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Avizo reconstruction of skull of Siamogale melilutra dataset
<p><em><strong>Siamogale melilutra</strong></em> is an extinct species of giant <a href="https://en.wikipedia.org/wiki/Otter">otter</a> from the late <a href="https://en.wikipedia.org/wiki/Miocene">Miocene</a> from <a href="https://en.wikipedia.org/wiki/Yunnan">Yunnan</a> province, <a href="https://en.wikipedia.org/wiki/China">China</a>. Ranking among the largest fossil otters, <em>Siamogale</em> represents a feeding <a href="https://en.wikipedia.org/wiki/Ecomorphology">ecomorphology</a> with no living analog. Its giant size and high mandibular strength confer <a href="https://en.wikipedia.org/wiki/Durophagy">shell-crushing capability</a> matched only by other extinct <a href="https://en.wikipedia.org/wiki/Molluscivore">molluscivores</a>, such as the marine bear <em><a href="https://en.wikipedia.org/wiki/Kolponomos">Kolponomos</a></em>.<sup><a href="https://en.wikipedia.org/wiki/Siamogale_melilutra#cite_note-1">[1]</a> </sup>The skull reveals a combination of otter-like and badger-like cranial and dental characteristics. The new species belongs to the <a href="https://en.wikipedia.org/wiki/Lutrinae">Lutrinae</a> because of its possession of a large infraorbital canal and ventral expansion of the mastoid process, among other traits.<sup><a href="https://en.wikipedia.org/wiki/Siamogale_melilutra#cite_note-2">[2]</a></sup> <em>Siamogale melilutra</em> was about 1.9 m (6.25 ft) in overall length and weighed at least 40 kg (88 pounds).<sup><a href="https://en.wikipedia.org/wiki/Siamogale_melilutra#cite_note-3">[3]</a></sup> The remains of the skull were found in China and were re-created with a special program called the CT scan which is able to reconstruct the skeleton without being damaged. (From https://en.wikipedia.org/wiki/Siamogale_melilutra).</p> <p>This dataset was generated by Avizo 2020.2. It represents our reconstruction of the skull of Siamogale melilutra. The original CT files are available on MorphoSource.org, media 000413248, DOI <a href="https://doi.org/10.17602/M2/M413248">10.17602/M2/M413248</a>. A mesh PLY file of the reconstructed skull generated from this Avizo dataset is also available at MorphoSource.org, media 000413543, DoI <a href="https://doi.org/10.17602/M2/M413543">10.17602/M2/M413543</a>.</p>
Mediterranean Cyclone tracks between 1979-2018 (40 years) from a high-resolution perspective using ECMWF ERA5 dataset
<p>The present dataset presents the trajectories of the 13,157 cyclones identified within the Mediterranean Region (MR) between 1979 and 2018 (40 years). These cyclone tracks were obtained using the new Cyclone Detection and Tracking Method (CDTM) described in Aragão e Porcù (2021) to take advantage of the recent availability of a high-resolution reanalysis dataset of ECMWF ERA5. The CDTM uses hourly data of Geopotential Height at 1000 hPa with a spatial resolution of 0.25°x0.25°, and the analysis' domain covers the area within 15°W to 48° E and 21° N to 54°N. Additionally, trying to eliminate artificial low-pressure cores, short-living thermal-lows or too weak cyclones as much as possible, the present study only considered cyclones lasting more than 24h.<br> The dataset presents hourly information for all cyclones from the cyclogenesis time to the cyclolysis time. Each record presents: [1] Cyclone ID (integer, 8 digits), [2] Cyclone centre longitude position (°E, real, 8 digits, 3 decimal digits), [3] Cyclone centre latitude position (°N, real, 8 digits, 3 decimal digits), [4] Year (integer, 4 digits), [5] Month (integer, 2 digits), [6] Day (integer, 2 digits), [7] Hour (integer, 2 digits), [9] Cyclone centre Geopotential Height at 1000 hPa (m, real, 9 digits, 3 decimal digits).<br> The analyses presented in Aragão e Porcù (2021) revealed that the proposed CDTM is capable to capture almost the totality of the observed cyclones, as well as describing its respective area of cyclogenesis, trajectories, and durations. More than an adaptation to a high-resolution dataset, the method brings as its primary contribution a suitable set of parameters to systematically identify and track the cyclonic activities in the Mediterranean, where cyclones do not have sizeable horizontal pressure gradients and present a shorter lifetime compared to open-ocean cyclones.</p> <p>Cite this article</p> <p>Aragão, L., Porcù, F. Cyclonic activity in the Mediterranean region from a high-resolution perspective using ECMWF ERA5 dataset. <em>Clim Dyn</em> (2021). https://doi.org/10.1007/s00382-021-05963-x</p>
