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1,267 results for “data paper”
Invasion Biology WikiProject Scientific Papers: Text Data Mining and LLM-based Information Extraction of Species, Locations, Habitats, and Ecosystems
<p>This dataset contains the abstract and full-text for publication DOIs from the Invasion Biology WikiProject (DOI: <a href="https://www.doi.org/10.5281/zenodo.12518036">10.5281/zenodo.12518036</a>). The data was retrieved using the <a href="https://ask.orkg.org/">ask.orkg.org</a> <a href="https://api.ask.orkg.org/docs#tag/Semantic-Neural-Search/operation/explore_documents_index_explore_get">API</a>. For the <a href="https://github.com/jd-coderepos/invasion-biology-IE/blob/main/scripts/ask-doi-list-fulltext-search.py">script</a> used to obtain the data, refer to the accompanying GitHub repository: <a href="https://github.com/jd-coderepos/invasion-biology-IE/" target="_blank" rel="noopener">https://github.com/jd-coderepos/invasion-biology-IE/</a>.</p> <p>The resulting CSV file includes the following fields: <code>"ASK ID"</code>, <code>"DOI"</code>, <code>"Title"</code>, <code>"Abstract"</code>, and <code>"Full-text"</code>.</p> <p>Of the 49,438 queried DOIs, the ASK database provided:</p> <ul> <li><strong>Total DOIs processed:</strong> 12,636</li> <li><strong>DOIs with neither abstract nor full-text:</strong> 36 (abstract token count was less than 10)</li> <li><strong>DOIs with abstracts but no full-text:</strong> 12,636</li> <li><strong>DOIs with both abstract and full-text:</strong> 2,834</li> </ul> <p>The second part of the dataset contains structured information extracted from the publications using the GPT-4o Large Language Model. This structured data is included in the zipped folder <code>structured-publications.zip</code>.</p> <p>The accompanying GitHub repository provides access to the code and scripts used at various stages of the information extraction (IE) process.</p> <p><strong>Theme of the Study:</strong><br>"Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models."</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>
Nucleus and cell segmentations for data in the mudRapp-seq paper
<p>Segmentation masks for images published with the paper describing </p> <p>"<em>Multiple direct RNA padlock probing in combination with in-situ sequencing (mudRapp-seq)</em>":</p> <blockquote> <p>Ahmad S, Gribling-Burrer AS, Schaust J, Fischer SC, Ambil UB, Ankenbrand MJ, Smyth RP. <em>Visualizing the transcription and replication of influenza A viral RNAs in cells by multiple direct RNA padlock probing and in-situ sequencing (mudRapp-seq)</em> (in review)</p> </blockquote> <p>Raw images are published in the <a href="https://www.ebi.ac.uk/bioimage-archive/">Bioimage Archive</a> (identifier pending). To use these masks, run the data formatting code in the accompanying code repository to get the raw data in the correct structure and extract this zip archive into the repository root (the folder structure in the archive matches the folder structure of the repository).</p> <p>Filenames in `analysis/segmentation` contain a hint about how they were created:</p> <ul> <li>cp: direct segmentation with a cellpose model (<a href="https://github.com/BioMeDS/mudRapp-seq/blob/main/models/cellpose/nuclei">nuclei</a>, <a href="https://github.com/BioMeDS/mudRapp-seq/blob/main/models/cellpose/cells">cells</a>)</li> <li>cpws: cell segmentation through watershed with nucleus masks as seeds</li> <li>cpmc: manually corrected cellpose segmentations</li> </ul> <p>Besides the final segmentation masks, the training data are included in `data/training` and the models in `models/cellpose`.</p> <p>Changes:</p> <ul> <li>v1.1 training data and models added</li> </ul>
Extended data for the paper: "SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters"
<p>Extended data 1 to 4 for the software article:<br>SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters. </p> <p>The extended data is tables and a Figure output and input from/to SentemQC runs relevant for the SentemQC paper.</p>
SQLite database to accompany the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring"
<p>This dataset is a SQLite database that accompanies methods and analysis described in the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring" (Balantic & Donovan 2019, Bioacoustics, https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1605309). </p> <p>A Github repository containing code for using the SQLite database also accompanies this paper at: <a href="https://github.com/cbalantic/false-positive-mitigation">http://github.com/cbalantic/false-positive-mitigation</a></p>
COSN paper data (The Chinese Open Science Network (COSN): Building an Open Science community from scratch)
<p>This is the dataset for generating figure1 and figure 3 in the manuscript <em>The Chinese Open Science Network (COSN): Building an Open Science community from scratch </em>(Accepted by AMPPS). Preprint at: <a href="https://doi.org/10.31234/osf.io/ac9by">https://doi.org/10.31234/osf.io/ac9by</a>.</p> <p>All the data and codes are available in repo: <a href="https://github.com/OpenSci-CN/COSN_AMPPS_Paper">COSN_AMPPS_Paper</a> Accepted Version.</p>
Vegetation Data Collected with Point Frame for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Across Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains point frame data for vegetation less than 1.3 m, including vascular plants, bryophytes, lichens, leaf litter and bare ground, as well as species codes used, as described in Jean et al. 2017 Canadian Journal of Forest Research. Samples of all encountered unknown species were collected for identification in the lab. Bryophyte nomenclature followed Anderson et al. (1990).
