research object study

Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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39 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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Structuring research methods and data with the research object model: genomics workflows as a case study

Kristina m hettne.

Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands

Harish Dharuri

Department of Zoology, University of Oxford, Oxford, UK

Katherine Wolstencroft

School of Computer Science, University of Manchester, Manchester, UK

Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

Khalid Belhajjame

Stian soiland-reyes, mark thompson, don cruickshank, lourdes verdes-montenegro.

Instituto de Astrofísica de Andalucía, Granada, Spain

Julian Garrido

David de roure, oscar corcho.

Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain

Graham Klyne

Reinout van schouwen, peter a c ‘t hoen, sean bechhofer, carole goble, associated data.

One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows.

We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as “which particular data was input to a particular workflow to test a particular hypothesis?”, and “which particular conclusions were drawn from a particular workflow?”.

Conclusions

Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well.

Availability

The Research Object is available at http://www.myexperiment.org/packs/428

The Wf4Ever Research Object Model is available at http://wf4ever.github.io/ro

Electronic supplementary material

The online version of this article (doi:10.1186/2041-1480-5-41) contains supplementary material, which is available to authorized users.

One of the main challenges for biomedical research lies in the integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms, for instance to explain the onset and progression of human diseases. Computer-assisted methodology is needed to perform these studies, posing new challenges for upholding scientific quality standards for the reproducibility of science. The aim of this paper is to describe how the research data, methods and metadata related to a workflow-centric computational experiment can be aggregated and annotated using standard Semantic Web technologies, with the purpose of helping scientists performing such experiments in meeting requirements for understanding, sharing, reuse and repurposing.

The workflow paradigm is gaining ground in bioinformatics as the technology of choice for recording the steps of computational experiments [ 1 – 4 ]. It allows scientists to delineate the steps of a complex analysis and expose this to peers using workflow design and execution tools such as Taverna [ 5 ], and Galaxy [ 6 ], and workflow sharing platforms such as myExperiment [ 7 ] and crowdLabs [ 8 ]. In a typical workflow, data outputs are generated from data inputs via a set of (potentially distributed) computational tasks that are coordinated following a workflow definition. However, workflows do not provide a complete solution for aggregating all data and all meta-data that are necessary for understanding the full context of an experiment. Consequently, scientists often find it difficult (or impossible) to reuse or repurpose existing workflows for their own analyses [ 9 ]. In fact, insufficient meta-data has been listed as one of the main causes of workflow decay in a recent study of Taverna workflows on myExperiment [ 9 ]. Workflow decay is the term used when the ability to re-execute a workflow after its inception has been compromised.

We will be able to better understand scientific workflows if we are able to capture more relevant data and meta-data about them; including the purpose and context of the experiment, sample input and output datasets, and the provenance of workflow executions. Moreover, if we wish to publish and exchange these resources as a unit, we need a mechanism for aggregation and annotation that would work in a broad scientific community. Semantic Web technology seems a logic choice of technology, given its focus on capturing the meaning of data in a machine readable format that is extendable and supports interoperability. It allows defining a Web-accessible reference model for the annotation of the aggregation and the aggregated resources that is independent of how data are stored in repositories. Examples of other efforts where Semantic Web technology has been used for the biomedical data integration includes the Semantic Enrichment of the Scientific Literature (SESL) [ 10 ] and Open PHACTS [ 11 ] projects. We applied the recently developed Research Object (RO) family of tools and ontologies [ 12 , 13 ] to preserve the scientific assets and their annotation related to a computational experiment. The concept of the RO was first proposed as an abstraction for sharing research investigation results [ 14 ]. Later, the potential role for ROs in facilitating not only the sharing but also the reuse of results, in order to increase the reproducibility of these results, was envisioned [ 15 ]. Narrowing down to workflow-centric ROs, preservation aspects were explored in [ 16 ], and their properties as first class citizen structures that aggregate resources in a principled manner in [ 13 ]. We also showed the principle of describing a (text mining) workflow experiment and its results by Web Ontology Language (OWL) ontologies [ 17 ]. The OWL ontologies were custom-built, which we argue is now an unnecessary bottleneck for exchange and interoperability. These studies all contributed to the understanding and implementation of the concept of an RO, but the data used were preliminary, and the studies were focused on describing workflows with related datasets and provenance information, rather than from the viewpoint of describing a scientific experiment of which workflows are a component.

A workflow-centric RO is defined as a resource that aggregates other resources, such as workflow(s), provenance, other objects and annotations. Consequently, an RO represents the method of analysis and all its associated materials and meta-data [ 13 , 15 ], distinguishing it from other work mainly focusing on provenance of research data [ 18 , 19 ]. Existing Semantic Web frameworks are used, such as (i) the Object Exchange and Reuse (ORE) model [ 20 ]; (ii) the Annotation Ontology (AO) [ 21 ]; and (iii) the W3C-recommended provenance exchange models [ 22 ]. ORE defines the standards for the description and exchange of aggregations of Web resources and provides the basis for the RO ontologies. AO is a general model for annotating resources and is used to describe the RO and its constituent resources as well as the relationships between them. The W3C provenance exchange models enable the interchange of provenance information on the Web, and the Provenance Ontology (PROV-O) forms the basis for recording the provenance of scientific workflow executions and their results.

In addition, we used the minimal information model “Minim”, also in Semantic Web format, to specify which elements in an RO we consider “must haves”, “should haves” and “could haves” according to user-defined requirements [ 23 ]. A checklist service subsequently queries the Minim annotations as an aid to make sufficiently complete ROs [ 24 ]. The idea of using a checklist to perform quality assessment is inspired by related checklist-based approaches in bioinformatics, such as the Minimum Information for Biological and Biomedical Information (MIBBI)-style models [ 25 ].

Case study: genome wide association studies

As real-world example we aggregate and describe the research data, methods and metadata of a computational experiment in the context of studies of genetic variation in human metabolism. Given the potential of genetic variation data in extending our understanding of genetic diseases, drug development and treatment, it is crucial that the steps leading to new biological insights can be properly recorded and understood. Moreover, bioinformatics approaches typically involve aggregation of disparate online resources into complex data parsing pipelines. This makes this a fitting test case for an instantiated RO. The biological goal of the experiment is to aid in the interpretation of the results of a Genome-Wide Association Study (GWAS) by relating metabolic traits to the Single Nucleotide Polymorphisms (SNPs) that were identified by the GWAS. GWA studies have successfully identified genomic regions that dispose individuals to diseases (see for example [ 26 ], for a review see [ 27 ]). However, the underlying biological mechanisms often remain elusive, which led the research community to evince interest in genetic association studies of metabolites levels in blood (see for example [ 28 – 30 ]). The motivation is that the biochemical characteristics of the metabolite and the functional nature of affected genes can be combined to unravel biological mechanisms and gain functional insight into the aetiology of a disease. Our specific experiment involves mining curated pathway databases and a specific text mining method called concept profile matching [ 31 , 32 ].

In this paper we describe the current state of RO ontologies and tools for the aggregation and annotation of a computational experiment that we developed to elucidate the genetic basis for human metabolic variation.

We performed our experiment using workflows developed in the open source Taverna Workflow Management System version 2.4 [ 5 ]. To improve the understanding of the experiment, we have added the following additional resources to the RO, using the RO-enabled myExperiment [ 33 ]: 1) the hypothesis or research question (what the experiment was designed to test); 2) a workflow-like sketch of the overall experiment (the overall data flow and workflow aims); 3) one or more workflows encapsulating the computational method; 4) input data (a record of the data that were used to reach the conclusions of an experiment); 5) provenance of workflow runs (the data lineage paths built from the workflow outputs to the originating inputs); 6) the results (a compilation of output data from workflow runs); 7) the conclusions (interpretation of the results from the workflows against the original hypothesis). Such an RO was then stored in the RO Digital Library [ 34 ]. RO completeness evaluation is checked from myExperiment with a tool implementing the Minim model [ 24 ]. Detailed description of the method follows.

Workflow development

We developed three workflows for interpreting SNP-metabolite associations from a previously published genome-wide association study, using pathways from the KEGG metabolic pathway database [ 35 ] and Gene Ontology (GO) [ 36 ] biological process associations from text mining of PubMed. To understand an association of a SNP with a metabolite, researchers would like to know the gene in the vicinity of the SNP that is affected by the polymorphism. Then, researchers examine the functional nature of the gene and evaluate if it makes sense given the biochemical characteristics of the metabolite with which it is associated. This typically involves interrogation of biochemical pathway databases and mining existing literature. We would like to evaluate the utility of background knowledge present in the databases and literature in facilitating a biological interpretation of the statistically significant SNP-metabolite pairs. We do this by first determining the genes closest to the SNPs, and then reporting the pathways that these genes participate in. We implemented two main workflows for our experiment. The first one mines the manually curated KEGG database of metabolic pathway and gene associations that are available via the KEGG REST Services [ 37 ]. The second workflow mines the text-mining based database of associations between GO biological processes and genes behind the Anni 2.1 tool [ 31 ] that are available via the concept profile mining Web services [ 38 ]. We also created a workflow to list all possible concept sets in the concept profile database, to encourage reuse of the concept profile-based workflow for matching against other concept sets than GO biological processes. The workflows were developed following the 10 Best Practices for workflow design [ 39 ]. The Best Practices were developed to encourage re-use and prevent workflow decay, and briefly consists of the following steps:

• Make a sketch workflow to help design the overall data flow and workflow aims, and to identify the tools and data resources required at each stage. The sketch could be created using for example flowchart symbols, or empty beanshells in Taverna.
• Use modules, i.e. implement all executable components as separate, runnable workflows to make it easier for other scientists to reuse parts of a workflow at a later date.
• Think about the output. A workflow has the potential to produce masses of data that need to be visualized and managed properly. Also, workflows can be used to integrate and visualise data as well as for analysing it, so one should consider how the results will be presented easily to the user.
• Provide input and output examples to show the format of input required for the workflow and the type of output that should be produced. This is crucial for the understanding, validation, and maintenance of the workflow.
• Annotate, i.e. choose meaningful names for the workflow title, inputs, outputs, and for the processes that constitute the workflow as well as for the interconnections between the components, so that annotations are not only a collection of static tags but capture the dynamics of the workflow. Accurately describing what individual services do, what data they consume and produce, and the aims of the workflow are all essential for use and reuse.
• Make it executable from outside the local environment by for example using remote Web services, or platform independent code/plugins. Workflows are more reusable if they can be executed from anywhere. If there is need to use local services, library or tools, then the workflow should be annotated in order to define its dependencies.
• Choose services carefully. Some services are more reliable or more stable than others, and examining which are the most popular can assist with this process.
• Reuse existing workflows by for example searching collaborative platforms such as myExperiment for workflows using the same Web service. If a workflow has been tried, tested and published, then reusing it can save a significant amount of time and resource.
• Test and validate by defining test cases and implementing validation mechanisms in order to understand the limitations of workflows, and to monitor changes to underlying services.
• Advertise and maintain by publishing the workflow on for example myExperiment, and performing frequent testing of the workflow and monitoring of the services used. Others can only reuse it if it is accessible and if it is updated when required, due to changes in underlying services.

The RO core model

The RO model [ 12 , 13 ] aims at capturing the elements that are relevant for interpreting and preserving the results of scientific investigations, including the hypothesis investigated by the scientists, the data artefacts used and generated, as well as the methods and experiments employed during the investigation. As well as these elements, to allow third parties to understand the content of the RO, the RO model caters for annotations that describe the elements encapsulated by the ROs, as well as the RO as a whole. Therefore, two main constructs are at the heart of the RO model, namely aggregation and annotation. The work reported on in this article uses version 0.1 of the RO model, which is documented online [ 12 ].

Following myExperiment packs [ 7 ], ROs use the ORE model [ 20 ] to represent aggregation. Using ORE, an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. Specifically, the RO extends ORE to define three new concepts: i) ro:ResearchObject is a sub-class of ore:Aggregation which represents an aggregation of resources. ii) ro:Resource is a sub-class of ore:AggregatedResource representing a resource that is aggregated within an RO. iii) ro:Manifest is a sub-class of ore:ResourceMap, representing a resource that is used to describe the RO.

To support the annotation of ROs, their constituent resources, as well as their relationship, we use the Annotation Ontology [ 21 ]. Several types of annotations are supported by the Annotation Ontology, e.g., comments, textual annotations (classic tags) and semantic annotations, which relate elements of the ROs to concepts from underlying domain ontologies. We make use of the following Annotation Ontology terms: i) ao:Annotation, which acts as a handle for the annotation. ii) ao:annotatesResource, which represents the resource(s)/RO(s) subjects to annotation. iii) ao:body, which describes the target of the annotation. The body of the annotation takes the form of a set of Resource Description Framework (RDF) statements. Note that it is planned for later revisions of the RO model to use the successor of AO, the W3C Community Open Annotation Data Model (OA) [ 40 ]. For our purposes, OA annotations follows a very similar structure using oa: Annotation, oa:hasTarget and oa:hasBody.

