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Data Curation

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. 

Source: https://libguides.uvic.ca/researchdata/researchdata/share/curationguides

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.

           Source: https://www.lib.uiowa.edu/data/share/

 

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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