ParseEduAbout/Data Sources

Why This Site Exists

At the core, 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.

ParseEdu was created to close that gap between raw education data and real execution by helping teams:

  • 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.