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Cyberinfrastructure for Public Access and Open Science

NSF · United States

What the organization said · quoted from the source

  • “address accessibility, discoverability, reliability, reproducibility, sustainability, and utility of data products in alignment with NSF and national goals for public access and open science”
  • “robust, synergistic collaborative team comprising skills from across communities of science/engineering, research data science, and information science discipline(s)”

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

excerpt as fetched · 3331 chars
The Cyberinfrastructure for Public Access and Open Science (CI PAOS) program within the Office of Advanced Cyberinfrastructure (OAC) aims to catalyze new and transformative socio-technical partnerships supporting research data infrastructure ecosystems across d omains through early-stage collaborative activities between cyberinfrastructure researchers, scie ntists , research computing experts, data management experts , research labs , university libraries , and other communities of practice. 
 The CI PAOS program supports the NSF Public Access Initiative by encouraging innovation across the CI ecosystem t o address accessibility, discoverability, reliability, reproducibility, sustainability, and utility of data products in alignment with NSF and national goals for public access and open science [ See: NSF Public Access Initiative , Office of Science and Technology Policy Memorandum on Ensuring Free, Immediate, and equitable Access to Federally funded Research , and Desirable Characteristics of Data Repositories for Federally Funded Research ] . CI PAOS builds on previous investments including those through Dear Colleague Letters NSF 19-069 , NSF 20 -068 , NSF 23-018 , and the FAIROS R esearch C oordination N etworks ( RCN ) p rogram solicitation ( NSF 22-553 ) . 
 NSF accepts proposals pursuant to this Program Description year-round. From time to time , NSF may also issue Dear Colleague Letters to encourage proposals on special thema tic interests and opportunities related to this progra m . 
 GUIDANCE TO POTENTIAL PROPOSERS 
 A primary feature of successful CI PAOS projects is a robust, synergistic collaborative team comprising skills from across communities of science/engineering, research data science, and information science discipline(s) and expertise in leveraging connections between cyberinfrastructure researchers and providers and data specialists . Leveraging international collaboration to build shared norms and address c hallenges related to developing and implementing PAOS policies and practices is encouraged . Research and education in science and engineering benefit immensely from international cooperation. P roposals with an international component are also welcome [See: International Collaborations Opportunities at NSF ] . Proposers must target one or more of th e following themes/pathways: Competency Building, Capability Building, and /or Community Building. 
 Competency Building 
 Open s cience /engineering-driven collaboration . A socio -technical collaborative approach in addressing disciplinary , interdisciplinary , domestic, and international data lifecycle challenges is critical to informing and guiding the development of principles, requirements , and standards of a CI ecosystem that fosters pipelines to good data management and pathways to access. Propos al s should clearly describe the goals, challenges, and rationale for the proposed data science and engineering project and include an explanation of the potential for transformative research and broader impacts on the open science ecosystem [ See: U.S. NSF Broader Impacts ] . Successful proposals will also clearly identify utilization science scenarios and use cases . 
 C apability Building 
 Exploratory and pilot activities. <span class="TextRun SCXW92189242 BCX4" lang="EN-US" xml:lang="EN-US" d
AI Opportunity AnalysisGenerated by POTL from the public listing (claude-haiku-4-5). The organization did not say any of this. Confidence 72/100.

Problem, as we read it

NSF seeks to fund collaborative cyberinfrastructure and data management projects that improve accessibility, discoverability, reliability, reproducibility, and sustainability of research data products across scientific domains. Organizations need to build competencies, capabilities, and communities around open science practices and data management infrastructure to align with federal public access and open science mandates.

Potential agent · Research Data Ecosystem Integration & Discoverability Agent

An AI agent that automates metadata harvesting, cross-repository discoverability indexing, and compliance validation across federated research data repositories to ensure alignment with FAIR (Findability, Accessibility, Interoperability, Reusability) principles and NSF public access requirements.

Capabilities

  • Automated metadata extraction and harmonization across heterogeneous data repositories
  • Cross-domain discoverability indexing and semantic linking
  • FAIR compliance validation and gap identification
  • Automated documentation of data lifecycle and provenance
  • Repository interoperability testing and standardization checks
  • Accessibility and reliability monitoring across CI ecosystem
  • Reproducibility assessment through automated analysis of data documentation

Inputs

  • Raw repository APIs and OAI-PMH endpoints
  • Heterogeneous metadata schemas from participating institutions
  • Data repository inventory and holdings
  • FAIR and public access compliance requirements
  • Domain-specific metadata standards documentation

Outputs

  • Unified metadata index with cross-repository discoverability
  • FAIR compliance reports and remediation recommendations
  • Standardized metadata mappings and transformation rules
  • Repository interoperability health dashboard
  • Accessibility and sustainability metrics reports
  • Best-practice guidance for data management pipelines

Likely integrations

  • Research data repository platforms (Zenodo, OSF, domain-specific repos)
  • OAI-PMH and Z39.50 metadata protocols
  • FAIR data metrics and evaluation systems
  • University library management systems
  • Research computing infrastructure platforms
  • Semantic web and linked data standards (RDF, JSON-LD)

Where a person stays on the loop

Domain scientists and data curators must review semantic mappings, FAIR compliance verdicts, and standardization recommendations to ensure accuracy and alignment with disciplinary norms before publication or policy implementation. Research data managers must validate reproducibility and lifecycle documentation assessments.

Security and compliance

Research data access control and authentication across federated repositories must be preserved; metadata harvesting must respect data sensitivity classifications and institutional policies; compliance with FERPA, HIPAA, and domain-specific data regulations required; API integration requires secure credential management and audit logging.

Our reasoning

  • Infer that the real problem is fragmentation: research data sits in heterogeneous, discipline-specific repositories with inconsistent metadata, standards, and access mechanisms, making it difficult for researchers to discover, access, and reproduce work across domains. The grant framing emphasizes 'accessibility' and 'discoverability' repeatedly, suggesting current state is opaque.
  • Infer that NSF expects grantees to solve socio-technical challenges—not just policy or human coordination, but actual engineering of data infrastructure, APIs, metadata standards, and automation to make the ecosystem interoperable. The emphasis on 'cyberinfrastructure researchers,' 'data management experts,' and 'engineering' indicates technical delivery is core.
  • Infer that successful proposals will demonstrate measurable improvements in FAIR compliance, cross-repository discovery, and reproducibility through pilots and use cases. The mention of 'utilization science scenarios and use cases' and prior Dear Colleague Letters suggests NSF wants concrete, replicable solutions, not conceptual frameworks.
  • Infer that automation and AI are implicit needs: manually harmonizing metadata across 50+ repositories, validating FAIR compliance per dataset, or testing interoperability standards is labor-intensive. An agent automating these tasks aligns with 'transformative' and 'early-stage collaborative activities' language and reduces implementation burden for partner institutions.
research data infrastructurecyberinfrastructure systemsdata repositoriesfederated systemsAPI standardsmetadata systemsresearch datasetsmetadataprovenance recordsrepository access logsdisciplinary data models
NSF · Cyberinfrastructure for Public Access and Open Science — POTL Signals