management of research data in clinical research

1. Data Management Plan
(DMP)

2. What it is
• Research data:- any information that has been collected,
generated or created to validate a scientific
question/phenomenon
• May take different forms including electronic and the traditional
paper based
• Researchers must ensure that data collected from human
subjects is properly handled
• This is a good practice as enshrined in responsible conduct of
Research charters such as the ICH Good Clinical Practice (GCP)

3. DATA MANAGEMENT PLAN
• Data management is the process of creating organized,
documented, accessible, and reusable research data
• Taking care of your research data
• Research data management helps improve research
workflows to make them more resilient, efficient,
maintainable, and reproducible
• Creating a data management plan earlier on in the study is
important. But, the process is ongoing and changes can be
made as the study evolves

4. Why is DMP important?
• Meet requirements of funding agencies, who often require
data management plans when applying for grants
• Ensure research integrity and replication
• Ensure your research data and records are accurate,
complete, authentic, and reliable
• Increase your own research efficiency - the less time you
have to spend cleaning up data messes and deciphering your
data, the more time you have to boost your own research
agenda!

5. • Save time and resources in the long run
• Enhance data security and minimize the risk of data loss
• Prevent duplication of effort by enabling others to use your
data
• Comply with ICH GCP and human subjects protection codes

6. DMP Process by Process

7. • Plan: Planning can include reviewing existing data sources,
addressing informed consent, considering costs, and
preparing a plan. Think about what data will be collected in
your project and what it will cost you.
• Create: Researchers produce data (experiment, observation,
measurement, simulation) and/or collect and organize third-
party data and materials. Metadata and related materials are
captured and created.

8. • Process: Data is converted to digital format (transcribed,
converted, digitized, curated) according to quality assurance
standards.
• Data is checked, validated, cleaned, recoded, versioned, and
as needed, anonymized.
• Analyze: Data is interpreted and analyzed to produce
research findings, publications, and intellectual outputs.
Data sources are cited.

9. • Preserve: Data is saved to formats that conform to curation
best practices, user documents and discovery metadata are
created, a digital identifier (i.e. DOI) is added and data is
linked to any published products, consideration is given to
security and Intellectual Property.
• Share: Access rights are confirmed (ethics and intellectual
property considerations). The data, along with user
documentation and metadata, are made accessible, e.g. on a
public domain server, or in a controlled repository.

10. • Reuse: Potentially useful data should be archived and reused
to answer critical questions. Secondary analysis is conducted
after any necessary data transformations are complete. Data
can be shared with other researchers following data sharing
procedures.