Design of an elastic porous injectable biomaterial for tissue regeneration and volume retention: raw dataset
<p>Raw dataset for the publication:</p> <p><strong>Design of an elastic porous injectable biomaterial for tissue regeneration and volume retention</strong></p>
Reconstructed high-rate SEIS data recorded during HP3 hammering from the NASA InSight mission to Mars
<p>The NASA InSight lander successfully placed a seismometer on the surface of Mars. Alongside, a hammering device was deployed that penetrated into the ground to attempt the first measurements of the planetary heat flow of Mars. The hammering of the heat probe generated repeated seismic signals that were registered by the seismometer. However, the broad frequency content of the seismic signals generated by the hammering extends beyond the Nyquist frequency governed by the seismometer's sampling rate of 100 samples per second. Here, we provide data that was reconstructed at a higher sampling rate of 2000 samples per second using a dedicated de-aliasing algorithm described in the accompanying article. This archive will be updated regularly with new data acquired on Mars. </p> <p>For a detailed data description and instructions on how to cite this dataset, please refer to the README file. </p>
Differential gene expression data of commercial compounds used to assess the performance of human TeraTox assay
<p>The dataset supplements the publication `Optimization of the <em>TeraTox</em> assay for preclinical teratogenicity assessment`. </p> <ul> <li>2022-02-18-TeraTox-commercial-logFC.gct: log2FC matrix of genes by compounds (in concentration ranges)</li> <li>2022-02-18-TeraTox-commercial-pScore.gct: p-scores (log 10 transformed p-values with the sign of logFC) of genes by compounds</li> <li>2022-02-18-TeraTox-commercial-featureData.txt: feature annotation in TSV format</li> <li>2022-02-18-TeraTox-commercial-phenoData.txt: sample annotation in TSV format</li> <li>2021-06-10-gcGeneFactorAnno-withPositiveCoefs.tsv: gene membership of germ-layer factors, with germ-layer annotation and average expression in copies per million (cpm).</li> </ul> <p>Citation: Jaklin, Manuela, Jitao David Zhang, Nicole Schäfer, Nicole Clemann, Paul Barrow, Erich Küng, Lisa Sach-Peltason, Claudia McGinnis, Marcel Leist, and Stefan Kustermann. “Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.” <em>Toxicological Sciences</em> 188, no. 1 (July 1, 2022): 17–33. <a href="https://doi.org/10.1093/toxsci/kfac046">https://doi.org/10.1093/toxsci/kfac046</a>.</p>
Classes of errors in DOI names: evaluation dataset
<p>This dataset contains the results of the evaluation of the methodology presented in the article <em>Identifying and correcting invalid citations due to DOI errors in Crossref data</em> (<a href="https://arxiv.org/abs/2111.11263">https://arxiv.org/abs/2111.11263</a>).</p> <p>The file named 10_random_citations_per_rule.csv contains 193 randomly selected citations from the corrected citations obtained by the process described in the article (<a href="https://doi.org/10.5281/zenodo.4892551">10.5281/zenodo.4892551</a>). They were extracted using the script called evaluation.py, which can be viewed in the GitHub repository <em>open-sci/2020-2021-grasshoppers-code </em>(<a href="https://doi.org/10.5281/zenodo.4723983">10.5281/zenodo.4723983</a>).</p>
S21 | UATHTARGETS | University of Athens Target List
<p>This is the collection associated with list S21 UATHTARGETS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S21 | UATHTARGETS | <strong>University of Athens Target List </strong></p> <p>Update 22/3/2020: added InChIKey file. Update 8/2/2022: new files from Dec 2021 with new compounds, NORMAN ID, classification and comments (provided by Maristina Nika). (v0.2.1 - attempted fix of CSV headers; v0.2.2 many small fixes flagged via PubChem deposit)</p> <p>Additional grant acknowledgement: Aristeia-Excellence: Transformation products of emerging pollutants in the aquatic environment (TREMEPOL project), 2012-2015, European Social Fund-Ministry of Education, <a href="http://tremepol.chem.uoa.gr/">http://tremepol.chem.uoa.gr/</a></p>
Dataset related to the manuscript: "An open-source integrated framework for the automation of citation collection and screening in systematic reviews"
