FAIR Data & Code Sharing
Resources for sharing CAMM research data, code, and documentation.
FAIR data and code practices help make research outputs Findable, Accessible, Interoperable, and Reusable.
These principles support NSF expectations for responsible data management and sharing, including clear documentation, useful metadata, appropriate repository practices, and connections between publications, datasets, code, and research workflows.
This page provides a starting point for the CAMM GitHub repository and CAMM data and code sharing practices.
CAMM GitHub
Code and research resources from CAMM.
The CAMM GitHub organization provides a central location for code, workflows, reporting templates, and related research resources developed by CAMM researchers.
CAMM researchers can use this space to share project code, documentation, computational workflows, and supporting materials connected to CAMM-supported publications and datasets.
Visit the CAMM GitHub RepositoryFAIR Principles
Make research outputs easier to discover, understand, and reuse.
FAIR stands for Findable, Accessible, Interoperable, and Reusable. These principles support responsible sharing of research data, code, metadata, and documentation.
Findable
Research outputs should be easy to locate through clear titles, repository organization, descriptions, keywords, and persistent links when available.
Accessible
Data and code should be stored where authorized researchers, collaborators, or readers can access them according to project, publication, and sponsor requirements.
Interoperable
Files, metadata, and documentation should use formats and descriptions that make it easier to connect outputs across tools, workflows, and research groups.
Reusable
Research outputs should include enough context, documentation, and licensing or access information for others to understand and reuse them appropriately.
CAMM Data-Sharing Context
Center-level practices supporting FAIR data and code sharing.
CAMM’s data management and sharing activities connect project documentation, shared storage, repository practices, metadata, and AI-ready materials datasets.
Roles & Reporting
FAIR Advocates and DMP support
CAMM’s Data Management Plan identifies roles, responsibilities, and routine reporting mechanisms to support data management and sharing across the Center.
Storage & Access
Shared data infrastructure
CAMM uses shared storage and transfer tools to help team members store, access, and share CAMM-generated data with collaborators.
AI-Ready Data
Center-Level Data Sharing Initiative
CAMM is developing a Center-level data-sharing platform to support AI-ready materials datasets, metadata tagging, discoverability, and common formats.
Sample Tracking
Structured experimental data workflows
CAMM is also developing sample tracking and experimental data management workflows that connect sample metadata, characterization data, analysis outputs, and reports.
Data Management
Preparing data and code for CAMM projects.
Please add details here regarding CAMM’s data management plan.