Support for workflow-centric ROs

A special kind of ROs that are supported by the model is what we call workflow-centric ROs, which, as indicated by the name, refer to those ROs that contain resources that are workflow specifications. The structure of the workflow in ROs is detailed using the wfdesc vocabulary [ 41 ], and is defined as a graph in which the nodes refers to steps in the workflow, which we call wfdesc:Process, and the edges representing data flow dependencies, wfdesc:DataLink, which is a link between the output and input parameters (wfdesc:Parameter) of the processes that compose the workflow. As well as the description of the workflow, workflow centric ROs support the specification of the workflow runs, wfprov:WorkflowRun, that are obtained as a result of enacting workflows. A workflow run is specified using the wfprov ontology [ 42 ], which captures information about the input used to feed the workflow execution, the output results of the workflow run, as well as the constituent process runs, wfprov:ProcessRun, of the workflow run, which are obtained by invoking the workflow processes, and the input and outputs of those process runs.

Support for domain-specific information

A key aspect of the RO model design is the freedom to use any vocabulary. This allows for inclusion of very domain-specific information about the RO if that serves the desired purpose of the user. We defined new terms under the name space roterms [ 43 ]. These new terms serve two main purposes. They are used to specify annotations that are, to our knowledge, not catered for by existing ontologies, e.g., the classes roterms:Hypothesis and roterms:Conclusion to annotate the hypothesis and conclusions part of an RO, and the property roterms:exampleValue to annotate an example value for a given input or output parameter given as an roterms:WorkflowValue instance. The roterms are also used to specify shortcuts that make the ontology easy to use and more accessible. For example, roterms:inputSelected associates a wfdesc:WorkflowDefinition to an ro:Resource to state that a file is meant to be used with a given workflow definition, without specifying at which input port or in which workflow run.

Minim model for checklist evaluation

When building an RO in myExperiment users are provided with a mechanism of quality insurance by our so-called checklist evaluation tool, which is built upon the Minim checklist ontology [ 23 , 44 ] and defined using Web Ontology Language. Its basic function is to assess that all required information and descriptions about the aggregated resources are present and complete. Additionally, according to explicit requirements defined in a checklist, the tool can also assess the accessibility of those resources aggregated in an RO, in order to increase the trust on the understanding of the RO. The Minim model has four key components, as illustrated by Figure  1 : 1) a Constraint, which associates a model (checklist) to use with an RO, for a specific assessment purpose, e.g. reviewing an RO containing sufficient information before being shared; 2) a Model, which enumerates of the set of requirements to be considered, which may be declared at levels of MUST, SHOULD or MAY be satisfied for the model as a whole; 3) a Requirement, which is the key part for expressing the concrete quality requirements to an RO, for example, the presence of certain information about an experiment, or liveness (accessibility) of a data server; 4) a Rule, which can be a SoftwareRequirementRule, to specify the software to be present in the operating environment, a ContentMatchRequirementRule, to specify the presence of certain pattern in the assessed data, or a DataRequirementRule, for specifying data resource to be aggregated in an RO.

An overview of the Minim model. An overview of the four components: a constraint, a model, a requirement, and a rule.

RO digital library

While myExperiment acts mainly as front-end to users, the RO Digital Library [ 34 ] acts as a back-end, with two complementary storage components: a digital repository to keep the content, as a triple store to manage the meta-data content. The ROs in the repository can be accessed via a Restful API [ 45 ] or via a public SPARQL endpoint [ 46 ]. All the ROs created in the myExperiment.org are also submitted to the RO Digital Library.

Workflow-centric RO creation process

Below we describe the steps that we conducted when creating the RO for our case study in an “RO-enabled” version of myExperiment [ 33 ]. The populated RO is intended to contain all the information required to re-run the experiment, or understand the results presented, or both.

Creating an RO

The action of creating an RO consists of generating the container for the items that will be aggregated, and getting a resolvable identifier for it. In myExperiment the action of creating an RO is similar to creating a pack. We filled in a title and description of the RO at the point of creation and got a confirmation that the RO had been created and had been assigned a resolvable identifier in the RO Digital Library (Figure  2 ).

Screenshots from myExperiment illustrating the process of creating a Research Object placeholder. Before pressing the “create” button the user can enter a title and description (A) , while pressing the “create” button will result in a placeholder Research Object with an identifier (B) .

Adding the experiment sketch

Using a popular office presentation tool, we made an experiment sketch and saved it as a PNG image. We then uploaded the image to the pack, selecting the type “Sketch”. As a result, the image gets stored in the Digital Library and aggregated in the RO. In addition, an annotation was added to the RO to specify that the image is of type “Sketch”. A miniature version of the sketch is shown within the myExperiment pack (Figure  3 ).

Workflow sketch. A workflow sketch showing that our experiment follows two paths to interpret genome wide association study results: matching with concept profiles and matching with KEGG pathways.

Adding the hypothesis

To specify the hypothesis, we created a text file that describes the hypothesis, and then upload it to the pack as type “Hypothesis”. The file gets stored in the Digital Library and aggregated in the RO, this time annotated to be of type “Hypothesis”.

Adding workflows

We saved the workflow definitions to files and uploaded them to the pack as type “Workflow”. MyExperiment then automatically performed a workflow-to-RDF transformation in order to extract the workflow structure according to the RO model, which includes user descriptions and metadata created within the Taverna workbench. The descriptions and the extracted structure gets stored in the RO Digital Library and associated with the workflow files as annotations.

Adding the workflow input file

The data values were stored in files that were then uploaded into the pack as “Example inputs”. Such files gets stored in the RO Digital Library and aggregated in the RO, and as “Example inputs”.

Adding the workflow provenance

Using the Taverna-Prov [ 47 ] extension to Taverna, we exported the workflow run provenance to a file that we uploaded to the pack as type “Workflow run”. Similar to other resources, the provenance file gets stored in the digital library with the type “Workflow run”, however as the file is in the form of RDF according to the wfprov [ 42 ] and W3C PROV-O [ 22 ] ontologies, it is also integrated into the RDF store of the digital library and available for later querying.

Adding the results

We made a compilation of the different workflow outputs to a result file in table format, uploaded to the pack as type “Results”. The file gets stored in the digital library and aggregated in the RO, annotated to be of the type “Results”.

Adding the conclusions

To specify the hypothesis, we created a text file that describes the hypothesis, and then uploaded it to the pack as type “Hypothesis”. The file gets stored in the digital library and aggregated in the RO, annotated to be of type “Conclusions”.

Intermediate step: checklist evaluation

At this point we checked how far we were from satisfying the Minim model, and were informed by the tool that the RO now fully satisfies the checklist (Figure  4 ).

Screenshot of the results from the second check with the checklist evaluation service. The results from checklist evaluation service show that the Research Object satisfies the defined checklist for a Research Object.

Annotating and linking the resources

We linked the example input file to the workflows that used the file by the property “Input_selected” (Figure  5 ). In this particular case, both workflows have the same inputs but they need to be configured in different ways. This is described in the workflow description field in Taverna.

Screenshot of the relationships in the RO in myExperiment. The relationships between example inputs and workflows in the Research Object have been defined in myExperiment.

The RO for our experiment is the container for the items that we wished to aggregate. In terms of RDF, we first instantiated an ro:ResearchObject in an RO-enabled version of myExperiment [ 33 ]. We thereby obtained a unique and resolvable Uniform Resource Identifier (URI) from the RO store that underlies this version of myExperiment. In our experimental setup this was http://sandbox.wf4ever-project.org/rodl/ROs/Pack405/ . It is accessible from myExperiment [ 48 ]. Each of the subsequent items in the RO was aggregated as an ro:Resource, indicating that the item is considered a constituent member of the RO from the point of view of the scientist (the creator of the RO).

Aggregated resources

We aggregated the following items: 1) the hypothesis (roterms:Hypothesis): we hypothesized that SNPs can be functionally annotated using metabolic pathway information complemented by text mining, and that this will lead to formulating new hypotheses regarding the role of genomic variation in biological processes; 2) the sketch (roterms:Sketch) shows that our experiment follows two paths to interpret SNP data: matching with concept profiles and matching with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Figure  3 ); 3) the workflows (wfdesc:Workflow): Figure  6 shows the workflow diagram for the KEGG workflow and Figure  7 shows the workflow diagram for the concept profile matching workflow. In Taverna, we aimed to provide sufficient annotation of the inputs, outputs and the functions of each part of the workflow to ensure a clear interpretation and to ensure that scientists know how to replay the workflows using the same input data, or re-run them with their own data. We provided textual descriptions in Taverna of each step of the workflow, in particular to indicate their purpose within the workflow (Figure  8 ); 4) the input data (roterms:exampleValue) that we aggregated in our RO was a list of example SNPs derived from the chosen GWAS [ 28 ]; 5) the workflow run provenance (roterms:WorkflowRunBundle): a ZIP archive that contains the intermediate values of the workflow run, together with its provenance trace expressed using wfprov:WorkflowRun and subsequent terms from the wfprov ontology. We thus stored process information from the input of the workflow execution to its output results, including the information for each constituent process run in the workflow run, modelled as wfprov:ProcessRun. The run data is: 3 zip files containing 2090 intermediate values as separate files totalling 9.7 MiB, in addition to 5 MiB of provenance traces; 6) the results (roterms:Result) were compiled from the different workflow outputs to one results file (see result document in the RO [ 49 ] Additional file 1 ). For 15 SNPs it lists the associated gene name, the biological annotation from the GWAS publication, the associated KEGG pathway, and the most strongly associated biological process according to concept profile matching. Our workflows were able to compute a biological annotation from KEGG for 10 out of 15 SNPs and 15 from mining PubMed. All KEGG annotations and most text mining annotations corresponded to the annotations by Illig et al [ 28 ]. An important result of the text mining workflow was the SNP-annotation “rs7156144- stimulation of tumor necrosis factor production”, which represents a hypothetical relation that to our knowledge was not reported before; 7) the conclusions (roterms: Conclusion): we concluded that our KEGG and text mining workflows were successful in retrieving biological annotations for significant SNPs from a GWAS experiment, and predicting novel annotations.

Taverna workflow diagram for the KEGG workflow. Blue boxes are workflow inputs, brown boxes are scripts, grey boxes are constant values, green boxes are Web services, purple boxes are Taverna internal services, and pink boxes are nested workflows.

Taverna workflow diagram for the concept profile mining workflow. Blue boxes are workflow inputs, purple boxes are Taverna internal services, and pink boxes are nested workflows.

Taverna workflow annotation example. An example of an annotation of the purpose of a nested workflow in Taverna.

As an example of our instantiated RO, Figure  9 provides a simplified view of the RDF graph that aggregates and annotates the KEGG mining workflow. It shows the result of uploading our Taverna workflow to myExperiment, as it initiated an automatic transformation from a Taverna 2 t2flow file to a Taverna 3 workflow bundle, while extracting the workflow structure and user descriptions in terms of the wfdesc model [ 41 ]. The resulting RDF document was aggregated in the RO and used as the annotation body of a ao:Annotation on the workflow, thus creating a link between the aggregated workflow file and its description in RDF. The Annotation Ontology uses named graphs for semantic annotation bodies. In the downloadable ZIP archive of an RO each named graph is available as a separate RDF document, which can be useful in current RDF triple stores that do not yet fully support named graphs. The other workflows were aggregated and annotated in the same way. The RO model further uses common Dublin Core vocabulary terms [ 50 ] for basic metadata such as creator, title, and description.

Simplified diagram showing part of the Research Object for our experiment. The Research Object contains the items that were aggregated by the “Research Object-enabled” version of myExperiment. Shown is the part of the RDF graph that aggregates and annotates the KEGG pathway mining workflow.

In some cases we manually inserted specified relations between the RO resources via the myExperiment user interface. An example is the link between input data and the appropriate workflow for cases when an RO has multiple workflows and multiple example inputs. In our case, both workflows have the same inputs, but they need to be configured in different ways. This was described in the workflow description field in Taverna which becomes available from an annotation body in the workflow upload process.

Checking for completeness of an RO: application of the Minim model

We also applied Semantic Web technology for checking the completeness of our RO. We implemented a checklist for the items that we consider essential or desirable for understanding a workflow-based experiment by annotating the corresponding parts of the RO model with the appropriate term from the Minim vocabulary (Table  1 ). Thus, some parts were annotated as “MUST have” with the property minim:hasMustRequirement (e.g. at least one workflow definition), and others as “SHOULD have” with the property minim:hasShouldRequirement (e.g. the overall sketch of the experiment). The complete checklist document can be found online in RDF format [ 51 ] and in a format based on the spreadsheet description of the workflow [ 52 ]. We subsequently used a checklist service that evaluates if an RO is complete by executing SPARQL queries on the Minim mappings. The overall result is a summary of the requirement levels associated with the individual items; e.g. a missing MUST requirement is a more serious omission than a missing SHOULD (or COULD) requirement. We justified the less strict requirements for some items to accommodate cases when an RO is used to publish a method as such. We found that treating the requirement levels as mutually exclusive (hence not sub properties) simplifies the implementation of checklist evaluation, and in particular the generation of results when a checklist item is not satisfied.

