Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7,195
datasets available to search
ShareScore release 0.9.0
Dataset results
7,195 results for “SET”
The Good & Bad bird and face data sets
<p>The Good & Bad data set consists of successful as well as unsuccessful generated samples synthesized by a conditional text-to-image GAN model. It can be used to enhance the propability of synthesizing Good latent vectors.</p>
Data Set of Public Procurement in México (2013-2020)
<p>This repository contains all data sets, manuals, and codes to reproduce the main results shown in the paper "Practices of public procurement and the risk of corrupt behavior before and after the government transition in México" (on revision in EPJ Data Science - Springer.) (arxive: <a href="https://arxiv.org/abs/2108.02653">arXiv:2108.02653</a>)</p>
Atmospheric visibility inferred from continuous-wave Doppler wind lidar, data set
<p>Visibility data from Pershore, UK, between 2018 and 2020</p>
Shoot senescence in perennials of seasonal habitats: Comparative analysis of a large set of species
<p>1. Senescence in plants is a hierarchical process affecting all their body parts from cells to whole organisms. Here we aim to fill the gap between the existing knowledge on leaf senescence and rapidly accumulating evidence on whole-plant senescence by addressing patterns and drivers of shoot senescence of herbaceous plants. This is a key process that determines photosynthetic gain late in the season and economy of soil-borne nutrients in seasonal climate.</p> <p>2. We present a comparative study of 231 temperate species, ranging from spring ephemeroids to species senescing in late autumn, in a common botanical garden collection, thus minimizing difference in environmental drivers affecting individual species. We assessed senescence by measuring size decline in the later part of the season.</p> <p>3. There were two main directions of variation in senescence trajectories: rate-date axis, separating early and fast senescing species from late and slowly senescing species, and the shape-asynchrony axis separating species with accelerating and synchronised senescence from non-accelerating senescence asynchronous among individual shoots. While accelerating senescence late in the season can be due to environmental harshness (frost), accelerating senescence early in the season is likely to be an indication of an active process.</p> <p>4. As expected, rate and shape of shoot senescence were associated both with leaf- and shoot-level traits. Species having leaves with high dry matter content senesced linearly and showed higher asynchrony among individual senescing shoots. Species with larger specific leaf area tended to senesce earlier and faster, and in synchronous and accelerating manner. Tall plants senesced later and in a more synchronous and accelerating manner.</p> <p>5. Species from different habitats varied in their senescence patterns. We confirmed the hypothesis that forest species postpone their senescence relative to open-habitats species, presumably to boost their photosynthetic balance, but did not confirm the hypothesis that plants from nutrient-poor habitats senesce earlier to retain soil-borne nutrients before the advent of frost.</p> <p>6. Synthesis. We show that while leaf senescence scales up to contribute to shoot senescence, shoot senescence has its additional independent drivers and is a phenomenon in its own right with clear environmental drivers. </p>
Compound activity data sets for 15 biological targets compiled from the ChEMBL and PubChem databases.
<p>Compound activity data sets for the 15 biological targets are deposited, along with structure-activity relationship matrices IDs. Active compounds were extracted from the ChEMBL database and inactive were from the PubChem database. Details of the data sets are described in the original publication. and the summary of the data sets is given in the readme.txt file. </p>
datacleanr manuscript data sets
<p>The data sets in the archive are used to generate figures for a research article introducing the datacleanr R application.</p>
TRINITY open access data repository data set 2 by Budapest University of Technology and Economics
<p>Horizon 2020 programme supports access to and reuse of research data generated by Horizon 2020 projects through the Open Research Data Pilot (ORDP). To support the validation of scientific results, the pilot focuses on providing access to data needed to validate the scientific results. There are several types of such data, e.g. machine learning data sets, models, measurements, statistical results of experiments, survey outcomes, etc.</p> <p>This deliverable summarizes the data that are expected to be collected in the course of the project and where and how they are stored. The aspect of providing open access to research data (as required by the European Commission’s Open Research Data Pilot, <a href="https://www.openaire.eu/what-is-the-open-research-data-pilot">https://www.openaire.eu/what-is-the-open-research-data-pilot</a>) is addressed in Section 3. Finally, in Section 4 we describe the data sets that were or are expected to be generated within the TRINITY projects and made freely available.</p>
