Acquisition from authorized sources
Data comes from files you provide, official APIs, bulk downloads, and public sources whose terms permit use. We scrape only where access is authorized.
Service
Research Data Engineering Services
From documents and fragmented sources to validated, research-ready datasets and reproducible pipelines.
We acquire data from authorized sources, extract and harmonize it, and document every transformation so your team can trust, audit, and re-run the result.
When teams need this
Situations this service is built for
- Hundreds of PDF reports or filings need to become one structured table.
- Spreadsheets or survey waves from different years use different column names, codes, and units.
- The dataset is rebuilt by hand each cycle, and nobody can reproduce last time's numbers.
- Analysis is blocked because the raw data is too large or too messy for existing scripts.
- A funder, collaborator, or reviewer asks how each value in the dataset was derived.
What we can deliver
Capabilities
Document and PDF extraction
Tables and fields are extracted from reports, filings, and scanned documents, with documented handling for layout variation and low-confidence values.
Cleaning and standardization
Consistent data types, units, codes, and missing-value conventions, agreed with your team and written down.
Joining and schema harmonization
Sources, identifiers, and changing definitions are reconciled into one documented schema, including longitudinal transformations across waves or years.
Validation and source reconciliation
Automated checks for ranges, totals, duplicates, and missingness, plus sample comparison back to the source documents.
Provenance and lineage
A record of which source and which transformation produced each field, so values can be traced and audited.
Reproducible, scalable pipelines
Versioned code, pinned environments, and ETL/ELT processing that handles larger volumes, with a runbook for re-running it.
Tangible artifacts
What you receive
Deliverables are agreed in the scope. Typical artifacts for this service include:
- Cleaned dataset (CSV, Parquet, or database tables, as agreed)
- Data dictionary and harmonization mapping
- Source reconciliation and validation report
- Processing code in version control
- Runbook for re-running the pipeline
Typical workflow
How the work usually runs
- 1
Discover & scope
Review the sources, permissions, target schema, and the definitions your researchers have approved.
- 2
Implement
Build extraction and transformation steps on a pilot sample first, then extend to the full set.
- 3
Validate & review
Run automated checks, reconcile against sources on a review sample, and walk through findings with your team.
- 4
Document & hand over
Deliver the dataset, dictionary, code, and runbook, with known limitations stated plainly.
Rigor and constraints
Data responsibility and scientific rigor
- Data permissions and licences are checked before acquisition. Unauthorized scraping is declined.
- Variable definitions come from your researchers. We implement them and record any ambiguity we find.
- Extraction error is measured on a review sample and reported, not hidden. We do not promise 100% accuracy.
- Sensitive or regulated data is accepted only after permissions and controls are assessed for the engagement.
Who this is for
Teams we typically support with this service
FAQ
Questions about Research Data Engineering
Can you work with scanned PDFs?
Often, depending on scan quality. We test a sample first and report how reliable extraction is for your documents before you commit to the full set.
Do you scrape websites?
Only where access is authorized, for example through an official API, terms that permit it, or written permission. We decline unauthorized scraping.
How do you report data quality?
Checks are defined with you at the start and reported at delivery, including known gaps and unresolved cases. We report what was checked rather than claim perfect accuracy.
Can you take over an existing pipeline?
Possibly. We review the code and documentation first, then agree on whether to extend, refactor, or rebuild it. This is scoped per engagement.
Discuss this with us
Tell us about your data and goals. The service is preselected in the inquiry form.