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3,409 results for “UK”
Mapping Croatian scientific diaspora in the UK
<p>This data contains anonymized answers from the study focusing on mapping Croatian researchers and scientists in the UK. We wanted to better understand circumstances and motivation behind the move to the UK, differences of doing research in UK and Croatia and finally extent, challenges and incentives for collaborations with Croatia. <br>This is part of the bigger project of Mapping Croatian scientific diaspora and was a pilot project for building platform mapa-znanstvenika.hr. <br>We collected 40 responses from broad range of researchers from early stages (bachelor, master, PhD students) to professors and lecturer, coming from broad range of scientific fields. </p>
COG-UK Viral Genome Sequences
<p>COG-UK Consortium has published dataset contains over 10K SARS-CoV-2 viral genome sequences available as open access. The current COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents a major threat to health in the UK and globally. To fully understand the transmission and evolution of the virus requires sequencing and analysing viral genomes at scale and speed. The numbers of samples calls for a rapid increase in the UK’s pathogen genome sequencing capacity rapidly and robustly. To provide this increased capacity to collect, sequence and analyse the whole genomes of virus samples in the UK, the COVID-19 Genomics UK (COG-UK) consortium is pooling the world-leading knowledge and expertise in genomics of the four UK Public Health Agencies, multiple regional University hubs, and large sequencing centres such as the Wellcome Sanger Institute.</p> <ul> <li>Protocols: https://www.cogconsortium.uk/protocols/</li> </ul>
Twitter analysis of the five main political leaders during the 2019 UK electoral campaign: from 12 October to 16 December 2019
<p>The analysis was conducted from 12 October to 16 December 2019 on Twitter through the study of the five main political leaders— Boris Johnson, Jeremy Corbyn, Jo Swinson, Nicola Sturgeon, and Nigel Farage — during the 2019 UK electoral campaign.</p>
DATA ANALYSIS - SARS-COV-2 ( Del69-70 VARIANT ) – NEW UK MUTANTS
<p>The data for S - genome sequence analysis known as Del69-70 is under variant of concern ( VOC ) . It is also termed as variant of investigation ( VUI ) . The data for VUI is statistically analysed by datewise and regionwise . The software used for data analysis is CURVE FINDER V.1.4 . The reproducibility of correlation and standard error is reported here for analysis of scattered data an attempt to study the Rational Fit and Harris Fit .</p>
Anthropogenic emissions of CH4, N2O, F-gases and BC from GAINS, for EU-countries plus CH, NO, UK developed under the EYE-CLIMA project - March 2025 update
<p><span>As part of the EYE-CLIMA project, GAINS emission data for CH<sub>4</sub>, N<sub>2</sub>O, BC and selected F-gases (HFC-125, HFC-134a, HFC-143a, HFC-23, HFC-32 and SF<sub>6</sub></span>) were released for all EU-27 countries plus UK, Switzerland, and Norway for the period 1990 to 2020 (with exception of F-gases, from 2005 only, and BC/CH<sub>4</sub> emissions from agricultural waste burning, from 2000). Results have been documented in EYE-CLIMA deliverable D2.8 (<a href="http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf">http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf</a>), and they are publicly available at the Zenodo repository under <a href="https://doi.org/10.5281/zenodo.11032177">https://doi.org/10.5281/zenodo.11032177</a>. All data is available on a 0.1°x0.1° grid and in monthly resolution. Emissions are attributed to the respective source categories according to GNFR.</p> <p>The motivation of an update resulted from the need to extending the emission data time series to 2023. With underlying statistics and national emission data currently available till 2022 only (the latter submitted to UNFCCC only by December 2024), the historical data series also could only be established for 2022. Here we use the GAINS scenario feature to extrapolate between 2022 historical data and the first scenario point, 2025 which is based on IEA’s Word Energy Outlook 2023 (https://www.iea.org/reports/world-energy-outlook-2023). Obviously, this also means that emission results for 2023 are not any more based on robust statistics but represent an extrapolation.</p> <p>Extrapolation of spatially explicit data is only possible when the spatial resolution conveys a realistic signal. For the sector “agricultural waste burning” (files with “AWB” as sector, see notation below) spatial allocation is based on actual observation from satellites. As such data products on agricultural fires have been made available until 2022 only, no spatial or temporal signal exists for 2023. The time series provided thus has to end in 2022. No recommendation can be given to modellers, other than to either use 2022 also for 2023 (understanding that the pattern will be strikingly different) or to use a five-year average (which will remove a lot of spatial specificity).