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• Published: 08 April 2024

A case study on the relationship between risk assessment of scientific research projects and related factors under the Naive Bayesian algorithm

• Xuying Dong 1 &
• Wanlin Qiu 1  

Scientific Reports volume  14 , Article number:  8244 ( 2024 ) Cite this article

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• Computer science
• Mathematics and computing

This paper delves into the nuanced dynamics influencing the outcomes of risk assessment (RA) in scientific research projects (SRPs), employing the Naive Bayes algorithm. The methodology involves the selection of diverse SRPs cases, gathering data encompassing project scale, budget investment, team experience, and other pertinent factors. The paper advances the application of the Naive Bayes algorithm by introducing enhancements, specifically integrating the Tree-augmented Naive Bayes (TANB) model. This augmentation serves to estimate risk probabilities for different research projects, shedding light on the intricate interplay and contributions of various factors to the RA process. The findings underscore the efficacy of the TANB algorithm, demonstrating commendable accuracy (average accuracy 89.2%) in RA for SRPs. Notably, budget investment (regression coefficient: 0.68, P < 0.05) and team experience (regression coefficient: 0.51, P < 0.05) emerge as significant determinants obviously influencing RA outcomes. Conversely, the impact of project size (regression coefficient: 0.31, P < 0.05) is relatively modest. This paper furnishes a concrete reference framework for project managers, facilitating informed decision-making in SRPs. By comprehensively analyzing the influence of various factors on RA, the paper not only contributes empirical insights to project decision-making but also elucidates the intricate relationships between different factors. The research advocates for heightened attention to budget investment and team experience when formulating risk management strategies. This strategic focus is posited to enhance the precision of RAs and the scientific foundation of decision-making processes.

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Introduction.

Scientific research projects (SRPs) stand as pivotal drivers of technological advancement and societal progress in the contemporary landscape 1 , 2 , 3 . The dynamism of SRP success hinges on a multitude of internal and external factors 4 . Central to effective project management, Risk assessment (RA) in SRPs plays a critical role in identifying and quantifying potential risks. This process not only aids project managers in formulating strategic decision-making approaches but also enhances the overall success rate and benefits of projects. In a recent contribution, Salahuddin 5 provides essential numerical techniques indispensable for conducting RAs in SRPs. Building on this foundation, Awais and Salahuddin 6 delve into the assessment of risk factors within SRPs, notably introducing the consideration of activation energy through an exploration of the radioactive magnetohydrodynamic model. Further expanding the scope, Awais and Salahuddin 7 undertake a study on the natural convection of coupled stress fluids. However, RA of SRPs confronts a myriad of challenges, underscoring the critical need for novel methodologies 8 . Primarily, the intricate nature of SRPs renders precise RA exceptionally complex and challenging. The project’s multifaceted dimensions, encompassing technology, resources, and personnel, are intricately interwoven, posing a formidable challenge for traditional assessment methods to comprehensively capture all potential risks 9 . Furthermore, the intricate and diverse interdependencies among various project factors contribute to the complexity of these relationships, thereby limiting the efficacy of conventional methods 10 , 11 , 12 . Traditional approaches often focus solely on the individual impact of diverse factors, overlooking the nuanced relationships that exist between them—an inherent limitation in the realm of RA for SRPs 13 , 14 , 15 .

The pursuit of a methodology capable of effectively assessing project risks while elucidating the intricate interplay of different factors has emerged as a focal point in SRPs management 16 , 17 , 18 . This approach necessitates a holistic consideration of multiple factors, their quantification in contributing to project risks, and the revelation of their correlations. Such an approach enables project managers to more precisely predict and respond to risks. Marx-Stoelting et al. 19 , current approaches for the assessment of environmental and human health risks due to exposure to chemical substances have served their purpose reasonably well. Additionally, Awais et al. 20 highlights the significance of enthalpy changes in SRPs risk considerations, while Awais et al. 21 delve into the comprehensive exploration of risk factors in Eyring-Powell fluid flow in magnetohydrodynamics, particularly addressing viscous dissipation and activation energy effects. The Naive Bayesian algorithm, recognized for its prowess in probability and statistics, has yielded substantial results in information retrieval and data mining in recent years 22 . Leveraging its advantages in classification and probability estimation, the algorithm presents a novel approach for RA of SRPs 23 . Integrating probability analysis into RA enables a more precise estimation of project risks by utilizing existing project data and harnessing the capabilities of the Naive Bayesian algorithms. This method facilitates a quantitative, statistical analysis of various factors, effectively navigating the intricate relationships between them, thereby enhancing the comprehensiveness and accuracy of RA for SRPs.

This paper seeks to employ the Naive Bayesian algorithm to estimate the probability of risks by carefully selecting distinct research project cases and analyzing multidimensional data, encompassing project scale, budget investment, and team experience. Concurrently, Multiple Linear Regression (MLR) analysis is applied to quantify the influence of these factors on the assessment results. The paper places particular emphasis on exploring the intricate interrelationships between different factors, aiming to provide a more specific and accurate reference framework for decision-making in SRPs management.

This paper introduces several innovations and contributions to the field of RA for SRPs:

Comprehensive Consideration of Key Factors: Unlike traditional research that focuses on a single factor, this paper comprehensively considers multiple key factors, such as project size, budget investment, and team experience. This holistic analysis enhances the realism and thoroughness of RA for SRPs.

Introduction of Tree-Enhanced Naive Bayes Model: The naive Bayes algorithm is introduced and further improved through the proposal of a tree-enhanced naive Bayes model. This algorithm exhibits unique advantages in handling uncertainty and complexity, thereby enhancing its applicability and accuracy in the RA of scientific and technological projects.

Empirical Validation: The effectiveness of the proposed method is not only discussed theoretically but also validated through empirical cases. The analysis of actual cases provides practical support and verification, enhancing the credibility of the research results.

Application of MLR Analysis: The paper employs MLR analysis to delve into the impact of various factors on RA. This quantitative analysis method adds specificity and operability to the research, offering a practical decision-making basis for scientific and technological project management.

Discovery of New Connections and Interactions: The paper uncovers novel connections and interactions, such as the compensatory role of team experience for budget-related risks and the impact of the interaction between project size and budget investment on RA results. These insights provide new perspectives for decision-making in technology projects, contributing significantly to the field of RA for SRPs in terms of both importance and practical value.

The paper is structured as follows: “ Introduction ” briefly outlines the significance of RA for SRPs. Existing challenges within current research are addressed, and the paper’s core objectives are elucidated. A distinct emphasis is placed on the innovative aspects of this research compared to similar studies. The organizational structure of the paper is succinctly introduced, providing a brief overview of each section’s content. “ Literature review ” provides a comprehensive review of relevant theories and methodologies in the domain of RA for SRPs. The current research landscape is systematically examined, highlighting the existing status and potential gaps. Shortcomings in previous research are analyzed, laying the groundwork for the paper’s motivation and unique contributions. “ Research methodology ” delves into the detailed methodologies employed in the paper, encompassing data collection, screening criteria, preprocessing steps, and more. The tree-enhanced naive Bayes model is introduced, elucidating specific steps and the purpose behind MLR analysis. “ Results and discussion ” unfolds the results and discussions based on selected empirical cases. The representativeness and diversity of these cases are expounded upon. An in-depth analysis of each factor’s impact and interaction in the context of RA is presented, offering valuable insights. “ Discussion ” succinctly summarizes the entire research endeavor. Potential directions for further research and suggestions for improvement are proposed, providing a thoughtful conclusion to the paper.

Literature review

A review of ra for srps.

In recent years, the advancement of SRPs management has led to the evolution of various RA methods tailored for SRPs. The escalating complexity of these projects poses a challenge for traditional methods, often falling short in comprehensively considering the intricate interplay among multiple factors and yielding incomplete assessment outcomes. Scholars, recognizing the pivotal role of factors such as project scale, budget investment, and team experience in influencing project risks, have endeavored to explore these dynamics from diverse perspectives. Siyal et al. 24 pioneered the development and testing of a model geared towards detecting SRPs risks. Chen et al. 25 underscored the significance of visual management in SRPs risk management, emphasizing its importance in understanding and mitigating project risks. Zhao et al. 26 introduced a classic approach based on cumulative prospect theory, offering an optional method to elucidate researchers’ psychological behaviors. Their study demonstrated the enhanced rationality achieved by utilizing the entropy weight method to derive attribute weight information under Pythagorean fuzzy sets. This approach was then applied to RA for SRPs, showcasing a model grounded in the proposed methodology. Suresh and Dillibabu 27 proposed an innovative hybrid fuzzy-based machine learning mechanism tailored for RA in software projects. This hybrid scheme facilitated the identification and ranking of major software project risks, thereby supporting decision-making throughout the software project lifecycle. Akhavan et al. 28 introduced a Bayesian network modeling framework adept at capturing project risks by calculating the uncertainty of project net present value. This model provided an effective means for analyzing risk scenarios and their impact on project success, particularly applicable in evaluating risks for innovative projects that had undergone feasibility studies.

