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Life Sciences

Protocols, instruments and findings in one layer.

Life sciences data is a lineage problem: every result should be traceable back through the sample, the protocol and the instrument that produced it.

See Document Intelligence All of Science & Health

A result traceable to the sample, the protocol and the instrument run.

The story

A result traceable to the sample, the protocol and the instrument run.

Where it starts

Instrument measurements

Readouts from sequencers, plate readers and imaging instruments. It is the first of 4 workloads running in Life Sciences.

The question it raises

Provenance chain

A result resolves to the sample, protocol version and instrument run that produced it.

Why one question is hard

From Design to Publish

Life Sciences data moves through 5 stages — Design → Prepare → Measure → Analyse → Publish. The shapes in play are Time series, SQL, JSON / documents, Objects, Vector, and answering one question means reading across all of them.

What PLOMID contributes

Lineage is the requirement, and everything else follows from it. These are the parts that record it.

  • Documents beside rows Fields that keep changing shape stay queryable instead of being exported into a separate document store.
  • One data layer Rows, documents and time-ordered events live in one system, so a question is asked once instead of once per store.
  • Retrieval as an access path Similarity search arrives as another way into the same data rather than a second copy to keep in step.
The environment

Experiment records, instrument data, sample lineage and publications.

Instruments produce measurements, samples and protocols are records, and observations are documents. PLOMID holds the lineage chain in one layer, so a result carries its provenance rather than requiring a reconstruction from three systems’ identifiers.

Data models in play

The shapes, in one layer.

5 shapes carry this domain. Choose a stage to read the operation, or a shape to see every stage that handles it.

PLOMID · Life Sciences design · prepare · measure · analyse · publish Select a stage
Stage

Design

Protocols, versions and reference material

JSON / documents · Vector

Workload map Life Sciences workload map. Every shape on it is a surface of the layer, and each stage names the part of the operation it carries.
  • Time series Measurements and events in time order
  • SQL Records, keys and joins
  • JSON / documents Documents and nested objects
  • Objects Large assets with queryable metadata
  • Vector Similarity and semantic retrieval
The data journey

How life sciences data reaches one layer.

Walk the path the data takes, from the environment that produces it to the questions it answers. Select a station, or a shape, to read each step.

A result traceable to the sample, the protocol and the instrument run.

Environment

The sample

Samples, aliquots and protocol versions with their lineage.

SQL

Where the data goes to work

Questions evidence has to answer.

Each one keeps the measurement with what qualifies it, read from the same layer rather than reconciled later.

Provenance chain

A result resolves to the sample, protocol version and instrument run that produced it.

  • JSON / documents
  • Vector

Instrument data with metadata

Readouts and run parameters stay together, so a run is reconstructable.

  • SQL

Search across evidence

Internal reports and literature are retrieved alongside the data they relate to.

  • Time series
  • JSON / documents

Laboratory operations

Instrument availability and maintenance read from the same layer as the experiments.

  • SQL
  • Objects
One environment · many workloads

What runs against life sciences data.

4 workload families over one set of shapes. Choose one to see what it moves and where it lands.

Readouts from sequencers, plate readers and imaging instruments.

  • Planned once against the layer, not once per store
  • Read beside the records it shares a key with
  • Persisted under one storage contract
Workload architecture

From measurement to evidence.

The work, the shapes it names and the path a request takes — with the protocol beside the measurement.

Life Sciences · workload architecture
Workloads

What runs against this data.

  • Instrument measurements
  • Sample and protocol records
  • Observations and images
  • References and literature
Data models

The shapes those workloads read and write.

  • Time series
  • SQL
  • JSON / documents
  • Objects
  • Vector
The layer

One path from a request to the data it names.

  • Planning Predicates narrow the work before it runs
  • Execution Records, fields and windows answered together
  • Transactions Readers and writers do not block each other
Surfaces

How the work reaches the layer.

  • SQL surface The query language the layer is documented in
  • Applications Services and jobs writing and reading as they run
  • Analytics & AI clients The same layer, the same access path
Deployment & residency

Where this data is allowed to run.

Laboratories keep their own infrastructure, and instruments write locally before anything central exists.

Deployment, residency and control
What you build next

Document Intelligence

Documents, their fields and their attachments treated as queryable data.

If Provenance chain is your question, start here.

See Document Intelligence Every solution
Next

Where to go next

Solutions The workload answers this industry leans on, with their models and status. Platform The data models in the product today, and the ones extending the layer. Architecture How a request is planned, executed and persisted once it reaches the layer.