</p> <p>The updated dataset covers files as follows (internally, all files now carry version number V05):</p> <p>ALL_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.csv</p> <p>BC_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>BC_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>HFC_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>N2O_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>SF6_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>This is version 2.0 of the dataset. It extends from version 1.0 by covering into the year 2023, but also benefits from a number of additional GAINS improvements. Emissions of emitted compounds are provided as kg/m²/s. File names follow the notation developed for the H-Europe project EYE-CLIMA, i.e. species _ variable-type _ sector _ region _ method (MOD=model) _ timestep _ fromTime _ toTime _ model _ institute _ version . filetype.</p> <p>This version is available at <a href="https://doi.org/10.5281/zenodo.15536170">https://doi.org/10.5281/zenodo.15536170</a>. The generic address of the dataset is <a href="https://doi.org/10.5281/zenodo.10886780">https://doi.org/10.5281/zenodo.10886780</a>, resolving to the latest update available at Zenodo. No further updates are planned in EYE-CLIMA, so this version is expected to also reflect the final update within the project.</p> <p>Compared to version 1.0, GAINS benefitted from a number of new developments such as the following:</p> <p>*) Previously, GAINS has been available in five-year timesteps only (with the aim of allowing for scenarios at that resolution). For data version 1.0, a makeshift solution was found to convert into annual data. A recent update now allows, for historic data, to store and retrieve information on an annual basis (from 1990).</p> <p>*) The energy data were obtained from IEA’s world energy balances 2024 (July version, https://www.iea.org/data-and-statistics/data-product/world-energy-balances#documentation), extending into 2022 and extrapolated towards 2025, downscaled from IEA to GAINS sectors and sub-sectors. Additionally, the annual activity of industrial production is estimated using a linear approach, based on five-year timestep data.</p> <p>*) Agricultural statistics were retrieved from Eurostat (and from FAO globally) and extended to 2022, extrapolated towards 2025.</p> <p>*) Interpretation of GAINS data was reconfirmed and updated in consultations with national experts of multiple EU countries. While the process resulted in revised emission projections to be used in the Clean Air Outlook 4 (see <a title="Protected by Check Point: https://environment.ec.europa.eu/topics/air/clean-air-outlook_en" href="https://protect.checkpoint.com/v2/r02/___https:/environment.ec.europa.eu/topics/air/clean-air-outlook_en___.YzJlOmlpYXNhOmM6bzoyYzdiNDRhNDI4Njc3ZjI5MGFjMTU1N2I2OWVmNzM2ZTo3OjE5OTM6ZTFiY2IzMDMxZGViNGE0MjI0ODRmNWQ4NzA3ZDY3Njc4M2U2NzUxNmEwNzQ0ODViNDBhODc1NmNhZmMzY2FlMjpoOkY6Tg"><span lang="EN-GB">https://environment.ec.europa.eu/topics/air/clean-air-outlook_en</span></a><span lang="EN-GB">). While the details of improvements on the individual aspects cannot be disclosed, they are useful to describe historic data most adequately, and have been integrated also in this assessment. That not only leads to changes in absolute emissions for a given year, but also affects trends that now are more plausible and confirmed through the exchange with the national experts.</span></p> <p><span lang="EN-GB">*) Technical adjustments have improved the precision of temporal allocation of emissions and the conversion of grid sizes to actual area.</span></p>
Plant-flower visitor network from Avon Gorge, UK
<table> <tbody> <tr> <td>Abstract</td> <td>This dataset gathers information on interactions between plants<br>and their flower visitors collected throughout 2004 (11 surveys covering local flowering season) the Avon Gorge (England), an iconic field site well known for its rare plant populations. The study area (1480 m2 ) included a broad<br>range of flowering plants, and overall the dataset shows information for 260 species (81 plant species, 179 insect species and morphospecies).</td> </tr> <tr> <td>Classification System</td> <td>all taxa were identified by specialist taxonomists</td> </tr> <tr> <td>Sampling Description</td> <td>A total of 11 survey visits were carried out from 10 May to 27 September 2004, this covering the main period of insect activity. Flower and insect surveys took place approximately every 14 days under dry conditions. In each flower abundance survey, a stratified random design was used to select 1 m2 quadrats in the study area. The area was divided into nine sub-areas based on habitat type and accessibility. Each sub-area was divided into 1 m2 quadrats and 2·5% (37) of these were randomly selected per sampling occasion. In each quadrat, the number of floral units of each plant species was recorded, defined as the distance that a small bee (c.1 cm length) would fly, rather than walk (Saville 1993). For example, in the Asteraceae, a flower unit is the entire inflorescence while in the Rosaceae, a flower unit is a single flower. Thus, the floral unit is defined from the bee’s perspective rather than by flower anatomy. Rare flowers which were missed using this method were included in the food web data as rare species with an abundance of two flower units (which was the lowest number of units observed in the plot for any species).