorship for those who deserve credit and can take responsibility for the work. The criteria are not intended for use as a means to disqualify colleagues from authorship who otherwise meet authorship criteria by denying them the opportunity to meet criterion #s 2 or 3. Therefore, all individuals who meet the first criterion should have the opportunity to participate in the review, drafting, and final approval of the manuscript.

The individuals who conduct the work are responsible for identifying who meets these criteria and ideally should do so when planning the work, making modifications as appropriate as the work progresses. We encourage collaboration and co-authorship with colleagues in the locations where the research is conducted. It is the collective responsibility of the authors, not the journal to which the work is submitted, to determine that all people named as authors meet all four criteria; it is not the role of journal editors to determine who qualifies or does not qualify for authorship or to arbitrate authorship conflicts. If agreement cannot be reached about who qualifies for authorship, the institution(s) where the work was performed, not the journal editor, should be asked to investigate. The criteria used to determine the order in which authors are listed on the byline may vary, and are to be decided collectively by the author group and not by editors. If authors request removal or addition of an author after manuscript submission or publication, journal editors should seek an explanation and signed statement of agreement for the requested change from all listed authors and from the author to be removed or added.

The corresponding author is the one individual who takes primary responsibility for communication with the journal during the manuscript submission, peer-review, and publication process. The corresponding author typically ensures that all the journal’s administrative requirements, such as providing details of authorship, ethics committee approval, clinical trial registration documentation, and disclosures of relationships and activities are properly completed and reported, although these duties may be delegated to one or more co-authors. The corresponding author should be available throughout the submission and peer-review process to respond to editorial queries in a timely way, and should be available after publication to respond to critiques of the work and cooperate with any requests from the journal for data or additional information should questions about the paper arise after publication. Although the corresponding author has primary responsibility for correspondence with the journal, the ICMJE recommends that editors send copies of all correspondence to all listed authors.

When a large multi-author group has conducted the work, the group ideally should decide who will be an author before the work is started and confirm who is an author before submitting the manuscript for publication. All members of the group named as authors should meet all four criteria for authorship, including approval of the final manuscript, and they should be able to take public responsibility for the work and should have full confidence in the accuracy and integrity of the work of other group authors. They will also be expected as individuals to complete disclosure forms.

Some large multi-author groups designate authorship by a group name, with or without the names of individuals. When submitting a manuscript authored by a group, the corresponding author should specify the group name if one exists, and clearly identify the group members who can take credit and responsibility for the work as authors. The byline of the article identifies who is directly responsible for the manuscript, and MEDLINE lists as authors whichever names appear on the byline. If the byline includes a group name, MEDLINE will list the names of individual group members who are authors or who are collaborators, sometimes called non-author contributors, if there is a note associated with the byline clearly stating that the individual names are elsewhere in the paper and whether those names are authors or collaborators.

3. Non-Author Contributors

Contributors who meet fewer than all 4 of the above criteria for authorship should not be listed as authors, but they should be acknowledged. Examples of activities that alone (without other contributions) do not qualify a contributor for authorship are acquisition of funding; general supervision of a research group or general administrative support; and writing assistance, technical editing, language editing, and proofreading. Those whose contributions do not justify authorship may be acknowledged individually or together as a group under a single heading (e.g. "Clinical Investigators" or "Participating Investigators"), and their contributions should be specified (e.g., "served as scientific advisors," "critically reviewed the study proposal," "collected data," "provided and cared for study patients," "participated in writing or technical editing of the manuscript").

Because acknowledgment may imply endorsement by acknowledged individuals of a study’s data and conclusions, editors are advised to require that the corresponding author obtain written permission to be acknowledged from all acknowledged individuals.

Use of AI for writing assistance should be reported in the acknowledgment section.

4. Artificial Intelligence (AI)-Assisted Technology

At submission, the journal should require authors to disclose whether they used artificial intelligence (AI)-assisted technologies (such as Large Language Models [LLMs], chatbots, or image creators) in the production of submitted work. Authors who use such technology should describe, in both the cover letter and the submitted work in the appropriate section if applicable, how they used it. For example, if AI was used for writing assistance, describe this in the acknowledgment section (see Section II.A.3). If AI was used for data collection, analysis, or figure generation, authors should describe this use in the methods (see Section IV.A.3.d). Chatbots (such as ChatGPT) should not be listed as authors because they cannot be responsible for the accuracy, integrity, and originality of the work, and these responsibilities are required for authorship (see Section II.A.1). Therefore, humans are responsible for any submitted material that included the use of AI-assisted technologies. Authors should carefully review and edit the result because AI can generate authoritative-sounding output that can be incorrect, incomplete, or biased. Authors should not list AI and AI-assisted technologies as an author or co-author, nor cite AI as an author. Authors should be able to assert that there is no plagiarism in their paper, including in text and images produced by the AI. Humans must ensure there is appropriate attribution of all quoted material, including full citations.

Next: Disclosure of Financial and Non-Financial Relationships and Activities, and Conflicts of Interest

Keep up-to-date Request to receive an E-mail when the Recommendations are updated.

Subscribe to Changes

• Utility Menu

FAS Research Administration Services

Guidelines on authorship and acknowledgement.

Disagreements may arise regarding who should be named as an author or contributor to intellectual work and the order in which individuals should be listed. These Guidelines are meant to serve as a set of standards that are shared by the academic community as a whole in order to help facilitate open communication through the adherence to common principles.  These principles apply to all intellectual products, whether published or prepared for internal use or for broad dissemination. For a printable pdf of these guidelines, please click here .

Applicability

These Guidelines apply to all faculty, students postdoctoral researchers, and staff. Ownership of research data and materials resulting from Harvard University (“University”) research activities rests with the University (see Research Data Ownership Policy ). 

Designing an ethical and transparent approach to authorship and publication of research, whether in a peer-reviewed journal or in an open access e-print or pre-print repository (e.g., arXiv, PsyArXiv), is a shared responsibility of all research team members but is primarily the responsibility of the Principal Investigator. The University recognizes that there are different standards across disciplines regarding authorship and the order in which authors are listed or acknowledged. Additionally, journals often specify their requirements in their guidance for authors and require attestations regarding individual authors intellectual contributions to the work. As a result, each laboratory, department, and/or school should engage in conversations regarding their own discipline-specific standards of authorship and, if needed, are encouraged to supplement the Guidelines herein with a description of these respective discipline-specific processes for deciding who should be an author and the order in which authors will be listed.

Note that these Guidelines are not intended for allegations related to research misconduct, defined as fabrication or falsification of data or plagiarism, which are subject to the Procedures for Responding to Allegations of Misconduct in Research and reviewed by the Committee on Professional Conduct (CPC).  

Criteria for Authorship

FAS and SEAS recommend that authorship consider the following criteria [1] ;

• Each author is expected to have made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work; or have drafted the work or substantively revised it; AND
• To have approved the submitted version (and any substantially modified version that involves the author’s contribution to the study); AND
• To have agreed both to be personally accountable for the author’s own contributions and to help ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated and resolved..

Some diversity exists across academic disciplines regarding acceptable standards for substantive contributions that would lead to attribution of authorship. Many journals have adopted discipline-specific standards. The University expects that researchers will act in accordance with accepted practice of the relevant research community. This Guidance is intended to allow for such variation of best practices within a specific discipline, while ensuring authorship is not inappropriately assigned.

Acknowledgment Versus Authorship

Financial sponsorship or donation of gift funding does not constitute criteria for authorship. Individuals who do not meet the recommended requirements for authorship, but have provided a valuable contribution to the work, should be acknowledged for their contributing role as appropriate to the publication. Authorship should not be conferred on those who have not made intellectual contributions to the work, or whose intellectual contributions are limited.

Implementation

Implementation of these Guidelines should include a commitment to collegiality, open communication, and expectation-setting throughout the research and scholarly process as well as the following considerations (see Authorship Best Practices Guidance (Addendum A) and Authorship Discussion Tool (Addendum B):

• Research groups should discuss authorship credit/criteria, presentation of joint work, and future direction of the research as early as practical, frequently during the course of their work, and as research team members begin or end their involvement. The Principal Investigator should initiate these discussions; however, any collaborator should feel free to raise questions or seek clarity throughout the course of the collaboration. Each lab or group may consider having a written document in place as guidance.
• All members of the research team are expected to adhere to good laboratory practices including maintaining an accurate laboratory notebook and annotating electronic files, as these practices will aide in identifying and clarifying individuals’ contributions to a project.
• Disposition of collaborative data and research materials should be mutually agreed upon among collaborators as early as practical and in accordance with any data-sharing and retention requirements.
• Laboratories, departments, centers, and programs supporting scholarly work should have available these Guidelines and a description of their discipline-specific processes of determining who should be an author, and the order in which authors are listed. These Guidelines should be included in the orientation of new research team members.

Authorship Disputes and Resolution

Disputes over authorship are best settled by the authors themselves; however, conflicts related to authorship may arise at any time during the research or scholarly process, resulting from differing perceptions of one’s contributions and resulting attribution of credit. It is expected that the resolution of disputes among collaborators will occur through open and collegial discourse, and mutual agreement is strongly encouraged. To facilitate this process, any prior decisions or discussions among authors, including verbal or written agreements between coauthors, should be reviewed and considered. These Guidelines and any documented customary practices in the relevant discipline should be applied, as appropriate. The authors should utilize the Authorship Discussion Tool (see Addendum B) in order to guide authors through a robust series of questions that can be jointly discussed by the authors in an effort to resolve the dispute.  Extending an invitation to a mutually agreed-upon party outside the group who is familiar with publication norms in the field to informally serve as a neutral facilitator may ensure that all viewpoints are considered and objectively applied. It is expected that most disputes will be resolved collegially among collaborators. Should an authorship dispute arise that includes a question of the veracity of underlying data supporting a manuscript or the misappropriation of the work of others , consultation with the Research Integrity Officer may be helpful to support resolution.

If the dispute cannot be resolved at the local level, it is the responsibility of the FAS Department Chair or SEAS Area Chair or their designee to take the lead in effecting a resolution of the dispute, assuming that the FAS Department Chair or SEAS Area Chair is not a direct party to the dispute and does not have a conflict of interest.

If strenuous, good faith efforts to resolve the dispute utilizing the Authorship Discussion Tool (see Addendum B) are unsuccessful, one or more of the parties may then contact their FAS Divisional Dean(s)/SEAS Area Dean, sharing the completed  Addendum B, which records the nature of the dispute and the efforts undertaken, and requesting further consideration. The FAS Divisional Dean(s)/SEAS Area Dean will review the submitted information and determine whether or not to appoint a committee to examine the case. As necessary, the Dean(s) will appoint a committee (and designate a committee chair), in consultation with the relevant FAS department(s)/SEAS area(s). The committee will consist of the following:

• A[n additional] faculty member from the field or fields relevant to the dispute
• Two faculty members from an adjacent field/department/area

FAS/SEAS Research Integrity Officer

• If the case involves a graduate student, an appropriate (non-student) representative from the Graduate School of Arts and Sciences
• If the case involves a postdoctoral researcher, an appropriate (non-postdoctoral) representative from the FAS Office of Postdoctoral Affairs

The committee will review the case and develop a recommendation to make to the authors. The committee chair will first inform the FAS Divisional Dean(s)/SEAS Area Dean of this recommendation and then inform the authors.

Related Resources

University Statement of Policy in Regard to Intellectual Property (IP Policy)

Graduate School of Arts and Sciences Office of Student Affairs

Harvard Ombuds Office

Committee on Publication Ethics (COPE) Authorship Resources

FAS/SEAS Procedures for Responding to Allegations of Research Misconduct

Harvard Medical School Authorship Guidelines

[1] As published in McNutt et al., Transparency in authors’ contributions and responsibilities to promote integrity in scientific publication. Proceedings of the National Academy of Sciences of the United States of America (PNAS) March 13, 2018 115 (11) 2557-2560. These criteria were adapted from the International Committee for Medical Journal Editors (ICMJE) framework for broader applicability across scientific fields.

Filter by Policy Area

• Faculty Research Policies
• Effort Policies
• Research Compliance Policies
• Research Finances Policies
• Proposal Submission Policies

Filter by Policy Type

• FAS Policies
• University Policies

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Plast Surg
• v.43(2); Jul-Dec 2010

Authorship issue explained

Surajit bhattacharya.

Editor, IJPS E-mail: ni.oc.oohay@hbtijarus

When it comes to the fact that who should be an author and who should not be offered ghost authorship, it seem we are all in agreement.[ 1 ] Each author should have participated sufficiently in the work to take responsibility for the content. Authorship credit should be based only on substantial contributions to (a) conception and design, or analysis and interpretation of data; and to (b) drafting the article or revising it critically for important intellectual content; and on (c) final revision of the version to be published. Conditions (a), (b), and (c) must all are met.

However, when it comes to the sequence of authorship there seems to be a grey zone and exploitation at both ends of the spectrum. We have come across aggrieved Unit Chiefs and displeased residents in almost equal numbers. It is important for young authors to understand that there are two positions that count, the first author and the last author. Attached to either position is the status associated with being the author for correspondence. The best combination when one is young is to be first author and the author for correspondence. As one’s career progresses, being last author and author for correspondence signals that this is a paper from one’s Unit, he/she is the main person responsible for its contents, and a younger colleague has made major contributions to the paper, hence he/she is designated as the first author. The guidelines here are not as well defined as for authorship in general, Riesenberg and Lundberg[ 2 ] have made certain very important and simple suggestions to decide the sequence of authorship:

• The first author should be that person who contributed most to the work, including writing of the manuscript
• The sequence of authors should be determined by the relative overall contributions to the manuscript.
• It is common practice to have the senior author appear last, sometimes regardless of his or her contribution. The senior author, like all other authors, should meet all criteria for authorship.
• The senior author sometimes takes responsibility for writing the paper, especially when the research student has not yet learned the skills of scientific writing. The senior author then becomes the corresponding author, but should the student be the first author? Some supervisors put their students first, others put their own names first. Perhaps it should be decided on the absolute amount of time spent on the project by the student (in getting the data) and the supervisor (in providing help and in writing the paper). Or perhaps the supervisor should be satisfied with being corresponding author, regardless of time committed to the project.
• A sensible policy adopted by many supervisors is to give the student a fixed period of time (say 12 months) to write the first draft of the paper. If the student does not deliver, the supervisor may then write the paper and put her or his own name first.

The second issue raised in this letter is about the use of plurals. Our insistence of avoiding pronouns I, me and mine in all publications is very sound and logical. Even if it is a single author paper, surgery is a team game and we are virtually powerless without our unsung colleagues - residents, nurses, technicians etc. By using plurals we recognize their vital role in our success story. Where as in a multiple author paper, the author has no option but to call it ‘our work’ instead on ‘my paper’, even when he is writing the paper all by himself / herself, there were many hands helping him / her and it is our Journal policy to acknowledge the same.

Main Navigation

• Contact NeurIPS
• Code of Ethics
• Code of Conduct
• Create Profile
• Journal To Conference Track
• Diversity & Inclusion
• Proceedings
• Future Meetings
• Exhibitor Information
• Privacy Policy

NeurIPS 2024, the Thirty-eighth Annual Conference on Neural Information Processing Systems, will be held at the Vancouver Convention Center

Monday Dec 9 through Sunday Dec 15. Monday is an industry expo.

Registration

Pricing » Registration 2024 Registration Cancellation Policy » . Certificate of Attendance

Our Hotel Reservation page is currently under construction and will be released shortly. NeurIPS has contracted Hotel guest rooms for the Conference at group pricing, requiring reservations only through this page. Please do not make room reservations through any other channel, as it only impedes us from putting on the best Conference for you. We thank you for your assistance in helping us protect the NeurIPS conference.

Announcements

• The call for High School Projects has been released
• The Call For Papers has been released
• See the Visa Information page for changes to the visa process for 2024.

Latest NeurIPS Blog Entries [ All Entries ]

Important dates.

If you have questions about supporting the conference, please contact us .

View NeurIPS 2024 exhibitors » Become an 2024 Exhibitor Exhibitor Info »

Organizing Committee

General chair, program chair, workshop chair, workshop chair assistant, tutorial chair, competition chair, data and benchmark chair, diversity, inclusion and accessibility chair, affinity chair, ethics review chair, communication chair, social chair, journal chair, creative ai chair, workflow manager, logistics and it, mission statement.

The Neural Information Processing Systems Foundation is a non-profit corporation whose purpose is to foster the exchange of research advances in Artificial Intelligence and Machine Learning, principally by hosting an annual interdisciplinary academic conference with the highest ethical standards for a diverse and inclusive community.

About the Conference

The conference was founded in 1987 and is now a multi-track interdisciplinary annual meeting that includes invited talks, demonstrations, symposia, and oral and poster presentations of refereed papers. Along with the conference is a professional exposition focusing on machine learning in practice, a series of tutorials, and topical workshops that provide a less formal setting for the exchange of ideas.

More about the Neural Information Processing Systems foundation »

Comprehensive analysis of digital twins in smart cities: a 4200-paper bibliometric study

• Open access
• Published: 27 May 2024
• Volume 57 , article number  154 , ( 2024 )

Cite this article

You have full access to this open access article

• Rasha F. El-Agamy   ORCID: orcid.org/0009-0005-0519-3870 1 , 2 ,
• Hanaa A. Sayed   ORCID: orcid.org/0000-0003-0728-6323 1 , 3 ,
• Arwa M. AL Akhatatneh   ORCID: orcid.org/0009-0009-2133-1822 1 ,
• Mansourah Aljohani   ORCID: orcid.org/0000-0001-5233-7738 1 &
• Mostafa Elhosseini   ORCID: orcid.org/0000-0002-1259-6193 1 , 4  

This survey paper comprehensively reviews Digital Twin (DT) technology, a virtual representation of a physical object or system, pivotal in Smart Cities for enhanced urban management. It explores DT's integration with Machine Learning for predictive analysis, IoT for real-time data, and its significant role in Smart City development. Addressing the gap in existing literature, this survey analyzes over 4,220 articles from the Web of Science, focusing on unique aspects like datasets, platforms, and performance metrics. Unlike other studies in the field, this research paper distinguishes itself through its comprehensive and bibliometric approach, analyzing over 4,220 articles and focusing on unique aspects like datasets, platforms, and performance metrics. This approach offers an unparalleled depth of analysis, enhancing the understanding of Digital Twin technology in Smart City development and setting a new benchmark in scholarly research in this domain. The study systematically identifies emerging trends and thematic topics, utilizing tools like VOSviewer for data visualization. Key findings include publication trends, prolific authors, and thematic clusters in research. The paper highlights the importance of DT in various urban applications, discusses challenges and limitations, and presents case studies showcasing successful implementations. Distinguishing from prior studies, it offers detailed insights into emerging trends, future research directions, and the evolving role of policy and governance in DT development, thereby making a substantial contribution to the field.

Avoid common mistakes on your manuscript.

1 Introduction

Digital Twin (DT) technology, a cornerstone of the Industry 4.0 era, represents a significant paradigm shift in how we interact with and understand physical systems and assets. Originating from Grieve's 2002 lecture at the University of Michigan (Grieves 2005 ) and later refined by NASA in 2010 (NASA 2010 ), the concept of DT has evolved into a sophisticated, multi-faceted approach to simulation and analysis. A Digital Twin is broadly defined as a digitally created virtual model of a physical object that leverages data to emulate the real-world behavior of the physical entity. It facilitates interaction and interoperability between the physical and virtual entities through interactive feedback, data integration, analysis, and iterative decision-making for optimized control, safety monitoring, and data analysis (Stark et al. 2017 ; Rosen et al. 2015 ).

Kritzinger et al. (Kritzinger et al. 2018 ) further categorized DT into three subtypes: digital model, digital shadow, and digital twin, each representing varying degrees of interaction and correlation between the physical and digital states.