<p>Dataset related to the manuscript: “An open-source integrated framework for the automation of citation collection and screening in systematic reviews”, to be used together with the code stored at https://github.com/AD-Papers-Material/BART_SystReviewClassifier to reproduce the results.</p> <p>There are three datasets:<br> - The Record data collected from the online scientific databases;<br> - The session journal which describes the search session, i.e., how many records were collected and from which source, for each query/session pairs.<br> - The session data which is the outcome of the classification and review tasks;</p>
OpenFOAM cases of the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers"
<p>This dataset contains the<em> underling data</em> for the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers”, submitted for the consideration and open review in Open Research Europe (ORE).</p> <p><strong>Validation1.tar.xz:</strong> OpenFOAM files and scripts for the simulation of flow and thermal structures in an enclosed environment (Wang and Chen, 2009).</p> <p><em>Wang, Miao; Chen, Qingyan (2009). Assessment of Various Turbulence Models for Transitional Flows in an Enclosed Environment (RP-1271). HVAC&R Research, 15(6), 1099–1119. doi:10.1080/10789669.2009.10390881</em></p> <p><strong>Validation2-kOmegaSSTModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using k-omega SST turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation2-RNGkEpsilonModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using RNG k-epsilon turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation3.tar.xz:</strong> OpenFOAM files and scripts for the simulation of strong natural convection in a model fire room (Murakami et al. 1995).</p> <p><em>Murakami, S., S. Kato, and R. Yoshie. 1995. Measurement of turbulence statistics in a model fire room by LDV. ASHRAE Transactions 101(2):287–301.</em></p> <p><strong>Validation4.tar.xz:</strong> OpenFOAM files and scripts for the simulation of thermal distribution in an open-aisle data center (Abdelmaksoud et al. 2013).</p> <p><em>W.A. Abdelmaksoud, T.Q. Dang, H. Ezzat Khalifa, R.R. Schmidt Improved computational fluid dynamics model for open-aisle air-cooled data center simulations J. Electron. Packag., 135 (2013), pp. 030901-30913</em></p> <p><strong>Results_Validation1.tar.xz:</strong> Simulation results of the Validation case 1.</p> <p><strong>Results_Validation2.tar.xz:</strong> Simulation results of the Validation case 2.</p> <p><strong>Results_Validation3.tar.xz:</strong> Simulation results of the Validation case 3.</p> <p><strong>Results_Validation4.tar.xz:</strong> Simulation results of the Validation case 4.</p> <p><strong>layout.csv:</strong> Input file for the Validation case 4.</p>
Bluetooth indoor localization Dataset
<p><strong>Bluetooth indoor localization Dataset</strong>: Collected to perform experimentation on how bluetooth signal strengths can be used to determine one of the indoor locations. The dataset has 6 transmission power, from Tx01 to Tx06; and in each dataset have 5 features with the bluetooth RSSI because the enviroment have 5 BLE4.0 and 1 categorical target that is the sector where the person is located (15 sectors total).</p>
ELKI Multi-View Clustering Data Sets Based on the Amsterdam Library of Object Images (ALOI)
<p>These data sets were originally created for the following publications:</p> <p><em>M. E. Houle, H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek</em><br> <strong>Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?</strong><br> In Proceedings of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM), Heidelberg, Germany, 2010.</p> <p><em>H.-P. Kriegel, E. Schubert, A. Zimek</em><br> <strong>Evaluation of Multiple Clustering Solutions</strong><br> In 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011, Athens, Greece, 2011.</p> <p>The outlier data set versions were introduced in:</p> <p><em>E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel</em><br> <strong>On Evaluation of Outlier Rankings and Outlier Scores</strong><br> In Proceedings of the 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, 2012.