Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,782
datasets available to search
ShareScore release 0.9.0
Dataset results
6,782 results for “project”
Job Ads in Retail and E-commerce sectors for INAIR project countries
<p><strong>INAIR</strong> (<a href="https://www.ai4retail.eu/en/">Increasing the Uptake of AI in Retail</a>) is a Coordination and Support Action funded by the European Union'’s Horizon Europe Research and Innovation programme - Grant Agreement No. 101133847. The project aims to contribute to reducing the AI skills gap of European MSMEs in Retail, to let them exploit the potential of AI for greening their businesses, support their competitiveness in the global market and ultimately contribute to reaching the digital decade target of 75%+ European companies adopting AI technologies by 2030. </p> <p>This dataset contains job advertisements from the retail and e-commerce sectors in Cyprus, Germany, Italy, Poland, and Romania, collected as part of the INAIR Horizon Europe project. This resource supports analyses of digital skill requirements and helps identify trends and gaps in the labor market for the retail and e-commerce sectors.</p> <p>Data was collected three times during the period from March to May 2024, resulting in a total of 44,494 job offers.</p>
EU project OCEAN:ICE Deliverable: D1.4 Gridded European circumpolar sea ice production fluxes
<p>This deliverable is a new dataset of sea ice production (SIP) in Antarctic coastal polynyas, critical regions for sea ice formation and dense water formation. Using Earth Observation data and atmospheric reanalysis, we use a heat budget method to estimate the SIP. We use sea ice concentration (SIC) from passive microwave sensors and ECMWF ERA5 reanalysis for near-surface wind speed and surface air temperature. Comparison against previous literature shows broad consistency in spatial patterns and magnitude of ice production, with notable variations across larger polynyas. Despite simplifications and thereby increased uncertainties in the absolute ice production values, the data set provides valuable insights into the dynamics and variability of Antarctic polynya SIP for the period 1992-2023.</p>
Images of The Art of Ikat: A Cambodian Renaissance exhibition - TEX-KR project
<p>Pictures of <em>The Art of Ikat: A Cambodian Renaissance</em> exhibition, a dissemination deliverable of the EU-funded MSCA TEX-KR project.<br><br><em>The Art of Ikat: A Cambodian Renaissance</em> took place from 22 February to 31 May 2024 at the Royal Danish Library / University of Copenhagen SøndreCampus, Karen Blixens Plads 7, 2300 Copenhagen S., Denmark. The opening event took place on 22 February 2024.<br>This exhibition was curated by Magali An Berthon, project leader for TEX-KR with the support of Morten Grymer-Hansen, associate researcher at the Centre for Textile Research, University of Copenhagen </p> <p>More information about the exhibition: <a title="The Art of Ikat exhibition page" href="https://www.traditionaltextilecraft.dk/exhibition-tex-kr" target="_blank" rel="noopener">Exhibition page on TEX-KR website</a></p> <p>This repository presents images of the installation process; images of the exhibition display, venue and original artworks commissioned for the exhibition, and images of the opening event.</p> <p>To credit the images appropriately:<br>- The photos of the installation process and opening event are credited <strong>© </strong>Luisa Dominguez Rey for TEX-KR. <br>- The photos of the exhibition display are credited <strong>©</strong>TEX-KR.</p> <p>These images are also available on this link: <a href="https://www.traditionaltextilecraft.dk/gallery" target="_blank" rel="noopener">Image gallery</a></p>
Model data for probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions
<p>Model data for probabilistic projections of the Amery Ice Shelf catchment, <br>Antarctica, under high ice-shelf basal melt conditions<br>======================================================================</p> <p>This archive contains ice-sheet model output and statistical models <br>used in the manuscript:<br>"Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under<br>high ice-shelf basal melt conditions" by:<br>Sanket Jantre, Matthew Hoffman, Nathan M. Urban, Trevor Hillebrand, Mauro<br>Perego, Stephen Price, John D. Jakeman</p> <p>OVERVIEW<br>--------</p> <p>Statistical models and corresponding datasets are archived in the file<br>Amery_UQ_Study_Statistical_Models.zip. That file contains a README that<br>describes its contents.<br>Contact for statistical models:<br>Sanket Jantre, Brookhaven National Laboratory, sjantre@bnl.gov</p> <p>The remaining files in this archive contain output from ice-sheet model <br>simulations using the MPAS-Albany Land Ice (MALI) model <br>(Hoffman et al. 2018, https://github.com/MALI-Dev/E3SM)<br>applied to a regional domain of the Amery Ice Shelf catchment<br>of Antarctica with mesh resolution varying from 4 to 20 km.