Alan Turing Institute

Academic institute focused on ethics and fairness around AI, machine learning, machine decision making

SCP - Synthetic Population Catalyst

The Synthetic Population Catalyst (SPC) makes it easier for researchers to work with synthetic population data in England. It combines a variety of data sources and outputs a single file in protocol buffer format, describing the population in a given study area, with a particular focus on socio-economic characteristics and interactions between individuals

Random forest models for gene expression experiments in Transformational Machine Learning

Almost all machine learning (ML) is based on representing examples using intrinsic features. When there are multiple related ML problems (tasks), it is possible to transform these features into extrinsic features by first training ML models on other tasks and letting them each make predictions for each example of the new task, yielding a novel representation. We call this transformational ML (TML). TML is very closely related to, and synergistic with, transfer learning, multi-task learning, and stacking. TML is applicable to improving any non-linear ML method. The models in this repository are for tests performed using random forests on a large scale gene expression problem. They are for the 978 landmark genes in the Library of Integrated Network-Based Cellular Signatures

The UK COVID-19 Vocal Audio Dataset

<p>The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech (speech not available in open access version) were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.</p><h3>Contents</h3><ul><li><strong>participant_metadata.csv</strong> row-wise, participant identifier indexed information on participant demographics and health status. Please see <a href="https://arxiv.org/pdf/2212.07738.pdf">A large-scale and PCR-referenced vocal audio dataset for COVID-19</a> for a full description of the dataset.</li><li><strong>audio_metadata.csv</strong> row-wise, participant identifier indexed information on three recorded audio modalities, including audio filepaths. Please see <a href="https://arxiv.org/pdf/2212.07738.pdf">A large-scale and PCR-referenced vocal audio dataset for COVID-19</a> for a full description of the dataset.</li><li><strong>train_test_splits.csv</strong> row-wise, participant identifier indexed information on train test splits for the following sets: 'Randomised' train and test set, Standard' train and test set, Matched' train and test sets, 'Longitudinal' test set and 'Matched Longitudinal' test set. Please see <a href="https://arxiv.org/abs/2212.08570">Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers</a> for a full description of the train test splits.</li><li><strong>audio/ </strong>directory containing all the recordings in .wav format<ul><li>Due to the large size of the dataset, to assist with ease of download, the audio files have been zipped into <strong>covid_data.z{ip, 01-24}.</strong> This enables the dataset to be downloaded in short periods, reducing the chances of a dropped internet connection scuppering progress. To unzip, first, ensure that all zip files are in the same directory. Then run the command 'unzip covid_data.zip' or right-click on 'covid_data.zip' and use a programme such as 'The Unarchiver' to open the file.</li><li>Once extracted, to check the validity of the download, please run the 'python Turing-RSS-Health-Data-Lab-Biomedical-Acoustic-Markers/data-paper/unit-tests.py. All tests should pass with no exceptions. Please clone the GitHub repo detailed below.</li></ul></li><li><strong>README.md</strong> full dataset descriptor.</li><li><strong>DataDictionary_UKCOVID19VocalAudioDataset_OpenAccess.xlsx </strong>descriptor of each dataset attribute with the percentage coverage.</li></ul><h3>Code Base</h3><p>The accompanying code can be found here: https://github.com/alan-turing-institute/Turing-RSS-Health-Data-Lab-Biomedical-Acoustic-Markers</p><h3>Citations:</h3><p>Please cite.</p><p>@article{coppock2022,</p><p> author = {Coppock, Harry and Nicholson, George and Kiskin, Ivan and Koutra, Vasiliki and Baker, Kieran and Budd, Jobie and Payne, Richard and Karoune, Emma and Hurley, David and Titcomb, Alexander and Egglestone, Sabrina and Cañadas, Ana Tendero and Butler, Lorraine and Jersakova, Radka and Mellor, Jonathon and Patel, Selina and Thornley, Tracey and Diggle, Peter and Richardson, Sylvia and Packham, Josef and Schuller, Björn W. and Pigoli, Davide and Gilmour, Steven and Roberts, Stephen and Holmes, Chris},</p><p> title = {Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers},</p><p> journal = {arXiv},</p><p> year = {2022},</p><p> doi = {10.48550/ARXIV.2212.08570},</p><p> url = {https://arxiv.org/abs/2212.08570},</p><p>}</p><p> </p><p>@article{budd2022,</p><p>   author={Jobie Budd and Kieran Baker and Emma Karoune and Harry Coppock and Selina Patel and Ana Tendero Cañadas and Alexander Titcomb and Richard Payne and David Hurley and Sabrina Egglestone and Lorraine Butler and George Nicholson and Ivan Kiskin and Vasiliki Koutra and Radka Jersakova and Peter Diggle and Sylvia Richardson and Bjoern Schuller and Steven Gilmour and Davide Pigoli and Stephen Roberts and Josef Packham Tracey Thornley Chris Holmes},</p><p>   title={A large-scale and PCR-referenced vocal audio dataset for COVID-19},</p><p>   year={2022},</p><p>   journal={arXiv},</p><p>   doi = {10.48550/ARXIV.2212.07738}</p><p>}</p><p>@article{Pigoli2022,</p><p>   author={Davide Pigoli and Kieran Baker and Jobie Budd and Lorraine Butler and Harry Coppock and Sabrina Egglestone and Steven G.\ Gilmour and Chris Holmes and David Hurley and Radka Jersakova and Ivan Kiskin and Vasiliki Koutra and George Nicholson and Joe Packham and Selina Patel and Richard Payne and Stephen J.\ Roberts and Bj\"{o}rn W.\ Schuller and Ana Tendero-Ca$\tilde{n}$adas and Tracey Thornley and Alexander Titcomb},</p><p>title={Statistical Design and Analysis for Robust Machine Learning: A Case Study from Covid-19},</p><p>   year={2022},</p><p>   journal={arXiv},</p><p>   doi = {10.48550/ARXIV.2212.08571}</p><p>}</p><p> </p><h3>The Dublin Core™ Metadata Initiative</h3><p> </p><p>- Title: The UK COVID-19 Vocal Audio Dataset, Open Access Edition.</p><p>- Creator: The UK Health Security Agency (UKHSA) in collaboration with The Turing-RSS Health Data Lab.</p><p>- Subject: COVID-19, Respiratory symptom, Other audio, Cough, Asthma, Influenza.</p><p>- Description:  The UK COVID-19 Vocal Audio Dataset Open Access Edition is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs and exhalations were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset Open Access Edition represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.</p><p>- Publisher: The UK Health Security Agency (UKHSA).</p><p>- Contributor: The UK Health Security Agency (UKHSA) and The Alan Turing Institute.</p><p>- Date: 2021-03/2022-03</p><p>- Type: Dataset</p><p>- Format:  Waveform Audio File Format audio/wave, Comma-separated values text/csv</p><p>- Identifier: <strong>10.5281/zenodo.10043978</strong></p><p>- Source: The UK COVID-19 Vocal Audio Dataset Protected Edition, accessed via application to <a href="https://www.gov.uk/government/publications/accessing-ukhsa-protected-data/accessing-ukhsa-protected-data">Accessing UKHSA protected data</a>.</p><p>- Language: eng</p><p>- Relation: The UK COVID-19 Vocal Audio Dataset Protected Edition, accessed via application to <a href="https://www.gov.uk/government/publications/accessing-ukhsa-protected-data/accessing-ukhsa-protected-data">Accessing UKHSA protected data</a>.</p><p>- Coverage: United Kingdom, 2021-03/2022-03.</p><p>- Rights: Open Government Licence version 3 (OGL v.3), © Crown Copyright UKHSA 2023.</p><p>- accessRights: When you use this information under the Open Government Licence, you should include the following attribution: The UK COVID-19 Vocal Audio Dataset Open Access Edition, UK Health Security Agency, 2023, licensed under the <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/">Open Government Licence v3.0</a> and cite the papers detailed above.</p><p> </p&gt

