A well-curated dataset is no longer optional — it is a publication requirement. AcademiQ makes your research dataset FAIR (Findable, Accessible, Interoperable, Reusable) with complete metadata, codebooks, data dictionaries, and compliance documentation that journals and institutional repositories require.
Research data curation is the active management, documentation, and preservation of research datasets so they can be understood, reproduced, and reused — by journal reviewers verifying your analysis, by other researchers building on your work, and by institutional repositories archiving science for the long term.
The reproducibility crisis in science has fundamentally changed what journals, funders, and institutions require of researchers. It is no longer sufficient to publish a paper summarising your findings — an increasing number of top-tier journals, particularly those in the Q1 category indexed on Scopus and Web of Science, now require a formal data availability statement, and many require the underlying dataset to be deposited in a recognised repository before or at the point of publication. Funders including EPSRC, UKRI, Wellcome Trust, and Horizon Europe now mandate that grant holders produce and follow a Data Management Plan (DMP) from the outset of a project.
FAIR data principles — Findable, Accessible, Interoperable, and Reusable — were formalised in a landmark 2016 paper in Scientific Data and have since been adopted by every major research funder and an expanding number of journals as the minimum standard for responsible data sharing. A dataset is Findable when it has a persistent identifier (such as a DOI), rich metadata, and is registered in a searchable resource. It is Accessible when it can be retrieved using standardised protocols, with clear documentation of any access restrictions. It is Interoperable when it uses standard vocabularies, formats, and schemas that allow data from different sources to be compared and combined. It is Reusable when it has a clear usage licence and is documented thoroughly enough for another researcher to understand and apply it without additional guidance.
Most researchers — including those at doctoral and postdoctoral level — have never been formally trained in data curation. The result is datasets that are technically valid for their own analysis but entirely opaque to anyone else: variable names like "var23" with no label, numeric codes with no key, file formats that cannot be opened without proprietary software, and no metadata that a repository search engine could index. AcademiQ solves all of this in a single engagement.
We produce complete standardised metadata following Dublin Core, DataCite, or discipline-specific schemas. We create detailed variable-level codebooks and human-readable data dictionaries. We conduct a systematic FAIR compliance assessment and produce the documentation you need to satisfy your journal's data policy. We prepare your dataset for deposit in Zenodo, Figshare, the UK Data Archive, or Mendeley Data, handling the technical submission requirements for each platform. And we write complete Data Management Plans compliant with every major UK, European, and North American funder's requirements.
Whether you need a standalone codebook for a dissertation appendix, a full FAIR-compliance package for a journal submission, or a DMP for a funding application — AcademiQ delivers within 24–72 hours depending on dataset size.
Complete, standardised metadata following Dublin Core, DataCite, or discipline-specific schemas. Makes your dataset discoverable in academic repositories.
Detailed variable-level codebook: variable name, label, type, coding instructions, valid values, and missing value codes — for every variable in your dataset.
Human-readable data dictionary explaining every variable, its source, measurement level, and role in your study.
Systematic assessment of your dataset against FAIR principles with recommendations and documentation for your data management plan.
Assistance preparing your dataset for submission to Zenodo, Figshare, UK Data Archive, Mendeley Data, or your institutional repository.
A complete DMP aligned to EPSRC, Wellcome Trust, UKRI, or Horizon Europe requirements — covering collection, storage, sharing, and preservation.
Top journals now require FAIR data statements and published datasets. We make compliance easy.
We know Zenodo, Figshare, UKDA, and Mendeley Data requirements inside out.
We advise on appropriate anonymisation procedures for datasets containing personal data under GDPR.
EPSRC, UKRI, Wellcome, and H2020 compliant Data Management Plans written from scratch.
You share your dataset and any existing documentation. We assess the current state against FAIR principles and identify gaps.
We produce the codebook, data dictionary, and metadata record to the relevant schema standard for your target repository.
We complete the FAIR compliance assessment and, if required, write the full Data Management Plan to funder specifications.
We prepare the final deposit package and guide you through submission to your chosen repository — or submit on your behalf.
FAIR stands for Findable, Accessible, Interoperable, and Reusable. It is a set of principles — now required by most major funders and many journals — ensuring that research data is properly documented, shared, and can be reused by other researchers.
Not all, but an increasing number of Q1 journals do require a data availability statement, and many require the underlying dataset to be deposited in a repository. We check your target journal's data policy.
A codebook provides variable-by-variable coding instructions (what each number means in a survey response). A data dictionary describes the dataset structure, variable types, and relationships. Both are usually required for repository submission.
Yes — we write DMPs compliant with EPSRC, UKRI, Wellcome Trust, Horizon Europe, and NSF requirements. We cover data collection, storage, security, sharing, and long-term preservation.
Yes — a standalone codebook for your dissertation appendix is one of the most common requests. We produce a fully formatted, professional codebook in 24–48 hours.
Share your dataset and tell us your target journal or repository. We'll have your codebook, metadata, and FAIR documentation ready within 24–72 hours.