NSF CI Compass

New studies have detected a rising number of reports of diseases in marine organisms such as corals, molluscs, turtles, mammals, and echinoderms over the past three decades. Despite the increasing disease load, microbiological, molecular, and theoretical tools for managing disease in the world's oceans are under-developed. Review of the new developments in the study of these diseases identifies five major unsolved problems and priorities for future research: (1) detecting origins and reservoirs for marine diseases and tracing the flow of some new pathogens from land to sea; (2) documenting the longevity and host range of infectious stages; (3) evaluating the effect of greater taxonomic diversity of marine relative to terrestrial hosts and pathogens; (4) pinpointing the facilitating role of anthropogenic agents as incubators and conveyors of marine pathogens; (5) adapting epidemiological models to analysis of marine disease.

Through Media to the Public and PolicymakersAt its inception, COMPASS focused on getting ocean issues onto the social agen-The Community Page is a forum for organizations and societies to highlight their efforts to enhance the dissemination and value of scientific knowledge.

New studies have detected a rising number of reports of diseases in marine organisms such as corals, molluscs, turtles, mammals, and echinoderms over the past three decades. Despite the increasing disease load, microbiological, molecular, and theoretical tools for managing disease in the world's oceans are under-developed. Review of the new developments in the study of these diseases identifies five major unsolved problems and priorities for future research: (1) detecting origins and reservoirs for marine diseases and tracing the flow of some new pathogens from land to sea; (2) documenting the longevity and host range of infectious stages; (3) evaluating the effect of greater taxonomic diversity of marine relative to terrestrial hosts and pathogens; (4) pinpointing the facilitating role of anthropogenic agents as incubators and conveyors of marine pathogens; (5) adapting epidemiological models to analysis of marine disease.

Nearest neighbor search (NNS) has a wide range of applications in information retrieval, computer vision, machine learning, databases, and other areas. Existing state-of-the-art algorithm for nearest neighbor search, Hierarchical Navigable Small World Networks (HNSW), is unable to scale to large datasets of 100M records in high dimensions. In this paper, we propose LANNS, an end-to-end platform for Approximate Nearest Neighbor Search, which scales for web-scale datasets. Library for Large Scale Approximate Nearest Neighbor Search (LANNS) is deployed in multiple production systems for identifying top-K (100 ≤ k ≤ 200) approximate nearest neighbors with a latency of a few milliseconds per query, high throughput of ~2.5k Queries Per Second (QPS) on a single node, on large (e.g., ~ 180M data points) high dimensional (50-2048 dimensional) datasets.

Sigma Gamma Epsilon, a national honor society for the Earth Sciences, held its 42nd Biennial Convention in conjunction with the annual meeting of the Geological Society of America. The Sigma Gamma Epsilon convention was held at the Westin Hotel in Charlotte, N.C. on November 3, 2012. This report provides a summary of the deliberations and actions of the participants at the convention.

The NSF CI Compass virtual workshop AI Meets CI: Intelligent Infrastructure for Major & Midscale Facilities (January 12–14, 2026) brought together NSF Major and Midscale Facility staff and a broader community to discuss how AI is currently used, what fails in practice, and what it would take to make AI-enabled capabilities supportable as part of facility cyberinfrastructure. The workshop included facility case studies, tool-focused discussions, and an interactive session focused on adoption and trust. This report synthesizes these discussions into cross-cutting themes, day-by-day highlights, and operating guidance intended to help facility leaders and program managers make well-grounded decisions. The workshop covered a range of facility workflows, including user and staff support over institutional documentation (Rubin, NEON); real-time data processing pipelines with embedded ML components (Rubin active optics system (AOS) and alert processing, CMS triggers and reconstruction); metadata and provenance generation as part of data acquisition (MagLab, CHESS); QA/QC and anomaly detection (OOI, NEON); data discovery and semantic search across heterogeneous holdings (DesignSafe/NHERI); and code assistance for development and analysis tasks (IceCube, multiple facilities). The workshop also addressed cross-cutting concerns, including security for retrieval-augmented systems (Trusted CI), adoption and human factors (Day 3 interactive session), and the gap between working prototypes and supported services. Three overarching messages emerged from these discussions. First, facilities should treat AI adoption as a service-management problem, not a tool selection exercise. The primary challenge is not getting a prototype to work once. The challenge lies in operating a capability over time with clear boundaries, traceability, and a defensible review posture. Second, the limiting factors are often “below the model.” Machine-actionable metadata, provenance, documentation stewardship, and governance boundaries determine whether AI-enabled workflows are safe and repeatable. Where these foundations are weak, AI tends to amplify confusion: it can produce plausible outputs that are difficult to verify, and it can encourage informal use that outpaces policy and security controls. Third, human factors are not secondary to AI adoption. Adoption is constrained by incentives, perceived risk, and validation capacity. As tools become more capable and more autonomous (i.e., able to chain multiple steps, retrieve information, and take actions without human intervention at each step), staff and users need better ways to keep track of what the system did, what evidence it used, and what is in scope for automation.