ME-ICA/tedana: 0.0.12

Summary This would ordinarily not have been released, but an issue with one of our dependencies means that people cannot install tedana right now. The most notable change (which will potentially change your results!) is that PCA is now defaulting to the "aic" criterion rather than the "mdl" criterion. What's Changed [DOC] Add JOSS badges by @tsalo in https://github.com/ME-ICA/tedana/pull/815[FIX] Fixes broken component figures in report when there are more than 99 components by @manfredg in https://github.com/ME-ICA/tedana/pull/824[DOC] Add manfredg as a contributor for code by @allcontributors in https://github.com/ME-ICA/tedana/pull/825DOC: Use RST link for ME-ICA by @effigies in https://github.com/ME-ICA/tedana/pull/832[DOC] Fixing a bunch of warnings & rendering issues in the documentation by @handwerkerd in https://github.com/ME-ICA/tedana/pull/840[DOC] Replace mentions of Gitter with Mattermost by @tsalo in https://github.com/ME-ICA/tedana/pull/842[FIX] The rationale column of comptable gets updated when no manacc is given by @eurunuela in https://github.com/ME-ICA/tedana/pull/855Made AIC the default maPCA option by @eurunuela in https://github.com/ME-ICA/tedana/pull/849[DOC] Improve logging of component table-based manual classification by @tsalo in https://github.com/ME-ICA/tedana/pull/852[FIX] Add jinja2 version pin as workaround by @jbteves in https://github.com/ME-ICA/tedana/pull/870 New Contributors @manfredg made their first contribution in https://github.com/ME-ICA/tedana/pull/824 Full Changelog: https://github.com/ME-ICA/tedana/compare/0.0.11...0.0.1

Mapping the values of IoT

We investigate the emerging meanings of “value” associated with the Internet of Things. Given the current political economy, we argue that the multiple meanings of “value” cannot be reduced to a single domain or discipline, but rather they are invariably articulated at the juxtaposition of three domains: social, economic, and technical. We analyse each of these domains and present domain challenges and cross-domain implications – drawing from an interdisciplinary literature review and gap analysis across sources from academia, business, and governments. We propose a functional model that aggregates these findings into a value-driven logic of the emerging global political economy enabled by digital technology in general and IoT in particular. These conceptual contributions highlight the critical need for an interdisciplinary understanding of the meaning of “value”, so that IoT services and products will create and sustain such concurrent meanings during their entire lifecycle, from design to consumption and retirement or recycling

Additional files of Polypharmacy during pregnancy and associated risk factors: a retrospective analysis of 577 medication exposures among 1.5 million pregnancies in the UK, 2000-2019

Additional files. Proportions of pregnancies with a prescribed medication – sensitivity analysis

Additional file 1: of Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9

Supplemental tables. (XLSX 62 kb