Data Management as Part of Research Planning

Review the entirety of this guidance document and associated resources as part of the data management process.

Effective data management must be designed into research activities from the very beginning. Once data are collected, generated, or processed, it becomes significantly harder, often prohibitively so, to retrofit them with the metadata, structure, identifiers, provenance records, and documentation required for reuse and AI readiness. Early planning ensures that teams create data and research artifacts that can be trusted, reused, and integrated into agentic workflows without costly rework. It also enables project leads to anticipate and budget for the full range of resource and capability needs—from storage, compute, and token usage to repository selection, schema or ontology alignment, and requirements for new standards or interoperability layers. Best practices in data management, whether public, non-public, open, or restricted, follow the FAIR principles for Findability, Accessibility, Interoperability, and Reusability. By incorporating data management into research planning, teams set themselves up to operate efficiently, meet funder expectations, and contribute durable, high-value artifacts to the broader scientific ecosystem.

Create a Data Management and Sharing Plan DMSP

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 data sharing², preservation, and stewardship beyond the performance period of the research activity. Maintain and update a machine-readable DMSP for the lifetime of the project.

1. 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.

2. Genesis Mission DMSP Tool. One resource available to support development of project and team DMSPs is the NNSA’s DMP tool.

Assign Data Stewards

Ensure all digital objects created by the project have an assigned data steward. The data steward serves as the main point-of-contact and is responsible for ensuring the accuracy, quality, and proper usage of the digital object. The person named as the data steward is often the person who generated the data or the project lead. Common tasks involve creating and/or reviewing data card content, ensuring associated metadata and documentation is accurate and complete, recommending data curation tools, and identifying appropriate repositories for long-term preservation and access and working with data curators and lab review processes on publication-quality metadata and governance. Other tasks and activities may include reviewing and approving access requests, providing guidance to downstream users of the digital object, and maintaining currency of metadata and digital format to ensure its accuracy and usability.

Plan Data Governance for the Full Data Lifecycle

Early in project set up, project team members should have a conversation with their institution’s tech transfer office to understand anticipated project outcomes and deliverables as detailed in the project’s statement/scope of work. Most, if not all, projects are governed by a project-specific set of agreements between the project team member institutions and DOE, such as the Other Transaction OT Agreement with DOE, AI Agreements, Project Data Use Agreements, and Bridge Agreements. The tech transfer office will be able to assist team members in planning appropriate governance, evaluating data disclosures, and determining action steps prior to any release or commercialization effort such as making data available for access and reuse licensing.

Project team members should follow home institution policies and procedures for disclosure of subject inventions, copyrights, and data generated or created as a result of the project. Early disclosure helps prepare researchers for appropriate governance and release of related publications and data.

Next Section: Reuse Data Responsibly

1. See specific requirements in the funding solicitation and/or award language that reflect overall DOE policy: https://www.energy.gov/datamanagement/doe-requirements-and-guidance-digital-research-data-management

2. See the definition of Data Sharing and note that this includes public and non-public data. ↩