Bryophyte Cover Summary Data for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Across Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains the summarized bryophyte (percent cover) data obtained from point frame measurements, as used in Jean et al. 2017 Canadian Journal of Forest Research. ?Samples of all encountered unknown species were collected for identification in the lab. Bryophyte nomenclature followed Anderson et al. (1990).
Data release for paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models"
<p>This data release for the paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models" [<a href="https://arxiv.org/abs/2010.05830">arXiv:2010.2010.05830</a>] contains posterior samples for the GW190412 binary black hole merger event obtained from public GWOSC data with the parallel bilby Bayesian inference package, dynesty nested sampler and a set of waveforms from the "generation X" of phenomenological waveform models: IMRPhenomXAS, IMRPhenomXHM, IMRPhenomXP, IMRPhenomXPHM, IMRPhenomT and IMRPhenomTHM. The provided file is a "meta file" that can be read with the <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a> python package. The posterior samples included correspond to runs [2,6,10,12,14,26] in Table III of the paper (standard settings for each waveform, standar priors and sampler settings of Nlive=2048 and Nact=10 or 50). If you make use of these samples, please cite both this data release and the paper.</p>
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
Data and script for the GenABEL paper
<p>This dataset contains the automatically collected data used for the overview paper about the GenABEL Project (Karssen et al, 2016, DOI:10.12688/f1000research.8733.1). Some data used for the paper was collected manually and is therefore not included in this dataset.</p> <p>The file "tracker_report-2016-04-16.csv" is an export of the bug reports from the GenABEL R-forge bug tracker on the date listed in the file name.</p> <p>The file "Analytics www.genabel.org Locatie Lennart 20150428-20160428.csv" is a custom export of the Google Analytics data for visits to the GenABEL website (www.genabel.org) in the period marked by the dates listed in the file name. The columns contain the ISO code of the country, city, number of sessions, number of new viewers, bounce percentage, pages per session and average session duration, respectively.</p> <p>The file analysis_GenABELpaper.org contains the source code<br> used for the automated data extraction for this paper in Emacs<br> Org mode literate programming format (http://orgmode.org, Schulte 2012, doi:10.18637/jss.v046.i03)</p>
Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"
<p>The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. </p> <p> </p> <h2> </h2>
Data for paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts"
<p>The forecasts and observation datasets are used in the paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts". https://doi.org/10.1016/j.jhydrol.2021.127301</p> <p>The forecast data is a subset of the "ensemble for machine learning dataset (ENS4ML)" from ECMWF. </p> <p>The Python codes are stored in Github: https://github.com/wentao-bnu/LeNet_CSG_Precip</p>
Data files belonging to the paper "Dealing with clustered samples for assessing map accuracy by cross-validation"
<p>Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data. </p> <p> </p> <p>The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection).</p> <p> </p> <p><strong>Files:</strong></p> <p>agb.tif = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (<a href="https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8">https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8</a>)<br> AGBstack.tif = covariates used for predicting AGB<br> aggArea.tif = coarse grid used for simulation in the model-based methods<br> ocs.tif = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (<a href="https://www.isric.org/explore/soilgrids">https://www.isric.org/explore/soilgrids</a>)<br> OCSstack.tif = covariates used for predicting OCS<br> strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples<br> TOTmask.tif = mask of the area covered by the covariates</p> <p> </p> <p><strong>Details and data sources of the covariates in AGBstack.tif and OCSstack.tif:</strong></p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Note</strong></p> </td> </tr> <tr> <td> <p>ai</p> </td> <td> <p>Aridity