Rethinking Scientific Data™


Registration Now Open for 2020 Fall Allotrope Connect Sept. 30th, Oct. 5th, 7th

For more information, click here.​

Register Here for the upcoming Allotrope Connect webinars: Sept. 30th, Oct. 5th, 7th

Covering areas of:

  • Value Realization of Allotrope Framework

  • Use Case(s) and Application of Technology

  • Expanding ADF Access for General Application(s)

  • Enabling and New Technology

Our Vision


Scientific data you can trust, at your fingertips. 

Our Mission

Revolutionize the way we acquire, share and gain insights from scientific data, through a community and the framework for standardization and linked data. 


Digital technology is ubiquitous in our daily lives.  We’re living in a technology revolution.  Data, pictures, movies, and music are a tap or click away.  We’re bringing that same revolution to the science we do in the workplace.


The members of Allotrope Foundation have teamed up to develop a single universal data format to store linked data that connects people with the raw data, results and evidence they need to do great science.  The Allotrope™ Ontologies and Data Models are used to create linked data that standardizes experimental parameters so we can remove human error and enhance scientific reproducibility.  Linked data connects results and evidence to people, places, equipment, processes and studies.  Linked data drives laboratory automation that fuels downstream analytics.  Collectively this framework enhances data integrity, quality and regulatory compliance in real-time before critical decisions are made.  In summary: Allotrope™ technology captures a connected experiment stored in a universal, shareable, archive-ready format. This is evidence you can trust and understand at your fingertips.


What can we achieve by rethinking how we collect and care for our scientific data?

Building a standards based framework into your infrastructure for scientific data can have some wide ranging benefits in terms of added value, quality, and the bottom line: Increased data integrity and scientific reproducibility; reduced manual effort and paper-based work; and significantly improved ability to find, share and leverage data.



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