Research Project – Definition, Types, Steps, Structure and Examples

A research project is a planned and systematic investigation designed to answer a specific question, examine a problem, test an explanation or produce new understanding. It normally involves reviewing existing knowledge, selecting suitable methods, gathering or analysing evidence, interpreting the results and communicating conclusions in an appropriate academic or professional format.

Introduction

Completing a research project requires more than finding information and writing about a topic. The researcher must define a manageable problem, develop an answerable question, select methods that fit that question and reach conclusions that are supported by evidence.

Research projects vary considerably. An undergraduate may analyse an existing dataset, a teacher may evaluate a classroom intervention, a business student may survey consumers, and a doctoral researcher may conduct a multi-stage mixed-methods study. Not every project involves experiments, surveys or entirely new data. Some projects use documents, archives, published literature, software, images, administrative records or previously collected datasets.

This guide explains what a research project is, how it differs from related academic work, the main types and components of projects, and the steps required to plan, conduct and report one responsibly.

Key Takeaways

• A research project is organised around a specific problem, question or objective.
• The research question should determine the design, data, methods and analysis.
• A project may use primary data, secondary data, existing literature or a combination.
• Feasibility, ethics and data management should be addressed before data collection begins.
• A research proposal describes the planned project; the completed project includes the evidence, analysis and conclusions.
• Artificial intelligence can support selected tasks, but the researcher remains responsible for accuracy, privacy, disclosure and academic integrity.

What Is a Research Project?

A research project is a time-bounded, systematic investigation undertaken to answer a defined research question or achieve a research objective. It uses an explicit process for locating or collecting evidence, analysing that evidence and reporting conclusions. Its scope may range from a small classroom study to a large collaborative or funded investigation.

The defining feature is not the length of the final paper. It is the organised relationship among the research problem, question, evidence, methods, analysis and conclusion.

For example, “social media and students” is only a broad topic. A research project might ask:

How is daily use of short-form educational videos associated with self-reported study engagement among first-year university students?

That question identifies a population, a behaviour and an outcome. The researcher can then decide what evidence is required and how it should be analysed.

Characteristics of a Good Research Project

A strong project normally has the following characteristics:

• Focused: It investigates a clearly delimited problem rather than an unlimited subject.
• Systematic: Decisions and procedures follow an organised plan.
• Evidence-based: Conclusions are connected to collected or reviewed evidence.
• Methodologically aligned: The design and analysis are appropriate for the question.
• Feasible: The project can be completed with the available time, access, skills and resources.
• Ethical: Participants, communities, data and intellectual contributions are treated responsibly.
• Transparent: Important decisions, limitations and changes are documented.
• Analytical: The project interprets evidence instead of merely describing or compiling it.
• Communicable: The process and findings can be explained to the intended audience.

A project does not have to produce a dramatic discovery. A well-designed replication, local evaluation, secondary analysis or critical synthesis can still make a useful contribution.

Research Project Compared With Related Academic Work

Term	Main purpose	When it is produced	Typical content
Research project	Conduct a complete investigation	From planning through final reporting	Problem, question, literature, methods, evidence, analysis and conclusions
Research proposal	Explain and justify a planned project	Before the main study begins	Background, question, proposed methods, feasibility, ethics, timeline and expected contribution
Research protocol	Specify procedures in operational detail	Before implementation	Eligibility, procedures, measures, analysis plan, data handling and deviations
Research paper	Communicate an argument, synthesis or completed study	During or after research	Introduction, evidence or methods, analysis, discussion and references
Thesis or dissertation	Demonstrate advanced independent research for a degree	At the end of a degree project	Extended literature review, methodology, findings, discussion and contribution
I-Search project	Investigate a personally meaningful question while reflecting on the search process	Commonly used as a teaching assignment	Personal motivation, search narrative, evaluated sources, learning and conclusions

A proposal is therefore part of many research projects, but it is not the completed project. Similarly, the final paper is an output of the project rather than the entire research process.

Types of Research Projects

Research projects can be classified in several ways. These categories overlap. For example, one project may be applied, qualitative, exploratory and based on primary interview data.

