Data Is Now Part of the Scholarly Record
Research visibility has traditionally been measured through publications. A researcher's impact was assessed by how many papers they published and how many times those papers were cited. This model is changing.
Research data, the datasets, code repositories, instruments, and materials that underlie published findings, is increasingly treated as a first-class scholarly output. It is:
- Citable: Data deposited in recognised repositories receives a Digital Object Identifier (DOI) and can be cited independently of any associated publication
- Evaluated: Research assessment frameworks in the UK, Australia, and increasingly globally include data and software outputs alongside publications
- Required: Most major funders (UKRI, Wellcome, NIH, Horizon Europe) now mandate open data deposition as a condition of funding
- Visible: Data repositories are indexed by Google Dataset Search, DataCite, and OpenAlex, creating additional discovery pathways
Researchers who treat their datasets as visibility assets, not just compliance boxes to tick, gain a meaningful edge in citation accumulation, funder assessment, and collaborative reach.
The FAIR Principles: The Standard Your Data Must Meet
FAIR data is the globally recognised standard for research data that is genuinely reusable. FAIR stands for:
Findable: The data has a persistent identifier (DOI), rich metadata, and is registered or indexed in a searchable resource.
Accessible: The data (or at minimum, its metadata) can be retrieved via a standard protocol. Ideally, it is open access; if restricted for legitimate reasons (privacy, commercial sensitivity), the access conditions are clearly stated.
Interoperable: The data uses standard formats and vocabularies that allow it to be integrated with other datasets and systems, CSV, JSON, HDF5, NetCDF rather than proprietary formats.
Reusable: The data has clear licensing information (Creative Commons CC BY is the most common for open research data), detailed provenance, and enough documentation for another researcher to understand and use it without contacting you.
Data that meets FAIR principles is more likely to be discovered, cited, and reused, which is the entire point.
Where to Deposit Your Data
The right repository depends on your discipline and funder requirements:
Zenodo, CERN's multidisciplinary repository. Accepts any research output (data, code, posters, presentations). Free. Issues DOIs automatically. Highly indexed. The most broadly applicable choice for researchers without a discipline-specific repository.
Figshare, Similar scope to Zenodo. Widely used in social sciences and humanities. Institutional versions of Figshare exist at many universities.
Dryad, Focused on ecology, evolution, and biology. Peer-reviewed submissions with a curation process that improves data quality and discoverability.
UK Data Service / ICPSR / GESIS, Major social science data archives. Accepted by funders as trusted repositories for social survey data.
GitHub / Zenodo integration, For code and software: maintain your repository on GitHub, then archive releases to Zenodo for a permanent DOI and citeable snapshot.
Institutional repositories, Many universities have a Research Data Management service with an institutional repository. This is often the fastest and most funder-compliant route.
Check your funder's preferred list: UKRI, Wellcome, and the Gates Foundation all maintain approved repository lists. Depositing in a non-approved repository may not satisfy your grant conditions.
Data Citations: Building a Parallel Citation Record
Every time your dataset is deposited with a DOI and cited in another researcher's paper, whether in their data availability statement or methods section, that is a citable impact. Data citations are tracked by:
- DataCite, the primary metadata registry for research data DOIs
- OpenAlex, includes data citations in its knowledge graph
- Google Dataset Search, surfaces datasets in web search results
- Scholix, links datasets to their associated publications bidirectionally
Your Google Scholar profile will not automatically show data citations, but your ORCID profile can list datasets as works, and DataCite tracks citation relationships independently.
The Data Management Plan
Most funders require a Data Management Plan (DMP) at the grant application stage. A DMP describes:
- What data will be collected or generated
- How it will be stored, secured, and backed up during the project
- Who will have access during the project
- How and where it will be shared after the project
- What will happen to it after the project ends (long-term preservation)
DMP templates exist for most funder requirements: DMP Online (UK) and DMPTool (US) provide guided template completion.
A well-structured DMP is not just a compliance document, it is a planning tool that forces decisions about formats, storage, and sharing that protect both the data and the researcher's ability to publish and share it later.
Data Management as a Visibility Strategy
The visibility argument for open data deposition is straightforward: papers published alongside openly available datasets consistently attract more citations than equivalent papers without associated data.
The mechanism: when a researcher can access both your paper and your data, they can:
- Verify your findings
- Reanalyse your data using their own methods
- Include your dataset in a meta-analysis or systematic review
- Build on your data in a follow-on study that cites your original paper and dataset
Each of these pathways generates citations that would not exist if the data were unavailable.
Research data is a visibility asset most researchers are not leveraging. The Researchvy Intelligence division covers data citation tracking and data visibility as part of a complete scholarly impact audit. For a structured programme covering every dimension of your visibility, including your data outputs, join a Digital Visibility Clinic. Also read our guide on altmetrics and research impact to understand how data outputs contribute to your broader impact profile.
