What is this guidance about…
The guidance will introduce This article introduces the concept of a qualitative data package that can be shared to comply with data requirements. It will provide provides tips for packaging your qualitative data to support research trustworthiness and transparency.
Introduction
As data sharing requirements grow, qualitative researchers are faced with the challenge of sharing the data underlying their results. Data sharing requirements are meant to ensure research integrity and trustworthiness and to enable new scholarly inquiries. To enable integrity checks and new inquiries, this guidance will focus on how qualitative researchers can produce a well-documented and organized data package that supports transparency and trustworthiness.
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If these questions raised any red flags for you, you might need to consider alternatives to openly sharing your data. There are many options such as de-identification, redaction, embargos, restricted access, applying terms of use, data confidentiality agreements, etc. For more information on ways to share data, please consult our guidance on Data Access Restrictions, Data Use Agreements, Sensitive Data, or Terms of Use and Licensing.
Conclusion
The guidance has informed you on how to construct a data package for qualitative research that will comply with data sharing requirements and support transparency and trustworthiness. We have compiled some additional resources below to assist you in the development of your package. The Data Curation Network (2023) has developed primers focused on preserving data types (e.g., Oral History, Twitter, Atlas. TI) that offer considerations for documentation, file formats, etc. The Qualitative Data Repository at Syracuse University provides valuable guidance on managing, preparing, and sharing data. UNC is a member of the Qualitative Data Repository (QDR), enabling UNC researchers to have access the QDR curation services. The Qualitative Data Sharing Toolkit provides guidance on planning and preparing qualitative data for sharing.
Resources
Data Curation Network. (2023). Data Curation Primers. https://github.com/DataCurationNetwork/data-primers
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