<br>In the insect surveys, an observation point was chosen for each flowering plant species by randomly selecting one of the quadrats where the species was present. All the flowering units that could be surveyed by a single observer (approximately a semi-circle with 1-m radius) were observed for 20 min. On consecutive sampling occasions, plant species were rotated through three time slots, the morning (09.00–12.00 h), early afternoon (12.00–15.00 h) and late afternoon (15.00–18.00 h), to allow each species to be observed equally over time. At least two floral units were observed per plant species per sample. All flower–visitor interactions were recorded, and all visitors observed were collected for identification. To estimate the overall abundance of each plant species, the average number of flower units per 1 m 2 quadrat was multiplied by the total area of the study site. To estimate the interaction frequency for each visitor–plant species pair, we divided the total number of visits recorded by the number of flower units observed (per 20 min) and then multiplied by the total number of floral units in the study plot. By collecting the insects, we did not allow for repeated visits by the same individual; hence, some visitation frequencies may be underestimated. However, collecting specimens is essential for identification of most visitor species. Hymenoptera, Diptera, and Coleoptera were identified by taxonomists either to species or to morphospecies. Lepidoptera were identified to species by the authors and Heteroptera and parasitoids were morphotyped by the authors.</td> </tr> </tbody> </table>
Disclosure UK Methodological Notes Data 2015, 2017, 2019
<p>Data extracted from Disclosure UK methodological notes for the years 2015, 2017 and 2019 for companies who made payment disclosures on Disclosure UK in the years 2015, 2017 and 2019. Data extracted relates to guidance from the The Prescription Medicines Code of Practice Authority and requirements in the code of conduct developed by the Association of the British Pharmaceutical Industry.</p>
Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK
<p>This is the updated version of building 3D model data. The revision includes appending attributes to the lod1 and lod2 shapefile and creating cityjson file for each 3D building model. All 3D building models are available in mesh (.obj), multipath shapefile, and cityjson (.json) now.</p> <p><strong>IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4).</strong></p> <p>Urban Big Data Centre of the University of Glasgow generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council. It is a large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR point clouds. </p> <p>The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.</p> <p>The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [1]-[2]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided in 5 km by 5 km tiles.</p> <p>Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[3]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles. The RMSE of Euclidean distances between each point in the point cloud to the reconstructed model was calculated to evaluate the LoD2 model construction. A table of RMSE and a note for a few problematic models are provided.</p>
UK asylum court decisions
<p>The dataset includes 35.305 court decisions related to UK asylum requests. The original owner of the data is the UK's <em>Upper Tribunal Immigration and Asylum Chamber (UTIAC)</em>.</p> <p>The court decisions were scraped from <a href="https://tribunalsdecisions.service.gov.uk/utiac">https://tribunalsdecisions.service.gov.uk/utiac</a> on 28th October 2021. </p> <p>For each court decision the dataset includes the original text of the court as well as a number of explanatory fields that have been generated following an information extraction process.</p> <p>The dataset includes the following fields:</p> <p>'Case title:', 'Appellant name:', 'Status of case:', 'Hearing date:', 'Promulgation date:', 'Publication date:', 'Last updated on:', 'Country:', 'Judges:', 'Document', 'Reference', 'Download', 'File', 'String', 'ID', 'Code label:', 'Heard at:', 'Decision:', 'Nationality:', 'Representation:', 'Appellant:', 'Respondent:', 'Decision label:', 'Appellant entity:', 'Respondent entity:', 'Country code', 'Keywords', 'Country guidance:', 'Case Notes:', 'Categories:']</p>
Ash (Fraxinus excelsior L.) in vitro survival data for the UKs Living Ash Project
