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62 results for “EU project”
Zenodo-communities for EU projects
<div> <div>Dataset of Zenodo communities associated with EU-funded projects. Only communities linked to a single EU project under either Horizon Europe, Horizon 2020, or Framework Programme 7 are included. Earlier Framework Programmes are not included, as Framework Programme 6 ended in 2006, and Zenodo was launched on May 8, 2013. The dataset was extracted from Zenodo on May 22, 2024, and contains data as of that date. It includes 2,724 communities linked to an EU-funded project.</div> </div>
EU MarcoPolo project | SO2 emission inventory over China
<p>The aposteriori SO<sub>2</sub> emissions for year 2014, in the domain from 102°E to 132°E and from 15°N to 55°N, in a 0.25°x0.25° spatial resolution and monthly temporal resolution, have been provided to the MarcoPolo project and can be found at <a href="http://users.auth.gr/mariliza/MarcoPolo/SO2_EmissionInventory/">http://users.auth.gr/mariliza/MarcoPolo/SO2_EmissionInventory/</a>. For details on the creation of the inventory refer to <a href="http://users.auth.gr/mariliza/MarcoPolo/D3.4_SO2_emission_estimates.pdf">http://users.auth.gr/mariliza/MarcoPolo/D3.4_SO2_emission_estimates.pdf</a> and for the inclusion of the SO2 emission inventory to the MarcoPolo Emission Database refer to: <a href="http://users.auth.gr/mariliza/MarcoPolo/D4.2_DescriptionMarcoPoloInventory.pdf">http://users.auth.gr/mariliza/MarcoPolo/D4.2_DescriptionMarcoPoloInventory.pdf</a> as well as <a href="http://users.auth.gr/mariliza/MarcoPolo/D4.3_assessment_impact_updated_emission_inventories_v2.0.pdf">http://users.auth.gr/mariliza/MarcoPolo/D4.3_assessment_impact_updated_emission_inventories_v2.0.pdf</a> .</p> <p>The main reference to this dataset is found here:</p> <p>Koukouli, M. E., Theys, N., Ding, J., Zyrichidou, I., Mijling, B., Balis, D., and van der A, R. J.: Updated SO<sub>2</sub> emission estimates over China using OMI/Aura observations, Atmos. Meas. Tech., 11, 1817–1832, https://doi.org/10.5194/amt-11-1817-2018, 2018.</p> <p>The netcdf data files contain the following structure:</p> <ul> <li>Dimensions <ul> <li>lat = 129</li> <li>lon = 121</li> </ul> </li> <li>Attributes <ul> <li>author = "MariLiza Koukouli"</li> <li>contact information = "mariliza@auth.gr"</li> <li>institution = "Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki"</li> <li>time frame = "2014"</li> <li>sector classification = "total emissions"</li> <li>emis_cat_name = "sulphur dioxide emissions"</li> <li>source_type_name = "sulphur dioxide emissions"</li> <li>pollutant_description = "updated sulphur dioxide emissions based on the CHIMERE model running the MEIC emissions and the OMI/Aura observations"</li> <li>unit_emissions = "Mg/month"</li> <li>nodata_value = "-9999.0"</li> </ul> </li> <li>Variables <ul> <li>float emissions(lon, lat)</li> </ul> </li> </ul>
List of capacity building resources for combating climate mis/disinformation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on combating climate change misinformation and disinformation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
List of capacity building resources for climate change adaptation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on climate change adaptation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
Projected fresh water use from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016
<p>The dataset contains projections of fresh water withdrawal and consumption from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016.</p> <p>The energy sector in this scope includes energy production (production of coal, oil and gas) and energy transformation in oil refineries and power plants (nuclear, solid fuels, oil, gas, biomass and geothermal).</p> <p>The information in provided on NUTS 2 level following the NUTS2 2013 definition.</p> <p>The dataset is explained in more detail in the report <a href="https://ec.europa.eu/jrc/en/publication/projected-fresh-water-use-european-energy-sector">Projected fresh water use from the European energy sector</a>.</p>
Financing conditions of renewable energy projects – results from an EU wide survey
