Data Management Plan Database
A Data Management Plan (DMP) describes how you will manage, store, secure, document, and share research data. DMPs can vary broadly across disciplines, methodologies, and data types. DMPs are a growing requirement for grants, and can also guide data practices for individuals and teams. DMP Assistant is a free webtool that guides you through drafting your DMP and the easiest way to start building a DMP.
Our database gathers examples from across the world including DMPs from the Digital Research Alliance of Canada, National Institutes of Health (NIH), Qualitative DMP Competition, DataOne, Digital Curation Centre, Liber, the Working Group on NIH DMSP Guidance, and UC San Diego Research Data Curation into one searchable, open-access platform.
Download the amalgamated dataset: https://doi.org/10.5683/SP3/SDITUG
Project Team: Rebeca Gaston Jothyraj (RDM Assistant - 2024), Shrey Acharya (RDM Assistant - 2023), Sarthak Behal (RDM Assistant 2022-23), Danica Evering and Isaac Pratt (RDM Specialists), Debbie Lawlor (Developer).
Data Management Plan Database
Search and Browse Data Management Plans
Displaying 1 - 30 of 34
This is an example of a data management plan from the NIH, intended to collect genomic data and subsequent phenotype data from cancerous mice models.
This is an example of a data management plan from the NIH, intended to collect clinical, demographic, and dialysis data from research participants.
This DMP aims to collect text, binary (ASCII, binary), images, audio, etc. data to develop advanced research computing frameworks and infrastructure for application in various fields like quantum chemistry, biochemistry, machine learning, and artificial intelligence.
This is a template DMP for use in systematic review projects in any field.
Developed by researchers for the University of Ottawa Heart Institute, this guidance and worked example extracts examples from Beck, Leblanc, and Morissette's systematic review protocol on depression screening of children and adolescents. It outlines a data management plan for a systematic review study.
Developed by researchers for the University of Ottawa Heart Institute, this guidance and worked example outlines a data management plan for a pre-clinical laboratory animal study. This DMP outlines a project to explore stroke treatments using biocellulose duroplasty with rat subjects.
Developed by researchers for the University of Ottawa Heart Institute, this guidance and worked example outlines a data management plan for a randomized controlled trial conducted with 100 human participants. Demographic data will be collected from participants, and then other data is collected by an electroencephalogram, electroculogram, electromyogram, as well as video and sound.
This is a sample data management plan from the NIH, intended for submission to the NIMH, presenting a proposal to collect and analyze genomic, phenotypic, and clinical data from human subjects.
This example DMP aims to collect genomic and phenotypic data from 36 human subjects.
Developed by researchers for the University of Ottawa Heart Institute, this guidance and worked example outlines a hypothetical genomic study into acute lymphoblastic leukaemia using blood drawn from patients, healthy volunteers, and mouse models.
This is an example of a data management plan from the NIH, intended for submission to the NICHD, that details a plan to collect and deidentify clinical, laboratory and genomic data from 1000 participants.
This is a sample data management plan from the NIH, intended for submission to the NHGRI, that provides an outline for a project involving human genomic and clinical data.
This DMP aims to collect questionnaires, personal data, computer-based tasks & output, ECG’s, EDA’s, videos, audio recordings, data analysis files, linkage data, interviews, and transcripts. This data will be used for the Promoting Healthy Families project, which will assess whether Triple P and COSP can help promote healthy family relationships.
This sample data management and sharing plan proposes a secondary data analysis of existing kidney magnetic resonance imaging (MRI) data. This secondary data will be accessed from the database of Genotypes and Phenotypes (dbGaP). The goal of this research is to produce a clinical dataset of kidney volume, estimated using a neural network.