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.
3,172
datasets available to search
ShareScore release 0.9.0
Dataset results
3,172 results for “public”
Pompeii - Public Water Fountain 12 and Well
This public water fountain was scanned as part of the 2022 Pompeii Water and Sanitation Project field school run by the University of Missouri. The fountain is situated at VI.1.19 and is made of grey peperino slabs. The relief on the spout stone is no longer visible, but was reportedly of Silenus. Behind the fountain is an old well, with the remains of a water tower against the nort eastern corner. The wall of the well facing the fountain has the remains of a lararium altar - when the fountain was originally discovered, a fresco of lares was still visible. The wells predate the fountains and were the original source of water before the aqueduct and fountains were built. More photos can be seen here: https://pompeiiinpictures.com/pompeiiinpictures/Fountains/Fountain%2060119.htm The model was generated in Agisoft Metashape from 541 photos taken using a Nikon Z5. Photo alignment was run on high, as was model generation - model has been decimated for export, and made manifold for 3D printing. Source: Objaverse 1.0 / Sketchfab
IUPUI Public Art - "Horizons"
**MESH PARAMETERS** **Object Description:** Digitized 400-pound Iron figure, sitting on wooden bench; Real-life approximate dimensions 25"x35"x55" **Watertight:** No <br> **TEXTURE PARAMETERS** **Count:** 1 **Size:** 8,192 x 8,192 **Delighting:** Yes Source: Objaverse 1.0 / Sketchfab
Pompeii - Public Water Fountain 01
This public water fountain was scanned as part of the 2022 Pompeii Water and Sanitation Project field school run by the University of Missouri. The fountain is situated on Via Stabiana in front of I.4.15 and is made of grey peperino slabs. The remains of a water tower made out of squared blocks of yellow tuff, which would have provided water to the fountains, are beside it. The relief on the spout stone is of a comedy mask. More photos can be seen here: https://pompeiiinpictures.com/pompeiiinpictures/Fountains/Fountain%2010415.htm The model was generated in Agisoft Metashape from 279 photos taken using a Nikon Z5. Photo alignment was run on high, as was model generation - model has been decimated for export, and made manifold for 3D printing. Source: Objaverse 1.0 / Sketchfab
Data for Secure communication in IP-based wireless sensor networks via a trusted gateway publication
<p>This archive file contains the raw data obtained from Contiki sensor nodes during Cooja experiments in the folders e2e, terminate, terminate_1st and plaintext.</p> <p>The archive accompagnies the IEEE ISSNIP 2015 publication titled "Secure communication in IP-based wireless sensor networks via a trusted gateway" by Floris Van den Abeele, Tom Vandewinckele, Jeroen Hoebeke, Ingrid Moerman and Piet Demeester.</p> <p><br /> Also included is the data_parser python script that converts the raw data into CSV files that are parseable by R. The script contains the definitions of the contents of the raw data files.<br /> Finally, the R scripts that use the CSV files to generate the plots from the paper are also included.</p>
Experimental data for the publication: "Evaluating scintillator performance in time-resolved, hard X-ray studies at synchrotron light sources"
<p>In accordance with the expectations outlined in <em><strong>Clarifications of EPSRC expectations on research data management</strong></em> (09/10/14) this data has been made publicly available to complement the open access publication "Evaluating scintillator performance in time-resolved, hard X-ray studies at synchrotron light sources". </p> <p>There are six data sets, corresponding to the six experimental data sets presented in the article. In each data set, which may be identified by their file names and reference to the article, the 1st column is the RF trigger - to - ICCD exposure delay in [ns], and the second column in the intensity recorded on the ICCD in [counts]. This intensity accounts for any online and offline processing outlined in the article, such as on-CCD exposures, dark frame correction etc. </p>
Services of AS CR Library in the area of scientific publications (not only) for AS CR Institutes