A review of factors affecting SRPs

Within the realm of SRPs management, the assessment and proficient management of project risks stand as imperative components. Consequently, a range of studies has been conducted to explore diverse methods and models aimed at enhancing the comprehension and decision support associated with project risks. Guan et al. 29 introduced a new risk interdependence network model based on Monte Carlo simulation to support decision-makers in more effectively assessing project risks and planning risk management actions. They integrated interpretive structural modeling methods into the model to develop a hierarchical project risk interdependence network based on identified risks and their causal relationships. Vujović et al. 30 provided a new method for research in project management through careful analysis of risk management in SRPs. To confirm the hypothesis, the study focused on educational organizations and outlined specific project management solutions in business systems, thereby improving the business and achieving positive business outcomes. Muñoz-La Rivera et al. 31 described and classified the 100 identified factors based on the dimensions and aspects of the project, assessed their impact, and determined whether they were shaping or directly affecting the occurrence of research project accidents. These factors and their descriptions and classifications made significant contributions to improving the security creation of the system and generating training and awareness materials, fostering the development of a robust security culture within organizations. Nguyen et al. concentrated on the pivotal risk factors inherent in design-build projects within the construction industry. Effective identification and management of these factors enhanced project success and foster confidence among owners and contractors in adopting the design-build approach 32 . Their study offers valuable insights into RA in project management and the adoption of new contract forms. Nguyen and Le delineated risk factors influencing the quality of 20 civil engineering projects during the construction phase 33 . The top five risks identified encompass poor raw material quality, insufficient worker skills, deficient design documents and drawings, geographical challenges at construction sites, and inadequate capabilities of main contractors and subcontractors. Meanwhile, Nguyen and Phu Pham concentrated on office building projects in Ho Chi Minh City, Vietnam, to pinpoint key risk factors during the construction phase 34 . These factors were classified into five groups based on their likelihood and impact: financial, management, schedule, construction, and environmental. Findings revealed that critical factors affecting office building projects encompassed both natural elements (e.g., prolonged rainfall, storms, and climate impacts) and human factors (e.g., unstable soil, safety behavior, owner-initiated design changes), with schedule-related risks exerting the most significant influence during the construction phase of Ho Chi Minh City’s office building projects. This provides construction and project management practitioners with fresh insights into risk management, aiding in the comprehensive identification, mitigation, and management of risk factors in office building projects.

While existing research has made notable strides in RA for SRPs, certain limitations persist. These studies limitations in quantifying the degree of influence of various factors and analyzing their interrelationships, thereby falling short of offering specific and actionable recommendations. Traditional methods, due to their inherent limitations, struggle to precisely quantify risk degrees and often overlook the intricate interplay among multiple factors. Consequently, there is an urgent need for a comprehensive method capable of quantifying the impact of diverse factors and revealing their correlations. In response to this exigency, this paper introduces the TANB model. The unique advantages of this algorithm in the RA of scientific and technological projects have been fully realized. Tailored to address the characteristics of uncertainty and complexity, the model represents a significant leap forward in enhancing applicability and accuracy. In comparison with traditional methods, the TANB model exhibits greater flexibility and a heightened ability to capture dependencies between features, thereby elevating the overall performance of RA. This innovative method emerges as a more potent and reliable tool in the realm of scientific and technological project management, furnishing decision-makers with more comprehensive and accurate support for RA.

Research methodology

This paper centers on the latest iteration of ISO 31000, delving into the project risk management process and scrutinizing the RA for SRPs and their intricate interplay with associated factors. ISO 31000, an international risk management standard, endeavors to furnish businesses, organizations, and individuals with a standardized set of risk management principles and guidelines, defining best practices and establishing a common framework. The paper unfolds in distinct phases aligned with ISO 31000:

Risk Identification: Employing data collection and preparation, a spectrum of factors related to project size, budget investment, team member experience, project duration, and technical difficulty were identified.

RA: Utilizing the Naive Bayes algorithm, the paper conducts RA for SRPs, estimating the probability distribution of various factors influencing RA results.

Risk Response: The application of the Naive Bayes model is positioned as a means to respond to risks, facilitating the formulation of apt risk response strategies based on calculated probabilities.

Monitoring and Control: Through meticulous data collection, model training, and verification, the paper illustrates the steps involved in monitoring and controlling both data and models. Regular monitoring of identified risks and responses allows for adjustments when necessary.

Communication and Reporting: Maintaining effective communication throughout the project lifecycle ensures that stakeholders comprehend the status of project risks. Transparent reporting on discussions and outcomes contributes to an informed project environment.

Data collection and preparation

In this paper, a meticulous approach is undertaken to select representative research project cases, adhering to stringent screening criteria. Additionally, a thorough review of existing literature is conducted and tailored to the practical requirements of SRPs management. According to Nguyen et al., these factors play a pivotal role in influencing the RA outcomes of SRPs 35 . Furthermore, research by He et al. underscored the significant impact of team members’ experience on project success 36 . Therefore, in alignment with our research objectives and supported by the literature, this paper identifies variables such as project scale, budget investment, team member experience, project duration, and technical difficulty as the focal themes. To ensure the universality and scientific rigor of our findings, the paper adheres to stringent selection criteria during the project case selection process. After preliminary screening of SRPs completed in the past 5 years, considering factors such as project diversity, implementation scales, and achieved outcomes, five representative projects spanning diverse fields, including engineering, medicine, and information technology, are ultimately selected. These project cases are chosen based on their capacity to represent various scales and types of SRPs, each possessing a typical risk management process, thereby offering robust and comprehensive data support for our study. The subsequent phase involves detailed data collection on each chosen project, encompassing diverse dimensions such as project scale, budget investment, team member experience, project cycle, and technical difficulty. The collected data undergo meticulous preprocessing to ensure data quality and reliability. The preprocessing steps comprised data cleaning, addressing missing values, handling outliers, culminating in the creation of a self-constructed dataset. The dataset encompasses over 500 SRPs across diverse disciplines and fields, ensuring statistically significant and universal outcomes. Particular emphasis is placed on ensuring dataset diversity, incorporating projects of varying scales, budgets, and team experience levels. This comprehensive coverage ensures the representativeness and credibility of the study on RA in SRPs. New influencing factors are introduced to expand the research scope, including project management quality (such as time management and communication efficiency), historical success rate, industry dynamics, and market demand. Detailed definitions and quantifications are provided for each new variable to facilitate comprehensive data processing and analysis. For project management quality, consideration is given to time management accuracy and communication frequency and quality among team members. Historical success rate is determined by reviewing past project records and outcomes. Industry dynamics are assessed by consulting the latest scientific literature and patent information. Market demand is gauged through market research and user demand surveys. The introduction of these variables enriches the understanding of RA in SRPs and opens up avenues for further research exploration.

At the same time, the collected data are integrated and coded in order to apply Naive Bayes algorithm and MLR analysis. For cases involving qualitative data, this paper uses appropriate coding methods to convert it into quantitative data for processing in the model. For example, for the qualitative feature of team member experience, numerical values are used to represent different experience levels, such as 0 representing beginners, 0 representing intermediate, and 2 representing advanced. The following is a specific sample data set example (Table 1 ). It shows the processed structured data, and the values in the table represent the specific characteristics of each project.

Establishment of naive Bayesian model

The Naive Bayesian algorithm, a probabilistic and statistical classification method renowned for its effectiveness in analyzing and predicting multi-dimensional data, is employed in this paper to conduct the RA for SRPs. The application of the Naive Bayesian algorithm to RA for SRPs aims to discern the influence of various factors on the outcomes of RA. The Naive Bayesian algorithm, depicted in Fig.  1 , operates on the principles of Bayesian theorem, utilizing posterior probability calculations for classification tasks. The fundamental concept of this algorithm hinges on the assumption of independence among different features, embodying the “naivety” hypothesis. In the context of RA for SRPs, the Naive Bayesian algorithm is instrumental in estimating the probability distribution of diverse factors affecting the RA results, thereby enhancing the precision of risk estimates. In the Naive Bayesian model, the initial step involves the computation of posterior probabilities for each factor, considering the given RA result conditions. Subsequently, the category with the highest posterior probability is selected as the predictive outcome.

Naive Bayesian algorithm process.

In Fig.  1 , the data collection process encompasses vital project details such as project scale, budget investment, team member experience, project cycle, technical difficulty, and RA results. This meticulous collection ensures the integrity and precision of the dataset. Subsequently, the gathered data undergoes integration and encoding to convert qualitative data into quantitative form, facilitating model processing and analysis. Tailored to specific requirements, relevant features are chosen for model construction, accompanied by essential preprocessing steps like standardization and normalization. The dataset is then partitioned into training and testing sets, with the model trained on the former and its performance verified on the latter. Leveraging the training data, a Naive Bayesian model is developed to estimate probability distribution parameters for various features across distinct categories. Ultimately, the trained model is employed to predict new project features, yielding RA results.

Naive Bayesian models, in this context, are deployed to forecast diverse project risk levels. Let X symbolize the feature vector, encompassing project scale, budget investment, team member experience, project cycle, and technical difficulty. The objective is to predict the project’s risk level, denoted as Y. Y assumes discrete values representing distinct risk levels. Applying the Bayesian theorem, the posterior probability P(Y|X) is computed, signifying the probability distribution of projects falling into different risk levels given the feature vector X. The fundamental equation governing the Naive Bayesian model is expressed as:

In Eq. ( 1 ), P(Y|X) represents the posterior probability, denoting the likelihood of the project belonging to a specific risk level. P(X|Y) signifies the class conditional probability, portraying the likelihood of the feature vector X occurring under known risk level conditions. P(Y) is the prior probability, reflecting the antecedent likelihood of the project pertaining to a particular risk level. P(X) acts as the evidence factor, encapsulating the likelihood of the feature vector X occurring.