The structure of a DT encompasses hardware and software components connected via middleware. The hardware typically includes IoT sensors and actuators, with the middleware playing a critical role in data management and communication between hardware and software. The software component, often an analytics engine, utilizes machine learning algorithms to transform raw data into actionable insights (Kritzinger et al. 2018 ). As depicted in Fig.  1 , this system encompasses the various components constituting the digital twin architecture.

Digital twin system structure. This diagram illustrates the essential components of a Digital Twin system, showcasing hardware with IoT sensors, middleware for data management, and a software analytics engine

Before going into the various applications of digital twins in various industries, it is important to comprehend the nature of digital twins and two closely connected ideas: digital model and digital shadow. These concepts are crucial for understanding this technology's depth (Kritzinger et al. 2018 ). The first concept is the Digital Model, a static digital representation of a physical object without automatic data exchange between the physical and digital entities. It can take many forms, including simulations, CAD files, 3D models, and mathematical algorithms. Digital models help design, optimize, and test by enabling the visualization, analysis, and manipulation of objects or systems in a digital context. A model is typically an estimation or prediction of how a system, process, or physical thing could function in the future or a certain setting. The second concept is Digital Shadow, which represents unidirectional information flow from the physical object to its digital counterpart, reflecting changes in the physical object. Through sensors, Internet of Things (IoT) devices, or other means, digital shadow gathers data from the asset (a database, a railroad system, or a banking platform). It delivers information that is fed into the model. This indicates that a digital shadow is current with the real object. It is helpful to understand it because it accurately depicts the asset in enough detail. The last concept is the Digital Twin, a dynamic, interactive digital representation capable of simulating, predicting, and interacting with data, showing a reciprocal impact between physical and digital states. Digital twins help with analysis, optimization, and predictive maintenance by simulating, monitoring, and controlling real-world systems or objects. They provide insights for enhancing effectiveness, dependability, and performance, as well as live feedback loops.

Figure  2 demonstrates the evolution from a basic digital model, lacking interactive data exchange, to a digital twin that dynamically mirrors and interacts with its physical counterpart, allowing for a two-way flow of information and continuous adaptation.

The progression from a static digital model to a dynamic digital twin, emphasizing reciprocal interaction between physical and digital assets

Digital twins' ability to reproduce physical items, processes, and systems in a virtual environment makes them useful in various applications. This technology has applications across various sectors and domains, providing several benefits and chances for innovation. For example, in industry, they are used for predictive maintenance, optimizing energy usage in smart buildings, and simulating traffic patterns in smart cities.

In the IoT sector, DTs are pivotal, acting as a critical bridge between the physical and digital realms. They allow for the seamless integration of digital and physical entities, enhancing maintenance capabilities and improving equipment performance monitoring (Fang et al. 2022 ; Mihai et al. 2022 ; Hinchy et al. 2019 ; Guo 2020; Wang and Luo 2021 ; Rajesh et al. 2019 ; Revetria et al. 2019 ). IoT can be seen as the vehicle that drives data to Digital Twins, enabling these virtual entities to replicate and interact with their physical counterparts in real-time. Digital Twins depend heavily on IoT technologies for data acquisition. IoT devices like sensors, RFID tags, and smart wearables collect data from the physical environment, which the digital twin then utilizes for various analyses. This data integration facilitated by IoT is crucial for applications ranging from predictive maintenance in industrial settings to real-time monitoring and augmented reality applications (Rajesh et al. 2019 ; Revetria et al. 2019 ). As described in sources monitoring (Fang et al. 2022 ; Mihai et al. 2022 ; Hinchy et al. 2019 ; Guo et al. 2020 ; Wang and Luo 2021 ; Rajesh et al. 2019 ; Revetria et al. 2019 ), IoT's role is not just about data collection but also about ensuring seamless integration of physical and virtual worlds, thus forming the backbone of any DT system.

While the Internet of Things (IoT) plays a major role in shaping and augmenting the capabilities of digital twins, machine learning augments these capabilities by allowing digital twins to analyze data, forecast, identify anomalies, optimize performance, customize experiences, and learn and improve continuously. Integrating machine learning with DT technology enables real-time, autonomous analysis of extensive data streams, enhancing decision-making and optimizing asset and system performance (Rathore et al. 2021 ; Dong et al. 2019 ; Zohdi 2020 ; Jaensch et al. 2018 ; He et al. 2019 ). Machine Learning, a pivotal branch of Artificial Intelligence, involves algorithms that enable systems to learn and adapt from data without being explicitly programmed. Its relationship with Digital Twin technology is synergistic. Digital Twins, virtual replicas of physical entities, systems, or environments, require advanced analytical capabilities to process and interpret the vast amount of data they receive. This is where Machine Learning comes into play. Machine Learning algorithms in DT systems facilitate the autonomous, real-time analysis of extensive data streams. These algorithms are adept at detecting patterns, making predictions, and optimizing processes based on the data ingested from the physical assets that the digital twins mirror. For instance, Rathore et al. 2021 (Rathore 2021) highlighted how applying advanced AI techniques to data within a DT system enables the creation of an 'intelligent' digital twin. This intelligence is manifested in capabilities like predictive maintenance, operational optimization, and dynamic decision-making based on a continuous stream of sensor and virtual data. The application of various machine learning models, such as Deep Neural Networks (DNNs) or Genetic Algorithms (GAs), is contingent upon the specific requirements and use cases of the intended digital twins (Dong et al. 2019 ; Zohdi 2020 ; Jaensch 2018; He et al. 2019 ). Therefore, Machine Learning is not just a complementary technology for Digital Twins but a fundamental enabler of their advanced functionalities.

In the sector of smart cities, DTs are used for urban planning, traffic management, environmental monitoring, energy management, waste management, public safety, infrastructure maintenance, water management, healthcare, public services, tourism, citizen engagement, economic development, and climate resilience. They provide real-time data crucial for emergency response, optimizing public transportation, and ensuring efficient city operations (Allam and Jones 2021 ; Bouzguenda et al. 2019 ; Svítek et al. 2019 ; Yu et al. 2021 ; Ghosh et al. 2016 ). Smart Cities represent urban areas that integrate various electronic data collection sensors to manage assets, resources, and services efficiently. Digital Twins, within the context of Smart Cities, act as sophisticated tools for urban planning, management, and enhancement of living conditions. They utilize data gathered via IoT devices and analyze it using machine learning algorithms to optimize city operations and decision-making processes. Besides, they contribute to traffic management, environmental monitoring, energy distribution, public safety, and more (Allam and Jones 2021 ; Bouzguenda et al. 2019 ; Svítek et al. 2019 ; Yu et al. 2021 ; Ghosh et al. 2016 ). For example, digital twins utilize data from sensors and cameras to optimize traffic flow and public transportation systems in traffic and transportation management. They use real-time data to monitor air and water quality in environmental monitoring. In energy management, digital twins aid in the operation of smart grids and in identifying potential energy conservation areas. These applications underline the comprehensive impact that Digital Twins, empowered by IoT and ML, can have in transforming urban environments into more efficient, sustainable, and responsive entities.

The primary aim of this paper is to engage in a comprehensive bibliometric analysis, examining the evolving landscape of Digital Twin technology within Smart Cities. The study is dedicated to methodically examining the scholarly dialogue, identifying predominant trends, and revealing key themes and collaborative networks in this area. We aim to provide a detailed, structured understanding of Digital Twin technology's role in urban development, filling a notable void in existing literature reviews. The survey's distinctiveness stems from its thorough data-gathering approach for bibliometric analysis in the field of Digital Twin technology and Smart Cities, selecting the Web of Science database for its broad interdisciplinary coverage and meticulously filtering over 4,220 pertinent articles, enhancing the depth and scope of analysis in these domains.

Our research found that various publications in various literary works are advancing the DT idea. Because there are so many articles available, academics have also published several survey papers that aim to review the current state-of-the-art in digital transformation (DT) development, inform other innovators about potential research gaps, questions, and directions, and point the industry toward potential DT use cases that could yield substantial business value in their particular domain.

Current literature predominantly concentrates on applying digital twin technology within specific facets of smart cities. For instance, Jafari et al. ( 2023 ) and He et al. ( 2023 ) explore the utilization of digital twin (DT) technology in enhancing various sectors of energy management within smart cities, encompassing transportation systems, power grids, and microgrids. Weil (2023) delves into the infrastructure elements of digital twins in smart cities, focusing on storage, computation, and network components. Nica et al. ( 2023 ) investigates Multi-Sensor Fusion Technology's role in sustainable urban governance networks. Dani et al. ( 2023 ) introduces an architectural framework underpinning the flow for digital twin platform development aimed at urban condition monitoring. Lam et al. ( 2023 ) outlines a use case for the 3D visualization of a smart village in Busan, South Korea, employing a 3D Geospatial platform. Paripooranan et al. ( 2020 ) suggests augmented reality (AR)-assisted DT as a pioneering approach towards the future transformation of human-centric industries. Mora (2023) highlights the importance of incorporating innovation management theories into the exploration of smart city transitions, offering novel insights and practical approaches to enhance the governance of smart cities through an innovation management lens. Ariyachandra and Wedawatta ( 2023 ) provides an overview of digital twin technologies' implications on disaster risk management, addressing the challenges of implementing digital twins in smart cities. Additionally, several reviews, including those by Weil (2023) and Wang (2024), focus on bibliometric analyses concerning digital twins in the realm of smart cities.

This work aims to support the other existing survey initiatives and provide a comprehensive comprehension of the DT. The paper gives an in-depth overview of the DT idea, architecture, enabling technologies, applications in smart cities, challenges, performance metrics, datasets, software, and use cases for deploying DTs in diverse industries, complementing prior research. This paper aims to fill a critical gap in understanding the expansive and evolving field of Digital Twin technology and its integration into Smart City development. This study is driven by the need to systematically synthesize and analyze the burgeoning body of research in this interdisciplinary area, providing clarity and direction for future studies. This survey's uniqueness and unprecedented nature stem from its comprehensive and systematic bibliometric analysis of over 4,220 articles on Digital Twin technology and Smart Cities. A focused examination of specialized areas such as datasets, platforms, and performance metrics marks this distinctiveness. The rigorous methodology involving the Web of Science database ensures in-depth interdisciplinary coverage. The survey's meticulous approach in formulating search strategies and selective filtration of relevant articles contributes to its depth and breadth, making it a unique contribution to the field. The significant contributions of this survey paper are listed below:

An overview of the DT definitions, concepts, and architecture in the literature

A Detailed bibliometric study of over 4,220 publications in Digital Twin technology and Smart Cities, including thematic trends analysis like AI and IoT integration.

Examination of datasets, platforms, and performance metrics specific to Digital Twins in urban settings and a critical evaluation of city models.

Applications in Smart Cities: Exploration of Digital Twin technology applications in urban development, encompassing urban planning, energy management, and public health.

Discussion of the challenges in implementing Digital Twin technology in Smart Cities, focusing on data integration, scalability, and security concerns.

Outlining potential research avenues based on current findings, indicating areas for further exploration.

Presentation of practical case studies demonstrating successful Digital Twin integration in urban development.

Summarizing the main findings and implications and a call to action for further research in this evolving field.

The paper's organization follows a clear and structured approach, beginning with Section  1 , an introduction that sets the stage for Digital Twin technology and Smart Cities. It progresses into Section  2 , which provides a detailed bibliometric study, covering objectives, methodology, data collection, and analysis, leading to key findings and implications. Then, Section  3 explores the applications of Digital Twins in Smart Cities. Section  4 discusses some technological aspects of DT. Section  5 presents some examples of datasets and software for developing DT. Section  6 states digital twin performance metrics according to its structure. Section  7 addresses the challenges associated with digital twins. Section  8 introduces some case studies for DT. Section  9 discusses smart city governance in the era of digital twins. Finally, Section  10 summarizes the paper's conclusions and presents future research directions.

2 Bibliometric study on digital twin and smart cities

The primary objective of this research is to perform a bibliometric analysis (Yu and Merritt 2023 ) to acquire a comprehensive understanding of emerging topics, prominent journals, and evolving research trends associated with the application of digital twin technology in smart cities. Additionally, the study aims to shed light on the potential challenges and future research trajectories concerning digital twin technology in the context of smart city development.

2.1 Research methodology

This investigation employed a systematic literature review (SLR) to meticulously explore, assess, and integrate the extant body of knowledge regarding the designated theme, adhering to a rigorously defined protocol (Kyriazopoulou 2015 ). Adopting the SLR methodology is instrumental in delineating the contemporary scholarly landscape of a given topic, thereby uncovering existing research voids and delineating avenues for forthcoming scholarly inquiries (Kitchenham et al. 2009 ). The SLR framework comprises three pivotal phases: planning, execution, and dissemination. The research inquiries were articulated during the planning stage, and criteria for identifying pertinent literature and determining search strategies were established. The execution stage entailed the meticulous gathering and vetting of scholarly works in alignment with the previously established criteria. This phase was initiated with an initial screening of the collected records through their titles and abstracts to ascertain their pertinence to the posed research questions, followed by an in-depth examination of the full-text articles. A bespoke form was devised for the methodical extraction of data, capturing essential information from the chosen articles, such as facets of digital twin components, smart city innovations, and the research lacunae identified therein. Subsequently, the dissemination phase involved the analytical consolidation and synthesis of the compiled literature. The process was underscored by a commitment to transparency and precision, with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework guiding the data acquisition methodology (Liberati et al. 2009 ).

2.1.1 Research questions

To delineate the scope of the SLR, the following research questions guided the study:

Q1: What are the components of digital twins in smart city applications?

Q2: What are the existing technologies used in the development of smart city development based on digital twins?

Q3: What are the research gaps and potential areas for future research?

2.1.2 Data collection

This section outlines the steps taken to collect relevant literature for the study. A PRISMA workflow diagram in Fig.  3 illustrates the study's search process. Initial literature searches were conducted in reputable databases such as Web of Science, Direct Science, and Scopus, which were chosen for their extensive coverage of scientific publications and advanced search capabilities. The research strategy applied an advanced search with keywords executed in the Web of Science and Scopus databases with a search string set to ("Digital twin," "virtual twin" or "virtual replica," and "smart city" or "smart cities"), for publications up to September 2023 and set to articles before 2018 were excluded. The period selected for the search is appropriate because there are few publications on digital twins and smart cities before 2018.

PRISMA workflow diagram

2.1.3 Inclusion and exclusion criteria

In this survey, the inclusion and exclusion criteria were meticulously established to ensure a focused and relevant analysis in the fields of Digital Twin technology and Smart Cities. This careful selection was pivotal in delineating the scope of the study.

Inclusion criteria:

Scope of content : Articles must focus on Digital Twin technology and its application within Smart Cities. This includes scholarly articles, conference proceedings, and review articles offering substantial insights into Digital Twin architectures, methodologies for Smart City implementation, technologies employed, and demonstrative case studies or laboratory setups.

Language : Only articles published in English are considered to ensure the clarity and accessibility of the content for our analysis.

Databases : Articles were sourced from the Web of Science, Scopus, and Direct Science databases to ensure a comprehensive and interdisciplinary coverage of the subject matter.

Publication period : Articles published from 2018 to 2023 were included to capture Digital Twin technology's evolution and current state in Smart Cities.

Detail requirements : Articles must present a detailed systematic architecture for a digital twin application and a clear methodology for Smart City implementation. They must also discuss the technologies used and provide demonstrative case studies or laboratory setups.

Exclusion criteria:

Language limitation : Articles published in languages other than English were excluded to maintain consistency and comprehensibility in the analysis.

Irrelevance : Publications unrelated to the direct intersection of Digital Twin technology and Smart Cities, lacking in detailed architecture, clear methodologies, technology discussion, or case studies, were excluded.

Duplication : Duplicated records identified across the databases were removed to ensure the uniqueness and accuracy of the analysis.

Date filter : Articles published before 2018 were excluded to focus the study on more recent developments and applications, reflecting the latest trends and innovations in the field.

The search produced 4,220 records from Web of Science, 382 from Scopus, and 24 from Direct Science. Through a preprocessing step, which involved removing duplicates and applying inclusion and exclusion criteria, 4,507 records were screened. This process yielded 4,073 articles that were deemed eligible for further analysis. The inclusion criteria were specifically targeted at articles that provided detailed systematic architectures for digital twin applications, methodologies for implementing smart cities, descriptions of technologies employed, and demonstrative case studies or laboratory setups.

2.2 Bibliometric study methodology

The methodology of a bibliometric study typically comprises a series of fundamental steps aimed at systematically analyzing scholarly literature within a specific field. These steps include formulating precise research questions to guide the analysis, identifying and selecting appropriate data sources, devising relevant search strategies using carefully chosen keywords, meticulously collecting and preparing the retrieved data, and employing established bibliometric techniques for rigorous data analysis. By adhering to this structured approach, researchers can effectively uncover trends and patterns in scientific publications and citations, thereby gaining valuable insights into the evolving landscape of their area of study (Mora et al. 2019 ). In line with these established practices, this research adopts a systematic approach for collecting, processing, and analyzing academic literature on digital twins within the context of smart cities.

2.3 Data analysis and visualization

This subsection outlines the methodologies and tools implemented to analyze and visualize the bibliometric data. For our study, VOSviewer was selected as the primary tool for managing and interpreting bibliographic data. We utilized network analysis methodologies to generate a range of visual representations. These included co-occurrence analyses, citation and co-citation maps, and keyword co-occurrence maps. Such visualizations were instrumental in uncovering patterns and discerning relationships within the collected dataset.

2.3.1 Publication trends

One of the key indicators in performance analysis is the annual number of publications. This metric serves as an indicator of research productivity. The data collected from 2011 to 2023 reveal a marked increase in publications focused on digital twin technology and smart cities. This surge in research output, demonstrating exponential growth, is depicted in Fig.  4 . This Figure underscores the significance and escalating interest in this interdisciplinary area. Figure  4 illustrates the yearly publication rates concerning digital twins and smart cities. Additionally, Table  1 provides a concise statistical analysis of these findings.

The publications rate of digital twin and smart cities by year from 2011 to 2023

2.3.2 Keyword analysis and research themes

This study's keyword co-occurrence analysis represents a systematic approach to understanding the prevailing keywords associated with digital twin technology and smart cities. The outcomes of this analysis, illustrated in Fig.  5 , reveal a range of predominant research themes and technologies pertinent to the domain of digital twins and smart cities.

Visualization of keyword co-occurrence network

Research themes in digital twin and smart cities:

Theme 1: Integration of AI and big data analytics in digital twins

This theme explores applying advanced deep learning techniques in processing and analyzing digital twin data. Key research areas are identified through terms such as "machine learning," "transfer learning," "simulation," "reinforcement learning," "cloud computing," "AI," "data analysis," "big data," and "forecasting."

Theme 2: Integration of digital twins with IoT

The focus here is IoT technologies, which are central to transmitting and collecting digital twin data. Relevant keywords include "wireless sensor network," "digital devices," "sensors," "5G", "communication," "wireless communications," and "monitoring."

Theme 3: Energy management in digital twins

This theme emphasizes the importance of energy efficiency and sustainability, highlighting keywords such as "energy efficiency," "energy utilization," "sustainability," and "renewable energy."

Theme 4: Security concerns in digital twins

Research in this area deals with the security aspects of digital twins, with keywords like "security," "privacy," "blockchain," and "fault diagnosis".

Theme 5: Cloud computing and digital twins

The final theme investigates the intersection of digital twins with cloud computing technologies, focusing on keywords such as "cloud computing," "edge computing," "fog computing," "blockchain," and "big data analytics."

Predominant technologies in the Digital Twin (DT) domain

Our study analyzed the authors' keywords to ascertain the most prominent technologies within the digital twin sphere. This investigation uses keyword frequency as a metric to identify the key technologies extensively employed in the digital twin (DT) domain. The term 'Internet of Things' (IoT) emerges as the most frequently cited keyword, demonstrated in Fig.  7 . This finding underscores the pivotal role of IoT in the digital twin field, highlighting its extensive research coverage and the ongoing need for in-depth exploration of IoT applications to enhance digital twins' efficacy. Additionally, "AI" and "machine learning" are prominently used to analyze and process large volumes of digital twin data. Other notable technologies such as "cloud computing," "virtual reality," and "digital twin security" have also gained traction. Collectively, these technologies contribute to the efficient storage, visualization, modeling, and security of digital twin data. The data presented in the accompanying table and Fig.  6 substantiate the findings discussed in this subsection.

