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 33
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 DMP aims to collect calculations, simulations, publications, presentations, and experimental design work. The goal of the project is to comprehend and control the quantum dynamics of light-induced excited states in condensed matter.
This is an example of a data management plan from the NIH, intended to collect clinical, demographic, and dialysis data from research participants.
This example data management plan from the Alliance intended to analyze the availability and usage of affordable rental housing. The project requires the collection of various forms of numeric, audio-visual and text-based data collected from a variety of sources such as surveys, focus groups etc.
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 mixed-methods research where data is obtained from surveys and qualitative interviews/focus groups.
This is a template DMP for researchers conducting statistics based research for Statistics Canada for use in external analyses and applications.
This is a template DMP for use in systematic review projects in any field.
This DMP aims to collect digital, tabular, and graphic data on plastic production parameters, environmental conditions, and inspectional measures among other factors to optimize the industrial extrusion process.
This example DMP aims to collect genomic and phenotypic data from 36 human subjects.
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 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 is a DMP example for a fictional research study that will use a mixed-methods approach to collect participant data from interviews, surveys, and focus groups. This example uses the new Alliance Simplified Template (Funding Application Stage) which focuses on 5 questions that are helpful for a DMP that meets new Tri-Agency requirements.
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.