Datasets for import testing

Bank Statement (PDF)

A one-page monthly bank statement — account summary and a dated transaction table with running balances. Synthetic data for testing PDF statement parsers, table extraction, and OCR.

File: PDF · Financial · 1 page
Use case: PDF editor testingData import

HL7 v2 ADT^A01 Admission Message

An HL7 v2.5 ADT^A01 patient-admission message — pipe/caret-delimited segments (MSH, EVN, PID, PV1, allergies, diagnosis). A fixture for testing healthcare integration engines and HL7 parsers. The patient is fictional.

File: HL7 · Healthcare
Use case: Data importConversion testing

X12 EDI 850 Purchase Order

An ANSI X12 850 purchase order — ISA/GS/ST envelope with BEG, N1, and PO1 line items. A fixture for testing EDI parsers and translators. Paired with the 810 invoice for the same order.

File: EDI · Edi
Use case: Data importConversion testing· Conversion set

X12 EDI 810 Invoice

An ANSI X12 810 invoice that references the 850 purchase order — ISA/GS/ST envelope with BIG, IT1 line items, and a total. The invoice twin of the purchase order, for testing EDI document flows.

File: EDI · Edi
Use case: Data importConversion testing· Conversion set

Clean CSV

A clean, well-formed CSV with a header and 20 rows — the baseline case for CSV parser testing.

File: CSV · CSV · 20 rows
Use case: CSV parsingData import

10,000-Row CSV

A CSV with 10,000 data rows — for testing streaming parsers, memory handling, and import performance.

File: CSV · CSV · 10,000 rows
Use case: CSV parsingData import

Flat JSON Array

A flat JSON array of ten simple objects — the baseline case for JSON parsing and mapping.

File: JSON · JSON · 10 records
Use case: JSON parsingData import

JSON Lines (JSONL)

A JSON Lines file with one object per line — for testing streaming/newline-delimited JSON parsers.

File: JSONL · JSON · 10 records
Use case: JSON parsingData import

Sample XML

A well-formed XML catalogue with nested elements and attributes — for testing XML parsers and XPath queries.

File: XML · XML
Use case: Data import

Sample YAML

A YAML configuration file with nested mappings, sequences, inline lists, and comments — for testing YAML parsers.

File: YAML · YAML
Use case: Data import

Sample SQLite Database

A real SQLite database with two related tables — users and orders joined by a foreign key — for testing database importers and SQL tooling.

File: SQLITE · Database
Use case: Data import

Fake Users Dataset (CSV, 500 rows)

A 500-row dataset of fake but realistic users (name, email, address, date of birth), generated with a fixed seed. Paired with a JSON twin.

File: CSV · Fake Datasets · 500 rows
Use case: Data importConversion testing· Conversion set

Fake Users Dataset (JSON, 500 records)

The 500-row users dataset as a JSON array — the format twin of the CSV version, for testing import and conversion.

File: JSON · Fake Datasets · 500 records
Use case: Data importConversion testing· Conversion set

Fake Orders Dataset (CSV, 1000 rows)

A 1000-row orders dataset whose user_id references the users dataset — a relational fixture for testing joins and import flows. Paired with a JSON twin.

File: CSV · Fake Datasets · 1,000 rows
Use case: Data importConversion testing· Conversion set

Fake Orders Dataset (JSON, 1000 records)

The 1000-row orders dataset as JSON — the format twin of the CSV version, relational to the users dataset.

File: JSON · Fake Datasets · 1,000 records
Use case: Data importConversion testing· Conversion set

NDJSON Stream

A newline-delimited JSON (NDJSON) stream of event records — for testing streaming JSON parsers.

File: NDJSON · JSON · 4 records
Use case: JSON parsingData import

E-commerce Products (CSV, 200 rows)

A realistic e-commerce product catalogue (200 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.

File: CSV · Ecommerce · 200 rows
Use case: Data importConversion testing+1· Conversion set

E-commerce Products (JSON, 200 records)

The e-commerce products table as a JSON array — the format twin of the CSV, for import and conversion testing.

File: JSON · Ecommerce · 200 records
Use case: Data importConversion testing· Conversion set

E-commerce Customers (CSV, 500 rows)

A realistic e-commerce customer directory (500 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.

File: CSV · Ecommerce · 500 rows
Use case: Data importConversion testing+1· Conversion set

E-commerce Customers (JSON, 500 records)

The e-commerce customers table as a JSON array — the format twin of the CSV, for import and conversion testing.

File: JSON · Ecommerce · 500 records
Use case: Data importConversion testing· Conversion set

E-commerce Orders (CSV, 2000 rows)

A realistic e-commerce order lines (customer_id → customers, product_id → products) (2000 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.

File: CSV · Ecommerce · 2,000 rows
Use case: Data importConversion testing+1· Conversion set

E-commerce Orders (JSON, 2000 records)

The e-commerce orders table as a JSON array — the format twin of the CSV, for import and conversion testing.

File: JSON · Ecommerce · 2,000 records
Use case: Data importConversion testing· Conversion set

E-commerce Database Schema (SQL)

A relational SQL schema (products, customers, orders with primary and foreign keys) plus sample INSERTs — the DDL twin of the e-commerce dataset, for testing schema import and migrations.

File: SQL · Ecommerce
Use case: Data importConversion testing

Stock OHLCV — Daily Candles (CSV, 252 rows)

A year of daily OHLCV stock candles (open/high/low/close/volume) as a seeded random walk — a realistic finance time-series for testing charting, indicators, and importers. JSON twin included.

File: CSV · Finance · 252 rows
Use case: Data importTime-series data+1· Conversion set

Stock OHLCV — Daily Candles (JSON, 252 records)

The daily OHLCV candles as a JSON array — the format twin of the CSV, for charting and time-series testing.

File: JSON · Finance · 252 records
Use case: Data importTime-series data· Conversion set

IoT Sensor Readings (CSV, 1440 rows)

A day of IoT sensor readings (temperature, humidity, pressure) at one-minute intervals from three sensors, with a realistic daily cycle plus noise — for testing time-series ingestion and downsampling. JSON twin included.

File: CSV · Iot · 1,440 rows
Use case: Data importTime-series data+1· Conversion set

IoT Sensor Readings (JSON, 1440 records)

The IoT sensor readings as a JSON array — the format twin of the CSV, for time-series testing.

File: JSON · Iot · 1,440 records
Use case: Data importTime-series data· Conversion set

FHIR R4 Patient Bundle (JSON)

A sample HL7 FHIR R4 bundle with a Patient plus vital-sign Observations, an Encounter, and a Condition — a realistic healthcare-interoperability fixture for testing FHIR parsers and mappers. Synthetic data, not a real person.

File: JSON · Healthcare
Use case: Data importConversion testing

Product JSON Schema (draft-07)

A JSON Schema (draft-07) describing a product object, with required fields, types, and constraints — paired with a conforming and a deliberately non-conforming instance for testing validators.

