A project exploring metadata uplift and quality through automation led by @supdey and @spuddybike. Funded by @ESRC and @DIRAC_HPC with support from @CLOSER_UK
Super pleased to announce that we are kicking off a project to explore ways to uplift and improve the quality of metadata in the social and population sciences using machine learning. The project website is at https://t.co/0hzuftRD1H and we'll be tweeting from here.
🆕Standardised scales (SS) added to Discovery, our research tool for longitudinal population studies!
This update will make it easier to determine if questions belong to a SS & identify where they are used across our studies & waves of data collection.
https://t.co/vwNryROUID
But if the advance from chatbots to ChatGPT is in automating the answers, can we automate the questions as well? Can we automate the prompt engineering?
Excellent points on creativity, enquiry and why it ain’t intelligence
https://t.co/fmFDPDv5Fq
Our final report of the @DiRAC_HPC funded Activity on 'Understanding the multiple dimensions of prediction of concepts in social and biomedical science questionnaires', jointly led by @spuddybike, is now out in https://t.co/7RkFZ7bplS
@MetadataUplift
Our first paper on metadata uplift using machine learning is out in @iassistdata https://t.co/MtyEiZ71Gq led by @supde. Funded by @ESRC and @DiRAC_HPC.
Encouraging feedback from the DDI-L community on our @ESRC funded ML pipeline approach for data extraction from longitudinal questionnaires. See slides from the talk here: https://t.co/bdFVAWhGle
#eddi2021@MetadataUplift
I'm really glad folks are looking into this and grateful to @strwbilly for this reporting highlighting the negative impacts of #AIhype
https://t.co/Hz0EB3x7CB
DDI-CDI is a @DDIAlliance candidate standard that is aimed at allowing cross domain integration. The paper raises some useful ways in which standards can work together for machine actionable data management
7 years worth of Twitter data was analysed to understand how the meaning of hundreds of emoji have changed and developed since 2012. Previously, this kind of analysis has been restricted to words. Link to paper: https://t.co/F9BIt8juc8
@Letxuga007 Of course one of big problems of location based assignment of measures is how heterogeneous many areas are, especially in urban areas even if you have my postcode it will cover everything from <10 to >90 income bracket.