The Naive Bayes, serving as the most elementary Bayesian network classifier, operates under the assumption of attribute independence given the class label c , as expressed in Eq. ( 2 ):

The classification decision formula for Naive Bayes is articulated in Eq. ( 3 ):

The Naive Bayes model, rooted in the assumption of conditional independence among attributes, often encounters deviations from reality. To address this limitation, the Tree-Augmented Naive Bayes (TANB) model extends the independence assumption by incorporating a first-order dependency maximum-weight spanning tree. TANB introduces a tree structure that more comprehensively models relationships between features, easing the constraints of the independence assumption and concurrently mitigating issues associated with multicollinearity. This extension bolsters its efficacy in handling intricate real-world data scenarios. TANB employs conditional mutual information \(I(X_{i} ;X_{j} |C)\) to gauge the dependency between attributes \(X_{j}\) and \(X_{i}\) , thereby constructing the maximum weighted spanning tree. In TANB, any attribute variable \(X_{i}\) is permitted to have at most one other attribute variable as its parent node, expressed as \(Pa\left( {X_{i} } \right) \le 2\) . The joint probability \(P_{con} \left( {x,c} \right)\) undergoes transformation using Eq. ( 4 ):

In Eq. ( 4 ), \(x_{r}\) refers to the root node, which can be expressed as Eq. ( 5 ):

TANB classification decision equation is presented below:

In the RA of SRPs, normal distribution parameters, such as mean (μ) and standard deviation (σ), are estimated for each characteristic dimension (project scale, budget investment, team member experience, project cycle, and technical difficulty). This estimation allows the calculation of posterior probabilities for projects belonging to different risk levels under given feature vector conditions. For each feature dimension \({X}_{i}\) , the mean \({mu}_{i,j}\) and standard deviation \({{\text{sigma}}}_{i,j}\) under each risk level are computed, where i represents the feature dimension, and j denotes the risk level. Parameter estimation employs the maximum likelihood method, and the specific calculations are as follows.

In Eqs. ( 7 ) and ( 8 ), \({N}_{j}\) represents the number of projects belonging to risk level j . \({x}_{i,k}\) denotes the value of the k -th item in the feature dimension i . Finally, under a given feature vector, the posterior probability of a project with risk level j is calculated as Eq. ( 9 ).

In Eq. ( 9 ), d represents the number of feature dimensions, and Z is the normalization factor. \(P(Y=j)\) represents the prior probability of category j . \(P({X}_{i}\mid Y=j)\) represents the normal distribution probability density function of feature dimension i under category j . The risk level of a project can be predicted by calculating the posterior probabilities of different risk levels to achieve RA for SRPs.

This paper integrates the probability estimation of the Naive Bayes model with actual project risk response strategies, enabling a more flexible and targeted response to various risk scenarios. Such integration offers decision support to project managers, enhancing their ability to address potential challenges effectively and ultimately improving the overall success rate of the project. This underscores the notion that risk management is not solely about problem prevention but stands as a pivotal factor contributing to project success.

MLR analysis

MLR analysis is used to validate the hypothesis to deeply explore the impact of various factors on RA of SRPs. Based on the previous research status, the following research hypotheses are proposed.

Hypothesis 1: There is a positive relationship among project scale, budget investment, and team member experience and RA results. As the project scale, budget investment, and team member experience increase, the RA results also increase.

Hypothesis 2: There is a negative relationship between the project cycle and the RA results. Projects with shorter cycles may have higher RA results.

Hypothesis 3: There is a complex relationship between technical difficulty and RA results, which may be positive, negative, or bidirectional in some cases. Based on these hypotheses, an MLR model is established to analyze the impact of factors, such as project scale, budget investment, team member experience, project cycle, and technical difficulty, on RA results. The form of an MLR model is as follows.

In Eq. ( 10 ), Y represents the RA result (dependent variable). \({X}_{1}\) to \({X}_{5}\) represent factors, such as project scale, budget investment, team member experience, project cycle, and technical difficulty (independent variables). \({\beta }_{0}\) to \({\beta }_{5}\) are the regression coefficients, which represent the impact of various factors on the RA results. \(\epsilon\) represents a random error term. The model structure is shown in Fig.  2 .

Schematic diagram of an MLR model.

In Fig.  2 , the MLR model is employed to scrutinize the influence of various independent variables on the outcomes of RA. In this specific context, the independent variables encompass project size, budget investment, team member experience, project cycle, and technical difficulty, all presumed to impact the project’s RA results. Each independent variable is denoted as a node in the model, with arrows depicting the relationships between these factors. In an MLR model, the arrow direction signifies causality, illustrating the influence of an independent variable on the dependent variable.

When conducting MLR analysis, it is necessary to estimate the parameter \(\upbeta\) in the regression model. These parameters determine the relationship between the independent and dependent variables. Here, the Ordinary Least Squares (OLS) method is applied to estimate these parameters. The OLS method is a commonly used parameter estimation method aimed at finding parameter values that minimize the sum of squared residuals between model predictions and actual observations. The steps are as follows. Firstly, based on the general form of an MLR model, it is assumed that there is a linear relationship between the independent and dependent variables. It can be represented by a linear equation, which includes regression coefficients β and the independent variable X. For each observation value, the difference between its predicted and actual values is calculated, which is called the residual. Residual \({e}_{i}\) can be expressed as:

In Eq. ( 11 ), \({Y}_{i}\) is the actual observation value, and \({\widehat{Y}}_{i}\) is the value predicted by the model. The goal of the OLS method is to adjust the regression coefficients \(\upbeta\) to minimize the sum of squared residuals of all observations. This can be achieved by solving an optimization problem, and the objective function is the sum of squared residuals.

Then, the estimated value of the regression coefficient \(\upbeta\) that minimizes the sum of squared residuals can be obtained by taking the derivative of the objective function and making the derivative zero. The estimated values of the parameters can be obtained by solving this system of equations. The final estimated regression coefficient can be expressed as:

In Eq. ( 13 ), X represents the independent variable matrix. Y represents the dependent variable vector. \(({X}^{T}X{)}^{-1}\) is the inverse of a matrix, and \(\widehat{\beta }\) is a parameter estimation vector.

Specifically, solving for the estimated value of regression coefficient \(\upbeta\) requires matrix operation and statistical analysis. Based on the collected project data, substitute it into the model and calculate the residual. Then, the steps of the OLS method are used to obtain parameter estimates. These parameter estimates are used to establish an MLR model to predict RA results and further analyze the influence of different factors.

The degree of influence of different factors on the RA results can be determined by analyzing the value of the regression coefficient β. A positive \(\upbeta\) value indicates that the factor has a positive impact on the RA results, while a negative \(\upbeta\) value indicates that the factor has a negative impact on the RA results. Additionally, hypothesis testing can determine whether each factor is significant in the RA results.

The TANB model proposed in this paper extends the traditional naive Bayes model by incorporating conditional dependencies between attributes to enhance the representation of feature interactions. While the traditional naive Bayes model assumes feature independence, real-world scenarios often involve interdependencies among features. To address this, the TANB model is introduced. The TANB model introduces a tree structure atop the naive Bayes model to more accurately model feature relationships, overcoming the limitation of assuming feature independence. Specifically, the TANB model constructs a maximum weight spanning tree to uncover conditional dependencies between features, thereby enabling the model to better capture feature interactions.

Assessment indicators

To comprehensively assess the efficacy of the proposed TANB model in the RA for SRPs, a self-constructed dataset serves as the data source for this experimental evaluation, as outlined in Table 1 . The dataset is segregated into training (80%) and test sets (20%). These indicators cover the accuracy, precision, recall rate, F1 value, and Area Under the Curve (AUC) Receiver Operating Characteristic (ROC) of the model. The following are the definitions and equations for each assessment indicator. Accuracy is the proportion of correctly predicted samples to the total number of samples. Precision is the proportion of Predicted Positive (PP) samples to actual positive samples. The recall rate is the proportion of correctly PP samples among the actual positive samples. The F1 value is the harmonic average of precision and recall, considering the precision and comprehensiveness of the model. The area under the ROC curve measures the classification performance of the model, and a larger AUC value indicates better model performance. The ROC curve suggests the relationship between True Positive Rate and False Positive Rate under different thresholds. The AUC value can be obtained by accumulating the area of each small rectangle under the ROC curve. The confusion matrix is used to display the prediction property of the model in different categories, including True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN).

The performance of TANB in RA for SRPs can be comprehensively assessed to understand the advantages, disadvantages, and applicability of the model more comprehensively by calculating the above assessment indicators.

Results and discussion

Accuracy analysis of naive bayesian algorithm.

On the dataset of this paper, Fig.  3 reveals the performance of TANB algorithm under different assessment indicators.

Performance assessment of TANB algorithm on different projects.

From Fig.  3 , the TANB algorithm performs well in various projects, ranging from 0.87 to 0.911 in accuracy. This means that the overall accuracy of the model in predicting project risks is quite high. The precision also maintains a high level in various projects, ranging from 0.881 to 0.923, indicating that the model performs well in classifying high-risk categories. The recall rate ranges from 0.872 to 0.908, indicating that the model can effectively capture high-risk samples. Meanwhile, the AUC values in each project are relatively high, ranging from 0.905 to 0.931, which once again emphasizes the effectiveness of the model in risk prediction. From multiple assessment indicators, such as accuracy, precision, recall, F1 value, and AUC, the TANB algorithm has shown good risk prediction performance in representative projects. The performance assessment results of the TANB algorithm under different feature dimensions are plotted in Figs.  4 , 5 , 6 and 7 .