File: JSON · Schema
Use case: Schema validationData import· Conversion set

Large Transactions Dataset (CSV, 50k rows)

A 50,000-row transactions dataset — an in-repo 'large' fixture for testing streaming CSV parsers, import performance, and pagination. Deterministic (fixed seed).

File: CSV · Large · 50,000 rows
Use case: Data importPerformance testing+1

Apache Access Log — Common Format (log)

An Apache access log in the Common Log Format (CLF) — client, timestamp, request line, status, and byte count. Deterministic, with reserved documentation IPs. Paired with a Combined-format twin for testing log parsers.

File: LOG · Logs
Use case: Log parsingData import+1· Conversion set

Apache Access Log — Combined Format (log)

An Apache access log in the Combined Log Format — the Common fields plus referer and user-agent. A realistic web-server log for testing access-log parsers, analytics, and grok patterns.

File: LOG · Logs
Use case: Log parsingData import+1· Conversion set

CSV with Mixed Null Representations

A CSV where missing values are written seven different ways — empty string, NULL, NA, N/A, null, None, and a dash — across text and numeric columns. A fixture for testing null-detection and coercion in CSV importers.

File: CSV · Edge Cases · 8 rows
Use case: CSV parsingError handling+1

CSV with Mixed-Type Columns

A CSV whose 'value' column mixes integers, floats, scientific notation, dates, hex, thousands separators, and whitespace, and whose 'flag' column mixes a dozen boolean spellings. A torture test for type inference and schema detection.

File: CSV · Edge Cases · 12 rows
Use case: CSV parsingError handling+1

Wide CSV — 1000 Columns

A CSV with 1000 columns and a handful of rows — a stress test for parsers, spreadsheet importers, and databases with column limits or per-row buffering assumptions. Deterministic (fixed seed).

File: CSV · Edge Cases · 5 rows
Use case: CSV parsingPerformance testing+1

CSV with Mixed Timestamp Formats

A CSV listing timestamps in eleven formats — ISO 8601 with Z and numeric offsets, millisecond precision, naive local, date-only, Unix epoch in seconds and milliseconds, US AM/PM, and RFC 1123. A fixture for testing date parsing and timezone normalisation.

File: CSV · Edge Cases · 11 rows
Use case: CSV parsingError handling+1

Star Schema — Date Dimension (CSV)

The date dimension of a star schema — one row per day with a YYYYMMDD surrogate key and calendar attributes (year, quarter, month, weekday). Joins to the sales fact table on date_key.

File: CSV · Star Schema · 31 rows
Use case: Data importData engineering+1· Conversion set

Star Schema — Product Dimension (CSV)

The product dimension of a star schema — surrogate key, SKU, name, category, and unit price. Joins to the sales fact table on product_key.

File: CSV · Star Schema · 20 rows
Use case: Data importData engineering+1· Conversion set

Star Schema — Customer Dimension (CSV)

The customer dimension of a star schema — surrogate key, name, city, country, and segment. Joins to the sales fact table on customer_key. Names are synthetic.

File: CSV · Star Schema · 40 rows
Use case: Data importData engineering+1· Conversion set

Star Schema — Sales Fact Table (CSV, 300 rows)

The sales fact table at the centre of a star schema — 300 sale lines with foreign keys to the date, product, and customer dimensions plus quantity and amount measures. A fixture for testing joins, star-schema imports, and BI tools.

File: CSV · Star Schema · 300 rows
Use case: Data importData engineering+1· Conversion set

Social Network Graph (GraphML)

A small synthetic social network in GraphML — 12 people in two communities with a few bridging ties, carrying node attributes (name, age, community) and weighted edges. Twinned with a GEXF file for testing graph importers and format converters.

File: GRAPHML · Graph
Use case: Graph dataConversion testing+1· Conversion set

Social Network Graph (GEXF)

The same 12-person social network in GEXF 1.3 (Gephi's native format) — node attributes and weighted edges. The format twin of the GraphML file, for testing Gephi imports and GraphML↔GEXF conversion.

File: GEXF · Graph
Use case: Graph dataConversion testing+1· Conversion set

Service Dependency Graph (GraphML, directed)

A directed service-dependency graph in GraphML — ten microservices (web, api, auth, db, cache, queue, worker, …) with their call/dependency edges. A fixture for testing directed-graph importers, cycle detection, and topological sorting.

File: GRAPHML · Graph
Use case: Graph dataData import

World Cities (40 rows, CSV)

A curated world-cities dataset — 40 major cities with country, latitude, longitude, and population. A realistic geospatial fixture for testing map plots, geocoding, and CSV→GeoJSON conversion. GeoJSON twin included.

File: CSV · Geospatial · 40 rows
Use case: GeospatialData import+1· Conversion set

World Cities (40 points, GeoJSON)

The same 40 world cities as a GeoJSON FeatureCollection of Points (longitude, latitude order per RFC 7946), each with name, country, and population properties. The mapping twin of the CSV, for testing GeoJSON parsers and map renderers.

File: GEOJSON · Geospatial
Use case: GeospatialData import+1· Conversion set

Survey Responses — Likert + NPS (CSV, 60 rows)

A customer-survey response set — 60 respondents rating five statements on a 1–5 Likert scale plus a 0–10 NPS score and a segment. A fixture for testing survey analysis, aggregation, and NPS/CSAT calculations.

File: CSV · Surveys · 60 rows
Use case: Data importConversion testing+1

Recommender Ratings — user/item/rating (CSV, 240 rows)

A MovieLens-style ratings log — 240 user/item/rating events with Unix timestamps across 50 users and 40 items. A fixture for testing recommender pipelines, collaborative-filtering loaders, and sparse-matrix builders.

File: CSV · Recommender · 240 rows
Use case: Data importModel evaluation+1

Bank Transactions (CSV, 60 rows)

A bank-transaction statement — 60 debits and credits across three accounts (masked numbers) with running balances, categories, and merchants. Synthetic data for testing statement parsers, categorisation, and reconciliation.

File: CSV · Banking · 60 rows
Use case: Data importConversion testing· Conversion set

Bank Transactions (JSON, 60 records)

The bank transactions as a JSON array — the format twin of the CSV, for import and reconciliation testing.

File: JSON · Banking · 60 records
Use case: Data importConversion testing· Conversion set

Payment Test Cards (CSV)

A reference list of well-known payment TEST card numbers (Visa, Mastercard, Amex, Discover, JCB, Diners) with sample CVV and expiry. These are published test values that pass the Luhn check but are not real accounts — for testing payment forms and card validators. Never use real card data.

File: CSV · Banking · 8 rows
Use case: Form testingData import

ISO 20022 Credit Transfer — pain.001 (XML)

A sample ISO 20022 customer credit-transfer initiation (pain.001.001.09) — a single payment with debtor/creditor parties, IBANs, BICs, amount, and remittance info. IBANs are published documentation examples; a fixture for testing payment-message parsers and bank integrations.

File: XML · Banking
Use case: Data importConversion testing+1

Real-estate Listings (CSV, 30 rows)

A property-listings dataset — 30 homes with address, type, price, beds/baths, size, year built, coordinates, and status. Synthetic data for testing listing importers, map plots, and price analytics.