<p><em>In-vitro</em> propagation and survival data sets (including nursery survival) of the ash plants generated i.e. <em>Fraxinus excelsior</em> L., plus the PCR primers used and conditions applied.</p> <p>Surveyed from a range of ash seed material taken from across the UK, and held at the UK ash collection hosted by the Earth Trust in Oxfordshire, UK.</p> <p>A more detailed analysis of this data is currently expected to be be published in the <em>Annals of Forest Science</em>, and which has already provisionally accepted this work for publication, subject to the underlying data being made available i.e. here</p> <p>The data deposited here represents the underlying data that will be presented in graphical form in the forthcoming paper by Fenning et al., plus the associated metadata and statistical analyses, along with the original .jpg of the photos used.</p>
Corpus of political tweets UK-EU-DEBATE-20-21
<p> </p> <p>The <em>UK-EU-DEBATE-20-21</em> corpus was collected within the framework of the collaborative research project OLiNDiNUM (<em><a href="https://olindinum.huma-num.fr">Observatoire LINguistique du DIscours NUMérique</a> / </em>Linguistic Observatory of Online Debate) to be part of a shared research archive of shared corpora and resources. </p> <p>The corpus was selected with a view to examining the UK-EU media debate on the COVID-19 vaccination campaign following a specific transformative moment: the signature of the Brexit withdrawal agreement by the UK and the EU at the end of January 2021.</p> <p>The data were retrieved through the Application Programming Interface of the social networking site Twitter, using the accounts of key political actors in the UK government and EU institutions over a period of 14 months (1 February 2020–31 March 2021). The composition of the corpus is illustrated in the table.</p> <p> </p> <table> <tbody> <tr> <td><em>Political Actor</em></td> <td><em>Role</em></td> <td><em>Account</em></td> <td><em>Tweets</em></td> </tr> <tr> <td>Boris Johnson</td> <td>UK Prime Minister</td> <td>@BorisJohnson</td> <td>1186</td> </tr> <tr> <td>Dominic R. Raab</td> <td>UK Foreign Secretary</td> <td>@DominicRaab</td> <td>1468</td> </tr> <tr> <td>Priti Patel</td> <td>UK Home Secretary</td> <td>@pritipatel</td> <td>941</td> </tr> <tr> <td>Ursula von der Leyen</td> <td>President of the European Commission</td> <td>@vonderleyen</td> <td>1338</td> </tr> <tr> <td>David Sassoli</td> <td>President of the European Parliament</td> <td>@EP_President</td> <td>554</td> </tr> <tr> <td>Charles Michel</td> <td>President of the Council of the European Union</td> <td>@eucopresident</td> <td>675</td> </tr> </tbody> </table> <p> </p> <p>The data are supplied in separate .csv files (tab-delimited format). Each row contains the text of the tweet (<em>data__text</em>) and the tweet identifier (<em>data__id</em>) as a header. The tweet identifier enables swift retrieval of the original tweet by searching https://twitter.com/anyuser/status/<em>data__id. </em></p> <p> </p>
Data from 18 fungicide trials on potato late blight in UK and Ireland 2013-2017
<p>The data set comprises records of disease incidence, crop growth stage and yield from untreated and treated plots.</p> <p>The data is provided as both a tab-separated text file and a binary R data file. The R files provides code to read and plot the data. The plot produced is also provided as a PNG file.</p> <p>The field trials were conducted by Corteva Agriscience, Germany.</p>
UK Administrative Shapefiles clipped to buildings (simplified at 100m)
<p>This dataset includes a series of modified UK administrative boundary shapefiles based on the 2011 census which are intended for use in more accurate visualisation of UK geospatial data analysis. There are two key features of these shapefiles: (1) administrative shapes have been clipped to the Ordnance Survey buildings shapefile, so that in choropleth visualisations relating to demographic data filled spaces represent populated areas of the UK rather than large undifferentiated blocks. (2) Shapefiles have been simplified to reduce loading and processing time, in the case of this repository at 100m. After testing, we have settled on a procedure to render buildings layer visually comprehensible at high zoom levels, by adding a small buffer, dissolving (so that individual overlapping shapes combine into a single more easily visualised shape) and then simplifying at 150m. It is important to emphasise that because of the use of simplification (using a Ramer–Douglas–Peucker algorithm), these shapefiles are not suitable for analysis as boundaries may not be suitably precise or accurate. For users interested in the process used to generate these files you can consult the codebase deposited on <a href="https://github.com/kidwellj/uk_census_shapes_clipped">github</a>.</p> <p>Many thanks to colleagues including Alasdair Rae for recommendations on technique used here. Computations were performed using the University of Birmingham's BEAR Cloud service, which provides flexible resource for intensive computational work to the University's research community. See <a href="http://www.birmingham.ac.uk/bear">http://www.birmingham.ac.uk/bear</a> for more details. Given the massive size of datasets involved (including the district buildings vector shapefile which is 1.4gb and consists of hundreds of thousands of individual shapes), this work would have been impossible without this invaluable resource. I hope that these files will be of use to colleagues who may not have access to similar large computational arrays and make the process of visualising UK boundary and census data more accurate and efficient.