How to Write a Data Availability Statement in 2026 (With Templates for Every Scenario)

Journal editors are rejecting manuscripts at submission stage — before peer review even begins — because the data availability statement is missing, vague, or structured incorrectly. Whether you are submitting a master’s thesis, a PhD chapter as a standalone paper, or your first journal article, knowing how to write a data availability statement is no longer optional. Across Nature Portfolio, Springer, Wiley, and PLOS journals, the statement is mandatory, and “data available on reasonable request” is increasingly flagged as insufficient. This guide gives you the exact steps, ready-to-use templates, and the repository workflow you need to get it right the first time.

What Is a Data Availability Statement?

Source: Springer Nature — official guidance on research data best practice for data availability statements.

A data availability statement (sometimes called a data access statement or data sharing statement) is a brief declaration published alongside a research article or thesis that describes how the underlying data can be accessed. It is not an abstract, a data appendix, or a methods section. Its sole purpose is to answer one question for any reader who wants to verify or build on your results: where is your data, and can I get it?

The statement typically answers four sub-questions:

• What data exist (type, format, scope)?
• Where is it located (repository name, supplementary file, institutional archive)?
• How can it be accessed (open link, email request, data-sharing agreement)?
• Are there restrictions, and why (ethics approval, third-party licence, national security)?

A DAS does not need to be long. Most published examples run between one and four sentences. Length is not the mark of quality — precision and completeness are.

Writing a strong DAS is also part of building a transparent, reproducible research record. For a broader context on why open science practices matter in 2026, see the site’s article on the reproducibility crisis in research — which explains how data-sharing norms emerged directly from the replication failures of the 2010s.

Who Requires a DAS — and Why It Matters in 2026

The mandate has spread rapidly. Understanding the policy landscape saves you from a painful revision request:

Even if your target journal has no stated policy, including a well-formed DAS signals methodological transparency to reviewers and examiners — a professional habit worth building from your first academic submission.

Step 1: Identify What Data Your Study Generates or Uses

Before you can write a single word of your statement, you need a clear picture of what data your research actually involves. Run through these questions:

1. Did you generate primary data? This includes survey responses, interview transcripts, experimental measurements, sensor readings, clinical observations, lab assay results, or any dataset you collected yourself.
2. Did you analyse third-party data? This includes publicly available datasets (e.g., ONS statistics, World Bank open data, NCBI GenBank sequences), proprietary data held under licence, or administrative records obtained via a data access agreement.
3. Is your work entirely theoretical or computational? Some papers — pure mathematical proofs, systematic reviews with a full PRISMA table, or modelling papers where all code and inputs are published — generate no new empirical dataset at all.
4. Are there legal, ethical, or contractual restrictions on your data? Anonymised survey data from adult participants is generally shareable. Data from children, clinical trials, or commercially sensitive industry partners is typically restricted.

Write down the answers. They directly determine which scenario template (Step 4) you will use and whether you need to deposit anything in a repository first. If your study required formal ethics approval, review your consent forms and ethics committee decision letter now — these documents often specify exactly what data may or may not be shared publicly. For a full walkthrough of the ethics approval process, see the guide on how to get ethics approval for your dissertation.

Step 2: Choose Where to Deposit Your Data

If your data can be shared openly, depositing it in a recognised repository is strongly preferred by journals — and is required by some. A repository provides a persistent URL, a DOI, versioning, and long-term preservation that a personal university webpage cannot guarantee.

Subject-Specific Repositories (First Choice)

When a field-specific repository exists, journals typically prefer it because the metadata standards and community norms are better suited to the data type:

• GenBank / SRA — genomics and nucleotide sequences
• ArrayExpress / GEO — gene expression data
• ICPSR — social science survey microdata
• Dryad — ecology, evolution, and the life sciences
• UK Data Service (ReShare) — UK social research data

Generalist Repositories (Excellent for Thesis Data)

Most humanities, education, and social science students — and any researcher without a discipline-specific home — should use one of these three:

• Zenodo — operated by CERN and OpenAIRE; free; accepts up to 50 GB per record; issues a DOI immediately on reserve. Ideal for datasets, code, and pre-prints.
• Figshare — 20 GB free private storage, unlimited public; issues DOIs on publication; integrates with many institutional repositories and journal submission portals.
• OSF (Open Science Framework) — 50 GB free per project; allows private or public sharing; assigns DOIs to projects and preregistrations; widely used in psychology, education, and social sciences.