File: CSV · Real Estate · 30 rows
Use case: Data importGeospatial+1· Conversion set

Real-estate Listings (JSON, 30 records)

The property listings as a JSON array — the format twin of the CSV, for import and mapping tests.

File: JSON · Real Estate · 30 records
Use case: Data importGeospatial+1· Conversion set

Flight Schedule (CSV, 30 rows)

A flight-schedule dataset — 30 flights with airline, origin/destination airports, departure/arrival times, aircraft, gate, and status. Synthetic data for testing travel apps, schedule parsers, and status boards.

File: CSV · Travel · 30 rows
Use case: Data importConversion testing· Conversion set

Flight Schedule (JSON, 30 records)

The flight schedule as a JSON array — the format twin of the CSV, for import and display tests.

File: JSON · Travel · 30 records
Use case: Data importConversion testing· Conversion set

Weather Forecast — 30 days (CSV, 30 rows)

A 30-day daily weather forecast for one city — high/low temperature, condition, precipitation, wind, and humidity. Synthetic (seeded) data for testing weather widgets, chart tools, and time-series import.

File: CSV · Weather · 30 rows
Use case: Data importTime-series data+1· Conversion set

Weather Forecast — 30 days (JSON, 30 records)

The daily forecast as a JSON array — the format twin of the CSV, for import and charting tests.

File: JSON · Weather · 30 records
Use case: Data importTime-series data+1· Conversion set

Sports League Standings (CSV)

A football-style league table — position, played, won/drawn/lost, goals for/against, goal difference, and points for ten teams, derived from a full round of synthetic match results. Paired with the results JSON.

File: CSV · Sports
Use case: Data importConversion testing· Conversion set

Sports Match Results (JSON)

The full round of match results behind the league table — home/away teams, scores, and dates as a JSON array. A fixture for testing sports-data importers and standings calculators.

File: JSON · Sports
Use case: Data importConversion testing· Conversion set

Employee Directory (CSV, 30 rows)

An employee directory — 30 staff with department, title, hire date, salary, and manager reference. Synthetic HR data for testing org-chart builders, directory imports, and payroll pipelines.

File: CSV · Hr · 30 rows
Use case: Data importConversion testing· Conversion set

Employee Directory (JSON, 30 records)

The employee directory as a JSON array — the format twin of the CSV, for HR import testing.

File: JSON · Hr · 30 records
Use case: Data importConversion testing· Conversion set

Payroll Summary (JSON)

A monthly payroll summary derived from the employee directory — headcount, annual and monthly gross totals, and a per-department breakdown. A fixture for testing finance/HR dashboards and JSON aggregation.

File: JSON · Hr
Use case: Data importConversion testing

CSV with Leading-Zero Identifiers

SKU identifiers that start with zeros. Spreadsheet tools often coerce these to numbers and drop the zeros — keep them as text.

File: CSV · Edge Cases · 3 rows
Use case: CSV parsingData import

CSV DST and Leap-Day Timestamps

Timestamps spanning a US spring-forward DST gap and leap-day dates for timezone and calendar edge-case testing.

File: CSV · Edge Cases · 4 rows
Use case: CSV parsingData import

CSV — UTF-8 with BOM

A CSV prefixed with a UTF-8 BOM (EF BB BF) and accented / CJK cells — for testing BOM-aware importers.

File: CSV · Encodings · 3 rows
Use case: CSV parsingEncoding detection+1

CSV — Semicolon Delimiter (EU)

European-style CSV using semicolons as delimiters and comma decimals — a common locale edge case.

File: CSV · Encodings · 2 rows
Use case: CSV parsingData import

CSV — Pipe Delimiter

A pipe-delimited (| ) tabular file — for testing non-comma delimiter autodetection.

File: CSV · Encodings · 2 rows
Use case: CSV parsingData import

Fixed-Width Positions File (TXT)

A classic fixed-width text extract with documented column positions — for COBOL-style / mainframe importer tests.

File: TXT · Encodings · 3 rows
Use case: CSV parsingData import+1

CSV — Hash Comment Lines

A CSV with # comment lines mixed among data — common in scientific exports; tests comment skipping.

File: CSV · Encodings · 2 rows
Use case: CSV parsingData import

CSV — Single Column

A one-column CSV of email addresses — edge case for delimiter sniffers that expect commas in every row.

File: CSV · Encodings · 2 rows
Use case: CSV parsingData import

SaaS Product Events (CSV)

Synthetic product-analytics events (signup/login/upgrade) for testing event pipelines and warehouse loads. CSV twin.

File: CSV · Saas · 24 rows
Use case: Data importCSV parsing· Conversion set

SaaS Product Events (JSON)

Synthetic product-analytics events (signup/login/upgrade) for testing event pipelines and warehouse loads. JSON twin.

File: JSON · Saas · 24 records
Use case: Data importJSON parsing· Conversion set

POS SKU Catalog (CSV)

A point-of-sale SKU catalog with tax codes and active flags — fictional Meridian Supply inventory. CSV twin.

File: CSV · Pos · 8 rows
Use case: Data importCSV parsing· Conversion set

POS SKU Catalog (JSON)

A point-of-sale SKU catalog with tax codes and active flags — fictional Meridian Supply inventory. JSON twin.

File: JSON · Pos · 8 records
Use case: Data importJSON parsing· Conversion set

Lab Measurements (CSV)

Synthetic lab readings (temperature, pressure, pH) with ISO timestamps for scientific ETL tests. CSV twin.

File: CSV · Lab · 16 rows
Use case: Data importCSV parsing· Conversion set

Lab Measurements (JSON)

Synthetic lab readings (temperature, pressure, pH) with ISO timestamps for scientific ETL tests. JSON twin.

File: JSON · Lab · 16 records
Use case: Data importJSON parsing· Conversion set

Marketing Attribution (CSV)

Synthetic multi-touch attribution click rows with UTM fields — for marketing analytics importers. CSV twin.

File: CSV · Marketing · 10 rows
Use case: Data importCSV parsing· Conversion set

Marketing Attribution (JSON)

Synthetic multi-touch attribution click rows with UTM fields — for marketing analytics importers. JSON twin.

File: JSON · Marketing · 10 records
Use case: Data importJSON parsing· Conversion set

FINANCE — Ledger Lines (CSV)

Synthetic finance dataset (ledger-lines) as CSV — for ETL and warehouse loader tests.

File: CSV · Finance · 8 rows
Use case: Data importCSV parsing· Conversion set

FINANCE — Ledger Lines (JSON)

JSON twin of the finance/ledger-lines mini-dataset.

File: JSON · Finance · 8 records
Use case: Data importJSON parsing· Conversion set

SUPPORT — Tickets (CSV)

Synthetic support dataset (tickets) as CSV — for ETL and warehouse loader tests.

File: CSV · Support · 6 rows
Use case: Data importCSV parsing· Conversion set

SUPPORT — Tickets (JSON)

JSON twin of the support/tickets mini-dataset.

