AI3SD Video: Data management: at the root of high-throughput experimentation
AI3SD Video: Data management: at the root of high-throughput experimentation
High-throughput experimentation (HTE) is an enabling technology that has had major effects on efficiency in small-molecule industrial chemistry, particularly pharma and agorchemicals. Data management and curation can be – perhaps should be – a guiding strategy for building up advantageous HTE capabilities, with futureproofing for goals including machine learning for reaction optimization. Beyond HTE, rapid access to analytical and project data enables chemists in any industrial role to make not only faster, but better decisions.
AI3SD Event, Chemistry, Data Decision Making, Data Quality, Data Science, Data Sharing, Datasets
Carson, Nessa
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Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
27 October 2021
Carson, Nessa
10b7346d-c1ff-4afd-a4c3-eeb4393bcb3a
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Carson, Nessa
(2021)
AI3SD Video: Data management: at the root of high-throughput experimentation.
Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan
(eds.)
AI3SD Autumn Seminar Series 2021.
13 Oct - 15 Dec 2021.
(doi:10.5258/SOTON/AI3SD0159).
Record type:
Conference or Workshop Item
(Other)
Abstract
High-throughput experimentation (HTE) is an enabling technology that has had major effects on efficiency in small-molecule industrial chemistry, particularly pharma and agorchemicals. Data management and curation can be – perhaps should be – a guiding strategy for building up advantageous HTE capabilities, with futureproofing for goals including machine learning for reaction optimization. Beyond HTE, rapid access to analytical and project data enables chemists in any industrial role to make not only faster, but better decisions.
Video
AI3SDAutumnSeminar-271021-NessaCarson
- Version of Record
More information
Published date: 27 October 2021
Additional Information:
Nessa Carson was born in Warrington, England. She received her MChem degree from Oxford University, before completing postgraduate studies in catalysis and organic methodology at the University of Illinois at Urbana-Champaign. She started in industry as a synthetic chemist for AMRI, then moved within the company to run the high-throughput automation facility on behalf of Eli Lilly in Windlesham, working across both the discovery and process chemistry arenas. She then worked in process development using automation at Pfizer. Nessa started at Syngenta in 2020, working in automation, reaction optimization, and data management. She maintains a website of useful chemistry resources, https://supersciencegrl.co.uk
Venue - Dates:
AI3SD Autumn Seminar Series 2021, 2021-10-13 - 2021-12-15
Keywords:
AI3SD Event, Chemistry, Data Decision Making, Data Quality, Data Science, Data Sharing, Datasets
Identifiers
Local EPrints ID: 469332
URI: http://eprints.soton.ac.uk/id/eprint/469332
PURE UUID: 016e855a-a87e-4b0b-a4ae-7a9b92d99b91
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Date deposited: 13 Sep 2022 16:47
Last modified: 17 Mar 2024 03:52
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Author:
Nessa Carson
Editor:
Mahesan Niranjan
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