<p>The dataset contains data related to financing conditions and costs of capital for onshore wind, solar PV and offshore wind within the EU. It provides data on minimum, maximum and average country and technology-specific values on costs of debt, debt service coverage ratios, loan tenors, debt size, costs of equity and WACC values. The data was collected between September 2019 and April 2020.</p> <p>The data contains values for onshore wind in Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Spain and Sweden. Furthermore, it contains values for solar PV in Czech Republic, Estonia, France, Greece, Hungary, Latvia, Portugal, Romania, Slovakia and Spain. Finally, it also contains values for offshore wind in Belgium, France, Germany and UK. </p> <p>The PDF files are survey questionnaires that were used for the data collection. This includes 1) a survey questionnaire used in an exploratory research phase, in which we identified the most relevant research aspects related to the impacts of auctions on costs of capital and financing 2) a survey questionnaire used for the focus-group countries (Germany, Denmark, Spain, Portugal and Greece), which includes a list of more extensive qualitative questions and 3) a survey questionnaire used for the focus-group countries (all other EU member states) and which focused only on collecting the quantitative data. </p>
i-Dreams H2020 EU Project: Sample dataset
<p>The overall objective of the <em>i</em>-DREAMS project is to setup a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (<em>i</em>-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment is made to monitor and determine if a driver is within acceptable boundaries of safe operation. Moreover, safety-oriented interventions were developed to inform or warn the driver real-time in an effective way as well as on an aggregated level after driving through an app- and web-based gamified coaching platform. The conceptual framework, which was tested in a simulator study and three stages of on-road trials in Belgium, Germany, Greece, Portugal and the United Kingdom on a total of 600 participants representing car, bus, and truck drivers, respectively. Specifically, the Safety Tolerance Zone (STZ) is subdivided into three phases, i.e. ‘Normal driving phase’, the ‘Danger phase’, and the ‘Avoidable accident phase’. For the real-time determination of this STZ, the monitoring module in the<em> i</em>-DREAMS platform continuously register and process data for all the variables related to the context and to the vehicle. Regarding the operator, however, continuous data registration and processing are limited to mental state and behavior. Finally, it is worth mentioning that data related to operator competence, personality, socio-demographic background, and health status, are collected via survey questionnaires. More information of the project can be seen from project website: https://idreamsproject.eu/wp/</p> <p>This dataset contains naturalistic driving data of various trips of participants recruited in i-Dreams project. Various different types of events are recorded for different intensity levels such as headway, speed, acceleration, braking, cornering, fatigue and illegal overtaking. Running headway, speed, distance, wipers use, handheld phone use, high beam use and other data is also recorded. Driver characteristics are also available but not part of this sample data. In the i-Dreams project, raw data for a particular trip was collected via CardioID gateway, Mobileye, wristband or CardioWheel. These trip data are fused using a feature-based data fusion technique, namely geolocation through synchronization and support vector machines. The system provided by CardioID integrates several data streams, generated by the different sensors that make up the inputs of the i-Dreams system. The sample dataset is fused, processed as well as aggregated to produce consistent time series data of trips for a particular time interval such as 30 secs/ 60 secs or 2- minutes intervals. More datasets can be acquired for analysis purposes by following the data acquisition process given in the data description file.</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>
Educational transformation and network learning dataset – qualitative data from an international collaborative EU-project
<p>We are releasing our dataset of workshop outcomes acquired from the annual consortium conferences organized by the international “NextFood” consortium. The purpose of this project is to develop new ways of educating the future sustainability leaders of the agrifood sector, making sure that the professionals (farmers, advisers, businesses, students) have the right set of skills and competences needed to tackle the sustainability challenges we face ahead. Data gathering started from May 2018 yielding considerable amount of data on achievements, challenges and action plans related to educational transformation. This dataset will be updated by the time of project finalization. This work was funded by the European Union, through the Horizon 2020 project “NextFood”, Grant agreement No. 771738.</p>