Types by Purpose

Type	Purpose	Example
Basic research	Develop or test general knowledge or theory	Investigating how working memory influences decision-making
Applied research	Address a practical problem	Testing ways to reduce appointment non-attendance
Evaluation research	Assess a programme, policy or intervention	Evaluating a university mentoring programme
Action research	Improve practice through cycles of action and reflection	A teacher testing and refining a classroom strategy
Design or development research	Create and evaluate an artefact, process or system	Developing and testing an accessible learning application
Replication research	Examine whether an earlier finding can be reproduced	Repeating a published experiment with a new sample

Types by Methodological Approach

Quantitative projects use numerical data to describe patterns, estimate relationships, compare groups or test hypotheses. Methods may include experiments, structured surveys, measurements and statistical analysis.

Qualitative projects investigate meanings, experiences, processes or contexts. They may use interviews, observations, documents, focus groups or visual materials and analyse them through approaches such as thematic, content, discourse or narrative analysis.

Mixed-methods projects intentionally combine quantitative and qualitative evidence. The two components should have a clear relationship; simply adding one interview question to a survey does not automatically create a meaningful mixed-methods design.

Types by Research Design

A project may be:

• Exploratory.
• Descriptive.
• Correlational.
• Comparative.
• Experimental or quasi-experimental.
• Case-study based.
• Cross-sectional.
• Longitudinal.
• Historical.
• Ethnographic.
• Phenomenological.
• Grounded-theory oriented.
• Systematic, scoping or other evidence review.

The correct design depends on what the researcher wants to know. A causal question usually requires a different design from a question about lived experience.

Types by Data Source

Primary-data projects collect evidence directly for the study through surveys, interviews, experiments, measurements, observation or fieldwork.

Secondary-data projects analyse evidence originally collected for another purpose, such as census files, public datasets, organisational records or archived survey data.

Evidence-synthesis projects systematically locate and analyse existing studies or documents.

Multisource projects combine two or more forms of evidence, such as interviews, policy documents and administrative statistics.

What Are the Main Components of a Research Project?

A completed research project commonly contains the following elements:

1. Title: A concise description of the topic, population, variables or central issue.
2. Abstract or executive summary: A brief account of the purpose, methods, principal findings and conclusion.
3. Introduction: The context, problem, purpose, objectives and research questions.
4. Literature review: A critical synthesis of relevant knowledge and the gap or need addressed.
5. Framework: The theoretical or conceptual ideas guiding the investigation, where appropriate.
6. Methodology: The design, setting, participants or materials, sampling, measures, procedures, analysis and ethical safeguards.
7. Results or findings: The analysed evidence, presented without concealing relevant outcomes.
8. Discussion: Interpretation of findings in relation to the question, literature and context.
9. Conclusion: The principal answer, contribution and implications.
10. Limitations: Factors affecting interpretation, transferability or generalisation.
11. Recommendations: Practical or research recommendations when justified by the findings.
12. References: Complete details of cited sources.
13. Appendices: Supporting materials such as instruments, consent documents, coding frameworks or supplementary tables.

The required format varies by discipline and institution. An engineering project may emphasise design specifications and performance tests, while a humanities project may organise the analysis thematically rather than using a conventional methods-results structure.

How to Conduct a Research Project

A research project can be completed through the following 12-step process. The process is iterative: findings from one step may require the researcher to revise an earlier decision.

1. Understand the Assignment or Research Context

Start by identifying the formal requirements:

• Expected output and word count.
• Submission date.
• Permitted methods.
• Required citation style.
• Assessment criteria.
• Available supervision.
• Ethics or approval procedures.
• Data-storage requirements.
• Whether individual or collaborative work is expected.

A technically sound study can still perform poorly if it does not meet the stated assignment or funding requirements.

2. Select a Broad Research Area

Choose an area that is academically relevant and sufficiently interesting to sustain prolonged work. Consider subjects encountered in lectures, previous assignments, professional practice, public debates or recent literature.

At this stage, the idea can remain broad. “Student use of generative AI,” for example, is an area rather than a research question.