</p> <p>Original files are under OGLv3 licenses. Derived data files, where possible are licensed for use under CC BY 4.0.</p> <p>Files include the following:</p> <p><em>Original unmodified data:</em></p> <ul> <li>infuse_ctry_2011.zip - original country level shapes, based on 2011 census, downloaded from https://borders.ukdataservice.ac.uk/ukborders/easy_download</li> <li>infuse_dist_lyr_2011.zip - original local authority shapes, based on 2011 census, downloaded from https://borders.ukdataservice.ac.uk/ukborders/easy_download</li> <li>TermsAndConditions.html - UK Data Service license details (OGLv3), applies to all the above</li> <li> GB_Postcodes.zip - UK postcode district shapes, prepared by Addy Pope, https://datashare.ed.ac.uk/handle/10283/2597</li> </ul> <p>Derived data files:</p> <ul> <li>OS_Open_Zoomstack_district_buildings.zip - buildings layer extracted from <a href="https://www.ordnancesurvey.co.uk/business-government/products/open-zoomstack">Ordnance Survey Zoomstack package</a>, licensed under <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">OGLv3</a> and exported to gpkg format.</li> <li>*_simplified_100m.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres.</li> <li>*_simplified_100m_buildings_overlay_simplified.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres, and then clipped to the buildings layer.</li> <li>*_simplified_100m_buildings_overlay_simplified.gpkg - Administrative shapes from above, simplified in R at a resolution of 100 metres, and then run against the buildings layer as a difference layer. Suitable for using as an overlay as the shapes are inverse.</li> </ul> <p>Users who wish to use these shapefiles in a reproducible research context may want to download individual files directly from this repository. To do so, you could use the following R code:</p> <pre><code># load packages require(sf) # load simplefeature data class, supercedes sp() and used for st_read # given the size and complexity even of simplified files here, ragg is highly recommended # for users on macos given inefficiencies in default R graphics device require(ragg) # create paths as needed if (dir.exists("data") == FALSE) { dir.create("data") } # download data files only if they aren't already present if (file.exists("data/infuse_dist_lyr_2011.shp") == FALSE) { download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_dist_lyr_2011.zip", destfile = "data/infuse_dist_lyr_2011.zip") unzip("infuse_dist_lyr_2011.zip", exdir = "data")} local_authorities <- st_read("data/infuse_dist_lyr_2011.shp")</code></pre> <p> </p>
UK HESA 2020 Academic women: Changing the Academic Gender Narrative through Open Access
<p>This Zenodo entry includes the full data files (.csv and .xlsx) for Figure 6: 'Percentages of women academic staff (headcount) in a subset of 165 United Kingdom universities by grouping, 2020', included in the manuscript "Changing the Academic Gender Narrative through Open Access", authored by members of the Curtin Open Knowledge Initiative (COKI). The analysis is of publicly available data sourced from the United Kingdom Higher Education Statistics Agency (HESA).</p>
Annex 1 – Actions and measures relevant to research integrity matched to the UK Concordat
<p>The present dataset is an Annex to the Discussion Document entitled “<a href="https://doi.org/10.5281/zenodo.6827947">Indicators of Research Integrity: An initial exploration of the landscape, opportunities and challenges</a>”. </p> <p>It consists in a longlist of actions and measures that organisations may put in place to support research integrity, building on a set of documents that we considered to represent the perspectives of the stakeholder groups mentioned in the UK Concordat to Support Research Integrity, including: </p> <ul> <li> <p>researchers; </p> </li> <li> <p>employers of researchers (i.e. bodies that conduct or host research; employ, support or host researchers; teach research students; or allow research to be carried out under their auspices); </p> </li> <li> <p>research funders; and </p> </li> <li> <p>other organisations (e.g. professional, statutory and regulatory bodies; academies and learned societies; professional and subject-specific representative bodies; journals and publishers; and organisations offering advice, guidance and support). </p> </li> </ul> <p>The table below provides an overview of the documents covered in the dataset. It should be noted that our selection of documents is not meant to imply that other efforts are of lesser importance: it is only a starting point for discussion and seeks to represent a breadth of stakeholder views. </p> <table> <tbody> <tr> <td> <p>Document </p> </td> <td> <p>Lead </p> </td> <td> <p>Main perspective(s) </p> </td> </tr> <tr> <td> <p><a