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Related Experiment Video

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

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Published on: November 22, 2019

Data management plans: the missing perspective.

Mary Williams¹, Jacqueline Bagwell², Meredith Nahm Zozus³

¹Duke Clinical Research Institute, United States.

Journal of Biomedical Informatics

|May 14, 2017

Summary

This summary is machine-generated.

Research funders have inconsistent requirements for Data Management Plans (DMPs). Identifying 43 key topics can help researchers create comprehensive plans for better data quality and reproducibility.

Keywords:

Data management Data management plan Data management planning Data quality Reproducibility Traceability

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Area of Science:

Research funding and data management practices
Scientific reproducibility and data integrity

Background:

Major research funders like the National Institutes of Health (NIH) and National Science Foundation (NSF) have specific requirements for data sharing and management plans.
Current funder requirements for Data Management Plans (DMPs) lack a definitive, comprehensive list of essential topics.
Existing DMP guidelines often prioritize post-publication data sharing over crucial upstream data quality and reproducibility measures.

Purpose of the Study:

To identify and review Data Management Plan (DMP) requirements across various research funders.
To establish a comprehensive list of DMP topics to guide researchers and funder evaluations.
To address the inconsistency and variability in current DMP requirements.

Main Methods:

Systematic review of DMP requirements from multiple research funders.
Identification and categorization of distinct topics within funder-mandated DMPs.
Analysis of the emphasis placed on different DMP components, such as data sharing versus data quality.

Main Results:

Forty-three distinct DMP topics were identified across funder requirements.
Significant inconsistency and high variability were found in the required or suggested DMP topics.
Funder requirements predominantly emphasize post-publication data sharing, with less focus on upstream data management practices that ensure quality and reproducibility.

Conclusions:

A standardized list of 43 DMP topics can significantly improve the comprehensiveness of Data Management Plans.
Equalizing emphasis on upstream activities and post-publication sharing will enhance research data quality, traceability, and reproducibility.
Comprehensive DMPs support effective research planning, funding applications, data management, and data sharing.