College of Information ScienceComputing Resources Working Group · Point people: Barney Maccabe and Greg Chism

Two-course pilot¶

Supplemental reference · Browse supplements · Current report

Supporting compute pilot. These DS/MLIS proposals retain their own $0 cash and Greg’s four-hour weekly envelope. They are not college-wide capacity findings or the working group’s selected solution. Return to working-group deliverables →

Proposed candidates: INFO 521 and LIS 671. Model 40 students each over 15 weeks. Term, offering, instructor agreement and actual enrollment remain unconfirmed.

Test components → size ACCESS CPU + GPU → replay on the target → ten-person canary → full-class rehearsal → teach → export and review.

INFO 521: Introduction to Machine Learning¶

Build a reproducible data-cleaning and exploratory-analysis notebook, then fit a small CPU model with held-out validation. Explain missingness, leakage, uncertainty and data provenance.

Inputs: Generate a synthetic civic-service dataset with no real residents; keep core CSV inputs below 250 MB and test peak memory against the awarded workspace. Real/public datasets require a rights and data-classification check.

Environment: Requested: Python, JupyterLab, pandas, scikit-learn, matplotlib and an approved reproducible package manifest; R/RStudio only if the course requires it.

Outputs and acceptance

Run the same environment from a browser and from the CPU fallback.
Produce a reproducible notebook with an environment manifest and checksummed inputs.
Evaluate a small model without requiring a GPU or a paid student account.

Run the whole assignment from a fresh student workspace; require execution without out-of-memory errors at the approved spec, reproducible key outputs and a complete exported submission.

LIS 671: Digital Curation and Digital Preservation¶

Audit a collection catalog, normalize names/dates/identifiers, document a metadata crosswalk, query an embedded catalog database and export a preservation-ready package with README, schema, provenance and checksums.

Inputs: Use a synthetic 10,000–100,000-record collection catalog with Dublin Core-style fields. Add public/licensed records only after rights review; exclude patron, circulation, donor and restricted collection records.

Environment: Requested: JupyterLab/Python, CSV/JSON/XML parsing and embedded SQLite; optional MARC parsing packages after approval. OpenRefine or other hosted applications require a separate provider fit check.

Outputs and acceptance

Explain how transformations change discoverability and preserve provenance.
Validate a metadata crosswalk and query structured collection records.
Export usable CSV/JSON/XML records and describe a suitable repository/retention route.

Execute the complete workflow in a fresh workspace; validate schema, record counts, identifiers and query results; reopen the exported package using the fallback environment.

People and support¶

Greg’s four weekly hours cover all his work, including EduCloud. The earlier two-course workload is over capacity and must be narrowed or supported by confirmed distinct staff. Two course support TAs and course leads have separate potential hours; course leads need no salary. A distinct independent backup is still unfilled. Do not promise a support window until commitments exist.

Use small-data CPU alternatives and assignment accommodations if GPU access fails. Keep rosters/grades in approved institutional systems. Test backups and exports before required use.

Hours and grant dependencies · Admission checks · Full plan text · Existing pilot brief PDF

Downloadable drafts retain the illustrative two-course workload, now flagged against the corrected four-hour weekly capacity. They do not establish a feasible staffing plan.