</p> <p> </p> <p>They are derived from the original image data available at <a href="https://aloi.science.uva.nl/">https://aloi.science.uva.nl/</a></p> <p>The image acquisition process is documented in the original ALOI work: <em>J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders</em>, <strong>The Amsterdam library of object images</strong>, Int. J. Comput. Vision, 61(1), 103-112, January, 2005</p> <p>Additional information is available at: <a href="https://elki-project.github.io/datasets/multi_view">https://elki-project.github.io/datasets/multi_view</a></p> <p>The following views are currently available:</p> <table> <tbody><tr> <th>Feature type</th> <th>Description</th> <th>Files</th> </tr> <tr> <td>Object number</td> <td>Sparse 1000 dimensional vectors that give the <em>true</em> object assignment</td> <td><a href="6355684/files/objs.arff.gz">objs.arff.gz</a></td> </tr> <tr> <td>RGB color histograms</td> <td>Standard RGB color histograms (uniform binning)</td> <td><a href="6355684/files/aloi-8d.csv.gz">aloi-8d.csv.gz</a> <a href="6355684/files/aloi-27d.csv.gz">aloi-27d.csv.gz</a> <a href="6355684/files/aloi-64d.csv.gz">aloi-64d.csv.gz</a> <a href="6355684/files/aloi-125d.csv.gz">aloi-125d.csv.gz</a> <a href="6355684/files/aloi-216d.csv.gz">aloi-216d.csv.gz</a> <a href="6355684/files/aloi-343d.csv.gz">aloi-343d.csv.gz</a> <a href="6355684/files/aloi-512d.csv.gz">aloi-512d.csv.gz</a> <a href="6355684/files/aloi-729d.csv.gz">aloi-729d.csv.gz</a> <a href="6355684/files/aloi-1000d.csv.gz">aloi-1000d.csv.gz</a></td> </tr> <tr> <td>HSV color histograms</td> <td>Standard HSV/HSB color histograms in various binnings</td> <td><a href="6355684/files/aloi-hsb-2x2x2.csv.gz">aloi-hsb-2x2x2.csv.gz</a> <a href="6355684/files/aloi-hsb-3x3x3.csv.gz">aloi-hsb-3x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-4x4x4.csv.gz">aloi-hsb-4x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-5x5x5.csv.gz">aloi-hsb-5x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-6x6x6.csv.gz">aloi-hsb-6x6x6.csv.gz</a> <a href="6355684/files/aloi-hsb-7x7x7.csv.gz">aloi-hsb-7x7x7.csv.gz</a> <a href="6355684/files/aloi-hsb-7x2x2.csv.gz">aloi-hsb-7x2x2.csv.gz</a> <a href="6355684/files/aloi-hsb-7x3x3.csv.gz">aloi-hsb-7x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-14x3x3.csv.gz">aloi-hsb-14x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-8x4x4.csv.gz">aloi-hsb-8x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-9x5x5.csv.gz">aloi-hsb-9x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-13x4x4.csv.gz">aloi-hsb-13x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-14x5x5.csv.gz">aloi-hsb-14x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-10x6x6.csv.gz">aloi-hsb-10x6x6.csv.gz</a> <a href="6355684/files/aloi-hsb-14x6x6.csv.gz">aloi-hsb-14x6x6.csv.gz</a></td> </tr> <tr> <td>Color similiarity</td> <td>Average similarity to 77 reference colors (not histograms) 18 colors x 2 sat x 2 bri + 5 grey values (incl. white, black)</td> <td><a href="6355684/files/aloi-colorsim77.arff.gz">aloi-colorsim77.arff.gz</a> (feature subsets are meaningful here, as these features are computed independently of each other)</td> </tr> <tr> <td>Haralick features</td> <td>First 13 Haralick features (radius 1 pixel)</td> <td><a href="6355684/files/aloi-haralick-1.csv.gz">aloi-haralick-1.csv.gz</a></td> </tr> <tr> <td>Front to back</td> <td>Vectors representing front face vs. back faces of individual objects</td> <td><a href="6355684/files/front.arff.gz">front.arff.gz</a></td> </tr> <tr> <td>Basic light</td> <td>Vectors indicating basic light situations</td> <td><a href="6355684/files/light.arff.gz">light.arff.gz</a></td> </tr> <tr> <td>Manual annotations</td> <td>Manually annotated object groups of semantically related objects such as cups</td> <td><a href="6355684/files/manual1.arff.gz">manual1.arff.gz</a></td> </tr> </tbody></table> <p><strong>Outlier Detection Versions</strong></p> <p>Additionally, we generated a number of subsets for outlier detection:</p> <table> <tbody><tr> <th>Feature type</th> <th>Description</th> <th>Files</th> </tr> <tr> <td>RGB Histograms</td> <td>Downsampled to 100000 objects (553 outliers)</td> <td><a href="6355684/files/aloi-27d-100000-max10-tot553.csv.gz">aloi-27d-100000-max10-tot553.csv.gz</a> <a href="6355684/files/aloi-64d-100000-max10-tot553.csv.gz">aloi-64d-100000-max10-tot553.csv.gz</a></td> </tr> <tr> <td> </td> <td>Downsampled to 75000 objects (717 outliers)</td> <td><a href="6355684/files/aloi-27d-75000-max4-tot717.csv.gz">aloi-27d-75000-max4-tot717.csv.gz</a> <a href="6355684/files/aloi-64d-75000-max4-tot717.csv.gz">aloi-64d-75000-max4-tot717.csv.gz</a></td> </tr> <tr> <td> </td> <td>Downsampled to 50000 objects (1508 outliers)</td> <td><a href="6355684/files/aloi-27d-50000-max5-tot1508.csv.gz">aloi-27d-50000-max5-tot1508.csv.gz</a> <a href="6355684/files/aloi-64d-50000-max5-tot1508.csv.gz">aloi-64d-50000-max5-tot1508.csv.gz</a></td> </tr> </tbody></table>
