Data Packaging Examples and Best Practices

Best Practices:

Each package should include metadata describing recording conditions, equipment, and any processing or editing steps, along with documentation noting consent status and collection context. When sharing audiovisual data, researchers should ensure consent aligns with NIH and HEAL data sharing expectations and should remove or obscure identifiable information, such as names, faces, or voices, to protect participant privacy.

Sharing data behind access controls is an appropriate safeguard to manage sensitive data concerns. Researchers may choose to share processed data derived from audiovisual recordings in addition to or rather than the raw recording files. For example, processed data may include redacted transcripts, coding schemas, or observational battery data.

Audiovisual Resource:

• Audiovisual Data Curation Primer (Data Curation Network)

Best Practices:

A well-prepared qualitative data package should include de-identified transcripts or text files, documentation describing the study design and analytic approach, and supporting materials, such as codebooks and interview guides, that provide essential reuse context. These components help ensure qualitative data are clearly documented, ethically shared, and useful for future research. When investigators worry that transforming the data files does not sufficiently de-identify them, access controls offer an additional layer of protection against deductive disclosure risk.

Qualitative Data Resources:

• Qualitative Data Curation Primer (Data Curation Network) provides practical guidance to prepare, document, and share qualitative research data in alignment with NIH and HEAL expectations for responsible, transparent, and reusable data.
• Managing Qualitative Data (Qualitative Data Repository) provides guidance to prepare and share qualitative research emphasizing ethical practices, clear documentation, and long-term data reuse.
• Guide for Sharing Qualitative Data at ICPSR outlines best practices to prepare and document qualitative data, so it can be shared responsibly, understood by others, and reused in ways that align with HEAL’s transparency and data stewardship goals.
• HEAL Public Access and Data Sharing policy describes HEAL’s expectations for protecting human data subject privacy.

Data Package example:

• Study Title: Examining the synergistic effects of cannabis and prescription opioid policies on chronic pain, opioid prescribing, and opioid overdose
• Data Package Link (NAHDAP)

Why is this a good example:

This dataset provides a well-documented example of questionnaire data, including multiple rounds of expert responses, clearly defined variables, and comprehensive supporting documentation. It illustrates how structured data can be shared in a trusted and access-controlled repository to promote discoverability, transparency, and reuse.

Components in the Data Package:

✅ Data file(s)
✅ README or Summary file
✅ Variable-level Metadata documentation
✅ Repository-specific documentation
✔️ Blank data collection instruments
✔️ Context or explanatory documents

Data Package examples:

• Study Title: Tracking the opioid epidemic with social media: an early warning system
• Data Package Link (Github)

Why this is a good example

This data package includes clear metadata, data dictionaries, and organized folders for raw data, processed outputs, and analysis code, making the workflow transparent and reproducible. By providing open access under an MIT license and including both documentation and code, it aligns with HEAL accessibility, transparency, and responsible data sharing principles.

Components in the Data Package:

✅ Data file(s)
✅ README or Summary file
✅ Variable-level Metadata documentation
✅ Repository-specific documentation
✔️ Code used to transform raw data to analytic dataset
✔️ Code used to conduct analyses
✔️ Publication Citation(s)
✔️ Study Protocol
✔️ Context or explanatory documents
✔️ Clear terms of reuse

Social Media-Related Resource:

• Twitter Data Curation Primer (Data Curation Network) offers practical guidance to organize and share social media datasets in a transparent, reproducible way.