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Matthew Evans: Difference between revisions
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(Created page with "{{Participant |Timezone=Europe/London (GMT+00:00/GMT+01:00) |Affiliation=UCLouvain, University of Cambridge |Projects=OPTIMADE }} {{Workshop Submission |Interest=I am a materials science researcher and open source software developer with a focus on open and machine-actionable data. Most recently, I have been developing small-scale tools and infrastructure for data ingestion in materials chemistry (somewhere between e-lab notebooks and full LIMS), and I would love to conn...") |
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|Materials=I provided some additional context in my previous answer, but one tool/ecosystem I work in is called OPTIMADE (https://optimade.org; https://github.com/Materials-Consortia/OPTIMADE), primarily as a developer of optimade-python-tools (https://github.com/Materials-Consortia/optimade-python-tools). At its core, OPTIMADE is an API specification for the layout, filtering and discoverability of resources describing crystal structures in the fields of materials science and chemistry. As a discipline, we are significantly less advanced than the equivalent life sciences endeavors in this area, in part due to lack of investment/potential payoff, and also due to the actual hierarchy of resources we are interested in describing. With OPTIMADE, we developed a common API format now used by ~20 data providers covering our field; these providers were typically already serving JSON APIs with their own bespoke formats, but jumping to a fully semantic API was not technically feasible for us as a community, currently run with little to no official funding off the back of graduate students and motivated PIs. The optimade-python-tools package (10.21105/joss.03458) aims to lower the barrier for researchers to serve their crystal structure data in an open, filterable way, such that our data ecosystem becomes more decentralized and diverse, beyond the listed providers at https://optimade.org/providers-dashboard. I believe some of the challenges we faced will be common across fields, and I am very interested in exploring ways the broader sci. community can unite to solve such challenges and put tools in the hands of front-line researchers. | |Materials=I provided some additional context in my previous answer, but one tool/ecosystem I work in is called OPTIMADE (https://optimade.org; https://github.com/Materials-Consortia/OPTIMADE), primarily as a developer of optimade-python-tools (https://github.com/Materials-Consortia/optimade-python-tools). At its core, OPTIMADE is an API specification for the layout, filtering and discoverability of resources describing crystal structures in the fields of materials science and chemistry. As a discipline, we are significantly less advanced than the equivalent life sciences endeavors in this area, in part due to lack of investment/potential payoff, and also due to the actual hierarchy of resources we are interested in describing. With OPTIMADE, we developed a common API format now used by ~20 data providers covering our field; these providers were typically already serving JSON APIs with their own bespoke formats, but jumping to a fully semantic API was not technically feasible for us as a community, currently run with little to no official funding off the back of graduate students and motivated PIs. The optimade-python-tools package (10.21105/joss.03458) aims to lower the barrier for researchers to serve their crystal structure data in an open, filterable way, such that our data ecosystem becomes more decentralized and diverse, beyond the listed providers at https://optimade.org/providers-dashboard. I believe some of the challenges we faced will be common across fields, and I am very interested in exploring ways the broader sci. community can unite to solve such challenges and put tools in the hands of front-line researchers. | ||
|Organizer Topics=Metadata, Research Data, Discovery | |||
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