GHG Dataset for the frontiers publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region"
<p>GHG Dataset used in the Frontiers Publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region". Additionally including CO2 besides N2O and CH4. Includes 3 cropping seasons.</p> <p>The data is also available online on the GHG flux visualisation and calculation tool "gasflxvis": https://sae-interactive-data.ethz.ch/gasflxvis/</p> <p>Further details on the calulation are provided both on gasflxvis and the Frontiers publication. Calculation procedure according the following PLOS ONE publication: http://dx.doi.org/10.1371/journal.pone.0200876</p>
iEEG Data for "Functional Group Bridge for Simultaneous Regression and Support Estimation"
<p>The repository contains analysis scripts and data used in Wang Z, Magnotti J, Beauchamp MS, Li M. Functional Group Bridge for Simultaneous Regression and Support Estimation, 2020. The data contains high-gamma brain responses across 8 subjects from "congruency" audio-visual experiment.</p>
Datasets to "Compressible test-field method and its application to shear dynamos"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Compressible test-field method and its application to shear dynamos" by M. J. Kapyla, M. Rheinhardt, & A. Brandenburg (Astrophys. J., in press, arXiv:2106.01107). If anything turns out to be incomplete, please email maarit.kapyla@aalto.fi or brandenb@nordita.org. </pre>
L3Pilot Open Data
<p>The L3Pilot Open Data contains processed data collected during the Piloting of pre-series automated prototype vehicles on public European roads.</p> <p>The dataset contains driving data in the form of performance indicators derived for all instances of certain driving scenarios such as Car Following or lane changes.</p> <p>Furthermore, it contains data from the questionnaires handed to both professional and ordinary driver piloting the vehicles.</p> <p>All data is provided as comma separated tables. The supplementing document provides all necessary information for working with the dataset and mentions all documents, where additional information can be found.</p> <p>Already executed analysis based on the collected data, of which this represents a subset, can be found in Deliverable D7.3 - Pilot Evaluation Results, available for Download at <a href="https://l3pilot.eu/downloads">l3pilot.eu/downloads</a><br> <br> Further publicly available dataset, such as trajectory data recorded with drones and an additional user survey, are linked on: <a href="https://l3pilot.eu/data">l3pilot.eu/data</a></p>
Human Kino-Dynamic Measurements Dataset for Factory-like Activities
<p>This dataset was created as a part of the study presented in IEEE Transactions on Human-Machine Systems with the title "An Online Multi-Index Approach to Human Ergonomics Assessment in the Workplace" by Marta Lorenzini, Wansoo Kim and Arash Ajoudani. This paper introduces an online approach to monitor kinematic and dynamic quantities on the workers, providing on the spot an estimate of the physical load required in their daily jobs. A set of ergonomic indexes is defined to account for multiple potential contributors to work-related musculoskeletal disorders (WMSDs), which remain one of the major occupational safety and health problems in the European Union nowadays. Thus, the continuous tracking of workers’ exposure to the factors that may contribute to their development is paramount. To evaluate the proposed framework, a throughout experimental analysis was conducted.</p> <p>Twelve healthy adult subjects were recruited in the experimental study to perform, in the laboratory settings, occupational activities that are commonly carried out by workers in the current industrial scenario. Three tasks were selected to encompass the most significant risk factors in the workplace: mechanical overloading of the body joints, variable and high-intensity interaction forces, and repetitive and monotonous movements. Accordingly, lifting/lowering of a heavy object, drilling, and painting with a lightweight tool were considered, respectively, in this study. While the subjects were carrying out such activities, the data regarding the whole-body motion and the forces exchanged with the environment (both ground reaction force (GRF) and interaction forces at the end-effector) were collected. In addition, ten surface electromyography (sEMG) sensors were placed on the body of each subject to measure muscle activity as a reference to the effective physical effort required for the tasks.