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AI3SD Video: AI insights from billions of dollars of ready-cleaned data

AI3SD Video: AI insights from billions of dollars of ready-cleaned data
AI3SD Video: AI insights from billions of dollars of ready-cleaned data
Two of the greatest pain points in Artificial Intelligence (AI)-assisted research workflows are data quantity and data organisation. Estimates place 60-80% of time in data science workflows is simply cleaning and arranging the data, dependent on researcher skill and the type of data. As AI relies on pattern recognition, the larger the dataset, the more likely the algorithm is to recognise a useful pattern. Due to organised unput via the Studies ELN and the underlying architecture of the database, extracting AI-ready data is made simple. We hold billions of dollars’ worth of data for our clients, and by working with each organisation to show them the untapped potential that existing projects already hold, then future research can be designed with these methodologies in mind to further boost research turnover and outcomes.
Bowers, Will
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Frey, Jeremy G.
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Kanza, Samantha
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Niranjan, Mahesan
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Bowers, Will
a39ba7b7-c0cc-4ae2-8a56-fa1212de27c0
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Bowers, Will (2022) AI3SD Video: AI insights from billions of dollars of ready-cleaned data. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom. 01 - 03 Mar 2022. (doi:10.5258/SOTON/AI3SD0202).

Record type: Conference or Workshop Item (Other)

Abstract

Two of the greatest pain points in Artificial Intelligence (AI)-assisted research workflows are data quantity and data organisation. Estimates place 60-80% of time in data science workflows is simply cleaning and arranging the data, dependent on researcher skill and the type of data. As AI relies on pattern recognition, the larger the dataset, the more likely the algorithm is to recognise a useful pattern. Due to organised unput via the Studies ELN and the underlying architecture of the database, extracting AI-ready data is made simple. We hold billions of dollars’ worth of data for our clients, and by working with each organisation to show them the untapped potential that existing projects already hold, then future research can be designed with these methodologies in mind to further boost research turnover and outcomes.

Video
ai4sd_march_2022_day_2_WillBowers_2
Available under License Creative Commons Attribution.
Download (226MB)

More information

Published date: 2 March 2022
Additional Information: Will Bowers is a Data Scientist for Dotmatics, but that’s only scraping the surface of what they do. As well as building out Dotmatics’ Platform AI capabilities, they are a part time writer, LGBTQ+ style icon, and emergent supervillain. They have studied at the University of Leicester, the University of Tulsa, Imperial College London, and the Institute of Cancer Research, before finding a home as a founding member of Dotmatics’ recent Science and Technology Specialism Team.
Venue - Dates: AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom, 2022-03-01 - 2022-03-03

Identifiers

Local EPrints ID: 470020
URI: http://eprints.soton.ac.uk/id/eprint/470020
PURE UUID: 8986880a-6f17-43c2-af82-0f7fe19b59c4
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 30 Sep 2022 16:41
Last modified: 01 Oct 2022 01:56

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Contributors

Author: Will Bowers
Editor: Jeremy G. Frey ORCID iD
Editor: Samantha Kanza ORCID iD
Editor: Mahesan Niranjan ORCID iD

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