Linking community and technology to enable FAIR data

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Linking community and technology to enable FAIR data

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 Proper data curation has benefits for researchers and others who create, use and reuse data.

Scientific research is becoming more dependent on large datasets of experimental observations, computer simulations, analyses and visualization. This places a stronger emphasis on digital data, which must be created and managed differently compared to the past. Proper data curation has benefits for researchers and others who create, use and reuse data.


Short-term benefits

  1. Improving access: Digital curation procedures allow continuing access to data and improve the speed of access to reliable data and the range of data that can be accessed.
  2. Improving data quality: Digital curation procedures assist in improving data quality, improving the trustworthiness of data, and ensuring that data are valid as a formal record (such as use as legal evidence).
  3. Encouraging data sharing and reuse: Digital curation procedures encourage and assist data sharing and use by applying common standards.

Long-term value

  1. Data protection: Digital curation procedures preserve data and protect it against loss and obsolescence.
  2. Reuse of data: Digital curation procedures allow data to be fully exploited through time (thus maximizing investment) by providing information about the context and provenance of data.


Scientific research has traditionally been project-based and short-term. Current project data may not be given a high priority nor created and maintained in such a way as to ensure long-term value towards future research. This traditional project-based research model does not support best practice in data curation. What incentives are there for scientists to participate in data curation?

1. Direct benefits to the scientist

Good data curation practices should ensure:

  • improved quality of research data
  • providing access to working data
  • external validation of conclusions
  • good record-keeping standards to data capture
  • enabling large amounts of data to be analyzed and developed across different locations by maintaining consistency in working practices and interpretations
  • managing relationships between different versions of dynamic or evolving datasets
  • valuable knowledge and data originating from short-term research projects does not become obsolete or inaccessible when funding expires
  • allowing data sets to be combined in new and innovative ways

Risk managament

Improved risk management could also provide additional benefits. Examples of risks include:

  • media, hardware or network failure
  • outdated media, hardware or software
  • insufficient funding to maintain data over time
  • data loss due to poor organizational structure

2. Public good obligations

Pressures are increasingly being brought to bear to make data more broadly available for public scrutiny.

Open access

Researchers can maximize their research impact by participating in open access initiatives. The return on public investment in research can also be maximised by making reports of that research more widely available and more widely cited, applied and used as the basis for further research.

3. Compliance

Compliance with funding body requirements

Research funding bodies increasingly require data curation mandates within grant funding applications. These might include the requirement for a data management plan, or for the deposit of data into publicly accessible data repositories.

Compliance with legislation and data-sharing policies

Compliance with legislation may require good data curation practice. Many countries have data protection acts and freedom of information acts. In addition, some areas of research have discipline-specific compliance requirements.

Compliance with publishers' requirements

Publishers in some disciplines insist that potential authors demonstrate data curation and require that specific conditions to be met before publication of research results.