File: JSON · Support · 6 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 01 (JSON)

Deterministic synthetic JSON batch 01 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 02 (JSON)

Deterministic synthetic JSON batch 02 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 03 (JSON)

Deterministic synthetic JSON batch 03 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 04 (JSON)

Deterministic synthetic JSON batch 04 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 05 (JSON)

Deterministic synthetic JSON batch 05 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 06 (JSON)

Deterministic synthetic JSON batch 06 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 07 (JSON)

Deterministic synthetic JSON batch 07 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 08 (JSON)

Deterministic synthetic JSON batch 08 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 09 (JSON)

Deterministic synthetic JSON batch 09 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 10 (JSON)

Deterministic synthetic JSON batch 10 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 11 (JSON)

Deterministic synthetic JSON batch 11 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 12 (JSON)

Deterministic synthetic JSON batch 12 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 13 (JSON)

Deterministic synthetic JSON batch 13 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 14 (JSON)

Deterministic synthetic JSON batch 14 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 15 (JSON)

Deterministic synthetic JSON batch 15 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 16 (JSON)

Deterministic synthetic JSON batch 16 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 17 (JSON)

Deterministic synthetic JSON batch 17 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 18 (JSON)

Deterministic synthetic JSON batch 18 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 19 (JSON)

Deterministic synthetic JSON batch 19 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 20 (JSON)

Deterministic synthetic JSON batch 20 for bulk-import smoke tests.

File: JSON · Synth · 5 records
Use case: Data importJSON parsing· Conversion set

Synth Batch 01 (CSV)

Deterministic synthetic CSV batch 01 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 02 (CSV)

Deterministic synthetic CSV batch 02 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 03 (CSV)

Deterministic synthetic CSV batch 03 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 04 (CSV)

Deterministic synthetic CSV batch 04 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 05 (CSV)

Deterministic synthetic CSV batch 05 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 06 (CSV)

Deterministic synthetic CSV batch 06 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 07 (CSV)

Deterministic synthetic CSV batch 07 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 08 (CSV)

Deterministic synthetic CSV batch 08 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 09 (CSV)

Deterministic synthetic CSV batch 09 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 10 (CSV)

Deterministic synthetic CSV batch 10 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 11 (CSV)

Deterministic synthetic CSV batch 11 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 12 (CSV)

Deterministic synthetic CSV batch 12 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 13 (CSV)

Deterministic synthetic CSV batch 13 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 14 (CSV)

Deterministic synthetic CSV batch 14 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 15 (CSV)

Deterministic synthetic CSV batch 15 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 16 (CSV)

Deterministic synthetic CSV batch 16 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 17 (CSV)

Deterministic synthetic CSV batch 17 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 18 (CSV)

Deterministic synthetic CSV batch 18 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 19 (CSV)

Deterministic synthetic CSV batch 19 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

Synth Batch 20 (CSV)

Deterministic synthetic CSV batch 20 for bulk-import smoke tests.

File: CSV · Synth · 5 rows
Use case: Data importCSV parsing· Conversion set

HR — Employees Mini (CSV)

Synthetic hr dataset (employees-mini) as CSV — for ETL and warehouse loader tests.

File: CSV · Hr · 8 rows
Use case: Data importCSV parsing· Conversion set

HR — Employees Mini (JSON)

JSON twin of the hr/employees-mini mini-dataset.

File: JSON · Hr · 8 records
Use case: Data importJSON parsing· Conversion set

IOT — Sensor Readings Mini (CSV)

Synthetic iot dataset (sensor-readings-mini) as CSV — for ETL and warehouse loader tests.

File: CSV · Iot · 12 rows
Use case: Data importCSV parsing· Conversion set

IOT — Sensor Readings Mini (JSON)

JSON twin of the iot/sensor-readings-mini mini-dataset.

File: JSON · Iot · 12 records
Use case: Data importJSON parsing· Conversion set

Time-series — Irregular Intervals (CSV)

Sensor readings at irregular minute gaps — tests time-series resampling and gap detection.

File: CSV · Timeseries · 8 rows
Use case: Time-series dataData import+1· Conversion set

Time-series — Irregular Intervals (JSON)

JSON twin of irregular-interval sensor readings for charting library tests.

File: JSON · Timeseries · 8 records
Use case: Time-series dataData import+1· Conversion set

Time-series — DST Spring Forward Gap (CSV)

Hourly load data skipping the 03:00 hour on DST spring-forward day — tests timezone/DST gap handling.

File: CSV · Timeseries · 4 rows
Use case: Time-series dataData import+1

Time-series — DST Fall Back Duplicate Hour (CSV)

Load readings with a repeated 01:00 local hour on fall-back DST — tests ambiguous timestamp handling.

File: CSV · Timeseries · 5 rows
Use case: Time-series dataData import+1

Time-series — Duplicate Timestamp Keys (CSV)

Two rows share the same timestamp key — tests deduplication and last-write-wins policies.

File: CSV · Timeseries · 3 rows
Use case: Time-series dataData import+2· Conversion set

Time-series — Duplicate Timestamp Keys (JSON)

JSON array with duplicate timestamp keys for ingestion pipeline tests.

File: JSON · Timeseries · 3 records
Use case: Time-series dataData import+1· Conversion set

Time-series — Gap Filled

Series with an explicit null value at a missing sample point.

File: CSV · Timeseries · 3 rows
Use case: Time-series dataData import+1

Time-series — Out Of Order

Timestamps out of chronological order — tests sort-on-ingest behaviour.

File: CSV · Timeseries · 3 rows
Use case: Time-series dataData import+1

Time-series — Mixed Tz

Same nominal instant expressed in Z, -05:00, and +09:00 offsets.

File: JSON · Timeseries · 3 rows
Use case: Time-series dataData import+1

Time-series — Nan Gaps

CSV with NaN string and empty cells representing missing measurements.

File: CSV · Timeseries · 3 rows
Use case: Time-series dataData import+1

Time-series — Millisecond

Sub-second millisecond timestamps for precision parsing tests.

File: JSON · Timeseries · 2 rows
Use case: Time-series dataData import+1

Time-series — Business Hours

Hourly order counts weekdays only (09–16 UTC) across three SAMPLE days.

File: CSV · Timeseries · 24 rows
Use case: Time-series dataData import+1

Time-series — Weekly Agg

Pre-aggregated weekly totals JSON for rollup/chart tests.

File: JSON · Timeseries · 1 row
Use case: Time-series dataData import+1

Time-series — Leap Day

Readings spanning Feb 29 leap day — calendar edge case.

File: CSV · Timeseries · 2 rows
Use case: Time-series dataData import+1

Time-series — Epoch Boundary

Timestamps at Unix epoch and classic 32-bit Y2038 boundary.

File: JSON · Timeseries · 2 rows
Use case: Time-series dataData import+1

Time-series — Sparse Events (JSONL)

Sparse event log in JSON Lines — two events hours apart for event-stream ingestion tests.

