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Dataset MTrill project
<p>This dataset includes data collected from 30 human subjects (anonymised) taking part in a syntactic priming experiment. All speakers of Brazilian Portuguese who were exposed to sentences with a relation of possession between nouns in Portuguese (e.g. A porta do quarto esta fechada) and were asked to freely translate them into English. We called this phase as "baseline phase" (Column "test" level Baseline).</p> <p>Following this phase, participants were requested to translate other sentences with the same structure using Google Translate, read the output of Google Translate out loud and, immediately after reading the output, participants were requested to describe images in English using words appearing on the screen of the computer- We called this phase as "priming" phase. (Column "test" level priming). </p> <p>All the sentences presented to participants could have been translated by them or by Google using a prepositional noun phrase (PNP) (e.g. the door of the room is open) or as a noun phrase (NP) (e.g. the room door is open)</p> <p> If participants used the same structure seen in the output of Google Translate (always noun phrase structure) when describing the images, then we considered participants were primed (column "prime" level "1") otherwise not primed (column "prime" level "0"). </p> <p>Column named "cumulativity" contains the proportion of noun phrase sentences out of the total sentences produced on the target trials before the current trial. </p> <p>Column named "EnglishTestGrade" contains participants' grades to an English Online Test The grades reflect participants' English proficiency level. </p> <p> </p> <p>Column named "structures" contain the structure of the sentences produced at the target trials (PNP or other and NP structures) </p> <p>OTHER FILES UPLOADED: </p> <p>1) Files used to run the experiment on Psychopy software (MTrill.psyexp_version2.psyexp copy and images_prime_target_version2 copy.xlsx)</p> <p>2) The 78 images and sentences used in the Priming phase: 60 items used in the trials of interest and 18 items used as filler trials </p> <p>3) The 26 images and sentences used in the baseline phase (pre-test phase): 20 items used in the trials of interest and 6 items used as filler trials </p> <p>4) All sentences used to test Google Translation before running the experiments (translated_sentences_experiment_MTrill.xlsx)</p> <p>5) Files with sentences used to text Google Translate prior choosing the sentences used in the experiment (stimuli_noun_phrase.docx and Noun_phrases_2.docx)</p> <p> </p>
The Outer Stellar Mass of Massive Galaxies: A SimpleTracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects
<p>These are the data for reproducing the results of the publication titled "The Outer Stellar Mass of Massive Galaxies: A Simple Tracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects" by Song Huang et al.</p> <p>Please see the Python scripts and Jupyter notebooks provided in the <a href="https://github.com/dr-guangtou/jianbing">jianbing</a> repo for examples about how to use these data files. And please contact dr.guangtou@gmail.com if you have any questions about these data.</p> <p>-------------------------------------------------------------------------------------------------</p> <p>Here is a brief description of all the files:</p> <p><strong>Data from N-body simulation:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_halos_0.7333_reduced_logmvir_13.npy?versionId=1648006b-a91a-4300-aadf-c4746d6f3ef2">mdpl2_halos_0.7333_reduced_logmvir_13.npy</a> <ul> <li>Basic information about the dark matter halos from MDPL2 simulation</li> <li>For scale factor = 0.7333 (or z~0.4).</li> <li>Only for halos with logMvir > 13.0.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_particles_0.7333_72m.npy?versionId=ff7d5847-df44-46f5-9bcc-d8a7f3cc040d">mdpl2_particles_0.7333_72m.npy</a> <ul> <li>Particle catalog of the a=0.7333 snapshot from MDPL2</li> <li>This is a down-sampled version with 72 million particles.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_theory_demo.pkl?versionId=7ed87c28-7adc-4987-9e00-b6223c744d42">topn_theory_demo.pkl</a> <ul> <li>These are the data used to create the theoretical demo of the TopN test.</li> <li>It is used for making the figures in <a href="https://github.com/dr-guangtou/jianbing/blob/master/notebooks/figure/fig1.ipynb">this notebook</a>.