RO items checklist

RO items for a workflow-based experiment annotated with the appropriate term from the Minim vocabulary.

In this paper we explored the application of the Semantic Web encoded RO model to provide a container data model for preserving sufficient information for researchers to understand a computational experiment. We found that the model indeed allowed us to aggregate the necessary material together with sufficient annotation (both for machines and humans). Moreover, mapping of selected RO model artefacts to the Minim vocabulary allowed us to check if the RO was complete according to our own predefined criteria. The checklist service can be configured to accommodate different criteria. Research groups may have different views on what is essential, but also libraries or publishers may define their own standards, enabling partial automation of the process of checking a submission against specific instructions to authors. Furthermore, the service can be run routinely to check for workflow decay, in particular decay related to references that go missing.

In using the RO model, we sought to meet requirements for sharing, reuse and repurposing, as well as interoperability and reproducibility. This fits with current trends to enhance reproducibility and transparency of science (e.g. see [ 53 – 55 ]). Reproducibility in computational science has been defined as a spectrum [ 55 ], where a computational experiment that is described only by a publication is not seen as reproducible, while adding code, data, and finally the linked data and execution data will move the experiment towards full replication. Adhering to this definition, our RO-enabled computational experiment comes close to fulfilling the ultimate golden standard of full replication, but falls short because it has not been analyzed using independently collected data. The benefit offered by the RO in terms of reproducibility is that it provides a context (RO) within which an evaluation of reproducibility can be performed. It does this by providing an enumerated and closed set of resources that are part of the experiment concerned, and by providing descriptive metadata (annotations) that may be specific to that context. This is not necessarily the complete solution to reproducible research, but at least an incremental step in that direction.

We have used RDF as the underlying data model for exchanging ROs. One of advantages is the ability to query the data, which becomes clear when we want to answer questions about the experiment, such as: 1) Which conclusions were drawn from a given workflow?; 2) Which workflow (run) supports a particular conclusion and which datasets did it use as inputs?; 3) Which different workflows used the same dataset X as input?; 4) Who can be credited for creating workflows that use GWAS data? The answers for the first two questions can readily be found using a simple SPARQL [ 56 ] query. Figure  10 shows the SPARQL query and the results as returned by the SPARQL endpoint of the RO Digital Library. Note that in our case we got two result rows, one for each of the workflows that were used to confirm the hypothesis. We emphasize that queries could also be constructed to answer more elaborate questions such as question 3 and 4. Without adding any complexity to the query or the infrastructure, it is possible to query over the entire repository of research objects. This effectively integrates all meta-data of any workflow-based experiment that was uploaded to the RO Digital Library via myExperiment. When more ROs have become available that use the same annotations as described in this paper, then we can start sharing queries that can act as templates. We did not explore further formalization in terms of rejecting or accepting hypotheses, since formulating such a hypothesis model properly would be very domain specific, such as current efforts in neuromedicine [ 57 ]. However, the RO model does not exclude the possibility to do so.

Screenshot showing a SPARQL query and its results. Query to obtain a reference to the data that was used as input to our workflows and the conclusions that we drew from evaluating the workflow results.

Applying the RO model in genomic working environments

An important criterion for our evaluation of the RO model and tools is that it should support researchers in preparing their digital methods and results for publication. We have shown that the RO model can be applied in an existing framework for sharing computational workflows (myExperiment). We used Taverna to create our workflows, and the wf4ever toolkit [ 58 ], including dLibra [ 59 ] that was extended with a triple store as a back end to store the ROs. The RO features of the test version of myExperiment that we used are currently under development for migration to the production version of myExperiment [ 60 ]. Creating an RO in the test version of myExperiment is not any different to a user than the action of creating a pack, completely hiding the creation of RDF objects under the hood. The difference lies in the support of the RO model, which allows the user to add data associated with a computational experiment in a structured way (a sketch representing the experimental setup, the hypothesis document, result files, etc.), and metadata in the form of annotations. Every piece of data in an RO can be annotated, either in a structured or machine-generated way like the automatic annotation of a wfdesc description of a workflow as provided by the workflow-to-RDF transformation service, or manually by the user at the time of resource upload, such as the annotation of an experiment overview as “Sketch”. Since RO descriptions are currently not a pre-requisite to publishing workflow results in journal, we hope that this support and streamlining of the annotation process will act as an incentive for scientists to start using the RO technology.

The representation of an RO in myExperiment as presented in this paper should be seen as a proof-of-concept. Crucial elements of a computational experiment are handled, but there is room for improvement. For example, the hypothesis and conclusions are at the moment only shown as downloadable text files and the content and provenance of a workflow run is not shown to the user. We found that more tooling is needed to make practical use of the provenance trace. It is detailed and focus is on data lineage, rather than the biological meaning of the recorded steps. Nevertheless, we regard this raw workflow data as highly valuable as the true record of what exactly was executed. It allows introspection of the data lineage, such as which service was invoked with exactly which data. By providing this proof-of-concept and the RO model as a reference model, we hope to stimulate developers of other genomic working environments such as Galaxy [ 6 ] and Genome Space [ 61 ] to start implementing the RO model as well, thus enabling scientists to share their investigation results as a complete knowledge package. Similarly, workflow systems use different workflow languages [ 62 , 63 ], and by presenting the workflow-to-RDF transformation service that handles the t2flow serialization format to transform a workflow to an RO, we hope to encourage systems that use other workflow languages to develop similar services to transform their workflows to ROs. This would allow for a higher-level understanding of workflow-based experiments regardless of the type of workflow system used.

It should be noted that although our ROs fully capture the individual data items of individual steps within workflow runs, this approach is not applicable to all scientific workflows. In fact, we have since further developed the provenance support for Taverna so that larger pieces of data are only recorded as URI references and not bundled within the ZIP file. The Taverna workflow system already supports working with such references; however many bioinformatics Web services still only support working directly with values. When dealing with references, the workflow run data only capture the URI and its metadata, and full access to the run data therefore would also depend on the continued availability (or mirroring) of those referenced resources, and their consistency would therefore later need to be verified against metadata such as byte size and Secure Hash Algorithm checksums.

Generalization to other domains

We acknowledge that apart from enabling the structured aggregation and annotation of digital ROs technically, scientists appreciate guidelines and Best Practices for producing high quality ROs. In fact, the minimal requirements for a complete RO that we implemented via the Minim model, were inspired by the 10 Best Practices that we defined for creating workflows [ 39 ]. An RO may be evaluated using different checklists for different purposes. A checklist description is published as linked data, and may be included in the RO, though we anticipate more common use will be for it to be published separately in a community web site. In our work to date, we have used checklist definitions published via Github (e.g. [ 64 ]), and are looking to create a collection of example checklist definitions to seed creation of checklists for different domains or purposes [ 65 ]. We envision that instructions to authors of ROs may differ between research communities, and publishers who wish to adopt RO technology for digital submissions may develop their own ‘Instructions to Authors’ for ROs. This could be implemented by different mappings of the Minim model.

Related work

The RO model was implemented as a Semantic Web model to provide a general, domain-agnostic reference that can be extended by domain specific ontologies. For instance, while the RO model offers terms pertaining to experimental science such as “hypothesis” and “conclusion”, extensions to existing models that also cover this area and are already in use in the life science domain could be considered. It is beyond the scope of this article to exhaustively review related ontologies and associated tools, but we wish to mention six that in our view are prime candidates to augment the RO family of ontologies and tools. The first is the Ontology for Biomedical Investigations (OBI) that aims to represent all phases of experimental processes, such as study designs, protocols, instrumentation, biological material, collected data and analyses performed on that data [ 66 ]. OBI is used for the ontological representation of the results of the Investigation-Study-Assay (ISA) metadata tools [ 67 ] that is the next on our list of candidates. ISA, developed by the ISA commons community, facilitates curation, management, and reuse of omics datasets in a variety of life science domains [ 68 ]. It puts spreadsheets at the heart of its tooling, making it highly popular for study capture in the omics domain [ 69 ]. The third candidate is the ontology for scientific experiments EXPO [ 70 ]. EXPO is defined by OWL-DL axioms and is grounded in upper ontologies. Its coverage of experiment terms is good, but we are unsure about its uptake by the community. Perhaps unfortunate for a number of good ontologies, we consider this an important criterion for interoperability. Four and five on our list relate to the annotation of Web Services (or bioinformatics operations in general): the EMBRACE Data and Methods (EDAM) ontology encompasses over 2200 terms for annotating tool or workflow functions, types of data and identifiers, application domains and data formats [ 71 ]. It is developed and maintained by the European Bioinformatics Institute and has been adopted for annotation of for instance the European Molecular Biology Open Software Suite. The myGrid-BioMoby ontology served as a starting point for the development of EDAM. This will facilitate the adoption of EDAM by for instance BioCatalogue,org and service-oriented tools such as Taverna, which would further broaden its user base and thereby its use for interoperability. The Semantic Automated Discovery and Integration (SADI) framework [ 72 ] takes semantic annotation of Web Services one step further. A SADI Web Service describes itself in terms of OWL classes, and produces and consumes instances of OWL classes. This enables instant annotation in a machine readable format when a workflow is built from SADI services. In addition, via a SADI registry suggestions can be made about which services to connect to which. SADI has clear advantages as an annotation framework. However, not all bioinformatics services are available as SADI services, while the conversion is not trivial without training in Semantic Web modelling. Therefore, SADI and RO frameworks could be strongly complementary for workflows that use a heterogeneous mix of service types. This would be further facilitated when both are linked to common ontologies such as EDAM. Finally, we highlight the recent development of models for microattribution and nanopublication that aim to provide a means of getting credit for individual assertions and making these available in a machine readable format [ 73 – 75 ]. Taking nanopublications as an example, we could “nanopublish” specific results from our experiment, such as the text mining-based association that we found between the SNP “rs7156144” and the biological process “stimulation of tumor necrosis factor production”. In addition to an assertion, a nanopublication consists of provenance meta-data (to ensure trust in the assertion) and publication information (providing attribution to authors and curators). Nanopublication and RO complement each other in two ways. On the one hand, nanopublications can be used to publish and expose valuable results from workflows and included in the RO aggregate. On the other hand, an RO could be referenced as part of the provenance of a nanopublication, serving as a record of the method that led to assertion of the nanopublication. Similar to the nanopublication and microattribution models, the Biotea and Elsevier Smart Content Initiative data models also aim to model scientific results, but are focused on encapsulating a collection of information that are related to the results reported in publications [ 76 , 77 ]. The relationship between an RO and these datasets is not much different from an RO with a nanopublication statement. An RO can be referenced by, e.g. the Biotea dataset, by its URI, which can provide detailed experimental information or provenance information about the results described by the Biotea dataset. In the meanwhile, an RO can also reference a Biotea dataset or an Elsevier linked dataset.

Summarizing, the RO model provides a general framework with terms for aggregating and annotating the components of digital research experiments, by which it can complement related frameworks that are already used in the life science domain such as EXPO, OBI, ISA, EDAM, SADI and nanopublication. We observe that models are partly complementary and partly overlapping in scope. Therefore, we stimulate collaboration towards the development of complementarity frameworks. For instance, we initiated an investigation of the combination of ISA, RO, and Nanopublication as a basis for general guidelines for publishing digital research artefacts (Manuscript in preparation).

Uptake by the research community

Beyond the RO presented in this paper, the RO model has been used to generate ROs within the domains of musicology [ 78 ] and astronomy using AstroTaverna [ 79 ]. In addition, we recently explored how an RO could be referenced as part of the provenance of nanopublications of genes that are differentially expressed in Huntington’s Disease (HD) with certain genomic regions [ 80 , 81 ]. The results from the in silico analysis of the differentially expressed genes were obtained from a Taverna data integration workflow and the RO itself was stored in the Digital Library. Using the PROV-O ontology, the nanopublication provenance was modelled to link to the workflow description in the RO. Since the RO was mostly automatically generated by the procedure described in this paper, the nanopublication refers to detailed provenance information without requiring additional modelling effort. To encourage further uptake by the research community we have developed the Web resource ResearchObject.org [ 82 ]. ResearchObject.org lists example ROs [ 83 ], presents the ongoing activities of the open RO community, and gathers knowledge about related developments and adoptions.

Applying the workflow-centric RO model and associated models such as Minim provides a digital method to increase the understanding of bioinformatics experiments. Crucial meta-data related to the experiment is preserved in a Digital Library by structured aggregation and annotation of hypothesis, input data, workflows, workflow runs, results, and conclusions. The Semantic Web representation provides a reference model for life scientists who perform computational analyses and for systems that support this, and can complement related annotation frameworks that are already in use in the life science domain.

Written informed consent was obtained from Kristina M Hettne to publish her picture in relation to the myExperiment pack "Interpreting GWAS results with pathways and text mining".