773782 COASTAL EU Project: stream data from SW Messinia, Greece
<p>In the framework of COASTAL Project (<a href="https://h2020-coastal.eu/">https://h2020-coastal.eu/</a>) , the Institute of Oceanography/HCMR coordinates the Multi-Actor Lab for the SW Messinia case study. The water quality of six small rivers was studied and evaluated to assess the environmental status of area. This kind of evaluation follows the European Water Framework Directive 2000/60/EU, which includes biotic and environmental characteristics, with emphasis on the Biological Quality Elements.</p> <p>Sampling and measurements were collected in seven periods, including macro-invertebrate fauna, physicochemical parameters and diatoms. The first one took place in October 2018, the second one in December 2018, the third in April 2019, the fourth in August 2019, the fifth in November - December 2019, the sixth in December 2020 and the last one in December 2021.</p>
773782 COASTAL EU Project: stream data from SW Messinia, Greece
<p>In the framework of COASTAL Project (<a href="https://h2020-coastal.eu/">https://h2020-coastal.eu/</a>) , the Institute of Oceanography/HCMR coordinates the Multi-Actor Lab for the SW Messinia case study. The water quality of six small rivers was studied and evaluated to assess the environmental status of area. This kind of evaluation follows the European Water Framework Directive 2000/60/EU, which includes biotic and environmental characteristics, with emphasis on the Biological Quality Elements.</p> <p>Sampling and measurements were collected in seven periods, including macro-invertebrate fauna, physicochemical parameters and diatoms. The first one took place in October 2018, the second one in December 2018, the third in April 2019, the fourth in August 2019, the fifth in November - December 2019, the sixth in December 2020 and the last one in December 2021.</p>
Polidoc.net CODEBOOK: National and Regional Manifestos and other Political Documents Collected for the Research Projects "Representation in Europe: Congruence between Preferences of Elites and Voters" (REPCONG) and "The Impact of EU Cohesion Policy on European Identification" (COHESIFY)
<p>The Political Documents Archive http://www.polidoc.net/ contains election manifestos, coalition agreements, government declarations and various other documents of political actors from developed democracies. Currently, the archive builds on a stock of more than 3000 political documents from 20 European countries. The aim of the repository is to provide political texts in order to facilitate scholarly research in different areas of comparative politics such as party competition, coalition politics, legislative decision-making or electoral behavior.</p> <p>National electoral manifestos have been collected in the course of the REPCONG project ("Representation in Europe: Policy Congruence between Citizens and Elites"), and the archive includes party manifestos for regional elections in several European democracies. Because the process of European integration resulted in a strengthening of regions in EU member states and in countries that want to join the European Union, the relevance of the regional level for political decision-making has increased during the last decades. Therefore, also the policy profiles of regional parties are required to get a full picture of democratic responsiveness in European states across all levels of the political system. The collection of regional manifestos was supported by the COHESIFY project (www.cohesify.eu), funded under the Horizon 2020 Framework Programme for Research and Innovation. The aim of COHESIFY is to study whether the European Structural and Investment Funds affect people’s support for and identification with the European project.</p> <p>The archive is freely accessible (after a simple registration) and meant to foster rigorous research in these areas by enabling scholars to produce valid and reliable findings from empirical studies of textual data rather than unnecessarily struggling to obtain and process texts.</p>
MODUL4R EU Founded Project Swarm Learning framework dataset
<p>MODUL4R EU Founded Project Swarm Learning framework dataset v1.0.0</p> <p>Contains values for:</p> <ul> <li>Capacitor type </li> <li>Grab Pressure (Pa)</li> <li>Leg Cutting (mm)</li> <li>Polarity</li> <li>Quality Metric</li> </ul>