</li> </ul> </li> </ul> <p><strong>Catalogs of Galaxies or Galaxy Clusters:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/camira_s16a_cluster_use_bsm.fits?versionId=ca274c83-4025-41c4-b993-3cc9074f08b2">camira_s16a_cluster_use_bsm.fits</a> <ul> <li>The HSC S16A CAMIRA cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_hsc_s16a_cluster_bsm.fits?versionId=11608e41-2427-4808-9060-a06139de165c">redmapper_hsc_s16a_cluster_bsm.fits</a> <ul> <li>The HSC S16A redMaPPer cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_sdss_cluster_bsm.fits?versionId=b977c4ed-11c9-4751-b32f-60883d2e81b0">redmapper_sdss_cluster_bsm.fits</a> <ul> <li>The SDSS DR8 redMaPPer clusters in the HSC S16A footprint.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_massive_logm_11.2.fits?versionId=603cb17c-bb64-4aa7-ae05-5ec61c7ee861">s16a_massive_logm_11.2.fits</a> <ul> <li>0.2 <z < 0.5 massive galaxies in the HSC S16A footprint.</li> </ul> </li> </ul> <p><strong>Galaxy-Galaxy Lensing Data:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_weak_lensing_medium.hdf5?versionId=593c4ba0-6d7d-4b83-b8d9-01740a351fcd">s16a_weak_lensing_medium.hdf5</a> <ul> <li>A compilation of the weak lensing data to calculate the DeltaSigma profiles.</li> <li>This includes the weak lensing source catalog, photometric redshift calibration file, and the random catalog.</li> <li>"medium" here means we applied the medium criteria for selecting source galaxies. Please refer to <a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.5658S/abstract">Speagle et al. (2019)</a> for the exact meaning of these criteria.</li> <li>We also have a "basic" and "strict" version. Please send your request if you need them.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_public_s16a_medium_precompute.hdf5?versionId=2216ecf9-b836-4dd5-a9dd-7e070e4977bf">topn_public_s16a_medium_precompute.hdf5</a> <ul> <li>A compilation of pre-computed lensing profiles for each individual object in a different galaxy or cluster samples for the TopN test.</li> <li>These are the data used to create the stacked DeltaSigma profiles.</li> <li>We also provide the "strict" and the "basic" versions if you want to test the robustness of the TopN tests against the different selections of source galaxies in weak lensing measurements. You just need these files to generate the stacked DeltaSigma profiles.</li> </ul> </li> </ul>
Datasets of SH's AI4ER MRes Project
<p>This dataset contains the data used a generated during the course of SH's AI4ER MRes project. The dataset consists of LiDAR and RGB data over Sepilok Forest Reserve, in Sabah, Malaysia, collected and processed by NERC and NEODAAS, along with 901 manually delineated tree crowns in the area, and tree crown delineations predicted by two models: a Mask R-CNN model developed by SH, and the ITCfast algorithm, developed by Tom Swinfield and optimised by SH. LiDAR data is given for two years, 2014 and 2020, which was used to calculate changes in the carbon stock of the forest. The LiDAR and RGB data are provided as tiffs, while tree crowns are provided as shapefiles.</p>
Associating Land Cover Changes with Climate Sensitive Infection in Fennoscandia, as part of the CLINF project: Example on Tick-Borne Diseases
<p>The data was used as part of the IJERPH article below. The GeoJSON and shapefile ZIP archive are two versions of the same geometries to represent geographically the districts whole of Fennoscandia and the Russian districts of Leningrad, St Petersburg, Vologda, Arkhangelsk, Nenetsia, Murmansk, Karelia, and Komi, making up 69 districts used for the analysis.</p> <p>Leibovici DG, Bylund H, Björkman C, Tokarevich N, Thierfelder T, Evengård B, Quegan S (2021). Associating Land Cover Changes with Patterns of Incidences of Climate Sensitive Infections: An Example on Tick-Borne Diseases in the Nordic Area. <strong><em>International Journal of Environmental Research and Public Health, 18(20):10963. <a href="https://doi.org/10.3390/ijerph182010963">doi:10.3390/ijerph182010963</a></em></strong></p> <p>Special Issue: <a href="https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects">https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects</a></p> <p> </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>
Data from a three-phase Delphi study used to investigate Knowledge Infrastructure for Research Data in Norway, KIRDN_Data; PhD project
