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 was created for the ConcePTION project, which aims to create an ecosystem that will collect information regarding the effects of medications used during pregnancy and breastfeeding. The DMP will outline the collection of healthcare data, surveillance data, research data, pharmacovigilance data, human reported data, human milk and blood samples,  animal milk and blood data, and human cells.

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.

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 DMP aims to collect demographic, clinical, and patient data in the form of MRI scans as well as patient placenta and tissue data from pregnant people diagnosed with fetal CHD.

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.

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 tweets from twitter, and use machine learning to analyze mental health patterns due to the Covid-19 pandemic.

This DMP aims to collect reaction time, choice, and eye-tracking data which will be used for a mock behavioral and eye-tracking project.

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 is a sample DMP that will be collecting quantitative data to develop a novel database for the new genome sequencing data for the pathogen, streptococcus pneumoniae.

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.

This DMP aims to collect data on nonrandomized observational population studies to identify gaps in study design and data collection methods to make it more standardized.