<p>Since 1994, the Library of the Academy of Sciences of the Czech Republic has been the coordinator of bibliographic database ASEP, which contains the records of publishing activities of 54 institutes of the Academy of Sciences of the Czech Republic (AS CR). Bibliographic records are collected in the Institatunional Repository of the ASCR, data is saved in the librarian system Advanced Rapid Library (ARL), the data is published as an on-line catalogue. The article describes how different groups of users – administrators, authors, representatives of institutes and AS CR can use this system. In details is decribed software ASEP Analytics which was created as a software extension that provides analytical reports derived by a combination of queries and calculations from the data stored in the ASEP database, which cannot be displayed directly in the catalogue.</p>
Compound activity records associated with original publications in ChEMBL 21
<p>Provided are two sets of compound activity records (set 1 and set 2) that were traced back to original publications and assembled from ChEMBL release 21. For each compound-target combination, the corresponding potency measurements and publications are provided. In addition, the list of unique publications is given for both sets 1 and 2.</p>
Data for publication: Autoadaptive motion modelling for MR-based respiratory motion estimation
<p>This repository contains four T1-weighted 2D MR slice datasets from multiple slice positions covering the entire thorax during free breathing and breath holds. The data was used to evaluate our novel autoadaptive respiratory motion model which we proposed in [1]. In particular, the datasets contain the following:</p> <ol> <li>Acquisition of all sagittal slice positions covering the thorax and one coronal slice position acquired during a breath hold.</li> <li>Results of registration between adjacent sagittal slice positions [control point displacements (cpp) and displacement fields (dfs)]</li> <li>40 dynamic acquisitions of each slice position also present in the breath-hold acquired during free breathing. </li> <li>Results of registration of the dynamic acquisitions to the respective breath-holds slices (cpp's and dfs's). </li> </ol> <p>The data is divided into 4 zip files, each containing the data of one volunteer. The folder structure for each is as follows:</p> <blockquote> <p>|-- bhs (breath hold data)<br /> | |-- images (images)<br /> | | |-- cor<br /> | | `-- sag<br /> | `-- mfs_slpos2slpos (registration results)<br /> | `-- sag<br /> `-- dyn (dynamic free-breathing data)<br /> |-- images (images)<br /> | |-- cor<br /> | `-- sag<br /> `-- mfs_tpos2tpos (registration results)<br /> |-- cor<br /> `-- sag</p> </blockquote> <p>Please, see our publication [1] for details on the acquisition sequence and registration used. </p> <p>--</p> <p>[1]: CF Baumgartner, C Kolbitsch, JR McClelland, D Rueckert, AP King, <em>Autoadaptive motion modelling for MR-based respiratory motion estimation</em>, Medical Image Analysis (2016), http://dx.doi.org/10.1016/j.media.2016.06.005</p>
Dataset accompanying publication: "Modulation of Cortical Oscillations by Low-Frequency Direct Cortical Stimulation is State-Dependent"
<p>Dataset accompanying publication:</p> <p>"Modulation of Cortical Oscillations by Low-Frequency Direct Cortical Stimulation is State-Dependent", Alagapan, Schmidt, Lefebvre, Hadar, Shin and Frohlich</p> <p>For questions, contact flavio_frohlich@med.unc.edu</p> <p>The mat file consists of the following Matlab variables</p> <ol> <li><strong>Electrode Distance</strong>: 3 x 1 cell array containing the arrays (trial x electrode) of distance from stimulating electrode to recording electrode for the three ECoG participants. (First array corresponds to P001, Second array corresponds to P005 and Third array corresponds to P008)</li> <li><strong>Spectra_Electrode_EC</strong>: 3 x 1 cell array consisting of nTrial x nFreq x nChannels x nEpochs matrices for each subject’s eyes-closed experiment. nTrial corresponds to number of trials, nFreq corresponds to frequencies at which spectral power is calculated, nChannels corresponds to number of electrodes in the analysis and nEpochs corresponds to “Before Stimulation”, “During Stimulation” and “After Stimulation” epochs.</li> <li><strong>Spectra_Electrode_EO</strong>: 3 x 1 cell array consisting of nTrial x nFreq x nChannels x nEpochs matrices for each subject’s eyes-open experiment. The dimensions are the same as above. The first array consists of task-engaged dataset from Participant P001.