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Data Curation & Management Toolkit: Basic Checklist

This guide is a brief introduction to Data Curation and the HKUST Data Management Services

Basic Checklist for Managing Data

Data Management Checklist   (based on NCSU's checklist)

1. What type of data will you or your group produce?

  • How will data be collected?
  • Will it be reproducible? What would happen if the data get lost or can't be used?
  • How much data will your project produce?
    • At what growth rate?
    • How often will it change?
  • Do you need special tools or software to create, process, or visualize the data?
  • Do you have a storage and backup strategy?
    • What is it?
    • Has anyone written it down anywhere? Can you find it easily?

2. How will you describe and document your data (standards & metadata)

  • How will you document how you collected or created the data? 
  • Is there good project and data documentation? 
    • Or any at all?  Can you find it easily?
  • What directory and file naming conventions for the data files will you use?
  • What project and data identifiers will be assigned? 
  • Is there a community standard for data sharing integration within the project?
    • Within the department or institution? Within your academic discipline?
    • Are you following it?

3.What steps will be taken to protect privacy, security, confidentiality, intellectual property or other rights?

  • Who controls it (e.g., PI, student, lab, HKUST, the funder)?
    • Is it written down somewhere who controls it, or who controls it for what purposes?
  • Are there any special privacy or security requirements (e.g., personal data, high-security data)?
  • Are there any embargo periods to uphold? 

4. If you allow others to reuse your data, how will the data be accessed and shared?

  • Any sharing requirements (e.g., funder data sharing policy)? 
  • Audience? Who can use it now? Who can use it later?
  • When will you publish it and where? 
  • Are there any special tools or software needed to work with the data?

5.How will the data be archived for preservation and long-term access?

  • How long should it be kept (e.g., 3-5 years, 10-20 years, permanently)?
  • What file formats? Are they long-lived? 
  • Do I want to store it at    or in a subject-based data repository?
  • Who will maintain my data for the long-term?

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