We extract learning materials, peer reviews, discipline classifications, and author profiles from MERLOT. 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 Learning Materials objects from merlot.org. All fields typed and schema-versioned.
"material_id": "M124981", "title": "Introduction to Quantum Mechanics", "author_name": "Dr. Sarah Jenkins", "material_type": "Simulation", "primary_discipline": "Science and Technology", "creative_commons_license": "CC BY-NC-SA", "average_rating": 4.5, "peer_review_count": 3
| # | material_id | title | external_url | author_name | material_type | date_added |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Peer Reviews objects from merlot.org. All fields typed and schema-versioned.
"review_id": "PR89231", "material_id": "M124981", "reviewer_name": "Prof. Alan Turing", "overall_rating": 5.0, "content_quality_rating": 5.0, "ease_of_use_rating": 4.5, "review_date": "2023-11-14", "review_text": "Excellent simulation that accurately demonstrates wave-particle duality."
| # | review_id | material_id | reviewer_name | reviewer_profile_url | review_date | overall_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Author Profiles objects from merlot.org. All fields typed and schema-versioned.
"member_id": "U45192", "name": "Dr. Sarah Jenkins", "affiliation": "University of Bristol", "role": "Faculty", "joined_date": "2018-04-22", "authored_materials_count": 14, "submitted_materials_count": 29
| # | member_id | name | affiliation | role | joined_date | submitted_materials_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Discipline Taxonomy objects from merlot.org. All fields typed and schema-versioned.
"category_id": "C912", "name": "Physics", "parent_category": "Science and Technology", "material_count": 4821, "member_count": 1205, "category_url": "https://www.merlot.org/merlot/materials.htm?category=2513", "last_updated": "2023-12-01T10:00:00Z"
| # | category_id | name | parent_category | material_count | member_count | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from merlot.org. All fields typed and schema-versioned.
"keyword": "thermodynamics", "discipline_filter": "Engineering", "position": 1, "material_id": "M99214", "title": "Thermodynamics Fundamentals", "rating": 4.2, "material_type": "Tutorial", "scraped_at": "2023-12-15T08:30:12Z"
| # | keyword | discipline_filter | position | material_id | title | author |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our MERLOT scraper navigates complex academic taxonomies, extracts nested peer reviews, and normalises user-submitted metadata into clean, queryable datasets.
Title, author, material type, target audience, and language metadata extracted for every learning resource.
Parse Creative Commons licensing details and usage rights directly from the material metadata.
Extract structured peer reviews, including composite scores for content quality, effectiveness, and ease of use.
Reconstruct MERLOT's deep discipline hierarchy, mapping every material to its primary and secondary academic categories.
Scrape member profiles, institutional affiliations, and contribution histories for faculty network analysis.
Optionally resolve and validate external OER URLs to detect link rot and flag unavailable resources.
Run targeted extractions based on specific keywords, material types, or academic disciplines.
Capture community ratings, member comments, and discussion threads attached to learning materials.
Run continuous pipelines to capture new submissions and updated peer reviews at a weekly or monthly cadence.
Brief in. Clean data out.
Provide specific disciplines, keywords, or request a full database extraction. We design the schema together.
We configure Scrapy crawlers, handle pagination limits, and map the unstructured metadata for merlot.org.
Schema validation, null-rate checks, and taxonomy verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from academic repositories involves navigating deep hierarchies and cleaning highly variable user inputs. Here is how we manage the complexity.
MERLOT organises materials into complex, multi-level academic disciplines. Our crawlers traverse this hierarchy recursively, ensuring every material is tagged with its full lineage, from broad category down to specific sub-discipline.
OER metadata is often submitted by users, leading to inconsistent formatting in author names, affiliations, and material types. We apply regex-based cleaning and standardisation rules during the extraction phase to deliver a normalised dataset.
MERLOT acts as a directory; the actual learning materials are hosted externally. Our pipeline can optionally perform HTTP HEAD requests on external URLs to verify if the resource is still online, flagging dead links in the final dataset.
For ongoing pipelines, we maintain a hash index of last-seen values per material. Subsequent runs only push diffs for new materials or updated peer reviews, reducing downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops, responding before you notice.
Learning management systems (LMS) and educational platforms ingest MERLOT metadata to populate their own resource directories.
Researchers use the peer-review corpus and material descriptions to train natural language processing models on academic text.
Instructional designers track available OERs within specific disciplines to build cost-effective, open-access course structures.
Institutions analyse composite peer-review scores and user ratings to recommend high-quality materials to their faculty.
Universities map author affiliations and co-authorship patterns to understand collaboration across institutions.
University libraries integrate structured MERLOT records into their catalogue search systems for broader resource discovery.
"MERLOT contains decades of curated educational resources, but extracting structured taxonomy and peer-review data requires parsing highly variable, user-submitted metadata."
Most teams underestimate the complexity of academic repositories. Extracting MERLOT requires deep taxonomy traversal, normalising unstructured metadata across thousands of disciplines, and managing external link rot. DataFlirt handles the extraction logic so your engineers can focus on building educational tools, not maintaining scrapers.
Everything supported by our merlot.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, deduplication, and retry logic. Playwright handles JavaScript rendering for dynamic UI elements. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies to ensure consistent access and avoid rate limits during large-scale taxonomy traversal.
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 merlot.org scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available metadata from MERLOT is generally permissible. DataFlirt targets only public, non-authenticated material listings, peer reviews, and author profiles. We do not extract private workspace data or circumvent authentication walls.
Our spiders are built to recursively traverse the entire discipline taxonomy. Every extracted material record includes an array of its parent categories, ensuring you retain the full academic context.
Yes. Because MERLOT links out to external sites, link rot is common. We can configure the pipeline to perform lightweight HTTP validation on the external URLs and append a status code to your dataset.
We typically run full catalogue refreshes on a weekly or monthly cadence, depending on client requirements. Incremental updates for specific high-velocity disciplines can be scheduled daily.
No. We extract the structured metadata, peer reviews, and URLs pointing to the materials. Downloading and hosting the underlying external files falls outside our standard metadata extraction service.
Absolutely. We provide a sample run of up to 500 materials or a specific academic sub-discipline during the scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full OER database dump or continuous updates for specific academic disciplines, we scope, build, and operate the pipeline. Tell us what you need.
