A conversation starts here Build a sourcing requirement around the relationship between composition, preparation, process, structure and measured performance. We help identify existing collections and coordinate their evaluation and licensing.
The following describe data types and relationships to discuss. Available records, scientific coverage and permitted use are established for each collection.
Ingredients, compositions, processing sequences, temperatures, pressures and durations can describe how a specimen was made. The useful record links those conditions to a sample or batch and identifies which steps were recorded.
Spectroscopy, diffraction and microscopy can provide complementary views of a material. Specify the original instrument files, acquisition settings, calibration context and sample links needed for your task.
Measured properties need units, test methods and conditions. Define the endpoints you need and how results should connect to the preparation history and characterization of the same material.
Variations, controls, repeats and unsuccessful runs can give context to a reported result. Describe whether your requirement needs a full experimental series and which omissions would limit its usefulness.
Training purpose changes the sourcing brief. Scientific breadth, labels and experimental independence should be considered alongside the volume of files.
Describe the material families, methods and range of experimental conditions your corpus should cover. Specify which raw signals, images and structured records are useful and whether multiple modalities need to be paired.
Define the relationship you want the model to learn, such as preparation conditions and measured properties. Identify the required labels, their definitions and the minimum context needed to interpret them.
Identify which specimens, batches or experimental series must remain independent of training. Related measurements can share underlying material even when they appear in different files.
For a requirement measured in gigabytes or terabytes, include the target number of independent experiments and scientific coverage. Discuss formats, preparation work, delivery batches and acceptance criteria during qualification.
Sample identity → synthesis → process conditions → characterization → property result
A possible requirement could connect preparation records with diffraction, microscopy and measured performance, including repeat and unsuccessful experiments where available.
A technically readable file is only one part of a useful experimental record.
Start with the model task, material family, measurements, linked information and approximate scale. A focused evaluation sample can be discussed before a wider acquisition.