File: JSONL · Timeseries · 2 records
Use case: Time-series dataData import+2

Parquet — Null-heavy Columns

Tiny Parquet table with null name/score/note cells — tests null handling in columnar readers.

File: PARQUET · Columnar · 3 rows
Use case: Data importConversion testing· Conversion set

Parquet — Dictionary-encoded Column

Parquet with dictionary-encoded string column — tests dictionary page decoding.

File: PARQUET · Columnar · 5 rows
Use case: Data importConversion testing

Parquet — Nested Struct Column

Parquet with a struct column — tests nested type round-trips.

File: PARQUET · Columnar
Use case: Data importConversion testing

Parquet — List Column

Parquet with a list-of-string column — tests repeated-field decoding.

File: PARQUET · Columnar
Use case: Data importConversion testing

Parquet — Empty Table

Empty Parquet file with schema but no rows — edge case for readers.

File: PARQUET · Columnar · 0 rows
Use case: Data importConversion testing

Parquet — Single Row

Single-row Parquet table — minimal columnar fixture.

File: PARQUET · Columnar · 1 row
Use case: Data importConversion testing

Parquet — Duplicate Key Column

Parquet with repeated key values — tests join/aggregation edge cases.

File: PARQUET · Columnar · 3 rows
Use case: Data importConversion testing

Parquet — Mixed Nullable Types

Parquet with bool/int/string/float columns including a null integer.

File: PARQUET · Columnar
Use case: Data importConversion testing

Healthcare Claim (FHIR SAMPLE JSON)

Synthetic FHIR Claim resource for healthcare billing parser tests — not a real patient.

File: JSON · Healthcare
Use case: Data importJSON parsing

Healthcare ORM HL7 Message (SAMPLE)

Sample HL7 v2 ORM order message with PID and OBR segments — fictional patient and order ids.

File: HL7 · Healthcare
Use case: Data import

Healthcare Patient Demographics (CSV)

Synthetic patient demographics CSV for EHR import and de-identification pipeline tests.

File: CSV · Healthcare · 2 rows
Use case: Data importCSV parsing

Healthcare Procedure Codes (JSON)

SAMPLE procedure code list with fees for medical billing calculator tests.

File: JSON · Healthcare
Use case: Data importJSON parsing

Healthcare Prescription (JSON)

Synthetic e-prescription JSON for pharmacy routing and validation tests.

File: JSON · Healthcare
Use case: Data import

Healthcare Lab Results (CSV)

Synthetic lab result rows for clinical data warehouse import tests.

File: CSV · Healthcare · 2 rows
Use case: Data importCSV parsing

Fintech Double-entry Ledger (JSON)

Balanced double-entry ledger SAMPLE for accounting engine validation tests.

File: JSON · Fintech
Use case: Data importJSON parsing

Fintech Transaction History (CSV)

Synthetic bank transaction history for personal-finance importer tests.

File: CSV · Fintech · 2 rows
Use case: Data importCSV parsing

Fintech Bank Statement Lines (CSV)

Synthetic bank statement line items for reconciliation tooling tests.

File: CSV · Fintech · 2 rows
Use case: Data import

Fintech SAR Compliance Flag (JSON)

Synthetic suspicious-activity flag JSON for AML compliance workflow tests.

File: JSON · Fintech
Use case: Data import

Inventory BOM Multi-level (JSON)

Multi-level bill-of-materials SAMPLE for MRP and assembly planning tests.

File: JSON · Inventory
Use case: Data importJSON parsing

Inventory SKU Catalog (CSV)

Fictional SKU catalog for ERP and POS import tests.

File: CSV · Inventory · 2 rows
Use case: Data importCSV parsing

Inventory Stock Levels (CSV)

Per-warehouse stock quantities for inventory reconciliation tests.

File: CSV · Inventory · 2 rows
Use case: Data import

Inventory Purchase Order (JSON)

Synthetic purchase order JSON for procurement workflow tests.

File: JSON · Inventory
Use case: Data import

Inventory Cycle Count (CSV)

Cycle-count variance SAMPLE for inventory audit reconciliation tests.

File: CSV · Inventory · 1 row
Use case: Data importCSV parsing

Feather — Null-heavy Columns

Feather/Arrow IPC twin of the null-heavy table — grouped with the Parquet nulls fixture.

File: FEATHER · Columnar · 3 rows
Use case: Data importConversion testing· Conversion set

Feather — Dictionary-encoded Column

Feather file with dictionary-encoded strings — Arrow IPC edge-case fixture.

File: FEATHER · Columnar
Use case: Data importConversion testing

Columnar Nulls Schema (JSON)

JSON description of nullable columns in the null-heavy columnar fixtures.

File: JSON · Columnar
Use case: Data importConversion testing+1· Conversion set

Parquet — Decimal as String Column

Amounts stored as strings in Parquet — common ingestion edge case for ETL parsers.

File: PARQUET · Columnar · 2 rows
Use case: Data importConversion testing

CSV Edge — Semicolon Eu

Tiny SAMPLE CSV (semicolon-eu) exercising delimiter/quoting edge behaviour.

File: CSV · CSV
Use case: CSV parsingError handling+1· Conversion set

CSV Edge — Pipe Delimited

Tiny SAMPLE CSV (pipe-delimited) exercising delimiter/quoting edge behaviour.

File: CSV · CSV
Use case: CSV parsingError handling+1· Conversion set

CSV Edge — Hash Commented

Tiny SAMPLE CSV (hash-commented) exercising delimiter/quoting edge behaviour.

File: CSV · CSV
Use case: CSV parsingError handling+1· Conversion set

CSV Edge — Quoted Embedded Comma

Tiny SAMPLE CSV (quoted-embedded-comma) exercising delimiter/quoting edge behaviour.

File: CSV · CSV
Use case: CSV parsingError handling+1· Conversion set

CSV Edge — Quoted Embedded Newline

Tiny SAMPLE CSV (quoted-embedded-newline) exercising delimiter/quoting edge behaviour.

File: CSV · CSV
Use case: CSV parsingError handling+1· Conversion set

CSV Edge — Empty Fields

Tiny SAMPLE CSV (empty-fields) exercising delimiter/quoting edge behaviour.

File: CSV · CSV
Use case: CSV parsingError handling+1· Conversion set

CSV Edge — Trailing Comma

Tiny SAMPLE CSV (trailing-comma) exercising delimiter/quoting edge behaviour.

File: CSV · CSV
Use case: CSV parsingError handling+1· Conversion set

CSV Edge — Header Only

Tiny SAMPLE CSV (header-only) exercising delimiter/quoting edge behaviour.

File: CSV · CSV
Use case: CSV parsingError handling+1· Conversion set

CSV Edge — Single Column

Tiny SAMPLE CSV (single-column) exercising delimiter/quoting edge behaviour.

File: CSV · CSV
Use case: CSV parsingError handling+1· Conversion set

CSV Edge — Mixed Quoting

Tiny SAMPLE CSV (mixed-quoting) exercising delimiter/quoting edge behaviour.