Strateole-2 data set associated to the publication "A seismic network in the stratosphere"
<p>NetCDF files of the pressure and temperature data of TSEN sensors, and GPS coordinates, on board EUROS gondolas of Strateole-2 project (stratospheric balloons deployed during fall 2021).</p> <p>One file per gondola, associated to the 4 balloons detecting the Flores quake (2021/12/14 3:20:35.8 GMT) and to a single balloon detecting the Northern Peru quake (2021/11/28 10:52:25.8 GMT).</p> <p>These data cover one hour before and 2 hours after the quake. The rest of the Strateole-2 data will be released by the project. This SUbset is associated to the publication "A seismic network in the stratosphere.</p>
Data sets used in "Neural network processing of holographic images"
<p>Included are the training, validation, and testing data sets for synthetic holograms (netCDF), the HOLODEC data set containing the RF07 examples (netCDF), and the two splits of manually labeled HOLODEC image tiles (numpy arrays). The source code for using the data sets can be found at https://github.com/NCAR/holodec-ml </p>
Raw Data for the article: Portal vein puncture-related complications during transjugular intrahepatic portosystemic shunt creation: Colapinto needle set vs Rösch-Uchida needle set
<p>Transjugular portal vein puncture is considered the riskiest step in TIPS creation with possible incidence of portal vein puncture-related complications (PVPC). The Colapinto and the Rösch-Uchida needle sets are two different needle sets currently available. To date, there have been no randomized control trials or systematic reviews which compare the incidence of PVPC when using the two different needle sets. The aim of this literature review is to assess the rate of PVPC associated with the different needle sets used in the creation of TIPS. From the described search, 1500 articles were identified and 34 met the inclusion criteria. Outcome measured was the prevalence of PVPC using the different needle sets. Overall 212 (3.6%) PVPC were reported in 5865 patients; 142 (3.5%) reported in 4000 cases using the Rösch-Uchida set and 70 (3.7%) in 1865 patients using the Colapinto set (p = 0.69). PVPC in TIPS creation are not related to the choice of needle set used in the procedure. To our knowledge, this is the first review of its kind, the results of which support the theory that while the rate of PVPC is influenced by many factors, choice of needle set does not seem to be one of them.</p>
Data set for publication: Interfacial Deposition of Titanium Dioxide at the Polarized Liquid– Liquid Interface
<p>The attached files are the raw data used to write the manuscript (.txt; .csv; .jpg): Interfacial Deposition of Titanium Dioxide at the Polarized Liquid– Liquid Interface</p>
Raw data set for Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production.
<p>This repository contains the raw data and code used to generate the results in the paper:</p> <p>Tanzer S.E., Blok K., Ramirez Ramirez A. Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production. 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15. 2021. doi: 10.2139/ssrn.3819778.</p> <p>also published as chapter 4 in the PhD dissertation ”Negative Emissions in the Industrial Sector”. The PhD was the department of Engineering Systems and Services, Faculty of Technology Policy, Management at the Delft University of Technology, between 2017-2022. </p> <p>This is intended to be a record of the exact data and code used to generate the results and graphics used in this publication. It is not necessarily designed for user-friendliness or tested to work on other machines and may contain extraneous data and files.</p> <p>To make use of the python black box modelling library for your own work, please check out the most recent public release, which can be found at https://zenodo.org/record/5800104#.YjUTnC8w30o</p>
Set of obfuscated spam dataset by using LeetSpeak transformations
<p>The usage of LeetSpeak and other text hiding tricks is often used by spammers in the distribution of unsolicited contents. To evaluate deobfuscation techniques and their impact on spam content classification, we preprocessed several popular public datasets to partially obfuscate the text. The datasets transformed are:</p> <ul> <li>YouTube Spam Collection [2, 3] which is available on <a href="https://www.dt.fee.unicamp.br/~tiago/youtubespamcollection/">https://www.dt.fee.unicamp.br/~tiago/youtubespamcollection/</a>.</li> <li>a subset of YouTube Comments [4, 5] which is available on <a href="http://mlg.ucd.ie/yt/">http://mlg.ucd.ie/yt/</a>.</li> <li>CSDMC2010 which is available on <a href="http://csmining.org/index.php/spam-email-datasets-.html">http://csmining.org/index.php/spam-email-datasets-.html</a>.</li> <li>TREC2007 which is available on <a href="https://plg.uwaterloo.ca/~gvcormac/treccorpus07/">https://plg.uwaterloo.ca/~gvcormac/treccorpus07/</a></li> </ul>
Teaching ESE Survey data set
<p>Teaching ESE Survey data set</p>
Data set for "Local and landscape correlates of coccinellid species richness, abundance, and assemblage change along a rural–urban gradient in Quintana Roo, Mexico."