High-frequency keywords in digital twin research

2.3.3 Analysis of geographical distribution

Examining the geographical distribution in the research and development of digital twin and smart cities technologies offers critical insights into the regional contributions, patterns of collaboration, and prospective areas for advancement. As depicted in Fig.  7 (A), our analysis reveals a broad geographical spread in the field's research activities. We identified key regions contributing significantly to the field by utilizing a citation metric analysis on our dataset, which set a minimum of ten documents and fifty citations per country. China emerges as the leading contributor in terms of citations, followed by the USA, the UK, Italy, and Germany. Furthermore, Fig.  7 (B) corroborates the leadership status of China, the USA, the UK, and Italy in this domain.

A Citations by country in digital twin and smart cities research. B Top 10 publishing countries in digital twin and smart cities research

2.3.4 Analysis of source co-citation

The source co-citation analysis conducted in our study highlights the prominent sources within the domain of digital twins and smart cities. Of 49,275 sources, 433 met the established criterion of a minimum of 50 citations per source. The findings of this analysis are presented in Fig.  8 . The most frequently co-cited journals include IEEE Access, the Journal of Manufacturing Systems, IEEE Transactions on Industrial Informatics, and IEEE Internet of Things. The analysis identified six distinct clusters, each represented by a unique color, as depicted in Fig.  7 (A).

A Co-citation network of sources in digital twin and smart cities research. B Bibliographic coupling network among countries in digital twin and smart cities research

2.3.5 Examination of international collaboration

The observed international collaboration in the digital twin and smart cities sector underscores research's global impact and relevance. Utilizing bibliographic coupling analysis on our dataset, with a set threshold of a minimum of 10 documents and 20 citations per country, 65 out of 103 countries met these criteria. A network visualization visually represents the bibliographic coupling among these countries in Fig.  8 (B).

This analysis collated data on each country's publications, citations, and total link strength. Each node in the figure symbolizes a country whose size reflects its publication count. The visualization reveals that China leads in a collaborative network, boasting approximately 1151 documents and a total link strength of 871,379. Following China are the USA, the UK, and England. Notably, the USA's most extensive collaborations were with China, England, and India, while China's primary collaborations were with the USA, England, and Germany.

The colors in Fig.  8 (B) delineate nine distinct clusters, indicating nations that frequently cite each other's research, suggesting closer collaboration within these groups. This mapping confirms that countries like China, the USA, the UK, Italy, and Germany are at the forefront in advancing research in digital twin and smart cities.

3 Applications of digital twin technology in smart city development

Digital twin technology offers a wide range of applications in smart city development, from optimizing traffic flow and energy usage to improving public safety, Environmental Monitoring and Management, Citizen-Centric Aspects, and Supply Chain Management and Enhancement. By creating virtual replicas of city infrastructure and systems, urban planners and policymakers can visualize potential changes and their impact before implementing them in the physical environment. Furthermore, digital twins can be instrumental in public safety by simulating emergency response scenarios and planning for effective evacuation routes in the event of natural disasters or other crises, as shown in Fig.  9 . As the adoption of digital twins continues to grow, their role in shaping the future of smart cities will become increasingly prominent.

Applications of digital twin technology in smart city development

3.1 Urban planning and management

Urban planning and management encompass the technical and political processes of utilizing land, infrastructure, and buildings within urban areas. This multifaceted domain includes urban design, land use, transportation, zoning, regulation, and environmental planning.

Urban planners and managers increasingly employ digital twin technology to enhance city functions like transportation and sustainability. Digital twins enable more informed decision-making and optimize planning, operations, finance, and strategy. In turn, such systems help reduce carbon emissions and expedite significant projects. Additionally, they enable the simulation of plans before implementation, allowing for the anticipation of potential challenges. The World Economic Forum 2022 recognized the role of digital twins in modeling future sustainable development by integrating digital technology with urban operational systems. This integration facilitates safer, more efficient urban activities. It creates low-carbon, sustainable environments through precise mapping, virtual-real integration, and intelligent feedback of physical and digital urban spaces (Yu and Merritt 2023 ).

Within urban planning and management, digital twins can represent entire cities or specific urban systems, assisting in various ways:

Real-time Monitoring: Integrating sensors and IoT devices with digital twins provides real-time data on urban processes like traffic flow, energy consumption, and air quality.

Simulation and Scenario Testing: Planners can use digital twins to simulate and test different scenarios, assessing the impacts of natural disasters or new transportation systems.

Optimization: Analyzing data from digital twins can identify and address inefficiencies in urban systems.

Public Engagement: Digital twins serve as interactive platforms for public involvement, allowing community members to view proposed changes and provide feedback.

Maintenance and Asset Management: They enable tracking urban infrastructure conditions and predicting maintenance needs.

System Integration: Digital twins facilitate understanding of interdependencies between various urban systems.

Support for Decision-Making: Providing a comprehensive view of the city and its systems, digital twins enhance the decision-making process, ensuring decisions are informed by accurate, up-to-date information.

3.2 Energy management

Energy systems form the backbone of smart cities, ensuring the quality and functionality of these urban environments. This section delves into the application of DTs in energy systems, encompassing transportation systems, power grids, and microgrids.

Digital Twin technology finds varied applications in transportation systems. It supports transportation system infrastructures in several ways, such as monitoring transport systems, traffic forecasting, energy system management, predicting the energy consumption of electric vehicles, IoT-based parking management, analyzing driver behavior, forecasting subway regenerative braking energy, studying pedestrian behavior, controlling health systems, and detecting cyber-physical attacks. DTs can significantly contribute to these areas. For instance, using DTs for transportation system monitoring can reduce maintenance costs. Beyond modeling and planning, DTs facilitate optimal traffic management and provide accurate and extensive traffic and electric vehicle (EV) data, contributing to sustainable development and efficient urban traffic control.

In traffic management, DT technology has been utilized to predict patterns of energy consumption and production (Ketzler et al. 2020 ). Additionally, DTs play a role in IoT-based parking management, improving user services by saving time and reducing parking costs.

Various studies have employed DT technology in diverse contexts. In (Yan et al. 2022 ), authors analyzed real drivers' and pedestrians' behavior using DTs. In (Liu et al. 2020 ; Damjanovic-Behrendt 2018 ), DTs of drivers and vehicles were used in real-time to relay critical information to drivers and vehicles in the physical world. Moreover, in (Crespi et al. 2023 ), the Electric Vehicles (EVs) model employed DTs to monitor the behavior and optimally manage charging programs, using energy consumption parameters and charging capacity and frequency for modeling the virtual twin.

A microgrid is an autonomous energy system characterized by distributed energy resources and interconnected loads. It functions as a manageable entity within the larger grid, enabling it to operate in either island mode or in conjunction with the grid (Ton and Smith 2012 ). The objective of microgrids is to enhance the functionality of energy systems in terms of sustainability, economic viability, efficiency, security, and overall energy management. Key aspects of microgrid performance include reliability, self-sufficiency, security, flexibility, and optimality. Studies on microgrids utilizing the Digital Twin (DT) framework have encompassed areas such as forecasting (Din and Marnerides 2017 ; He et al. 2017 ), management and monitoring (Xu et al. 2019 ; Park et al. 2020 ), fault prediction (Nowocin 2017 ; Goia et al. 2022 ), and security (Huang et al. 2021 ).

The development and implementation of DT-based power grids are instrumental in improving network behavior under various conditions. Network studies employing DT include diverse analyses such as restoration (Biagini et al. 2020 ), reliability (Podvalny and Vasiljev 2021 ), prediction (Park et al. 2020 ), addressing uncertainty (Raqeeb et al. 2022 ), energy hub management (Kuber et al. 2022 ), and ensuring both physical and cyber security. Each of these analyses offers unique insights into network behavior. In reference (Endsley 2016 ), Situation Awareness (SA) is the ability to perceive the elements in a specific environment, understand their properties, and anticipate their future statuses. SA is crucial in augmenting decision-making, especially in complex systems like the Energy Internet of Things (EIoT) (He et al. 2023 ). It provides essential information critical for such systems' operation, enhancing efficiency and effectiveness.

In the application of Digital Twin technology in power systems, several significant challenges emerge:

IT infrastructure limitations: Existing infrastructure often falls short in supporting the data analysis demands of DT environments.

High-performance computing needs: Utilizing high-performance GPUs and cloud services from major providers is essential for adequate support.

Connectivity issues: Software errors and power outages present obstacles in real-time monitoring.

Cybersecurity risks: The extensive data exchange in DT systems heightens vulnerability to cyber-attacks, necessitating secure platforms.

Standardization requirement: The absence of standardized protocols impedes DT development, highlighting the need for unified approaches for model definition, storage, and execution.

The exploration of digital twin applications in energy management reveals several key areas for future development:

Advancements in big models: Addressing challenges in AI, such as limited model generalization and the need for high-quality data, by developing larger, more adaptable models.

Virtual twin structures in power systems: Detailed modeling of power system entities using virtual twins, enabling dynamic visualization and strategy development for urban transformation.

Application of theoretical models: Utilizing chaos and complex system theories (Mir et al. 2022 ) to understand and optimize the nonlinearities in power systems, offering a novel approach to managing system complexities.

3.3 Traffic and mobility management

Traffic and Mobility Management in Smart Cities (Xu et al. 2023 ), enhanced by Digital Twin (DT) technology, represents a significant advancement in urban planning and logistics. DTs enable:

Real-time traffic simulation : Mimicking urban traffic flow to identify and alleviate congestion points.

Public transportation optimization : Analyzing patterns to improve transit routes and schedules.

Pedestrian flow management : Ensuring safer and more efficient pedestrian movement.

Pollution reduction : Aiding in strategies to lower emissions through traffic regulation.

Emergency response enhancement : Assisting in quicker and more efficient routing for emergency services.

Data-driven decision making : Utilizing sensor data for informed traffic management decisions.

Sustainable urban planning : Contributing to long-term urban sustainability goals through efficient mobility solutions.

These applications of DT in traffic and mobility management significantly contribute to creating more livable, efficient, and sustainable urban environments.

3.4 Environmental monitoring and management

Digital twin technology is increasingly integral in urban development, offering real-time insights and solutions for environmental management:

Optimization and prediction: Digital twins, as virtual representations of physical entities, enable process optimization, change monitoring, and future scenario prediction (Wang et al. 2023 ).

Environmental monitoring applications: Usage in water quality monitoring, detecting pollutants, and adapting to changing environmental conditions.

Data integration in smart cities: Interconnection of multiple digital twins, using diverse data sources like temperature and humidity, to forecast environmental conditions (Ivanov et al. 2020 ).

Sensor utilization: Various sensors capture essential environmental data for digital twin construction, including Kinect v2 depth cameras and electronic gloves for manufacturing systems (Nikolakis et al. 2018 ).

Food industry monitoring: Application in monitoring and predicting food quality, employing wireless sensors for environmental factors like humidity and temperature (Defraeye et al. 2019 ).

Agricultural management: Use in agriculture for crop growth monitoring and simulating interventions, aiding in remote farm management (Verdouw et al. 2021 ).

Healthcare applications: Implementation in healthcare for environmental monitoring and mental health management using smartwatch sensors (Bagaria et al. 2019 ).

These diverse applications showcase the role of digital twins in enhancing urban planning, agriculture, healthcare, and more.

3.5 Public health and safety

In the development of Smart Cities, Digital Twin Technology plays a crucial role in enhancing public health and safety (Erol et al. 2020 ). It offers a dynamic and integrated approach to managing complex urban health challenges through simulation and analysis. This technology's applications include:

Disease outbreak prediction and management : Leveraging real-time data to simulate disease spread and plan responses.

Emergency preparedness : Using simulations for natural disasters or public safety incidents to enhance response strategies.

Resource optimization in healthcare : Improving the allocation of healthcare resources like hospital beds and emergency services.

Environmental health monitoring : Tracking and analyzing environmental factors that impact public health, such as pollution levels.

Public safety and incident response : Simulating various scenarios to optimize law enforcement and emergency services.

These applications demonstrate the transformative impact of Digital Twin Technology in public health and safety within Smart Cities.

3.6 Citizen-centric aspects

Technological advancements have focused on urban development and infrastructure management, employing physical sensors such as the Internet of Things (IoT) and satellites (Borrmann et al. 2018 ). However, not all city digital twin implementations have citizen engagement in mind. A citizen-centric digital twin (CCDT) approach views citizens as integral components of a data-driven city, with human sensors playing a key role in addressing city-scale challenges (Saeed et al. 2022 ). This approach distinguishes itself from traditional digital twin frameworks by prioritizing citizens as the central element and integrating technologies like processing, data acquisition, and visualization to enhance citizen involvement in infrastructure governance.

Developing a CCDT requires the execution of numerous processes and technologies. One such technology involves sensors like Volunteered Geographic Information (VGI) (White et al. 2021 ), which transfer data from the actual city to its digital twin, followed by analysis using various analytical tools. Managing data from diverse sources at a city scale presents challenges in scalability, reliability, and the performance of real-time analytics and modeling (Langenheim et al. 2022 ).

The study by Abdeen et al. (Abdeen et al. 2023 ) indicates a scarcity of publications in this field over the past five years. However, a rising trend in CDT interest post-2017 was noted, with publications doubling by the end of 2022 compared to 2019. Research works (Ford and Wolf 2020 ; Fan et al. 2021 ) discuss the application of digital technologies in catastrophic situations and emergency responses. The capabilities of intelligent digital twins in various application fields have been examined (Shahat 2021; Deren et al. 2021 ), with the latter focusing on hazards like epidemic services, traffic control, and flood monitoring. (Shahat et al. 2021 ) concentrated on data simulations, fusion, administration, and collaboration. (Charitonidou 2022 ) addressed citizen participation in decision-making, highlighting that limited variables and processes and overlooking social aspects of urban contexts can render citizen input integration ineffective.

In the literature, various data acquisition mechanisms are employed to support CCDTs. One prominent method is the use of open-source data platforms (OSDP), providing spatiotemporal performance data relevant to CCDT applications like disaster management (Ghaith et al. 2022 ) and public services monitoring (Diakite et al. 2022 ). However, the effectiveness of CCDTs can be compromised if data is unreliable. Another mechanism is crowdsourcing, which generates large quantities of data and is particularly useful when remote or IoT sensors are unavailable (Trusov and Limonova 2020 ). Nevertheless, citizens' data errors or bogus inputs can affect CCDT effectiveness (Trusov and Limonova 2020 ). Visionary concepts for disaster city digital twins with extensive data (images, text, geo maps) have been proposed to enrich CCDT content (Fan et al. 2021 ).

Remote sensors are effective in modeling 3D city aspects of CCDTs and for large-scale urban monitoring (Fan and Mostafavi 2019 ; Fan et al. 2020 ). Geospatial platforms storing and managing data from individual vehicles or pedestrians have been proposed (Lee et al. 2022 ), though data accessibility to all stakeholders remains challenging for CCDT integration. Furthermore, IoT sensors (Nochta et al. 2020 ), deployed in large numbers and integrated effectively, facilitate urban data monitoring but require advanced communication infrastructure.

Advanced AI algorithms also play a crucial role in CCDTs, enhancing citizen engagement. Genetic algorithms (Fan et al. 2021 ) have been used to study the range of disruptions during hazard events, while Convolutional Neural Networks (CNN) (Pang et al. 2021 ) and Burst detection algorithms (Fan et al. 2021 ) help analyze crowdsourced data and social media frequencies providing insights into citizen perspectives and infrastructure governance through CCDTs.

3.7 Supply chain management and enhancement

The supply chain, encompassing the entire spectrum from raw material sourcing to the distribution of finished products, has seen a transformative integration of Digital Twin technology in recent years. Digital Twins, as virtual models of physical assets, offer real-time monitoring, analysis, and optimization across all facets of the supply chain, ranging from procurement to distribution (Tao et al. 2017 ). According to van der Valk et al. (van der Valk et al. 2022 ), these digital replicas enable two-way data exchange between the digital and physical worlds, providing professionals with exceptional visibility and traceability. This level of insight facilitates the identification of complex behavioral patterns and proactive problem detection, which is crucial for maintaining operational continuity.

As Gerlach et al. (Gerlach et al. 2021 ) highlight, Digital Twins are instrumental in offering real-time inventory insights, enabling the simulation of various scenarios, and assisting in planning and forecasting. These capabilities can result in significant cost reductions and process efficiency improvements. The study by Srai et al. (Srai and Settanni 2019 ) explores the optimization opportunities that Digital Twins offer in areas such as transportation resource management, demand–supply analysis, customer service improvement, and revenue enhancement. They also emphasize the role of technology in identifying and addressing inefficiencies.

The influence of Digital Twins in improving stock availability, a key aspect of manufacturing operations, is underscored by Abouzid et al. (Abouzid and Saidi 2023 ). Furthermore, (Lugaresi et al. 2023 ) introduces the concept of "technological labelers" like IoT devices, cloud computing, and advanced analytics, which are crucial in developing a comprehensive digital twin of a company's value chain. IoT integration, in particular, is noted for significantly enhancing supply chain efficiency by providing real-time data and contextual insights. In addition, distorted demand signals can result in various supply chain challenges, which can be effectively addressed using Digital Twin technology (Abouzid and Saidi 2023 ).

In conclusion, the discourse delves into the exploration and development of digital twins within automated manufacturing systems, showcasing the expansive potential of this technology in modernizing and streamlining supply chain management processes (Abideen et al. 2021 ).

4 Technological aspects of digital twin

This section explores the fundamental components and emerging advancements in digital twin technology, covering various technological aspects essential for understanding Digital Twins across disciplines. Each component offers unique insights into critical subjects, including distinctions between Digital Twins and Building Information Modeling (BIM) and the development of Cognitive Twins. By studying frameworks like the Five-Layer Architecture and advancements such as Cloud and Edge Computing integration, this section aims to reveal the technological foundations driving the evolution of Digital Twins. Readers will gain a deeper understanding of the technological breakthroughs shaping the future of Digital Twins and their applications through this comprehensive examination.

4.1 Distinction between digital twins and Building Information Modeling (BIM)

In construction technology, the emerging potential of digital twins and the rapid advancement of smart technologies has garnered significant interest. Although 'digital twin' is a relatively new term in the construction research literature, it is often conflated with Building Information Modeling (BIM), leading to some conceptual ambiguity. It is imperative to clarify the differences between these two concepts.

Building Information Modeling (BIM) is a digital representation of a building or structure's physical and functional characteristics. It is a tool architects, engineers, and construction professionals utilize to create detailed digital models of buildings, encompassing all systems and components, including architectural, plumbing, electrical, and HVAC systems. BIM enables the creation of accurate construction plans, virtual walkthroughs, and performance testing under various scenarios. (Wang and Meng 2021 ) defined BIM as a method that integrates geometric and non-geometric data. The 3D model, often called the BIM model and realized through object-oriented software, is a critical component of BIM (Cerovsek 2011 ). However, BIM primarily manages static data and requires external technologies to update models with real-time data (White 2021). In construction projects and asset management, a vast amount of non-geometric data is essential for informed decision-making but is often underutilized (Khudhair et al. 2021 ). BIM models have limited capacity to handle large volumes of dynamic and multifaceted data, necessitating advanced storage and processing technologies. These limitations can lead to data underutilization, inefficient decision-making, and financial implications. The advent of digital twin technology offers a solution to overcome these constraints inherent in BIM.