<br>The contents of these files are described below.<br>Contact for MALI simulations:<br>Matthew Hoffman, Los Alamos National Laboratory, mhoffman@lanl.gov</p> <p>ENSEMBLES<br>---------</p> <p>The ensembles consist of all or a subset of 200 model runs with values for 6<br>parameters sampled from a Sobol' sequence over a specified range.</p> <p>Parameters and Sampled Ranges:<br>1. Ice stiffness scaling factor, Cφ: (0.8, 1.2)<br>2. Basal friction scaling factor, Cμ: (0.8, 1.2)<br>3. Basal slip exponent, q: (0.1, 0.333)<br>4. Calving yield stress, σmax: (80, 180) kPa<br>5. Ice-shelf melt coefficient, γ0: (9620, 471000) m yr−1<br>6. Ice-shelf basal melt rate, m: (12, 58) Gt yr−1</p> <p>SCENARIOS<br>---------</p> <p>The archive contains output from 4 scenarios described in the manuscript:</p> <p>Historical relaxation (RELX): For each ensemble member, we conducted a 50 year<br>relaxation from the initial condition using historical climate forcing to<br>integrate out fast transient behavior. For surface mass balance, we applied a<br>1995–2017 climatological average from RACMO2.3p1 (Van Wessem et al., 2014; van<br>den Broeke, 2019). The ocean thermal forcing was the observation-based<br>climatology compiled for ISMIP6-Antarctica, which uses data from 1995–2018<br>(Jourdain et al., 2020; Nowicki et al., 2020). The 50 year relaxation duration<br>was chosen as the most rapid adjustments occur in the first few decades of<br>integration, while the long term adjustment to a fully steady state takes<br>thousands of years. Relaxation to full steady state would require<br>substantially more computing resources than our entire set of ensembles and<br>also leads to the complication of different runs having potentially very<br>different initial states. Future improvements to model initialization that<br>account for surface elevation change (Perego et al., 2014) may reduce model<br>drift and adjust this requirement. The final model state in each run at the<br>end of RELX was given the nominal date of January 1, 2015, and all three<br>projection ensembles were branched from these states.</p> <p>Control projection (CTRL): The CTRL projection ensemble were an extension of<br>the RELX configurations, continuing the same surface mass balance and ocean<br>thermal forcing from January 1, 2015, to January 1, 2300. This ensemble was<br>used to assess model drift relative to the forced response of the climate<br>scenarios. </p> <p>SSP1-2.6 projection (SSP1): Our SSP1 projection used annual surface mass<br>balance and ocean thermal forcing derived from a UKESM SSP1-2.6 climate<br>scenario (expAE10 from Seroussi et al., 2024). Surface mass balance and ocean<br>thermal forcing were applied as anomalies relative to the climatological mean<br>forcings in RELX/CTRL to avoid issues related to climate model bias and abrupt<br>changes in forcing. This ensemble was also run from January 1, 2015, to<br>January 1, 2300.</p> <p>SSP5-8.5 projection (SSP5) Our SSP5 projection used the UKESM SSP5-8.5<br>projection forcings (expAE05 from Seroussi et al., 2024), again applied as<br>anomalies and from 2015 to 2300.</p> <p><br>OUTPUT<br>------</p> <p>Each ensemble directory contains at the base level:<br>* branch_ensemble.cfg - configuration file for the ensemble_generator test<br> case (https://mpas-dev.github.io/compass/latest/users_guide/landice/test_groups/ensemble_generator.html) <br> of COMPASS (Configuration Of Model for Prediction Across Scales Setups, https://github.com/MPAS-Dev/compass)<br> used to set up the ensemble.<br>* mesh_vars.nc - netCDF file containing the mesh variables. This file is the<br> same for all ensembles. These fields need to be appended to an output file to<br> visualize in, e.g., Paraview. This can be done with the command:<br> ncks -A mesh_vars.nc output.nc</p> <p>Each ensemble contains subdirectories for each run numbered 000-199. Each run<br>directory contains the files:<br>* globalStats.nc - scalar metrics at every time step<br>* output.nc - spatial fields at 10 year intervals<br>* run_info.cfg - summary of parameter values</p> <p>Notes:<br>* The SSP1 and SSP5 ensembles contain a subset of the full 200 runs<br> because some runs were filtered out as being unnecessary during model<br> calibration (see manuscript).<br>* The RELX ensemble is run for 50 years with an arbitrary start year of 2000.<br> The CTRL, SSP1, and SSP5 ensembles are started in 2015 from the final state<br> of the corresponding RELX run (nominally 2050). Because spatial data has<br> been saved in this archive at 10 year intervals, the first output for the<br> three projection ensembles is 2020. To get the initial condition for the<br> projection ensembles, use the 2050 state from RELX for the corresponding<br> model run.