3. Conduct a Preliminary Literature Search

Read enough reliable literature to understand:

• Important concepts and terminology.
• Major theories and debates.
• Common methods.
• Established findings.
• Contradictions or uncertainties.
• Populations or settings that remain understudied.
• Data and instruments that may already be available.

Keep searchable notes and record full citation details from the beginning. Preliminary reading should narrow the project, not become an endless attempt to read everything ever published.

4. Define the Research Problem and Question

A research problem identifies the issue, uncertainty or knowledge gap that justifies investigation. The research question states exactly what the project will attempt to answer.

A good question is:

• Clear.
• Specific.
• Researchable.
• Relevant.
• Ethically acceptable.
• Feasible with available resources.
• Appropriately ambitious for the academic level.

Broad question:

How does technology affect education?

More focused question:

How do first-year undergraduate students describe the influence of AI-generated feedback on their revision decisions in an academic-writing course?

The revised question indicates that a qualitative interview or diary design may be appropriate.

5. Establish the Aim, Objectives and Hypotheses

The aim states the overall purpose. The objectives divide that aim into specific tasks.

Example:

Aim: To examine undergraduate students’ experiences of using AI-generated feedback during revision.

Objectives:

1. Identify the types of AI feedback students use.
2. Explore how students evaluate the credibility of that feedback.
3. Examine when students accept, modify or reject AI suggestions.
4. Identify perceived benefits and risks.

A hypothesis is appropriate when the project predicts a testable relationship or difference. Exploratory and many qualitative projects do not require hypotheses.

6. Assess Scope and Feasibility

Before finalising the question, evaluate:

• Time: Can recruitment, approval, collection, analysis and writing be completed?
• Access: Can the researcher reach the participants, documents, equipment or data?
• Sample: Is an adequate and appropriate sample realistically obtainable?
• Skills: Does the project require unfamiliar statistical, linguistic, laboratory or programming expertise?
• Cost: Are travel, software, transcription, incentives or equipment affordable?
• Risk: Could delays, low recruitment or technical failure make completion impossible?
• Ethics: Does the topic involve vulnerable people, sensitive data, deception or more than minimal risk?

A smaller completed study is more valuable than an ambitious but unfinished one.

7. Select the Research Design and Methods

The design should follow the question.

Research question	Possible design and evidence
What proportion of students use a service?	Cross-sectional survey
Is an intervention associated with improved scores?	Experimental or quasi-experimental comparison
How do participants experience a process?	Qualitative interviews or observations
How has a policy changed over time?	Document analysis and longitudinal secondary data
What is known across existing studies?	Systematic or scoping review
Does a prototype meet user needs?	Design, usability testing and evaluation

The methodology should describe:

• Population or unit of analysis.
• Sampling strategy.
• Inclusion and exclusion criteria.
• Data-collection instrument.
• Procedures.
• Variables or qualitative concepts.
• Analysis plan.
• Quality safeguards.
• Ethical protections.

Avoid selecting a method merely because it is familiar. A large survey cannot compensate for a vague question or poorly designed measures.

8. Address Ethics, Integrity and Data Management

Ethics is not limited to medical experiments. Projects involving people, identifiable records, private communications, online communities or sensitive topics may require review or formal permission.

Where applicable, address:

• Voluntary informed consent.
• The right to withdraw.
• Risks and potential benefits.
• Fair participant selection.
• Privacy and confidentiality.
• Anonymisation or pseudonymisation.
• Secure storage and access controls.
• Treatment of vulnerable participants.
• Conflicts of interest.
• Researcher safety.
• Community or cultural considerations.
• Responsible authorship and acknowledgement.

The Belmont Report identifies respect for persons, beneficence and justice as foundational principles for human-participant research (National Commission, 1979). Researchers must also follow their institution’s rules and applicable national law.

Create a data-management plan covering file formats, naming conventions, version control, backups, documentation, retention, access and appropriate sharing. Open-data expectations do not override consent, privacy, legal or contractual restrictions.

