Data Integration
Machine Learning, Artificial Intelligence, Large Language Models, and the many other analysis methods rely on data. Data as the basis for a model or data as the basis for a solution-specific fit.
Data comes from different sources. Contextualize unifies these sources through a highly modular framework.
Unity through Isolation
“How wonderful that we have met with a paradox. Now we have some hope of making progress.”
— Niels Bohr (Danish Physicist, 1885-1962)
Deloitte, the Wall Street Journal, and many others have declared data the new currency. Enterprises and organizations around the globe have come to recognize its value and, as they have, the world of data has and will continue to become increasingly fractured.
But the value of data is not intrinsic. It comes from making the causal connections between events; by relating properties from data silos collected across time and space. It comes from exploration, from visualization, from analysis. It comes from exposure.
Through Carta, Contextualize has solved the read problem. By isolating data from compute from application, data can remain as protected or as open as you, the data owner, wish while still giving your customers access to the data they need to train AI/ML models, perform analyses, or generate plots.
Through Carta, companies connect applications and computing infrastructure to data resources once and leverage that connection over-and-again. Carta brings together the value of data from isolated silos without risking exposure.
Maximizing Data’s Value
Extracting information from data requires expertise. Extracting information automatically requires expertise and refinement. Whether that knowledge comes from in-house subject matter experts, consultants, or academic collaborators, those insights are readily captured in the Contextualize ecosystem.
In many applications, refining analyses is like fitting a line to one point. With only one example data set, it is impossible to generalize and refine an algorithm, just like it’s impossible to find the right line given just one point.
Contextualize has created an ecosystem and partnerships that allows even the most complex algorithms to be developed securely, refined collaboratively, shared safely, and deployed reliably.
Case Study: Classifying Pores in 3D Printed Metal Parts
X-ray computed tomography (XCT) is used in medicine, inspection, and manufacturing. Despite developments in metals additive manufacturing, also known as 3D-printing, pores, cracks, and other defects often form during the print. However, these defects form randomly throughout the part, making it difficult to correlate one print with another. And with hundreds of defects and hundreds-of-thousands of data points, every defect is near a point,
Images created by the Author.
So the question is no longer whether the defects are near a feature, but are they near the same kind of feature, and what kind of features are there? For that, Contextualize researchers developed a unique spatial correlation algorithm to identify the spatial features that were unique because they correlated to an increased number of defects.
Images created by the Author.
This video is easy to interpret: more pores formed near the surface. While this one proved straightforward, the same approach works even when the answer isn’t quite so obvious–like when the laser suddenly changes directions during the print, moves faster, or moves slower.
Our Currently Available Applications
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Collections
For researchers, students, and lab-based workers. Input data consistently, correctly, and quickly for uploading to your desired data store(s).
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Digraph
Quick and easy exploration of a project’s data structure and corresponding metadata and results. Digraph app contains statistical summaries of the entire project.
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Dashboards
Quick and easy plotting of one or more datasets. Great for initial exploration, identifying outliers, and creating plots for presentations.
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Carta Compute
In partnership with Seven Bridges, we offer a low code/no code environment to store, share, and reuse code. Track compute spend, code changes and failures, setup routine analyses, and share complex algorithms with even the most novice programmers.