<br>* Spatial field output at 1 year intervals, as well as run input, log, and<br> restart files are available from the corresponding contact. The complete<br> run data is about 2 TB.</p> <p><br>CITATION<br>--------<br>If you find these data useful, please cite the DOI for this <br>archive (10.5281/zenodo.11166628), as well as our manuscript<br>submitted to The Cryosphere.</p> <p>This archive is approved by Los Alamos National Laboratory<br>for public release under LA-UR-24-24969; distribution is unlimited.</p> <p> </p> <p>REFERENCES<br>----------</p> <p>Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T.,<br>Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R., and Bertagna, L.:<br>MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth<br>system modeling using Voronoi grids, Geoscientific Model Development, 11,<br>3747–3780, 2018.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H.,<br>Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in<br>the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134,<br>https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H.,<br>Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A.,<br>Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M.,<br>Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C.<br>M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R.<br>S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.:<br>Experimental protocol for sea level projections from ISMIP6 stand-alone ice<br>sheet models, The Cryosphere, 14, 2331–2368,<br>https://doi.org/10.5194/tc-14-2331-2020, 2020.</p> <p>Perego, M., Price, S., and Stadler, G.: Optimal Initial Conditions for<br>Coupling Ice Sheet Models to Earth System Models, Journal of Geophysical<br>Research: Earth Surface, 119, 1894–1917, https://doi.org/10.1002/2014JF003181,<br>2014.</p> <p>Seroussi, H., et al. 2024. ISMIP6 Projections 2300 Antarctica Protocol.<br>https://theghub.org/groups/ismip6/wiki/ISMIP6-Projections2300-Antarctica. </p> <p>van den Broeke, M.: RACMO2.3p1 annual surface mass balance Antarctica<br>(1979-2014), PANGAEA - Data Publisher for Earth & Environmental Science,<br>https://doi.org/10.1594/PANGAEA.896940, 2019.</p> <p>van Wessem, J., Reijmer, C., Morlighem, M., Mouginot, J., Rignot, E., Medley,<br>B., Joughin, I., Wouters, B., Depoorter, M., Bamber, J., Lenaerts, J., Van De<br>Berg, W., Van Den Broeke, M., and Van Meijgaard, E.: Improved Representation<br>of East Antarctic Surface Mass Balance in a Regional Atmospheric Climate<br>Model, Journal of Glaciology, 60, 761–770,<br>https://doi.org/10.3189/2014JoG14J051, 2014.</p>
European funded projects related to integrated robotic sensing
<p>This repository contains the metadata of 1371 projects in tabular form (combinedProjectData.xlsx). The data was extracted from the European Union’s COmmunity Research and Development Information Service (CORDIS) repository.</p> <p>The dataset was used to support a review of the latest advancements in integrated robotic sensing. CORDIS was interrogated using a Boolean search, combining multiple chosen search terms using precise logical relationships, such as AND and OR. This search approach was used to obtain precise and relevant search results by specifying the relationships among the search terms, saving time and effort while minimising the likelihood of encountering irrelevant or unrelated material. The following Boolean search string was used: “(‘robot’ OR ‘robotic’ OR ‘robotically’ OR ‘roboti?ed’) AND (‘non-destructive’ OR ‘inspection’ OR ‘evaluation’ OR ‘NDT’ OR ‘NDE’ OR ‘sensing’ OR ‘sensor’)”. This resulted in searching projects whose title and short description (teaser) contained at least one of the words in the first set of brackets and at least one in the second set. Note that the “?” in ‘roboti?ed’ allowed looking for the presence of both the British English word “robotised” and the respective American English version “robotized”.</p> <p>Additionally, the search results were filtered according to the funding schemes. For the sake of reviewing the recent landscape, only projects funded through the HORIZON 2020 and HORIZON EUROPE schemes were considered. The described filtered search returned 1371 projects. The resulting metadata was extracted from the CORDIS repository for each of the found projects: the project start date, the end date, the total cost, the total EU contribution, the fields of science related to the project, the coordinating institution, and the participating institutions. The fields of science of each project are given as a list of strings detailing the fields of science related to the project. Each string shows a variable-depth hierarchy from the broadest classification to specific fields (e.g., “engineering and technology/materials engineering/composites”), following the hierarchical framework adopted by the European Commission. Finally, whereas each project has one and only one coordinator, it can have none, one or multiple participants. For the coordinator and each participant (if present), the following information was extracted: country of the coordinating/participating institution, amount of EU contribution received, amount of other funds available to the institution and the project outcome in terms of peer-reviewed journal papers, conference contributions and filed patents.