Digital twins and BIM represent two distinct technological applications in the construction sector, differentiated by their functions. BIM is most effective in the design and construction phases, while digital twins excel in building maintenance and operations. A digital twin system involves data linkage that transfers information between the physical asset and its virtual counterpart. This indicates that a BIM (Building Information Modeling) model is the initial step toward developing a digital twin in the construction industry. Digital twin technology integrates the BIM model with the physical world, enabling bidirectional data exchange. This connection allows for the real-time updating of the BIM model, enhancing asset implementation and management decision-making. The synergy between BIM and Digital Twin technology has the potential to revolutionize the construction industry. By combining the detailed architectural and structural information provided by BIM with the real-time operational data and analysis capabilities of Digital Twin technology, construction professionals can create comprehensive, accurate digital models of buildings. These models can then continuously monitor and optimize building performance in real-time.

4.2 Framework of the five-layer architecture in digital twins

A digital twin, essentially a digital representation of a physical entity, process, or person contextualized in a virtual environment, is a pivotal tool for organizations to simulate real-world scenarios and outcomes, thus enhancing decision-making capabilities (Moosavi et al. 2021 ). As shown in Fig.  10 , the architecture of digital twins is typically structured into five principal layers, as outlined in (Jones et al. 2020 ):

Five-layer architecture in digital twins

Physical layer : This foundational layer comprises the actual physical objects or entities. It utilizes sensor technology for data acquisition and can receive commands from the virtual layer. This layer provides real-time data feedback to the digital twin model.

Data sensing layer : Responsible for collecting diverse information types, this layer employs various sensors to monitor the system's status and operational process in detail. The data heterogeneity and variety stem from diverse data generation sources, such as IoT sensors, information systems, and wearable devices.

Data transmission layer : As a crucial link, this layer ensures data transmission between the physical and virtual layers. It leverages communication integration protocols and interactive security technologies to facilitate this transfer (Lohtander et al. 2018 ).

Virtual layer : In this layer, the components of the real world are digitally reconstructed. It builds a collection of digital twins using data transmitted from the physical layer, enhanced with historical or integrated network data. This layer dynamically tunes itself based on the real-time data from the physical layer and can be influenced by modifications made in the application layer.

Application layer : This layer visualizes the data and simulations derived from the virtual layer, presenting a graphical model that staff can easily interpret. Modifications in the physical or virtual layer parameters can lead to simulation changes. These can then be revised and optimized based on the observed or extrapolated results.

Each layer in this five-tier architecture plays a distinct yet interconnected role, collectively enabling the digital twin to function as a comprehensive, dynamic system for analyzing, simulating, and enhancing real-world processes and entities.

4.3 Integration of cloud and edge computing in digital twin environments

Cloud computing represents a large-scale computational approach that leverages the Internet to facilitate sharing computing, storage, and other resources, accessible anytime and anywhere on demand. In contrast, edge computing is a novel computational model that processes a portion of data using distributed computing, storage, and network resources between data sources and cloud computing centers.

Edge computing is increasingly recognized for its potential to enhance privacy, reduce latency, conserve energy and costs, and boost reliability. It is particularly well-suited for Digital Twin (DT) scenarios that demand low latency, high bandwidth, high reliability, and stringent privacy measures. In DT-assisted edge computing setups, the framework includes user devices, edge servers, resource devices, and the DT itself. User equipment initiates task requests to the edge server, which then allocates computing resources to the task, with the DT deployed within the edge server. Reviewed literature demonstrates the application of Cloud and Edge technologies in various contexts. Cloud storage is universally employed in these studies. Earlier research also utilized cloud computing for user interfaces, with cloud-rendered 3D models, as indicated in (Xu et al. 2021 ), or through GUIs accessible via web applications as in (Urbina Coronado 2018). While initial studies relegated intensive data processing and analytics to cloud computing due to its superior resource access, recent advancements in edge device capabilities have led to the implementation of edge computing, employing techniques for heavy data analysis or machine learning (Cathey et al. 2021 ; Lu et al. 2021a ; Zhang et al. 2022 ).

Recent digital twin research, such as (Alam and El Saddik 2017 ), employs edge computing, describing a framework where each device is represented as a cloud-based digital twin. This hierarchical architecture involves higher-level digital twins composed of simpler units in a master/slave relationship, enhancing the communicability of traditional cyber-physical systems with cloud servers' advanced computational and storage capacities. Focus on edge-based architectures is evident in (Dong 2019; Lu et al. 2021a ), with research by Dong (Dong 2019) on enhancing energy efficiency in 5G services through deep neural networks and Lu (Lu et al. 2020 ) exploring the use of digital twins in-network replication and machine learning via federated learning.

In industry, studies such as (Lu et al. 2021b ; Zhang et al. 2022 ) concentrate on Smart Vehicles, driven by the rise in edge computing power. Other research, including (Liu et al. 2019 ; Martinez-Velazquez et al. 2019 ), investigates the application of digital twins in healthcare, aiming to provide high-quality, real-time care to senior citizens. Most other studies, such as (Xu et al. 2021 ; Urbina Coronado et al. 2018 ; Hu et al. 2018 ; Bellavista et al. 2021 ), are categorized under Smart Manufacturing, focusing on industrial productivity improvements. Cloud-based digital twins play a crucial role in optimizing IoT device energy consumption and operational efficiency (Li et al. 2020 ), detecting and preventing potential system failures (Cathey et al. 2021 ), and ensuring data privacy and integrity (Wen et al. 2020 ). Thus, cloud computing and IoT emerge as complementary technologies, synergistically advancing the development of smart, interconnected systems.

Research in the Oil and Gas industry reflects a systematic adoption of digital twins and cloud/edge computing. For instance, (Pivano et al. 2019 ) discusses offloading simulations and data analysis to public cloud servers to access greater computational resources and avoid complex local IT infrastructures. Tygesen et al. (Tygesen et al. 2018 ) highlight the role of high-performance cloud computing in wave load modeling, which is essential for maintaining offshore platform integrity. (ASME 2018 ) describes the use of cloud data lakes for data verification and physical model feeding. At the same time, a microservices-based approach has been presented for designing and implementing digital twins using open-source tools (Zborowski 2018 ).

4.4 Implementation of augmented reality in digital twin technology

Augmented Reality (AR) is a technology that merges the real with the virtual, facilitating real-time interaction and 3D registration (Damjanovic-Behrendt and Behrendt 2019 ). It enhances user experience by superimposing graphics, video streams, or holograms onto the physical world (Yin et al. 2023 ). It is supported by various devices such as AR head-mounted displays (HMD), tablets, head-up displays (HUD), projectors, VR HMD with cameras, and 2D screen augmentations.

AR's enhancements are primarily derived from its visualization, interaction, 3D registration, and information collection capabilities as a unified device (Billinghurst et al. 2015 ). AR contributes to Digital Twin (DT) technology in several dimensions. In the virtual twin dimension, AR provides visualization of non-registered geometry, data, workflows, and basic status monitoring and alerting for operators. It also allows users to update DT information, for example, by scanning barcodes or adding annotations. However, the full potential of AR in augmenting DTs remains underexploited. In the hybrid twin dimension, AR enables multi-modal interactions and on-site registered visualization, with a need for further exploration and utilization in cyber-physical interaction functions. In the cognitive twin dimension, AR-assisted DT, bolstered by edge-cloud computing systems, is poised to play a more significant role in areas like visual programming, human–robot collaboration (HRC), product design, and human ergonomics, marking promising future directions for AR-assisted DT.

Applications of AR-assisted DT span a wide range of physical scenarios, encompassing the entire product lifecycle, including the management of production facilities and services. The production process and service phases include design, production, distribution, maintenance, and end-of-life stages, as illustrated in Fig.  11 .

Applications of AR-Assisted digital twins in engineering lifecycle, including design, production, distribution, maintenance, and end-of-life stages

The systematic design process involves prototyping, pilot runs, and testing operations. Real-time data from product usage, collected by sensors, informs smart product service design or redesign, integrating DT in mapping virtual and physical objects (Praschl and Krauss 2022 ). Research in AR device utilization for design falls into three categories: product design (Zheng et al. 2018 ), service design, and system design. Service design, a creative and user-centric process for enhancing or creating new services (Chang et al. 2020 ), is supported by several studies focusing on operation training (Blomkvist et al. 2023 ), driving and flight guidance (Moya et al 2020b ), smart environments (Vidal-Balea et al. 2021 ), smart cities (Lacoche and Villain 2022 ; Ssin et al. 2021a ), and smart wetlands (Ssin et al. 2021b ), aiming to deliver user-centered services that cater to the needs of users and stakeholders.

System design entails developing architecture, components, and core algorithms for AR-assisted DT scenarios. Moya et al. (Aheleroff et al. 2020 ; Moya et al. 2020a ) introduced two self-learning DT systems with screen augmentation for fluid behavior prediction and beam load analysis.

The production process includes goods fabrication or service provision, subdivided into process planning and scheduling (Wiegand et al. 2018 ), monitoring and control (Lemos et al. 2022 ), assembly (Kritzler et al. 2017 ), and robotics-related works (Židek et al. 2021 ). Real-time machine status monitoring and interactive control are prevalent in research, as demonstrated by Paripooranan et al. (Paripooranan et al. 2020 ), who developed an AR-enabled 3D printer DT for alerting abnormal statuses.

In distribution, warehouse management utilizes AR and DT, as shown by Petković et al. (Petković et al. 2019 ) in their use of a warehouse system DT (comprising the warehouse, automated guided vehicles (AGV), and operators with AR HMD) to test a human intention estimation algorithm.

Maintenance work encompasses various strategies and can be categorized into reactive, preventive, and predictive maintenance (Petković et al. 2019 ), adopting different approaches within the AR-assisted DT framework.

4.5 Hybrid twins in mixed reality applications

Mixed Reality (MR) applications offer an interactive experience that blends real and virtual environments, akin to Augmented Reality (AR) (Damjanovic-Behrendt and Behrendt 2019 ). Additionally, the term Extended Reality (XR) encompasses Virtual Reality (VR), AR, and MR and has been included in the research scope. The enhancements brought about by AR are examined across three distinct dimensions of the digital twin: the virtual twin, hybrid twin, and cognitive twin, as depicted in Fig.  12 .

Layered framework for digital twin classification based on augmented reality devices' perceptual capabilities

The virtual twin dimension encompasses data transmission from physical to virtual realms, non-registered visualization, and essential status monitoring and alerting functions based on sensor data. When enhanced by Augmented Reality (AR) devices' perceptual capabilities, this dimension can improve the data transmission process from the physical to the virtual space and suitably update Digital Twin (DT) information. Beyond IoT sensor data, on-site information such as barcodes and workspace details can also be gathered through AR applications, exemplified in warehouse management (Xia et al. 2022 ).

Reference (John Samuel et al. 2022 ) discusses the concept of hybridization in DTs, focusing on refining DT accuracy through self-adaptation and data-driven estimation techniques. This approach integrates physics-based model predictions with process measurements, creating a hybrid digital twin (HT) that facilitates the soft-sensing of otherwise hard-to-predict data.

The hybrid twin dimension emphasizes analysis and feedback from the virtual to the physical world, such as context information-related analysis, visual registration, multi-modal interaction and control, and the functionalities based on these aspects. Traditional DTs manage real-time data analysis, including simulation, prediction, diagnosis, and optimization, feeding back the analysis outcomes from the virtual to the physical world. AR-assisted DTs enhance this analysis with on-site data, adding capabilities like object localization, scene understanding, and cyber-physical interaction computation. For instance, in human–robot collaboration (HRC) assembly (Johansen et al. 2023 ), the hybrid twin dimension offers immersive visual registration beyond traditional 2D interfaces, displaying geometry and key data overlaid on the physical entity in the correct position. In contexts such as assembly (Liu et al. 2022 ; Zhao and Sun 2020 ), maintenance (Meier et al. 2021 ; Li et al. 2021 ; Rabah et al. 2018 ), and manual or semi-automated tasks (Koteleva et al. 2021 ; Rebmann et al. 2020 ; Mandl et al. 2017 ), operators can reference on-site instructions and guidance to work more efficiently. Additionally, geometry overlay for inspection (Catalano et al. 2022 ; Xie et al. 2020 ) or motion preview (Kim and Olsen 2021 ) aids operators in verifying the shape or movement of physical entities against planned outcomes. Users can also add geometry-linked or position-related annotations through AR.

Akroyd et al. (Akroyd et al. 2022 ) introduced the concept of the Universal Digital Twin, a digital twin that leverages a dynamic knowledge graph to enable cross-domain interoperability for DTs.

4.6 Development of cognitive twins in digital twin technology

Cognitive twins represent an advanced form of digital twins endowed with high-level cognitive capabilities encompassing machine and human intelligence. These cognitive twins are designed to address complex and unpredictable situations using enhanced computational power dynamically. Augmented Reality (AR) significantly contributes to the development of cognitive twins as it can function as a wearable computational unit within the edge-cloud architecture (Li et al 2022 ). HoloLens 2, a widely-used AR device, notably possesses substantial computing power (1 T FLOP) compared to wearable devices like sensors. This capability allows training models on high-power devices and their subsequent deployment on HoloLens 2, highlighting one of AR's key benefits to digital twins.

Cognitive Digital Twins (CDTs), originating from the domains of Industry 4.0 and Smart Cities, are recognized for their ability to support autonomous activities (Um et al. 2018 ; Liu et al. 2023 ; Zheng et al. 2021 ). Semantic technologies, including ontology and Knowledge Graph (KG), are vital in interlinking digital twins in virtual spaces. These technologies eliminate ambiguity across heterogeneous systems, thus enhancing digital interoperability and enabling cooperative decision-making (Rožanec et al. 2022 ). As defined by (Pan et al. 2021 ), ontology involves a set of formal and explicit vocabularies characterized by shareability and reusability, describing domain-specific knowledge, entities' attributes, and their interrelationships. While early research primarily focused on utilizing ontology for data modeling and sharing (Rožanec et al. 2022 ), recent studies emphasize that integrating semantics with digital twin technologies can advance the capability and interoperability of CDTs in autonomous and cooperative decision-making (Zheng et al. 2021 ).

The knowledge graph has become increasingly important in developing and managing CDTs because it can delineate relationships between real-world entities or link data (Liu et al. 2023 ). For instance, recent research has explored using knowledge graphs and digital twins in managing assets and tasks in smart manufacturing systems (Guarino et al. 2009 ) and underwater ship inspections (Zheng et al. 2023 ). Some studies have concentrated on methodologies that leverage knowledge graphs to create semantic data models for shaping digital twins (Waszak et al. 2022 ).

Furthermore, the evolving flexibility and customization in futuristic smart manufacturing are closely linked with human intelligence. For example, in human–robot collaboration (HRC) tasks aimed at improving human ergonomics (Steinmetz et al. 2022 ), operators can adjust robot poses through gesture-based interactions with the robot's digital twin. After receiving instructions from human operators, the robot digital twin learns to perform better and meet human needs. Additionally, in the timber prefabrication process (Dimitropoulos et al. 2021 ), AR provides effective interaction methods to enhance mutual understanding between operators and collaborative robots, ultimately facilitating harmonious task sharing.

4.7 Classification of digital twins by scale

Digital twins can be categorized into various types based on their scale and comprehensiveness, including component, asset, system, and process twins (Amtsberg et al. 2021 ).

Component twins : This approach suits large, complex digital twins. The adaptation and uncertainty quantification of the model in such applications can be framed as a Bayesian state estimation problem. Here, data from the physical world is used to infer which models from a model library best represent the digital twins. This approach strategically selects specific components for replication in the digital twins to avoid data redundancy and reduce costs. Microsoft has developed the Azure Digital Twins (ADT) platform (Cinar et al. 2020 ), facilitating model creation and offering a graph API for querying and interacting with these digital twins. The ADT platform enables users to visualize and examine the relationships among components, such as creating 3-D digital twins of a factory with a user-friendly interface. This interface allows operators to monitor the state of each machine. A notable challenge in this scenario involves loading each 3-D object instance into the scene. Repeated loading of the same object in different locations can lead to inefficiencies.

To address this, future developments in component twins could involve a system where a single instance of a 3-D object is streamed, loaded into memory, and rendered multiple times as needed. This approach would optimize the handling of 3-D objects in digital twin environments, enhancing efficiency and reducing the computational load.

Asset twins : This methodology focuses on creating data-driven digital twins using a library of physics-based reduced-order models. When a single model library is shared among numerous assets, this approach can effectively scale to applications requiring a substantial number of digital twins (Krzyczkowski 2019 ). Asset twins involve an estimation process wherein online sensor data from a physical asset determines which models from the library should be integrated into the digital twin. Future advancements in asset twins should enhance the robustness of model selection, particularly in the context of corrupted data. Implementing mechanisms to improve robustness and incorporating various damage models to detect and classify actual asset damage is also essential. GE Healthcare (Kapteyn et al. 2020 ) has noted the application of asset twins in healthcare, addressing challenges such as staffing model design and surgical block schedule optimization.

System twin : Operating at a higher level, system twins amalgamate different assets to form a complete functional system, such as a vehicle's brake system (Aghdam et al. 2021 ). These twins offer insights into asset interactions, thereby augmenting overall performance.

Process twin : Process twins utilize high-performance computing to optimize equipment and manufacturing processes. This is achieved by integrating multidimensional process knowledge models (Aghdam et al. 2021 ). Manufacturers can attain unparalleled efficiency and deeper insights by combining production processes with economic considerations.

Application : A digital twin system integrating Virtual Reality (VR) and Artificial Intelligence (AI) technologies has been developed to monitor and analyze welder behavior. This system exemplifies the practical application of digital twin technology in understanding and improving specific work processes.

5 Datasets, data models, and software for developing digital twins

The transformation of physical assets into digital twins involves an in-depth asset data collection process, which is then utilized to form an exact digital counterpart. This procedure is essential for asset management and predictive maintenance. There are variant data models and datasets used to underpin the digital twin initiative and significantly enhance the effectiveness and capabilities of digital twin implementations while reducing development efforts and optimizing the total cost of ownership. Many software applications have recently been used to create and manage digital twins. This section presents samples of Data models, Datasets, and software applications.

5.1 Smart city data models and datasets

To illustrate the potential of digital twins in smart cities, let us consider examples of digital twin data models and datasets that provide valuable insights for urban planning and management. Digital twin data can be applied in both tangible and virtual realms. These data are pivotal for asset monitoring, operational optimization, and safety enhancement in physical settings. On the other hand, virtual landscapes enable realistic simulations, training endeavors, and strategic planning. This dual use of digital twins highlights their adaptability, effectively bridging the real and digital domains.

One of the cornerstones of DT design and development is modeling data. Data originate from heterogeneous sources, use various protocols, and include their own data attributes, attribute types, and relationships. In order to ensure interoperability, it is necessary not only to standardize the communication between DT components but also to standardize the data format that flows through these components.

3D city modeling transcends mere data acquisition and processing, extending into data management, storage, and exchange. Consequently, open and standardized data models and exchange formats are essential for 3D city modeling. CityGML and its streamlined counterpart, CityJSON (Ledoux et al. 2019 ), are the most established data formats for 3D city models. These formats facilitate representations ranging from basic to richly detailed, depending on the required level of detail (LoD). The building model is depicted in five levels of detail, from LOD0 to LOD4, with higher LoDs offering more detail and accuracy. The aim is to manage the complexity of 3D models effectively.

In their study, the authors (Lei et al. 2022 ) assess 40 authoritative 3D city models that have emerged since 2013. This evaluation yields both quantitative and qualitative insights. The framework developed offers a thorough and structured comprehension of the landscape of semantic 3D geospatial data while also serving as an evaluated compilation of open 3D city models.

In (Ledoux et al. 2019 ), digital twin (DT) initiatives in cities are classified based on the nature of their digital replicas (static or dynamic, i.e., incorporating sensor or IoT data) and the extent of data integration (the data connection between the physical and digital worlds). Various static datasets utilize digital model integration, including Helsinki 3D + , Espoo, Vienna, Zurich 3-4D, and Amsterdam3D. Meanwhile, dynamic datasets such as Digital Twin Munich, Rennes 3D, Virtual Gothenburg, and Sofia-Bulgaria employ digital shadow integration. Furthermore, dynamic datasets like DUET, Fishermans, and Virtual Singapore implement digital twin integration. It can be inferred that most initiatives are digital shadows, given that data connections from the real world to the digital copy are automated. At the same time, the reverse typically involves manual processes (human interventions adapting the physical world). This bidirectional connection warrants further exploration.

