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Figure 5 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
Figure 5 - Distribution of the observations over time, collected in research projects or by volunteer contributors.
Supplementary material 2 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
Checklist of the taxa recorded in the database : Data type: Table
Figure 6 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
Figure 6 - Distribution of the localities of the observations (punctual type) inside (black circles) and outside (open circles) protected areas (grey areas).
Figure 7 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
Figure 7 - Distribution of the localities of the observations (punctual type) inside (black circles) and outside (open circles) a 2 km buffer around the main roads (bands).
Model outputs: Quantitative assessment of fire and vegetation properties in historical simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project
<p>This dataset contains the fire model outputs of various variables used in the initial FireMIP benchmarking paper.</p>
Video 1 Disseminatio strategy WP10 EcoStack (EU2020) project
<p>First video of EcoStack project presentation. In this video Project goals are presented.</p>
Text-fig. 9. Alternating field demagnetization - sample No. 230, Section No. 2 (reversed polarity). Top left - DRM vector directions during demagnetization process, white circles - projection of vector directions to the upper hemisphere; top right - Zijderveld diagram, black circles - projection of vector directions into xy plane, white circles - projection of vector directions into xz plane; bottom left - normalised magnetization intensity values during the alternation field demagnetization. in New Updated Results Of Paleomagnetic Dating Of Cave Deposits Exposed In Za Hájovnou Cave, Javoříčko Karst
Text-fig. 9. Alternating field demagnetization - sample No. 230, Section No. 2 (reversed polarity). Top left - DRM vector directions during demagnetization process, white circles - projection of vector directions to the upper hemisphere; top right - Zijderveld diagram, black circles - projection of vector directions into xy plane, white circles - projection of vector directions into xz plane; bottom left - normalised magnetization intensity values during the alternation field demagnetization.
Attachment, titled: Sustainability Construction in Value Engineering (VE) and Its Application on a Housing Project in Depok Region
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Historic version of the Environments - EOL project: final v2 version
the last version of the resource derived from the EOL v2 text
Investigating the role and effect of engineering professional ethics in reducing claims and lawsuits in construction projects
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Deep Electrical Resistivity Tomography - SENECA Project Antarctica
<p>This repository collects all the files of the <strong>Deep Electrical Resistivity Tomography (DERT) geophysical survey</strong> held in Antarctica (Dry Valleys) on December 2019- January 2020, as part of the SENECA Project (PrincipaI Investigator Livio Ruggiero, INGV, now at ISPRA).</p> <p>Files in this repository are organized as follows:</p> <ul> <li>The <strong>dem</strong> folder contains the Lidar xyz digital elevation model of the surveyed area (Fountain et al., 2017) that was used to appropriately model the terrain during the mesh generation step of the data processing.</li> <li>The <strong>field_data</strong> folder contains the DERT resistivity/chargeability datasets acquired by using the IRIS Instruments Fullwaver system (12 VFullwaver boxes, 50 meters MN dipoles) and a VIP5000 transmitter used to inject current at TX electrodes. More details on file types can be found in the folder's <em>README</em> file.</li> <li>The <strong>processing</strong> folder contains the resistivity inversions files for each profile. More details on file types can be found in the folder's <em>README</em> file.</li> <li>The <strong>topography</strong> folder hosts the csv files with the sensor positions for each profile from GPS survey and the processed files with the definitive coordinates of the RX and TX electrodes in the UTM WGS84 coordinate system. The conversion table txt files are also derived: these are used to translate the "dummy" coordinates of the raw datasets to the real positions.