</p> <p>Thus, the project metadata extracted from CORDIS was thoroughly analysed. The "fieldsOfScience_SunburstPlot.xlsx" file contains a sunburst chart that offers a lucid overview of the diverse scientific disciplines of the selected projects. The analysis of the fields of science strings has revealed a hierarchical depth going up to the seventh classification level, showing great permeance of robotic NDT and robotic sensing into numerous and specific fields.</p>
DESI 2017_2022 ODDEA Project Complete Database
<p>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em><span>Project ID: HORIZON MSCA-SE 101086381)</span></em>. The file contains data for 27 EU countries in the years 2017 - 2022. The file contains 4 data sheets processed in the arrangement from general data (DESI Total) to detailed data (dimensions, sub-dimensions and indicators). The file also contains raw data collected in five sheets by topic and a sheet with a list of all indicators with their description.</p> <p>Dataset consist of two files. Excel file presents data and Word file presents DESI 2022 dimensions and indicators list. </p> <p> </p>
Sicomb project roll up
Open the record for dataset details and reuse information.
"Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation"
<p>We build DISE2021 datasets from 95 images from DISEC2013 dataset [12], 70 images from RDCL dataset [22], and 324 images from RVL-CDIP dataset [14]. The composed datasets contains various types of documents, multiple languages, and typography features. Firstly, all the images are ensured and verified to be in a straight position. Secondly, we use the generating algorithm as in [12] to generate skew images in the range −15 to +15 skew degree. The dataset is split into two development/test sets by a ratio of 0.7/0.3 that results in 3399 development images and 1491 testing images. When generating the skew dataset in the range from −44.9 to 44.9 skew degree, we double the augmented image that results in 6980 development images and 2800 testing images. </p> <p>Note: This datasets are built upon three other datasets: DISEC 2013, RVL-CDIP, RDCL 2017. So I urge you to respect their LICENSE.</p>
Images from field research in Cambodia - May 2022 for the TEX-KR project
<p>These pictures were taken by Magali An Berthon for the EU-funded MSCA TEX-KR project as part of a period of fieldwork in Phnom Penh, Cambodia in May 2022. Following copyright restrictions for this institution, this repository only shows a selection of images from the National Museum of Cambodia, the building, the inside galleries, a viewing of the textiles in the collection with textile conservator Kong Kuntheary, and images of an interview with retired librarian Lim Yi with translator Moeung Seyha. The repository also showcases general views of Phnom Penh and two pictures of Tuol Sleng Genocide Museum. </p> <p>All these images must be credited © Magali An Berthon for TEX-KR. </p>
Data related to feedstock supply, logistics and engineering work for Pilot 5 (Biosolvents) in WaysTUP! project
<p><span>The objectives of this dataset were to describe the feedstock supply, logistics and engineering work carried out for PILOT 5. A description of the design, construction and final start-up of the pilot is also included. </span><span>PILOT 5 is located in Athens (Greece). Feedstock material is source-separated biowaste provided by the Municipality of Vari-Voula-Vouliagmeni under the supervision of SUST. The necessary logistics for continuous and stable feedstock supply were arranged by SUST and verified by NTUA. </span><span>PILOT 5 is a pre-existing installation developed in the framework of the LIFE WASTE2BIO project (LIFE11 ENV/GR/000949 and</span><span> is installed in the premises of NTUA</span><span>. For the purposes of the WaysTUP!, this prototype plant was upgraded in order to meet the project’s needs. </span><span>It includes a dehydration unit, a bioconversion unit and a distillation unit for the recovery of the produced ethanol. The working volume of the bioreactors is 400L and up to</span><span> </span><span>80kg/d dried biowaste could be treated depending on the selected operational parameters. </span></p>
Data related to the conduction of WACs in each Pilot of the project WaysTUP!
