The Genesis Mission anticipates a research environment in which AI-enabled workflows accelerate the full research, development, and deployment lifecycle: generating new data, connecting that data with existing scientific and mission datasets, supporting model development and validation, and enabling faster movement from discovery to impact. In this environment, data is not only an input to autonomous agents and digital twin research assistants; it is a managed and evolving mission asset. To support this vision, Genesis requires a data architecture that can make diverse data and data-related artifacts discoverable, understandable, appropriately governed, and reusable across distributed infrastructure, while preserving the stewardship responsibilities of the programs, facilities, laboratories, and partners that produce and maintain them.
This data management and sharing guidance supports researchers and data stewards in adopting practices that enhance the discovery, access, interpretation, reuse, attribution, and actionability of data by both humans and machines. The outcome is a machine-actionable web of scientific information where AI-ready data, code, models, workflows, and related assets are richly described and validated to accelerate scientific insight.
Effective data management begins at project inception and continues throughout its lifecycle, with provenance and lineage captured from the start to ensure trustworthiness and long-term value.
This information offers guidance and resources to help teams establish practices that keep their data and digital products usable, trustworthy, and impactful. Updates will follow as tools and best practices evolve.
Resources
Data Management and Sharing Plans
Create a Data Management and Sharing Plan (DMSP) that satisfies all applicable funder and DOE requirements, identifies the data and other digital products that will be created and used, and covers the full data lifecycle from creation or collection to stewardship beyond the performance period of the research activity. Maintain and update a machine readable DMSP for the lifetime of the project.
DOE Requirements and Guidance for Digital Research Data Management. These requirements can be extended or augmented by specific language in the funding solicitation and/or award terms and conditions. Understand the requirements for your project.
DMP Planning Session Tool. A browser-based facilitation tool for guiding teams through a structured Data Management Plan planning meeting
Data Card Templates
The published technical report provides a description of the data card including supporting ontologies and controlled vocabularies, links to versioned data card templates, corresponding schema, and supporting artifacts including a validation plug-in.
Data Cards for Standardized Metadata Across DOE-Aligned Data Initiatives: Toward Transparent, Interoperable, and Governed Dataset Documentation. DOI: https://doi.org/10.2172/3377514
Genesis data card schema, template and supporting tools [Dataset]. Energy Data eXchange (EDX), Jefferson Lab. https://doi.org/10.18141/3376151.
Data Management as Part of Research Planning
Provides guidance on how to plan to produce reproducible data.
Explore Data Management as Part of Research Planning
Identify Appropriate Repositories
The DOE recommends using repositories that align with the "Desirable Characteristics of Data Repositories for Federally Funded Research". Many repositories already exist to support domain-specific data.
Understand if your program or participating institutions already have required or recommended repositories. Plan early in your project for any effort related to preparing the repository for your data or preparing your data for the repository.
Repository Map: The repository map represents a resource and not a requirement. Teams should contact their program manager for any questions about repository selection. Teams are encouraged to recommend additional repositories for potential inclusion in the Repository Map using the “Suggest a Repository” button on the interactive map homepage. Users of the map should verify each repository against their own requirements.
Prepare for Data Preservation and Sharing
Before data can be shared and reused, it must be robustly stored and ultimately preserved beyond the lifetime of the project. Data governance rules, which specify who can have access and for what purpose, may change throughout the lifecycle of the data. At the end of the project, teams are expected to publish data to appropriate community repositories as well as preserve pre-publication artifacts for their own future reuse by depositing these artifacts in a repository with the appropriate access controls.
Explore Preparing for Data Preservation and Sharing
Additional Acknowledgement language for work related to Genesis Mission:
Please reference the standardized approach below for acknowledging support from the U.S. Department of Energy (DOE) for digital products, including but not limited to, data, models, agents, git repos, websites, etc.:
Acknowledgment: "This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of [insert the SC Program Office funding the award, e.g., Basic Energy Sciences], [Add any additional acknowledgements or information requested by the sponsoring SC Program Office] under Award Number(s) [Enter the award number(s)]."
Suggested language for use in the additional acknowledgement section above for U.S. Department of Energy, Genesis Mission products:
For products from explicitly funded Genesis Mission activities, use this templated approach:
[Program Office] as part of the Genesis Mission National Science and Technology Challenges [under Award Number(s)…]
For products and teams who use Genesis Mission public resources in their own research, but were not funded under an explicit Genesis Mission program line, use this templated approach:
This work utilized resources made available by the U.S. Department of Energy’s Genesis Mission
Contribute to a FAIR, Machine-Actionable Web of Science
Best practices in data management, whether public, non-public, open, or restricted, follow the FAIR principles for Findability, Accessibility, Interoperability, and Reusability.
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