href="https://ukrio.org/wp-content/uploads/UKRIO-Self-Assessment-Tool-for-The-Concordat-to-Support-Research-Integrity-V2.pdf">UKRIO Self-Assessment Tool for The Concordat to Support Research Integrity</a> </p> </td> <td> <p>UK Research Integrity Office (UKRIO) </p> </td> <td> <p>Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.1371/journal.pbio.3000737">The Hong Kong Principles for assessing researchers: Fostering research integrity</a> </p> </td> <td> <p>Moher et al. (academic article) </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://www.vitae.ac.uk/vitae-publications/reports/research-integrity-a-landscape-study">Research integrity: a landscape study</a> </p> </td> <td> <p>UK Research and Innovation (UKRI), Vitae, UK Research Integrity Office (UKRIO), UK Reproducibility Network (UKRN) </p> </td> <td> <p>All stakeholders </p> </td> </tr> <tr> <td> <p><a href="https://wellcome.org/reports/what-researchers-think-about-research-culture">What Researchers Think About the Culture They Work In</a> </p> </td> <td> <p>Wellcome </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://www.allea.org/wp-content/uploads/2017/05/ALLEA-European-Code-of-Conduct-for-Research-Integrity-2017.pdf">The European Code of Conduct for Research Integrity</a> </p> </td> <td> <p>All European Academies (ALLEA) </p> </td> <td> <p>All stakeholders </p> </td> </tr> <tr> <td> <p><a href="http://www.enrio.eu/wp-content/uploads/2019/03/INV-Handbook_ENRIO_web_final.pdf">Handbook on Research Integrity</a> </p> </td> <td> <p>European Network for Research Ethics and Integrity (ENERI) </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://sops4ri.eu/wp-content/uploads/Guideline-for-Promoting-RI-in-RFOs_final.pdf">Guideline for Promoting Research Integrity in Research Funding Organisations</a> </p> </td> <td> <p>Standard Operating Procedures for Research Integrity (SOPs4RI) </p> </td> <td> <p>Research funders </p> </td> </tr> <tr> <td> <p><a href="https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/guideline-for-promoting-research-integrity-in-research-performing-organisations_horizon_en.pdf">Guideline for Promoting Research Integrity in Research Performing Organisations</a> </p> </td> <td> <p>Standard Operating Procedures for Research Integrity (SOPs4RI) </p> </td> <td> <p>Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.24318/cope.2018.1.3">Cooperation between research institutions and journals on research integrity cases: guidance from the Committee on Publication Ethics</a> </p> </td> <td> <p>Committee on Publication Ethics (COPE) </p> </td> <td> <p>Publishers and Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.24318/cope.2019.1.4">COPE Retraction Guidelines</a> </p> </td> <td> <p>Committee on Publication Ethics (COPE) </p> </td> <td> <p>Publishers </p> </td> </tr> </tbody> </table> <p>Find more outputs of this project in the <a href="https://zenodo.org/communities/research-integrity-indicators/">dedicated Zenodo community</a>. </p>
UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits
<p>Summary-level GWAS data for 53 traits generated by <a href="https://www.genomicsplc.com/">Genomics plc</a> as presented in:</p> <p>Thompson D. et al. UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits (<a href="https://doi.org/10.1101/2022.06.16.22276246">https://doi.org/10.1101/2022.06.16.22276246</a>)</p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at <a href="mailto:research@genomicsplc.com">research@genomicsplc.com</a></p> <p><strong>NOTES</strong></p> <p>These analyses were carried out using the full UK Biobank (UKB) imputation data release (v3b). After removal of exclusions and withdrawals, a subset of 337,151 UKB individuals, the White British Unrelated (WBU) subgroup, was defined as the intersection of two sample groups created by Bycroft et al 2018 (Nature 562, 203-209): the ‘White British ancestry’ group (UKB Data Field 22006) and the ‘used in genetic principal components’ group (UKB Data Field 22020), the latter being high quality samples that were filtered to avoid closely related individuals. All GWAS analyses were performed on the WBU subgroup.</p> <p>Phenotypes were defined as described in Supplementary Table 1 ‘Phenotype definitions’ using a combination of Hospital Episode Statistics, Cancer Registry reports (where applicable) and self-report responses, with the exception of coronary artery disease (CAD). GWAS data was generated for both a “narrow” and a “broad” definition of CAD. The former was used as part of the training data for the Enhanced CAD PRS, the latter was used as part of the training data for the Enhanced CVD PRS. The phenotype definitions for “narrow” and a “broad” CAD are as follows:</p> <table> <tbody> <tr> <td>Narrow CAD<br> (includes angina)</td> <td>ICD10 codes (where .X indicates all subcodes) from both hospital and death records: I21, I22, I23, I24.1, I25.2, I20.X. ICD9 codes: 410-412, 42979, 413.X. OPCS-4 codes (K40.1–40.4, K41.1–41.4, K45.1–45.5,K49.1–49.2, K49.8–49.9, K50.2, K75.1–75.4, K75.8–75.9), self-reported heart attack (UKB codes 1075 in field 20002; code 1 in field 6150), self-reported coronary angioplasty (ptca) or coronary artery bypass graft (UKB codes 1070 and 1095 in field 20004), self-reported angina.