Many research projects and similar initiatives mainly focus on collecting and providing IoT data generated from smart cities. For example, the ODAA platform ( 2016 ) Footnote 1 provides open access to data collected from the City of Aarhus using IoT infrastructure deployed within the city. The datasets within the ODAA are categorized across various applications, including energy, population and society, transport, education, and more. Moreover, San Francisco Open Data ( 2024 ) Footnote 2 and the City of Chicago Data Portal ( 2024 ) Footnote 3 provide a centralized collection of relevant smart city datasets that are publicly accessible.

For example, the NYC Open Data Initiative has already leveraged digital twin technology to improve urban planning and citizen engagement. By providing access to a wide range of open data, including information on infrastructure, public services, and environmental factors, the initiative has empowered citizens to actively participate in shaping the city's future.

5.2 Software for digital twin creation and management

Numerous digital twin software applications are available for creating and managing digital twins in buildings, cities, and urban systems. Some notable examples include:

Autodesk revit (Autodesk 2019 ): This software is extensively used for Building Information Modeling (BIM) and is acclaimed for its comprehensive design, documentation, and collaboration tools. It enables architects, engineers, and construction professionals to create detailed 3D models and provides extensive data for informed decision-making throughout a building's lifecycle.

Esri cityengine (Badwi et al. 2022 ): CityEngine is a robust software tool for crafting 3D city models. It is utilized by urban planners and designers to generate detailed and lifelike representations of cities, offering capabilities for cityscape generation, urban environment modeling, and simulation of various urban scenarios. It also integrates with GIS data to enhance city models with geographic information and analysis.

Bentley systems openbuildings designer (Mainisa et al. 2023 ): This BIM software provides advanced building design and construction modeling tools. Architects, engineers, and construction professionals use it for detailed 3D modeling, structural analysis, and effective collaboration throughout the building lifecycle.

Unity reflect (Nämerforslund 2022 ): Unity Reflect is a platform that creates interactive and immersive experiences with digital twins. It supports real-time, high-fidelity 3D modeling for virtual and augmented reality environments, enhancing visualization, interaction, and decision-making processes.

Siemens city performance tool (Al-Obaidy et al. 2022 ): Specifically tailored for urban planning and management, this tool offers a comprehensive platform for analyzing and optimizing the performance of urban systems.

iLens from knowledge lens : This leading Industrial IoT solution addresses Industry 4.0 needs with capabilities in Interface Connectivity, Edge Computing, Monitoring and Control, and Predictive Analytics. iLens is powering diverse industries globally, including Automation, Manufacturing, Energy, and Utilities.

Iotics : Iotics' innovative digital twin technology enables seamless communication across an entire digital ecosystem. It bridges gaps between various entities, from sensors to power stations and individual trains to entire airplane networks, transcending organizational boundaries and differing data languages while maintaining security.

Kavida.ai : This supply chain digital twin platform assists enterprises in making intelligent resiliency decisions. It builds supply chain digital twins using artificial intelligence to help enterprises prevent and mitigate disruptions in real time or before they occur.

MODS reality : This cloud-based application hosts a digital twin of a facility in a point cloud environment, enhancing engineering and streamlining scheduling and work execution management for maintenance and minor modifications, thereby maximizing performance and profitability.

Twinzo : As a mobile-first live digital twin platform focused on operational excellence, twinzo visualizes and reconstructs live data in 3D, offering novel ways to analyze and consume information. It helps customers save significant operational costs and increase production output.

VEERUM's digital twin : This application is a leading visualization and analytics tool that combines CAD, geospatial, document management, IoT, and operational systems. It delivers considerable cost and time savings in operations, maintenance, reliability, and complex capital construction projects.

WillowTwinTM : Revolutionizing the built world, WillowTwinTM is a pioneering software platform for real estate and infrastructure assets. It provides a central hub for all asset data, turning siloed datasets into a virtual replica of the built form. The platform enables proactive, data-driven decision-making in real-time to reduce costs, increase profits, and manage risks.

6 Digital twin performance metrics

Extensive research has been conducted on digital twins (DTs) and their applications, yet a standard method for assessing DT performance remains elusive. Establishing a method for evaluating the performance of DTs is essential for enhancing or monitoring processes and systems within a business context. Such a method could guide researchers and practitioners in developing more effective digital twins (Psarommatis and May 2022 ).

There have been limited studies focusing on specific methodologies for assessing DT performance. Chen et al. (Chen et al. 2021 ) proposed a DT maturity model for managing industrial assets based on Gemini principles, facilitating quantitative evaluation of DT flexibility and implementation levels. Chakraborty et al. (Chakraborty and Adhikari 2021 ) assessed DT performance in a multi-time scale dynamical system using an efficient framework that leverages expectation maximization and a sequential Monte Carlo sampler for developing machine learning-based DTs. Shangguan et al. (Shangguan et al. 2020 ) evaluated DT performance for fault diagnosis using a predefined threshold technique, focusing on accuracy (ACC), specificity (SPE), and sensitivity. Psarommatis et al. (Psarommatis and May 2022 ) introduced a systematic approach for measuring DT performance and flexibility, quantifying it based on four key performance indicators (KPIs). Additionally, they introduced DTflex as a new KPI to evaluate the flexibility of digital twins.

6.1 Performance metrics categories

Although there are no well-established methods or Key Performance Indicators (KPIs) in the field for thoroughly assessing the performance of Digital Twins (DT), this study suggests classifying performance metrics according to three essential elements: software, hardware, and data management middleware. This paradigm makes it possible to evaluate the system's efficacy in detail. A thorough analysis of the body of prior research and industry norms guided the choice of these indicators. We aimed to find measures that captured the essential elements of DT performance by combining knowledge from several sources.

The proposed metrics ensure an adequate evaluation by focusing on DT performance characteristics within each component. For example, metrics about hardware components evaluate attributes like scalability, communication dependability, and sensor precision. These metrics were selected to represent the fundamental hardware performance features essential to DT's operation. Similarly, metrics related to middleware for data management emphasize security, scalability, and efficiency, highlighting middleware's vital role in integrating and controlling data streams. Finally, software component metrics highlight the significance of strong software functions for DT performance by addressing factors such as model integrity, simulation accuracy, and user interface responsiveness. Each metric recommended in this section is supported by its relevance to real-world DT implementations and alignment with broader business or operational objectives. These measures help stakeholders make well-informed decisions by offering practical insights about DT performance. Including these measures also attempts to create a standard framework for assessing DT performance in various applications and domains.

6.1.1 Hardware components

Sensor accuracy: Precision and reliability of physical sensors.

Communication reliability: Efficiency of data transmission between sensors and the digital counterpart.

Hardware scalability: Ability to expand hardware components with increasing data volumes.

Latency in data acquisition: Time taken to acquire and transmit sensor data.

Hardware failure rate: Frequency and severity of failures in sensors or actuators.

6.1.2 Data management middleware

Data integration efficiency: Ease of integrating data from various sources into the DT.

Middleware latency: Time taken for middleware processes to complete tasks.

Data accuracy and consistency: Precision and consistency in data storage and management by middleware.

Scalability of middleware: Ability to handle increasing data volumes without performance degradation.

Data security protocols: Effectiveness of security protocols in protecting data during storage and transit.

6.1.3 Software components

Model fidelity: Accuracy and completeness of the digital model representing the entity.

Simulation accuracy: Precision of simulations compared to real-world scenarios.

Quality of visualization: Clarity and detail of visual representations in the user interface.

User interface responsiveness: Speed and responsiveness of the software interface to user actions.

IoT device integration: Compatibility and integration with various IoT devices.

Scalability of software: Capacity to handle increasing computational loads and data processing demands.

Software security: Protections against cyber threats and unauthorized access.

Interactivity and control: Responsiveness of software to user inputs and control commands.

Updating and maintenance efficiency: Ease of updating and maintaining software components.

Effectiveness of decision support: Capability of the software to provide meaningful insights.

6.2 Best practices for evaluating digital twin performance

As noted by Peter Drucker, Mgt. consultant and author, “You cannot manage what you cannot measure.” This principle is equally applicable to digital twins. The confusion matrix employed in data science can measure digital twins' performance ARC Advisory Group ( 2024 ). Footnote 4 Assessing the performance of digital twins necessitates a thorough approach that considers multiple aspects, including hardware, data management middleware, and software components. Below are some essential practices for effectively assessing the performance of digital twins. The formulation of these best practices necessitated a thorough examination of the current literature on DT performance evaluation. Consultations with some stack holders were also conducted. By combining information from various sources, we hoped to convey the multidimensional nature of DT performance and provide meaningful advice to practitioners and researchers alike. Furthermore, the methods were iteratively refined to ensure their usefulness and applicability across various situations and industries.

Each practice recommended in this section is based on known management principles and its ability to address important difficulties in DT performance evaluation. For example, the emphasis on objective definition and particular Key Performance Indicators (KPIs) demonstrates the significance of goal alignment and measurement precision in achieving effective DT efforts. Similarly, data quality, security assessment, and scalability analysis methods emphasize these variables' importance in assuring the dependability and efficacy of distributed computing systems.

Objective definition: Clearly articulate the goals and objectives of the digital twin implementation to align performance indicators with broader business or operational objectives.

Establish specific KPIs: Identify and set specific Key Performance Indicators (KPIs) that align with the objectives, ensuring they are measurable, relevant, and linked to desired outcomes.

Multidimensional evaluation: Assess performance across multiple dimensions, including accuracy, responsiveness, scalability, security, and usability.

Regular review and update of metrics: Given the evolving nature of digital twin environments, performance metrics should be regularly reviewed and updated to maintain relevance and accuracy.

Focus on data quality and integrity: Emphasize metrics related to data accuracy, consistency, and integrity, as the quality of the digital twin largely depends on the reliability of its data.

Incorporate end-user experience metrics: Include metrics that gauge user satisfaction and adoption, such as visualization quality, interaction, and ease of use.

Measure latency and responsiveness: Evaluate latency in data collection, middleware processing, and software responsiveness to ensure real-time or near-real-time capabilities.

Security performance assessment: Implement metrics to evaluate the efficacy of security measures, including data encryption protocols.

Scalability analysis: Examine the digital twin's scalability, focusing on how well it accommodates increasing data volumes, user numbers, and processing requirements.

Simulation accuracy verification: Regularly validate the accuracy of simulations and virtual representations against actual world scenarios to ensure the digital twin's reliability.

Benchmarking: Compare performance against industry standards or best practices to understand how the digital twin stacks up against similar implementations.

Utilize monitoring technologies: Deploy monitoring technologies that offer real-time insights into the digital twin's operation, enabling proactive issue identification and resolution.

Develop a continuous improvement process: Establish a process for continuous improvement that integrates user feedback and ongoing evaluations, fostering a culture of perpetual enhancement.

By adhering to these practices, organizations can establish a robust framework for assessing and improving the performance of their digital twins, ensuring that these technologies deliver maximum value and effectively contribute to strategic objectives. To sum up, this section's performance indicators are the outcome of a systematic approach guided by academic research and industry observations. We hope to give readers a thorough grasp of how these measures support efficient DT evaluation procedures by outlining the reasoning behind their selection and their applicability to DT performance assessment.

7 Challenges associated with digital twins

Understanding the obstacles encountered while deploying digital twin technology is critical for its successful adoption and improvement. This section elucidates the difficulties various components of digital twin systems face, shedding light on their origins and implications. The challenges outlined are meticulously identified through an extensive review of literature and insights from field and industry experts, signifying their significance in the successful deployment and operation of digital twin systems. This analysis integrates multiple sources to pinpoint these hurdles as key challenges. The study organizes the identified challenges into three main aspects of digital twin technology: hardware, data management middleware, and software. This categorization facilitates a thorough understanding of the complex problems impacting different aspects of digital twin systems. A thorough examination of these challenges across the hardware, data management middleware, and software components aids in bridging the current research gap. Whereas prior studies often discussed these challenges in broad strokes, (Tuhaise et al. 2023 ) divided them into three categories: data transmission, interoperability, and data integration. This research details specific problems within each distinct component of the digital twin framework, thereby offering an in-depth analysis of the inherent obstacles in digital twins. It identifies hardware-related challenges, such as the complexity of sensor integration and issues with hardware reliability, suggesting solutions like adopting standardized sensor interfaces and employing predictive maintenance strategies. Furthermore, the study uncovers problems in data management middleware, including data integration bottlenecks and interoperability issues, recommending developing scalable middleware systems and adopting universal standards to enhance interoperability. The research outlines security vulnerabilities and algorithmic complexity regarding software components, proposing using advanced analytical tools and robust cybersecurity measures as solutions.

By delineating these issues across hardware, middleware, and software components, the study enhances the understanding of digital twin technology and offers actionable recommendations for enhancing the technology’s effectiveness and resilience. As digital twin technology continues to evolve, the findings underscore the necessity of concentrating on these components to surmount challenges and fully exploit the technology's potential across various applications and industries. The examination of digital twin elements and their associated challenges is visually summarized in Fig.  13 , which consists of three parts: (a) delineates the components of a digital twin, (b) identifies the challenges specific to each component, and (c) proposes solutions to these challenges.

Overview of digital twin components, associated challenges, and proposed solutions. Part ( a ) delineates the core components of a Digital Twin (DT). In part ( b ), a detailed breakdown highlights the challenges inherent in each component. Part ( c ) provides insightful solutions strategically proposed to address these challenges and enhance the effectiveness of Digital Twin implementation

7.1 Hardware components

Hardware components are the foundation of digital twin systems, comprising sensors, actuators, and other physical devices. Challenges within this component include:

Sensor integration complexity: Integrating diverse sensors for real-time data poses compatibility and synchronization issues.

Hardware reliability: Ensuring long-term sensor and actuator reliability is essential.

Proposed solutions involve adopting standardized sensor interfaces and implementing predictive maintenance strategies to mitigate these challenges.

7.2 Data management middleware

Middleware plays a crucial role in managing and processing the vast amount of data generated by digital twin systems. Challenges within this component include:

Data integration bottlenecks: Handling diverse data streams can lead to processing delays.

Interoperability issues: Different standards may hinder middleware system compatibility.

Proposed solutions include developing scalable middleware architectures and embracing industry-wide standards for improved interoperability.

7.3 Software components

Software components encompass the algorithms and analytical tools for real-time data analysis and decision-making. Challenges within this component include:

Algorithmic complexity: Complex algorithms for real-time analytics and decision-making need streamlining.

Security vulnerabilities: Software components are susceptible to cybersecurity threats.

Proposed solutions involve utilizing advanced analytical tools and robust cybersecurity protocols to address these challenges.

In conclusion, addressing the challenges linked with digital twins requires a deep understanding of their core components: hardware, data management middleware, and software. This analysis has unveiled various obstacles, from hardware constraints to data integration complexities and software interoperability challenges. A comprehensive perspective is provided by examining these issues across the distinct hardware, middleware, and software components. It is essential to identify and tackle the limitations associated with hardware, the challenges within middleware, and the issues related to software interoperability to enhance the efficiency and robustness of digital twin systems. As digital twin technology evolves, prioritizing these areas will be critical for navigating difficulties and leveraging the technology’s capacity in diverse applications and industries.

8 Case studies

Case studies in the realm of smart cities and digital twins serve as vital illustrations of these technologies in practical scenarios:

Dubai's "Happiness Agenda": A smart city initiative using big data to enhance urban living and measure "happiness" across various criteria. The objective was to involve every citizen in shaping future cities, particularly focusing on citizen engagement. Dubai’s "Happiness Agenda" implementation represents a notable example of a smart city involving its residents in urban development. Dubai has positioned itself as one of the "happiest" places to live by defining citizen "happiness" across multiple criteria. It uses big data analysis to allocate urban resources strategically, enhancing the city's overall "Happiness Index" (Zakzak 2019 ).

West Cambridge site and IFM building: These case studies explore adaptable digital twins at the building level, integrating various data sources and AI-driven decision-making. The West Cambridge site of the University of Cambridge in the UK was chosen as a case study due to its diverse facilities, which include university buildings, sports centers, residence areas, main roads, parking places, and restaurants. This variety allows for testing and evaluating the proposed dynamic digital twin system across different types of infrastructure. Additionally, the site's size and complexity offer an ideal environment to assess the effectiveness of the technology. Access to extensive data sources, collaboration opportunities with experts, and relevance to the academic community further contribute to its suitability as a testbed for the study (Qiuchen Lu et al. 2019 ).

Herrenberg, Germany: A case study demonstrating the use of digital twin technology in urban planning and city management. The case study of Herrenberg might illustrate the implementation and benefits of digital twins in improving urban planning, infrastructure management, and citizen engagement within the city. Herrenberg was selected as a case study for the digital twin due to its relevance to urban challenges, accessibility of diverse data sources, the potential for collaboration with local stakeholders, engagement of the community, and suitability in terms of size and complexity for testing the digital twin technology (Dembski et al. 2020 ).

Cambridge Sub-region: A digital twin pilot is developed, integrating diverse data streams for urban planning and decision-making. The authors stress the significance of including diverse data like IoT sensors, satellite images, social media, and government records to ensure an all-encompassing and precise city representation. The case study presented in the paper is about developing a digital twin pilot for the Cambridge Sub-region. It highlights how integrating various data streams and simulation models can assist urban planning, resource allocation, and decision-making processes. The case study provides insights into the potential benefits of using a city-level digital twin for improving efficiency, sustainability, and resilience in urban environments (Wan et al. 2019 ).

Málaga City: Implementing cognitive analytics in smart city management to enhance transportation, energy, and public services. The focus is on enhancing various aspects of urban life, such as transportation, energy management, waste management, and public services. The case study of Málaga City demonstrates the practical implementation of cognitive analytics to improve decision-making processes, optimize resource allocation, and ultimately enhance the quality of life for its residents (Pérez and Toledo 2017 ).

Ålesund, Norway: The study explores the role of a data-driven digital twin in enhancing urban systems and services within a smart city framework. It suggests using high-quality 3D graphical digital twins (GDTs) of cities to generate 4D visualizations of geolocalized time-series data to enhance citizen engagement. Through a case study conducted in Ålesund, Norway, the methodology utilizes readily available hardware and a game engine to develop immersive environments for presenting complex data sourced from GIS, BIM, demographics, and IoT. The approach emphasizes scalability, transferability, versatility in data integration, adherence to privacy regulations, and dependable data delivery. The paper introduces a pioneering smart city GDT framework, which capitalizes on interactive features and advancements in metrology (Major et al. 2021 ).

Case study in Greece: Details the development and application of digital twins tailored for smart cities, focusing on urban infrastructure improvements. The case study probably illustrates how digital twins optimize city systems, improve efficiency, and facilitate decision-making processes in Greek urban environments. This study might showcase practical examples of implementing digital twin technology to address challenges and enhance the overall functioning of a smart city in Greece (Evangelou et al. 2022 ).

Each case study offers unique insights into the deployment and impact of digital twin technology in various urban settings, highlighting its potential to improve city management and living standards. Table 2 offers an overview of each paper's focus areas, case studies, and key highlights, showcasing their distinct contributions and applications in the field of digital twins in smart cities.

9 Smart city governance in the era of digital twins: addressing challenges and leveraging opportunities

In the evolving discourse on smart cities and digital twin technologies, a critical examination of multi-level governance, organizational practices, and governance dimensions emerges as pivotal. The collective contributions from the referenced studies provide a comprehensive overview of the challenges and strategies in implementing smart city initiatives across different governance frameworks and geographical contexts.

