Data Availability Statements for Engineering and Computer Science Papers

Why Data Availability Matters More Than Ever

Engineering and Computer Science research has entered an era where scientific discoveries are increasingly driven by data. From artificial intelligence and machine learning to cybersecurity, robotics, Internet of Things (IoT), software engineering, computer vision, and computational modeling, modern research often depends on datasets, algorithms, simulations, and digital resources that support published findings.

For authors, a Data Availability Statement is more than a publication requirement. It demonstrates a commitment to research integrity, encourages confidence in published findings, and helps other researchers validate, reproduce, or extend previous work.

At Crosslink Studies (CLS), transparency and responsible scholarly communication are fundamental principles of the editorial process. Preparing an accurate and informative Data Availability Statement helps authors strengthen their manuscript while supporting reviewers, editors, and future researchers throughout the research lifecycle.

What Is a Data Availability Statement?

A Data Availability Statement is a dedicated section within a manuscript that explains the availability of the data used to support the research findings.

Rather than simply indicating whether data exists, the statement informs readers and reviewers:

• Whether the research data can be accessed.
• Where the data is stored.
• Under what conditions it may be shared.
• Whether any restrictions apply.
• Who should be contacted if access is required.

Why Engineering and Computer Science Research Requires Data Transparency

Engineering and Computer Science disciplines frequently generate digital resources that extend beyond traditional research manuscripts. These may include large datasets, software code, trained machine learning models, simulation outputs, hardware configurations, benchmark results, or experimental logs.

Without access to these supporting resources, reviewers may find it difficult to verify results, while future researchers may struggle to reproduce published work. A well-prepared Data Availability Statement addresses these challenges by documenting how the supporting evidence can be accessed or why it cannot be shared.

What Types of Data Should Be Considered?

The definition of “research data” varies depending on the nature of the study. In Engineering and Computer Science, data may include:

Experimental Data

Information collected from laboratory experiments, prototype testing, hardware evaluations, or engineering measurements.

Computational Data

Many studies rely on computational workflows and digital outputs. Examples include simulation results, numerical models, optimization outputs, computational analyses and benchmark performance data.

Software and Source Code

Research software often plays a central role in validating Engineering and Computer Science studies. Supporting materials may include source code, scripts, software libraries, configuration files, application programming interfaces (APIs) and build instructions. Providing software resources improves reproducibility and facilitates future research.

Machine Learning Resources

Artificial Intelligence research commonly produces valuable digital assets. These may include training datasets, validation datasets, test datasets, pre-trained models, and hyperparameter configurations.

Why Journals Request Data Availability Statements

Editors increasingly recognize that scientific conclusions should be supported by accessible evidence whenever possible. A Data Availability Statement helps journals determine whether authors have adequately documented the research process and considered long-term accessibility of supporting materials.

These statements provide several important benefits:

• Strengthen research transparency.
• Improve reproducibility.
• Facilitate peer review.
• Increase confidence in published findings.
• Encourage responsible data management.
• Support future collaboration and innovation.

Common Types of Data Availability Statements

The content of a Data Availability Statement depends on the nature of the research and the availability of supporting materials. Below are several common scenarios.

Data Publicly Available

When datasets are openly accessible through trusted repositories, authors should clearly indicate where the data can be found. An effective statement identifies the repository and provides sufficient information for readers to locate the dataset.

Data Available Upon Reasonable Request

Some datasets cannot be made publicly available because of confidentiality agreements, institutional policies, intellectual property considerations, or ongoing research. In these situations, authors should explain that data may be shared upon reasonable request while describing any applicable conditions.

Data Included Within the Article or Supplementary Materials

For smaller studies, all supporting data may already be presented within the manuscript or supplementary files. Authors should clearly indicate that no additional datasets are required beyond the published materials.

Data Not Publicly Available

Occasionally, legal, ethical, commercial, or security considerations prevent public data sharing. Examples include:

• Proprietary industrial research
• Sensitive cybersecurity information
• Confidential engineering designs
• Personal or institutional privacy concerns
• National security restrictions

Best Practices for Preparing a High-Quality Data Availability Statement

A strong statement should be concise, accurate, and transparent. Authors are encouraged to follow several best practices.

Be Specific

Avoid vague statements such as: “Data available upon request.”

Instead, explain:

• What data is available.
• Who maintains it.
• Under which conditions it can be accessed.

Ensure Consistency

The Data Availability Statement should match the manuscript, supplementary materials, and any referenced repositories. Inconsistencies between these sources may create confusion during editorial assessment.

Consider Long-Term Accessibility

Whenever possible, data should be deposited in reliable repositories capable of preserving digital resources over time.

Stable storage enhances reproducibility and ensures continued access for future researchers.

Protect Sensitive Information

Transparency should never compromise ethical or legal responsibilities. Before sharing datasets, authors should verify that confidential, proprietary, or personally identifiable information has been removed or appropriately protected. Responsible data sharing balances openness with privacy and intellectual property considerations.

Common Mistakes Authors Should Avoid

Editors frequently encounter Data Availability Statements that provide insufficient or inaccurate information.

Examples include:

• Omitting the statement entirely.
• Providing contradictory information.
• Referring to datasets that cannot be located.
• Failing to describe access restrictions.
• Using temporary storage locations.
• Sharing incomplete datasets.
• Providing undocumented software or code.
• Including broken repository links.
• Using unclear or generic descriptions.

Preparing for Submission to Crosslink Studies

Before submitting your manuscript to Crosslink Studies (CLS), consider reviewing your Data Availability Statement using the following checklist:

• Does the manuscript include a dedicated Data Availability Statement?
• Is the description accurate and complete?
• Are datasets clearly identified?
• Are software and code documented where appropriate?
• Are repository details consistent throughout the manuscript?
• Have ethical and confidentiality considerations been addressed?
• Does the statement accurately reflect the availability of all supporting research materials?

Building Trust Through Transparent Research

Scientific progress depends not only on innovative ideas but also on the ability of researchers to verify, reproduce, and build upon previous work. Data Availability Statements play a vital role in achieving this objective by documenting how the evidence supporting published research can be accessed and evaluated.

For Engineering and Computer Science researchers, where digital resources often form the foundation of scientific conclusions, thoughtful data management has become an essential component of scholarly publishing rather than an optional consideration.

At Crosslink Studies (CLS), authors are encouraged to view Data Availability Statements as an opportunity to strengthen their research rather than simply satisfy editorial requirements. Clear documentation, responsible data sharing, and transparent reporting contribute to a more credible, collaborative, and trustworthy scientific community.

By preparing comprehensive Data Availability Statements before submission, researchers enhance not only the quality of their manuscripts but also the long-term value, impact, and reproducibility of their published work.