Prediction accuracy of TANB algorithm on different budget investments.

Prediction accuracy of TANB algorithm on different team experiences.

Prediction accuracy of TANB algorithm at different risk levels.

Prediction accuracy of TANB algorithm on different project scales.

From Figs.  4 , 5 , 6 and 7 , as the level of budget investment increases, the accuracy of most projects also shows an increasing trend. Especially in cases of high budget investment, the accuracy of the project is generally high. This may mean that a higher budget investment helps to reduce project risks, thereby improving the prediction accuracy of the TANB algorithm. It can be observed that team experience also affects the accuracy of the model. Projects with high team experience exhibit higher accuracy in TANB algorithms. This may indicate that experienced teams can better cope with project risks to improve the performance of the model. When budget investment and team experience are low, accuracy is relatively low. This may imply that budget investment and team experience can complement each other to affect the model performance.

There are certain differences in the accuracy of projects under different risk levels. Generally speaking, the accuracy of high-risk and medium-risk projects is relatively high, while the accuracy of low-risk projects is relatively low. This may be because high-risk and medium-risk projects require more accurate predictions, resulting in higher accuracy. Similarly, project scale also affects the performance of the model. Large-scale and medium-scale projects exhibit high accuracy in TANB algorithms, while small-scale projects have relatively low accuracy. This may be because the risks of large-scale and medium-scale projects are easier to identify and predict to promote the performance of the model. In high-risk and large-scale projects, accuracy is relatively high. This may indicate that the impact of project scale is more significant in specific risk scenarios.

Figure  8 further compares the performance of the TANB algorithm proposed here with other similar algorithms.

Performance comparison of different algorithms in RA of SRPs.

As depicted in Fig.  8 , the TANB algorithm attains an accuracy and precision of 0.912 and 0.920, respectively, surpassing other algorithms. It excels in recall rate and F1 value, registering 0.905 and 0.915, respectively, outperforming alternative algorithms. These findings underscore the proficiency of the TANB algorithm in comprehensively identifying high-risk projects while sustaining high classification accuracy. Moreover, the algorithm achieves an AUC of 0.930, indicative of its exceptional predictive prowess in sample classification. Thus, the TANB algorithm exhibits notable potential for application, particularly in scenarios demanding the recognition and comprehensiveness requisite for high-risk project identification. The evaluation results of the TANB model in predicting project risk levels are presented in Table 2 :

Table 2 demonstrates that the TANB model surpasses the traditional Naive Bayes model across multiple evaluation metrics, including accuracy, precision, and recall. This signifies that, by accounting for feature interdependence, the TANB model can more precisely forecast project risk levels. Furthermore, leveraging the model’s predictive outcomes, project managers can devise tailored risk mitigation strategies corresponding to various risk scenarios. For example, in high-risk projects, more assertive measures can be implemented to address risks, while in low-risk projects, risks can be managed more cautiously. This targeted risk management approach contributes to enhancing project success rates, thereby ensuring the seamless advancement of SRPs.

The exceptional performance of the TANB model in specific scenarios derives from its distinctive characteristics and capabilities. Firstly, compared to traditional Naive Bayes models, the TANB model can better capture the dependencies between attributes. In project RA, project features often exhibit complex interactions. The TANB model introduces first-order dependencies between attributes, allowing features to influence each other, thereby more accurately reflecting real-world situations and improving risk prediction precision. Secondly, the TANB model demonstrates strong adaptability and generalization ability in handling multidimensional data. SRPs typically involve data from multiple dimensions, such as project scale, budget investment, and team experience. The TANB model effectively processes these multidimensional data, extracts key information, and achieves accurate RA for projects. Furthermore, the paper explores the potential of using hybrid models or ensemble learning methods to further enhance model performance. By combining other machine learning algorithms, such as random forests and support vector regressors with sigmoid kernel, through ensemble learning, the shortcomings of individual models in specific scenarios can be overcome, thus improving the accuracy and robustness of RA. For example, in the study, we compared the performance of the TANB model with other algorithms in RA, as shown in Table 3 .

Table 3 illustrates that the TANB model surpasses other models in terms of accuracy, precision, recall, F1 value, and AUC value, further confirming its superiority and practicality in RA. Therefore, the TANB model holds significant application potential in SRPs, offering effective decision support for project managers to better evaluate and manage project risks, thereby enhancing the likelihood of project success.

Analysis of the degree of influence of different factors

Table 4 analyzes the degree of influence and interaction of different factors.

In Table 4 , the regression analysis results reveal that budget investment and team experience exert a significantly positive impact on RA outcomes. This suggests that increasing budget allocation and assembling a team with extensive experience can enhance project RA outcomes. Specifically, the regression coefficient for budget investment is 0.68, and for team experience, it is 0.51, both demonstrating significant positive effects (P < 0.05). The P-values are all significantly less than 0.05, indicating a significant impact. The impact of project scale is relatively small, at 0.31, and its P-value is also much less than 0.05. The degree of interaction influence is as follows. The impact of interaction terms is also significant, especially the interaction between budget investment and team experience and the interaction between budget investment and project scale. The P value of the interaction between budget investment and project scale is 0.002, and the P value of the interaction between team experience and project scale is 0.003. The P value of the interaction among budget investment, team experience, and project scale is 0.005. So, there are complex relationships and interactions among different factors, and budget investment and team experience significantly affect the RA results. However, the budget investment and project scale slightly affect the RA results. Project managers should comprehensively consider the interactive effects of different factors when making decisions to more accurately assess the risks of SRPs.

The interaction between team experience and budget investment

The results of the interaction between team experience and budget investment are demonstrated in Table 5 .

From Table 5 , the degree of interaction impact can be obtained. Budget investment and team experience, along with the interaction between project scale and technical difficulty, are critical factors in risk mitigation. Particularly in scenarios characterized by large project scales and high technical difficulties, adequate budget allocation and a skilled team can substantially reduce project risks. As depicted in Table 5 , under conditions of high team experience and sufficient budget investment, the average RA outcome is 0.895 with a standard deviation of 0.012, significantly lower than assessment outcomes under other conditions. This highlights the synergistic effects of budget investment and team experience in effectively mitigating risks in complex project scenarios. The interaction between team experience and budget investment has a significant impact on RA results. Under high team experience, the impact of different budget investment levels on RA results is not significant, but under medium and low team experience, the impact of different budget investment levels on RA results is significantly different. The joint impact of team experience and budget investment is as follows. Under high team experience, the impact of budget investment is relatively small, possibly because high-level team experience can compensate for the risks brought by insufficient budget to some extent. Under medium and low team experience, the impact of budget investment is more significant, possibly because the lack of team experience makes budget investment play a more important role in RA. Therefore, team experience and budget investment interact in RA of SRPs. They need to be comprehensively considered in project decision-making. High team experience can compensate for the risks brought by insufficient budget to some extent, but in the case of low team experience, the impact of budget investment on RA is more significant. An exhaustive consideration of these factors and their interplay is imperative for effectively assessing the risks inherent in SRPs. Merely focusing on budget allocation or team expertise may not yield a thorough risk evaluation. Project managers must scrutinize the project’s scale, technical complexity, and team proficiency, integrating these aspects with budget allocation and team experience. This holistic approach fosters a more precise RA and facilitates the development of tailored risk management strategies, thereby augmenting the project’s likelihood of success. In conclusion, acknowledging the synergy between budget allocation and team expertise, in conjunction with other pertinent factors, is pivotal in the RA of SRPs. Project managers should adopt a comprehensive outlook to ensure sound decision-making and successful project execution.

Risk mitigation strategies

To enhance the discourse on project risk management in this paper, a dedicated section on risk mitigation strategies has been included. Leveraging the insights gleaned from the predictive model regarding identified risk factors and their corresponding risk levels, targeted risk mitigation measures are proposed.

Primarily, given the significant influence of budget investment and team experience on project RA outcomes, project managers are advised to prioritize these factors and devise pertinent risk management strategies.

For risks stemming from budget constraints, the adoption of flexible budget allocation strategies is advocated. This may involve optimizing project expenditures, establishing financial reserves, or seeking additional funding avenues.

In addressing risks attributed to inadequate team experience, measures such as enhanced training initiatives, engagement of seasoned project advisors, or collaboration with experienced teams can be employed to mitigate the shortfall in expertise.

Furthermore, recognizing the impact of project scale, duration, and technical complexity on RA outcomes, project managers are advised to holistically consider these factors during project planning. This entails adjusting project scale as necessary, establishing realistic project timelines, and conducting thorough assessments of technical challenges prior to project commencement.

These risk mitigation strategies aim to equip project managers with a comprehensive toolkit for effectively identifying, assessing, and mitigating risks inherent in SRPs.

This paper delves into the efficacy of the TANB algorithm in project risk prediction. The findings indicate that the algorithm demonstrates commendable performance across diverse projects, boasting high precision, recall rates, and AUC values, thereby outperforming analogous algorithms. This aligns with the perspectives espoused by Asadullah et al. 37 . Particular emphasis was placed on assessing the impact of variables such as budget investment levels, team experience, and project size on algorithmic performance. Notably, heightened budget investment and extensive team experience positively influenced the results, with project size exerting a comparatively minor impact. Regression analysis elucidates the magnitude and interplay of these factors, underscoring the predominant influence of budget investment and team experience on RA outcomes, whereas project size assumes a relatively marginal role. This underscores the imperative for decision-makers in projects to meticulously consider the interrelationships between these factors for a more precise assessment of project risks, echoing the sentiments expressed by Testorelli et al. 38 .