</p> <p>The whole experimental procedure was carried out in accordance with the Declaration of Helsinki and the protocol was approved by the ethics committee azienda sanitaria locale (ASL) Genovese N.3 (Protocol IIT_HRII_ERGOLEAN 156/2020).</p>
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs
<p>This is a GeoTIFF version of the <a href="http://hydrology.irpi.cnr.it/download-area/sm2rain-data-sets/">SM2RAIN-ASCAT (2007-2021): global daily satellite rainfall from ASCAT soil moisture</a> data set v1.1 (Brocca et al. 2019). Conversion steps are available <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Few important notes:</p> <ul> <li>Daily values are stored as integers, whereas in the NetCDF the dataset is rounded to one decimal place.</li> <li>The NetCDF has also a Quality Flag for a better and more informed use of the data (here omitted).</li> <li>P05, P50 and P95 indicate quantiles derived per pixel.</li> </ul> <p>Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong><a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a></strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images.</p> <p>Monthly averages and s.d. of precipitation are available in the files:</p> <ul> <li>clm_precipitation_sm2rain.*_m_10km_s0..0cm_2007..2021_v1.5.tif = monthly precipitation in mm,</li> <li>clm_precipitation_sm2rain.*_sd.10_10km_s0..0cm_2007..2021_v1.5.tif = standard deviation of precipitation in mm * 10 per month (multiplied by 10 so Integers can be used),</li> </ul> <p>Downscaled monthly averages (1 km) are also available (<a href="https://doi.org/10.5281/zenodo.1435912">https://doi.org/10.5281/zenodo.1435912</a>).</p> <p>To cite this data set please refer to the <strong><a href="https://doi.org/10.5281/zenodo.2591214">original copy</a></strong> of the data set.</p> <ul> <li>Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). <strong><a href="https://doi.org/10.5194/essd-11-1583-2019">SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations</a></strong>. Earth Syst. Sci. Data, 11, 1583–1601. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a></li> </ul>
Dataset Dental research data availability and quality according to FAIR principles
<p>This dataset contains open access publications in EPMC dental journals from 2016 to 2021 and 500 non-open access dental publications. We evaluated the level of compliance with the FAIR principles. The original dataset and codebook are attached. </p>
Long-term MODIS LST day-time and night-time temperatures, sd and differences at 1 km based on the 2000–2020 time series
<p>Layers include: Land Surface Temperature daytime monthly median value 2000–2017, Land Surface Temperature daytime monthly sd value 2000–2017, Land Surface Temperature daytime monthly day-night difference 2000–2017. Derived using the <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/MOD11A2">data.table package and quantile function in R</a>. We derived four standard statistics: (1) lower 2.5% probability (l.025), median (m), upper 97.5% probability (u.975) and standard deviation (sd). Updated long-term values for 2000–2022+ are pending.</p> <p>Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong><a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a></strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images.</p> <p>For more info about the MODIS LST product see: <a href="https://lpdaac.usgs.gov/products/mod11a2v006/"><strong>https://lpdaac.usgs.gov/products/mod11a2v006/</strong></a>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>lst = variable: land surface temperature,</li> <li>mod11a2.oct.day = determination method: MOD11A2 product, day time values for October,</li> <li>d = median value / sd = standard deviation / u.975 = aggregation/statistics method: 97.5% probability upper quantile,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: from 2000 to 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>
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.