Index</p> </td> <td> <p><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></p> </td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio1</p> </td> <td> <p>Mean annual air temperature [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio5</p> </td> <td> <p>Mean daily maximum air temperature of the warmest month [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio7</p> </td> <td> <p>Annual range of air temperature [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio12</p> </td> <td> <p>Annual precipitation [kg/m<sup>2</sup>]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio15</p> </td> <td> <p>Precipitation seasonality [kg/m<sup>2</sup>]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>gdd10</p> </td> <td> <p>Growing degree days heat sum above 10°C</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>clay</p> </td> <td> <p>Clay content [g/kg] of the 0-5cm layer</p> </td> <td> <p><a href="https://soilgrids.org/">https://soilgrids.org/</a></p> <p> </p> </td> <td> <p>Only used for AGB</p> </td> </tr> <tr> <td> <p>sand</p> </td> <td> <p>Sand content [g/kg] of the 0-5cm layer</p> </td> <td><a href="https://soilgrids.org/">https://soilgrids.org/</a></td> <td>as above</td> </tr> <tr> <td> <p>pH</p> </td> <td> <p>Acidity (Ph(water)) of the 0-5cm layer</p> </td> <td><a href="https://soilgrids.org/">https://soilgrids.org/</a></td> <td>as above</td> </tr> <tr> <td> <p>glc2017</p> </td> <td> <p>Landcover 2017</p> </td> <td> <p><a href="https://land.copernicus.eu/global/products/lc">https://land.copernicus.eu/global/products/lc</a>, reclassified to: closed forest, open forest, natural non-forest veg., bare & sparse veg. cropland, built-up, water</p> </td> <td> <p>Categorical variable</p> </td> </tr> <tr> <td> <p>dem</p> </td> <td> <p>Elevation</p> </td> <td> <p><a href="https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem">https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem</a></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>cosasp</p> </td> <td> <p>Cosine of slope aspect</p> </td> <td> <p>Computed with the terra package from elevation</p> </td> <td>Computed @25m resolution; next aggregated to 0.5km</td> </tr> <tr> <td> <p>sinasp</p> </td> <td> <p>Sine of slope aspect</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TPI</p> </td> <td> <p>Topographic position index</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TRI</p> </td> <td> <p>Terrain ruggedness index</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TWI</p> </td> <td> <p>Topographic wetness index</p> </td> <td> <p>Computed with SAGA from 500m resolution (aggregated) dem</p> </td> <td> </td> </tr> <tr> <td> <p>gedi</p> </td> <td> <p>Forest height</p> </td> <td> <p><a href="https://glad.umd.edu/dataset/gedi">https://glad.umd.edu/dataset/gedi</a></p> </td> <td> <p>Zone: NAFR</p> </td> </tr> <tr> <td> <p>xcoord</p> </td> <td> <p>X coordinate</p> </td> <td> <p>Using a mask created from the other covariates</p> </td> <td> </td> </tr> <tr> <td> <p>ycoord</p> </td> <td> <p>Y coordinate</p> </td> <td>Using a mask created from the other covariates</td> <td> </td> </tr> <tr> <td> <p>Dcoast</p> </td> <td> <p>Distance from coast</p> </td> <td> <p>Using a land mask created from the other covariates</p> </td> <td> </td> </tr> </tbody> </table> <p> </p>
Data for the paper 'Reducing networks of ethnographic codes co-occurrence in anthropology'
<p>Pseudonymized data supporting the paper "Reducing networks of ethnographic codes co-occurrence in anthropology", published in "Advances in Quantitative Ethnography. Fourth International Conference on Quantitative Ethnography (ICQE 2022), Copenhagen, Denmark, October 15–19, 2022, Proceedings", and edited by Amanda Barany and Crina Damsa. The paper is part of the POPREBEL project. The data were gathered in the spring and summer of 2021, as a part of a larger research project on populism in Central and Eastern Europe, to be completed by the end of 2022. They consist of 17 semi-structured interviews with Polish-speaking Internet users, who used social media to seek and share information about health against the backdrop of the COVID-19 pandemic. Research participants were asked about their opinion on the current state of affairs in their respective countries, and their political choices over the years and at present.</p> <p><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data).</p>
Survey questions and raw data for the study in the paper "Educational Technology for Tutors – What are Useful Tools and Information?"