</td> </tr> <tr> <td>Broad CAD<br> (includes angina and all ischaemic heart disease)</td> <td>As for Narrow CAD, plus ICD10 codes I24.X, I25X, and ICD9 codes 414.X (where .X indicates all subcodes).</td> </tr> </tbody> </table> <p>Note that there is no GWAS for cardiovascular disease (CVD) per se. This is because the UKB training data for the Enhanced CVD PRS consisted of separate GWASs for “narrow” CAD and ischaemic stroke.</p> <p>All analyses included Age at assessment, sex (for non-sex specific traits), genotyping chip, and 10 principal components as covariates.</p> <p>GWAS summary statistics for each trait were generated by applying PLINK 2.0 to the WBU subgroup, using a logistic regression for disease traits, and a linear regression model for quantitative traits. For chromosome X variants males were treated as having 0 or 2 alternative alleles.</p> <p>The results are not adjusted for genomic control.</p> <p><strong>DATA FILE CONTENT DESCRIPTION (DISEASE TRAITS)</strong></p> <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size (log odds ratio)</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ncase</td> <td>Number of cases</td> </tr> <tr> <td>ncontrol</td> <td>Number of controls</td> </tr> </tbody> </table> <p><strong>DATA FILE CONTENT DESCRIPTION (QUANTITATIVE TRAITS)</strong></p> <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ntotal</td> <td>Total sample size</td> </tr> </tbody> </table> <p><strong>FILE NAMES</strong></p> <p>The following is a list of traits and their corresponding file names.</p> <p><em><strong>DISEASE TRAITS</strong></em></p> <table> <tbody> <tr> <td>Age-related macular degeneration</td> <td>amd_strict_UKB_WBU.csv.gz</td> </tr> <tr> <td>Alzheimer's disease</td> <td>alzheimers_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Asthma</td> <td>asthma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Atrial fibrillation</td> <td>atrial_fibrillation_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bipolar disorder</td> <td>bipolar_disorder_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bowel cancer</td> <td>CRC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Breast cancer</td> <td>BC_UKB_WBU_women.csv.gz</td> </tr> <tr> <td>Coeliac disease</td> <td>celiac_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Narrow coronary artery disease</td> <td>NARROW_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Broad coronary artery disease</td> <td>BROAD_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Crohn's disease</td> <td>crohns_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Epithelial ovarian cancer</td> <td>OC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Hypertension</td> <td>HT_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ischaemic stroke</td> <td>IS_stroke_UKB_WBU.csv.gz</td> </tr> <tr> <td>Melanoma</td> <td>melanoma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Multiple sclerosis</td> <td>multiple_sclerosis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Osteoporosis</td> <td>OP_WBU_training.csv.gz</td> </tr> <tr> <td>Prostate cancer</td> <td>PC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Parkinson's disease</td> <td>parkinsons_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Primary open angle glaucoma</td> <td>POAG_WBU_training.csv.gz</td> </tr> <tr> <td>Psoriasis</td> <td>psoriasis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Rheumatoid arthritis</td> <td>rheumatoid_arthritis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Schizophrenia</td> <td>schizophrenia_UKB_WBU.csv.gz</td> </tr> <tr> <td>Systemic lupus erythematosus</td> <td>lupus_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 1 diabetes</td> <td>t1d_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 2 diabetes</td> <td>T2D_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ulcerative colitis</td> <td>ulcerative_colitis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Venous thromboembolic disease</td> <td>VTE_UKB_WBU.csv.gz</td> </tr> </tbody> </table> <p><em><strong>QUANTITATIVE TRAITS</strong></em></p> <table> <tbody> <tr> <td>Age at menopause</td> <td>age_at_menopause_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein A1</td> <td>apolipoprotein_a1_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein B</td> <td>apolipoprotein_b_UKB_WBU.csv.gz</td> </tr> <tr> <td>Body mass index</td> <td>bmi_UKB_WBU.csv.gz</td> </tr> <tr> <td>Calcium</td> <td>calcium_UKB_WBU.csv.gz</td> </tr> <tr> <td>Docosahexaenoic acid</td> <td>docosahexaenoic_acid_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated bone mineral density T-score</td> <td>BMD_WBU_training.