A conversation starts here Source experimental datasets for AI pretraining, fine tuning and model evaluation. Materials science and life sciences, with the measurements, context and licensing your project needs.
SIB Scientific · Data sourcing and licensing · Austin, Texas
Scientific AI needs observations from real experiments. SIB helps teams define a data requirement, identify relevant holders and arrange a licence around the intended use.
Specify the range of materials, biological systems, methods and experimental conditions your training plan needs. Raw signals, images and structured records can serve different parts of the model.
Connect a narrower scientific question with the right measurements and labels. Examples include predicting a material property from preparation conditions or connecting research images to experimental outcomes.
Define what must stay separate from training. Samples, experimental series, studies, sites and acquisition periods provide useful boundaries when designing an evaluation collection.
The experimental relationships matter as much as the file type. Define the raw measurements, associated records and outcomes that your model needs to learn from.
Connect synthesis and processing histories with characterization, measured properties and performance. Source against a specific family of materials, an experimental range or a model task.
For thin films, semiconductors and other materials, discuss which measurements link to a specimen and which are only associated with a batch.
Explore materials dataConnect molecular measurements and research imaging with study design, sample preparation and experimental results. Scope the biological systems, measurement methods and labels needed for your task.
Begin with collection level descriptions. For human research data, establish the permitted access and handling workflow before any exchange.
Explore life science dataTell us the target volume, the number of independent experiments and the scientific coverage your training run needs.
A large image archive, a spectral library and a linked experimental series carry different information. We use both the data volume and the underlying science to guide a search.
Collection size and delivery are established during sourcing and qualification.
Raw files preserve the signal. Methods, sample identities and outcomes explain what it represents. Explore how those records can connect in a collection.
Link raw instrument files, images, preparation records and measured properties through stable specimen and batch identifiers. Review native formats, units, repeats and missing fields before defining a training collection.
Define acceptance criteria before committing to a collection.
Your technical team sets the requirements. SIB coordinates descriptions, open questions and any agreed evaluation sample with the holder.
Materials, biological systems, conditions and outcomes represented, together with known gaps.
Sample and batch identifiers, repeats, missing values and links between measurements.
Raw formats, schemas, units, file sizes and any conversion or preparation work.
Collection history, control of the records and the training or commercial uses to address in the licence.
We start with buyer demand, identify relevant data holders and coordinate the technical and commercial work needed to assess a collection.
Model task, scientific domain, modalities, target scale, intended use and timing.
Find holders and establish their records, experimental context and open questions.
Agree the information or sample needed for review, with access and handling settled first.
Collection scope, permitted use, acceptance, preparation and delivery arrangements.
Send a short requirement covering the domain, model task, data types and approximate scope.
Discuss a data requirementDescribe the experiments already completed, the records you hold and how they connect.
Describe your collection