Acknowledgements

The research reported in this paper is supported by the EU Wf4Ever STREP project (270129) funded under EU FP7 (ICT-2009.4.1), the EP/G026238/1 EPSRC project myGrid: A Platform for e-Biology Renewal, the IMI-JU project Open PHACTS (grant agreement n 115191), and grants received from the Netherlands Bioinformatics Centre (NBIC) under the BioAssist program.

We gratefully acknowledge Matt Gamble for his advice on the Minim model.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

KMH participated in the design and the coordination of the study, participated in the design and creation of the workflows and the Web services used in the text-mining workflow, created the Research Object, and drafted the manuscript. HKD participated in the design of the study, participated in the design and creation of the workflows, and helped to draft the manuscript. JZ, KB, SSR, OC, GK, SB participated in the design of the semantic models and helped to draft the manuscript. KW, CG, LVM, JG, DR, PBH participated in the design of the study and helped to draft the manuscript. CG also prepared and co-supervised the work on Minim. EM performed the connection to the nanopublication model and helped to draft the manuscript. MT designed and performed the SPARQL queries and helped to draft the manuscript. DC implemented the requirements for creating a Research Object in myExperiment and helped to draft the manuscript. RS designed and implemented the web services used by the text-mining workflow. MR conceived of the study, participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.

Contributor Information

Kristina M Hettne, Email: [email protected] .

Harish Dharuri, Email: [email protected] .

Jun Zhao, Email: [email protected] .

Katherine Wolstencroft, Email: [email protected] .

Khalid Belhajjame, Email: ku.ca.nam.sc@bdilahk .

Stian Soiland-Reyes, Email: ku.ca.retsehcnam.sc@seyer-dnalios .

Eleni Mina, Email: [email protected] .

Mark Thompson, Email: [email protected] .

Don Cruickshank, Email: ku.ca.notos.sce@cgd .

Lourdes Verdes-Montenegro, Email: se.aai@sedruol .

Julian Garrido, Email: se.aai@odirragj .

David de Roure, Email: [email protected] .

Oscar Corcho, Email: se.mpu.if@ohcroco .

Graham Klyne, Email: [email protected] .

Reinout van Schouwen, Email: moc.liamg@tuonier .

Peter A C ‘t Hoen, Email: ln.cmul@neoH_t_.C.A.P .

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Research Objectives – Types, Examples and Writing Guide

Table of Contents

Research Objectives

Research objectives refer to the specific goals or aims of a research study. They provide a clear and concise description of what the researcher hopes to achieve by conducting the research . The objectives are typically based on the research questions and hypotheses formulated at the beginning of the study and are used to guide the research process.

Types of Research Objectives

Here are the different types of research objectives in research:

• Exploratory Objectives: These objectives are used to explore a topic, issue, or phenomenon that has not been studied in-depth before. The aim of exploratory research is to gain a better understanding of the subject matter and generate new ideas and hypotheses .
• Descriptive Objectives: These objectives aim to describe the characteristics, features, or attributes of a particular population, group, or phenomenon. Descriptive research answers the “what” questions and provides a snapshot of the subject matter.
• Explanatory Objectives : These objectives aim to explain the relationships between variables or factors. Explanatory research seeks to identify the cause-and-effect relationships between different phenomena.
• Predictive Objectives: These objectives aim to predict future events or outcomes based on existing data or trends. Predictive research uses statistical models to forecast future trends or outcomes.
• Evaluative Objectives : These objectives aim to evaluate the effectiveness or impact of a program, intervention, or policy. Evaluative research seeks to assess the outcomes or results of a particular intervention or program.
• Prescriptive Objectives: These objectives aim to provide recommendations or solutions to a particular problem or issue. Prescriptive research identifies the best course of action based on the results of the study.
• Diagnostic Objectives : These objectives aim to identify the causes or factors contributing to a particular problem or issue. Diagnostic research seeks to uncover the underlying reasons for a particular phenomenon.
• Comparative Objectives: These objectives aim to compare two or more groups, populations, or phenomena to identify similarities and differences. Comparative research is used to determine which group or approach is more effective or has better outcomes.
• Historical Objectives: These objectives aim to examine past events, trends, or phenomena to gain a better understanding of their significance and impact. Historical research uses archival data, documents, and records to study past events.
• Ethnographic Objectives : These objectives aim to understand the culture, beliefs, and practices of a particular group or community. Ethnographic research involves immersive fieldwork and observation to gain an insider’s perspective of the group being studied.
• Action-oriented Objectives: These objectives aim to bring about social or organizational change. Action-oriented research seeks to identify practical solutions to social problems and to promote positive change in society.
• Conceptual Objectives: These objectives aim to develop new theories, models, or frameworks to explain a particular phenomenon or set of phenomena. Conceptual research seeks to provide a deeper understanding of the subject matter by developing new theoretical perspectives.
• Methodological Objectives: These objectives aim to develop and improve research methods and techniques. Methodological research seeks to advance the field of research by improving the validity, reliability, and accuracy of research methods and tools.
• Theoretical Objectives : These objectives aim to test and refine existing theories or to develop new theoretical perspectives. Theoretical research seeks to advance the field of knowledge by testing and refining existing theories or by developing new theoretical frameworks.
• Measurement Objectives : These objectives aim to develop and validate measurement instruments, such as surveys, questionnaires, and tests. Measurement research seeks to improve the quality and reliability of data collection and analysis by developing and testing new measurement tools.
• Design Objectives : These objectives aim to develop and refine research designs, such as experimental, quasi-experimental, and observational designs. Design research seeks to improve the quality and validity of research by developing and testing new research designs.
• Sampling Objectives: These objectives aim to develop and refine sampling techniques, such as probability and non-probability sampling methods. Sampling research seeks to improve the representativeness and generalizability of research findings by developing and testing new sampling techniques.

How to Write Research Objectives

Writing clear and concise research objectives is an important part of any research project, as it helps to guide the study and ensure that it is focused and relevant. Here are some steps to follow when writing research objectives:

• Identify the research problem : Before you can write research objectives, you need to identify the research problem you are trying to address. This should be a clear and specific problem that can be addressed through research.
• Define the research questions : Based on the research problem, define the research questions you want to answer. These questions should be specific and should guide the research process.
• Identify the variables : Identify the key variables that you will be studying in your research. These are the factors that you will be measuring, manipulating, or analyzing to answer your research questions.
• Write specific objectives: Write specific, measurable objectives that will help you answer your research questions. These objectives should be clear and concise and should indicate what you hope to achieve through your research.
• Use the SMART criteria: To ensure that your research objectives are well-defined and achievable, use the SMART criteria. This means that your objectives should be Specific, Measurable, Achievable, Relevant, and Time-bound.
• Revise and refine: Once you have written your research objectives, revise and refine them to ensure that they are clear, concise, and achievable. Make sure that they align with your research questions and variables, and that they will help you answer your research problem.

Example of Research Objectives

Examples of research objectives Could be:

Research Objectives for the topic of “The Impact of Artificial Intelligence on Employment”:

• To investigate the effects of the adoption of AI on employment trends across various industries and occupations.
• To explore the potential for AI to create new job opportunities and transform existing roles in the workforce.
• To examine the social and economic implications of the widespread use of AI for employment, including issues such as income inequality and access to education and training.
• To identify the skills and competencies that will be required for individuals to thrive in an AI-driven workplace, and to explore the role of education and training in developing these skills.
• To evaluate the ethical and legal considerations surrounding the use of AI for employment, including issues such as bias, privacy, and the responsibility of employers and policymakers to protect workers’ rights.

When to Write Research Objectives

• At the beginning of a research project : Research objectives should be identified and written down before starting a research project. This helps to ensure that the project is focused and that data collection and analysis efforts are aligned with the intended purpose of the research.
• When refining research questions: Writing research objectives can help to clarify and refine research questions. Objectives provide a more concrete and specific framework for addressing research questions, which can improve the overall quality and direction of a research project.
• After conducting a literature review : Conducting a literature review can help to identify gaps in knowledge and areas that require further research. Writing research objectives can help to define and focus the research effort in these areas.
• When developing a research proposal: Research objectives are an important component of a research proposal. They help to articulate the purpose and scope of the research, and provide a clear and concise summary of the expected outcomes and contributions of the research.
• When seeking funding for research: Funding agencies often require a detailed description of research objectives as part of a funding proposal. Writing clear and specific research objectives can help to demonstrate the significance and potential impact of a research project, and increase the chances of securing funding.
• When designing a research study : Research objectives guide the design and implementation of a research study. They help to identify the appropriate research methods, sampling strategies, data collection and analysis techniques, and other relevant aspects of the study design.
• When communicating research findings: Research objectives provide a clear and concise summary of the main research questions and outcomes. They are often included in research reports and publications, and can help to ensure that the research findings are communicated effectively and accurately to a wide range of audiences.
• When evaluating research outcomes : Research objectives provide a basis for evaluating the success of a research project. They help to measure the degree to which research questions have been answered and the extent to which research outcomes have been achieved.
• When conducting research in a team : Writing research objectives can facilitate communication and collaboration within a research team. Objectives provide a shared understanding of the research purpose and goals, and can help to ensure that team members are working towards a common objective.

Purpose of Research Objectives

Some of the main purposes of research objectives include:

• To clarify the research question or problem : Research objectives help to define the specific aspects of the research question or problem that the study aims to address. This makes it easier to design a study that is focused and relevant.
• To guide the research design: Research objectives help to determine the research design, including the research methods, data collection techniques, and sampling strategy. This ensures that the study is structured and efficient.
• To measure progress : Research objectives provide a way to measure progress throughout the research process. They help the researcher to evaluate whether they are on track and meeting their goals.
• To communicate the research goals : Research objectives provide a clear and concise description of the research goals. This helps to communicate the purpose of the study to other researchers, stakeholders, and the general public.

Advantages of Research Objectives

Here are some advantages of having well-defined research objectives:

• Focus : Research objectives help to focus the research effort on specific areas of inquiry. By identifying clear research questions, the researcher can narrow down the scope of the study and avoid getting sidetracked by irrelevant information.
• Clarity : Clearly stated research objectives provide a roadmap for the research study. They provide a clear direction for the research, making it easier for the researcher to stay on track and achieve their goals.
• Measurability : Well-defined research objectives provide measurable outcomes that can be used to evaluate the success of the research project. This helps to ensure that the research is effective and that the research goals are achieved.
• Feasibility : Research objectives help to ensure that the research project is feasible. By clearly defining the research goals, the researcher can identify the resources required to achieve those goals and determine whether those resources are available.
• Relevance : Research objectives help to ensure that the research study is relevant and meaningful. By identifying specific research questions, the researcher can ensure that the study addresses important issues and contributes to the existing body of knowledge.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Aims and Objectives – A Guide for Academic Writing
• Doing a PhD

One of the most important aspects of a thesis, dissertation or research paper is the correct formulation of the aims and objectives. This is because your aims and objectives will establish the scope, depth and direction that your research will ultimately take. An effective set of aims and objectives will give your research focus and your reader clarity, with your aims indicating what is to be achieved, and your objectives indicating how it will be achieved.

Introduction

There is no getting away from the importance of the aims and objectives in determining the success of your research project. Unfortunately, however, it is an aspect that many students struggle with, and ultimately end up doing poorly. Given their importance, if you suspect that there is even the smallest possibility that you belong to this group of students, we strongly recommend you read this page in full.

This page describes what research aims and objectives are, how they differ from each other, how to write them correctly, and the common mistakes students make and how to avoid them. An example of a good aim and objectives from a past thesis has also been deconstructed to help your understanding.

What Are Aims and Objectives?

Research aims.

A research aim describes the main goal or the overarching purpose of your research project.

In doing so, it acts as a focal point for your research and provides your readers with clarity as to what your study is all about. Because of this, research aims are almost always located within its own subsection under the introduction section of a research document, regardless of whether it’s a thesis , a dissertation, or a research paper .

A research aim is usually formulated as a broad statement of the main goal of the research and can range in length from a single sentence to a short paragraph. Although the exact format may vary according to preference, they should all describe why your research is needed (i.e. the context), what it sets out to accomplish (the actual aim) and, briefly, how it intends to accomplish it (overview of your objectives).

To give an example, we have extracted the following research aim from a real PhD thesis:

Example of a Research Aim

The role of diametrical cup deformation as a factor to unsatisfactory implant performance has not been widely reported. The aim of this thesis was to gain an understanding of the diametrical deformation behaviour of acetabular cups and shells following impaction into the reamed acetabulum. The influence of a range of factors on deformation was investigated to ascertain if cup and shell deformation may be high enough to potentially contribute to early failure and high wear rates in metal-on-metal implants.

Note: Extracted with permission from thesis titled “T he Impact And Deformation Of Press-Fit Metal Acetabular Components ” produced by Dr H Hothi of previously Queen Mary University of London.

Research Objectives

Where a research aim specifies what your study will answer, research objectives specify how your study will answer it.

They divide your research aim into several smaller parts, each of which represents a key section of your research project. As a result, almost all research objectives take the form of a numbered list, with each item usually receiving its own chapter in a dissertation or thesis.