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (creatinine based)</td> <td>egfr_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (cystatin based)</td> <td>egfr_cys_UKB_WBU.csv.gz</td> </tr> <tr> <td>Glycated haemoglobin</td> <td>hba1c_UKB_WBU_nodiabetes.csv.gz</td> </tr> <tr> <td>High density lipoprotein cholesterol</td> <td>hdl_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Height</td> <td>height_UKB_WBU.csv.gz</td> </tr> <tr> <td>Intraocular pressure</td> <td>iop_WBU_training.csv.gz</td> </tr> <tr> <td>Low density lipoprotein cholesterol</td> <td>ldl_UKB_WBU_nostatins.csv.gz</td> </tr> <tr> <td>Omega-6 fatty acids</td> <td>omega_6_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Omega-3 fatty acids</td> <td>omega_3_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphatidylcholines</td> <td>phosphatidylcholines_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphoglycerides</td> <td>phosphoglycerides_UKB_WBU.csv.gz</td> </tr> <tr> <td>Polyunsaturated fatty acids</td> <td>polyunsaturated_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Resting heart rate</td> <td>resting_heart_rate_UKB_WBU.csv.gz</td> </tr> <tr> <td>Remnant cholesterol (Non-HDL, Non-LDL cholesterol)</td> <td>remnant_cholesterol__UKB_WBU.csv.gz</td> </tr> <tr> <td>Sphingomyelins</td> <td>sphingomyelins_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total cholesterol</td> <td>total_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total fatty acids</td> <td>total_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total triglycerides</td> <td>total_triglycerides_UKB_WBU.csv.gz</td> </tr> </tbody> </table>
Benthic and pelagic biomass and silicon cycling in the Severn Estuary, UK
<p>This dataset contains benthic and pelagic data on biomass, chlorophyll fluorescence and silicon cycling in the Severn Estuary, UK, from 2016. The sampled periods coincided with the seasons, and for clarity, are referred to here as; winter (January-March), spring (April-June), summer (July-September) and autumn (October-December). See readme files for details.</p>
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{coppock2024audio,</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 = {Nature Machine Intelligence},</p> <p> year = {2024},</p> <p> doi = {https://doi.org/10.1038/s42256-023-00773-8}</p> <p>}</p> <p>@article{budd2024,</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> journal={Scientific Data},</p> <p> year={2024},</p> <p> doi = {https://doi.org/10.1038/s41597-024-03492-w}</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.10043977</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>
Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungary, Switzerland
<p><strong>Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungay, Switzerland</strong></p> <p>In the UNTWIST project, we have carried out a total of 18 focus groups in Denmark, Germany, Hungary, Spain, Switzerland and the United Kingdom. They explore RWPP voters’ subjective perceptions of their needs and demands, their horizon of expectations, and their level of ‘gender fatigue’. Groups’ design followed two minimum criteria: same-sex composition (with a minimum of two same sex -male and female- groups per country) and voting behaviour (current voters of RWPP who have previously voted for mainstream parties or abstained or have doubts about RWPP and mainstream or abstain in case of voting for the first time).</p> <p> The composition of the groups varied between 6 and 10 participants per group in all but one partner’s country. In Denmark, all focus groups experienced dropouts. These unforeseen issues led to conducting the focus groups with fewer participants than was initially designed.</p> <p> In 83% of countries, the empirical composition of focus groups was considered and controlled for participants’ age, social class position and level of education.</p> <p>Finally, groups were same-sex moderated.</p> <p>Two comprised folders are provided. One contains the anonymised transcriptions of 18 Focus Groups carried out for WP2 of the UNTWIST project in their local languages. The other contains the IA-translated (Deepl) version of the same focus groups. Please note that the translations have not been human-supervised. </p> <p>FG_CHE_1 Female <br>Female Group, Switzerland</p> <p>FG_CHE_2 Male<br>Male Group, Switzerland </p> <p>FG_DEN_1 Female <br>Female Groups, Denmakr</p> <p>FG_DEN_2 Male<br>Male Group, Denmark </p> <p>FG_DEN_3 Male <br>Male Group, Denmark</p> <p>FG_DEN_4 Mixed <br>Mix Male and Female Group, Switzerland</p> <p>FG_ESP_1 Male<br>Male Group, Spain</p> <p>FG_ESP_2 Male<br>Male Group, Spain </p> <p>FG_ESP_3 Female <br>Female Group, Spain</p> <p>FG_ESP_4 Female<br>Female Group, Spain</p> <p>FG_GBR_1 Female <br>Female Group, UK</p> <p>FG_GBR_2 Male <br>Male Group, UK</p> <p>FG_GER_1 Female <br>Female Group, Germany</p> <p>FG_GER_2 Male<br>Male Group, Germany</p> <p>FG_HUN_1 Female <br>Female Group, Hungary</p> <p>FG_HUN_2 Female <br>Female Group, Hungary</p> <p>FG_HUN_3 Male<br>Male Group, Hungary </p> <p>FG_HUN_4 Male<br>Male Group, Hungary </p>
UK shale gas air and water quality data
<p>Datasets for UK coal bed methane compositions (Airth field), shale gas composition from Bowland shale operations and produced water composition from UK Airth field.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.