In sum, this paper furnishes a holistic comprehension of the Naive Bayes algorithm’s application in project risk prediction, offering robust guidance for practical project management. The paper’s tangible applications are chiefly concentrated in the realm of RA and management for SRPs. Such insights empower managers in SRPs to navigate risks with scientific acumen, thereby enhancing project success rates and performance. The paper advocates several strategic measures for SRPs management: prioritizing resource adjustments and team training to elevate the professional skill set of team members in coping with the impact of team experience on risks; implementing project scale management strategies to mitigate potential risks by detailed project stage division and stringent project planning; addressing technical difficulty as a pivotal risk factor through assessment and solution development strategies; incorporating project cycle adjustment and flexibility management to accommodate fluctuations and mitigate associated risks; and ensuring the integration of data quality management strategies to bolster data reliability and enhance model accuracy. These targeted risk responses aim to improve the likelihood of project success and ensure the seamless realization of project objectives.

Achievements

In this paper, the application of Naive Bayesian algorithm in RA of SRPs is deeply explored, and the influence of various factors on RA results and their relationship is comprehensively investigated. The research results fully prove the good accuracy and applicability of Naive Bayesian algorithm in RA of science and technology projects. Through probability estimation, the risk level of the project can be estimated more accurately, which provides a new decision support tool for the project manager. It is found that budget input and team experience are the most significant factors affecting the RA results, and their regression coefficients are 0.68 and 0.51 respectively. However, the influence of project scale on the RA results is relatively small, and its regression coefficient is 0.31. Especially in the case of low team experience, the budget input has a more significant impact on the RA results. However, it should also be admitted that there are some limitations in the paper. First, the case data used is limited and the sample size is relatively small, which may affect the generalization ability of the research results. Second, the factors concerned may not be comprehensive, and other factors that may affect RA, such as market changes and policies and regulations, are not considered.

The paper makes several key contributions. Firstly, it applies the Naive Bayes algorithm to assess the risks associated with SRPs, proposing the TANB and validating its effectiveness empirically. The introduction of the TANB model broadens the application scope of the Naive Bayes algorithm in scientific research risk management, offering novel methodologies for project RA. Secondly, the study delves into the impact of various factors on RA for SRPs through MLR analysis, highlighting the significance of budget investment and team experience. The results underscore the positive influence of budget investment and team experience on RA outcomes, offering valuable insights for project decision-making. Additionally, the paper examines the interaction between team experience and budget investment, revealing a nuanced relationship between the two in RA. This finding underscores the importance of comprehensively considering factors such as team experience and budget investment in project decision-making to achieve more accurate RA. In summary, the paper provides crucial theoretical foundations and empirical analyses for SRPs risk management by investigating RA and its influencing factors in depth. The research findings offer valuable guidance for project decision-making and risk management, bolstering efforts to enhance the success rate and efficiency of SRPs.

This paper distinguishes itself from existing research by conducting an in-depth analysis of the intricate interactions among various factors, offering more nuanced and specific RA outcomes. The primary objective extends beyond problem exploration, aiming to broaden the scope of scientific evaluation and research practice through the application of statistical language. This research goal endows the paper with considerable significance in the realm of science and technology project management. In comparison to traditional methods, this paper scrutinizes project risk with greater granularity, furnishing project managers with more actionable suggestions. The empirical analysis validates the effectiveness of the proposed method, introducing a fresh perspective for decision-making in science and technology projects. Future research endeavors will involve expanding the sample size and accumulating a more extensive dataset of SRPs to enhance the stability and generalizability of results. Furthermore, additional factors such as market demand and technological changes will be incorporated to comprehensively analyze elements influencing the risks of SRPs. Through these endeavors, the aim is to provide more precise and comprehensive decision support to the field of science and technology project management, propelling both research and practice in this domain to new heights.

Limitations and prospects

This paper, while employing advanced methodologies like TANB models, acknowledges inherent limitations that warrant consideration. Firstly, like any model, TANB has its constraints, and predictions in specific scenarios may be subject to these limitations. Subsequent research endeavors should explore alternative advanced machine learning and statistical models to enhance the precision and applicability of RA. Secondly, the focus of this paper predominantly centers on the RA for SRPs. Given the unique characteristics and risk factors prevalent in projects across diverse fields and industries, the generalizability of the paper results may be limited. Future research can broaden the scope of applicability by validating the model across various fields and industries. The robustness and generalizability of the model can be further ascertained through the incorporation of extensive real project data in subsequent research. Furthermore, future studies can delve into additional data preprocessing and feature engineering methods to optimize model performance. In practical applications, the integration of research outcomes into SRPs management systems could provide more intuitive and practical support for project decision-making. These avenues represent valuable directions for refining and expanding the contributions of this research in subsequent studies.

Data availability

All data generated or analysed during this study are included in this published article [and its Supplementary Information files].

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Xuying Dong and Wanlin Qiu played a key role in the writing of Risk Assessment of Scientific Research Projects and the Relationship between Related Factors Based on Naive Bayes Algorithm. First, they jointly developed clearly defined research questions and methods for risk assessment using the naive Bayes algorithm at the beginning of the research project. Secondly, Xuying Dong and Wanlin Qiu were responsible for data collection and preparation, respectively, to ensure the quality and accuracy of the data used in the research. They worked together to develop a naive Bayes algorithm model, gain a deep understanding of the algorithm, ensure the effectiveness and performance of the model, and successfully apply the model in practical research. In the experimental and data analysis phase, the collaborative work of Xuying Dong and Wanlin Qiu played a key role in verifying the validity of the model and accurately assessing the risks of the research project. They also collaborated on research papers, including detailed descriptions of methods, experiments and results, and actively participated in the review and revision process, ensuring the accuracy and completeness of the findings. In general, the joint contribution of Xuying Dong and Wanlin Qiu has provided a solid foundation for the success of this research and the publication of high-quality papers, promoted the research on the risk assessment of scientific research projects and the relationship between related factors, and made a positive contribution to the progress of the field.

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Dong, X., Qiu, W. A case study on the relationship between risk assessment of scientific research projects and related factors under the Naive Bayesian algorithm. Sci Rep 14 , 8244 (2024). https://doi.org/10.1038/s41598-024-58341-y

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Beginner’s Guide to Project Risk Identification Complete with Workshop Toolkit

By Kate Eby | October 10, 2022

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Every project comes with risks, from external threats to sudden opportunities. We’ve compiled best practices, tools, techniques, and expert tips that will guide you through the project risk identification process.

Included on this page, you’ll find best practices for project risk identification , a toolkit for hosting an effective risk identification workshop , and a guide to the six phases of the project risk identification lifecycle .

What Is Project Risk Identification?

Project risk identification is the first step in the risk management process. During this step, managers identify events that might influence a project. Identifying risks helps teams prepare for any outcome. Risks can be either positive or negative.

Mary Beth Imbarrato, Owner of MBI Consulting , shares how risk arises in projects: “Projects introduce change. Change can introduce risks, surprises and unknown issues or challenges. Risk identification is not a static activity.”.

Identifying project risks early and often is the key to minimizing their impact. In order to identify as many risks as possible, project managers need to learn about the various types of risk and where to look for them.

Why Is Risk Identification Important in Projects?

Risk identification is important because it increases the chances of project success. Many projects fail because managers do not identify important risks. Early risk identification helps the project team respond to risks quickly and effectively.

“By definition, risk management is the process of identifying, tracking, and managing potential risks that can impact your scope,” shares Amy Black , Director of Security, Privacy, and Risk at RSM US LLP. “Risk identification is no different. Without proper tracking, the risk can delay or be a significant failure point for the success of your end deliverables. This will impact cost, schedule, and performance.”

The benefits of early project risk identification include the following:

• Fewer Delays: Stay on schedule by identifying risks that could cause delays. 
• Better Adaptability: Minimize the impact of negative risks, and maximize the impact of the negative risks by identifying them early on.
• Fewer Surprise Expenses: By helping you avoid delays and resource shortages, project risk identification decreases the number of surprise expenses or penalties.  
• Increased Chance of Success: Decrease the chances of project failure caused by unforeseen risks. 

“It is not an ‘if’ but ‘when’ something goes wrong in your project,” says Alexis Nicole White , a Project Management Professional (PMP)®, Scrum master, and project delivery consultant with North Highland. “It is important to identify all those things that can go wrong within your project or program as early as possible and associate an impact to each item. Failure to identify risks will result in costly delays. Subsequently, it can impact other project areas such as your budget, resources, and key success metrics.”

When Should Risks Be Identified in a Project?

Risk identification is an iterative process. Teams should first identify risks during project planning. Then, it is best practice to continue identification throughout the entire project. The project manager, project team, and all relevant stakeholders should participate.

Project managers will prepare a process and cadence for identifying and evaluating risks. The earlier a project manager identifies a risk, the better the team can mitigate its effects and proceed without losing time. “Risks should be captured during all facets of the project. Proactively identifying potential risks during the planning and initiation of a project will save you time and money down the road,” states Black.