PERCEIVE: WP3: Effectiveness of communication strategies of EU projects
<p>This data set contains all the relevant data referred to PERCEIVE WP3, as tasks within WP3 are logically connected. Data analyzed within WP3, but collected in WP5, are included in the data set “<em>PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels</em>” (<a href="http://doi.org/10.5281/zenodo.1038041">http://doi.org/10.5281/zenodo.1038041</a>). Data analyzed in Task3.1 are mostly based on transcripts already included in the data set “<em>PERCEIVE. WP1. Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries. Task1.2. Focus group with Cohesion Policy practitioners</em>” (focus groups and other interviews).</p> <p>Task3.2 data are the results of a European wide online survey (at the moment this document is being compiled, the website of the survey is no longer online) targeting policy communicators and focused on three strategic aspects of communicating policy: a) factors of success and barriers, b) support from central institutions and c) communication mix and storytelling.</p> <p>Task3.3 data regard the analysis of social media communication from EU communication offices at both local and European level. Data covers a sentiment analysis performed on the Facebook homepages of Local Management Authorities (LMA) of PERCEIVE case study regions as well as twitter networks and timelines for international accounts and hashtags. </p> <p>Task3.4 data contain elements used in the statistical modeling of communication efforts (see Deliverable 3.4, <a href="http://doi.org/10.6092/unibo/amsacta/6111">http://doi.org/10.6092/unibo/amsacta/6111</a> or <a href="http://doi.org/10.5281/zenodo.1318144">http://doi.org/10.5281/zenodo.1318144</a>). Data cover the algorithmic clustering of topics detected in Task5.3, data derived from the PERCEIVE survey, the code (R programming environment) used to run regression analyses, as well as the results of the analyses themselves.</p> <p>Task3.5 data refer to secondary publicly available data. Namely the collection of the PANORAMA magazine available at INFOREGIO, the Directorate of Regional Policy web portal (<a href="https://ec.europa.eu/regional_policy/en/information/publications/panorama-magazine/">https://ec.europa.eu/regional_policy/en/information/publications/panorama-magazine/</a>), and Eurobarometer data on “awareness” available at the Open Data Portal of the EC (<a href="http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm">http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm</a>). The textual content of PANORAMA magazine has been content analyzed and the results are made available as a .csv table of concepts’ frequencies per magazine issue.</p>
WoS and Scopus records for the bibliometric analysis in the output D2.2 Digital transformation of research and innovation roadmap of the reSEArch-EU project
<p>These files represent the exported WoS and Scopus records, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.</p>
Results of the MECinFire experiment in Fed4FIRE+ EU project
<p><strong>Multi-access Edge Computing (MEC)</strong> has been proposed as the means to drastically minimize the service access latency, by bringing computational resources and services closer to the wireless network edge. Edge resources are planned to be extensively used in the 5G network deployments, as they are able to meet stiff latency demands required from services being developed around this ecosystem (e.g. AR/VR, e-Health, Industry 4.0, etc.), by providing computational capabilities where such services can be executed close to the network edge. At the same time, 5G networks redefine the operation of traditional base station units, by disaggregating them and operating part of them in the Cloud, thus creating Cloud-RANs. These Cloud-RANs can also be heterogeneous, allowing users to access the network through multiple wireless technologies (e.g. dual access through 5G-NR and LTE). In this project, we blend the novel disaggregated and heterogeneous base station architecture with the MEC concept, and develop and experiment with the deployment of the edge computing services even closer to the network edge. In MECinFIRE we developed a software prototype that allows services to be executed close or over the machines hosting the radio access services for the network access. Our experiment provides several proof-of-concept experiments that illustrate the applicability and benefits of our solution in real 5G networks. The experiment was evaluated in a real testbed environment, while measuring KPIs regarding the end-to-end user to service latency.</p> <p>This repository contains the dataset of the experimental results produced by the MECinFIRE Project within the FED4FIRE+.</p>
