We extract public school directories, IPEDS datasets, district demographics, and financial statistics from nces.ed.gov. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Public Schools (CCD) objects from nces.ed.gov. All fields typed and schema-versioned.
"nces_school_id": "062271003230", "school_name": "Los Angeles Center for Enriched Studies", "district_name": "Los Angeles Unified", "total_students": 1642, "fte_teachers": 68.5, "student_teacher_ratio": 23.97, "title_i_status": "Eligible for TAS"
| # | nces_school_id | state_school_id | school_name | district_name | nces_district_id | physical_address |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Postsecondary (IPEDS) objects from nces.ed.gov. All fields typed and schema-versioned.
"unitid": "110635", "institution_name": "University of California-Berkeley", "sector": "Public, 4-year or above", "control": "Public", "total_enrollment": 45307, "tuition_in_state": 14395, "tuition_out_of_state": 44149
| # | unitid | opeid | institution_name | address | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for School Districts objects from nces.ed.gov. All fields typed and schema-versioned.
"nces_district_id": "3620580", "district_name": "New York City Public Schools", "total_schools": 1859, "total_students": 938, "total_revenue": 38472910000, "total_expenditures": 37281920000
| # | nces_district_id | state_district_id | district_name | county_name | supervisory_union_number | agency_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Financial Aid & Costs objects from nces.ed.gov. All fields typed and schema-versioned.
"unitid": "110635", "academic_year": "2023-2024", "published_tuition": 14395, "on_campus_room_board": 20530, "pct_receiving_pell": 27, "avg_pell_grant": 5420
| # | unitid | institution_name | academic_year | published_tuition | books_and_supplies | on_campus_room_board |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Graduation & Retention objects from nces.ed.gov. All fields typed and schema-versioned.
"unitid": "110635", "cohort_year": 2017, "full_time_retention_rate": 97, "grad_rate_150_pct": 93, "transfer_out_rate": "None", "pell_grant_grad_rate": 90
| # | unitid | institution_name | cohort_year | full_time_retention_rate | part_time_retention_rate | grad_rate_150_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our NCES scraper automates data extraction across the Common Core of Data (CCD), IPEDS, and College Navigator. We handle the ASP.NET legacy systems so you get clean, queryable data.
Extract directory information, enrollment counts, demographic breakdowns, and Title I status for over 100,000 public elementary and secondary schools.
Capture institutional characteristics, admissions, enrollment, retention rates, and financial statistics for colleges and universities.
Scrape district-level revenue sources, instructional expenditures, and capital outlays mapped to NCES district IDs.
Extract consumer-facing data including net price calculators, varsity athletic teams, and campus security statistics.
NCES frequently changes variable names across years. We map historical datasets to a unified schema for longitudinal analysis.
Extract FTE counts, student-to-teacher ratios, and average salary data for instructional staff across institutions.
Automate complex search forms, manage heavy __VIEWSTATE payloads, and handle legacy pagination structures.
Automated downloading, parsing, and conversion of NCES Access databases and CSV dumps into structured warehouse formats.
Configure pipelines to detect new data releases annually or biennially and ingest them directly into your warehouse.
Brief in. Clean data out.
Specify required datasets: CCD, IPEDS, PSS, or specific College Navigator variables. We design the target schema.
We configure crawlers to navigate ASP.NET forms, manage session state, and parse nested tabular data.
Schema validation, cross-referencing totals against NCES summary reports, and standardising entity IDs.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Government websites present unique scraping challenges. Here is how we extract structured data from legacy ASP.NET infrastructure.
NCES search interfaces rely on ASP.NET Web Forms. Our crawlers extract, preserve, and transmit the __VIEWSTATE and __EVENTVALIDATION hidden fields required to paginate through results without session resets.
NCES alters column names and variable definitions between survey years. We maintain a mapping layer that standardises historical variables into a consistent schema, enabling reliable time-series analysis.
Many NCES datasets are distributed as MS Access databases or zipped flat files. Our pipelines automate the download, extraction, and conversion of these legacy formats into modern columnar formats like Parquet.
Government servers often have strict rate limits. We configure conservative concurrency settings and distribute requests across residential proxies to ensure reliable extraction without triggering firewall blocks.
We implement validation logic that compares extracted component values (e.g., male + female enrollment) against reported totals to identify parsing errors caused by UI updates.
Companies estimate total addressable market (TAM) by filtering districts based on enrollment size, Title I status, and technology budgets.
Researchers analyse longitudinal IPEDS data to study enrollment trends, tuition inflation, and graduation rate disparities.
PropTech platforms enrich property listings with local school district boundaries, student-teacher ratios, and demographic data.
Think tanks evaluate the impact of funding changes by correlating district financial expenditures with student outcomes.
Sales teams build targeted lead lists of school districts and universities based on institutional characteristics and locale codes.
Universities analyse regional high school graduation pipelines using CCD data to optimise their recruitment strategies.
"NCES holds the definitive statistical record of American education, but extracting longitudinal data across its fragmented ASP.NET interfaces requires extensive engineering."
Most teams underestimate the investment required: reliable NCES scraping requires handling heavy ASP.NET ViewState payloads, navigating legacy search forms, extracting data from nested tables, and normalising schemas that change year over year. DataFlirt absorbs that complexity so your engineers can focus on analysis, not infrastructure maintenance.
Everything supported by our nces.ed.gov scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles concurrent requests and data extraction, while Playwright manages complex ASP.NET interactions and JavaScript rendering on newer NCES dashboards.
Pandas and Airflow orchestrate the transformation layer, mapping disparate historical variables into a clean, unified schema before warehouse delivery.
Pipelines run on Kubernetes with automated retry logic and alerting via Prometheus and Grafana. All state and metadata are stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About nces.ed.gov scraping, legality, and pipeline operations.
Ask us directly →Yes. The National Center for Education Statistics (NCES) provides public domain government data. Scraping publicly available aggregate statistics, directories, and survey results is permissible. DataFlirt does not attempt to access restricted-use datasets or protected student-level information.
Our crawlers are engineered to parse and maintain the __VIEWSTATE, __EVENTVALIDATION, and hidden form fields required by legacy ASP.NET applications. This allows us to programmatically submit searches and paginate through results without breaking the session.
Yes. We can extract historical survey data across multiple academic years. Because NCES frequently changes variable definitions, we employ a mapping layer to normalise historical data into a consistent schema for longitudinal analysis.
NCES datasets are typically updated on an annual or biennial schedule, depending on the specific survey. Our pipelines can be configured to monitor for new data releases and automatically ingest the updates into your warehouse.
Yes. We extract complete institutional profiles from College Navigator, including tuition costs, net price calculator outputs, enrollment demographics, and campus security statistics.
No. Restricted-use data requires formal application, government approval, and adherence to strict physical and virtual security protocols. We only extract publicly available datasets.
Yes. We map changing variable names and category definitions in the Common Core of Data (CCD) to provide a unified schema, ensuring your downstream analytics are not broken by NCES reporting changes.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete export of the IPEDS database or targeted public school directories — we scope, build, and operate the pipeline. Tell us what you need.
