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6,782 results for “project”
ArMoR Cluster: 7 research projects fight Antimicrobial Resistance in livestock farming (updated version)
<p>Supported by the European Commission, Horizon Dissemination Booster (HRB) contributes to an effective transfer of research and innovation project results to policy makers, industry and society by offering various services as dissemination, exploitation strategy and business plan development to projects. Within Horizon Results Booster programme (HRB), 7 research projects AMRILS, AVANT, BM-FARM, FARMCARE, DISARM, HealthyLivestock and ROADMAP have formed the "ArMoR Cluster" to develop a conceptual framework to improve understanding of AMR in livestock systems.</p> <p>The video is available on YouTube: <a href="https://www.youtube.com/watch?v=ACbnyu3PhOY">https://www.youtube.com/watch?v=ACbnyu3PhOY</a></p> <p>For any further questions please contact us at:</p> <ul> <li><strong><a href="https://zenodo.org/record/avant@rtds-group.com">avant@rtds-group.com</a></strong> (project AVANT)</li> </ul>
Data Table - Digital Scholarship, PhD project "Bridging Data Science and Intellectual History: Computing the Nodes and Edges in the Old University of Louvain (1425-1797)"
<p>How were academic networks configured in the premodern world, and how did they change? This doctoral project seeks to offer a data-driven answer by computing networks at and around the Old University of Louvain (1425-1797), a crucial hub for the transfer of knowledge in late medieval and early modern Europe. Drawing upon datasets under construction from the teams of the PIs at KU Leuven and UCLouvain, this project sets out to plot and visualize networks of students, scholars and their ‘books’ over almost four centuries, thus integrating data from demographic, prosopographical and book historical datasets. This will lead to a better understanding of how academic communities evolved in the past, and it will help to assess how their organization and structure promoted or hindered the creation and transfer of knowledge in premodern Europe. Hence, the doctoral research project offers an innovative test case to develop novel understandings of networks (e.g.. new tested ways to define nodes and edges) in datasets on scholars and ‘literati’, and it will be able to compare these new results to interpretations of human capital indices in the past. As such, the project creates a pioneering pilot for integrating data science into the field of intellectual and early modern history. This enhanced collaboration between KU Leuven and UCLouvain on a theme related to their common past is timely in view of 600 years Leuven/Louvain in 2025.</p>
Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code
<p>This package contains artifacts for <strong>15</strong> real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to <strong>v8</strong> of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following:</p> <p>Original data:</p> <ul> <li>For each project, its <strong>original</strong> artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> <p>Fire Drill in issue-tracking data:</p> <ul> <li><strong>Ground truth</strong> for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> <p>Fire Drill in source-code data:</p> <ul> <li><strong>Four</strong> technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>
Defense Resiliency Project (DRP) Alaska Community Hazard and Permafrost Mitigation Plans
<p>Hazard and Permafrost Mitigation Plans for Alaska communities to be included in the DRP study</p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2023-01]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
14C, d13C and d15N for bones from the Femern project
<p>This file contains basic information (species, 14C age, d13C and d15N) for animal bones analysed in the Femern project, as well as average d13C values for wood samples.</p> <p>This is supplementary data for Philippsen, B. (2023) Changing diet in a changing world. In: Groß, D. and Rothstein, M. (eds) Changing Identities in a Changing World.</p>
KI Process Model Developed for SIAM Project
<p>Process Model Diagram for KI Development Process improved with practices from SCRUM, XP, Design Thinking, and UX Methods by using the Method Engineering Tools Developed in the SIAM Project. </p> <p>Updated with the source XMI file for Ki Process Specification (KiProcess.mprocesos) and the corresponding ecore metamodel (mprocesos.ecore)</p> <p> </p> <p>*The names of the elements in the process are in Spanish.</p>
AptaShape dataset and R analysis files, oDIN oLIVER project, Laborotory of Jørgen Kjems, Aarhus University
<p>.Aarhus University, February 2023. </p> <p>The attached dataset consists of a test and validation dataset obtained during the oDIN oLIVER project (https://projects.au.dk/odin/oliver). </p> <p>The datasets contain two cohorts of patients suffering from liver diseases. The metadata files present common clinically relevant parameters. The focus is on identifying patterns and protein markers for Non-Alcoholic Fatty Liver Disease (NAFLD) and Non-Alcoholic SteatoHepatitis (NASH). </p> <p>The uploaded folder also contains the R scripts used to generate the report presented here: https://doi.org/10.5281/zenodo.7629808</p> <p>Contact information</p> <p>Daniel Miotto Dupont</p> <p><a href="http://inano.au.dk/"><strong>Interdisciplinary Nanoscience Center - INANO-MBG, iNANO-huset</strong></a><br> Gustav Wieds Vej 14<br> <a href="https://www.au.dk/en/map/?b=1590">building 1590</a>, 316<br> 8000 Aarhus C<br> Denmark<br> <a href="mailto:dmd@inano.au.dk">dmd@inano.au.dk</a></p> <p> </p>