Clusters of fast and slow correlated particles, identified as dynamical heterogeneities (DHs), con-stitute a central aspect of glassy dynamics. A key ingredient of the glass transition scenario is asignificant increase of the cluster size $\xi$4 as the transition is approached. In need of easy-to-computetools to measure $\xi$4 , the dynamical susceptibility $\chi$4 was introduced recently, and used in various ex-perimental works to probe DHs. Here, we investigate DHs in dense microgel suspensions using imagecorrelation analysis, and compute both $\chi$4 and the four-point correlation function G4 . The spatialdecrease of G4 provides a direct access to $\xi$4 , which is found to grow significantly with increasingvolume fraction. However, this increase is not captured by $\chi$4 . We show that the assumptions thatvalidate the connection between $\chi$4 and $\xi$4 are not fulfilled in our experiments.

The CI CoE Pilot project was funded in 2018 and charged with creating a blueprint for a cyberinfrastructure (CI) Center of Excellence, dedicated to supporting NSF major facilities’s (MFs) CI that is critical to achieve science outcomes. To better inform the Pilot's creation of that blueprint, the Pilot team began researching the way MFs manage their data and the cyberinfrastructure (CI) that enables the capture, transformation, and dissemination of science data. From this research, the Pilot team created a lifecycle model to illustrate the way data flows through an MF's CI for the purpose of guiding the Pilot team's work supporting MFs and developing the blueprint. This technical report explains this data lifecycle and discusses how it applies to MFs such as IceCube, LIGO, Rubin Observatory, NEON, and OOI. The team continues to refine this model as it broadens its outreach and support activities to other MFs.

The NSF CI Compass virtual workshop AI Meets CI: Intelligent Infrastructure for Major & Midscale Facilities (January 12–14, 2026) brought together NSF Major and Midscale Facility staff and a broader community to discuss how AI is currently used, what fails in practice, and what it would take to make AI-enabled capabilities supportable as part of facility cyberinfrastructure. The workshop included facility case studies, tool-focused discussions, and an interactive session focused on adoption and trust. This report synthesizes these discussions into cross-cutting themes, day-by-day highlights, and operating guidance intended to help facility leaders and program managers make well-grounded decisions. The workshop covered a range of facility workflows, including user and staff support over institutional documentation (Rubin, NEON); real-time data processing pipelines with embedded ML components (Rubin active optics system (AOS) and alert processing, CMS triggers and reconstruction); metadata and provenance generation as part of data acquisition (MagLab, CHESS); QA/QC and anomaly detection (OOI, NEON); data discovery and semantic search across heterogeneous holdings (DesignSafe/NHERI); and code assistance for development and analysis tasks (IceCube, multiple facilities). The workshop also addressed cross-cutting concerns, including security for retrieval-augmented systems (Trusted CI), adoption and human factors (Day 3 interactive session), and the gap between working prototypes and supported services. Three overarching messages emerged from these discussions. First, facilities should treat AI adoption as a service-management problem, not a tool selection exercise. The primary challenge is not getting a prototype to work once. The challenge lies in operating a capability over time with clear boundaries, traceability, and a defensible review posture. Second, the limiting factors are often “below the model.” Machine-actionable metadata, provenance, documentation stewardship, and governance boundaries determine whether AI-enabled workflows are safe and repeatable. Where these foundations are weak, AI tends to amplify confusion: it can produce plausible outputs that are difficult to verify, and it can encourage informal use that outpaces policy and security controls. Third, human factors are not secondary to AI adoption. Adoption is constrained by incentives, perceived risk, and validation capacity. As tools become more capable and more autonomous (i.e., able to chain multiple steps, retrieve information, and take actions without human intervention at each step), staff and users need better ways to keep track of what the system did, what evidence it used, and what is in scope for automation.

Clusters of fast and slow correlated particles, identified as dynamical heterogeneities (DHs), con-stitute a central aspect of glassy dynamics. A key ingredient of the glass transition scenario is asignificant increase of the cluster size $ξ$4 as the transition is approached. In need of easy-to-computetools to measure $ξ$4 , the dynamical susceptibility $χ$4 was introduced recently, and used in various ex-perimental works to probe DHs. Here, we investigate DHs in dense microgel suspensions using imagecorrelation analysis, and compute both $χ$4 and the four-point correlation function G4 . The spatialdecrease of G4 provides a direct access to $ξ$4 , which is found to grow significantly with increasingvolume fraction. However, this increase is not captured by $χ$4 . We show that the assumptions thatvalidate the connection between $χ$4 and $ξ$4 are not fulfilled in our experiments.