<p>Data set for original article "Local and landscape correlates of coccinellid species richness, abundance, and assemblage change along a rural–urban gradient in Quintana Roo, Mexico"<strong> </strong>accepted at Biotropica.</p> <p>We studied changes in coccinellid assemblage composition, diversity, species richness and abundance in domestic gardens along a rural to urban gradient in southeastern Mexico, and identified local and lanscape variables that determine their species richness and abundance. </p>
A nuclear phylogenomic study of the angiosperm order Myrtales, exploring the potential and limitations of the universal Angiosperms353 probe set
<p>The premise of this study is to further advance the understanding of the species-rich, economically and ecologically important Myrtales, an angiosperm order in the rosid clade comprising nine families, approximately 400 genera and almost 14000 species occurring on all continents (except Antarctica). The potential of High Throughput Sequencing (HTS) and target enrichment with the Angiosperms353 probe kit is evaluated on a sample of 485 species in 305 genera (76% of all genera in the order). Results provide technical considerations for processing HTS data and the most comprehensive phylogenetic hypothesis for the order to date. Relationships at all ranks such as the relationship of the early diverging families, often reflect previous studies, but gene conflict is evident, and relationships previously found to be uncertain often remain so. HTS and the Angiosperms353 probe kit are powerful tools for phylogenomic analysis, but better understanding of the genetic data available is required to enable selection of genes and gene trees that account for incomplete lineage sorting and hybridisation events. </p>
Test Image Set for Pyramid Generation
<p>A set of 7 images for a test of pyramid generation of the OMERO.server</p> <p>Varying sizes and file formats</p>
Data set for: History-dependent domain and skyrmion formation in 2D van der Waals magnet Fe3GeTe2
<p>This data repository contains the experimental data utilised to produce the figures shown in History-dependent domain and skyrmion formation in 2D van der Waals magnet Fe3GeTe2, by M. T. Birch, L. Powalla, S. Wintz, O. Hovorka, K. Litzius, J. C. Loudon, L. A. Turnbull, V. Nehruji, K. Son, C. Bubeck, T. G. Rauch, M. Weigand, E. Goering, M. Burghard, G. Schütz.</p> <p>Experimental data analysis were performed using Python via Jupyter notebooks, utilising matplotlib, scipy, numpy and h5py libraries. Jupyter notebooks are provided to process some of the experimental data to form the figures shown in the paper.</p> <p><br> Data data is divided into folders:</p> <p>Characterisation_Data - contains EDX, TEM and AFM characterisation of the bulk FGT material and exfoliated flakes.</p> <p>Magnetometry_Data - Includes data files from the MPM3 magnetometry utilised to study the magnetisation of the bulk FGT crystal as a function of temperature and applied magnetic field. The two sub folders divide the files into data acquired with the field applied either parallel to the c axis (OOP - out of plane), or perpendicular to the c axis (IP - in plane).</p> <p>STXM_Data - Contains Scanning Transmission X-ray Microscopy data acquired at the MAXYMUS instrument at the BESSY II synchrotron, Berlin. The data is divided into sections acquired following different sample histories, as reported in the main paper: field sweep, field-cooled and zero-field cooled. Folders containing the extracted skyrmion sizes and stripe domain sizes are included, as well as Python notebooks for plotting Fig. 3, 4 and 5 of the main paper.</p> <p>Spectra_Data - Contains x-ray absorption spectra acquired on a bulk FGT crystal via total electron yield method (Bulk spectra), and spectra acquired on the exfoliated FGT flake by a transmission method (Flake Spectra). The accompanying jupyter notebook demonstrates the plotting and application of XMCD sum rules to the experimental data.</p> <p>LTEM_Data - Contains images acquired via Lorentz Transmission Electron Microscopy of an FGT flake, measured as a function of temperature and applied magnetic field at various tilt angles.