</li> <li><strong>MI_Summary</strong>: 8 x 1 cell array consisting of 3 x 1 cell arrays of modulation indexes for the three participants. The 8 arrays stand for the modulation indexes in different epochs and different frequencies. Refer <strong>MI_Summary_Names</strong></li> <li><strong>MI_Summary_Names</strong>: 8 x 1 cell array consisting of strings denoting the arrays in <strong>MI_Summary</strong>. <strong>During</strong> in text corresponds to “During Stimulation” epoch and <strong>After</strong> corresponds to “After Stimulation” epoch.</li> <li><strong>f</strong>: Frequencies at which spectral power was estimated.</li> <li><strong>NetworkModel</strong>: Matlab struct containing the time series generated by the network model and corresponding spectra. The <strong>timeseries</strong> consists of 4 columns – 1<sup>st</sup> column corresponds to time, 2<sup>nd</sup> column corresponds to task-engaged state data, 3<sup>rd</sup> column corresponds to eyes-open state data and 4<sup>th</sup> column corresponds to eyes-closed state data. The <strong>spectra </strong>struct consists of spectral powers estimated in the different epochs. 1<sup>st</sup> column of each epoch array corresponds to task-engaged state, 2<sup>nd</sup> column corresponds to eyes-open state and the 3<sup>rd</sup> column corresponds to eyes-closed state.</li> <li><strong>SummationModel</strong>: Matlab struct containing the time series generated by the summation model and the peak values in spectra before and during stimulation by varying the two strength parameters. The columns correspond to stimulation strength while rows correspond to oscillation strength. The oscillation strength parameter was varied from 0.5 to 50 in steps of 0.5 and the stimulation strength parameter was varied from 0.1 to 10 in steps of 0.1. </li> </ol> <p> </p>
Texas Archaeology Publications--Network Data
<p>This dataset was harvested from Scopus using the search "Texas AND archaeology." These data were then used to generate a citation network used to explain how the work of Texas archaeologists articulates with one another. </p>
Supplementary Dataset S8 for publication "Universality in human cortical folding in health and disease"
<p>The data sources and method of extracting the data are described in the publication "Universality in human cortical folding in health and disease". PNAS 2016</p> <p>2019 update: The matlab code for extraction of relevant variables from Freesurfer subjects is now published on <a href="https://doi.org/10.5281/zenodo.3608675">Zenodo</a> and <a href="https://github.com/cnnp-lab/CorticalFoldingAnalysisTools">Github</a>.</p>
Facts Extracted from EuPMC OA publications 2016-05-01 to 2016-05-31
<p>Named entities were extracted from the fulltext Open Access articles in EuropePMC by loading the text into ElasticSearch and querying with the content of the ContentMine dictionaries (http://github.com/ContentMine/dictionaries)</p>
globalbioticinteractions/AfricaTreeDatabase: prepare for publication to zenodo
<p>Seltzer, Carrie; Wysocki, William; Palacios, Melissa; Eickhoff, Anna; Pilla, Hannah; Aungst, Jordan; Mercer, Aaron; Quicho, Jamie; Voss, Neil; Xu, Man; J. Ndangalasi, Henry; C. Lovett, Jon; J. Cordeiro, Norbert (2015): Plant-animal interactions from Africa. figshare. https://dx.doi.org/10.6084/m9.figshare.1526128 . To be included in http://globalbioticinteractions.org .</p>
Raw fMRI data from 12 rats used in the manuscript "Mapping of hemodynamic responses to the sensorymotor stimulation in a rodent model: a BOLD fMRI study" submitted to PlosOne for publication
<p>Raw data are from twelve male adult Wistar rats (Charles River Laboratories, Paris-France) weighing 300 ± 20g.</p> <p>Rats were initially anesthetized (induction) with 3% isoflurane and were maintained under 0,7-0.8% (sedation along with a muscle relaxation) during fMRI session. </p> <p>Each rat was submitted to two fMRI sessions: one with TE of 30 ms and and other with TE of 40 ms.</p> <p>For fMRI acquisition, electrodes were inserted subcutaneously in the palmar surface of the right hindpaw of each rat and electrical stimulation (current pulses with a 1.7 mA amplitude, 10 ms duration and 8 Hz frequency) was applied in a block-design starting with a resting period of 25s as a baseline followed by 25s stimulation, repeated 8 times.</p> <p>Ten 1-mm thick contiguous axial slices, from -6.36 mm to +2.64 mm to Bregma, were acquired with a two-shot gradient echo planar imaging (GE EPI) pulse sequence (2.56 cm2 FOV; 64x64 matrix size; a TR of 1000 ms; a flip angle of 50°) resulting in the pixel size of 0.4 mm.</p> <p>All imaging experiments were performed on a 4.7T Bruker (Biospec 47/40, Bruker, GmbH, Ettlingen,Germany) with a horizontal bore magnet equipped with a 12 cm gradient coil (Bruker BGA12, 400 mT/m) and interfaced to AVANCE III console. Two actively decoupled RF coils were used: a 7.2-cm diameter volume coil for transmission and a 2-cm diameter surface coil (Rapid Biomedical, Rimpar, Germany) positioned on the top of the animal's head for reception.</p>
