FAIRplus Data Usage Areas

version: v0.1

Corresponding FAIR indicators v0.1

Data Usage Areas

Each organization or research community might have different business goals for re-using data. These different re-use areas require different attention for improving findability, accessibility and interoperability of data sets. Following table provides an overview of these data re-use areas, and for each area what action can be taken to improve data management processes and potential benefits.

Data Usage Area Aim Expected Benefit
Data Interpretability Improve reuse of data by another person.
- Provide metadata about how data is organized and structured.
- Document the content of data files, and their relations.
- Identify and validate data types and formats
- Reduces time spent for each person examining and understanding existing research data.
Data Integration Improve data consolidation & harmonization.
- Annotate data with common vocabularies, ontologies.
- Use of common master data and reference data (if available).
- Use common data profiles, models, schemas for semantic modelling(if available).
- Improve interoperability by mapping terminologies (e.g. via identifier linksets).
- Reduces time for data cleaning and integration.
- Increases the likelihood of linking datasets with other sets automatically.
Data Repurposing Improve reuse of data in another context, such as with different research hypotheses.
- Document research hypothesis and data inclusion and exclusion criteria
- Document reference materials, such as cell lines and microorganisms.
- Document different steps of research lifecycle and their data outputs.
- Provide raw data or primary data, not only derived and analyzed data sets.
- Provide a variety of research outcomes, such as negative results.
- Reduces the resources spent for generating data for research hypotheses.
- Improves repurposing of data as part of a new study.
Data Reproducibility Improve repeatability, replicability, reproducibility of research outcomes
- Provide documentation and guidelines for describing research protocols.
- Provide provenance of experiment such as measuring tools, locations, conditions, hypothesis, time periods, study design (power analysis, sample sizes)
- Identify the key resources such as antibodies, model organisms and software.
- Share materials, software, and other tools used for data analysis
- Ensures transparency, gives confidence in understanding study.
- Increases the likelihood of attaining results by a different or same research team, using the same or different experiment setups.

Potential future Data Usage areas:

  • Regulatory Reporting: Easily fulfill the mandatory reporting requirements

  • Data Analytics (machine actionable data): Easy access by services and running algorithms and computational models

Maturity of each data usage area is measured via a set of indicators

Data Usage Area Related Set of Indicators
Data Interpretability F+S03, F+S04, F+A01, F+A02, F+A04, F+A05
Data Integration F+A02, F+A03, F+A04, F+A05, F+A06
Data Repurposing F+S01, F+S02, F+S04, F+S05, F+S06, F+S07, F+S08a, F+S08b, F+S08c, F+S08d
Data Reproducibility F+S02, F+S05, F+S08a, F+S08b, F+S08c