<p>A modified three-phase Delphi study was used to explore the knowledge infrastructure for research data in Norway. The study includes different stakeholders involved in research data sharing. A Delphi study is characterised by the use of an expert panel to elicit opinions on a shared reality from different perspectives. Data collection is performed in several rounds with the intention of reaching consensus or solving an issue. </p> <p>A group of 24 experts took part in the study. The group consisted of policy-makers, representatives of national service providers, and researchers and research support staff from four Norwegian universities. The participants were invited based on their involvement in the development of policies, infrastructure or data-related research support. The research support staff were recruited to include representatives from different research support services at the universities, including libraries, research offices and IT departments. While the researchers were selected from based on their receival of EU funding with requirements of data management plans.</p> <p>Data were collected in three phases. The first phase, the ‘exploration phase’, was conducted using open interviews lasting approximately one hour in January/February 2018. The purpose of this phase was to obtain an initial overview of the panel members opinions’ on issues regarding research data management.</p> <p>In the second phase, the ‘evaluation phase’, conducted in August/September 2018, participants answered a survey containing nine questions on topics such as data stewardship, DMPs, ethical aspects of data sharing and core functions in a research data infrastructure. The survey was designed to further explore issues and tensions uncovered in the first interviews. Several of the questions were formulated as statements that the participants were asked to agree or disagree upon. </p> <p>The third, ‘concluding phase’ was conducted using interviews in March/April 2019. These interviews lasted approximately 30 minutes and were based on results from the questionnaire as well as the first interview. Participants were asked whether they had thoughts on the preliminary findings of the study. </p> <p>Based on requests from some of the participants, the questions were sent to all participants prior to the data collection, in all three phases. The participants were also sent the transcripts from the interviews and were asked for permission to share the complete material or parts of the data material to which they contributed. </p>
RhECAST project
<p><em>This repository is periodically updated</em>.</p> <p>The <a href="https://cordis.europa.eu/project/id/101105219"><strong>RhECAST - Rural landscape hEritage and CArbon sequeSTration (HORIZON-MSCA-2022-PF-01)</strong></a> addresses the urgent need for sustainable land use strategies in the face of the global climate emergency. Supported by the Marie Skłodowska-Curie Actions programme, RhECAST focuses on agroforestry, a time-honored practice in European countries known for its rural benefits and potential for carbon sequestration. The project specifically targets the Po-Venetian Plain in Italy, a region facing significant atmospheric pollution, and combines landscape archaeology, computer-based modelling, and archaeological soil geochemistry to explore the long-term CO2 sequestration of historical agroforestry practices. RhECAST's innovative approach offers insights into sustainable landscape management and climate change mitigation, with the potential for replication in similar European regions such as Germany, Spain, and Portugal. Through this repository, we provide the data, publications, methods, and models used in RhECAST, fostering further research and application in the field of sustainable land use and carbon sequestration.</p> <h3> Overview</h3> <p>This dataset is part of the RhECAST project, focusing on sustainable land use strategies, agroforestry, and computer-based modelling to tackle climate change. It includes geospatial data and Python scripts for processing and analysis.</p> <ul> <li><strong>RhECAST_2.0.zip</strong>: This .zip file contains the latest version of the carbon sequestration model based on historical LULC changes between 1929 - 2024 in the Po-Venetian Plain (Northern Italy).</li> <li><strong>ISTAT_Catasto_Agrario_1929.zip</strong>: This ZIP file contains all the PDF scans of the <em>Catasto Agrario</em> <em>1929 </em>by the Italian National Institute of Statistics (ISTAT).</li> <li><strong>Publications.zip </strong>: this folder includes all the project peer-reviewd publications.</li> </ul>
Announcement: Milestones and the 4th Data Release of Project SWAP (SWAP DR4)
