Data Packaging Examples and Best Practices

Best Practices:

NIH-funded clinical trials must be registered on ClinicalTrials.gov, and the assigned CTN number should be included in all data packages. For human subjects studies focused on pain, investigators must incorporate the HEAL Pain Common Data Elements (CDEs) to ensure data collection consistency, enable cross-study comparisons, and support integration with other HEAL-funded research. To promote consistency, interoperability, and reuse, clinical trial data may be organized using standardized formats such as Clinical Data Interchange Standards Consortium (CDISC) standards, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), or another standard that fits the data type. Each data package should include de-identified participant-level data, a detailed data dictionary, study protocols, analysis code, and informed consent materials that describe data use and sharing. Maintaining traceability from raw data to analyzed results ensures transparency, reproducibility, and NIH HEAL data sharing requirement alignment.

Resources:

• Clinical Trials Data Curation Primer (Data Curation Network) guides researchers through key steps, offering best practices and tools to ensure trial data are well organized, shareable, and reusable.
• Rethinking Clinical Trials: A Living Textbook is an expert-curated resource for pragmatic clinical trial design, conduct, and reporting, offering best practices, case studies, and tools to help researchers improve trial execution and data sharing.

Data Package example:

• Study title: CTN-0093: Validation of a Community Pharmacy-based Prescription Drug Monitoring Program Risk Screening Tool (PHARMSCREEN)
• Data Package Link (NIDA Data Share)

Why this is a good example:

This dataset is openly accessible (but repository access requires a name and email) and includes thorough documentation describing data preparation and de-identification. It also provides a data dictionary, null value file, and study-level metadata with the protocol. Together, these elements make it a clear, well-structured example of a tabular data package that aligns with HEAL data sharing expectations.

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
✔️ Blank data collection instruments
✔️ Context or explanatory documents

Data Package example:

• Study Title: Developing equilibrative nucleoside transporter inhibitors as non-opioid pain therapeutics
• Data Package Link (Mendeley)

Why this is a good example:

This dataset contains a well-structured experimental data package supporting transparency and reuse across multiple research domains. It combines diverse data types (biochemical assays, imaging, electrophysiology, and behavioral studies) with detailed documentation that links methods, results, and analysis workflows. By providing both raw and processed data under an open data license, it demonstrates best practices for sharing complex, multimodal research in alignment with HEAL and FAIR principles.

Components in the Data Package:

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

Lab Data-Related Resources:

• A Practical Guide to data management and sharing for biomedical laboratory researchers (Experimental Neurology) includes sections on data standards, documentation, metadata, data dictionaries, protocols, code, and workflows for lab-created data.