9. Write the Proposal, Protocol and Project Plan

The proposal explains what will be investigated, why it matters and how the study will be conducted. A detailed protocol adds operational instructions that make the procedures consistent and reproducible.

A practical plan should include:

• Research question and objectives.
• Deliverables.
• Task sequence.
• Milestones.
• Responsibilities.
• Dependencies.
• Resources.
• Approval dates.
• Recruitment period.
• Analysis period.
• Writing and revision time.
• Risks and contingency actions.

Allow buffer time. Ethics review, participant recruitment, data access and transcription often take longer than expected.

10. Pilot the Procedures and Collect the Evidence

A pilot is a small-scale test of the proposed procedures. It can reveal ambiguous questions, technical problems, unrealistic timings, recruitment difficulties or missing response options.

After making justified revisions, carry out the approved data-collection process consistently. Maintain an audit trail recording:

• Dates and versions.
• Recruitment outcomes.
• Departures from the protocol.
• Equipment or software problems.
• Missing data.
• Decisions made during fieldwork.
• Reasons for excluding observations.

Do not silently alter the question or method after seeing the results. Necessary changes should be documented and distinguished from the original plan.

11. Analyse and Interpret the Findings

Analysis converts raw evidence into findings relevant to the research question.

Quantitative analysis may include:

• Data cleaning.
• Descriptive statistics.
• Visualisation.
• Confidence intervals.
• Hypothesis tests.
• Regression or other models.
• Sensitivity checks.

Qualitative analysis may include:

• Transcription and familiarisation.
• Coding.
• Category or theme development.
• Comparison across cases.
• Reflexive note-taking.
• Examination of contradictory evidence.
• Use of illustrative extracts.

Separate the result from its interpretation. A statistical association does not automatically demonstrate causation, and a participant quotation does not by itself establish that a theme is widespread.

Interpret findings in relation to the question, prior literature, study context and limitations. Avoid claiming more than the design and evidence support.

12. Write, Revise, Report and Archive the Project

Begin writing before all data collection is finished. The background, literature review and parts of the methodology can usually be drafted earlier.

During revision, check:

• Does every section help answer the research question?
• Are the methods described accurately?
• Can readers distinguish planned from exploratory analyses?
• Are tables and figures understandable without guesswork?
• Are findings reported even when they contradict expectations?
• Are limitations specific rather than ceremonial?
• Do recommendations follow from the evidence?
• Are all citations complete and accurate?
• Have confidential details been removed?

Use a discipline-appropriate reporting guideline where one exists. PRISMA 2020, for example, provides a checklist and flow diagrams for systematic reviews (Page et al., 2021).

After submission or publication, retain materials according to institutional, ethical and legal requirements. Where appropriate, share non-sensitive data, code, instruments or documentation through a recognised repository.

Research Project Planning Template

Planning item	Guiding question	Project output
Problem	What is uncertain, inadequate or unresolved?	Problem statement
Question	What exactly will the study answer?	Main research question
Contribution	Who benefits from the answer and how?	Rationale
Evidence	What information is required?	Data requirements
Design	What structure can answer the question?	Research design
Sample or material	Who or what will be studied?	Sampling plan
Collection	How will evidence be obtained?	Procedures and instruments
Analysis	How will evidence be transformed into findings?	Analysis plan
Ethics	What risks, rights and permissions apply?	Ethics application or safeguards
Data management	How will files be protected and documented?	Data-management plan
Feasibility	What limits time, access, cost or skill?	Scope and contingency plan
Communication	How will findings be reported?	Report, presentation or other output

Example 12-Week Research Project Timeline

Weeks	Main activities
1–2	Confirm requirements, conduct preliminary reading and narrow the topic
3	Finalise the problem, question, aim and objectives
4	Select the design, sample, instruments and analysis plan
5	Prepare proposal, ethics materials and data-management plan
6	Pilot instruments and make approved revisions
7–8	Collect or obtain data
9	Clean, organise, transcribe or code data
10	Complete the main analysis
11	Write results, discussion, conclusion and limitations
12	Revise, proofread, check references and submit

This schedule is illustrative. Projects requiring formal ethics review or external data access may need a substantially longer preparation period.