<p>The data include the questions data set, the answers dataset and the codebook for the questions conducted with soscisurvey (https://www.soscisurvey.de/de/index). The survey itself can be imported in soscisurvey (via the XML data) and reused.</p> <p>The answers are unedited.</p>
Supporting Data for the paper titled "The Intensity, Directionality and Statistics of Underwater Noise from Melting Icebergs"
<p>The dataset contains:</p> <p>a) 13 audio files, with names including date, track number and channel; format: WAV files</p> <p>b) data from magnetic compass used to calculate noise directionality; format: txt files with lines containing date, time and magnetic direction (degrees)</p> <p>c) GPS tracks of the boat and attached acoustic buoy; format: txt files with NMEA codes</p> <p>d) GPS tracks around each iceberg tracked; format: txt files with UTM coordinates</p> <p>The study was founded by National Science Centre Poland grant no. 2013/11/N/ST10/01729 and partially supported within statutory activities No 3841/E-41/S/2018 of the Ministry of Science and Higher Education of Poland. Partial support for this work was also provided by US Office of Naval Research, Grant No. N00014-17-1-2633.</p> <p>Corresponding author: Oskar Glowacki, oglowacki@igf.edu.pl</p>
Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing (code, data and scripts to reproduce paper results)
<p>This repository contains the data, code, and scripts required to reproduce the results of the paper "Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing" by Daniele De Sensi, Salvatore Di Girolamo and Torsten Hoefler, presented at the 2019 International Conference for High Performance Computing, Networking, Storage, and Analysis. </p> <p>This repository does not contains the code of the library used to automatically tune the routing algorithm, which can be found at http://doi.org/10.5281/zenodo.3372785</p>
Accompanying data for paper "Electrical and Thermal Conductivity of Complex-Shaped Contact Spots"
<div> </div> <div>This repository contains the numerical data of the conductivity of complex-shaped contact spots on isotropic and linear conducting half-space obtained by Boundary and Finite Element methods. These data were used to construct some figures from the manuscript "Electrical and Thermal Conductivity of Complex-Shaped Contact Spots". The data is organized in folders corresponding to different types of contact spots: annular, flower-, star- and gear-shaped, Koch's snowflake, and self-affine spots. Each folder contains the results of numerical simulations in the form of `.npz` files, which can be loaded using `numpy` library in Python. The data is used to construct figures in the manuscript and can be used to reproduce the results or to perform additional analysis.</div> <div> </div>
The data that support the findings of a review paper "From urban data to city-scale models: A review of traffic simulation case studies"
<p>This dataset contains the data that were used in a review paper "From urban data to city-scale models: A review of traffic simulation case studies". It contains the following files:</p> <ul> <li>keywords with counts.txt - list of keywords and their counts in the considered corpus of traffic simulation case studies. The data were used to produce Figure 2 and Figure 3 in the paper.</li> <li>Papers analysis.xlsx - Excel file containing the data on the reviewed studies. The document has the following sheets: <ul> <li> Appendix A - contains a table short reference, location, simulation period, spatial scale, simulated units and marked categories for a paper;</li> <li>Geography - contains data on geographical distribution of simulated areas between world regions and countries, these data were used to produce Figure 4 in the paper;</li> <li>Software tools - contains data on simulation tools used in the studies. </li> <li>Journals and conferences - contains data on where the reviewed papers were published.</li> </ul> </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.