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 - 9 of 9
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 DMP example show uses a case study of relativistic astrophysics and gravitational physics data to show how management strategies can support computational reproducibility in High-Performance Computing. Data are the inputs and outputs of the numerical simulation model.
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.