As examined in one study, the integration of Chinese new authoritarian principles into smart government transitions highlights the inherent tensions between state-level directives and local-level implementation, underscoring the complexity of multi-level governance in authoritarian regimes (Zhang and Mora 2023 ). This perspective is enriched by a nuanced exploration of organizational practices within smart city development, revealing how bureaucratic, technocratic, and participatory logics intersect to shape decision-making and citizen engagement in smart city projects (Mora et al. 2023a ). Furthermore, the identification of three key governance dimensions—institutional context for urban innovation, urban innovation ecosystem, and urban digital innovation—provides a framework for understanding the governance mechanisms essential for fostering smart city transitions (Mora et al. 2023b ).

Critical analysis across the studies reveals common challenges in smart city governance, such as interoperability and compatibility issues within the digital ecosystem and integrating a technological dimension in urban development. These challenges underscore the importance of addressing interoperability and compatibility to enhance city planning and management effectively (Quek et al. 2023 ). The discourse extends to the critical analysis of smart urbanism in non-Western contexts, notably in India and Africa, where issues of urban informality, equity, and the inclusivity of smart city initiatives are brought to the forefront (Prasad et al. 2023 ; Tonnarelli and Mora 2023 ). These analyses highlight the necessity of adopting equitable and inclusive smart city development approaches that consider the needs and priorities of all urban dwellers, particularly marginalized communities.

Moreover, the call for empirical studies and the integration of innovation management theory into smart city governance research emphasizes the need for practical guidance and theoretical advancements in managing urban digital innovation (Mora et al. 2023b ). The exploration of human-cyber-physical interactions further illuminates the evolving relationship between technology, governance, and societal dynamics, advocating for a holistic approach that balances technological advancements with ethical and sociocultural considerations (Quek et al. 2023 ).

In conclusion, the amalgamated insights from these studies advocate for a pragmatic, contextually informed, and inclusive approach to smart city governance. By addressing the multifaceted challenges of interoperability, governance, and citizen engagement, and by incorporating a critical perspective on urban informality and inclusivity, this body of work contributes significantly to the scholarly discourse on smart cities and digital twins. The emphasis on empirical research, innovation management, and the integration of technology in urban development underscores the dynamic interplay between technology, governance, and urban development strategies in the quest for sustainable and equitable urban futures.

9.1 Role of DT in smart city governance

Smart city governance constitutes a complex framework fundamental to the effective realization and long-term viability of smart city endeavors. It encompasses the strategic alignment of policies, technological systems, and multifaceted collaborations amongst stakeholders by overarching urban development goals. Digital twin technology plays a pivotal role in enhancing smart city governance by offering innovative solutions across various components:

Policy and strategy formulation : Crafting policies and strategies that guide smart city initiatives in service of the city's broader objectives (Beckers 2022). Digital twins assist in crafting policies and strategies by providing valuable insights derived from real-time data and simulations. City authorities can utilize digital twins to assess the impact of different policies and strategies on urban systems, enabling informed decision-making aligned with broader city objectives.

Collaborative ecosystem : Fostering partnerships spanning government entities, the private sector, academic institutions, and the citizenry, thus leveraging collective knowledge and resources (Beckers 2022). Digital twins foster collaboration among government agencies, private sector entities, academic institutions, and citizens by providing a platform for data sharing and analysis. This collaborative ecosystem enhances collective knowledge and resource utilization, facilitating more effective governance practices and co-creating solutions to urban challenges.

Technological infrastructure : Establishing and administering the technological basis, encompassing data management and digital platforms, that underpins smart city operations (Zhang and Mora 2023 ). As a foundational element of smart city operations, digital twins contribute to establishing and managing the technological infrastructure required for governance. They enable comprehensive data management and visualization, empowering city administrators to monitor urban systems, identify emerging trends, and respond proactively to issues in real-time.

Ethical considerations : Prioritizing ethical concerns by safeguarding data privacy and security and ensuring the equitable deployment of technology (Mora et al. 2023a ). Digital twins support ethical governance by prioritizing data privacy, security, and equitable technology deployment. Through robust data encryption protocols and access controls, digital twins safeguard sensitive information, ensuring that governance processes remain transparent, accountable, and inclusive for all stakeholders.

Public participation : Stimulating citizen involvement in the governance process promotes transparency and inclusiveness (Mora et al. 2023b ). Digital twins facilitate public participation by providing accessible platforms for citizen feedback, collaboration, and co-design of urban solutions. By incorporating citizen inputs into decision-making processes, digital twins help ensure that governance strategies align with community needs and preferences.

Sustainability : Championing sustainable development practices integrated within smart city projects to prioritize environmental stewardship and long-term resilience (Quek et al. 2023 ). By simulating various scenarios and assessing the environmental impact of proposed policies and projects, digital twins enable city authorities to prioritize sustainability and resilience in urban planning and decision-making processes.

9.2 Smart city governance challenges

The pursuit of smart city objectives is frequently hindered by governance crises, underscoring the complexities of managing urban digital transformations. Digital twins offer innovative solutions to navigate the complexities of urban governance and enhance decision-making processes. Here, we explore how digital twins can be utilized to tackle key challenges in smart city governance:

Data privacy and security concerns: Contending with data privacy and security risks associated with the vast collection and storage of urban data (Mora et al. 2023a ). Digital twins incorporate robust data encryption protocols and access controls, ensuring the protection of sensitive information within smart city systems. Digital twins help mitigate privacy and security risks associated with urban data collection and storage by enabling secure data management and transmission.

Digital divide and inequity: Mitigating the digital divide can potentially intensify social disparities within urban communities (Prasad et al. 2023 ). Digital twins promote inclusivity and bridge the digital divide by providing accessible platforms for citizen engagement and participation in governance processes. Through user-friendly interfaces and interactive visualization tools, digital twins empower all citizens to contribute to decision-making, regardless of their technological literacy or socioeconomic status.

Regulatory and legal challenges: Navigating the disparity between the swift pace of technological progress and prevailing regulatory frameworks (Tonnarelli and Mora 2023 ). Digital twins assist city authorities in navigating regulatory and legal frameworks by providing comprehensive data analytics and scenario modeling capabilities. Digital twins facilitate informed policy-making and ensure alignment with legal standards and industry regulations by simulating the impact of proposed regulations and assessing compliance requirements.

Fragmented governance structures: Surmounting the intricacies of multi-stakeholder governance structures, which can obstruct coordinated action (Zhang and Mora 2023 ). Digital twins serve as centralized platforms for data integration and collaboration, overcoming the challenges of fragmented governance structures. By consolidating diverse datasets from multiple stakeholders and domains, digital twins enable seamless information sharing and coordination, fostering synergy among various governmental entities and stakeholders.

Resource constraints: Confronting limitations in financial, technical, and operational capacities is vital to the success of smart city ventures (Quek et al. 2023 ). Digital twins optimize resource utilization and operational efficiencies within smart city governance through predictive analytics and optimization algorithms. By identifying inefficiencies and optimizing resource allocation, digital twins help cities overcome resource constraints and maximize the impact of limited financial, technical, and operational resources.

By integrating digital twin technologies, smart city administration can address these challenges, paving the way for innovative solutions and sustainable urban development. Digital twins offer a comprehensive and data-driven approach to governance, enabling cities to enhance decision-making processes, accountability, and transparency, ultimately enhancing the quality of life for urban residents. While digital twin technologies have the potential to significantly improve urban management through advanced data analytics, simulation, and optimization, their seamless integration into smart city governance requires careful consideration of governance issues. This includes addressing concerns related to data privacy, fostering collaboration among stakeholders, and upholding ethical principles.

10 Conclusions and future research directions

This survey paper employs a meticulous bibliometric methodology, selecting the Web of Science database for its comprehensive coverage and developing precise search criteria to gather over 4,220 relevant articles. The analysis uses advanced tools like VOSviewer for network analyses and visualizations, including co-authorship and keyword co-occurrence maps, enabling a detailed examination of trends and relationships in Digital Twin technology and Smart Cities research. This methodological rigor ensures the study's reliability and contributes to its uniqueness in the field. This survey comprehensively reviews over 4200 publications in the domain of Digital Twins and Smart Cities. It outlines the evolution, applications, and integration of Digital Twins with IoT and AI in urban development. The survey distinguishes itself through extensive bibliometric analysis, focusing on datasets, platforms, software, and performance metrics, and it offers unique insights into the challenges and opportunities within the field. The findings include emerging trends, key thematic areas, and a detailed exploration of various Smart City applications. The paper concludes with implications for urban developers, policymakers, and researchers and recommendations for future research directions. The field of Digital Twin (DT) and Smart Cities is ripe for future research, aiming to overcome current challenges and explore new frontiers. Detailed investigation and development in this area are essential for realizing the full potential of DT technologies in urban environments. The discussions pave the way for sustainable and equitable urban futures, recognizing the dynamic interplay between technology, governance, and urban development strategies.

Future research should focus on:

Enhanced data integration : Developing more efficient methods for integrating diverse data sources within DT systems.

Scalability solutions : Creating scalable DT models suitable for larger and more complex urban environments.

Advanced security protocols : Strengthening cybersecurity measures for DT systems to ensure data privacy and security.

Sophisticated analytical tools : Incorporating cutting-edge AI and machine learning techniques for predictive analytics and decision-making.

Expanding IoT capabilities : Extending the use of IoT in DTs for comprehensive real-time data collection and monitoring.

Sustainable urban development : Leveraging DTs for resource management, focusing on sustainability and environmental conservation.

Citizen engagement models : Developing DTs prioritizing citizen involvement in urban planning and management.

Policy and governance studies : Examining the influence of policy in guiding DT implementation and addressing ethical concerns.

Economic impact assessment : Evaluating the economic implications of DTs, including cost analysis and return on investment.

Real-world case studies : Documenting extensive case studies to assess DTs' practical impact and challenges in urban settings.

Investigating future technological advancements: new applications, and the role of policy and governance in Digital Twins development.

The findings of this paper are poised to influence future research, policy-making, and practical applications in Smart Cities and Digital Twins in significant ways:

Informing future research directions: The comprehensive review of over 4,200 publications provides valuable insights into the current state of Digital Twins and Smart Cities research. Researchers can utilize this information to identify gaps in existing literature and prioritize areas for further investigation. For example, identifying challenges such as data integration bottlenecks and security vulnerabilities can guide future research efforts toward developing solutions to these pressing issues.

Guiding policy development: Policymakers can leverage the findings of this paper to inform the development of policies and regulations related to Digital Twin technology and its application in Smart Cities. By understanding the challenges and opportunities associated with Digital Twins, policymakers can create frameworks that promote innovation while addressing data privacy, cybersecurity, and ethical considerations.

Improving urban planning and management: The insights provided by this paper can assist urban planners and city managers in making informed decisions about adopting and implementing Digital Twins in Smart Cities. By understanding Digital Twin technology's potential benefits and challenges, city officials can develop strategies to optimize urban infrastructure, improve resource management, and enhance citizen services.

Driving technological innovation: The paper identifies emerging trends and technological advancements in Digital Twin technology, such as the integration of AI and IoT, as well as the development of scalable models and advanced security protocols. These insights can inspire innovation in academia and industry, leading to the development of new tools, platforms, and solutions that push the boundaries of Digital Twin technology and its applications in Smart Cities.

Finally, the findings of this article have the potential to spark advances in research, policymaking, and practical applications connected to Digital Twins and Smart Cities, resulting in more efficient, sustainable, and resilient urban development.

Data availability

Data is provided within the manuscript.

http://www.odaa.dk

https://data.sfgov.org

https://data.cityofchicago.org

https://www.arcweb.com/industry-best-practices/measuring-digital-twin-performance-maturity-confusion-matrix

Abdeen FN, Shirowzhan S, Sepasgozar SME (2023) Citizen-centric digital twin development with machine learning and interfaces for maintaining urban infrastructure. Telematics Inform 84:102032. https://doi.org/10.1016/j.tele.2023.102032

Article   Google Scholar  

Abideen AZ, Sundram VPK, Pyeman J, Othman AK, Sorooshian S (2021) Digital twin integrated reinforced learning in supply chain and logistics. Logistics 5(4):84. https://doi.org/10.3390/logistics5040084

Abouzid I, Saidi R (2023) Digital twin implementation approach in supply chain processes. Sci Afr 21:e01821. https://doi.org/10.1016/j.sciaf.2023.e01821

Aghdam ZN, Rahmani AM, Hosseinzadeh M (2021) The role of the internet of things in healthcare: future trends and challenges. Comput Methods Programs Biomed 199:105903. https://doi.org/10.1016/j.cmpb.2020.105903

Aheleroff S, Zhong RY, Xu X, Feng Z, Goyal P (2020) Digital twin enabled mass personalization: a case study of a smart wetland maintenance system. Volume 2: manufacturing processes; manufacturing systems; nano/micro/meso manufacturing; quality and reliability. https://doi.org/10.1115/msec2020-8363

Akroyd J, Harper Z, Soutar D, Farazi F, Bhave A, Mosbach S, Kraft M (2022) Universal digital twin: land use. Data-Centric Eng 3. https://doi.org/10.1017/dce.2021.21

Alam KM, El Saddik A (2017) C2PS: a digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access 5:2050–2062. https://doi.org/10.1109/access.2017.2657006

Allam Z, Jones DS (2021) Future (post-COVID) digital, smart and sustainable cities in the wake of 6G: digital twins, immersive realities and new urban economies. Land Use Policy 101:105201. https://doi.org/10.1016/j.landusepol.2020.105201

Al-Obaidy AHMJ, Khalaf SM, Hassan FM (2022) Application of CCME index to assess the water quality of tigris river within Baghdad City, Iraq. IOP Conf Ser: Earth Environ Sci 1088(1):012004. https://doi.org/10.1088/1755-1315/1088/1/012004

Amtsberg F, Yang X, Skoury L, Wagner H-J, Menges A (2021) iHRC: an AR-based interface for intuitive, interactive and coordinated task sharing between humans and robots in building construction. Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC). https://doi.org/10.22260/isarc2021/0006

ARC Advisory Group (2024) Measuring digital twin performance maturity with the confusion matrix. Retrieved from https://www.arcweb.com/industry-best-practices/measuring-digital-twin-performance-maturity-confusion-matrix . Accessed 10 May 2024

Ariyachandra MRMF, Wedawatta G (2023) Digital twin smart cities for disaster risk management: a review of evolving concepts. Sustainability 15(15):11910. https://doi.org/10.3390/su151511910

ASME (2018) Retracted: “Kinematic analysis of the motion of a six degrees of freedom wave energy converter based on the concept of the stewart-gough platform” [ASME 2018 37th international conference on ocean, offshore and arctic engineering, Volume 10: ocean renewable energy, Madrid, Spain, June 17–22, 2018, Conference sponsors: ocean, offshore and arctic engineering division, ISBN: 978–0–7918–5131–9. Paper No. OMAE2018–78601, pp V010T09A047; 10 pages]. Volume 10: ocean renewable energy. https://doi.org/10.1115/OMAE2018-78601

Autodesk Revit Architecture Certification (2019) Mastering Autodesk® Revit® 2020, 1033–1035. Portico. https://doi.org/10.1002/9781119570189.app3

Badwi IM, Ellaithy HM, Youssef HE (2022) 3D-GIS parametric modelling for virtual urban simulation using CityEngine. Ann GIS 28(3):325–341. https://doi.org/10.1080/19475683.2022.2037019

Bagaria N, Laamarti F, Badawi HF, Albraikan A, Martinez Velazquez RA, El Saddik A (2019) Health 4.0: digital twins for health and well-being. Connected health in smart cities. pp 143–152. https://doi.org/10.1007/978-3-030-27844-1_7

Bellavista P, Giannelli C, Mamei M, Mendula M, Picone M (2021) Application-driven network-aware digital twin management in industrial edge environments. IEEE Trans Industr Inf 17(11):7791–7801. https://doi.org/10.1109/tii.2021.3067447

Biagini V, Subasic M, Oudalov A, Kreusel J (2020) The autonomous grid: automation, intelligence and the future of power systems. Energy Res Soc Sci 65:101460. https://doi.org/10.1016/j.erss.2020.101460

Billinghurst M, Clark A, Lee G (2015) A survey of augmented reality. Foundations and Trends® in Human–Computer Interaction, 8(2–3):73–272. https://doi.org/10.1561/1100000049

Blomkvist J, Clatworthy S, Holmlid S (2023) Interlude 1: materiality in design from a practitioner perspective: interview with Markus Edgar Hormeß, Adam Lawrence and Marc Stickdorn (26 April 2022). The materials of service design. pp 83–88. https://doi.org/10.4337/9781802203301.00019

Borrmann A, König M, Koch C, Beetz J (2018). Building information modeling: why? What? How? Building information modeling. pp 1–24. https://doi.org/10.1007/978-3-319-92862-3_1

Bouzguenda I, Alalouch C, Fava N (2019) Towards smart sustainable cities: a review of the role digital citizen participation could play in advancing social sustainability. Sustain Cities Soc 50:101627. https://doi.org/10.1016/j.scs.2019.101627

Catalano M, Chiurco A, Fusto C, Gazzaneo L, Longo F, Mirabelli G, Nicoletti L, Solina V, Talarico S (2022) A digital twin-driven and conceptual framework for enabling extended reality applications: a case study of a brake discs manufacturer. Procedia Comput Sci 200:1885–1893. https://doi.org/10.1016/j.procs.2022.01.389

Cathey G, Benson J, Gupta M, Sandhu R (2021) Edge centric secure data sharing with digital twins in smart ecosystems. 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). https://doi.org/10.1109/tpsisa52974.2021.00008

Cerovsek T (2011) A review and outlook for a ‘Building Information Model’ (BIM): a multi-standpoint framework for technological development. Adv Eng Inform 25(2):224–244. https://doi.org/10.1016/j.aei.2010.06.003

Chakraborty S, Adhikari S (2021) Machine learning based digital twin for dynamical systems with multiple time-scales. Comput Struct 243:106410. https://doi.org/10.1016/j.compstruc.2020.106410

Chang T-W, Hsiao C-F, Chen C-Y, Huang W-X, Datta S, Mao W-L (2020) Fabricating behavior sensor computing approach for coexisting design environment. Sensors Mater 32(7):2409. https://doi.org/10.18494/sam.2020.2809

Charitonidou M (2022) Urban scale digital twins in data-driven society: challenging digital universalism in urban planning decision-making. Int J Archit Comput 20(2):238–253. https://doi.org/10.1177/14780771211070005

Chen L, Xie X, Lu Q, Parlikad AK, Pitt M, Yang J (2021) Gemini principles-based digital twin maturity model for asset management. Sustainability 13(15):8224. https://doi.org/10.3390/su13158224

Cinar ZM, Nuhu AA, Zeeshan Q, Korhan O (2020) Digital twins for industry 4.0: a review. Industrial engineering in the digital disruption era, 193–203. https://doi.org/10.1007/978-3-030-42416-9_18

City of Chicago Data Portal (2024) Chicago Data Portal. https://data.cityofchicago.org . Accessed 10 May 2024

Crespi N, Drobot AT, Minerva R (2023) The digital twin: what and why? The digital twin.pp 3–20. https://doi.org/10.1007/978-3-031-21343-4_1

Damjanovic-Behrendt V, Behrendt W (2019) An open source approach to the design and implementation of Digital Twins for Smart Manufacturing. Int J Comput Integr Manuf 32(4–5):366–384. https://doi.org/10.1080/0951192x.2019.1599436

Damjanovic-Behrendt V (2018) A digital twin-based privacy enhancement mechanism for the automotive industry. 2018 International Conference on Intelligent Systems (IS). https://doi.org/10.1109/is.2018.8710526

Dani AAH, Supangkat SH, Lubis FF, Nugraha IGBB, Kinanda R, Rizkia I (2023) Development of a smart city platform based on digital twin technology for monitoring and supporting decision-making. Sustainability 15(18):14002. https://doi.org/10.3390/su151814002

Defraeye T, Tagliavini G, Wu W, Prawiranto K, Schudel S, Assefa Kerisima M, Verboven P, Bühlmann A (2019) Digital twins probe into food cooling and biochemical quality changes for reducing losses in refrigerated supply chains. Resour Conserv Recycl 149:778–794. https://doi.org/10.1016/j.resconrec.2019.06.002