Alan Zucker, Founding Principal at  Project Management Essentials , confirms the need to identify risks throughout the lifecycle of a project. “The business case and project charter should identify the project’s opportunities — why are we undertaking this effort — and the threats that could derail it. We should continue identifying risks until the project is formally closed. New risks will materialize from internal projects or external sources,” he says.  

Although risk identification is a continuous process, it should begin before project risk assessment and project risk analysis , and before you finalize your project risk management plan.

How to Identify Project Risks

In order to identify project risks, project managers first need a clear definition of risk. Next, they should use techniques such as brainstorming sessions to determine all possible risk events. Finally, they should document these risks for later reference.

Risk identification is not static. Risks can and will change throughout a project’s lifecycle. “Some risks may be applicable at the start of a project (e.g., resource allocation) and can be closed later in the project lifecycle,” says Imbarrato. “Risks can arise at any stage of a project effort: initiation, planning, execution, or closing. The risk response plan will need to be part of all regularly scheduled meetings with the project team. The timing of those meetings will depend on the complexity, the criticality, and the length of the project.”

Six Phases of the Project Risk Identification Lifecycle

Six phases comprise the project risk identification lifecycle. These include creating a statement template, conducting a SWOT analysis, researching risks, reviewing internal and external risks, cross-checking risks, and creating a final risk statement.

In the Guide to the Project Management Book of Knowledge (PMBOK® Guide), the risk identification lifecycle indicates that the risk management plan should provide a statement for “a fully specified risk statement.”   This includes the cause, event, time window, impact, and effect on the project’s objective for each risk. 

These are the six phases of the project risk identification lifecycle: 

• Create a Statement Template: A statement template allows you to capture the same key pieces of information for each risk. A risk statement template might look like this: Because of <cause>, <event> could occur during <time window>, which could lead to <impact> with an <effect on a project objective>.
• Conduct a SWOT Analysis: Basic identification begins with analyzing the strengths, weaknesses, opportunities, and threats (SWOT) associated with the project. For example, a threat may be that market competitors have more brand recognition than you do.
• Research Risks: Project managers can identify risks while conducting interviews, reviewing assumptions, brainstorming with their teams, and researching similar projects. 
• Review External Risks: Many risks will come from within the project team or company. However, everyone should be on the lookout for external risks that can impact the outcome of a project. It’s essential to gather knowledge from as many outside sources as possible. For example, you might interview a market specialist familiar with competitors to evaluate the actual market share of your company or project and that of your competitors.
• Cross-Check Risks: It’s important that all risks are relevant to the project scope and work breakdown structure (WBS) . The project manager will ensure each risk corresponds to an element in the WBS. 
• Create a Final Risk Statement: The project manager will create a risk statement for each risk in the list. A final risk statement might look like this: Because competitors have more brand recognition, the customer may choose another product before evaluating our product, which could lead to fewer opportunities and have a profound effect on expected product sales and revenue.

Once you complete these steps, you can begin your project risk mitigation efforts.

Project Risk Identification Steps

The steps of identifying project risks align with the phases of the risk lifecycle. The first step is to build the risk statement template. After the internal team and stakeholders identify relevant risks, finalize each risk statement using the template.

Risk Identification Inputs

Project risks can come from anywhere. Review all inputs to better understand potential risks. Common inputs include your project management plan, project documents, enterprise environmental factors (EEFs), and organizational process assets (OPAs). 

For each input, review every element to ensure that you identify every major risk to your project.

Here are the main risk identification inputs:

• Project Management Plan: A project management plan includes cost management, scheduling, quality control, human resources, scope, schedule, and budget. For example, it is not uncommon to identify risk within the budget, especially if it is too low to cover all project expenses.
• Project Documents: Project documents include the project charter, stakeholder register, costs, duration, performance reports, resource requirements, and procurement documents. For example, insufficient resources to complete the project pose an enormous risk.
• Enterprise Environmental Factors (EEFs): EEFs include industry information, important benchmarks, research and studies, and attitudes toward risk. For example, industry competitors can pose a risk to a project. 
• Organizational Process Assets (OPAs): OPAs include risk registers from previous projects and lessons from the project manager, experts, and the project team. For example, if a subject matter expert reviews your project, they will likely find additional risks.

Risk Identification Tools and Techniques 

Each project manager will have their preferred tools and techniques for identifying risks. Gathering data through brainstorming sessions, consulting experts, and conducting a SWOT analysis are all common methods for identifying risks. 

These are some helpful risk identification tools and techniques to try:

• Expert Judgment: Experience and subject matter expertise might be enough to identify some project risks. Consult experts to ensure that you haven’t missed key risks.
• Data Gathering: Project managers might host brainstorming sessions with the project team and external stakeholders to dive into potential risks. Checklists, questionnaires, and interviews can help you discover important risks.
• Root Cause Analysis: Root cause analysis means identifying the actual risk as opposed to the symptoms of the risk. Conducting a SWOT analysis and critically reviewing project requirements and assumptions will likely bring hidden risks to the forefront. 
• Collaboration: Group or individual meetings, workshops, and brainstorming sessions are all great ways to unearth new risks and eliminate risks that are outside the project's scope.
• Hybrid Approach: Most project managers combine two or more techniques to uncover risks throughout the project.

Project Risk Identification Framework

The project risk identification framework is a tool that standardizes risk identification. Knowing the current and potential risks helps improve the likelihood of project success. Keep everyone on the same page about risks by establishing a common framework.

Each business will create or adopt its own unique framework. In the The Journal of International Technology and Information Management, Jack T. Marchewka puts forth this framework for identifying project risks. Marchewka’s framework is a helpful example of how to standardize risk identification. 

In Marchewka’s model, project value is at the core of the risk identification framework. Just outside the center are project elements, such as quality and budget, that significantly impact project success. The next tier includes internal and external risks, which may be outside a project manager’s control. The next layer contains known, unknown-known, and unknown-unknown risks. 

Known risks are entirely certain. Unknown-known risks are certain, but some details might be unclear. For example, you might know that you have to hire an engineer for your project, but you don’t know the exact negotiated salary. Unknown-unknown risks are those that the project manager cannot predict. For example, a recession or sudden bankruptcy are unknown-unknowns. The outermost layer of Marchewka’s framework contains the project lifecycle phases because risk identification may occur at any point during the project.

Project Risk Identification Example

Follow along with the risk identification steps to start risk management in any project. After you create your statement template, move through each phase of risk identification to ensure that you identify as many risks as possible.

Here is how to apply these risk identification steps to a CRM software project:

• Conduct a SWOT Analysis: The SWOT analysis for the CRM software project will help the team analyze the projects’ strengths and weaknesses, as well as identify opportunities and threats. Threats may include the lack of end-user involvement in requirements, CRM competitors, or no brand recognition in the CRM market. 
• Research Risks: While performing research, you might come across a previous software project that is similar to your current project. If the earlier project team encountered unexpected problems with conflicting priorities or late-stage requirement changes from leaders, add these to your risk register. These are important risks to note as they reflect historical behaviors of the company.
• Review External Risks: As you meet with external resources, you might find that the CRM market is saturated with competitors. The risk is that there will be less demand for your product. You might have to consider how to differentiate your product or brand to avoid the immense competitive pressures of an already saturated market.  
• Cross-Check Risks: Ensure each risk is within the project scope and corresponds to the work deliverables. It is possible that a risk you have identified is out of scope for the project and doesn’t need to be addressed in your risk mitigation plan.  
• Because competitors have more brand recognition, the customer might choose another product before evaluating our product, which could lead to fewer opportunities and have a profound effect on expected product sales and revenue.
• Because end-users are not involved in requirement development, the software might not meet their needs once the software is available, which could lead to additional development costs and have an impact on product suitability, product sales, and revenue.

Project Risk Identification Workshop Toolkit

This project risk identification workshop toolkit will help you conduct a successful risk identification workshop. The toolkit includes all the information you will need to run your own workshop, from brainstorming questions to communication guidelines.  

The guide includes questions that will help the team think about, discuss, and prioritize potential risks. Following the risk identification workshop, you will document and track all risks in the risk register. 

Download a Project Risk Identification Workshop Toolkit for Microsoft Word | Google Docs

For more useful templates and resources, see our comprehensive list of project risk management templates .

Project Risk Identification Best Practices

Planning for project threats and opportunities is essential to project success. Following project risk identification best practices will help prevent surprises that could derail your project. For example, identify risks early and often throughout the project lifecycle.

Successful project risk identification shares many of the benefits of general project risk management . These are some best practices to help you optimize your project risk identification: 

• Detect Risks Early: Project risk identification should happen in the earliest stages of project planning. “Early detection is key. If we can identify risks early enough, we can avoid delays in our overall project timeline and schedule,” shares Black.
• Collect Stakeholder Input: Stakeholders will have important insights into potential risks, so you should always consult them when identifying risks. “Communication is key to capturing as many risks as possible. While you may not perceive something as a risk, another stakeholder or project contributor may experience a significant impact,” says Black. “Bringing risks up during project status meetings or status read-outs is imperative.”
• Review Risks Often: Risks change over the lifecycle of a project. “New risks can arise as the project unfolds. It is critical for project managers to understand that risks are not something they can document and then store. Risk identification and response will never be in a final state until the project ends,” states Imbarrato. 
• Analyze Risk Impact: Part of identifying risks is understanding how impactful each risk event will be. White suggests conducting a risk analysis: “Analyze how risk can impact your project and evaluate the outcome of the risks.”
• Learn Lessons from Past Projects: Always review similar past projects as part of your risk identification process. “Many risks from prior projects will manifest in future ones, so old risk registers can be a good starting point for review,” suggests Zucker. “Also, use checklists from other projects in your organization,” says Zucker. “Many people execute similar projects and encounter the same risks. A checklist reduces the effort and ensures we do not forget a common risk.”
• Review Industry Data: Project managers don’t need to start from scratch when identifying risks. “Many industries have standard prompt lists, benchmarks, and common risks that can be a starting point and help us look beyond our current horizon,” says Zucker.
• Interview All Relevant Personnel: The more people you consult about risk identification, the less likely you are to run into surprises during project execution. “Brainstorm possible risks with the project team and key stakeholders,” says Zucker. “Also, interview subject matter experts who can provide insight into potential risks.”