<h3>I am thrilled to announce four significant milestones for <strong>Project SWAP (Severe Weather Archive of the Philippines)</strong>:</h3> <ol> <li> <h3><strong>Manuscripts published in Asia-Pacific Journal of Atmospheric Sciences (APJAS):<br><br></strong>I'm excited to share that our recent research papers have been published in Springer Nature’s Asia-Pacific Journal of Atmospheric Sciences (APJAS)! These studies focus on hailstorm events in the Philippines, offering new insights into the dynamics and impacts of these intense weather phenomena. Please kindly see the following/respective DOI links. </h3> <h3>Ibañez, M.P.A., Manalo, J.A., Capuli, G.H., Olaguera, L.M.P. (2025). Spatiotemporal Analysis of Hail Events in the Philippines. Asia-Pac J Atmos Sci 61, 24. <a href="https://doi.org/10.1007/s13143-025-00409-4" target="_blank" rel="noopener">https://doi.org/10.1007/s13143-025-00409-4</a><br><br>Capuli, G.H. (2025). Friday the 13th Hailstorm in the Province of Bulacan, Philippines (13 August 2021): A Case Study. Asia-Pac J Atmos Sci 61, 13. <a href="https://doi.org/10.1007/s13143-025-00396-6" target="_blank" rel="noopener">https://doi.org/10.1007/s13143-025-00396-6</a><br><br>Meanwhile, our case study on the 27 May 2024 Tornadic Supercell in Pampanga Province is currently under peer review in Springer Nature’s Natural Hazards. I'm looking forward to sharing more about this severe weather event once the review process is complete. You may read this, as a preprint, through: <a href="https://doi.org/10.48550/arXiv.2504.20559" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2504.20559</a><br><strong><br></strong></h3> </li> <li> <h3><strong>Integration of Tornado and Waterspout data in the Tornado Archive (TA):<br><br></strong>I'm pleased to share that tornado and waterspout data from Project SWAP (particularly the SWAP DR3) have now been officially integrated into the Tornado Archive (TA; Maas et al. 2024) marking the first time that severe weather events from the Philippines have been globally imported and visualized through this platform.<br><br>You can visit the Blog/Update regarding the addition of the Philippine severe weather data to the TA platform through this <a href="https://tornadoarchive.com/home/2025/05/31/2024-u-s-tornado-data-is-live-and-v2-3-1/" target="_blank" rel="noopener">link</a>. Meanwhile, you can also visit the interactive visualization tool provided by TA through their <a href="https://tornadoarchive.com/home/tornado-archive-data-explorer/" target="_blank" rel="noopener">Data Explorer</a>.<br><br>Maas, M., Supinie, T., Berrington, A., Emmerson, S., Aidala, A., & Gavan, M. (2024). The Tornado Archive: Compiling and Visualizing a Worldwide, Digitized Tornado Database. Bulletin of the American Meteorological Society, 105(7), E1137-E1152. <a href="https://doi.org/10.1175/BAMS-D-23-0123.1" target="_blank" rel="noopener">https://doi.org/10.1175/BAMS-D-23-0123.1</a><br><strong><br></strong></h3> </li> <li> <h3><strong>2nd Part of Project SWAP:<br></strong></h3> <h3>I’m thrilled to share that the second part of our Project SWAP (Severe Weather Archive of the Philippines) is now available as a preprint! The article establishes the baseline climatology of severe weather events across Luzon, with a particular focus on the convective environments associated with hail-bearing severe storms. </h3> <h3>This provides a clearer picture of the atmospheric conditions that favor hail development in the Philippines. The manuscript is currently under peer review, but the preprint is now accessible for reading to anyone interested.</h3> <h3><a href="#h_63672142712561760428569038" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2510.09530</a><br><br></h3> </li> <li> <h3><strong>Towards the 4th Data Release of SWAP (SWAP DR4):<br><br></strong>As the 2025 severe weather season across the Philippine archipelago gradually winds down (based on insights from the Project SWAP Part 1 article), preparations are now underway for the fourth data release of the Severe Weather Archive of the Philippines (SWAP DR4) - slated for release in the first quarter of 2026 (next year). You may see the discussion of this update in the next Philippine Meteorological Society Annual Conference.<br><br>SWAP DR4 will highlight and document severe convective events observed throughout 2025, continuing our effort to build a comprehensive archive of hailstorms, tornadoes, and waterspouts across the country.