<p><span>The objective of this dataset is to report on the development of a representative methodology for the conduction of Waste Analysis Campaigns (WACs) of all different types of urban biowaste that will be used as feedstocks for the biobased products within WaysTUP!. These include: meat waste, fish waste, coffee waste, source separated biowaste, used cooking oils, coffee oil from spent coffee grounds, cellulosic rejections of MSWTP, nappies, carton and paper rejections of MSW, sewage sludge, olive oil mill waste and sawdust. T</span><span>he methodology for setting up a WAC is proposed suggesting strict sampling plans, sampling procedures and preservation times in order to ensure that the outcomes of the samplings are representative. This dataset also includes a common protocol for laboratory analysis for the detailed physicochemical characterization of urban biowaste according to the needs and specifications of each pilot plant and feedstock. T</span><span>he laboratory protocol includes the methods of typical analyses that must be performed according to the type (solid, oil etc.) of samples. Guidelines for the uniform representation of results are also included indicating that t</span><span>he sampling records must also accompany the characterization reports.</span></p>
STRIDE I-5 Project Materials
<p>This folder contains products developed from STRIDE I-5 project.</p> <p>This project builds on a previously funded STRIDE project (D4) where a simulation extension was built using the micro simulator VISSIM to accurately represent vehicle autonomy and connectivity, and their operational and environmental effects.</p> <p>In this project, the research team expanded the functionality of the simulation extension to:</p> <p>i. Model human driven vehicles in the presence of AVs. An aggressive merging behavior model was implemented in VISSIM to study potential queue-jumping behavior at a freeway on-ramp. The research team also considered implementing this model in the open-source simulator SUMO.</p> <p>ii. Consider advanced vehicle dynamics and enable users to customize driver, vehicle, operating environment, and operating mode separately.</p> <p>iii. Incorporate a real-time optimization tool (RIO) previously developed with funding from the National Science Foundation (NSF). RIO jointly optimizes vehicle trajectories and signal control by taking advantage of CAV technologies.</p> <p> </p> <p>In this project, the research team conducted the following educational activities:</p> <p>i. Developed and conducted a nation-wide survey to understand the needs of the CAV education.</p> <p>ii. Developed six instructional modules for CAV education.</p> <p>iii. Developed a training module to model CAVs in microsimulation environment.</p> <p> </p> <p>The products developed from this project are:</p> <p>i. An enhanced simulation extension that can be used as a “plug and play” solution to model CAVs and human driven vehicles in presence of CAVs with options to consider advanced vehicle dynamics and incorporate signal/trajectory optimization.</p> <p>ii. Educational modules on how CAVs impact planning, design, modeling, and analysis of transportation facilities along with a training module to model CAVs using VISSIM</p>
Data for "Unusually Low Dust Activity in North Africa in June 2023: Causes, Impacts and Future Projections"
<p>Datasets for manuscript entitled "Unusually Low Dust Activity in North Africa in June 2023: Causes, Impacts and Future Projections"</p>
Results of quasi static test on CFRP/CFRP joints and CFRP/Al joint with different surface treatments within project _Spoke11_WP2_Task2.3_MOST
<p>Quasi-static single lap joint test on CFRP CFRP joints and CFRP aluminum joints. Two different epoxy adhesives have been used and different chemical surface treatments used in order to try to enhance the mechanical resistance of the single lap joint.</p>
FIGURE 7 in The Belgrade PaleoBlitz: A pilot project to engage amateur paleontologists
FIGURE 7. Participants' deliverables two months after the PaleoBlitz.