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Project Risk Assessment in 2024: Guide With Templates & Examples

You are organizing a product rollout, the premier milestone of your company this year. You have invited a thousand guests, expecting to witness a big gala event. Everything seems to be perfect before the event begins until you get two phone calls.

Your keynote speaker didn’t make it to her flight, and your caterer won’t be arriving due to some kitchen situation.

What do you do?

Just like life, your projects often throw some unexpected crises at you. These are what we call risks. And you should never allow yourself to ask what to do when risks happen because, by then, it’s too late and the damage will be significant. What you can and should do is to conduct a project risk assessment to anticipate such scenarios. 

Don’t worry, even if you lack formal training in project management, risk assessment is quite straightforward. In this article, we’ll show you how with a few project risk assessment templates to help you follow the process.

Project Risk Assessment Guide Table of Contents

• Identify Risks
• Analyze Risks
• Determine Risk Response
• Document Risks
• Factors to consider when creating project risk assessment

As the Project Management Institute (PMI) defines it, risk is an unexpected event that can have an effect on your project, including its stakeholders, processes, and resources. Risk can affect your project positively or negatively. Take note that risk assessment is just one aspect of your life as the project leader. But it is a critical part of your strategy whatever project management methodology you’re using.

As a manager, you have your fair share of exposure to risks at varying levels. 

Sometimes, risks are mistaken as issues, but there is a significant difference. Issues are events or problems that are already currently happening. Examples of issues include lack of manpower to work on a project, insufficient funding, and an immensely tight timeline.

Meanwhile, risks are problems that may happen in the future. The last-minute no-show of the performers and the caterer in the scenario above is an example of risk. We can also consider the immediate mass resignation of significant staff members a risk. Here are more examples of risks vs. issues in project management .

The purpose of preparing for project risk assessment is to acquire an awareness of the kinds of risks your project may encounter and the degree of damage they may bring. The following tips below will walk you through the important parts of this endeavor, including properly framing the project risk assessment definition.

To help you make a more accurate risk assessment and streamline its tedious process, you can turn to project management software with risk management; for example, what you’ll find in monday.com features .

Try out monday.com with their free trial

Project Risk Assesment Guide

1. identify risks.

Identifying of risk should be done as early as possible in the project and carried out throughout the project timeline, as risks affect significant project milestones. Throughout the years, you or your predecessors might have created a catalog of risks in the company server that the business have encountered in completed projects.

However, risks can also be identified during brainstorming with seasoned project members and other stakeholders. You can use the Crawford Slip method, where during a meeting, an attendee writes one suggestion per each piece of paper. This is a very simple yet effective way of gathering and collating suggestions and ideas. Just a note, you’ll need the cooperation of teams from other departments to get the best ideas. If you’re getting less-than-ideal attention from them, a shrug of the shoulder here and there, that’s one of the signs you need project management software or upgrade to a better one to optimize collaboration across the board.

In identifying risks, a risk category document is very useful in determining areas that are prone to risks. Risks may fall under the following categories:

• Organizational
• Project management.

Your project risk assessment checklist should include the relevant stakeholder accountable to action for each.

Here’s a sample risk category checklist:

Credits: northam.wa.gov.au

2. Analyze risks

This step entails examining the probability of a risk, how a risk event may impact project objectives and outcomes, and the appropriate steps that can be taken to mitigate the negative effects of risk. Here are the two things to consider at this stage:

• Likelihood. How probable will a certain risk occur in your project? PMI identifies the likelihood of risk occurrence as high, medium-high, medium-low, and low.  Knowing the likelihood a certain risk will occur will help your team to prepare for it. For example, it is more probable for the bank to reject your loan application for funding than for that same bank to be set on fire by a lightning.
• Impact. An effective project planning will have a project risk assessment matrix of the various levels of impacts of a risk (categorized as catastrophic, critical, and marginal) on cost, schedule, scope, and quality of outcome. This will allow your team to identify which area of the project will bear the brunt of the risk with the biggest impact. This, in turn, will enable you to allocate manpower, budget, or technology for prevention or solution. An example of a catastrophic risk is the last-minute cancellation of a venue, which will greatly affect the whole event.

A project risk assessment matrix helps you analyze each risk based on the two factors above. You can vary the model, but essentially here’s how this template looks like (pay most attention to the red boxes):

Credits: The Program Manager

3. Determine risk response

Project risk assessment planning tools offered by some project management sites, such as monday.com, target to achieve the following results:  eliminate the risk, reduce the probability of the occurrence of risk, and weaken the impact of the risk on the project. However, while it is best to develop a workflow to avoid the risk, it is still a rational move to set up a risk response guide for every project. This may mean factoring the risk in the project plan and schedule, increasing the funding or budget, and adding manpower and resources to the project, among other things.

It is simply not possible to completely eliminate all of the risk in a project. Some risks will persist at lower levels with weaker effects. These are called residual risks. Despite the diminished impact, residual risks need to be identified and assessed as you do the big-impact risks.

Here’s a project risk assessment example with an action plan, illustrating clearly what to do per risk occurrence:

4. Document risks

It is not enough, that you as project manager are able to identify, plan for, and solve risks events. A project folder or file needs to be created at the end of each project to provide transparency and awareness of the project’s timeline, workflow, and risks. This document sums up the reports above, plus adds insights on and citation of best practices on how the risks were handled. It will help other managers get a glimpse of the ins and outs of a project similar to yours. Having a cloud-based project management software like monday.com helps you to collate these details in one place for future reference.

Factors to consider when creating a project risk assessment

Being a project manager is not all about fighting and putting out fires. Here are more tips to get your project moving despite the hiccups.

• Make risk assessment the mainstay of your projects. It is ignorance to assume that projects will never encounter risks.  An effective project risk assessment and management is essential in the success of any project. Although this will incur an additional step on your part, the benefit you will reap from embedding risk assessment and management can never be underestimated.
• Inform stakeholders about risks. In the “Attack by Stratagem” chapter of the Art of War, one of the best books for project managers , Sun Tzu declared that the source of an army’s strength is in unity, not size.  Risk managers can learn a nugget of wisdom here. Sometimes, upper management is unaware of the details of failed projects. And there are instances where the instrument to solve the problem is already available but wasn’t used because the team has not been informed of its availability nor existence. Remember to include communicating risks and mitigation plans to other stakeholders in your project. You can do this during project update meetings as a default part of the agenda. This way, you clearly communicate that risks are vital parts of the project and should be given sufficient attention.
• Clarify ownership. Most projects involve different departments and stakeholders. It is important that stakeholders are clear with their ownership of the project or phase of the project. And this should be done at the beginning of the project or before a risk occurs. This way, each stakeholder will carry out tasks to decrease occurrences of risks on their part and will not be surprised by any additional expenses incurred.

Get the right tool for project risk assessment

While some managers have a wealth of experiences under their belt that help them to instinctively carry out steps and processes in analyzing and managing risks in their projects, it does not hurt for new managers to do some research as well as to adopt and utilize project risk assessment tools, checklists, and templates from websites. An example is monday.com.

monday.com is an online project management software that empowers managers to drive projects and teams effectively. It offers project risk assessment tools and templates that will save you time on the paperwork and give you more time to keep your team focused on achieving project success.

By Stephanie Seymour

Stephanie Seymour is a senior business analyst and one of the crucial members of the FinancesOnline research team. She is a leading expert in the field of business intelligence and data science. She specializes in visual data discovery, cloud-based BI solutions, and big data analytics. She’s fascinated by how companies dealing with big data are increasingly embracing cloud business intelligence. In her software reviews, she always focuses on the aspects that let users share analytics and enhance findings with context.

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Risk Assessment Form for clinical research projects

Instructions.

This Risk Assessment Form is a step-by-step guide for sponsor-investigators that helps them ensure that the potential risks of a clinical research project are addressed according to current good clinical practice (GCP) requirements. It therefore supports setting up a risk-based quality management system (QMS) as well. Plenty of examples help you to identify quality risks specific to your clinical research project, evaluate these risks, and develop strategies to overcome them.

The user-friendly Risk Assessment Form can be used to make a step-by-step assessment of a clinical research project's potential risks; it is in line with current GCP requirements.

This form helps you to identify potential quality risks of your clinical research project, evaluate these risks, and develop strategies to overcome them. As risks differ widely with different trials, the form’s instructions provide many examples for additional guidance. This form covers risks at both the system level and the clinical trial level.

This form is suitable for both clinical trials initiated in an academic setting and for project leaders running research projects according to the Human Research Ordinance (HRO). Part A applies to the system level and can be reused for different research projects running at the same site.

This form was developed by the SCTO’s Auditing Platform and first published in December 2019.