</li> </ul> <p><strong>Acknowledgments</strong></p> <p>This work is part of the PNRA 2018/D3.01 SENECA project and was financially supported by CNR (PNRA 2018 n° 00253 linea D, prot.73633/2019, SENECA PROJECT: Source and origin of greenhouses gases in Antarctica). All the data were collected during the XXXV Italian expedition in Antarctica, we thank PNRA and UTA ENEA for the logistic support. SENECA is a joint project of<br>international cooperation between Italy, New Zealand and Norway. We thank the Antarctica New Zealand for the scientific, logistical and technical support and the Scott Base personnel for hosting our research team. We acknowledge the support of the Research Council of Norway (NFR) through the HOTMUD project number 288299 and its Centres of Excellence funding scheme, project<br>number 223272 (CEED). </p> <p><strong>Credits</strong></p> <ul> <li>Jacob Anderson: data acquisition, data interpretation</li> <li>Massimiliano Ascani: data acquisition</li> <li>Bob Dagg: data acquisition</li> <li>Federico Fischanger: survey design, data processing and interpretation</li> <li>Richard Hardie: data acquisition</li> <li>Matteo Lupi: conceptual developement, data interpretation</li> <li>Adriano Mazzini: data acquisition</li> <li>Claudio Mazzoli: data acquisition</li> <li>Valentina Romano: survey design, data collection, data processing and interpretation</li> <li>Livio Ruggiero: principal Investigator of the SENECA project.</li> <li>Alessandra Sciarra: data collection</li> <li>Maria Chiara Tartarello: data collection </li> <li>Gary Wilson: data collection, data interpretation</li> <li>Rachel Worthington: data collection </li> </ul> <p> </p> <p><strong>References:</strong></p> <p>Fountain, A.G., Fernandez-Diaz, J.C., Obryk, M., Levy, J., Gooseff, M., Van Horn, D.J., Morin, P., & Shrestha, R., (2017), High resolution elevation mapping of the McMurdo Dry Valleys, Antarctica, and surrounding regions, Earth Syst. Sci. Data, 9, 435-443.</p>
DeepSEA project complete datset and code
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Dataset related to NAPCAUMAB Project, article title: "Real-practice analysis of potential antibiotic interactions in patients treated with Immune Checkpoint Inhibitors: an observational study from an Italian referral cancer center"
<p>The datasets uploaded are related to data collected for NAPCAUMAB observational project, which aims to analyze potential antibiotic interactions in cancer patients treated with Immune Checkpoint Inhibitors (ICIs) and the outcomes of immunotherapy tretament in these patients in terms of Progression Free Survival (PFS) and Overall Survival (OS). </p>
SUINK project presentation
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A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics
<p><strong>Motivation</strong></p> <p>Increasing heat stress due to climate change poses significant risks to human health and can lead to widespread social and economic consequences. Evaluating these impacts requires reliable datasets of heat stress projections. </p> <p><strong>Data Record</strong></p> <p><strong>CMIP6</strong></p> <p>We present a global dataset projecting future dry-bulb, wet-bulb, and wet-bulb globe temperatures under 1-4°C global warming scenarios (at 0.5°C intervals) relative to the preindustrial era, using outputs from 16 CMIP6 global climate models (GCMs) (Table 1). All variables were retrieved from the historical and SSP585 scenarios which were selected to maximize the warming signal.</p> <p>Wet-bulb and wet-bulb globe temperature are calculated using the Davies-Jones[1] and Liljegren[2] approach respectively.</p> <p>The dataset was bias-corrected against ERA5 reanalysis by incorporating the GCM-simulated climate change signal onto the ERA5 baseline (1950-1976) at a 3-hourly frequency. It therefore includes a 27-year sample for each GCM under each warming target.</p> <p>The data is provided at a fine spatial resolution of 0.25° x 0.25° and a temporal resolution of 3 hours, and is stored in a self-describing NetCDF format. Filenames follow the pattern "VAR_bias_corrected_3hr_GCM_XC_yyyy.nc", where:</p> <ul> <li> <p>"VAR" represents the variable (Ta, Tw, WBGT for dry-bulb, wet-bulb, and wet-bulb globe temperature, respectively),</p> </li> <li> <p>"GCM" denotes the CMIP6 GCM name,</p> </li> <li> <p>"X" indicates the warming target compared to the preindustrial period,</p> </li> <li> <p>"yyyy" represents the year index (0001-0027) of the 27-year sample</p> </li> </ul> <p><strong>Table 1 </strong>CMIP6 GCMs used for generating the dataset for Ta, Tw and WBGT.</p> <div> <table> <tbody> <tr> <td> <p>GCM</p> </td> <td> <p>Realization</p> </td> <td> <p>GCM grid spacing</p> </td> <td> <p>Ta</p> </td> <td> <p>Tw</p> </td> <td> <p>WBGT</p> </td> </tr> <tr> <td> <p>ACCESS-CM2</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>BCC-CSM2-MR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.1ox1.125o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CanESM5</p> </td> <td> <p>r1i1p2f1</p> </td> <td> <p>2.8ox2.8o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CMCC-CM2-SR5</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.94ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CMCC-ESM2</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.94ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CNRM-CM6-1</p> </td> <td> <p>r1i1p1f2</p> </td> <td> <p>1.4ox1.4o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> </td> </tr> <tr> <td> <p>EC-Earth3</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.7ox0.7o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>GFDL-ESM4</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.0ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>HadGEM3-GC31-LL</p> </td> <td> <p>r1i1p1f3</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>HadGEM3-GC31-MM</