File: CSV · CSV
Use case: CSV parsingError handling+1· Conversion set

Directed Acyclic Pipeline - GraphML

A six-stage software pipeline DAG for testing dependency import, cycle checks and reproducible topological sorting. This GraphML member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: GRAPHML · Graph
Use case: Graph dataConversion testing+1· Conversion set

Directed Acyclic Pipeline - GEXF 1.3

A six-stage software pipeline DAG for testing dependency import, cycle checks and reproducible topological sorting. This GEXF 1.3 member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: GEXF · Graph
Use case: Graph dataConversion testing+1· Conversion set

Directed Acyclic Pipeline - JSON edge list

A six-stage software pipeline DAG for testing dependency import, cycle checks and reproducible topological sorting. This JSON edge list member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: JSON · Graph
Use case: Graph dataConversion testing+1· Conversion set

Directed Dependency Cycle - GraphML

A four-service directed cycle whose only valid topological result is failure, for dependency-analysis regression tests. This GraphML member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: GRAPHML · Graph
Use case: Graph dataConversion testing+1· Conversion set

Directed Dependency Cycle - GEXF 1.3

A four-service directed cycle whose only valid topological result is failure, for dependency-analysis regression tests. This GEXF 1.3 member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: GEXF · Graph
Use case: Graph dataConversion testing+1· Conversion set

Directed Dependency Cycle - JSON edge list

A four-service directed cycle whose only valid topological result is failure, for dependency-analysis regression tests. This JSON edge list member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: JSON · Graph
Use case: Graph dataConversion testing+1· Conversion set

Disconnected Directed Graph - GraphML

A directed graph with two linked islands and one isolated node, designed for weak-component and isolated-node tests. This GraphML member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: GRAPHML · Graph
Use case: Graph dataConversion testing+1· Conversion set

Disconnected Directed Graph - GEXF 1.3

A directed graph with two linked islands and one isolated node, designed for weak-component and isolated-node tests. This GEXF 1.3 member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: GEXF · Graph
Use case: Graph dataConversion testing+1· Conversion set

Disconnected Directed Graph - JSON edge list

A directed graph with two linked islands and one isolated node, designed for weak-component and isolated-node tests. This JSON edge list member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: JSON · Graph
Use case: Graph dataConversion testing+1· Conversion set

Weighted Directed Multigraph - GraphML

A weighted acyclic multigraph with two parallel source-to-router edges, checking multiplicity and floating-point weight preservation. This GraphML member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: GRAPHML · Graph
Use case: Graph dataConversion testing+1· Conversion set

Weighted Directed Multigraph - GEXF 1.3

A weighted acyclic multigraph with two parallel source-to-router edges, checking multiplicity and floating-point weight preservation. This GEXF 1.3 member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: GEXF · Graph
Use case: Graph dataConversion testing+1· Conversion set

Weighted Directed Multigraph - JSON edge list

A weighted acyclic multigraph with two parallel source-to-router edges, checking multiplicity and floating-point weight preservation. This JSON edge list member carries the same node IDs, edge IDs, directions and weights as its two format twins.

File: JSON · Graph
Use case: Graph dataConversion testing+1· Conversion set

GDPR Sample Data Export (ZIP)

A small ZIP shaped like a GDPR subject-access export — profile JSON, activity CSV, consents JSON, and a README. All PII is fictional SAMPLE data.

File: ZIP · Exports · 4 members
Use case: Data importConversion testing+1

GDPR Export — JSON Only (ZIP, SAMPLE)

SAMPLE subject-access ZIP with JSON profile/preferences only — fictional PII.

File: ZIP · Exports · 3 members
Use case: Privacy export testingData import+1

GDPR Export — CSV Heavy (ZIP, SAMPLE)

SAMPLE GDPR-style ZIP dominated by activity/devices/sessions CSVs — fictional PII.

File: ZIP · Exports · 4 members
Use case: Privacy export testingCSV parsing+1

GDPR Export — Nested Paths (ZIP, SAMPLE)

SAMPLE nested-folder GDPR export with JSON + JSONL members — fictional PII.

File: ZIP · Exports · 4 members
Use case: Privacy export testingData import+1

GDPR Messages Export (JSON, SAMPLE)

Standalone SAMPLE messages JSON fragment for privacy-export parsers.

File: JSON · Exports
Use case: Privacy export testingData import

GDPR Activity Log (JSONL, SAMPLE)

SAMPLE activity JSONL for subject-access export pipelines.

File: JSONL · Exports
Use case: Privacy export testingData import

Sentiment Classification Dataset (JSONL)

A labelled sentiment-classification dataset in JSON Lines — 24 short product-review-style sentences balanced across positive, negative, and neutral. Fully synthetic; a fixture for testing text-classification loaders, tokenizers, and JSONL parsers.

File: JSONL · Nlp · 24 records
Use case: ML training dataNLP datasets+2

Model Benchmark Results (JSON)

A model-evaluation summary in JSON — per-task scores for a fictional model across sentiment, NER, summarization, translation, and QA, each with its metric and sample size. A fixture for testing eval dashboards and leaderboard importers.

File: JSON · Eval
Use case: Model evaluationJSON parsing+1

Coverage — Per-File Table (CSV)

The same coverage as a flat table with a TOTAL row, for spreadsheets, trend charts and diffing two runs without an XML parser. The TOTAL row is the arithmetic sum of the four file rows, so it doubles as a checksum on any tool that regenerates it. Every file in this group describes the same four-file source tree and reports 127/140 lines, 17/24 branch outcomes and 18/20 functions, so a converter can be diffed against a known answer.

File: CSV · Coverage · 5 rows
Use case: CSV parsingConversion testing+1· Conversion set

Test Case Matrix — Twelve Documented Cases (CSV)

Twelve test cases with level, priority, the requirement each covers, preconditions, steps and expected result — the table a test-management import expects. Eleven of the twelve are automated, and the requirement column joins this file to the traceability matrix next to it.

File: CSV · Plans · 12 rows
Use case: CSV parsingData import+1· Conversion set

Requirements Traceability Matrix with One Gap (CSV)

Thirteen requirements mapped to the test cases that cover them — and one, REQ-PAY-02, deliberately covered by nothing. A coverage-gap report that does not surface that row is not working, which makes this the answer key for exactly that check.

File: CSV · Plans · 13 rows
Use case: CSV parsingData import+1· Conversion set

SBOM Component Inventory (CSV)

The same fictional component tree flattened to a spreadsheet-friendly CSV — name, version, licence, depth, purl and a SHA-256 prefix — for testing the export path SBOM tools offer alongside JSON. Every package, version, hash and licence is fictional — the tree describes nothing real.

File: CSV · Sbom · 10 rows
Use case: CSV parsingData import+1· Conversion set

Lockfile Pin Summary (CSV)

Declared range against resolved version for every fictional package pinned by the lockfiles in this category — the table a review needs to see which pins drifted from their manifest ranges. Every package, version, hash and licence is fictional — the tree describes nothing real.

