Science charter commitments

The HRL Science Committee Charter, particularly Appendix D, describes the commitments that guide data collection, publication, and analysis activities under the HRL Science Program. This page summarizes those commitments so that HRL Program partners understand the expectations that underpin the rest of this documentation hub. Use it as a quick refresher before diving into detailed guidance on the data lifecycle, standards, or common activities.

Core charter commitments

The HRL Science Committee charter expresses principles that support open, reproducible, ethical science. The principles are operationalized through common practices across the full data lifecycle.

Data collection

  • HRL parties collect data using approved protocols documented in system-level science plans.
  • Data Producers capture complete metadata at the point of collection, including metadata describing who collected the data, when and where the data were collected, equipment settings, and calibration routines.
  • Field quality assurance and quality control activities (e.g., duplicates, blanks, environmental notes, and calibration logs) are recorded and carried forward into publication packages.
  • Sensitive or Tribal sovereignty-linked observations are identified early and handled under agreements co-developed with Tribes and partner entities.

More information:

Data Collection Governance and Roles

Data management

  • Datasets are stored in machine-readable, non-proprietary formats and structured according to tidy data principles where practical.
  • Quality control steps are scripted, version-controlled, and documented through validation logs.
  • Provenance is preserved from raw inputs through static data publication, ingestion, and synthesis; every workflow records source DOIs, code versions, and processing parameters.
  • CARE-aligned data handling requirements are embedded in metadata, storage locations, and access controls.

More information:

FAIR and CARE Data Static Data Publication Data Ingestion and Standardization Metadata Standards

Data access

  • Static HRL datasets receive DOIs (e.g., via EDI) and are discoverable through the HRL data catalog, search, and APIs.
  • Metadata packages provide plain-language summaries, schema information, keywords, and access notes.
  • Programmatic access routes (bulk download, APIs, SDKs) are maintained so analysts and partners can use the data without manual, one-off transfers.

More information:

Data Storage and Serving Reference Resources

Integration and analysis

  • All analyses are implemented through structured R or Python workflows managed in HRL GitHub repositories that follow the HRL Style and Development Guide.
  • Continuous integration tests verify that code runs, schemas match expectations, and outputs reproduce.
  • Derived synthesis datasets, indicators, and models are versioned, documented, and returned to the Central Data Team for curation and publication.
  • Analytical workflows include diagnostics, uncertainty statements, and clear linkage to input datasets so results can be validated and reused.

More information:

Data Ingestion and Standardization Analysis and Synthesis Reporting and Communication

Program-wide priorities

HRL data governance and management emphasizes three cross-cutting priorities that help HRL meet its commitments.

Efficiency and responsiveness

  • Modular pipelines and reusable templates reduce duplication and support repeatable annual and triennial reporting.
  • Thorough documentation and coding standards enable rapid onboarding of new collaborators and contractors and coherence of data and code products across the entire program.
  • Feedback loops between Data Producers, the Central Data Team, and Synthesis Teams surface issues quickly, allow the data and analysis enterprise to evolve to meet program needs, and inform adaptive management decisions.

Scalable, future-proof infrastructure

  • Cloud-native, open, and standards-based technologies allow HRL to ingest increasing data volumes and adopt new tools as needed.
  • Storage, compute, and orchestration platforms are selected for durability and portability over the eight-year program horizon.
  • Large or complex datasets (e.g., imagery, LiDAR, and modeling outputs) are handled with architectures that support efficient storage, retrieval, and processing.

Program-wide learning

  • Shared templates, training curricula, office hours, and documentation keep practices aligned across agencies.
  • Lessons from synthesis and reporting cycles feed updates into protocols, standards, and governance decisions.
  • Knowledge is captured in repositories, catalogs, and documentation sites so it persists beyond individual personnel changes.