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Data Curation, Preservation, and Reuse

This guide aims to help researchers through the data curation process at any stage in a projects lifecycle.

Data Curation Introduction

What is data curation?

Data Curation is the documentation, management, and preservation of research data to produce datasets that are FAIR: Findable, Accessible, Interoperable, and Reusable. By curating research data we add value by enhancing data sets for current use, as well as future discovery and reuse. This guide will also explore data citation as a step in the data curation process.


What is research data?

Research data are items of recorded information "necessary to validate or reproduce your research findings, or to gain a richer understanding of them." (University of Edinburgh - Information Services). Research data encompasses a broad set of categories, from physical specimens to sensor output to the software developed to support analysis of data. Some examples of research data include: 

  • Documents (plain-text, rich-text, PDF, spreadsheets)
  • Laboratory notebooks, field notebooks, diaries
  • Questionnaires, transcripts, survey responses
  • Audio and video recordings
  • Images such as film, photographs, digital scans, or medical scans such as computed tomography
  • Protein or genetic sequences
  • Spectra
  • Slides, artifacts, specimens, samples
  • Collection of digital objects acquired and generated during the process of research
  • Database contents (video, audio, text, images)
  • Software including code and documentation, models, algorithms and scripts
  • Methodologies and workflows
  • Standard operating procedures and protocols

Why curate and share your data?

  • To fulfill funder requirements. NIH, NSF, NEH, and many other research funding sources expect researchers to publish and preserve access to their data.
  • Some journals and societies require data publishing and preservation, i.e., PLOSNatureDryad partner journals, to name a few.
  • To increase the impact of your research. Sharing detailed research data is associated with increased citation rate (PLoS ONE).
  • To address questions about reproducibility
  • To speed the rate of discoveries in your discipline.
  • To establish priority and a public record.



How can this guide help me?

This guide will assist researchers at any stage of a project in the preparation of their project's data and metadata in order to ensure it is compliant with FAIR principles: Findable, Accessible, Inter-operable, and Re-usable. The included tutorial will guide you, step by step, through the data curation process. 

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