According to the Good Clinical Practice Guideline from the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), referred to as ICH GCP E6(R2), it is the sponsor’s responsibility to ensure that an adequate QMS is in place to manage a clinical trial and address potential risks. Using this form helps you bring your risk assessment in line with the current requirements of ICH GCP E6(R2).

According to the Declaration of Helsinki and data privacy guidelines, it is the project leader’s responsibility to ensure that an adequate QMS is in place that can be used to oversee the project and address potential risks to the research project.

Watch this video for a quick introduction on how to manage your study risks with the Risk Assessment Form.

The complementary user instructions provide many practical examples and contain a step-by-step guide on how to complete the form.

• User Instructions for the Risk Assessment Form (pdf, 319.6 KB)

Our templates and tools are free. As a publicly funded organisation, we strive to inform the public about the impact of our work and to continually improve our services. We therefore kindly ask you to leave us your email address so we can contact you with a short one-time survey. You may download the document without providing your personal data.

This form is licensed under CC BY-NC 4.0. Its content can be shared and adapted as long as you follow the terms of the license. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ .

• Risk Assessment Form (docx, 6.56 MB)

Related tools and resources

Guidance for electronic health record (ehr) system requirements in switzerland.

This guidance provides an overview of the requirements an EHR system should meet to ensure that the data recorded in it are valid and reliable for use in clinical research projects.

Guidelines for Risk-Based Monitoring

These guidelines describe the risk­-based monitoring procedures for clinical trials and help determine the recommended risk category for a clinical trial.

Risk-Based Monitoring (RBM) Score Calculator

The Risk-Based Monitoring (RBM) Score Calculator is a web-based questionnaire that helps you identify the best monitoring strategy for your clinical trial.

EHR Systems Study Site Assessment Template

The EHR Systems Study Site Assessment Template can be used to assess the regulatory conformity of electronic health record systems that host the source data for clinical research projects.

Safety reporting forms for clinical research projects

This set of comprehensive safety reporting forms addresses all aspects of safety reporting for clinical research projects.

Questions or suggestions?

Feel free to contact us with any questions or feedback related to this resource!

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Research risk assessment

It's the responsibility of the principal investigators (PI) and researchers to identify reasonably foreseeable risks associated with their research and control the risks so far as is reasonably practicable.

All participants and research assistants have the right to expect protection from physical, psychological, social, legal and economic harm at all times during an investigation. Certain research may also present reputational, legal and / or economic risks to the University.

As part of the ethical approval process for research involving human participants you are required to identify potential risks associated with your research and the action you will take to mitigate risk. You may be asked to submit your risk assessment.

The risk assessment process is a careful examination of what could cause harm, who/what could be harmed and how. It will help you to determine what risk control measures are needed and whether you are doing enough. 

Risk assessment responsibility

The PI and researchers need to take responsibility for all assessments associated with their projects. Occasionally you may need research workers or students to risk assess an aspect of the work and you will need to check the assessments are adequate and sign them off.

Risk assessors need to be competent and you’ll need to ensure they have adequate training and resource to do the assessments. There is risk assessment training available and help and advice help and advice help and advice from your Health and Safety Advisory Service link advisor and safety specialists (for health and safety risks), or the REO Research Governance team for other risks. In some cases, the hazards are so unique to the research that the PI and their team might be the only people who know the work well enough to make valid judgements about the risk and justify their conclusions.

Risk assessment process

The risk assessment process is a careful examination of what could cause harm, who/what could be harmed and how. It will help you to determine what risk control measures are needed and whether you are doing enough.

To simplify the process you can use the health and safety risk assessment templates, risk estimation tool and guidance for all risks associated with your research project. Please refer to the research risk estimation guidance under how to carry out a risk assessment below to assist you. 

Research risks

Typical risks that need to be considered as part of research ethics are:

• Social risks: disclosures that could affect participants standing in the community, in their family, and their job.
• Legal risks: activities that could result in the participant, researchers and / or University committing an offence; activities that might lead to a participant disclosing criminal activity to a researcher which would necessitate reporting to enforcement authorities; activities that could result in a civil claim for compensation.
• Economic harm: financial harm to participant, researcher and / or University through disclosure or other event.
• Reputational risk: damage to public perception of University or the University/researchers’ reputation in the eyes of funders, the research community and / or the general public. 
• Safeguarding risks:   Risk to young people, vulnerable adults and / or researcher from improper behaviour, abuse or exploitation. Risk to researcher of being in a comprising situation, in which there might be accusations of improper behaviour.
• Health and safety risks: risks of harm to health, physical injury or psychological harm to participants or the researcher. Further information on health and safety risks is given below.

Health and safety risks

The potential hazards and risks in research can be many and varied. You will need to be competent and familiar with the work or know where to obtain expert advice to ensure you have identified reasonably foreseeable risks. Here are some common research hazards and risks:

• Location hazards Location hazards Location hazards and risks are associated with where the research is carried out. For example: fire; visiting or working in participant’s homes; working in remote locations and in high crime areas; overseas travel; hot, cold or extreme weather conditions; working on or by water. Also hazardous work locations, such as construction sites, confined spaces, roofs or laboratories. For overseas travel, you will need to check country / city specific information, travel health requirements and consider emergency arrangements as part of your research planning, by following the University’s overseas travel  health and safety standard .  
• Activity hazards Activity hazards Activity hazards and risks associated with the tasks carried out. For example: potentially mentally harmful activities; distressing and stressful work and content; driving; tripping, or slipping; falling from height; physically demanding work; lifting, carrying, pushing and pulling loads; night time and weekend working.
• Machinery and equipment Machinery and equipment Machinery and equipment . For example: ergonomic hazards, including computer workstations and equipment; contact with electricity; contact with moving, rotating, ejecting or cutting parts in machinery and instruments; accidental release of energy from machines and instruments.
• Chemicals and other hazardous substances . The use, production, storage, waste, transportation and accidental release of chemicals and hazardous substances; flammable, dangerous and explosive substances; asphyxiating gases; allergens; biological agents, blood and blood products. You’ll need to gather information about the amount, frequency and duration of exposure and carry out a COSHH or DSEAR assessment which will inform whether you may need health surveillance for yourself and / or your research participants.
• Physical agents Physical agents Physical agents . For example: excessive noise exposure, hand-arm vibration and whole body vibration; ionising radiation; lasers; artificial optical radiation and electromagnetic fields. You’ll need to gather information about the amount, frequency and duration of exposure inform whether you may need health surveillance for yourself and / or your research participants.

When to carry out a risk assessment

Carrying out initial risk assessments as part of the planning process will help you identify whether existing resources and facilities are adequate to ensure risk control, or if the project needs to be altered accordingly. It will also help you to identify potential costs that need to be considered as part of the funding bid.

Once the project is approved, research specific risk assessments need to be carried out before work starts.

The research may need ethical approval if there is significant risk to participants, researchers or the University.

How to carry out a risk assessment

The University standard on risk assessments provides guidance, tips on getting it right, as well as resources and the forms to help you produce suitable and sufficient risk assessments and must be used.

• Risk assessment template (.dotx)
• Flow chart to research risk assessment (.pdf)
• Research risk assessment: Risk estimation tool (.pdf)
• Example of a Social Science research risk assessment (.pdf)

Refer to carrying out a risk assessment carrying out a risk assessment carrying out a risk assessment for step by step guidance.

Risk assessments must relate to the actual work and must be monitored by the PI. If there are significant changes to the activities, locations, equipment or substances used, the risk assessment will need to reviewed, updated and the old version archived. Risk assessments should also consider the end of projects, arrangements for waste disposal, equipment, controlled area decommission and emergencies. 

Things to consider:

• The risks may be specialist in nature or general. Information can found from legislation, sector guidance, safety data sheets, manufacturers equipment information, research documents, forums and health and safety professionals.
• Practical research might involve less well-known hazards. Do you or your team have the expertise to assess the risk adequately? Do you know who to go to for expert advice?
• The capabilities, training, knowledge, skills and experience of the project team members. Are they competent or are there gaps?
• In fast changing research environments, is there a need to carry out dynamic risk assessments? Are they understood and recorded?
• The right personal protective equipment for the hazards identified and training in how to use it.
• Specific Occupational Health vaccinations, health surveillance and screening requirements identified and undertaken. With physical agents and substances you’ll need to make an informed decision about the amount, frequency and duration of exposure. If you need help with this contact HSAS.
• Associated activities: storage, transport/travel, cleaning, maintenance, foreseeable emergencies (eg spillages), decommissioning and disposal.
• The safe design, testing and maintenance of the facilities and equipment.
• Planned and preventative maintenance of general plant and specialist equipment.

These risk assessments relate to the actual work and must be monitored by the PI. If there are significant changes to the activities, locations, equipment or substances used, the risk assessment will need to reviewed, updated and the old version archived. Risk assessments should also consider the end of projects, arrangements for waste disposal, equipment and controlled area decommission and emergencies.

Training 

If you would like training on risk assessment, please book onto one of our courses:

• Research Risk Assessment (for research staff and students in Humanities and Social Sciences)
• Research Risk Assessment (for research staff and students in Faculty of Science of Health only)
• Carrying out a risk assessment Carrying out a risk assessment Carrying out a risk assessment
• People especially at risk People especially at risk People especially at risk
• IOSH/USHA/UCEA guidance on managing health and safety in research (.pdf) 
• Research governance: Ethical approval

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