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|