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The Spectrus Platform uniquely empowers R&D organizations to digitize, process, assemble, and share connected analytical and chemical data.
Spectrus is the most viable platform for analytical data standardization today.
The Spectrus Platform offers a single, uniform data format for all major analytical techniques and instrument vendor formats (open source formats and emerging standards are also supported—including AnIML, JCAMP, ADF from Allotrope, and more).
Spectrus provides native support for >150 data formats—a result of decades-long partnerships with analytical instrument vendors.
Spectrus portfolio applications provide chemically intelligent tools for analytical data processing and interpretation, handling analytical data just like a scientist would.
Features include:
Context ensures that insights can be gleaned from data. Spectrus uniquely helps connect relevant chemical information, analytical data, interpretations, and meta data to preserve context. Contextual data is knowledge.
The integration of structure representation in Spectrus enables direct correlation of structural features with analytical spectra and chromatograms, ensuring context.
Rich, contextual, connected multi-technique analytical data stored in a central Spectrus database ensures that R&D organizations can store and share knowledge for effective decision-making and collaborative science.
Chemically intelligent search means you can search database records by structure, sub-structure, text/numerical values, and numerous spectral features (peak/exact/similar).
Knowledge in Spectrus databases is shared by R&D organizations across their global networks. Data may be accessed locally or remotely, in accordance with appropriate permission-based rules.
A variety of commercial-off-the-shelf (COTS) software on the Spectrus Platform provides tools for processing, analysis, and databasing of multi-technique analytical data.
This Strategy Brief is intended to help organizational stakeholders to review analytical data accessibility needs, provide an overview of ACD/Labs software and services, and propose practical steps for realizing an automated ADMS.
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In this white paper we provide commentary on factors that impact analytical data standardization and a vision of the considerations and requirements that such an undertaking should include.
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In an era where analytical instruments are becoming more and more sophisticated, and the amount and types of data collected are increasing exponentially, we partnered with Cell Associates and C&EN to conduct a formal industry survey on Analytical Data Management. Read this eBook to discover our key findings on how laboratories manage all the analytical data they produce.
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Commonly used informatics technologies do not effectively manage the unique challenges of analytical data management. While data abstraction serves a purpose, it also has limitations since important details, knowledge, and contextual information can be lost.
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We have found that a number of common misconceptions exist about validated environments. Many arise because previous deployments of software accompanied the installation of new hardware, or have involved informatics systems that are the source of data and reports submitted directly to regulatory authorities. Here we clear up some of the grey areas that seem to have become industry myths.
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Coding can take scientists from participants to owners of their data. More and more it is becoming clear that every organization needs to employ workers with data science skills. As more scientists become data scientists, the move towards digitalized data will accelerate.
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