Following the example of the research aim shared above, here are it’s real research objectives as an example:

Example of a Research Objective

• Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.
• Investigate the number, velocity and position of impacts needed to insert a cup.
• Determine the relationship between the size of interference between the cup and cavity and deformation for different cup types.
• Investigate the influence of non-uniform cup support and varying the orientation of the component in the cavity on deformation.
• Examine the influence of errors during reaming of the acetabulum which introduce ovality to the cavity.
• Determine the relationship between changes in the geometry of the component and deformation for different cup designs.
• Develop three dimensional pelvis models with non-uniform bone material properties from a range of patients with varying bone quality.
• Use the key parameters that influence deformation, as identified in the foam models to determine the range of deformations that may occur clinically using the anatomic models and if these deformations are clinically significant.

It’s worth noting that researchers sometimes use research questions instead of research objectives, or in other cases both. From a high-level perspective, research questions and research objectives make the same statements, but just in different formats.

Taking the first three research objectives as an example, they can be restructured into research questions as follows:

Restructuring Research Objectives as Research Questions

• Can finite element models using simplified experimentally validated foam models to represent the acetabulum together with explicit dynamics be used to mimic mallet blows during cup/shell insertion?
• What is the number, velocity and position of impacts needed to insert a cup?
• What is the relationship between the size of interference between the cup and cavity and deformation for different cup types?

Difference Between Aims and Objectives

Hopefully the above explanations make clear the differences between aims and objectives, but to clarify:

• The research aim focus on what the research project is intended to achieve; research objectives focus on how the aim will be achieved.
• Research aims are relatively broad; research objectives are specific.
• Research aims focus on a project’s long-term outcomes; research objectives focus on its immediate, short-term outcomes.
• A research aim can be written in a single sentence or short paragraph; research objectives should be written as a numbered list.

How to Write Aims and Objectives

Before we discuss how to write a clear set of research aims and objectives, we should make it clear that there is no single way they must be written. Each researcher will approach their aims and objectives slightly differently, and often your supervisor will influence the formulation of yours on the basis of their own preferences.

Regardless, there are some basic principles that you should observe for good practice; these principles are described below.

Your aim should be made up of three parts that answer the below questions:

• Why is this research required?
• What is this research about?
• How are you going to do it?

The easiest way to achieve this would be to address each question in its own sentence, although it does not matter whether you combine them or write multiple sentences for each, the key is to address each one.

The first question, why , provides context to your research project, the second question, what , describes the aim of your research, and the last question, how , acts as an introduction to your objectives which will immediately follow.

Scroll through the image set below to see the ‘why, what and how’ associated with our research aim example.

Note: Your research aims need not be limited to one. Some individuals per to define one broad ‘overarching aim’ of a project and then adopt two or three specific research aims for their thesis or dissertation. Remember, however, that in order for your assessors to consider your research project complete, you will need to prove you have fulfilled all of the aims you set out to achieve. Therefore, while having more than one research aim is not necessarily disadvantageous, consider whether a single overarching one will do.

Research Objectives

Each of your research objectives should be SMART :

• Specific – is there any ambiguity in the action you are going to undertake, or is it focused and well-defined?
• Measurable – how will you measure progress and determine when you have achieved the action?
• Achievable – do you have the support, resources and facilities required to carry out the action?
• Relevant – is the action essential to the achievement of your research aim?
• Timebound – can you realistically complete the action in the available time alongside your other research tasks?

In addition to being SMART, your research objectives should start with a verb that helps communicate your intent. Common research verbs include:

Table of Research Verbs to Use in Aims and Objectives

Last, format your objectives into a numbered list. This is because when you write your thesis or dissertation, you will at times need to make reference to a specific research objective; structuring your research objectives in a numbered list will provide a clear way of doing this.

To bring all this together, let’s compare the first research objective in the previous example with the above guidance:

Checking Research Objective Example Against Recommended Approach

Research Objective:

1. Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.

Checking Against Recommended Approach:

Q: Is it specific? A: Yes, it is clear what the student intends to do (produce a finite element model), why they intend to do it (mimic cup/shell blows) and their parameters have been well-defined ( using simplified experimentally validated foam models to represent the acetabulum ).

Q: Is it measurable? A: Yes, it is clear that the research objective will be achieved once the finite element model is complete.

Q: Is it achievable? A: Yes, provided the student has access to a computer lab, modelling software and laboratory data.

Q: Is it relevant? A: Yes, mimicking impacts to a cup/shell is fundamental to the overall aim of understanding how they deform when impacted upon.

Q: Is it timebound? A: Yes, it is possible to create a limited-scope finite element model in a relatively short time, especially if you already have experience in modelling.

Q: Does it start with a verb? A: Yes, it starts with ‘develop’, which makes the intent of the objective immediately clear.

Q: Is it a numbered list? A: Yes, it is the first research objective in a list of eight.

Mistakes in Writing Research Aims and Objectives

1. making your research aim too broad.

Having a research aim too broad becomes very difficult to achieve. Normally, this occurs when a student develops their research aim before they have a good understanding of what they want to research. Remember that at the end of your project and during your viva defence , you will have to prove that you have achieved your research aims; if they are too broad, this will be an almost impossible task. In the early stages of your research project, your priority should be to narrow your study to a specific area. A good way to do this is to take the time to study existing literature, question their current approaches, findings and limitations, and consider whether there are any recurring gaps that could be investigated .

Note: Achieving a set of aims does not necessarily mean proving or disproving a theory or hypothesis, even if your research aim was to, but having done enough work to provide a useful and original insight into the principles that underlie your research aim.

2. Making Your Research Objectives Too Ambitious

Be realistic about what you can achieve in the time you have available. It is natural to want to set ambitious research objectives that require sophisticated data collection and analysis, but only completing this with six months before the end of your PhD registration period is not a worthwhile trade-off.

3. Formulating Repetitive Research Objectives

Each research objective should have its own purpose and distinct measurable outcome. To this effect, a common mistake is to form research objectives which have large amounts of overlap. This makes it difficult to determine when an objective is truly complete, and also presents challenges in estimating the duration of objectives when creating your project timeline. It also makes it difficult to structure your thesis into unique chapters, making it more challenging for you to write and for your audience to read.

Fortunately, this oversight can be easily avoided by using SMART objectives.

Hopefully, you now have a good idea of how to create an effective set of aims and objectives for your research project, whether it be a thesis, dissertation or research paper. While it may be tempting to dive directly into your research, spending time on getting your aims and objectives right will give your research clear direction. This won’t only reduce the likelihood of problems arising later down the line, but will also lead to a more thorough and coherent research project.

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• insurgent learning
• convivial research
• facilitation
• direct action
• guerrilla media

Objects of Study

Often left unspecified and mostly assumed or taken for granted, an object of study is one of the most fundamental technologies of any investigative process. Drawing from the work of Jorge Gonzalez, we approach an object of study as a socially constructed research tool that works best when explicit, transparent, and strategic. A comprehensive object of study should organize at least nine components: Title, Area of Interest, Topic, Research Question, Practical Problem, Research Problem, Techniques, Information Produced, and Glossary. The complete object of study should manage a number of obligations required of any investigation. Thus, an object of study frames a research question, articulates a claim, formulates co-generated information, facilitates techniques to co-produce knowledge, and proposes a system(s) of information. A successful object of study manages the epistemological, theoretical, and methodological contributions of the research.

A collectively articulated object of study should be treated as emergent, evolving with greater strategic focus and clarity through the dynamic interaction of a community of struggle organizing itself into an emergent research collective. An object of study is less likely to objectify a community of struggle when it is collectively generated through convivial processes. In a convivial approach, we insist that an object of study should be collectively determined and that its articulation should reflect the specific interests of a community struggle.

Convivial Research Guides

Object of study workshop handout

Interactive Object of Study

Objects of Study web page

Strategic Texts

Bell Hooks, “Feminist Scholarship: Ethical Issues,” in Talking Back: Thinking Feminist, Thinking Black(Boston: Southend Press, 1989): 42-48;

Marta Malo de Molina, “Common Notions;”

Linda Tuhiwai Smith, “Imperialism, History, Writing, Theory,” in Decolonizing Methodologies: Research and Indigenous Peoples (London: Zed Books, 2002): 19-41;

Nate Holdren and Sebastian Touza, “Introduction to Colectivo Situaciones,” ephemera 5:4 (November 2005): 595-601;

Colectivo Situaciones, “Something More on Research Militancy: Footnotes on Procedures and (In)Decisions,”ephemera 5:4 (November 2005): 602-614;

Colectivo Situaciones, “On the Researcher-Militant;”

Mario Barrera and Geralda Vialpando, eds., Action Research: In Defense of the Barrio (Los Angeles: Aztlan Publications, 1974.

Module 1: Introduction: What is Research?

Learning Objectives

By the end of this module, you will be able to:

• Explain how the scientific method is used to develop new knowledge
• Describe why it is important to follow a research plan

The Scientific Method consists of observing the world around you and creating a  hypothesis  about relationships in the world. A hypothesis is an informed and educated prediction or explanation about something. Part of the research process involves testing the  hypothesis , and then examining the results of these tests as they relate to both the hypothesis and the world around you. When a researcher forms a hypothesis, this acts like a map through the research study. It tells the researcher which factors are important to study and how they might be related to each other or caused by a  manipulation  that the researcher introduces (e.g. a program, treatment or change in the environment). With this map, the researcher can interpret the information he/she collects and can make sound conclusions about the results.

Research can be done with human beings, animals, plants, other organisms and inorganic matter. When research is done with human beings and animals, it must follow specific rules about the treatment of humans and animals that have been created by the U.S. Federal Government. This ensures that humans and animals are treated with dignity and respect, and that the research causes minimal harm.

No matter what topic is being studied, the value of the research depends on how well it is designed and done. Therefore, one of the most important considerations in doing good research is to follow the design or plan that is developed by an experienced researcher who is called the  Principal Investigator  (PI). The PI is in charge of all aspects of the research and creates what is called a  protocol  (the research plan) that all people doing the research must follow. By doing so, the PI and the public can be sure that the results of the research are real and useful to other scientists.

Module 1: Discussion Questions

• How is a hypothesis like a road map?
• Who is ultimately responsible for the design and conduct of a research study?
• How does following the research protocol contribute to informing public health practices?

Email Updates

21 Research Objectives Examples (Copy and Paste)

Research objectives refer to the definitive statements made by researchers at the beginning of a research project detailing exactly what a research project aims to achieve.

These objectives are explicit goals clearly and concisely projected by the researcher to present a clear intention or course of action for his or her qualitative or quantitative study. 

Research objectives are typically nested under one overarching research aim. The objectives are the steps you’ll need to take in order to achieve the aim (see the examples below, for example, which demonstrate an aim followed by 3 objectives, which is what I recommend to my research students).

Research Objectives vs Research Aims

Research aim and research objectives are fundamental constituents of any study, fitting together like two pieces of the same puzzle.

The ‘research aim’ describes the overarching goal or purpose of the study (Kumar, 2019). This is usually a broad, high-level purpose statement, summing up the central question that the research intends to answer.

Example of an Overarching Research Aim:

“The aim of this study is to explore the impact of climate change on crop productivity.” 

Comparatively, ‘research objectives’ are concrete goals that underpin the research aim, providing stepwise actions to achieve the aim.

Objectives break the primary aim into manageable, focused pieces, and are usually characterized as being more specific, measurable, achievable, relevant, and time-bound (SMART).

Examples of Specific Research Objectives:

1. “To examine the effects of rising temperatures on the yield of rice crops during the upcoming growth season.” 2. “To assess changes in rainfall patterns in major agricultural regions over the first decade of the twenty-first century (2000-2010).” 3. “To analyze the impact of changing weather patterns on crop diseases within the same timeframe.”

The distinction between these two terms, though subtle, is significant for successfully conducting a study. The research aim provides the study with direction, while the research objectives set the path to achieving this aim, thereby ensuring the study’s efficiency and effectiveness.

How to Write Research Objectives

I usually recommend to my students that they use the SMART framework to create their research objectives.

SMART is an acronym standing for Specific, Measurable, Achievable, Relevant, and Time-bound. It provides a clear method of defining solid research objectives and helps students know where to start in writing their objectives (Locke & Latham, 2013).

Each element of this acronym adds a distinct dimension to the framework, aiding in the creation of comprehensive, well-delineated objectives.

Here is each step:

• Specific : We need to avoid ambiguity in our objectives. They need to be clear and precise (Doran, 1981). For instance, rather than stating the objective as “to study the effects of social media,” a more focused detail would be “to examine the effects of social media use (Facebook, Instagram, and Twitter) on the academic performance of college students.”
• Measurable: The measurable attribute provides a clear criterion to determine if the objective has been met (Locke & Latham, 2013). A quantifiable element, such as a percentage or a number, adds a measurable quality. For example, “to increase response rate to the annual customer survey by 10%,” makes it easier to ascertain achievement.
• Achievable: The achievable aspect encourages researchers to craft realistic objectives, resembling a self-check mechanism to ensure the objectives align with the scope and resources at disposal (Doran, 1981). For example, “to interview 25 participants selected randomly from a population of 100” is an attainable objective as long as the researcher has access to these participants.
• Relevance : Relevance, the fourth element, compels the researcher to tailor the objectives in alignment with overarching goals of the study (Locke & Latham, 2013). This is extremely important – each objective must help you meet your overall one-sentence ‘aim’ in your study.
• Time-Bound: Lastly, the time-bound element fosters a sense of urgency and prioritization, preventing procrastination and enhancing productivity (Doran, 1981). “To analyze the effect of laptop use in lectures on student engagement over the course of two semesters this year” expresses a clear deadline, thus serving as a motivator for timely completion.

