Machine-ready data accessibility
Accessing and extracting key experimental data across informatics is critical for secondary analysis and applying modern data science practices to merged data sets.
The inability to access and analyze experimental data can drive pain in the pursuit of eliminating rework and improving quality in scientific activities. R&D organizations are increasingly challenged to get more from their data through analysis and data science practices, which requires the ability to extract and access the necessary data.
What is Data Space?
Data Space is a service that enables for all spreadsheet data to be extracted in a machine-ingestible file format from IDBS platforms. This data can then be processed in a variety of ways, depending on the end user’s requirements, including importing it into the organization’s existing data pipeline or Data Lake. Having the data available in an external analysis system allows for more advanced manipulation and interrogation of the data than is available in the main platform, such as visualizing hundreds of different batches on the same graph or running principal component analysis.
Real-time access – Data is continually updated based on user save events and can be configured to be extracted as spreadsheets are updated, creating a real-time data pipeline.
Highly performant – Built to complement existing IDBS platform infrastructure, Data Space is a new data service that won’t impact performance of core system operations.
Machine-ready and flexible – Data available in both JSON and CSV file formats and ready to be ingested by machines. Extraction can be done via REST API call manually or programmatically through languages like Python.
An Example Use Case
Figure 1: Data capture within an E-WorkBook experiment (Solubility)
Figure 2: The E-WorkBook Spreadsheet with the data
Figure 3: That same data extracted from E-WorkBook via Data space and loaded into and external tool as a table…
Figure 4: …and as a clustering for visual data consumption
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