Scientific Data Management Platforms for Multi-Entity Life Sciences Groups

Summary

We build scientific data management platforms for life sciences groups made up of legally independent research entities, where the most valuable material sits as documents on local file shares and in departmental tools. This kind of platform gives that material one address: central storage, search across the whole estate, and enforcement of exactly who may see what. It is deliberately narrower than a warehouse. Consolidation, navigation and controlled sharing come first, with analytical modelling layered on once the foundation is trustworthy.

The Challenge

Scientists rediscover work that already exists elsewhere in the group because nothing indexes it. The entities are separate legal persons, so isolation is a legal requirement rather than a preference, and folder conventions are not an access control model. Much of the source material is scanned, and a PDF without a text layer is invisible to search while appearing indexed. Personal data inside clinical documents falls under data protection law and often needs pseudonymisation before it crosses a border. Subsidiary systems are frequently isolated both technically and organisationally, so a plan that assumes clean source integrations everywhere will stall. And a repository without required metadata at upload degrades into a dumping ground no index can rescue.

The Solution

Repository and pipeline

• Central object repository - object storage with a dedicated prefix or bucket per entity, versioning enabled so newer revisions do not displace earlier ones, and lifecycle rules moving cold material to cheaper storage tiers.
• Ingestion - managed transfer for bulk and scripted uploads alongside browser upload, with format handling for the document types that actually occur, typically DOCX, PDF and CSV.
• Extraction and indexing - event-driven functions triggered on object creation that extract text, run optical character recognition on scanned pages, capture metadata and write to a search index.
• Search - keyword query with filters by entity, document type, study and date, extended with semantic retrieval over embeddings so a query using different terminology than the document still returns it.
• Access control - entitlements evaluated at query time, so restricted documents never appear in a result list rather than being blocked only at download.

Isolation and compliance

Each data owner defines who outside their own organisation may read their material, enforced through fine-grained policies rather than folder conventions. Encryption uses keys held under the owning entity's own key policy, and every read, share and permission change is written to an immutable audit trail. Records supporting regulatory submissions carry retention and integrity expectations, and electronic records used in regulated processes have to be attributable and tamper-evident.

Resilience and room to grow

Storage and compute spread across availability zones, with replication for critical buckets and a restore path that is tested rather than assumed. Ingestion is queued and retried, so a malformed document parks in a dead-letter path instead of stalling the pipeline. The design anticipates its own successor: a metadata catalogue and a consistent object layout let the same repository later serve as the landing zone for a warehouse or lakehouse, with catalogued tables, scheduled transformation jobs and analytical queries over structured extracts.

Sequence of work

An honest assessment of the existing estate comes first, because it decides scope: what is centralised now by upload, and what waits for a source-system integration. The ownership and permission model, the document taxonomy and the ingestion contract are settled before pipeline code is written. Ownership is decided per document at ingestion, since retrofitting it onto a loaded repository is painful. Extraction coverage is measured rather than assumed, and search relevance is evaluated against real queries from working scientists rather than default ranking.

What This Delivers

Research material becomes findable across the group instead of only within each silo, and ownership of every document is explicit at the moment it lands. Data owners share deliberately with named counterparts and can withdraw that access, backed by an audit trail that answers who saw what. Versioning stays visible, so a reader knows whether they hold the current revision. The result is a foundation an analytics layer can be built on, rather than another store that has to be migrated again.

Technologies and Tools

• AWS as the platform: Amazon S3 with object versioning and lifecycle rules, managed SFTP transfer, event-driven functions, and KMS keys scoped per entity
• OpenSearch for keyword and faceted search, with embedding-based semantic retrieval alongside it
• Text extraction and optical character recognition for scanned material, handling DOCX, PDF and CSV
• Multi-zone deployment with queued ingestion, retries and dead-letter handling