<br><br>For the upcoming release, SWAP DR4 will include several significant updates and improvements:</h3> </li> </ol> <ul> <li> <h3>Expanded documentation, now approaching 1,500 recorded severe weather events across the Philippines.</h3> </li> <li> <h3>Enhanced geographic accuracy, with refined latitude–longitude coordinates for all entries.</h3> </li> <li> <h3>Increased reliability, as many events are now supported by multiple independent information sources. </h3> </li> <li> <h3>Improved metadata documentation, with additional “indicators” for accuracy and reliability included in the accompanying PDF files.</h3> </li> <li> <h3>Integration of proximity rawinsonde observations (RAOBs), in line with our previous commitment, SWAP DR4 will feature more (if not all) proximity soundings associated with each event, analyzed using SounderPy of <a href="https://doi.org/10.21105/joss.08087" target="_blank" rel="noopener">Gillett (2025)</a>. Apologies for the delay. </h3> </li> </ul> <h3>As indicated, this project can be important to uncovering the Philippine's thermodynamic and kinematic environment on various timescales, essentially creating a baseline climatology to further understand both convective processes and long term climate linkages. However, any analyses using this archive will require careful consideration of biases therein, many of which we have discussed in the journal article (as also described).</h3> <h3>Kindly see the attached table I created in this announcement for the versions of Project SWAP and other details. For now, <a href="https://doi.org/10.5281/zenodo.15035188" target="_blank" rel="noopener">SWAP DR3</a> will be good to use and is imported/carried to this announcement. If you have comments, kindly use the SWAP Contact Form below. </h3> <table> <tbody> <tr> <td> <h3>Indexing number of Versions</h3> </td> <td> <h3>Description</h3> </td> <td> <h3>w/ Zenodo DOI?</h3> </td> <td> <h3>Is it citable? </h3> </td> </tr> <tr> <td> <p>Version 1.0, 2.0, 3.0, and so on...</p> </td> <td> <p>Major Data Release/Major Update</p> </td> <td> <p>Yes</p> </td> <td> <p>Yes*</p> </td> </tr> <tr> <td> <p>Version 1.1, 1.2, 1.3, and so on...</p> </td> <td> <p>Incremental Update/Patch</p> </td> <td> <p>Yes</p> </td> <td> <p>Yes*</p> </td> </tr> <tr> <td> <p>Version x.x.1, x.x.2, x.x.3, and so on...</p> </td> <td> <p>Announcement</p> </td> <td> <p>Yes</p> </td> <td> <p>No</p> </td> </tr> <tr> <td> <h3>*Note: You can just cite all the versions for your ease. See the Additional Description. </h3> </td> </tr> </tbody> </table>
The Global Carbon Project's fossil CO2 emissions dataset
<p>The <a href="https://www.globalcarbonproject.org/">Global Carbon Project</a> (GCP) has been publishing estimates of global and national fossil CO2 emissions since 2001. In the first instance these were simple re-publications of data from another source, but over subsequent years refinements have been made in response to feedback and identification of inaccuracies. In this article (PDF document) we describe the history of this process leading up to the methodology used in the 2025 release of the GCP's fossil CO2 dataset.</p> <p>The fossil CO2 emissions dataset is included in both its standard, absolute form, and per capita, with associated metadata files in JSON format. A file indicating the source(s) of each data point is also provided.</p> <p>This is the initial release of the 2025 dataset.</p>
Natural Earth data in Goode's Homolosine projection
<p>Produced from NaturalEarth <a href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_map_subunits.zip">1:50m Admin0 - Details map sub units cultural vector</a>data (version 5.1.1) and with <a href="https://zenodo.org/record/1841337">Vectors for Goode's Homolosine projection</a></p> <p> </p> <p>Created with QGIS 3.20.3</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding dataset for the analyses and results achieved in the CS Track project in the research line on participation analyses, which is also reported in the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. The usernames have been anonymised.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable, and in the above-mentioned publication. As such, it <strong>does not</strong> represent <strong>raw data</strong>, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>In this Zenodo entry, several files can be found. The structure is as follows (<code>files</code> and <strong>folders </strong>and<strong> </strong><em>descriptions</em>).