You’re not expected to fit every single element of the SMART framework in one objective, but across your objectives, try to touch on each of the five components.

Research Objectives Examples

1. Field: Psychology

Aim: To explore the impact of sleep deprivation on cognitive performance in college students.

• Objective 1: To compare cognitive test scores of students with less than six hours of sleep and those with 8 or more hours of sleep.
• Objective 2: To investigate the relationship between class grades and reported sleep duration.
• Objective 3: To survey student perceptions and experiences on how sleep deprivation affects their cognitive capabilities.

2. Field: Environmental Science

Aim: To understand the effects of urban green spaces on human well-being in a metropolitan city.

• Objective 1: To assess the physical and mental health benefits of regular exposure to urban green spaces.
• Objective 2: To evaluate the social impacts of urban green spaces on community interactions.
• Objective 3: To examine patterns of use for different types of urban green spaces. 

3. Field: Technology

Aim: To investigate the influence of using social media on productivity in the workplace.

• Objective 1: To measure the amount of time spent on social media during work hours.
• Objective 2: To evaluate the perceived impact of social media use on task completion and work efficiency.
• Objective 3: To explore whether company policies on social media usage correlate with different patterns of productivity.

4. Field: Education

Aim: To examine the effectiveness of online vs traditional face-to-face learning on student engagement and achievement.

• Objective 1: To compare student grades between the groups exposed to online and traditional face-to-face learning.
• Objective 2: To assess student engagement levels in both learning environments.
• Objective 3: To collate student perceptions and preferences regarding both learning methods.

5. Field: Health

Aim: To determine the impact of a Mediterranean diet on cardiac health among adults over 50.

• Objective 1: To assess changes in cardiovascular health metrics after following a Mediterranean diet for six months.
• Objective 2: To compare these health metrics with a similar group who follow their regular diet.
• Objective 3: To document participants’ experiences and adherence to the Mediterranean diet.

6. Field: Environmental Science

Aim: To analyze the impact of urban farming on community sustainability.

• Objective 1: To document the types and quantity of food produced through urban farming initiatives.
• Objective 2: To assess the effect of urban farming on local communities’ access to fresh produce.
• Objective 3: To examine the social dynamics and cooperative relationships in the creating and maintaining of urban farms.

7. Field: Sociology

Aim: To investigate the influence of home offices on work-life balance during remote work.

• Objective 1: To survey remote workers on their perceptions of work-life balance since setting up home offices.
• Objective 2: To conduct an observational study of daily work routines and family interactions in a home office setting.
• Objective 3: To assess the correlation, if any, between physical boundaries of workspaces and mental boundaries for work in the home setting.

8. Field: Economics

Aim: To evaluate the effects of minimum wage increases on small businesses.

• Objective 1: To analyze cost structures, pricing changes, and profitability of small businesses before and after minimum wage increases.
• Objective 2: To survey small business owners on the strategies they employ to navigate minimum wage increases.
• Objective 3: To examine employment trends in small businesses in response to wage increase legislation.

9. Field: Education

Aim: To explore the role of extracurricular activities in promoting soft skills among high school students.

• Objective 1: To assess the variety of soft skills developed through different types of extracurricular activities.
• Objective 2: To compare self-reported soft skills between students who participate in extracurricular activities and those who do not.
• Objective 3: To investigate the teachers’ perspectives on the contribution of extracurricular activities to students’ skill development.

10. Field: Technology

Aim: To assess the impact of virtual reality (VR) technology on the tourism industry.

• Objective 1: To document the types and popularity of VR experiences available in the tourism market.
• Objective 2: To survey tourists on their interest levels and satisfaction rates with VR tourism experiences.
• Objective 3: To determine whether VR tourism experiences correlate with increased interest in real-life travel to the simulated destinations.

11. Field: Biochemistry

Aim: To examine the role of antioxidants in preventing cellular damage.

• Objective 1: To identify the types and quantities of antioxidants in common fruits and vegetables.
• Objective 2: To determine the effects of various antioxidants on free radical neutralization in controlled lab tests.
• Objective 3: To investigate potential beneficial impacts of antioxidant-rich diets on long-term cellular health.

12. Field: Linguistics

Aim: To determine the influence of early exposure to multiple languages on cognitive development in children.

• Objective 1: To assess cognitive development milestones in monolingual and multilingual children.
• Objective 2: To document the number and intensity of language exposures for each group in the study.
• Objective 3: To investigate the specific cognitive advantages, if any, enjoyed by multilingual children.

13. Field: Art History

Aim: To explore the impact of the Renaissance period on modern-day art trends.

• Objective 1: To identify key characteristics and styles of Renaissance art.
• Objective 2: To analyze modern art pieces for the influence of the Renaissance style.
• Objective 3: To survey modern-day artists for their inspirations and the influence of historical art movements on their work.

14. Field: Cybersecurity

Aim: To assess the effectiveness of two-factor authentication (2FA) in preventing unauthorized system access.

• Objective 1: To measure the frequency of unauthorized access attempts before and after the introduction of 2FA.
• Objective 2: To survey users about their experiences and challenges with 2FA implementation.
• Objective 3: To evaluate the efficacy of different types of 2FA (SMS-based, authenticator apps, biometrics, etc.).

15. Field: Cultural Studies

Aim: To analyze the role of music in cultural identity formation among ethnic minorities.

• Objective 1: To document the types and frequency of traditional music practices within selected ethnic minority communities.
• Objective 2: To survey community members on the role of music in their personal and communal identity.
• Objective 3: To explore the resilience and transmission of traditional music practices in contemporary society.

16. Field: Astronomy

Aim: To explore the impact of solar activity on satellite communication.

• Objective 1: To categorize different types of solar activities and their frequencies of occurrence.
• Objective 2: To ascertain how variations in solar activity may influence satellite communication.
• Objective 3: To investigate preventative and damage-control measures currently in place during periods of high solar activity.

17. Field: Literature

Aim: To examine narrative techniques in contemporary graphic novels.

• Objective 1: To identify a range of narrative techniques employed in this genre.
• Objective 2: To analyze the ways in which these narrative techniques engage readers and affect story interpretation.
• Objective 3: To compare narrative techniques in graphic novels to those found in traditional printed novels.

18. Field: Renewable Energy

Aim: To investigate the feasibility of solar energy as a primary renewable resource within urban areas.

• Objective 1: To quantify the average sunlight hours across urban areas in different climatic zones. 
• Objective 2: To calculate the potential solar energy that could be harnessed within these areas.
• Objective 3: To identify barriers or challenges to widespread solar energy implementation in urban settings and potential solutions.

19. Field: Sports Science

Aim: To evaluate the role of pre-game rituals in athlete performance.

• Objective 1: To identify the variety and frequency of pre-game rituals among professional athletes in several sports.
• Objective 2: To measure the impact of pre-game rituals on individual athletes’ performance metrics.
• Objective 3: To examine the psychological mechanisms that might explain the effects (if any) of pre-game ritual on performance.

20. Field: Ecology

Aim: To investigate the effects of urban noise pollution on bird populations.

• Objective 1: To record and quantify urban noise levels in various bird habitats.
• Objective 2: To measure bird population densities in relation to noise levels.
• Objective 3: To determine any changes in bird behavior or vocalization linked to noise levels.

21. Field: Food Science

Aim: To examine the influence of cooking methods on the nutritional value of vegetables.

• Objective 1: To identify the nutrient content of various vegetables both raw and after different cooking processes.
• Objective 2: To compare the effect of various cooking methods on the nutrient retention of these vegetables.
• Objective 3: To propose cooking strategies that optimize nutrient retention.

The Importance of Research Objectives

The importance of research objectives cannot be overstated. In essence, these guideposts articulate what the researcher aims to discover, understand, or examine (Kothari, 2014).

When drafting research objectives, it’s essential to make them simple and comprehensible, specific to the point of being quantifiable where possible, achievable in a practical sense, relevant to the chosen research question, and time-constrained to ensure efficient progress (Kumar, 2019). 

Remember that a good research objective is integral to the success of your project, offering a clear path forward for setting out a research design , and serving as the bedrock of your study plan. Each objective must distinctly address a different dimension of your research question or problem (Kothari, 2014). Always bear in mind that the ultimate purpose of your research objectives is to succinctly encapsulate your aims in the clearest way possible, facilitating a coherent, comprehensive and rational approach to your planned study, and furnishing a scientific roadmap for your journey into the depths of knowledge and research (Kumar, 2019). 

Kothari, C.R (2014). Research Methodology: Methods and Techniques . New Delhi: New Age International.

Kumar, R. (2019). Research Methodology: A Step-by-Step Guide for Beginners .New York: SAGE Publications.

Doran, G. T. (1981). There’s a S.M.A.R.T. way to write management’s goals and objectives. Management review, 70 (11), 35-36.

Locke, E. A., & Latham, G. P. (2013). New Developments in Goal Setting and Task Performance . New York: Routledge.

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Animism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 10 Magical Thinking Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Social-Emotional Learning (Definition, Examples, Pros & Cons)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ What is Educational Psychology?

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How to appropriately choose research subjects

Affiliation.

• 1 Consulting Center of Biomedical Statistics, Academy of Military Medical Sciences, Beijing 100850, China. [email protected]
• PMID: 21419075
• DOI: 10.3736/jcim20110303

The research subject is the first key element of the three key elements in the research design. An appropriate selection of research subjects is crucial to the success of the research. This article summarizes the general principles for the selection of research subjects, the types and numbers of research subjects and the common mistakes that researchers tend to make in the selection of the research subjects. This article also provides the methodology suggestions for the selection of research subjects.

• Patient Selection*
• Research Design*

Research Object Crate

RO-Crate has been developed as a schema.org-based JSON lightweight approach to the next generation Research Object serialization.

2020-10-30: The specification RO-Crate 1.1 has been released. Join the community to help further develop RO-Crate!

Workshop on Research Objects (RO2019)

The RO2019 workshop was at IEEE eScience Conference 2019 in San Diego, US. This successful workshop followed the initial RO2018 . Proceedings of accepted papers and talks are available, along with links to slides and posters .

Researchobject.org aims to map the landscape of initiatives and activity in the development of Research Objects , an emerging approach to the publication, and exchange of scholarly information on the Web. Research Objects aim to improve reuse and reproducibility by:

• Supporting the publication of more than just PDFs , making data , code , and other resources first class citizens of scholarship
• Recognizing that there is often a need to publish collections of these resources together as one shareable , cite-able resource.
• Enriching these resources and collections with any and all additional information required to make research reusable , and reproducible !

Research objects are not just data, not just collections, but any digital resource that aims to go beyond the PDF for scholarly publishing!

Going beyond the PDF

Science advances on a foundation of trusted discoveries. Reproducing an experiment is one important approach that scientists use to gain confidence in their conclusions. Marcia McNutt, Editor-in-Chief of Science

The reuse and reproduction of scientific experiments as they are described in publications can be hard. Often it requires additional information, data, tooling or support beyond that provided in the text of a traditional publication.

As part of one research investigation you might for example have:

• Slides hosted on slideshare,
• Code in a github repository,
• Data in figshare,
• Data in ArrayExpress.

A growing number of activities are developing new mechanisms, or repurposing existing mechanisms in order to describe and associate resources like this together, in a machine-readable manner, so that they can be more easily shared, and exchanged.

The goal of research objects is to improve the potential for understanding and reuse of research by making sure that the information that is needed to make a published resource useful is associated with it, and shared as a whole.

There are a growing number of, individuals, groups, and initiatives – all trying to improve the state of scholarly publication. These range from domain specific to general, and from the practical and immediately actionable, to the more visionary and experimental.

What is emerging from these activities is a common set of goals and principles – features that are required required to support research that is Findable , Accessible , Interoperable , and Reusable (FAIR).

To understand more about the principles , goals of the Research Object approach, head over to the Overview page.

To discover the range of on going activities, and mechanisms that can be used to build research objects, check out the RO Initiatives & Resources page.

Get Involved!

ResearchObject.org is a community site aimed at gathering information, ideas, and interest around the topic of modernizing scholarly publication. You can contribute using GitHub issues or pull requests.

Most of the current RO activities are happening as part of the RO-Crate community which you are welcome to join !

Copyright and license

Unless otherwise noted, the documentation and images on this website is Open Source and licensed as Apache License, version 2.0 :

• Copyright © 2013-2014 University of Oxford, UK
• Copyright © 2013-2020 The University of Manchester, UK

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0</a </small>

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Please see the file NOTICE for required attributions.