File: CSV · Lockfiles · 9 rows
Use case: CSV parsingData import+1· Conversion set

OSV Vulnerability Records (JSON Lines)

All four SAMPLE advisories as newline-delimited OSV records — the bulk shape an advisory database is loaded from, where each line must parse independently. Advisory identifiers use the invented NOVUS-SAMPLE namespace with SAMPLE-CVE aliases; no identifier here refers to a published CVE, GHSA or OSV record, and no package named exists.

File: JSONL · Vulnerability · 4 records
Use case: JSON parsingData import+1· Conversion set

Vulnerability Summary (CSV)

The four SAMPLE advisories flattened to one row each — the export a security review circulates, and the reference answer for any converter run against the Trivy, Grype or OSV reports here. Advisory identifiers use the invented NOVUS-SAMPLE namespace with SAMPLE-CVE aliases; no identifier here refers to a published CVE, GHSA or OSV record, and no package named exists.

File: CSV · Vulnerability · 4 rows
Use case: CSV parsingData import+1· Conversion set

Severity Scoring and SLA Matrix (CSV)

A severity-band table mapping CVSS ranges to remediation SLAs and gate actions, with the finding count each band has in this fixture set — so a policy engine can be tested end to end. Advisory identifiers use the invented NOVUS-SAMPLE namespace with SAMPLE-CVE aliases; no identifier here refers to a published CVE, GHSA or OSV record, and no package named exists.

File: CSV · Vulnerability · 5 rows
Use case: CSV parsingConfig testing+1· Conversion set

SLSA Build Level Matrix (CSV)

How the nine fictional dependencies distribute across SLSA build levels, with the policy outcome for each band — the table that turns the VSA's dependencyLevels object into something reviewable. Every package, version, hash and licence is fictional — the tree describes nothing real.

File: CSV · Provenance · 4 rows
Use case: CSV parsingData import+1· Conversion set

Dependency Graph (GraphML)

The dependency graph in GraphML, with typed attribute keys for node version, licence and depth and for edge range — the interchange format graph databases and analysis tools import. Every package, version, hash and licence is fictional — the tree describes nothing real.

File: GRAPHML · Graphs
Use case: Graph dataConversion testing+1· Conversion set

Transitive Dependency Closure (CSV)

Every edge in the fictional dependency graph as one row — parent, child, declared range, resolved version, depth and whether the edge is direct — the form a spreadsheet or SQL import can aggregate. Every package, version, hash and licence is fictional — the tree describes nothing real.

File: CSV · Graphs · 10 rows
Use case: CSV parsingGraph data+1· Conversion set

Kestrel Bay Territory — OpenStreetMap XML Twin

The towns and roads in OSM XML: tagged nodes, three highway ways sharing the junction node at Kestrel Bay, and a route relation over them. All object IDs are negative — the convention for objects that have never been uploaded — so nothing here can be mistaken for real OSM data.

File: OSM · Territory · UTF-8
Use case: GeospatialConversion testing+1· Conversion set

GPX — Route (rte) Rather Than a Track

A `rte` element: an ordered list of waypoints describing a planned route, with no timestamps because it has not been travelled yet. Many importers only look for `trk` and report this perfectly valid file as empty.

File: GPX · Gpx · UTF-8
Use case: GeospatialConversion testing+1· Conversion set

GPX — Standalone Waypoints Only

Five `wpt` elements for the towns of the territory, with names, symbols and descriptions but no track or route. It is the GPX equivalent of a point layer, and the third of the three top-level GPX content types.

File: GPX · Gpx · UTF-8
Use case: GeospatialConversion testing+1· Conversion set

NMEA 0183 — Mixed GGA/RMC/GSA/GSV/VTG Log

A twelve-fix receiver log with the five sentence types a GPS unit normally interleaves, including three-part GSV satellite-in-view sets. Every checksum is correct, so a parser that verifies them should accept the whole file.

File: NMEA · Nmea · ASCII
Use case: GeospatialTime-series data+1· Conversion set

NMEA 0183 — GGA Sentences Only

The same twelve fixes reduced to GGA alone. GGA carries time-of-day but no date and no speed, so a parser that only reads GGA cannot place the track on a calendar — a real limitation that this file makes obvious rather than leaving to be discovered later.

File: NMEA · Nmea · ASCII
Use case: GeospatialTime-series data+1· Conversion set

NMEA 0183 — Receiver With No Fix

Six cycles of a receiver that has power but no satellite lock: GGA fix quality 0, RMC status V, and empty coordinate fields throughout. Parsers that split on commas and index positionally read these empty fields as zeros and plot the track at Null Island.

File: NMEA · Nmea · ASCII
Use case: GeospatialError handling+1· Conversion set

NMEA 0183 — Bad Checksum and Truncated Sentence

A log with one sentence whose checksum has been altered and a final sentence truncated mid-transmission with no checksum at all — the two ways a serial GPS feed actually degrades. A correct parser drops both and keeps everything else.

File: NMEA · Nmea · ASCII
Use case: GeospatialError handling+1· Conversion set

KML — Point Placemarks With Descriptions

Five town Placemarks with plain-text descriptions and no styling — the smallest useful KML and the baseline for the rest of this subcategory. Coordinates match the master territory, so a KML-to-GeoJSON conversion can be diffed against a known answer.

File: KML · Kml · UTF-8
Use case: GeospatialConversion testing+1· Conversion set

KML — LineString Placemarks With tessellate

The three roads as LineString Placemarks with `tessellate` set, which tells a globe renderer to drape the line over terrain rather than drawing it as a straight chord. Converters that drop the flag produce lines that tunnel through hills.

File: KML · Kml · UTF-8
Use case: GeospatialConversion testing+1· Conversion set

KMZ — Single doc.kml, No Resources

The minimal KMZ: a ZIP holding exactly one `doc.kml` at the archive root and nothing else. Readers are supposed to open the first .kml they find, and most look for the conventional name — this file satisfies both rules.

File: KMZ · Kml · 1 member
Use case: GeospatialConversion testing+1· Conversion set

kmz

658 B

KMZ — Bundled Icon Referenced by a Relative Path

A KMZ whose style points at `files/markers/town.png` inside the same archive. Resolving that relative href against the archive rather than the filesystem is the whole job of a KMZ reader, and it is where extract-then-parse implementations break.

File: KMZ · Kml · 2 members
Use case: GeospatialConversion testing+1· Conversion set

Shapefile — Towns Attributes (.dbf, dBase III)

The attribute table as a dBase III file: fixed-width, space-padded, ASCII numerics. Field names are capped at ten characters and there is no type for dates-with-time or for nested values, which is why shapefile export loses so much of a modern schema.

File: DBF · Shapefile · 5 records
Use case: GeospatialData import+1· Conversion set

Shapefile — Roads Attributes (.dbf with a decimal field)

The road attribute table, including a numeric field declared with two decimal places. DBF stores numbers as right-justified ASCII, so the declared width and decimal count are the only thing that says 9.40 is not 940.