</p> <ul> <li><code>corresponding_calculations.html</code> <ul> <li><em>Quarto-notebook to view in browser</em></li> </ul> </li> <li><code>corresponding_calculations.qmd</code> <ul> <li><em>Quarto-notebook to view in RStudio</em></li> </ul> </li> <li><strong>assets</strong> <ul> <li><strong>data</strong> <ul> <li><strong>annotations</strong> <ul> <li><code>annotations.csv</code> <ul> <li><em>List of annotations made per day for each of the analysed projects</em></li> </ul> </li> </ul> </li> <li><strong>comments</strong> <ul> <li><code>comments.csv </code> <ul> <li><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></li> </ul> </li> </ul> </li> <li><strong>rolechanges</strong> <ul> <li><code>478_rolechanges.csv</code> <ul> <li><em>List of roles per user to determine number of role changes </em></li> </ul> </li> <li><code>1104_rolechanges.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> <li><strong>totalnetworkdata</strong> <ul> <li><strong>Edges</strong> <ul> <li><code>478_edges.csv</code> <ul> <li><em>Network data (edge set) for the given projects (without time slices)</em></li> </ul> </li> <li><code>1104_edges.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> <li><strong>Nodes</strong> <ul> <li><code>478_nodes.csv</code> <ul> <li><em>Network data (node set) for the given projects (without time slices)</em></li> </ul> </li> <li><code>1104_nodes.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> </ul> </li> <li><strong>trajectories</strong> <ul> <li><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></li> <li><strong>478</strong> <ul> <li><strong>Edges</strong> <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> <li><strong>Nodes</strong> <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> </li> <li> <p><strong>1104</strong> </p> <ul> <li> <p><strong>Edges</strong> </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><strong>Nodes</strong> </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> </ul> </li> <li> <p>...</p> </li> </ul> </li> </ul> </li> <li><strong>scripts</strong> <ul> <li><code>datavizfuncs.R</code> <ul> <li><em>script for the data visualisation functions, automatically executed from within </em><code>corresponding_calculations.qmd</code></li> </ul> </li> <li><code>import.R</code> <ul> <li><em>script for the import of data, automatically executed from within </em><code>corresponding_calculations.qmd</code></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>corresponding_calculations_files</strong> <ul> <li>f<em>iles for the html/qmd view in the browser/RStudio</em></li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The data is grouped according to given criteria (e.g., <code>project_title </code>or <code>time</code>). Accordingly, the respective files can be found in the data structure</p>
Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)
<p><strong>Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project.</strong></p> <ul> <li> <p><em>H2020 EuroSea project:</em><br> The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: <a href="https://eurosea.eu/">https://eurosea.eu/</a>). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626).</p> </li> <li> <p><em>Task 2.3:</em><br> Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report <a href="https://doi.org/10.3289/eurosea_d2.1">Barceló-Llull et al. (2020)</a> and the complete analysis is available here <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France).</p> </li> <li> <p><em>Subtask 2.3.1:</em><br> Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT.</p> </li> <li> <p><em>Where?</em><br> The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase.</p> </li> </ul> <p><strong>Report with the full analysis</strong></p> <p>The complete analysis can be found in this report: <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>.</p> <p><strong>Codes for the analysis</strong></p> <p>The codes generated to develop Subtask 2.3.1 can be found on GitHub: <a href="https://github.com/bbarcelollull/EuroSea_subTask_2.3.1">https://github.com/bbarcelollull/EuroSea_subTask_2.3.1</a></p> <p><strong>The dataset</strong></p> <p>The dataset includes:</p> <p>1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. </p> <p>2) Simulated configurations (or sampling strategies) in each region (PKL file format).</p> <p>3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42).</p> <p>4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)).</p> <p>5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).</p> <p> </p>
Problem discovery and resolution activities in the Apache HTTP Server Project (March 2001- March 2013).