RO-Crate 1.0 specification released

Posted on 15 November 2019

2019-09-24: Workshop on Research Objects (RO2019)

Posted on 20 June 2019

2018-10-03 Being FAIR: Enabling Reproducible Data Science

Posted on 15 October 2018

2018-10-29 Workshop on Research Objects

Posted on 19 April 2018

2017-12-05 FAIRy Stories for Christmas

Posted on 5 December 2017

2017-11-15 Managing Digital Research Objects in an Expanding Science Ecosystem

Posted on 1 December 2017

• identifiers

2017-11-27 BioCompute Objects

Posted on 9 November 2017

2017-10-24 Revamped ROHub portal officially released

Posted on 27 October 2017

2017-10-21 Keynote at SemSci ISWC 2017

Posted on 21 October 2017

• semantic web

2017-09-27 eLife Reproducible Portable Publications

Posted on 27 September 2017

• publication
• Reproducibility

2017-07-22 Common Workflow Language Viewer

Posted on 22 July 2017

2017-07-17 Being Reproducible (SSBSS Summer School)

Posted on 17 July 2017

2016-05-18 Make Research Reproducible Again

Posted on 18 May 2016

2016-01-28 Reproducibility Using Semantics

Posted on 29 January 2016

2016-01-28 ROHub

Posted on 28 January 2016

2016-01-28 Research Objects, FAIRDOM and SEEK4Science

2016-01-28 aspects of reproducibility in earth science, bagit for transferring and archiving research objects.

Posted on 17 July 2015

• specification

Research Objects at BOSC

Research objects biocaddie webinar, why publish and be so damned hard to find.

Posted on 23 March 2015

JISC Digifest Keynote

Posted on 11 March 2015

Combining Docker & R for Reproducible Research

Posted on 10 March 2015

RO+ISA+Nanopublication: What can you do when you put all three of them together?

Posted on 6 August 2014

Example of Encoding an RO using RDF-a

Posted on 28 February 2014

Our Workshop on "What Bioinformaticians need to know about digital publishing beyond the PDF2" has been accepted for ISMB2014

Posted on 14 January 2014

The Launch of Research Object Creator Tool (Give it a try!)

Posted on 10 January 2014

Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome

Posted on 17 November 2013

10th International Conference on Preservation of Digital Objects

Posted on 2 September 2013

From Preserving Data to Preserving Research:Curation of Process and Context

Posted on 21 May 2013

The Now and Future of Data Publishing, a symposium, 22 May 2013, Oxford, UK

Posted on 16 May 2013

Posted on 14 May 2013

Wf4Ever in ISMB/ECCB 2013

10th eswc 2013 semantics and big data.

Posted on 4 May 2013

Launch of W3C Research Object for Scholarly Communication Community Group

Posted on 20 April 2013

Innovative Statistics Project Ideas for Insightful Analysis

Table of contents

• 1.1 AP Statistics Topics for Project
• 1.2 Statistics Project Topics for High School Students
• 1.3 Statistical Survey Topics
• 1.4 Statistical Experiment Ideas
• 1.5 Easy Stats Project Ideas
• 1.6 Business Ideas for Statistics Project
• 1.7 Socio-Economic Easy Statistics Project Ideas
• 1.8 Experiment Ideas for Statistics and Analysis
• 2 Conclusion: Navigating the World of Data Through Statistics

Diving into the world of data, statistics presents a unique blend of challenges and opportunities to uncover patterns, test hypotheses, and make informed decisions. It is a fascinating field that offers many opportunities for exploration and discovery. This article is designed to inspire students, educators, and statistics enthusiasts with various project ideas. We will cover:

• Challenging concepts suitable for advanced placement courses.
• Accessible ideas that are engaging and educational for younger students.
• Ideas for conducting surveys and analyzing the results.
• Topics that explore the application of statistics in business and socio-economic areas.

Each category of topics for the statistics project provides unique insights into the world of statistics, offering opportunities for learning and application. Let’s dive into these ideas and explore the exciting world of statistical analysis.

Top Statistics Project Ideas for High School

Statistics is not only about numbers and data; it’s a unique lens for interpreting the world. Ideal for students, educators, or anyone with a curiosity about statistical analysis, these project ideas offer an interactive, hands-on approach to learning. These projects range from fundamental concepts suitable for beginners to more intricate studies for advanced learners. They are designed to ignite interest in statistics by demonstrating its real-world applications, making it accessible and enjoyable for people of all skill levels.

Need help with statistics project? Get your paper written by a professional writer Get Help Reviews.io 4.9/5

AP Statistics Topics for Project

• Analyzing Variance in Climate Data Over Decades.
• The Correlation Between Economic Indicators and Standard of Living.
• Statistical Analysis of Voter Behavior Patterns.
• Probability Models in Sports: Predicting Outcomes.
• The Effectiveness of Different Teaching Methods: A Statistical Study.
• Analysis of Demographic Data in Public Health.
• Time Series Analysis of Stock Market Trends.
• Investigating the Impact of Social Media on Academic Performance.
• Survival Analysis in Clinical Trial Data.
• Regression Analysis on Housing Prices and Market Factors.

Statistics Project Topics for High School Students

• The Mathematics of Personal Finance: Budgeting and Spending Habits.
• Analysis of Class Performance: Test Scores and Study Habits.
• A Statistical Comparison of Local Public Transportation Options.
• Survey on Dietary Habits and Physical Health Among Teenagers.
• Analyzing the Popularity of Various Music Genres in School.
• The Impact of Sleep on Academic Performance: A Statistical Approach.
• Statistical Study on the Use of Technology in Education.
• Comparing Athletic Performance Across Different Sports.
• Trends in Social Media Usage Among High School Students.
• The Effect of Part-Time Jobs on Student Academic Achievement.

Statistical Survey Topics

• Public Opinion on Environmental Conservation Efforts.
• Consumer Preferences in the Fast Food Industry.
• Attitudes Towards Online Learning vs. Traditional Classroom Learning.
• Survey on Workplace Satisfaction and Productivity.
• Public Health: Attitudes Towards Vaccination.
• Trends in Mobile Phone Usage and Preferences.
• Community Response to Local Government Policies.
• Consumer Behavior in Online vs. Offline Shopping.
• Perceptions of Public Safety and Law Enforcement.
• Social Media Influence on Political Opinions.

Statistical Experiment Ideas

• The Effect of Light on Plant Growth.
• Memory Retention: Visual vs. Auditory Information.
• Caffeine Consumption and Cognitive Performance.
• The Impact of Exercise on Stress Levels.
• Testing the Efficacy of Natural vs. Chemical Fertilizers.
• The Influence of Color on Mood and Perception.
• Sleep Patterns: Analyzing Factors Affecting Sleep Quality.
• The Effectiveness of Different Types of Water Filters.
• Analyzing the Impact of Room Temperature on Concentration.
• Testing the Strength of Different Brands of Batteries.

Easy Stats Project Ideas

• Average Daily Screen Time Among Students.
• Analyzing the Most Common Birth Months.
• Favorite School Subjects Among Peers.
• Average Time Spent on Homework Weekly.
• Frequency of Public Transport Usage.
• Comparison of Pet Ownership in the Community.
• Favorite Types of Movies or TV Shows.
• Daily Water Consumption Habits.
• Common Breakfast Choices and Their Nutritional Value.
• Steps Count: A Week-Long Study.

Business Ideas for Statistics Project

• Analyzing Customer Satisfaction in Retail Stores.
• Market Analysis of a New Product Launch.
• Employee Performance Metrics and Organizational Success.
• Sales Data Analysis for E-commerce Websites.
• Impact of Advertising on Consumer Buying Behavior.
• Analysis of Supply Chain Efficiency.
• Customer Loyalty and Retention Strategies.
• Trend Analysis in Social Media Marketing.
• Financial Risk Assessment in Investment Decisions.
• Market Segmentation and Targeting Strategies.

Socio-Economic Easy Statistics Project Ideas

• Income Inequality and Its Impact on Education.
• The Correlation Between Unemployment Rates and Crime Levels.
• Analyzing the Effects of Minimum Wage Changes.
• The Relationship Between Public Health Expenditure and Population Health.
• Demographic Analysis of Housing Affordability.
• The Impact of Immigration on Local Economies.
• Analysis of Gender Pay Gap in Different Industries.
• Statistical Study of Homelessness Causes and Solutions.
• Education Levels and Their Impact on Job Opportunities.
• Analyzing Trends in Government Social Spending.

Experiment Ideas for Statistics and Analysis

• Multivariate Analysis of Global Climate Change Data.
• Time-Series Analysis in Predicting Economic Recessions.
• Logistic Regression in Medical Outcome Prediction.
• Machine Learning Applications in Statistical Modeling.
• Network Analysis in Social Media Data.
• Bayesian Analysis of Scientific Research Data.
• The Use of Factor Analysis in Psychology Studies.
• Spatial Data Analysis in Geographic Information Systems (GIS).
• Predictive Analysis in Customer Relationship Management (CRM).
• Cluster Analysis in Market Research.

Conclusion: Navigating the World of Data Through Statistics

In this exploration of good statistics project ideas, we’ve ventured through various topics, from the straightforward to the complex, from personal finance to global climate change. These ideas are gateways to understanding the world of data and statistics, and platforms for cultivating critical thinking and analytical skills. Whether you’re a high school student, a college student, or a professional, engaging in these projects can deepen your appreciation of how statistics shapes our understanding of the world around us. These projects encourage exploration, inquiry, and a deeper engagement with the world of numbers, trends, and patterns – the essence of statistics.

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Research: What Companies Don’t Know About How Workers Use AI

• Jeremie Brecheisen

Three Gallup studies shed light on when and why AI is being used at work — and how employees and customers really feel about it.

Leaders who are exploring how AI might fit into their business operations must not only navigate a vast and ever-changing landscape of tools, but they must also facilitate a significant cultural shift within their organizations. But research shows that leaders do not fully understand their employees’ use of, and readiness for, AI. In addition, a significant number of Americans do not trust business’ use of AI. This article offers three recommendations for leaders to find the right balance of control and trust around AI, including measuring how their employees currently use AI, cultivating trust by empowering managers, and adopting a purpose-led AI strategy that is driven by the company’s purpose instead of a rules-heavy strategy that is driven by fear.

If you’re a leader who wants to shift your workforce toward using AI, you need to do more than manage the implementation of new technologies. You need to initiate a profound cultural shift. At the heart of this cultural shift is trust. Whether the use case for AI is brief and experimental or sweeping and significant, a level of trust must exist between leaders and employees for the initiative to have any hope of success.

• Jeremie Brecheisen is a partner and managing director of The Gallup CHRO Roundtable.

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ASU, UA rank in top 1% of global universities, per a recent study. Here’s why

Both Arizona State University and the University of Arizona ranked among the top 1% of schools across the world in this year’s Center for World University rankings , putting them ahead of schools like the University of Connecticut and Georgetown University.

The group compiled data from more than 20,000 institutions internationally, studying graduate employability and academics. Among the U.S. rankings, UA landed in 48th, ASU in 67th and NAU in 185th. All three schools were credited for their research influence.

That was key in developing the overall rankings according to the organization. Faculty citations and research made up 40% of the scores given.

In fiscal year 2022, ASU spent $797.2 million on research, growing by 18% from the year prior. In fiscal year 2023, UA spent even more with $954 million in investments. Both schools are in the top 4% nationally for research spending. A large portion of research funding in the state is from federal investments.

While NAU spent less at around $77.5 million in fiscal year 2023, the school has continued to expand its research budget. NAU is also likely to reach R1 status next year after the new criteria goes into effect . An R1 institution is a school recognized within the highest tier of research universities.

The Arizona Board of Regents, the body overseeing the state's public university system, sets research spending targets for each year to support what the board said is "essential to our state’s economic future" in a 2023 report . The system has surpassed those benchmarks.

This year’s rankings come at a critical point for UA, which is working to recover from a multimillion-dollar budget shortfall discovered last fall. Through a hiring freeze and several other budget cuts, school officials now expect to bring a deficit that was once $117 million down to $52 million by next year.

The fiscal headaches were blamed on a variety of issues including a decentralized budgeting model and inflation. But in the fall, UA President Robert Robbins acknowledged large investments on merit scholarships for out-of-state students and research initiatives also made an impact.

"We made a bet on spending money," Robbins said in a November meeting. "We just overshot."

In February, Robbins told The Arizona Republic the school would grow its research spending to $1 billion in the coming years. He said he didn’t regret the move but admitted the school had been using money from its reserves to fund it.

UA searching for its next president Here's what students, faculty want to see

UA was ranked among the top 0.5% of schools internationally this year, compared to ASU in the top 1% and NAU in the top 4%. The study placed UA faculty highly, at 69th globally, making it one of the school’s standout characteristics in the rankings.

Throughout ongoing budget discussions, faculty have long called on university leaders to protect its academic staff, citing it as the “engine” of the R1 land-grant university. While the school’s president and interim chief financial officers have said they too are committed to protecting UA’s academic mission, many faculty are uneasy, saying cuts on the horizon would go on to hurt students.

