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Data GovernanceData ArchitectureData IntegrationKnowledge ManagementAPI Integrationgrant · government

Findable Accessible Interoperable Reusable Open Science

NSF · United States

What the organization said · quoted from the source

  • “Research, education, and socio-technical cyberinfrastructure development capacities that advance sustainable multi-disciplinary findable, accessible, interoperable, reusable (FAIR) research data management (RDM) and open science capabilities”
  • “Developing FAIROS data portals, research data commons, RDM as a national service”
  • “Lowering barriers to accessing, curating, integrating, linking, managing, sharing, and storing data across many disciplinary domains, irrespective of data size”
  • “The program supports innovation across the cyberinfrastructure (CI) ecosystem to address accessibility, data curation, research data management, discoverability, reliability, reproducibility, preservation, sustainability, and utility of research products, including data software, and code”

Full text of the listing is at the original posting. We keep an excerpt; we never edit it.

excerpt as fetched · 2446 chars
The FAIROS Program seeks to support a broad range of transformative open science activities including but not limited to i.) Research, education, and socio-technical cyberinfrastructure development capacities that advance sustainable multi-disciplinary findable, accessible, interoperable, reusable (FAIR) research data management (RDM) and open science capabilities, ii.) Piloting new models of scientific communication and publication that improve efficiency and accessibility, iii.) Developing FAIROS data portals, research data commons, RDM as a national service, and iv.) Lowering barriers to accessing, curating, integrating, linking, managing, sharing, and storing data across many disciplinary domains, irrespective of data size.
The program supports innovation across the cyberinfrastructure (CI) ecosystem to address accessibility, data curation, research data management, discoverability, reliability, reproducibility, preservation, sustainability, and utility of research products, including data software, and code, developed as part of funded projects.
FAIROS proposals must select one of two tracks to focus on, either: 1) Disciplinary Improvements to targeted scientific communities, or 2) Cross-Cutting Improvements that apply to many or most scientific disciplines. In the case of proposals focused on Disciplinary Improvements, it is strongly recommended that prospective PIs contact a program officer from the list of Cognizant Program Officers in the directorate closest to the major disciplinary impact of the proposed work to ascertain that the scientific focus and budget of the proposed work are appropriate for this solicitation. In the case of proposals focused on Cross-Cutting Improvements, it is strongly recommended that prospective PIs contact the cognizant program officer from the Office of Advanced Cyberinfrastructure (OAC).
After selecting either Disciplinary Improvements or Cross-Cutting Improvements in which to focus research, the proposal must include the kinds of activities relevant to the selected track. Standard research proposals are the only type of proposal accepted in response to this solicitation.
The FAIROS Program is undertaken in support of the US NSF Public Access Initiative.
For more information on the US NSF Public Access Initiative please visit  https://new.nsf.gov/public-access .
AI Opportunity AnalysisGenerated by POTL from the public listing (claude-haiku-4-5). The organization did not say any of this. Confidence 75/100.

Problem, as we read it

NSF seeks to fund research and infrastructure projects that address FAIR (Findable, Accessible, Interoperable, Reusable) research data management, open science capabilities, data portals, research commons, and barriers to data access, curation, and sharing across scientific disciplines. Organizations must propose either disciplinary-specific or cross-cutting improvements to cyberinfrastructure, research data management practices, or scientific communication models.

Potential agent · FAIR Data Discovery and Integration Agent

An agent that assists funded research teams in automatically ingesting, cataloging, and linking research datasets across disciplinary domains using FAIR principles, standardizing metadata, generating discovery indices, and establishing interoperability between data portals and research commons platforms.

Capabilities

  • Automated metadata extraction and standardization
  • Cross-disciplinary data discovery and linking
  • FAIR compliance validation and scoring
  • Data curation workflow automation
  • API integration between heterogeneous data systems
  • Research data lifecycle management
  • Discoverability index generation

Inputs

  • Raw research datasets from multiple disciplines
  • Existing metadata in various formats
  • Data governance policies and requirements
  • Disciplinary domain ontologies and standards
  • User access and permissions specifications

Outputs

  • FAIR-compliant standardized metadata records
  • Discoverable indexed datasets
  • Interoperability mappings and links between repositories
  • Data curation recommendations
  • Accessibility and reusability assessments
  • Automated preservation recommendations

Likely integrations

  • Data portal systems
  • Research commons platforms
  • Disciplinary repositories
  • Preservation infrastructure
  • Scientific publication systems
  • API gateways

Where a person stays on the loop

Data stewards and domain experts must review and approve metadata standardization decisions, FAIR compliance scorecards, and any automated recommendations for data restructuring or linking before implementation in production systems.

Security and compliance

Research data may contain sensitive information; data access controls must enforce institutional and funder restrictions. Metadata standardization must not expose sensitive research information. Data linkage and integration must respect privacy-preserving practices. API integrations require authentication and authorization frameworks. Compliance with HIPAA, FERPA, or disciplinary-specific data regulations may apply.

Our reasoning

  • Infer that funded institutions will struggle with metadata standardization and cross-disciplinary data linking because research practices vary widely and existing data silos prevent discoverability; an agent could automate this by mapping domain-specific vocabularies to FAIR standards.
  • Infer that barriers to data access and curation stem from manual, repetitive work across heterogeneous data systems; workflow automation and API-driven integration are technical components that agents could accelerate.
  • Infer that proposals will need to demonstrate measurable improvements in FAIR compliance and data interoperability; agents could provide automated compliance scoring and monitoring tools to validate impact.
  • Infer that data commons and RDM-as-a-service platforms require ongoing data ingestion, deduplication, and curation; agents could handle bulk ingestion and routine curation tasks, freeing human stewards for complex decisions.
Research data management systemsData portalsData commons platformsMetadata standardsAPIsData discovery toolsPreservation systemsOpen science infrastructureResearch datasetsMetadataCode repositoriesScientific publicationsDisciplinary dataMulti-domain scientific data
NSF · Findable Accessible Interoperable Reusable Open Science — POTL Signals