</p> <p>Analysis_Scripts - Contains python notebook for plotting the magnetic phase diagrams, and plotting the thickness dependence estimation from the x-ray absorption spectra data.</p> <p>Meanfield_Simulations - Contains the raw output files of the simulations, the .h5 files, and Python notebook plotting scripts. Summary pdf files show the output of all simulation runs for the field sweep performed at each temperature. </p> <p><br> The latest version of this Data Set can be found at the Zenodo DOI:<br> 10.5281/zenodo.6346695</p> <p>Any further requests for materials are welcome, send any inquiries to Max Birch at birch@is.mpg.de</p>
Global forest reference set with time series annual change information
<p><strong>Brief introduction of the reference set:</strong></p> <ul> <li>The initial sample points were stratified. The ratio of non-forest, forest and forest change stratum area is about 67:27:6. In order to increase the number of forest and forest change sample points, the ration of forest sample, forest change sample and non-forest sample points is roughly 4:1:1. The information of stratum identifier is provided in this sample set. The study generated 12925 initial sample points which were further reduced to 10339 points (6252 persisting forest sample points, 2049 change sample points and 2038 persisting non-forest sample points). The details are shown in Table 1. </li> </ul> <table> <caption>Table 1. Initial and finial sample size in each stratum</caption> <tbody> <tr> <td><strong>Stratum name</strong></td> <td><strong>Percent area of each stratum</strong></td> <td><strong>Initial sample size</strong></td> <td><strong>Final sample size</strong></td> <td><strong>Eliminated sample size</strong></td> </tr> <tr> <td>Forest</td> <td>27%</td> <td>8332</td> <td>6252</td> <td>1644</td> </tr> <tr> <td>Non-forest</td> <td>67%</td> <td>1892</td> <td>2038</td> <td>207</td> </tr> <tr> <td>Forest change</td> <td>6%</td> <td>2701</td> <td>2049</td> <td>735</td> </tr> <tr> <td>Sum</td> <td>100%</td> <td>12925</td> <td>10339</td> <td>2586</td> </tr> </tbody> </table> <p> </p> <ul> <li>The sample set will be provided in shapefile version. (The eliminated sample points will not be provided.) In this version, the provided attributions are shown in Table 2.</li> </ul> <p> </p> <table> <caption>Table 2. Details of provided attributions in this version</caption> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>per_f</td> <td>unchanged forest (0: No 1: Yes)</td> </tr> <tr> <td>per_nonf</td> <td>unchanged non-forest (0: No 1: Yes)</td> </tr> <tr> <td>change</td> <td>forest change (0: No 1: Yes)</td> </tr> <tr> <td>loss</td> <td>forest loss times</td> </tr> <tr> <td>gain</td> <td>forest gain times</td> </tr> <tr> <td>times</td> <td>change times (gain + loss)</td> </tr> <tr> <td>changeinfo</td> <td>year and order of change</td> </tr> <tr> <td>initial_s</td> <td>initial stratum identifier: change/perf (persisting forest)/nonf (persisting non-forest)</td> </tr> </tbody> </table> <p> </p> <ul> <li>This reference set will be updated regularly. Now it's version 1.1 (from 2000 to 2020).</li> </ul> <p> </p> <p><strong>NOTES:</strong></p> <ol> <li>Please be cautious while using the sample points with multiple change times. </li> <li>We encourage people to send us feedback if you found some mistakes while using this sample set via email.</li> <li>Welcome discussions around potential collaborations. 【You can email Jing (guoj15@tsinghua.org.cn).】</li> </ol> <p> </p> <p><strong>Citation:</strong></p> <p>Please cite the dataset including the version number and the following paper when using this data: </p> <p>Jing Guo, Zhiliang Zhu & Peng Gong (2022) A global forest reference set with time series annual change information from 2000 to 2020, International Journal of Remote Sensing, 43:9, 3152-3162, DOI: <a href="https://doi.org/10.1080/01431161.2022.2088256">10.1080/01431161.2022.2088256</a></p>
Linearity Data Set
<p>Data behind Linearity Fig 1, Fig 2, and Fig 3, and the full data set for 175 SPARC galaxies</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.