For most thesis students at UK, Australian, Irish, or North American universities, Zenodo is the lowest-friction starting point: no institutional affiliation required, CERN infrastructure, and a well-understood DOI minting process.

If you are considering preregistering your study before data collection — a practice that strengthens your DAS and your open-science record — the step-by-step guide on how to preregister a study on OSF or AsPredicted explains the full workflow, including how a preregistration DOI can be cited alongside your data DOI.

Step 3: Get a Persistent Identifier (DOI)

A persistent identifier — almost always a DOI — is what separates a citable, permanently accessible dataset from a link that might break in three years. Every major publisher guidance document, including Nature Portfolio’s data policy and the PLOS data availability guidelines, specifies that a DOI or accession number must be included in the statement whenever data are in a public repository.

Here is how to get a DOI from Zenodo, which covers most thesis-related scenarios:

1. Create a free account at zenodo.org/signup. Link your ORCID for automatic harvest of your record to your researcher profile.
2. Click the “+” icon and select “New upload”.
3. Drag and drop your data files. Acceptable formats include CSV, Excel, SPSS .sav, NVivo .nvpx, plain text, or any non-proprietary format. Zenodo accepts up to 100 files and 50 GB total.
4. Under “Basic information”, select Resource type: Dataset, enter a clear title (e.g., “Survey data supporting: [Your thesis title]”), add your name as creator, and write a brief description.
5. Click “Reserve DOI”. Zenodo immediately provides a DOI string (e.g., 10.5281/zenodo.XXXXXXX). The DOI is not yet live — it activates only when you click Publish.
6. If your paper is not yet accepted, save a draft. Add the DOI to your data availability statement as a placeholder and submit to the journal. Publish the Zenodo record when the paper is accepted.

Figshare and OSF follow similar workflows. OSF assigns DOIs at the project level; for datasets, you can create a dedicated OSF component and request a DOI for that component specifically.

Step 4: Select Your Scenario and Template

The four canonical scenarios cover nearly every situation you will encounter. Match your answers from Step 1 to the correct template below.

Scenario A — Data in a Public Repository

Use this when you have deposited your data in Zenodo, Figshare, OSF, Dryad, or a subject-specific repository and obtained a DOI or accession number.

Scenario B — Data Available on Request

Use this when your data can be shared but cannot be deposited publicly — for example, because it contains indirectly identifiable participant information that ethics approval requires be handled with additional safeguards.

Scenario C — No New Data Generated

Use this for theoretical papers, mathematical analyses, purely narrative literature reviews without a systematic search dataset, or modelling work where all code and inputs are already published.

Scenario D — Third-Party or Restricted-Access Data

Use this when you analysed data owned by a third party (government administrative records, commercial datasets, data held by a partner organisation) that you do not have permission to redistribute.

Scenario E — Data in Supplementary Files

Use this when all data are embedded in supplementary tables, appendices, or files submitted alongside the manuscript.

Step 5: Write and Polish Your Statement

With your scenario chosen and your DOI or contact details in hand, follow this four-part structure to assemble a precise statement:

1. State what data exist. Name the data type specifically — “anonymised semi-structured interview transcripts”, “raw accelerometer time-series files”, “coded thematic analysis matrices” — rather than using generic phrasing like “the data from this study”.
2. State where they are or how to reach them. Give the repository name and the persistent identifier (DOI, accession code, project URL), or give the contact email for on-request data.
3. Note any restrictions and their cause. One sentence. Cite the ethics reference number or legal basis if applicable.
4. Add a licence if relevant. For open data, state the licence (e.g., “under a CC BY 4.0 licence”) so reusers know their rights immediately.