Supporting Students in Team-Based Software Development Projects: An Exploratory Study - Dataset
<p>Dataset for the paper "Supporting Students in Team-Based Software Development Projects: An Exploratory Study"</p>
EAMT Project Dataset - MTUX, Productivity and Quality in TPE and IPE
<p>This dataset has been collected thanks to the funding of the European Association for Machine Translation (EAMT) in its Sponsorship for Students Activities 2021. The name of the research project funded was "Machine Translation User Experience (MTUX): The Neglected Element in MT".</p> <p>The dataset contains the information about the interaction of 15 professional translators with different machine translation post-editing modalities (namely, traditional and interactive post-editing). Results include MTUX and translation quality and productivity scores.</p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2023-02]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
Data for Computer Vison final project
<ul> <li>dataset.zip contains images used for our colorization project. We collected those images by filtering the places dataset. Statistically, we obtain 36500 images for training, 18250 images for validation and 18250 images for testing. </li> <li>colorized_images.zip contains the test images inferred by the SOTA approaches we tried.</li> </ul>
Project BRIDGE - Presentation video
<p>Video presentation of Project BRIDGE<br> MP4 format - Duration 00:03:07</p>
Computational Philosophy: Reflections on the PolyGraphs Project
<p>Talk at the <a href="https://www.digital-philosophy.org/">Philosophy [in:of:for:and] Digital Knowledge Infrastructures</a> online workshop (08/09/2022).</p>
FIG. 2 in Clearing up the Crystal Ball: Understanding Uncertainty in Future Climate Suitability Projections for Amphibians
FIG. 2.—Mean predicted change in suitable climate for amphibian species by citation and model parameters from the meta-analysis. Points represent means and bars represent 95% confidence intervals. Mean predicted change in suitable climate was calculated for each amphibian order within each study for each of the model settings, including Representative Concentration Pathway, Year, and Dispersal Limitation. Triangles indicate estimates from the case study, and circles indicate all other studies. A color version of this figure is available online.
FIG. 1 in Clearing up the Crystal Ball: Understanding Uncertainty in Future Climate Suitability Projections for Amphibians
FIG. 1.—Map of countries for which suitable climate has been projected under future climate scenarios for at least one amphibian species. Colors represent the number of studies in which a given country was included. A color version of this figure is available online.
FIG. 3 in Clearing up the Crystal Ball: Understanding Uncertainty in Future Climate Suitability Projections for Amphibians
FIG. 3.—Percent change in climate suitability predicted for each species in the case study. For each species, predicted percent change in climate suitability is shown for the year 2050 under 13 general circulation models and two representative concentration pathways (2.6, 8.5) climate scenarios for both the default Maxent species distribution models (SDMs; regularization multiplier ¼ 1; open red boxes) and the best-fit SDM (Akaike information criterion; ΔAICc ¼ 0; shaded blue boxes) for three levels of dispersal: no dispersal, limited, or unlimited. Boxplots show median, interquartile range, and minimum and maximum values. A color version of this figure is available online.
Mechanistic models project bird invasions with accuracy
<p>This ZIP file contains all 1) input data [occurrence data and environmental predictors] used, 2) all R scripts used, and 3) all model outputs.</p>
Online appendix of "On the Effectiveness of Automated Tracing from Model Changes to Project Issues"
<p>This is the online appendix of the paper "On the Effectiveness of Automated Tracing from Model Changes to Project Issues", authored by Wouter van Oosten, Randell Rasiman, Fabiano Dalpiaz, Toine Hurkmans, which appears in the Information & Software Technology Journal (2023).</p> <p>The appendix includes the copy of the LCDTrace repository (https://github.com/RELabUU/LCDTrace) that was used in the paper, as well as the data and the scripts that can be used to replicate our statistical analysis. There are specific README files to better explore the package.</p>
HortiMED Project Video
<p>This <strong>project video</strong> has been developed by INKOA to provide clear information on the motivation behind HortiMED project, the objectives of HortiMED and the proposed approach to attain them.</p> <p>The aim of HortiMED is to provide farmers with innovative tools to enable resource efficient year round greenhouse cultivation by harnessing the potential of both simple and advanced technologies for smart nutrient, irrigation and climate control, and integrated pest management taking into account their feasibility and cost-effectiveness at individual greenhouse level. HortiMED is part of the PRIMA Programme supported by the European Union (Grant Number 1915). The contents of this video are the sole responsibility of the consortium and the PRIMA Foundation is not responsible for any use that may be made of the information it contains.</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.