Research Project Examples

Example 1: Education

Topic: AI-generated feedback in undergraduate writing.

Question: How do first-year students evaluate and use AI-generated feedback when revising academic essays?

Design: Qualitative study.

Evidence: Revision diaries and semi-structured interviews.

Analysis: Reflexive thematic analysis.

Potential contribution: A clearer understanding of when students consider automated feedback useful, confusing or unreliable.

Example 2: Business

Topic: Delivery reliability and online customer loyalty.

Question: To what extent do delivery delays and complaint-resolution time predict intention to purchase again from an online retailer?

Design: Cross-sectional quantitative study.

Evidence: Customer survey data and, where authorised, service-performance records.

Analysis: Descriptive statistics and regression modelling.

Limitation: A cross-sectional association would not by itself prove that delivery performance causes future loyalty.

Example 3: Computer Science

Topic: Accessibility of a university navigation application.

Question: Does a redesigned interface reduce task-completion time and errors for users with low vision?

Design: Design-and-evaluation project.

Evidence: Usability tasks, completion time, error rates and participant feedback.

Analysis: Quantitative task comparison combined with qualitative usability findings.

Ethical consideration: Accessible consent procedures and protection of participant data.

Example 4: Public Health

Topic: Geographic differences in access to vaccination services.

Question: How does travel time to vaccination facilities vary across urban and rural districts?

Design: Secondary-data spatial analysis.

Evidence: Publicly available facility locations, road-network data and population estimates.

Analysis: Geographic accessibility measures and comparison across districts.

Limitation: Geographic availability does not necessarily represent affordability, acceptability or actual service use.

Advantages and Limitations of Research Projects

Advantages	Limitations
Develops critical thinking and methodological skills	Can require substantial time and coordination
Produces evidence about a defined problem	Access to participants or data may be restricted
Connects theory with practice	Findings may be limited by sample, setting or design
Builds skills in analysis, writing and communication	Poor planning can create avoidable delays
May contribute to policy, practice or future research	Ethical and legal requirements can constrain procedures
Encourages independent and collaborative learning	Software, equipment or specialised skills may be needed

Limitations do not automatically invalidate a project. Their purpose is to help readers understand the conditions under which the findings should be interpreted.

Common Research Project Mistakes

Choosing a Topic That Is Too Broad

“Climate change,” “artificial intelligence” or “mental health” cannot be investigated meaningfully in a small project without further delimitation.

Correction: Specify the population, setting, concept, relationship and period.

Starting Data Collection Without a Clear Question

Collecting large amounts of information does not guarantee that the project will answer anything important.

Correction: Define the question and analysis plan before deciding what data to collect.

Confusing Description With Analysis

A literature review that lists one study after another, or a results section that repeats percentages without interpretation, remains descriptive.

Correction: Compare, synthesise, explain patterns and connect the evidence to the research question.

Using Convenient but Unsuitable Methods

A survey may be easy to distribute but inappropriate for understanding a complex personal experience.

Correction: Choose the method according to the question, not convenience alone.

Ignoring Approval Requirements

Recruiting participants before approval can make the data unusable and expose participants or the researcher to risk.

Correction: Confirm institutional requirements before recruitment, access or collection.

Treating Software Output as the Analysis

Statistical and qualitative software organises or calculates information; it does not decide whether the assumptions, coding or interpretations are defensible.

Correction: Understand and justify every analytical decision.

Making Causal Claims From Non-Causal Designs

A correlation or cross-sectional comparison usually cannot establish that one factor caused another.

Correction: Match the wording of conclusions to the design.

Leaving Writing Until the End

Late writing exposes gaps when there is little time to correct them.

Correction: Maintain a research log and draft sections throughout the project.

Research Projects in Modern Academic Practice

Modern projects increasingly include digital collaboration, structured data management, transparent workflows and reusable research outputs.

