Science only works if it's reproducible
FAIR² Data Management makes it easier to share reproducible data—and get recognition for the work behind it.
Science Only Works If It’s Reproducible
FAIR² Data Management helps researchers publish data that others can reproduce, trust, and reuse.
Reproducibility isn’t a luxury—it’s what makes science science. Without it, findings can’t be verified, extended, or built upon. And yet, across disciplines, too much research is published without the clarity, structure, or transparency needed to reproduce results or reuse the underlying data.
For decades, the focus has been on the narrative—the figures, the conclusions, the paper itself—while the dataset behind it has often been treated as a black box or an afterthought. But that’s backward. Data is a primary research output. If we want science to be cumulative and collaborative, the data itself must be reproducible, reusable, and ready to support new discoveries.
Take a common example: a dataset of behavioral response times from a cognitive psychology study. Without clear documentation of how the task was implemented, what counts as a valid response, how outliers were handled, or how “mean response time” was calculated, another researcher—even one in the same field—can’t meaningfully reuse that dataset. At best, they must contact the authors and hope for a reply. At worst, the data remains inert: findable, perhaps, but not truly accessible.
FAIR² Data Management was created to change that. It supports researchers in preparing data that others can understand, reuse, and trust—while ensuring that the work involved is recognised and rewarded. FAIR² makes reproducible data not just possible, but practical.
Reproducibility Begins with Clarity
To reproduce an analysis or reuse a dataset, you first have to understand it. What do the variables mean? How were they generated? What processing steps have already been applied? What assumptions were made?
Simply putting a file online isn’t enough. A CSV without context is just a spreadsheet of numbers or labels—meaningless without a map.
FAIR² helps researchers provide that map by supporting clarity at every level:
Organise and describe variables clearly.
A dataset with a column labeled Score tells you very little. FAIR² prompts researchers to specify: What is the score measuring? On what scale? Was it transformed or aggregated? For example, a Score field might actually be a mean accuracy score from a 10-trial working memory task, z-scored relative to a control group. FAIR² encourages researchers to express this explicitly.
Connect each variable to its origin.
Say a dataset includes a field called theta_power. Without context, it’s unclear whether this refers to EEG spectral power during resting state or during a specific task epoch—or how it was computed. FAIR² supports linking each variable to the protocol, device, software, and preprocessing pipeline that produced it. That could mean connecting theta_power to an EEG task condition, a device calibration profile, and a signal processing notebook that includes bandpass filtering and artifact rejection.
Include analysis workflows and code.
Reproducibility often breaks down in derived variables—especially those used in figures or statistical tests. With FAIR², researchers can upload or link to Jupyter notebooks or scripts that show exactly how key variables were derived. A flexibility_index field, for instance, might be generated by subtracting shift costs between two task blocks. FAIR² allows the scoring code and logic to be captured, versioned, and displayed alongside the data.
Use community-relevant terms and standards.
FAIR² supports structured metadata in formats like JSON-LD and works with the researcher’s community to align terminology with relevant ontologies. For example, a dataset on zebrafish neural imaging might integrate terms from the Zebrafish Information Network (ZFIN), while a plant phenotyping dataset might draw on the Plant Ontology (PO). This ensures interoperability without imposing one-size-fits-all schemas.
Track data versions and history.
If a researcher later updates the dataset—say, correcting a coding error in a task label or adding an additional participant group—FAIR² maintains version control and tracks what changed. That means others can cite the exact version they used, and future users can explore how the dataset evolved.
Responsible Sharing Deserves Recognition
Even when researchers want to share well, the current academic system offers little incentive to do it properly. Most of the labor behind clean, reusable data—curating variables, standardizing labels, writing documentation, preparing analysis code—goes unrewarded. FAIR² is designed to make that work visible and valuable.
Here’s how:
Formal publication through a peer-reviewed Data Article.