Benzonitrile on Si(001). XPS, NEXAFS, and STM data. Accepted for publication in PCCP Sept. 2016
<p>This data set contains original XPS and NEXAFS data collected at the Australian Synchrotron. The data are the results of experiments investigating benzonitrile adsorption to the Si(001) surface. The results were written up and have been accepted for publication in Physical Chemistry Chemical Physics in Sept. 2016. The publication date is not yet known. </p>
452,000,000 public Git commits on GitHub (October 2016)
<p>What's inside</p> <p>part-000xx.lzo - LZO archives with the data (refer to "Format").</p> <p>part-000xx.lzo.index - LZO index files so that the archives are splittable in Hadoop.</p> <p>stats.csv.gz - GZIP-ed CSV file with some repository statistics related to the commits.</p> <p>Format</p> <p>part-000xx - text, one line per repository, every line is JSON with the following scheme:</p> <p>{ "r": "repository name", "c": [{ "h": "git hash", "a": "author's email hash", "t": "date and time commit was created", "m": "commit message" }, ...] }</p> <p>Date and time format is <em>mostly</em> Go language's time.Time.String(), I recommend to use dateutil.parse() to parse it with Python.</p> <p>Commit message contains explicit \r and \n symbols in order to be a single line.</p> <p>stats.csv has 4 columns: repository name, number of commits, number of contributors, average length of the commit messages.</p>
The public goods game on multiplex networks
<p>The code and data used to create the figures in chapters 3-7 in the thesis "The public goods game on multiplex networks". The data is labelled for which figure it corresponds to.</p>
Austrian Science Fund (FWF) Publication Cost Data 2016
<p>Following the approach for the datasets in 2013 (http://dx.doi.org/10.6084/m9.figshare.988754), 2014 (https://dx.doi.org/10.6084/m9.figshare.1378610.v14) and 2015 (https://doi.org/10.6084/m9.figshare.3180166), the Austrian Science Fund (FWF) is making the publication costs spent in 2016 (esp. for Open Access) publically available.</p> <p>The dataset includes payments for publications of authors funded by the Austrian Science Fund (FWF) via following programmes:</p> <p>Peer-Reviewed Publications: https://www.fwf.ac.at/en/research-funding/fwf-programmes/peer-reviewed-publications/</p> <p>Stand-Alone Publications: https://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/</p>
Publication Data from Koch et al 2017
<p>Contains the dataset and the MATLAB toolbox used for Koch et al. (2017) "Atlas registration for edema corrected MRI lesion volume in mouse stroke models"</p> <p><strong>data_koch_et_al_jcbfm_2017.zip</strong></p> <p>T2 weighted MRI data in NIFTI format from stroke mice 24 h post surgery. Contains raw data (images before registration, manually delineated hemisphere and lesion masks) and processed data (automatically generated brain mask and an additional version of raw images in registration to the Allen mouse brain atlas). Also contains a copy of some datasets for which slices in the image stack were removed. Includes an excel file describing the group assignment of each mouse (middle cerebral artery occlusion, photothrombosis or sham, full number of slices or partial number of slices) and describing files for each mouse. Histological data (TTC stainings) and corresponding regions of interest in ImageJ format are stored in a separate folder.</p> <p><strong>Toolbox available at GITHUB (!!note new address compared to original JCBFM publication!!)</strong></p> <p><a href="https://github.com/ChariteExpMri/antx2">https://github.com/ChariteExpMri/antx2</a></p> <p>When downloading the toolbox at GITHUB please respect legal issues and carefully read the provided README file.</p>
Publication data of Dog eye movements are slower than human eye movements
<p>Publication data of Dog eye movements are slower than human eye movements</p> <p> https://doi.org/10.16910/jemr.12.8.4</p> <p>The data in the previous version contained unnecessary information, which has been removed in this updated version.</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.