We extract lesson plans, syllabus metadata, standards alignments, and provider details from OER Commons. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Resource Metadata objects from oercommons.org. All fields typed and schema-versioned.
"resource_id": "73921", "title": "Introduction to Algebraic Functions", "material_type": "Lesson Plan", "education_level": "High School", "subject_areas": "['Mathematics', 'Algebra']", "license_type": "CC BY-NC-SA 4.0", "language": "English"
| # | resource_id | title | url | abstract | material_type | education_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Standards Alignment objects from oercommons.org. All fields typed and schema-versioned.
"resource_id": "73921", "standard_type": "Common Core State Standards", "standard_code": "CCSS.MATH.CONTENT.HSA.REI.B.3", "standard_description": "Solve linear equations and inequalities in one variable.", "alignment_degree": "Strong Alignment", "jurisdiction": "National"
| # | resource_id | standard_type | standard_code | standard_description | alignment_degree | learning_domain |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Provider & Author objects from oercommons.org. All fields typed and schema-versioned.
"author_name": "Sarah Jenkins", "provider_org": "OpenMath Initiative", "hub_affiliation": "STEM Educators Hub", "contribution_count": 42, "member_since": "2019-04-12", "location": "California"
| # | author_name | provider_org | provider_url | hub_affiliation | contribution_count | member_since |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Evaluations & Reviews objects from oercommons.org. All fields typed and schema-versioned.
"review_id": "REV-99214", "resource_id": "73921", "rating_score": 4.5, "review_text": "Excellent breakdown of linear functions with practical examples.", "rubric_criteria": "Subject Matter Accuracy", "date_posted": "2023-11-05"
| # | review_id | resource_id | reviewer_name | rating_score | rubric_criteria | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Collections & Hubs objects from oercommons.org. All fields typed and schema-versioned.
"collection_id": "COL-441", "hub_name": "OER STEM Hub", "collection_title": "Algebra I Fundamentals", "resource_count": 128, "member_count": 3450, "tags": "['Algebra', 'Grade 9', 'Equations']"
| # | collection_id | hub_name | collection_title | description | resource_count | member_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our OER Commons scraper handles every layer of the platform, from deeply nested educational taxonomies to dynamically loaded standard alignments and external resource linking.
Extract title, abstract, material type, education level, and subject areas mapped directly to standard educational ontologies.
Capture Common Core, NGSS, and state specific standard codes linked to individual lesson plans and modules.
Navigate and extract the hierarchical structure of subjects, ensuring resources remain categorised correctly in your database.
Extract specific Creative Commons license types, copyright notices, and usage permissions for compliance auditing.
Monitor specific educational hubs, extracting author profiles, contributing organisations, and submission histories.
Capture user reviews, rubric scores, and peer evaluations to assess the quality of educational materials.
Extract direct URLs for external PDFs, videos, and interactive simulations hosted outside the OER Commons domain.
Map the exact hierarchy of curated collections and folders to replicate OER hubs in your own environment.
Run continuous pipelines to detect newly added resources, updated syllabus files, or changed standard alignments.
Brief in. Clean data out.
Provide subject areas, grade levels, specific hubs, or keyword sets. We design the extraction schema together.
We configure Scrapy crawlers, taxonomy parsers, and session management to navigate the OER Commons directory.
Schema validation, null rate checks, and taxonomy normalisation testing before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Educational repositories feature complex metadata schemas and deeply nested structures. Here is how we extract clean data.
OER Commons utilises complex metadata schemas to describe educational content. Our parsers normalise these nested fields into flat, queryable database records without losing the semantic relationships between subjects and grade bands.
Many educational standards and alignments are loaded dynamically via JavaScript when a user interacts with the page. We run full Playwright browser sessions to trigger these network requests and capture the complete alignment data.
A significant portion of OER content links out to third party sites or direct file downloads. Our crawlers resolve these redirect chains to provide the final destination URL, ensuring your users do not hit dead links.
Subjects in OER Commons are highly hierarchical. We extract the full breadcrumb path for every resource, allowing you to filter by broad categories or highly specific subtopics.
For large curriculum catalogues, we maintain a hash index of last seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Learning Management Systems ingest open resources to provide teachers with immediate access to aligned lesson plans.
School districts map available open source materials against their specific state standards to build cost effective curricula.
Machine learning teams use structured lesson plans and rubrics to train educational assistants and grading models.
Researchers analyse the adoption rates, review scores, and distribution of open educational resources across different demographics.
Policy makers track the availability of STEM materials aligned to NGSS standards to identify funding gaps.
Educational publishers analyse existing open materials to identify underserved subjects and grade levels for new content creation.
"OER Commons holds the largest structured repository of open educational materials, but standardising its complex taxonomies requires purpose built extraction infrastructure."
Most teams underestimate the difficulty of parsing nested educational standards and multi layered metadata. DataFlirt handles the JavaScript rendering, taxonomy normalisation, and asset link resolution so your engineers can focus on integrating the curriculum data rather than maintaining scrapers.
Everything supported by our oercommons.org 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 crawl orchestration and deduplication. Playwright handles JavaScript rendering for dynamic standard alignments. Combined via scrapy-playwright middleware.
Custom parsers translate complex LRMI and Dublin Core metadata into clean, typed JSON schemas suitable for immediate database ingestion.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About oercommons.org scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from OER Commons is generally permissible. DataFlirt targets only public, non authenticated educational metadata, standards, and reviews. We do not extract private group discussions or user account credentials.
We use JavaScript rendering to load the complete alignment trees, then parse the hierarchical data into flat, relational structures mapping the resource ID to the specific Common Core or NGSS code.
We extract the direct URLs and metadata for external assets, but we do not download and store the actual PDF or video files in our data delivery. You receive the structured metadata and the links to retrieve the media.
Yes. We can scope the pipeline to monitor specific hubs, collections, or provider profiles, ensuring you only receive data relevant to your target demographic or subject area.
We can run daily or weekly diff checks against specific categories or hubs to capture newly added resources, updated syllabus files, and new peer evaluations.
Yes, we extract the structured rubric criteria, numeric scores, and qualitative text from the peer review and evaluation sections of the resources.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete dump of STEM resources or continuous updates for state standards alignments, we scope, build, and operate the pipeline. Tell us what you need.
