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https://issues.apache.org/jira/browse/AIRAVATA-3975?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Lahiru Jayathilake updated AIRAVATA-3975:
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Labels: gsoc gsoc2026 (was: )
> Allocation Research Impact & Analytics Dashboard
> ------------------------------------------------
>
> Key: AIRAVATA-3975
> URL: https://issues.apache.org/jira/browse/AIRAVATA-3975
> Project: Airavata
> Issue Type: Task
> Components: Airavata System, Custos Airavata Integration
> Reporter: Lahiru Jayathilake
> Priority: Major
> Labels: gsoc, gsoc2026
>
> h2. Summary
> Build a research impact tracking pipeline and analytics dashboard for Custos
> that connects compute allocations to their research outcomes (publications,
> citations) and provides visual analytics on allocation distribution and usage
> patterns. This becomes part of the Custos allocation management layer, giving
> PIs and administrators visibility into how compute resources translate into
> research output.
> h2. Problem
> HPC centers grant compute allocations to research projects through programs
> like ACCESS-CI (Accelerate, Maximize, Explore, Discover) and internal
> discretionary pools. These allocations consume significant resources (CPU
> hours, GPU hours, storage), but there is limited visibility into the research
> outcomes they produce.
> PIs and administrators want to understand not just "how many credits were
> consumed" but "what did those credits produce?" When a project uses 50,000
> GPU hours, what publications came out of that work? How does resource
> consumption correlate with research output across different scientific
> domains? This kind of traction data is valuable for reporting, future
> allocation decisions, and demonstrating the value of the compute
> infrastructure.
> At the same time, allocation analytics (resource distribution across sites,
> comparison by scientific domain, usage patterns by allocation type) are
> useful for understanding how resources are being distributed and consumed
> across the system.
> h2. Description
> This project builds two connected components within the Custos allocation
> management layer:
> 1. Research Impact Pipeline (primary focus)
> Build a data pipeline that cross-references compute allocations with
> published research outcomes:
> * Publication discovery: Given an allocation project (PI name, institution,
> project title/abstract, field of science), search external publication
> repositories to find related publications. Sources to integrate with:
> ** Semantic Scholar API (free, structured, good coverage)
> ** Crossref API (DOI-based metadata, citation counts)
> ** ORCID API (if the PI's ORCID is known, pull their works directly)
> ** Google Scholar (broad coverage, limited API access)
> ** ACM Digital Library, IEEE Xplore, or other domain-specific repositories
> as applicable
> * Matching logic: Design a strategy to link publications to specific
> allocations. This is not trivial since publications don't always cite their
> compute allocation. Approaches to explore:
> ** Match by PI name + institution + time window (allocation period)
> ** Match by keywords from the allocation abstract against publication
> titles/abstracts
> ** Match by acknowledgment text mining (some papers acknowledge allocation
> grant numbers)
> ** Match by ORCID works if the PI's ORCID is linked
> * Periodic sync: The pipeline should run periodically (configurable interval)
> to discover new publications and update citation counts for previously
> matched ones.
> * Data model: Store matched publications with metadata (title, authors,
> venue, year, DOI, citation count, match confidence, match method) linked to
> the allocation project.
> 2. Allocation Analytics
> Build analytics views that visualize allocation data:
> * Resource distribution across HPC sites (e.g., which sites are getting the
> most CPU, GPU, storage allocations)
> * Allocation breakdown by scientific domain / field of science
> * Comparison across allocation types (Accelerate vs. Maximize vs. Explore vs.
> Discover)
> * Top projects by resource allocation
> * Trends over time
> 3. Dashboard UI (React/TypeScript)
> Build a dashboard that brings both components together:
> * Allocation detail view: When navigating to a specific allocation/project,
> show:
> ** Allocation metadata (PI, institution, field of science, resources granted,
> dates)
> ** Matched publications with citation counts, DOIs, and match confidence
> ** Resource usage summary (if usage data is available)
> * Analytics views:
> ** Aggregated research impact metrics (total publications, citations across
> projects)
> ** Allocation distribution charts (by site, by domain, by allocation type)
> ** Visual comparisons (scatter plots, bar charts, radar charts by scientific
> domain)
> * Role-based views:
> ** PI view: their own projects, publications, and resource usage
> ** Admin view: cross-project analytics, research impact overview, resource
> distribution
> 4. Backend API (Go)
> * REST endpoints to serve allocation data, publication matches, and analytics
> aggregations
> * Integration with the ACCESS-CI allocations API
> (https://allocations.access-ci.org/) as a data source for project metadata
> * Endpoints for triggering and monitoring the publication discovery pipeline
> h2. Expected Deliverables
> * Research impact pipeline that cross-references allocations with
> publications from external repositories (Semantic Scholar, Crossref, ORCID,
> etc.)
> * Publication matching algorithm with configurable strategies and confidence
> scoring
> * Allocation analytics backend with aggregation queries (by site, domain,
> allocation type)
> * React/TypeScript dashboard with allocation detail views (including matched
> publications) and analytics visualizations
> * Go REST API serving allocation data, publication matches, and analytics
> * Documentation covering the matching strategy, data sources, and API
> specification
> h2. Required Skills
> * Go (backend API and pipeline)
> * React/TypeScript (dashboard UI)
> * REST API design and integration with external APIs
> * SQL / database modeling
> * Data visualization (charting libraries)
> * Familiarity with academic publication APIs (Semantic Scholar, Crossref), or
> willingness to learn
> h2. Resources
> * Custos repository: github.com/apache/airavata-custos
> * Allocation management module: allocations/ directory in the repository for
> the existing ACCESS-CI integration and allocation data model
> * ACCESS-CI allocations API: https://allocations.access-ci.org/ provides
> current project data including PI, institution, field of science, resources,
> and allocation type
> * Semantic Scholar API: https://api.semanticscholar.org/ (free, structured
> publication search and citation data)
> * Crossref API: https://api.crossref.org/ (DOI metadata and citation counts)
> * ORCID API: https://info.orcid.org/documentation/ (researcher works and
> affiliations)
> * Google Scholar: https://scholar.google.com/ (broad coverage, limited API
> access)
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