Every FAIR² Data Package is accompanied by a Data Article that focuses entirely on the dataset itself—its structure, provenance, limitations, and potential for reuse. For example, a Data Article might explain how multi-site fMRI data were harmonized across scanners using specific calibration routines and preprocessing templates, detail dropout rates, and describe exclusion criteria. This context is essential for reuse and isn’t typically available in traditional methods sections.
Persistent identifiers for citation and tracking.
FAIR² assigns DOIs to both datasets and their associated Data Articles. That means if your dataset is reused in a cross-cohort study or to benchmark a machine learning algorithm, the reuse is citable—and tracked. This builds a traceable record of impact over time.
Contributor roles captured and credited.
FAIR² supports formal attribution of data-related work using the CRediT taxonomy and Role Ontology. So, if a postdoc built the preprocessing pipeline, a technician ensured signal quality, or a student handled data validation, each of those roles can be documented and linked to their ORCID. This ensures credit for real contributions—not just for writing the paper.
Increased discoverability through FAIR² Data Portals.
FAIR² datasets don’t disappear into supplementary files or static repositories. They’re published in a structured, interactive portal designed for real exploration—complete with interactive visualizations, AI chat, and richly linked documentation. That means your data doesn’t just sit on a server—it’s findable, usable, and valuable to researchers across disciplines, educators building learning materials, and funders looking for evidence of openness, impact, and reuse. FAIR² helps your dataset fulfill funder mandates while increasing its scientific reach.
FAIR² Makes High-Quality Sharing Achievable
Even when researchers support the idea of reproducible data, implementation is hard. What does “enough documentation” mean? How much detail is too much? Where do you start?
FAIR² is designed to guide researchers through that process step by step—with intelligent support and feedback at every stage.
AI guidance from start to finish.
The FAIR² AI Data Steward automatically analyzes your uploaded dataset and supporting documentation to identify how each variable was produced. It maps data fields to the methods, instruments, and processing steps that generated them—linking, for example, a time-series variable to its sampling rate, filtering pipeline, and device settings, or a survey response to its source instrument, version, and scoring rules. When something is unclear or missing, it prompts you with targeted, context-aware questions. Instead of starting from scratch, you refine what the system has already inferred—making the process faster, clearer, and easier to get right.
Plain-language inputs, structured outputs.
Researchers can describe variables in natural language—“This score is the average response time on incongruent trials”—and FAIR² converts that into machine-readable, standards-aligned metadata. This dual format keeps data accessible for humans and interoperable for machines.
Validation checks before publication.
FAIR² performs pre-publication quality checks, flagging ambiguous field names, missing definitions, inconsistent formats, or unlinked variables. Researchers get a validation report and can iterate before the data goes live.
Once published, every FAIR² dataset includes a full set of tools to support exploration and reuse:
A peer-reviewed Data Article
An interactive data portal with summaries and filters
An AI assistant that can answer “What does this variable measure?” or “How was this derived?”
A Jupyter notebook with starter code for reproducing analyses or building on the dataset
An audio narrative that introduces the dataset’s structure, use cases, and reuse potential
Reproducible Data Is How Science Moves Forward
Science doesn’t stop at the end of a paper. It grows through reuse. When datasets are structured, documented, and trusted, they can power new analyses, validation studies, training sets, benchmarks, and unexpected cross-domain applications.
Imagine a dataset of sleep EEG recordings originally collected to study aging. With clear documentation, it could also support:
ML researchers developing new artifact removal techniques
Device developers benchmarking consumer sleep trackers
Comparative studies on sleep in neurological disorders
Educational modules in signal processing or circadian physiology
But none of that happens if the dataset isn’t reproducible.
FAIR² helps researchers make their data truly usable—not just available. It supports the technical work of structuring and documenting datasets, and the social infrastructure of credit, publication, and discoverability. It respects the realities of research today while building toward the ecosystem science needs.
FAIR² doesn’t replace the hard work of science. It makes that work easier to reuse, more visible, and more valuable. Because science only works if it’s reproducible.
Ready to publish reproducible, high-impact data?
Learn more about FAIR² Data Management and apply to join the pilot.