p> </td> <td> <p>r1i1p1f3</p> </td> <td> <p>0.55ox0.83o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>KACE-1-0-G</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>KIOST-ESM</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.9ox1.9o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MIROC-ES2L</p> </td> <td> <p>r1i1p1f2</p> </td> <td> <p>2.8ox2.8o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MIROC6</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.4ox1.4o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-HR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.93ox0.93o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-LR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.85ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> </tbody> </table> </div> <p><strong>ERA5</strong></p> <p>We also provide hourly Tw and WBGT derived from ERA5 reanalysis during 1950-2023 to enable analyses of heat stress changes from historical period to a warmer climate.</p> <p><strong> </strong></p> <p><strong>Data Access</strong></p> <p>An inventory of the dataset is available in this repository. The complete dataset, approximately 57 TB in size, is freely accessible via Purdue Fortress' long-term archive through Globus. The bias-corrected CMIP6 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?origin_id=6538f53a-1ea7-4c13-a0cf-10478190b901&origin_path=%2F">Globus Link1</a>, and the ERA5 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?destination_id=63242aea-d3e0-4aa4-9372-0e19dd0c6539&destination_path=%2F">Globus Link2</a>. After clicking the link, users may be prompted to log in with a Purdue institutional Globus account. You can switch to your institutional account, or log in via a personal Globus ID, Gmail, GitHub handle, or ORCID ID. Alternatively, the dataset can be accessed by searching for the universally unique identifier (UUID)—"6538f53a-1ea7-4c13-a0cf-10478190b901" for CMIP6, and “63242aea-d3e0-4aa4-9372-0e19dd0c6539” for ERA5 dataset—in Globus.</p> <p><strong>Dataset Validation</strong></p> <p>We validate the bias-correction method and show that it significantly enhances the GCMs' accuracy in reproducing both the annual average and the full range of quantiles for all metrics within an ERA5 reference climate state. This dataset is expected to support future research on projected changes in mean and extreme heat stress and the assessment of related health and socio-economic impacts.</p> <p>For a detailed introduction to the dataset and its validation, please refer to our data descriptor currently under review at Scientific Data. We will update this information upon publication.</p> <p><strong><br><br><br></strong></p>
CitiObs project Factsheet
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Appendix to the paper titled: Which teamwork challenges do computing students face in a project-based learning course in research methods?
<p>Instructions on reflection reports, interview guide, and example student projects.</p>
GMDN-EMDN Mapping Project - Data Release
<h2>Description</h2> <pre>This release contains the datasets described in the paper "A Data-driven Algorithm Streamlining Medical Device Nomenclature Interoperability Through Large Language Models". The repository provides the necessary input data to reproduce the results presented in the paper, as well as all the output files described. Access to the files can be obtained, following a reasonable request, by contacting the Corresponding Author at <code>enrico.caiani@polimi.it</code>.</pre> <h2>Included in this release</h2> <div> <ul> <li><code>EMDN.csv</code>: EMDN dataset</li> <li><code>foiclass.zip</code>: FDA Product Code Classification Database</li> <li><code>GUDID_full_release.zip</code>: FDA GUDID data of American medical devices</li> <li><code>italian_medical_device_full_list.csv</code>: Italian medical devices dataset</li> <li>All output files described in the paper</li> <li>Additional files for further analysis</li> </ul> </div> <div> <h2>Acknowledgements</h2> </div> <div> <div> <pre>We thank the <a href="https://www.salute.gov.it/portale/home.html" target="_blank" rel="noopener">Italian Ministry of Health</a>, the <a href="https://www.fda.gov/" target="_blank" rel="noopener">U.S. Food and Drug Administration</a>, and the <a href="https://commission.europa.eu/index_en" target="_blank" rel="noopener">European Commission</a> for providing the datasets used in this project with public access.<br><br>For more information, please visit either this <a href="https://doi.org/10.5281/zenodo.13891146" target="_blank" rel="noopener">Zenodo repository</a> or the <a href="https://github.com/riccardogibello/md_nomenclature_mapping_project.git" target="_blank" rel="noopener">official GitHub repository</a>.</pre> </div> </div>
Data Set For Projection-Based Density Matrix Renormalization Group in Density Functional Theory Embedding
<p><span>Funded by the National Science Center grant no 2021/43/I/ST4/02250 </span></p>
Idrabel tubes installation project LIFE SEDREMED
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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.