File: DBF · Shapefile · 3 records
Use case: GeospatialData import+1· Conversion set

Shapefile — Parcels Attributes (.dbf)

The parcel attribute table, joined to the .shp purely by record order — there is no key and no explicit link. Deleting a geometry without deleting its row shifts every subsequent attribute onto the wrong shape, silently.

File: DBF · Shapefile · 3 records
Use case: GeospatialData import+1· Conversion set

Shapefile Bundle — Kestrel Bay Towns (zipped component set)

A zipped shapefile containing the full five-file point set — .shp, .shx, .dbf, .prj and .cpg. Zipping is how shapefiles are actually exchanged, because a bare .shp is meaningless without its sidecars — and it is the upload format most web GIS tools accept.

File: ZIP · Shapefile · 5 members
Use case: GeospatialConversion testing+1· Conversion set

Shapefile Bundle — Kestrel Bay Parcels (zipped component set)

A zipped shapefile containing a three-file polygon set with no .cpg, so the DBF encoding is undeclared. Zipping is how shapefiles are actually exchanged, because a bare .shp is meaningless without its sidecars — and it is the upload format most web GIS tools accept.

File: ZIP · Shapefile · 4 members
Use case: GeospatialConversion testing+1· Conversion set

Shapefile DBF — Accented Attributes in UTF-8

The same four accented attribute rows written in UTF-8. Both files are exactly the same size because DBF fields are fixed-width in bytes, not characters — so the UTF-8 table fits fewer characters per field, and a reader that guesses the encoding produces mojibake rather than an obvious failure.

File: DBF · Shapefile · 4 records
Use case: GeospatialEncoding detection+1· Conversion set

Shapefile DBF — Accented Attributes in CP1252

The same four accented attribute rows written in CP1252. Both files are exactly the same size because DBF fields are fixed-width in bytes, not characters — so the UTF-8 table fits fewer characters per field, and a reader that guesses the encoding produces mojibake rather than an obvious failure.

File: DBF · Shapefile · 4 records
Use case: GeospatialEncoding detection+1· Conversion set

GeoPackage — Three Feature Layers in One SQLite Container

The territory as an OGC GeoPackage: an ordinary SQLite database with the three required metadata tables and three feature layers whose geometry columns hold GeoPackage binary blobs. Unlike a shapefile it carries several geometry types, long column names and real typed columns in one file.

File: GPKG · Geopackage
Use case: GeospatialConversion testing+1· Conversion set

GeoPackage — Aspatial Attributes Table (no geometry column)

A GeoPackage holding an `attributes` table with no geometry at all — the standard's own answer to a lookup table you want to ship alongside your layers. Its gpkg_contents row has null bounds and a null SRS, which is what readers that assume every row is spatial trip over.

File: GPKG · Geopackage · 10 rows
Use case: GeospatialData import+1· Conversion set

MBTiles — Raster Tile Pyramid, Zoom 4 to 8

A small raster tile pyramid covering the invented territory, one flat colour per zoom level so a wrongly addressed tile is obvious on sight. The row axis follows the TMS convention, which is flipped relative to the XYZ scheme web maps use — the single most common reason an MBTiles layer renders upside down.

File: MBTILES · Geopackage
Use case: GeospatialData import+1· Conversion set

MBTiles — Structurally Valid Tileset With No Tiles

Every required MBTiles table and metadata key, and not one tile row. This is what a failed or interrupted tile build actually produces, and it is the fixture that finds code which reports success because the file opened and the schema validated.

File: MBTILES · Geopackage
Use case: GeospatialError handling+1· Conversion set

OSM XML — Multipolygon Relation With Outer and Inner Ways

Wither Lake and its island as an OSM multipolygon relation: two closed ways tagged separately and joined by a relation whose members carry outer and inner roles. OSM has no polygon primitive at all, so reconstructing the hole means reading the relation rather than the ways.

File: OSM · Gml Osm · UTF-8
Use case: GeospatialConversion testing+1· Conversion set

OSM XML — Intentionally Invalid: Way and Relation With Dangling References

Intentionally invalid: a way referencing node -9 and a relation referencing way -999, neither of which is in the file. This is the normal state of a bounding-box extract, where objects are clipped at the boundary — so a reader has to decide between rejecting the file and building partial geometry, and must say which.

File: OSM · Gml Osm · UTF-8
Use case: GeospatialError handling+1· Conversion set

FITS BINTABLE — Typed Columns with TUNIT (.fits)

A FITS binary table with four typed columns, big-endian numerics packed with no inter-field padding and TUNIT declaring milli-jansky on the two flux columns. It is the fixture for a table reader that has to build a row struct from TFORM codes rather than guess widths.

File: FITS · Fits · 12 rows
Use case: Scientific dataSerialization testing+1· Conversion set

HDF5 Compound Record dtype — Mixed Field Types (.h5)

A compound-dtype dataset mixing an integer key, two float measurements, a fixed-width byte string and a boolean flag in a single record. It is the fixture for readers that must map an HDF5 record type onto a native struct without reordering or repadding the fields.

File: H5 · Hdf5 · 10 records
Use case: Scientific dataSerialization testing+1· Conversion set

NumPy .npy — Structured Record Array with Units in Field Names (.npy)

A 16-record structured array carrying a value, its uncertainty and a quality flag, with the physical unit encoded in the field-name suffix because .npy has nowhere else to put it. The header descriptor is a list of field tuples rather than a single type string, which is the parsing branch this file exercises.

File: NPY · Arrays · 16 records
Use case: Scientific dataSerialization testing+1· Conversion set

Units on a Second Header Row — Not Data (.csv)

The same instrument export with units on a second header row instead of inside the column names, which is how a great many laboratory instruments write CSV. An importer that treats row two as data types every measurement column as text and then quietly refuses to plot anything.

File: CSV · Measurements
Use case: Scientific dataCSV parsing+2· Conversion set

Gravimetric Mass — TSV With Bracketed Units and Spaces in Headers (.tsv)

Ten weighings as tab-separated values with headers that contain spaces, brackets and parentheses — 'u(mass) [g]' — the way balance software actually writes them. Column names like these break naive slug-based header handling long before the numbers become a problem.

File: TSV · Measurements · 10 rows
Use case: Scientific dataEditor testing+1· Conversion set

Three-Molecule SDfile Library — Multi-Record (.sdf)

Three molecules of different sizes in one SDfile, each with its own connection table and four data fields, separated by the $$$$ terminator. It is the fixture for streaming SDfile readers that have to split on the terminator rather than load the whole file.

File: SDF · Chemistry · 3 records
Use case: Scientific dataEditor testing+1· Conversion set

SMILES Library — Ten Structures Including Aromatics and Salts (.smi)

Ten tab-delimited SMILES covering the notation features that trip parsers: lowercase aromatic atoms, ring-closure digits, branch parentheses, explicit charges in brackets and a dot-disconnected salt. Every structure is small enough to verify by hand.

File: SMI · Chemistry · 10 records
Use case: Scientific dataEditor testing+1· Conversion set