Data Management

Pre-step

1. Is the data complete and in their final state? (We can’t accept ‘live’ data) ☐

Data collection and documentation

2. Is there a good project and data documentation? ☐
   • Is there a Data Dictionary? ☐
     A data dictionary is a collection of names, definitions, and attributes about data elements that are being used or captured in a database, information system, or part of a research project.
     • Need help with the Data Dictionary?
   • Is there a Readme file? ☐
     A readme file contains information about the other files in a directory or archive of computer software. It usually supports answering the following questions: What data have been collected? How were the data collected? Why were the data collected? When were the data collected? How can the data be accessed? Are special tools or software needed to read and work with the data?
     • Need an example for the Readme file?
   • Do you have a codebook file describing what project and data identifiers were assigned? If this file includes identifying personal information of research participants, please do not include this file. ☐
     Take survey research as an example: the codebook usually contains survey objectives, a description of the survey design and methodology, a copy of the survey questionnaire, etc.
     • Need an example for this codebook file?
   • Are there any interview/survey questionnaire templates? ☐
   • Is there a [other type of data documentation file]? ☐
3. Is there any proprietary filetypes? If so, convert them to open file types if possible. ☐
   • Here are links to what are the proprietary filetypes and open filetypes
   • Reference Library of Congress Recommended Formats Statement to choose the open format you need
   • How to convert file types from proprietary to open:
     • Data Curation Network
     • FAIR Cookbook (life sciences focus)

Sharing and management

4. Are there any limits on making your data public (e.g. embargo periods, contracts, non-disclosure agreements)? ☐
5. Do you have a open data repository in mind to deposit and share your data? ☐
   • Have you ensured this repository is trusted and sustainable? ☐
     Such as checking the repository’s page to see how they preserve data, ensure whether the repository has a preservation and backup strategy.
   • Does the repository allow the total volume (in GB or TB) of your data collected?
6. In addition to the depositor of the data you generate, who else has a right to see or use this data? And who else should have access to edit the repository record? ☐
   • If the data were sourced from an external provider or contains third party copyright material, you must seek permission first. 
7. Are there requirements on how long data needs to be preserved? ☐
8. Are there any special privacy or security requirements (e.g. personal data, confidential, high-security data)? Have you removed this data? ☐
9. Did you document the contact that people can reach out to him/her to request the data sharing permission? ☐
10. Did you identify your copyright statement of rights? ☐
    When you create new research, copyright law automatically gives you full ownership rights in your creation. You can grant different access to your data to other people who have access to your data. It will help deter infringement and declare what rights you wish to maintain. Copyright statement usually contains a copyright symbol, a date, an author’s name and a statement of rights.
    • All rights reserved? Some rights reserved (For knowledge sharing only, data is not available for commercial use)? No right reserved?
      • Need an example of copyright statements?

It is good practice to document basic information about your data so that you or your research partners can more easily interpret and re-use the data in the future. 

Some research fields offer metadata standards for data documentation. Adhering to one of these standards increases the findability and utility of your data in the broader research community.