Keep the total statement under 100 words. Longer is rarely better — journals impose word counts, and brevity forces precision.

After drafting, run a four-question check:

• Does the DOI or URL actually resolve? Test it in a browser.
• Is the repository or contact detail up to date?
• Does the statement cover all datasets cited in the paper — not just the primary one?
• Have you matched the exact template wording your target journal specifies?

Step 6: Place and Cite the Statement Correctly

Placement differs by venue:

• Journal articles: Most journals provide a dedicated “Data Availability” section in the manuscript template, positioned after the Conclusions or Discussion and before the References. If no dedicated section exists, place the statement in the Acknowledgements section.
• Thesis / dissertation: Place a brief DAS either in the front matter (after the abstract, before the table of contents) or at the end of the relevant empirical chapter. Some universities specify exact location in their thesis format guidelines — check your institution’s regulations.

If your dataset has its own DOI, you should also cite it formally in your reference list. The DataCite minimum citation format is:

Author(s). (Year). Dataset title [Dataset]. Repository Name. https://doi.org/XXXXX

Adding this as a reference ensures the dataset appears in citation indices and is counted in your academic output — an increasingly important factor for early-career researchers building their research profile.

Writing a DAS for a Thesis vs. a Journal Article

The template language is identical, but context matters:

A common time-saving move: deposit your thesis data to Zenodo at the same time as you submit to your institutional repository. The DOI you receive can then be inserted into both your thesis DAS and any subsequent journal articles from that thesis, maintaining a consistent, citable data trail across all outputs.

Transparent reporting of your data is also closely linked to your methodology chapter. If you want a deeper look at structuring that chapter, the guide on how to write a research methodology chapter covers the full data collection and analysis sections in detail.

For researchers who want to go beyond the DAS and understand the full framework for making data findable, accessible, interoperable, and reusable, the detailed guide to FAIR data principles for research data management explains each principle with practical examples for thesis students and early-career researchers.

Common Mistakes and How to Avoid Them

1. Using a broken or placeholder URL. Always verify that the DOI or repository link resolves before submission. A reviewer who clicks a dead link may recommend rejection on transparency grounds alone.
2. Saying “available on request” without a reason. As noted in the Springer Nature data availability guidance, journals now require a specific reason for non-deposit. State the ethics, legal, or contractual basis.
3. Omitting secondary datasets. If your study re-analyses public data (e.g., OECD statistics, a public genomic database), you must reference those sources in the DAS too, not just your own generated data.
4. Mismatching the statement to the journal template. Wiley uses “The data that support the findings of this study…”; Nature uses “The datasets generated during and/or analysed during the current study…”. The phrasing difference is deliberate — editorial systems may flag deviations.
5. Waiting too long to deposit. For PLOS journals, data must be publicly available at the time of publication — not on acceptance, and not “in due course”. Build in two weeks to upload, add metadata, and verify the deposit before your submission date.
6. Conflating the DAS with data documentation. A DAS is not a README, a codebook, or a data dictionary. Those belong inside the repository. The DAS is a pointer — one or two sentences that tell the reader where to find the full documentation.

If your study uses qualitative data — interview transcripts, ethnographic notes, thematic codes — the question of what to deposit is more nuanced. See the guide on conducting semi-structured interviews for your thesis for coverage of participant consent and anonymisation decisions that directly affect what you can include in a public repository deposit.

Once your data is secure, the next step in your publishing workflow is the manuscript submission itself. The step-by-step guide on how to write a cover letter for journal submission walks you through every required element — including how to reference your DAS and preprint disclosure in the letter itself.

Frequently Asked Questions

Sources and Further Reading

• Springer Nature — Data Availability Statements guidance
• PLOS ONE — Data Availability policy
• Nature Portfolio — Data Availability Statements and Data Citations policy (PDF)
• Wiley — Data Sharing Policies and standard templates
• Zenodo — How to create a new upload and reserve a DOI
• Cranfield University Library — Data Availability Statements guide