ParseEduAbout/Data Sources

Why This Site Exists

Education data is publicly available but in messy formats and various locations. ParseEdu is a normalized district data spine keyed on LEAID/NCES ID (the canonical district identifier), so every downstream workflow starts from consistent IDs and standardized fields.

  • Search and validate districts quickly
  • Match messy uploaded lists to canonical LEAIDs
  • Enrich records with reliable national fields
  • Build targeted district lists using defensible filters
  • Export decision-ready data for pipeline, territory, and pricing workflows

The goal is simple: reduce guesswork, reduce manual cleanup, and make every district decision faster and more defensible.

Data Sources (Current)

NCES (National Center for Education Statistics): Used for district-level education reference data. Includes CCD 2023-24 enrollment fields in the product.

CCD (Common Core of Data, NCES): Explicitly surfaced as a source for enrollment totals. Used in district profiles and enrichment/export fields.

CRDC (Civil Rights Data Collection, U.S. Department of Education OCR): Explicitly surfaced as CRDC 2021-22. Used for enrollment cross-reference and student population measures (EL, IDEA, Section 504, schools reported).

SAIPE 2023 (U.S. Census Bureau poverty estimates): Used for child poverty indicators: child poverty rate (ages 5-17), children in poverty (5-17), population (5-17), and total population. Product includes a `poverty_source` field for source labeling.

U.S. Department of Education ecosystem: Represented throughout the site.

ParseEdu normalized district directory (internal unified layer): Canonical LEAID-anchored district records. Includes operational fields like district identity, status, contact info, and coverage metadata. Powers search, matching, list building, and exports.

Our Data Philosophy

  • Canonical first: LEAID/NCES ID is the backbone.
  • Source visible: where possible, source labels are shown alongside values.
  • Coverage-aware: not every district has every field, and missing values are handled explicitly.
  • Human-in-the-loop: users can override low-confidence matches.
  • Workflow over dashboards: the value is in execution speed, not just display.