Molecular measurements
Proteomics and other molecular research outputs need sample preparation, instrument methods and relevant processing information. Specify whether your task requires raw signals, processed measurements or both.
Research data / Life science research data
Life science data
for scientific AI.
Build a sourcing requirement around molecular measurements, research images and the experimental information that connects them. We help identify existing collections and coordinate their evaluation and licensing.
Research imagingSIB SCI
Measurements and context
Follow the sample
through the study.
The following describe data types and relationships to discuss. Study coverage, available records and permitted use are established for each collection.
Research imaging
Images gain context from acquisition settings, specimen relationships and annotations. Define whether the model needs individual fields, linked sections, repeated observations or another specified image structure.
Assays and experimental outcomes
Experimental readouts need label definitions, controls and measurement timing. Describe the result you want to model and which information should connect it to the sample that produced it.
Study structure
Study identifiers, sample relationships, replicates and inclusion criteria help describe the collection. Aggregate counts and coverage are a useful first step before discussing any sample access.
Your model task
Define the learning objective
and the study boundaries.
The scientific question determines which measurements, annotations and relationships should be present.
Pretraining
Describe the biological systems, study types and measurement methods your corpus should cover. Specify whether molecular outputs and images need to be linked to the same samples or can support separate learning objectives.
Fine tuning
Define the narrower task, such as image interpretation or prediction of a specified assay response. State which annotations or experimental outcomes are required and how their meaning should be documented.
Model evaluation
Describe the sample groups, studies, sites or acquisition periods that should remain separate from training. Multiple observations from a shared source require attention when defining independent evaluation data.
Scale and delivery
For a requirement spanning gigabytes or terabytes, describe independent sample counts and study coverage as well as file volume. Storage, preparation work, access and delivery are agreed for the specific collection.
Illustrative requirement
A linked research collection
Study design → sample preparation → molecular measurement and image → annotation → experimental result
A possible requirement could combine research imaging and molecular measurements linked to the same experimental samples.
Collection review
Review the study
behind the files.
Use a focused evaluation to identify both useful context and limitations.
- What defines an independent sample, and which measurements are repeated observations?
- Which modalities are paired, and what identifiers preserve those relationships?
- How were annotations and outcome labels created and reviewed?
- Which preparation methods, controls and acquisition conditions are documented?
- Which study groups are represented, and where are records missing or excluded?
- What use and access arrangements can be considered for this collection?
Start with a summary
Discuss scope before
sharing records.
Begin with an aggregate description. For collections involving human research, please leave patient records and images out of the website enquiry. Any later review requires an agreed recipient, purpose and access arrangement.
Define your life science requirement.
Start with the model task, measurement types, study context, required labels and approximate scale. We can develop the details with you.