3D Point Cloud of a railway slope - MOMIT (Multi-scale observation and monitoring of railway infrastructure threats) EU project - H2020-EU.3.4.8.3. - Grant agreement ID: 777630
<p>3D point cloud of a railway trench in Lavancia-Épercy (France). The 3D point cloud has been generated from pictures obtained by means of a UAV (DJI Matrice 600 Pro) and processed using Agisoft Metashape. The 3D point cloud is composed of 110,356,682<strong> </strong> million points containing XYZ and RGB information.</p> <p>The original file is in .bin format and is compressed in zip format.</p> <p> </p>
VineScout EU project - Summer 2020 data
<p>The Excel file contains several tabs (tests T1, T2, T3, T4, T5, T6, and T8 in 2020) with the following columns for each datum: Hour (Spanish summer time. The UTC is that value minus 2 hours), Minutes, Seconds, Latitude, and Longitude of the VineScout robot antenna, East, North, East, and North of the crop (vineyard of the right side), Altitude, Canopy temperature, Ambient temperature, Pressure, and Humidity, Compass heading, NDVI, PRI (averaged), Row, Irrigation rate, Space, and Scholander pressure when it was measured</p>
European Study on Risk Factors for Violence in Mental Disorder and Forensic Care: a multicentre project. The EU-Viormed Dataset
<p>The aims of this project, through a series of interlinked work packages, designed by researchers and practicing clinicians, and lead by an experienced management team, is to explore and map these differences, test new risk assessment tools and identify and share best practice where it exists. It aims are fourfold. Firstly to describe forensic psychiatry services as they exist today in 2017 across the European Union. Secondly to identify risk factors for violence in a unique international forensic sample and thirdly to test for the very first time in a related EU sample two contrasting methods of violence risk assessment. Finally it explored what works for these often marginalised patients, their families and their carers, at an operational, clinical and ethical level.<br> These aims were achieved through two studies designs:<br> Study 1: case-control retrospective design in which forensic patients with Schizophrenia<br> Spectrum Disorders (SSDs) who have a history of interpersonal violence and live in forensic<br> units will be compared to non-violent patients with SSDs living in the community.<br> Study 2: prospective cohort study, with 6 and 12 month follow-up, to test the predictive validity<br> of the leading structured professional clinical judgement guide for violence prediction, the<br> HCR-20v3, the Forensic Psychiatry and Violence Tool (FoVOx) and the Mental Illness and<br> Suicide Tool (OXMIS, https://oxrisk.com/)</p>
Nordic trial reporting project: Raw data from EU Clinical Trials Registry (EUCTR) and ClinicalTrials.gov
<p>Uploaded on behalf of the author team for the research project "<strong>Systematic evaluation of clinical trial reporting at medical universities and university hospitals in the Nordic countries</strong>".</p><p><strong>Raw data</strong> from EU Clinical Trials Registry (EUCTR) and ClinicalTrials.gov:</p><p><strong>EUCTR</strong>: We retrieved the latest dataset for the EU Trials Tracker of EUCTR trials on Nov 27, 2022, reflecting data from Nov 7, 2022 (1,2). We also used a custom web scraper that automatically extracts data from EUCTR country protocols and results sections (variables described in Appendix Table 2), developed by the EU Trials Tracker team (2).<br>References: <br>1. Goldacre B, DeVito NJ, Heneghan C, Irving F, Bacon S, Fleminger J, Curtis H. Compliance with requirement to report results on the EU Clinical Trials Register: cohort study and web resource. BMJ. 2018 Sep 12;362:k3218.<br>2. EU Trials Tracker — Who's not sharing clinical trial results? [Internet]. [cited 2022 Aug 30]. Available from: http://eu.trialstracker.net/</p><p><strong>ClinicalTrials.gov</strong>: We downloaded the complete Aggregate Analysis of ClinicalTrials.gov dataset (AACT, http://aact.ctti-clinicaltrials.org/) on Nov 27, 2022, reflecting data from Nov 9, 2022. </p><p>See our GitHub and preregistered protocol for more details:<br>https://github.com/cathrineaxfors/nordic-trial-reporting<br>https://osf.io/97qkv/</p>
ScienceDex guides
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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.