CHRONIC KIDNEY DISEASE SCREENING IN COMMUNITY PHARMACY: CRIERFAC PROJECT
<p>Chronic kidney disease (CKD) is a major health problem. Early detection improves the prognosis of these patients, but the disease does not show its face until it is more advanced. The aim is to screen for the disease in community pharmacies in order to refer patients to primary care physicians.</p> <p>Data from the 2116 patients recruited are presented in a single Excel table where each line corresponds to one patient. It contains patient demographics data, risk factors for chronic kidney disease, creatinine and glomerular filtration data, pharmacist intervention and physician response.</p>
Recording of the webinar "Six Pathways to Smart Certification of BioBased Systems" in the frame of the 3-CO project
<p>In this webinar on 7th June 2024 six different Horizon Europe projects presented themselves to the public: 3-CO (as the organiser), BioReCer, SUSTRACK and the BIOBASEDCERT Cluster (Star4bbs, Harmonitor, Sustcert4Biobased).</p> <p>Speakers were: Maarit Haltunen (3-CO), Pedro Villanueva Rey (BioReCer), Gülsah Yilan (SUSTRACK), Costanza Rossi, Luana Ladu, Iris Vural Gürsel (Cluster).</p> <p>Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p>
ITU Complete Database ODDEA Project
<p>UN International Telecommunication Union (ITU) Database for ODDEA Project. </p> <p>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381). It consists of </em>178 digitalization indicators (with breakdowns) for selected countries: EU, UK, USA, Montenegro, ASEAN. 2017-2023 collected in separate Excel files. </p>
Dataset for FEM modelling of emission reference material (ERM) developed in the EMPIR project 20NRM04 MetrIAQ
<p>This dataset contains the numerical data used in the project 20NRM04 MetrIAQ for the FEM model simulations on the emission reference material (ERM) with the software COMSOL 6.2. The data published here are freely accessible with a text editor.<br>The accompanying document '<em>FEM data and file name explanations.pdf'</em> contains additional information for each data file.<br>A detailed description of the development of the FEM model can be found in the '<em>Guideline with recommendations on the customised design and dimensioning of the ERM, including the uncertainty of the numerical model developed for the prediction of the emission rate. The numerical model will (i) support the customised generation of the ERM (ii) simulate the transport processes inside the ERM and the compound release into test chamber air and (iii) enable the prediction of the emissions for each of the selected target VOC</em>' (<a href="https://doi.org/10.5281/zenodo.12749007" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12749007</a>).</p>
Broadcast of the BioReCer project in a Spanish radio show (synchronised version)
<p>In a 20 min talk show format organised by EURADIA/BETANIA, Cetaqua, Anfaco and Unitelma explained BioReCer (https://biorecer.eu/) to the general public. The broadcast took place at https://radioecogestiona.com/</p> <p>CETAQUA explained what the project consists of and its advantages.<br>ANFACO commented on the case studies they are working on.<br>UNITELMA explained the importance of stakeholders in the project and how to be part of it.</p> <p>BioReCer (Biological Resources Certifications Schemes) aims at assessing and complementing current certification schemes for biological resources according to the new EU sustainability goals to enhance bio-based circular systems.</p> <p>This will be achieved by including new criteria that align with EU taxonomy and EU corporate due diligence regulations into guidelines for certifying biological resources’ sustainability, origin, tracking and traceability (T&T), and by ensuring applicability at EU and global scale.</p> <p>By promoting the sustainability and trade of biological resources, BioReCer will increase the added value, use, as well as social acceptance of bio-based products.</p> <p><em>Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></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.