Depending on the discipline, researchers may use:

• Academic databases and citation indexes for literature searching.
• Reference managers for storing sources and producing citations.
• Survey, laboratory or field-data platforms.
• Statistical software, programming languages or qualitative-analysis tools.
• Version-control systems for code and documents.
• Electronic research notebooks.
• Preregistration or protocol registries.
• Data, code and preprint repositories.
• Persistent researcher identifiers.
• Reporting-guideline checklists.

Preregistration records a research plan before the main analysis and helps distinguish planned confirmatory work from later exploratory decisions. It is useful for many hypothesis-testing projects but is not a substitute for good design, ethical review or transparent reporting.

Research data should be managed so that it is appropriately findable, accessible, interoperable and reusable where legal, ethical and disciplinary conditions allow (Wilkinson et al., 2016). “Accessible” does not necessarily mean openly available to everyone; sensitive data may require controlled access.

Using Artificial Intelligence in a Research Project

Artificial intelligence can assist with limited research tasks, but it should not replace scholarly judgement or conceal the researcher’s contribution.

Potentially appropriate uses, subject to institutional rules, include:

• Generating search terms for later database searching.
• Explaining unfamiliar statistical or programming concepts.
• Checking code for possible errors.
• Suggesting alternative headings or organisational structures.
• Improving grammar in researcher-written text.
• Producing preliminary summaries that are independently checked.
• Creating practice questions or mock interview prompts.

Researchers should not:

• Treat AI output as a reliable scholarly source.
• Cite references without opening and verifying the originals.
• Upload confidential interviews, participant records or unpublished manuscripts to an unapproved system.
• Allow AI to invent observations, quotations, data, results or citations.
• use AI-generated analysis without understanding and checking it.
• Present AI-generated writing as independent work when disclosure is required.
• List an AI system as an author.

UNESCO recommends a human-centred approach that addresses privacy, governance and human responsibility (Miao & Holmes, 2023). Current publication guidance also places responsibility for accuracy, originality and disclosure on human authors rather than AI systems (International Committee of Medical Journal Editors, 2026).

Before using any AI tool, check the assignment rules, institutional policy, funder terms, ethics approval, data-protection requirements and intended journal policy. Keep a record of significant uses and disclose them where required.

Final Research Project Checklist

Before submitting the project, confirm that:

• The title accurately represents the investigation.
• The research problem is clear.
• The question is answerable and consistently stated.
• The objectives match the question.
• The literature review is analytical and current enough for the topic.
• The design and methods are justified.
• Required ethics approval and permissions were obtained.
• Data-management procedures are documented.
• The analysis addresses the research question.
• Results are separated from unsupported interpretation.
• Unexpected or negative findings are not hidden.
• Limitations are specific and honest.
• Conclusions do not exceed the evidence.
• Tables, figures, citations and appendices are complete.
• AI or other assistance has been disclosed according to applicable rules.
• The final document meets institutional formatting requirements.

FAQs

Conclusion

A research project is a complete process for investigating a defined question through systematic and ethically responsible use of evidence. Successful projects align the problem, question, design, data and analysis while remaining realistic about time, access and limitations. Careful planning, transparent documentation and continuous writing make the final report more credible and considerably easier to complete.

References

• Center for Open Science. (n.d.). Preregistration. Retrieved June 22, 2026, from https://www.cos.io/initiatives/prereg
• EQUATOR Network. (n.d.). What is a reporting guideline? Retrieved June 22, 2026, from https://www.equator-network.org/about-us/what-is-a-reporting-guideline/
• International Committee of Medical Journal Editors. (2026). Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals. https://www.icmje.org/recommendations/
• Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
• National Academies of Sciences, Engineering, and Medicine. (2017). Fostering integrity in research. The National Academies Press. https://doi.org/10.17226/21896
• National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. U.S. Department of Health and Human Services. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/
• National Institutes of Health. (2020, October 29). Final NIH policy for data management and sharing (Notice No. NOT-OD-21-013). https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-013.html
• Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., . . . Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
• UK Research Integrity Office. (2025). Code of practice for research (Version 3.5). https://ukrio.org/ukrio-resources/publications/code-of-practice-for-research/
• Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., . . . Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, Article 160018. https://doi.org/10.1038/sdata.2016.18