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

Data Management Plans Overview

Search and Browse Data Management Plans

Displaying 1 - 3 of 3

DMP Example: Economic and Social Research

Original Source

University of Leeds; Digital Curation Centre (DCC)

Includes two example DMPs from the University of Leeds covering research involving the collection of primary data by use of interviews and focus groups.

DyVirt Digital data management plan

Original Source

The University of Sheffield; University of Vienna (PHAIDRA)

This DMP was created for the DyVirt project which aims to create an early stage researcher training network that will address the challenge of modeling the performance of engineering structures in dynamic environments. It will consist of the collections of computer-based simulation models.

Examining the combined effectiveness of early language and later reading intervention in primary aged children

Original Source

Bangor University

This DMP aims to collect demographic data, numeric data, and raw and standardized score measurements. This data will aid with investigating the combined effectiveness of language and literacy interventions on reading outcomes in primary school children.