Operational data exists, analysis often does not.
Food banks collect a lot of operational data. Intake forms capture donations by department and donor. Distribution records capture pounds out, households served, and recipient sites. Most of this data sits in spreadsheets that staff use as ledgers rather than as analytical surfaces.
The result is that decisions about donor outreach, department balance, distribution timing, and inventory holds are often made without a rigorous read of the data the organization already produces. Demand patterns over time are visible in the records but rarely summarized. Donor concentration is observable but rarely measured. Economic disparities in community food access show up in the distribution data but rarely surface in the form staff and donors actually see.
The lab's premise: a food bank's own operational data can answer most of its operational questions, if the analysis surface is built for the data the form actually produces.
Intake, donation, and distribution records.
The research draws on intake forms, donation logs, and distribution records from local Pennsylvania food banks. The intake forms follow a JotForm-style wide-sparse layout: each department (produce, dairy, frozen, bakery, canned goods, and so on) fills out only the columns relevant to its work and leaves the rest blank. The same wide-sparse pattern is common across food banks that adopt off-the-shelf form tools.
Specific organization names, identifying details, and dataset identifiers are held private. The Pantry Dashboard ships with a synthetic sample workbook of the same shape so the analysis surface is publicly demonstrable without exposing any organization's records.
Schema-aware ingestion, then operational views.
The analysis layer treats the form's blanks as not-applicable rather than as missing data. Aggregations skip nulls, top-N computations run over rows that actually carry the relevant field, and per-department summaries respect the wide-sparse structure rather than fighting it.
On top of that ingestion, the work spans four threads:
- Time-series operations. Monthly totals, rolling means, season-over-season comparison.
- Donor concentration. Top-N share, repeat-donor rates, donor-type breakdowns.
- Distribution analysis. Per-site pounds and households, intake-to-distribution gap.
- Audience-aware presentation. The same data shown differently to staff, board, donors, and the public, with privacy-preserving transformations applied per audience.
The dashboard runs entirely client-side in the browser. No part of the file is uploaded. Close the tab and the file is gone.
The Pantry Dashboard.
The public deliverable of this research project is the Pantry Dashboard: a browser-based, audience-aware operational dashboard tailored for food bank workflows. The dashboard reads an intake spreadsheet in any layout, auto-detects roles for common header patterns, and builds seven operational views around the data: monthly overview, departments, donor concentration, distribution, trends, side-by-side comparison, and a row-level raw data view.
An audience toggle in the header switches between staff, board, donors, and public modes. The toggle drives which views appear, which columns are visible, and whether identifiers are anonymized. The mapping is configurable, so any food bank can adapt it to its own reporting policies without changing code structure.
For research and exploratory purposes only. Not a substitute for operational decision-making without independent verification.
For the scientific community.
An audience-aware operational layer for food bank data has three audiences. For food bank staff, it makes routine operational questions answerable without exporting to a paid BI tool: which donors carry the month, where the distribution gap sits, how households served are trending. For donors and boards, it gives the same picture in a privacy-respecting view that does not expose identifiers. For researchers studying community food systems, the surfaced demand and access patterns inform donor strategy and community-level policy in ways that the raw spreadsheet cannot.
The open-source release makes the same capability available to any food bank without a license fee. Any organization with a similar intake spreadsheet can use it without modification; the column-detection logic recognizes common header patterns and assigns roles automatically.
Where things stand.
In preparation. The Pantry Dashboard is publicly available as the research project's first deliverable. Subsequent research outputs are pending.