<p>This is a dynamic visualization of problem discovery and resolution activities observed in the in the development of the Apache HTTP Server Project during the period March 2001- March 2013. The nodes in the network represent problems (software bugs). Anthropomorphic icons represent participants (software developers). The network edges connect participants to problems. Numerical labels record the internal identification numbers or participants and problems. The visible clusters represent the software modules. The central node is the project core module. Participants move closer to problems that attract their attention. When a participant allocates attention to a problem, an edge emerges connecting the two. The edge is green when a participant opens a bug report (i.e., when he discovers a new problem), red when the participant closes the bug report (i.e., when she solves an existing problem), and yellow when any other action is recorded. Problems (white nodes) are green when they first appear. They turn red immediately before being closed, and are yellow when the corresponding bug report is being modified. The animation advances by 0.05 seconds every day of historical time.</p> <p>The animation is produced using the Gource server control visualization tool developed by Andrew Caldwell (<a href="https://gource.io/">https://gource.io/</a>)</p>
ELISITY project; Dataset with the photospheric RFLH and various physical parameters for the ARs examined in the project
<p>Dataset with values of the photospheric relative field line helicity (RFLH) in two gauges and of various physical parameters (energies, helicities, quality metrics) for the seven solar active regions examined in the ELISITY project</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>
UKRI Digital Research Infrastructure Mapping Survey Dataset (for Net Zero Scoping Project)
<p>This dataset was generated as an output for the DRI Mapping exercise carried out during the UKRI Net Zero Digital Research Infrastructure (DRI) Scoping Project undertaken from 2021-2023. The "README.md" provides more information about the dataset and how to use it.</p> <p>The report associated with this dataset is available at:</p> <p>https://doi.org/10.5281/zenodo.7805987</p>
Example of Force Digital Calibration Certificate used in ComTraForce 18SIB08 project to demonstrate Digital Twin concept
<p>Force Digital Calibration Certificate (DCC) was developed in the frameworks of 18SIB08 ComTraForce project. It was used to demonstrate the way of data connection between the physical object (force transducer) and virtual object (Finite Element model) within the developed Digital Twin concept. The developed at PTB v3.1.2 xsd schema was used to convert analog calibration certificate to machine readable XML format. The DCC covers static and continuous calibration processes. Note that the current Force DCC is not a Good Practice example. Please follow further developments of force DCC Good Practice example at https://gitlab.com/ptb/dcc.</p>
Soil water content before and after rain events, May-July 2014-2016, Hainich, Germany, project AquaDiva
<p>This dataset contains soil water content data used for the analysis published in Fischer-Bedtke et al., (2023). It gives spatially distributed soil water contents evaluated at a rain event scale covering the following periods:</p> <p>May 5 - July 26 2014</p> <p>May 13 - July 28 2015</p> <p>May 25 - July 24 2016</p> <p>The enclosed files cover two different soil depth: topsoil (soil depth 7 cm) and subsoil (soil depth 27 cm).</p> <p>Fischer et al. (2023) give details about the included data and should be cited along the with the dataset when using the data.</p> <p><strong>Related datasets</strong></p> <p>Design information on the soil water content measurement points, including position to the next tree, hydraulic soil properties can be found in the follwowing associated dataset</p> <p>Metzger, Johanna Clara, Hildebrandt, Anke, & Filipzik, Janett. (2023). Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8065170</p> <p> </p> <p><strong>References</strong></p> <p>Fischer, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023.</p>