Hundreds of people attended a general faculty meeting last month to go over the school’s deficit. The group later passed a resolution asking the administration to stop ongoing layoffs until “detailed, transparent, and clear financial data and guidance on financial rules” is available.

Helen Rummel covers higher education for The Arizona Republic. Reach her at [email protected] . Follow her on X, formerly Twitter: @helenrummel .

'Surprisingly' high number of adults in the U.S. at risk of heart syndrome

Nearly 90% of adults over age 20 in the United States are at risk of developing heart disease , an alarming new study suggests. 

While the unexpectedly high number doesn't mean that the majority of adults in the U.S. have full-blown heart disease, it does indicate that many are at risk of developing the condition, even younger people.

Researchers identified people at high risk using a recently defined syndrome that takes into account the strong links between heart disease, obesity, diabetes and kidney disease, according to the research published Wednesday in JAMA.

The American Heart Association alerted doctors in October about cardiovascular-kidney-metabolic (CKM) syndrome , a condition which affects major organs in the body, including the brain, heart, liver and kidneys. CKM is diagnosed in stages ranging from zero — no risk factors for heart disease — to 4 — people with diagnosed heart disease plus excess body fat, metabolic risk factors such as hypertension and diabetes, or kidney disease.

For the new study, researchers analyzed almost a decade’s worth of data from more than 10,000 people who were participating in the National Health and Nutrition Examination Survey (NHANES).

“We absolutely were surprised that almost 90% of people met the criteria,” said study co-author Dr. Rahul Aggarwal, a cardiology fellow at Brigham and Women’s Hospital, Harvard Medical School, in Boston. “It was much higher than we anticipated in a database that included younger adults.”

Especially concerning was the finding that almost 50% of the NHANES participants were at stage 2 of CKM, meaning that they were at moderate risk because they had either high blood sugar, hypertension, high cholesterol or chronic kidney disease, Aggarwal said.

Just more than a quarter of the group — people listed as stage 1 — were at increased risk of developing heart disease because of being obese or overweight, having excess belly fat and fat around their organs, but didn't have specific symptoms.

The researchers found that 15% of the participants had advanced disease, a number that remained fairly constant between 2011 and 2020.  

“I think one of the biggest factors contributing to the fact that the percentage of people in advanced stages is not improving is obesity, which is very prevalent in the U.S.,” Aggarwal said, adding that 40% of people in America are obese. Another 32% are overweight based on body mass index calculations , according to the Centers for Disease Control and Prevention. 

Carrying excess pounds increases the likelihood a person will have high blood pressure, high blood sugar and high cholesterol, although some have metabolic risk factors even if they are at a healthy weight.

 Participants older than 65 were more likely to be at an advanced stage than people between 45 to 64. But being young wasn’t as protective as one might assume. Only 18% of people ages 20 through 44 were at stage zero. That is, they had no risk factors.  

The new findings show that health care providers need to be picking up on these conditions earlier “before they lead to downstream effects,” such as increased risk of heart attack, heart failure and stroke, Aggarwal said. “We need to diagnose earlier and be more aggressive at treating people.”

Latest news on heart health

• Science shows how a surge of anger could raise heart attack risk.
• Intermittent fasting linked to a higher risk of cardiovascular death, new analysis finds.
• High levels of a common B vitamin linked to heart disease.

Adopting lifestyle changes, such as improved diets and increased activity, can help protect against heart attack and stroke.The findings also show that “young adults, those younger than 45, are not as healthy as we thought they were,” Aggarwal said. 

Experts were also surprised by the high rates of CKM. 

“It is alarming that 90% of the population is at least stage 1 and only 10% have no risk factors,” said Dr. Sripal Bangalore, a professor of medicine and director of invasive and interventional cardiology at NYU Langone Health in New York City. 

He blames the epidemic of overweight and obesity for those numbers. 

“We have a lot of work to do to reduce the rates of overweight and obesity,” Bangalore said. “If we can do that, then hopefully we can reduce the number of people who progress to stage 2 and also move the needle down for higher stages.”

The inclusion of kidney disease in the risk assessments for cardiovascular disease makes a lot of sense, said Dr. Adriana Hung, a kidney specialist and epidemiologist and a professor of medicine at the Vanderbilt University Medical Center in Nashville, Tennessee. 

“Kidney disease magnifies cardiovascular disease,” she said. “Some studies show that a patient has as much as six times the risk of dying from cardiovascular disease if kidney disease is also present.”

The new, broader approach to heart disease is likely to help identify more people who are at risk, said Dr. Robert Rosenson, director of lipids and metabolism for the Mount Sinai Health System in New York City. 

“The main message from this study should be that many common behaviors are leading to an accumulation of diseases over one’s lifetime, which will impact quality of life and survival,” he said. 

The large numbers of people with CKM in this study are related to overweight and obesity, insulin resistance and a diet that is high in fat and salt, Rosenson added. 

People need to realize that it’s not just the heart that is being harmed by unhealthy diets and lack of exercise, he said, but that lifestyle factors also have an effect on cognition.

Linda Carroll is a regular health contributor to NBC News. She is coauthor of "The Concussion Crisis: Anatomy of a Silent Epidemic" and "Out of the Clouds: The Unlikely Horseman and the Unwanted Colt Who Conquered the Sport of Kings." 

MATH Seminar: “What is an infinity operad? (part I)”, Redi Haderi, 1:30PM May 20 2024 (EN)

You are cordially invited to the Topology Seminar organized by the Department of Mathematics.

Speaker: Redi Haderi (Bilkent University)

“What is an infinity operad? (part I)”

Abstract: We propose a combinatorial model for non-symmetric infinity operads. Our approach is simplicial, except that the simplicial objects we study take values in a category of sets in which morphisms assign lists of elements in the codomain to an element in the domain. We briefly discuss ordinary operads and their algebras in order to motivate our constructions. This is joint work with Özgün Ünlü.

Date: Monday, May 20, 2024 Time: 13:30 UTC+3 Place: Mathematics Seminar Room SA – 141 & ZOOM

To request the event link, please send a message to  [email protected]

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70 years after brown v. board of education, new research shows rise in school segregation.

As the nation prepares to mark the 70th anniversary of the landmark U.S. Supreme Court ruling in Brown v. Board of Education , a new report from researchers at Stanford and USC shows that racial and economic segregation among schools has grown steadily in large school districts over the past three decades — an increase that appears to be driven in part by policies favoring school choice over integration.

Analyzing data from U.S. public schools going back to 1967, the researchers found that segregation between white and Black students has increased by 64 percent since 1988 in the 100 largest districts, and segregation by economic status has increased by about 50 percent since 1991.

The report also provides new evidence about the forces driving recent trends in school segregation, showing that the expansion of charter schools has played a major role.  

The findings were released on May 6 with the launch of the Segregation Explorer , a new interactive website from the Educational Opportunity Project at Stanford University. The website provides searchable data on racial and economic school segregation in U.S. states, counties, metropolitan areas, and school districts from 1991 to 2022. 

“School segregation levels are not at pre- Brown levels, but they are high and have been rising steadily since the late 1980s,” said Sean Reardon , the Professor of Poverty and Inequality in Education at Stanford Graduate School of Education and faculty director of the Educational Opportunity Project. “In most large districts, school segregation has increased while residential segregation and racial economic inequality have declined, and our findings indicate that policy choices – not demographic changes – are driving the increase.” 

“There’s a tendency to attribute segregation in schools to segregation in neighborhoods,” said Ann Owens , a professor of sociology and public policy at USC. “But we’re finding that the story is more complicated than that.”

Assessing the rise

In the Brown v. Board decision issued on May 17, 1954, the U.S. Supreme Court ruled that racially segregated public schools violated the Equal Protection Clause of the Fourteenth Amendment and established that “separate but equal” schools were not only inherently unequal but unconstitutional. The ruling paved the way for future decisions that led to rapid school desegregation in many school districts in the late 1960s and early 1970s.

Though segregation in most school districts is much lower than it was 60 years ago, the researchers found that over the past three decades, both racial and economic segregation in large districts increased. Much of the increase in economic segregation since 1991, measured by segregation between students eligible and ineligible for free lunch, occurred in the last 15 years.

White-Hispanic and white-Asian segregation, while lower on average than white-Black segregation, have both more than doubled in large school districts since the 1980s. 

Racial-economic segregation – specifically the difference in the proportion of free-lunch-eligible students between the average white and Black or Hispanic student’s schools – has increased by 70 percent since 1991. 

School segregation is strongly associated with achievement gaps between racial and ethnic groups, especially the rate at which achievement gaps widen during school, the researchers said.  

“Segregation appears to shape educational outcomes because it concentrates Black and Hispanic students in higher-poverty schools, which results in unequal learning opportunities,” said Reardon, who is also a senior fellow at the Stanford Institute for Economic Policy Research and a faculty affiliate of the Stanford Accelerator for Learning . 

Policies shaping recent trends 

The recent rise in school segregation appears to be the direct result of educational policy and legal decisions, the researchers said. 

Both residential segregation and racial disparities in income declined between 1990 and 2020 in most large school districts. “Had nothing else changed, that trend would have led to lower school segregation,” said Owens. 

But since 1991, roughly two-thirds of districts that were under court-ordered desegregation have been released from court oversight. Meanwhile, since 1998, the charter sector – a form of expanded school choice – has grown.

Expanding school choice could influence segregation levels in different ways: If families sought schools that were more diverse than the ones available in their neighborhood, it could reduce segregation. But the researchers found that in districts where the charter sector expanded most rapidly in the 2000s and 2010s, segregation grew the most. 

The researchers’ analysis also quantified the extent to which the release from court orders accounted for the rise in school segregation. They found that, together, the release from court oversight and the expansion of choice accounted entirely for the rise in school segregation from 2000 to 2019.

The researchers noted enrollment policies that school districts can implement to mitigate segregation, such as voluntary integration programs, socioeconomic-based student assignment policies, and school choice policies that affirmatively promote integration. 

“School segregation levels are high, troubling, and rising in large districts,” said Reardon. “These findings should sound an alarm for educators and policymakers.”

Additional collaborators on the project include Demetra Kalogrides, Thalia Tom, and Heewon Jang. This research, including the development of the Segregation Explorer data and website, was supported by the Russell Sage Foundation, the Robert Wood Johnson Foundation, and the Bill and Melinda Gates Foundation.   

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Object Studies

Introductions to Material Culture

• © 2023
• Cyrus Mulready 0

Department of English, State University of New York at New Paltz, New Paltz, USA

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• Takes an interdisciplinary perspective not found in other material culture textbooks
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Table of contents (9 chapters)

Front matter, personal objects.

Cyrus Mulready

Objects and Local History

A history of the world in coffee cups, in the middle: subjects, objects, and theories of things, collecting things: the psychology of accumulation, from museums to hoarders, the things we read, consuming objects, thinking with things, epilogue: a department of object studies, back matter.

• Material culture
• Memory studies
• History of things
• Global history
• Object biography
• Consumerism

About this book

Object Studies: Introductions to Material Culture is a textbook that introduces students to an interdisciplinary approach to material cultural study. This text helps reveal how everyday objects from pens and coffee cups to our most cherished keepsakes help define our collective histories and personal narratives. Object Studies is organized around accessible and engaging chapters on objects with “model  essays” that present original projects designed to engage students with a series of concepts and research activities. Each will demonstrate a key methodology tied to specific learning outcomes, but all chapters will be intertwined in their attention to the project of developing the core skills of “object studies”: careful viewing, writing detailed descriptions, setting out and testing research hypotheses, and telling stories through material artifacts. Aimed towards undergraduate students taking courses in material culture as well as postgraduate students embarking on independent research projects these chapter “studies” are practically oriented and demonstrate research projects that can be undertaken either in a course or even through personal study. Chapters in Object Studies conclude with research questions, suggestions on methodology, and a discursive bibliography designed to help students pursue their own projects based on these examples.

--David Scott Kastan,  George M. Bodman Professor of English, Yale University

Authors and Affiliations

About the author.

Cyrus Mulready is Associate Professor of English at the State University of New York at New Paltz, where he teaches courses on book history, material culture, Shakespeare, and early British literature. He has published on Shakespeare, book history, and pedagogy, and was the 2017 recipient of the SUNY Chancellor’s Award for Excellence in Teaching. 

Bibliographic Information

Book Title : Object Studies

Book Subtitle : Introductions to Material Culture

Authors : Cyrus Mulready

DOI : https://doi.org/10.1007/978-3-031-09027-1

Publisher : Palgrave Macmillan Cham

eBook Packages : Literature, Cultural and Media Studies , Literature, Cultural and Media Studies (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

Softcover ISBN : 978-3-031-09026-4 Published: 07 March 2023

eBook ISBN : 978-3-031-09027-1 Published: 06 March 2023

Edition Number : 1

Number of Pages : XV, 165

Topics : Contemporary Literature , Literary Theory , Digital Humanities , Literature and Technology/Media

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