Dembski F, Wössner U, Letzgus M, Ruddat M, Yamu C (2020) Urban digital twins for smart cities and citizens: the case study of Herrenberg, Germany. Sustainability 12(6):2307. https://doi.org/10.3390/su12062307

Deren L, Wenbo Y, Zhenfeng S (2021) Smart city based on digital twins. Comput Urban Sci 1(1). https://doi.org/10.1007/s43762-021-00005-y

Diakite AA, Ng L, Barton J, Rigby M, Williams K, Barr S, Zlatanova S (2022) Liveable city digital twin: a pilot project for the city of Liverpool (NSW, Australia). ISPRS Ann Photogramm Remote Sens Spatial Inf Sci X-4/W2-2022:45–52. https://doi.org/10.5194/isprs-annals-x-4-w2-2022-45-2022

Dimitropoulos N, Togias T, Zacharaki N, Michalos G, Makris S (2021) Seamless human-robot collaborative assembly using artificial intelligence and wearable devices. Appl Sci 11(12):5699. https://doi.org/10.3390/app11125699

Din GMU, Marnerides AK (2017) Short term power load forecasting using Deep Neural Networks. 2017 International Conference on Computing, Networking and Communications (ICNC). https://doi.org/10.1109/iccnc.2017.7876196

Dong R, She C, Hardjawana W, Li Y, Vucetic B (2019) Deep learning for hybrid 5G services in mobile edge computing systems: learn from a digital twin. IEEE Trans Wireless Commun 18(10):4692–4707. https://doi.org/10.1109/twc.2019.2927312

Endsley MR (2016) Designing for situation awareness. CRC Press. https://doi.org/10.1201/b11371

Erol T, Mendi AF, Dogan D (2020) The digital twin revolution in healthcare. 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). https://doi.org/10.1109/ismsit50672.2020.9255249

Evangelou T, Gkeli M, Potsiou C (2022) Building digital twins for smart cities: a case study in Greece. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci X-4/W2-2022:61–68. https://doi.org/10.5194/isprs-annals-x-4-w2-2022-61-2022

Fan C, Mostafavi A (2019) A graph-based method for social sensing of infrastructure disruptions in disasters. Comput-Aided Civ Infrastruct Eng 34(12):1055–1070. https://doi.org/10.1111/mice.12457 . (Portico)

Fan C, Zhang C, Yahja A, Mostafavi A (2021) Disaster City Digital Twin: a vision for integrating artificial and human intelligence for disaster management. Int J Inf Manage 56:102049. https://doi.org/10.1016/j.ijinfomgt.2019.102049

Fan C, Jiang Y, Mostafavi A (2020) Social sensing in disaster city digital twin: integrated textual–visual–geo framework for situational awareness during built environment disruptions. J Manag Eng 36(3). https://doi.org/10.1061/(asce)me.1943-5479.0000745

Fang X, Wang H, Liu G, Tian X, Ding G, Zhang H (2022) Industry application of digital twin: from concept to implementation. Int J Adv Manuf Technol 121(7–8):4289–4312. https://doi.org/10.1007/s00170-022-09632-z

Ford DN, Wolf CM (2020) Smart cities with digital twin systems for disaster management. J Manag Eng 36(4). https://doi.org/10.1061/(asce)me.1943-5479.0000779

Gerlach B, Zarnitz S, Nitsche B, Straube F (2021) Digital supply chain twins—conceptual clarification, use cases and benefits. Logistics 5(4):86. https://doi.org/10.3390/logistics5040086

Ghaith M, Yosri A, El-Dakhakhni W (2022) Digital twin: a city-scale flood imitation framework. Proceedings of the Canadian society of civil engineering annual conference 2021. pp 577–588. https://doi.org/10.1007/978-981-19-1065-4_48

Ghosh D, Chun SA, Shafiq B, Adam NR (2016) Big data-based smart city platform. Proceedings of the 17th international digital government research conference on digital government research. https://doi.org/10.1145/2912160.2912205

Goia B, Cioara T, Anghel I (2022) Virtual power plant optimization in smart grids: a narrative review. Future Internet 14(5):128. https://doi.org/10.3390/fi14050128

Grieves MW (2005) Product lifecycle management: the new paradigm for enterprises. Int J Prod Dev 2(1/2):71. https://doi.org/10.1504/ijpd.2005.006669

Guarino N, Oberle D, Staab S (2009) What is an ontology? Handbook on ontologies. pp 1–17. https://doi.org/10.1007/978-3-540-92673-3_0

Guo D, Zhong RY, Lin P, Lyu Z, Rong Y, Huang GQ (2020) Digital twin-enabled Graduation Intelligent Manufacturing System for fixed-position assembly islands. Robot Comput-Integr Manuf 63:101917. https://doi.org/10.1016/j.rcim.2019.101917

He X, Ai Q, Wang J, Tao F, Pan B, Qiu R, Yang B (2023) Situation awareness of energy internet of things in smart city based on digital twin: from digitization to informatization. IEEE Internet Things J 10(9):7439–7458. https://doi.org/10.1109/jiot.2022.3203823

He Y, Deng J, Li H (2017) Short-term power load forecasting with deep belief network and copula models. 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). https://doi.org/10.1109/ihmsc.2017.50

He X, Ai Q, Qiu RC, Zhang D (2019) Preliminary exploration on digital twin for power systems: challenges, framework, and applications. arXiv preprint arXiv:1909.06977. https://www.semanticscholar.org/paper/Preliminary-Exploration-on-Digital-Twin-for-Power-He-Ai/360cd3a26ad4ab5f3685d676d36001348dd6e1d6 . Accessed 10 May 2024

Hinchy EP, O’Dowd NP, McCarthy CT (2019) Using open-source microcontrollers to enable digital twin communication for smart manufacturing. Procedia Manuf 38:1213–1219. https://doi.org/10.1016/j.promfg.2020.01.212

Hu L, Nguyen N-T, Tao W, Leu MC, Liu XF, Shahriar MR, Al Sunny SMN (2018) Modeling of cloud-based digital twins for smart manufacturing with MT connect. Procedia Manuf 26:1193–1203. https://doi.org/10.1016/j.promfg.2018.07.155

Huang J, Zhao L, Wei F, Cao B (2021) The application of digital twin on power industry. IOP Conf Ser: Earth Environ Sci 647(1):012015. https://doi.org/10.1088/1755-1315/647/1/012015

Ivanov S, Nikolskaya K, Radchenko G, Sokolinsky L, Zymbler M (2020) Digital twin of city: concept overview. 2020 Global Smart Industry Conference (GloSIC). https://doi.org/10.1109/glosic50886.2020.9267879

Jaensch F, Csiszar A, Scheifele C, Verl A (2018) Digital twins of manufacturing systems as a base for machine learning. 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). https://doi.org/10.1109/m2vip.2018.8600844

Jafari M, Kavousi-Fard A, Chen T, Karimi M (2023) A review on digital twin technology in smart grid, transportation system and smart city: challenges and future. IEEE Access 11:17471–17484. https://doi.org/10.1109/access.2023.3241588

Johansen ST, Unal P, Albayrak Ö, Ikonen E, Linnestad KJ, Jawahery S, Srivastava AK, Løvfall BT (2023) Hybrid and cognitive digital twins for the process industry. Open Eng 13(1). https://doi.org/10.1515/eng-2022-0418

John Samuel I, Salem O, He S (2022) Defect-oriented supportive bridge inspection system featuring building information modeling and augmented reality. Innov Infrastruct Solut 7(4). https://doi.org/10.1007/s41062-022-00847-3

Jones D, Snider C, Nassehi A, Yon J, Hicks B (2020) Characterising the Digital Twin: a systematic literature review. CIRP J Manuf Sci Technol 29:36–52. https://doi.org/10.1016/j.cirpj.2020.02.002

Kapteyn MG, Knezevic DJ, Huynh DBP, Tran M, Willcox KE (2020) Data-driven physics-based digital twins via a library of component-based reduced-order models. Int J Numer Methods Eng 123(13):2986–3003. https://doi.org/10.1002/nme.6423 . (Portico)

Ketzler B, Naserentin V, Latino F, Zangelidis C, Thuvander L, Logg A (2020) Digital twins for cities: a state of the art review. Built Environ 46(4):547–573. https://doi.org/10.2148/benv.46.4.547

Khudhair A, Li H, Ren G, Liu S (2021) Towards future BIM technology innovations: a bibliometric analysis of the literature. Appl Sci 11(3):1232. https://doi.org/10.3390/app11031232

Kim J, Olsen D (2021) From BIM to inspection: a comparative analysis of assistive embedment inspection. Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC). https://doi.org/10.22260/isarc2021/0123

Kitchenham B, Pearl Brereton O, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering – a systematic literature review. Inf Softw Technol 51(1):7–15. https://doi.org/10.1016/j.infsof.2008.09.009

Koteleva N, Valnev V, Frenkel I (2021) Investigation of the effectiveness of an augmented reality and a dynamic simulation system collaboration in oil pump maintenance. Appl Sci 12(1):350. https://doi.org/10.3390/app12010350

Kritzinger W, Karner M, Traar G, Henjes J, Sihn W (2018) Digital Twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11):1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474

Kritzler M, Funk M, Michahelles F, Rohde W (2017) The virtual twin. Proceedings of the seventh international conference on the internet of things. https://doi.org/10.1145/3131542.3140274

Krzyczkowski D (2019) Introducing azure digital twins. Apress. https://doi.org/10.1007/978-1-4842-5375-5

Kuber S, Sharma M, Bonetti A, Harispuru C, Soroush A (2022) Virtual testing of protection systems using digital twin technology. 2022 75th Annual Conference for Protective Relay Engineers (CPRE). https://doi.org/10.1109/cpre55809.2022.9776572

Kyriazopoulou C (2015) Smart city technologies and architectures - a literature review. Proceedings of the 4th International Conference on Smart Cities and Green ICT Systems. https://doi.org/10.5220/0005407000050016

Lacoche J, Villain É (2022) Prototyping context-aware augmented reality applications for smart environments inside virtual reality. Proceedings of the 17th international joint conference on computer vision, imaging and computer graphics theory and applications. https://doi.org/10.5220/0010768800003124

Lam P-D, Han J, Kwon K-R, Ok S-Y, Lee S-H (2023) Semantic 3D city model data visualization for smar t city digital twin. J Korea Multimed Soc 26(2):116–130. https://doi.org/10.9717/kmms.2023.26.2.116

Langenheim N, Sabri S, Chen Y, Kesmanis A, Felson A, Mueller A, Rajabifard A, Zhang Y (2022) Adapting a digital twin to enable real-time water sensitive urban design decision-making. Int Arch Photogramm Remote Sens Spat Inf Sci XLVIII-4/W4-2022:95–100. https://doi.org/10.5194/isprs-archives-xlviii-4-w4-2022-95-2022

Ledoux H, Arroyo Ohori K, Kumar K, Dukai B, Labetski A, Vitalis S (2019) CityJSON: a compact and easy-to-use encoding of the CityGML data model. Open Geospatial Data Softw Standards 4(1). https://doi.org/10.1186/s40965-019-0064-0

Lee A, Lee K-W, Kim K-H, Shin S-W (2022) A geospatial platform to manage large-scale individual mobility for an urban digital twin platform. Remote Sens 14(3):723. https://doi.org/10.3390/rs14030723

Lei B, Stouffs R, Biljecki F (2022) Assessing and benchmarking 3D city models. Int J Geogr Inf Sci 37(4):788–809. https://doi.org/10.1080/13658816.2022.2140808

Lemos MR, Cardoso VF, Otani M, Da Costa Nunes R, Da Silva VJ, De Lucena Junior VF (2022) Navigation robot training with Deep Q-Learning monitored by Digital Twin. 2022 IEEE International Conference on Consumer Electronics (ICCE). https://doi.org/10.1109/icce53296.2022.9730282

Li W, Rentemeister M, Badeda J, Jöst D, Schulte D, Sauer DU (2020) Digital twin for battery systems: cloud battery management system with online state-of-charge and state-of-health estimation. J Energy Storage 30:101557. https://doi.org/10.1016/j.est.2020.101557

Li C, Zheng P, Li S, Pang Y, Lee CKM (2022) AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop. Robot Comput-Integr Manuf 76:102321. https://doi.org/10.1016/j.rcim.2022.102321

Li Y, Zhang Z, Li X, Guan S (2021) Research on equipment maintenance guidance technology based on MR and digital twin. Proceedings of the 2021 5th international conference on electronic information technology and computer engineering. https://doi.org/10.1145/3501409.3501454

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, Clarke M, Devereaux PJ, Kleijnen J, Moher D (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol 62(10):e1–e34. https://doi.org/10.1016/j.jclinepi.2009.06.006

Liu Y, Zhang L, Yang Y, Zhou L, Ren L, Wang F, Liu R, Pang Z, Deen MJ (2019) A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access 7:49088–49101. https://doi.org/10.1109/access.2019.2909828

Liu S, Wang XV, Wang L (2022) Digital twin-enabled advance execution for human-robot collaborative assembly. CIRP Ann 71(1):25–28. https://doi.org/10.1016/j.cirp.2022.03.024

Liu Y, Pan S, Folz P, Ramparany F, Bolle S, Ballot E, Coupaye T (2023) Cognitive digital twins for freight parking management in last mile delivery under smart cities paradigm. Comput Ind 153:104022. https://doi.org/10.1016/j.compind.2023.104022

Liu Y, Wang Z, Han K, Shou Z, Tiwari P, L. Hansen JH (2020) Sensor fusion of camera and cloud digital twin information for intelligent vehicles. 2020 IEEE intelligent vehicles symposium (IV). https://doi.org/10.1109/iv47402.2020.9304643

Lohtander M, Ahonen N, Lanz M, Ratava J, Kaakkunen J (2018) Micro manufacturing unit and the corresponding 3D-model for the digital twin. Procedia Manuf 25:55–61. https://doi.org/10.1016/j.promfg.2018.06.057

Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2021a) Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks. IEEE Trans Industr Inf 17(7):5098–5107. https://doi.org/10.1109/tii.2020.3017668

Lu Y, Maharjan S, Zhang Y (2021b) Adaptive edge association for wireless digital twin networks in 6G. IEEE Internet Things J 8(22):16219–16230. https://doi.org/10.1109/jiot.2021.3098508

Lu Q, Parlikad AK, Woodall P, Don Ranasinghe G, Xie X, Liang Z, Konstantinou E, Heaton J, Schooling J (2020) Developing a digital twin at building and city levels: case study of West Cambridge Campus. J Manag Eng 36(3). https://doi.org/10.1061/(asce)me.1943-5479.0000763

Lugaresi G, Jemai Z, Sahin E (2023) Digital twins for supply chains: current outlook and future challenges. ECMS 2023 Proceedings Edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni. https://doi.org/10.7148/2023-0451

Mainisa M, Eka Priana S, Zulhedi Z (2023) Implementasi bim dalam permodelan 3d pembangunan gedung kantor cabang bri batusangkar menggunakan software openbuildings designer. Ensiklopedia Res Community Serv Rev 2(3):147–155. https://doi.org/10.33559/err.v2i3.1771

Major P, Li G, Hildre HP, Zhang H (2021) The use of a data-driven digital twin of a smart city: a case study of Ålesund, Norway. IEEE Instrum Meas Mag 24(7):39–49. https://doi.org/10.1109/mim.2021.9549127

Mandl B, Stehling M, Schmiedinger T, Adam M (2017) Enhancing workplace learning by augmented reality. Proceedings of the seventh international conference on the internet of things. https://doi.org/10.1145/3131542.3140265

Martinez-Velazquez R, Gamez R, El Saddik A (2019) Cardio twin: a digital twin of the human heart running on the edge. 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA). https://doi.org/10.1109/memea.2019.8802162

Meier N, Müller-Polyzou R, Brach L, Georgiadis A (2021) Digital twin support for laser-based assembly assistance. Procedia CIRP 99:460–465. https://doi.org/10.1016/j.procir.2021.03.066

Mihai S, Yaqoob M, Hung DV, Davis W, Towakel P, Raza M, Karamanoglu M, Barn B, Shetve D, Prasad RV, Venkataraman H, Trestian R, Nguyen HX (2022) Digital twins: a survey on enabling technologies, challenges, trends and future prospects. IEEE Commun Surv Tutorials 24(4):2255–2291. https://doi.org/10.1109/comst.2022.3208773

Mir M, Yaghoobi M, Khairabadi M (2022) A new approach to energy-aware routing in the Internet of Things using improved Grasshopper Metaheuristic Algorithm with Chaos theory and Fuzzy Logic. Multimed Tools Appl 82(4):5133–5159. https://doi.org/10.1007/s11042-021-11841-9

Moosavi J, Naeni LM, Fathollahi-Fard AM, Fiore U (2021) Blockchain in supply chain management: a review, bibliometric, and network analysis. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-13094-3

Mora L, Deakin M, Reid A (2019) Combining co-citation clustering and text-based analysis to reveal the main development paths of smart cities. Technol Forecast Soc Chang 142:56–69. https://doi.org/10.1016/j.techfore.2018.07.019

Mora L, Appio FP, Foss NJ, Arellano-Gault D, Zhang X (2023a) Organizing for smart city development: research at the crossroads. Introduction to the special issue. Organ Stud 44(10):1559–1575. https://doi.org/10.1177/01708406231197815

Mora L, Gerli P, Ardito L, Messeni Petruzzelli A (2023b) Smart city governance from an innovation management perspective: theoretical framing, review of current practices, and future research agenda. Technovation 123:102717. https://doi.org/10.1016/j.technovation.2023.102717

Moya B, Badías A, Alfaro I, Chinesta F, Cueto E (2020a) Digital twins that learn and correct themselves. Int J Numer Methods Eng 123(13):3034–3044. https://doi.org/10.1002/nme.6535 . (Portico)

Moya B, Alfaro I, Gonzalez D, Chinesta F, Cueto E (2020b) Physically sound, self-learning digital twins for sloshing fluids. PLoS ONE 15(6):e0234569. https://doi.org/10.1371/journal.pone.0234569

Nämerforslund T (2022) Digital twin performance: unity as a platform for visualizing interactive digital twins. Thesis - Institution of Information Systems and Technology

National Aeronautics and Space Administration (NASA) (2010) Encyclopedia of geography. https://doi.org/10.4135/9781412939591.n797

Nica E, Popescu GH, Poliak M, Kliestik T, Sabie O-M (2023) Digital twin simulation tools, spatial cognition algorithms, and multi-sensor fusion technology in sustainable urban governance networks. Mathematics 11(9):1981. https://doi.org/10.3390/math11091981

Nikolakis N, Alexopoulos K, Xanthakis E, Chryssolouris G (2018) The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor. Int J Comput Integr Manuf 32(1):1–12. https://doi.org/10.1080/0951192x.2018.1529430

Nochta T, Wan L, Schooling JM, Parlikad AK (2020) A socio-technical perspective on urban analytics: the case of city-scale digital twins. J Urban Technol 28(1–2):263–287. https://doi.org/10.1080/10630732.2020.1798177

Nowocin JK (2017) Microgrid risk reduction for design and validation testing using controller hardware in the loop (Doctoral dissertation, Massachusetts Institute of Technology)

ODAA platform (2016) http://www.odaa.dk . Accessed 10 May 2024

Pan S, Trentesaux D, McFarlane D, Montreuil B, Ballot E, Huang GQ (2021) Digital interoperability in logistics and supply chain management: state-of-the-art and research avenues towards Physical Internet. Comput Ind 128:103435. https://doi.org/10.1016/j.compind.2021.103435

Pang J, Huang Y, Xie Z, Li J, Cai Z (2021) Collaborative city digital twin for the COVID-19 pandemic: a federated learning solution. Tsinghua Sci Technol 26(5):759–771. https://doi.org/10.26599/tst.2021.9010026