QuantMig microsimulation population projection model and migration scenarios for 31 European countries
<p>This open data deposit contains the data and model code of QuantMig-Mic microsimulation population projection model for 31 European countries and accompanies deliverables D8.3: Model outputs for dissemination and D8.1: Microsimulation projection model.</p> <p>This Zenodo deposit contains datasets of model input (baseline population and immigration database) and output data (demography and components output tables) and model code with parameters of the Baseline scenario (termed Default in the model code) deposited in QuantMig_mic.zip file. To view the code the users must first install MODGEN software (or can view code files in Visual Studio). All scenarios share the same parameters except the immigrant population - to change the immigration assumptions the users can import immigration assumptions for any other scenario from the ImmigDataBase.csv and change it in the immigration module using the MODGEN user interface or using Visual Studio.</p> <p><strong>The file structure and codebook for the data files is included in the cover note file "readme_quantmig_datasets.pdf"</strong></p> <p>Detailed <strong>information about the QuantMig-Mic microsimulation model, its modules and parameters</strong>:</p> <p>Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf</p> <p>Instructions how to install MODGEN can be found in:</p> <p>Marois, G. and Potančoková, M. (2022) QuantMig-mic microsimulation tool. QuantMig Project Deliverable D8.1. International Institute for Applied Systems Analysis (IIASA). http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.1%20v1.1.pdf </p> <p>Detailed <strong>information about the QuantMig migration scenarios</strong> can be found in:</p> <p>Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf</p> <p>A <strong>guide to the datasets</strong> and the codebook can be found in: <strong>readme_quantmig_datasets.pdf</strong></p> <p><strong>Countries included in the model: </strong></p> <p>Austria, Belgium, Bulgaria, Croatia, Czechia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom</p> <p> </p>
BVNA Community Informatics Project Dataset I
<p>This collection comprises geospatial datasets used to create the Beaverdam Valley Neighborhood Association community map and the resulting map in pdf and jpeg formats. This scope of the map covers the borders of Buncombe County, North Carolina, the city limits of Asheville, NC, and the three registered neighborhoods of the Beaverdam Valley (Beaverdam Valley, Hills of Beaverdam, and Beaverdam Run). The geospatial data includes the following layers and associated files: </p> <ul> <li>"AVL City Limits.geojson": City of Asheville GIS municipal boundary data</li> <li>"AVL City Limits.qmd": QGIS metadata file for the above </li> <li>"AVL Neighborhoods.geojson": City of Asheville GIS registered neighborhood data</li> <li>"AVL Neighborhoods.qmd": QGIS metadata file for the above</li> <li>"Buncombe_County_Parcels.geojson": Buncombe County GIS parcel data. </li> <li>"Buncombe_County_Parcels.qmd": QGIS metadata file for the above</li> <li>"BV Boundaries.geojson": Beaverdam Valley Neighborhood boundaries. </li> <li>"BV Boundaries.qmd": QGIS metadata file for the above</li> <li>"BV Parcel Intersection.geojson": Intersection of the Beverdam Valley Neighborhood boundaries with the Buncombe County Parcel data.</li> <li>"BV Parcel Intersection.qmd": QGIS metadata file for the above</li> <li>"BVNA_Map_2022_v2.pdf": BVNA CIP Community Map</li> <li>"BVNA_Map_2022_v2_825.jpg": BVNA CIP Community Map</li> <li>"City Limits.geojson": Buncombe county boundaries and city limits boundaries witin the county. </li> <li>"QGIS BVNA CIP.zip": Zip file containing the above layers in a QGIS project folder and file.</li> </ul> <p>About the Project: The Beaverdam Valley Neighborhood Association (BVNA) Community Informatics Project aims to gain deeper understanding of the Beaverdam Valley community and to work towards gathering and sharing information about the community and its history. This collection represents a deliverable produced under the 2022-2023 City of Asheville Neighborhood Matching Grant program. </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.