Paripooranan CS, Abishek R, Vivek DC, Karthik S (2020) An implementation of AR enabled Digital Twins for 3-D printing. 2020 IEEE International Symposium on Smart Electronic Systems (ISES) (Formerly INiS). https://doi.org/10.1109/ises50453.2020.00043

Park H-A, Byeon G, Son W, Jo H-C, Kim J, Kim S (2020) Digital Twin for operation of microgrid: optimal scheduling in virtual space of Digital Twin. Energies 13(20):5504. https://doi.org/10.3390/en13205504

Pérez JG, Toledo DG (2017) Cognitive analytics of smart cities. Proceedings of the 18th annual international conference on digital government research. https://doi.org/10.1145/3085228.3085265

Petković T, Puljiz D, Marković I, Hein B (2019) Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses. Robot Comput-Integr Manuf 57:182–196. https://doi.org/10.1016/j.rcim.2018.11.004

Pivano L, Nguyen DT, Bruun Ludvigsen K (2019) Digital Twin for drilling operations – towards cloud-based operational planning. Day 3 Wed, May 08, 2019. https://doi.org/10.4043/29316-ms

Podvalny SL, Vasiljev EM (2021) Digital twin for smart electricity distribution networks. IOP Conf Ser: Mater Sci Eng 1035(1):012047. https://doi.org/10.1088/1757-899x/1035/1/012047

Prasad D, Alizadeh T, Dowling R (2023) Smart city planning and the challenges of informality in India. Dialogues in human geography, 204382062311566. https://doi.org/10.1177/20438206231156655

Praschl C, Krauss O (2022) Geo-referenced occlusion models for mixed reality applications using the microsoft HoloLens. Proceedings of the 17th international joint conference on computer vision, imaging and computer graphics theory and applications. https://doi.org/10.5220/0010775200003124

Psarommatis F, May G (2022) A standardized approach for measuring the performance and flexibility of digital twins. Int J Prod Res 61(20):6923–6938. https://doi.org/10.1080/00207543.2022.2139005

Qiuchen Lu V, Parlikad AK, Woodall P, Ranasinghe GD, Heaton J (2019) Developing a dynamic digital twin at a building level: using Cambridge campus as case study. International Conference on Smart Infrastructure and Construction 2019 (ICSIC). https://doi.org/10.1680/icsic.64669.067

Quek HY, Sielker F, Akroyd J, Bhave AN, von Richthofen A, Herthogs P, van der Laag Yamu C, Wan L, Nochta T, Burgess G, Lim MQ, Mosbach S, Kraft M (2023) The conundrum in smart city governance: interoperability and compatibility in an ever-growing ecosystem of digital twins. Data Policy 5. https://doi.org/10.1017/dap.2023.1

Rabah S, Assila A, Khouri E, Maier F, Ababsa F, Bourny V, Maier P, Mérienne F (2018) Towards improving the future of manufacturing through digital twin and augmented reality technologies. Procedia Manuf 17:460–467. https://doi.org/10.1016/j.promfg.2018.10.070

Rajesh PK, Manikandan N, Ramshankar CS, Vishwanathan T, Sathishkumar C (2019) Digital Twin of an automotive brake pad for predictive maintenance. Procedia Comput Sci 165:18–24. https://doi.org/10.1016/j.procs.2020.01.061

Raqeeb A, Bonetti A, Carlsson A, Harispuru C, Pustejovsky M, Wetterstrand N (2022) Functional digital twins of relay protection and relay test equipment enabling benefits in training and remote support. 16th International Conference on Developments in Power System Protection (DPSP 2022). https://doi.org/10.1049/icp.2022.0925

Rathore MM, Shah SA, Shukla D, Bentafat E, Bakiras S (2021) The role of AI, machine learning, and big data in digital twinning: a systematic literature review, challenges, and opportunities. IEEE Access 9:32030–32052. https://doi.org/10.1109/access.2021.3060863

Rebmann A, Knoch S, Emrich A, Fettke P, Loos P (2020) A multi-sensor approach for Digital Twins of manual assembly and commissioning. Procedia Manuf 51:549–556. https://doi.org/10.1016/j.promfg.2020.10.077

Revetria R, Tonelli F, Damiani L, Demartini M, Bisio F, Peruzzo N (2019) A real-time mechanical structures monitoring system based on Digital Twin, Iot and augmented reality. 2019 Spring Simulation Conference (SpringSim). https://doi.org/10.23919/springsim.2019.8732917

Rosen R, von Wichert G, Lo G, Bettenhausen KD (2015) About the importance of autonomy and Digital Twins for the future of manufacturing. IFAC-PapersOnLine 48(3):567–572. https://doi.org/10.1016/j.ifacol.2015.06.141

Rožanec JM, Lu J, Rupnik J, Škrjanc M, Mladenić D, Fortuna B, Zheng X, Kiritsis D (2022) Actionable cognitive twins for decision making in manufacturing. Int J Prod Res 60(2):452–478. https://doi.org/10.1080/00207543.2021.2002967

Saeed Z, Mancini F, Glusac T, Izadpanahi P (2022) Future city, digital twinning and the urban realm: a systematic literature review. Buildings 12(5):685. https://doi.org/10.3390/buildings12050685

San Francisco Open Data (2024) San Francisco Open Data. https://data.sfgov.org . Accessed 10 May 2024

Shahat E, Hyun CT, Yeom C (2021) City Digital Twin potentials: a review and research agenda. Sustainability 13(6):3386. https://doi.org/10.3390/su13063386

Shangguan D, Chen L, Ding J (2020) A Digital Twin-based approach for the fault diagnosis and health monitoring of a complex satellite system. Symmetry 12(8):1307. https://doi.org/10.3390/sym12081307

Srai J, Settanni E (2019) Supply chain digital twins: opportunities and challenges beyond the hype

Ssin S, Cho H, Woo W (2021a) GeoACT: augmented control tower using virtual and real geospatial data. Interact Des Archit 48:122–142. https://doi.org/10.55612/s-5002-048-006

Ssin S, Cho H, Woo W (2021b) GeoVCM: virtual urban digital twin system augmenting virtual and real geo-spacial data. 2021 IEEE International Conference on Consumer Electronics (ICCE). https://doi.org/10.1109/icce50685.2021.9427709

Stark R, Kind S, Neumeyer S (2017) Innovations in digital modelling for next generation manufacturing system design. CIRP Ann 66(1):169–172. https://doi.org/10.1016/j.cirp.2017.04.045

Steinmetz C, Schroeder GN, Sulak A, Tuna K, Binotto A, Rettberg A, Pereira CE (2022) A methodology for creating semantic digital twin models supported by knowledge graphs. 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA). https://doi.org/10.1109/etfa52439.2022.9921499

Svítek M, Skobelev P, Kozhevnikov S (2019) Smart city 5.0 as an urban ecosystem of smart services. Stud Comp Intell:426–438. https://doi.org/10.1007/978-3-030-27477-1_33

Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2017) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94(9–12):3563–3576. https://doi.org/10.1007/s00170-017-0233-1

Ton DT, Smith MA (2012) The U.S. Department of Energy’s Microgrid Initiative. Electr J 25(8):84–94. https://doi.org/10.1016/j.tej.2012.09.013

Tonnarelli F, Mora L (2023) Smart urbanism in Africa: when theories do not fit with contextual practices. Reg Stud:1–11. https://doi.org/10.1080/00343404.2023.2235407

Trusov A, Limonova EE (2020) The analysis of projective transformation algorithms for image recognition on mobile devices. Twelfth International Conference on Machine Vision (ICMV 2019). https://doi.org/10.1117/12.2559732

Tuhaise VV, Tah JHM, Abanda FH (2023) Technologies for digital twin applications in construction. Autom Constr 152:104931. https://doi.org/10.1016/j.autcon.2023.104931

Tygesen UT, Jepsen MS, Vestermark J, Dollerup N, Pedersen A (2018) The true digital twin concept for fatigue re-assessment of marine structures. Volume 1: offshore technology. https://doi.org/10.1115/omae2018-77915

Um J, Popper J, Ruskowski M (2018) Modular augmented reality platform for smart operator in production environment. 2018 IEEE Industrial Cyber-Physical Systems (ICPS). https://doi.org/10.1109/icphys.2018.8390796

Urbina Coronado PD, Lynn R, Louhichi W, Parto M, Wescoat E, Kurfess T (2018) Part data integration in the shop floor digital twin: mobile and cloud technologies to enable a manufacturing execution system. J Manuf Syst 48:25–33. https://doi.org/10.1016/j.jmsy.2018.02.002

van der Valk H, Strobel G, Winkelmann S, Hunker J, Tomczyk M (2022) Supply chains in the era of Digital Twins – a review. Procedia Comput Sci 204:156–163. https://doi.org/10.1016/j.procs.2022.08.019

Verdouw C, Tekinerdogan B, Beulens A, Wolfert S (2021) Digital twins in smart farming. Agric Syst 189:103046. https://doi.org/10.1016/j.agsy.2020.103046

Vidal-Balea A, Blanco-Novoa O, Fraga-Lamas P, Vilar-Montesinos M, Fernández-Caramés TM (2021) Collaborative augmented digital twin: a novel open-source augmented reality solution for training and maintenance processes in the shipyard of the future. The 4th XoveTIC conference. https://doi.org/10.3390/engproc2021007010

Wan L, Nochta T, Schooling JM (2019) Developing a city-level digital twin –propositions and a case study. International Conference on Smart Infrastructure and Construction 2019 (ICSIC). https://doi.org/10.1680/icsic.64669.187

Wang P, Luo M (2021) A digital twin-based big data virtual and real fusion learning reference framework supported by industrial internet towards smart manufacturing. J Manuf Syst 58:16–32. https://doi.org/10.1016/j.jmsy.2020.11.012

Wang W, He F, Li Y, Tang S, Li X, Xia J, Lv Z (2023) Data information processing of traffic digital twins in smart cities using edge intelligent federation learning. Inf Process Manage 60(2):103171. https://doi.org/10.1016/j.ipm.2022.103171

Wang H, Meng X (2021) BIM-supported knowledge management: potentials and expectations. J Manag Eng 37(4). https://doi.org/10.1061/(asce)me.1943-5479.0000934

Waszak M, Lam AN, Hoffmann V, Elvesater B, Mogos MF, Roman D (2022) Let the asset decide: digital twins with knowledge graphs. 2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C). https://doi.org/10.1109/icsa-c54293.2022.00014

Wen T, Dobson E, Hvaara R (2020) Mesh learning: a cloud and edge–based computing network providing data–driven solutions to the oil and gas industry. Day 2 Tue, November 03, 2020. https://doi.org/10.4043/30365-ms

White G, Zink A, Codecá L, Clarke S (2021) A digital twin smart city for citizen feedback. Cities 110:103064. https://doi.org/10.1016/j.cities.2020.103064

Wiegand G, Mai C, Liu Y, Hußmann H (2018) Early take-over preparation in stereoscopic 3D. Adjunct proceedings of the 10th international conference on automotive user interfaces and interactive vehicular applications. https://doi.org/10.1145/3239092.3265957

Xia L, Zheng P, Li X, Gao RX, Wang L (2022) Toward cognitive predictive maintenance: a survey of graph-based approaches. J Manuf Syst 64:107–120. https://doi.org/10.1016/j.jmsy.2022.06.002

Xie X, Lu Q, Rodenas-Herraiz D, Parlikad AK, Schooling JM (2020) Visualised inspection system for monitoring environmental anomalies during daily operation and maintenance. Eng Constr Archit Manag 27(8):1835–1852. https://doi.org/10.1108/ecam-11-2019-0640

Xu Y, Sun Y, Liu X, Zheng Y (2019) A Digital-Twin-assisted fault diagnosis using deep transfer learning. IEEE Access 7:19990–19999. https://doi.org/10.1109/access.2018.2890566

Xu W, Cui J, Li L, Yao B, Tian S, Zhou Z (2021) Digital twin-based industrial cloud robotics: framework, control approach and implementation. J Manuf Syst 58:196–209. https://doi.org/10.1016/j.jmsy.2020.07.013

Xu H, Berres A, Yoginath SB, Sorensen H, Nugent PJ, Severino J, Tennille SA, Moore A, Jones W, Sanyal J (2023) Smart mobility in the cloud: enabling real-time situational awareness and cyber-physical control through a Digital Twin for traffic. IEEE Trans Intell Transp Syst 24(3):3145–3156. https://doi.org/10.1109/tits.2022.3226746

Yan M, Gan W, Zhou Y, Wen J, Yao W (2022) Projection method for blockchain-enabled non-iterative decentralized management in integrated natural gas-electric systems and its application in digital twin modelling. Appl Energy 311:118645. https://doi.org/10.1016/j.apenergy.2022.118645

Yin Y, Zheng P, Li C, Wang L (2023) A state-of-the-art survey on augmented reality-assisted Digital Twin for futuristic human-centric industry transformation. Robot Comput-Integr Manuf 81:102515. https://doi.org/10.1016/j.rcim.2022.102515

Yu G, Wang Y, Hu M, Shi L, Mao Z, Sugumaran V (2021) RIOMS: an intelligent system for operation and maintenance of urban roads using spatio-temporal data in smart cities. Futur Gener Comput Syst 115:583–609. https://doi.org/10.1016/j.future.2020.09.010

Yu X, Merritt J (2023) Comparison of city digital twin case studies. Digital twins for smart cities, 123–137. https://doi.org/10.1680/dtsc.66007.123

Zakzak L (2019) Citizen-centric smart city development. Proceedings of the 20th annual international conference on digital government research. https://doi.org/10.1145/3325112.3325236

Zborowski M (2018) Finding meaning, application for the much-discussed “Digital Twin.” J Petrol Technol 70(06):26–32. https://doi.org/10.2118/0618-0026-jpt

Zhang J, Mora L (2023) Nothing but symbolic: Chinese new authoritarianism, smart government, and the challenge of multi-level governance. Gov Inf Q 40(4):101880. https://doi.org/10.1016/j.giq.2023.101880

Zhang K, Cao J, Zhang Y (2022) Adaptive digital twin and multiagent deep reinforcement learning for vehicular edge computing and networks. IEEE Trans Industr Inf 18(2):1405–1413. https://doi.org/10.1109/tii.2021.3088407

Zhao X, Sun Y (2020) Augmented reality assembly guidance method based on situation awareness. In: Proceedings of the 2020 the 10th international workshop on computer science and engineering. WCSE, pp 165–173

Zheng P, Lin T-J, Chen C-H, Xu X (2018) A systematic design approach for service innovation of smart product-service systems. J Clean Prod 201:657–667. https://doi.org/10.1016/j.jclepro.2018.08.101

Zheng X, Lu J, Kiritsis D (2021) The emergence of cognitive digital twin: vision, challenges and opportunities. Int J Prod Res 60(24):7610–7632. https://doi.org/10.1080/00207543.2021.2014591

Zheng P, Li S, Fan J, Li C, Wang L (2023) A collaborative intelligence-based approach for handling human-robot collaboration uncertainties. CIRP Ann 72(1):1–4. https://doi.org/10.1016/j.cirp.2023.04.057

Židek K, Hladký V, Pitel’ J, Demčák J, Hošovský A, Lazorík P (2021) SMART production system with full digitalization for assembly and inspection in concept of industry 4.0. Future access enablers for ubiquitous and intelligent infrastructures. pp 181–192. https://doi.org/10.1007/978-3-030-78459-1_13

Zohdi TI (2020) A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments. Comput Methods Appl Mech Eng 363:112907. https://doi.org/10.1016/j.cma.2020.112907

Article   MathSciNet   Google Scholar  

Download references

Acknowledgements

The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, for funding this research work through project number 445-5-961.

This research was funded by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, project number 445-5-961.

Author information

Authors and affiliations.

College of Computer Science and Engineering, Taibah University, 46421, Yanbu, Saudi Arabia

Rasha F. El-Agamy, Hanaa A. Sayed, Arwa M. AL Akhatatneh, Mansourah Aljohani & Mostafa Elhosseini

Computer Science Department, Faculty of Science, Tanta University, Tanta, 31527, Egypt

Rasha F. El-Agamy

Department of Computer Science, Faculty of Computers and Information, Assiut University, Assiut, 71516, Egypt

Hanaa A. Sayed

Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt

Mostafa Elhosseini

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, M.E.; methodology, R.E., H.A., A.A., M.A.; software, A.A. and M.A.; validation, H.A., R.E. and M.E.; formal analysis, R.E.. and A.A.; investigation, A.A. and H.A; resources, M.A.; data curation, R.E., and H.A; writing—original draft preparation, R.E., H.A., A.A., and M.A.; writing—review and editing, R.E., H.A., and M.E.; visualization, R.E., and H.A; supervision, M.E., H.A., and R.E.; project administration, M.E.; funding acquisition, M.E. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Mostafa Elhosseini .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

El-Agamy, R.F., Sayed, H.A., AL Akhatatneh, A.M. et al. Comprehensive analysis of digital twins in smart cities: a 4200-paper bibliometric study. Artif Intell Rev 57 , 154 (2024). https://doi.org/10.1007/s10462-024-10781-8

Download citation

Accepted : 29 April 2024

Published : 27 May 2024

DOI : https://doi.org/10.1007/s10462-024-10781-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Digital twin
• Smart city development
• Bibliometric study
• IoT integration
• Find a journal
• Publish with us
• Track your research

Cloud Radiative Effects Associated With Daily Weather Regimes

May 28th, 2024

Key Findings

• This study Investigated weather and climate connections, with an emphasis on the effect of high-impact weather on Earth’s radiation budget, in order to better constrain climate models and improve predictions.
• Daily weather patterns were categorized into different types and the cloud radiative effects (CRE) associated with each type were measured.
• This study shows that precipitation days account for roughly 80% of global longwave (LW) and shortwave (SW) CRE due to their large frequency and high intensity in CRE.
• Despite being rare globally (13%), storm days (atmospheric rivers, tropical storms, and mesoscale convective systems) account for 32% of global LW CRE and 27% of SW CRE due to their higher intensity in LW and SW CRE.

Ming Zhao . Geophysical Research Letters. DOI: 10.1029/2024GL109090

This research Investigates the effect of high-impact storms on Earth’s radiation budget, in order to better constrain climate models and improve weather and climate forecasts. Using detailed satellite observations and reanalysis data, the author categorized daily weather patterns into different types and measured the cloud radiative effects (CRE) associated with each type. The weather patterns included non-precipitation days, drizzle, wet non-storm days, and storm days, encompassing events like atmospheric rivers, tropical storms, and mesoscale convective systems.

The results show that precipitation days, which include both drizzle and wet days, contribute to about 80% of global longwave (LW) and shortwave (SW) CRE due to their high frequency and intensity. Even though storm days are rare globally (only 13%), they collectively contribute to approximately 32% of global LW CRE and 27% of SW CRE because of their stronger impact on both LW and SW CRE. These findings are important for understanding how different weather systems influence the Earth’s radiation balance and will help improve the accuracy of climate models.

Clouds cover about two-thirds of the Earth’s surface and are often organized into coherent systems by large-scale atmospheric flows. They can either warm the Earth by trapping outgoing longwave radiation or cool it by reflecting shortwave solar radiation back to space. The net effect depends on factors such as cloud height, type, and optical properties. The impact of clouds on the CRE can be deduced from satellite observations comparing upwelling radiation in cloudy and non-cloudy regions. Given the large magnitude of CRE, clouds have the potential to significantly influence climate feedback.

Indeed, cloud feedback has been identified as the primary source of uncertainties in climate models’ projections of future climate since the first IPCC assessment report. Unfortunately, a reliable observational constraint on global cloud feedback remains elusive due to the short satellite record, as well as other complexities. Reducing model biases in simulations of the observed present-day CRE will help narrow down cloud feedback uncertainty and improve climate models’ fidelity in future projections.

These results will be useful for understanding the roles of various weather systems in Earth’s radiative budget and for more in-depth climate model evaluations.

Share this:

« Previous research highlight