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.
9,152
datasets available to search
ShareScore release 0.9.0
Dataset results
9,152 results for “Networking”
Drainage network for a region in central Buenos Aires province, 30m resolution raster, EPSG:5347
<p>This drainage network was obtained using the Copernicus-30 Global Digital-Surface-Model, tiles (S37-38, W60) with QGIS and PCraster plugin: local direction drainage and flow accumulation algorithms. It helps to clarify the surrounding drainage pattern at cities like Azul, Cacharí and Tandil, for flood alleviation schemes.</p>
Identity Lexicon and Keyword Counts for "The Life of a Tie: Social Origins of Network Diversity"
<p>Prototype identity lexicon for the categories of occupation (e.g., "reporter" at Boston Globe), familial roles (e.g., proud "father"), political affiliation (life-long "democrat"), and cultural and sports interests (e.g., "hiphop", "NFL").</p> <p>Also includes a CSV file with counts of each identity keywords matched for all 572K users in our dataset. </p> <p> </p>
Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks
<p>Data used to train multiclass and binary neural networks to analyse SiPM (Silicon Photomultiplier) signals in a multiplexed array of 16 detectors and detect the signal detector origin. Data acquired using an oscilloscope. Results compared with previous anger logic methods. </p> <p>Dataset used in the publication</p> <p>"Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks"</p> <p>https://doi.org/10.1088/2057-1976/ad4f73</p>
Exploring The Spatial Structure of Interregional Supply Chain: A Multilayer Network Approach
<p><span>This research aims to elucidate the organizational patterns of interregional economic interdependence to enhance our comprehension of the national economy's structure at a regional scale. Employing a multilayer network model, this study represents economic interdependence among Indonesian regions, utilizing the InterRegional Input-Output (IRIO) table. Through the application of various metrics, such as degree and strength distribution, assortativity coefficient, and global and local rich club coefficient, to the multilayer IRIO network, we uncover the organizational patterns of economic exchanges between provinces and economic sectors within Indonesia. Our findings demonstrate that a multilayer network approach reveals the heterogeneous and complex structure of the national economy at the regional level. By analyzing the assortativity pattern and global rich-club coefficient, we illustrate that the IRIO network exhibits a hierarchical organization, where significant provincial-sector nodes are interconnected and form dense rich clubs, extending from a few structural cores to peripheral regions. Additionally, we identify distinct connectivity patterns of non-rich nodes based on their incoming and outgoing relations. The insights gained from this study have implications for the macro-control of regional development.</span></p>
Dataset for "Flavor network and the principles of food pairing"
<p>Dataset for Ahn, Y. Y., Ahnert, S. E., Bagrow, J. P., & Barabási, A. L. (2011). Flavor network and the principles of food pairing. <em>Scientific reports</em>, <em>1</em>(1), 196. https://doi.org/10.1038/srep00196</p> <p> </p>
Data for "CryoDRGN-ET: Deep reconstructing generative networks for visualizing dynamic biomolecules inside cells"
<p>Trained models weights, training parameters, sampled density maps, reconstructed density maps for featured classes, and plotting scripts are included for each of the following datasets and training runs:</p> <ul> <li><em>M. pneumoniae</em> ribosome, initial training run with all 18,466 particles, 1 tilt per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 10 tilts per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 41 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, initial training run with all 119,031 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 93,281 filtered particles, 10 tilts per particle</li> <li><em>S. cerevisiae</em> ribosome, training run with 30,657 particles in the non-rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 62,624 particles in the rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, initial training run with all 33,492 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, training run with all 5,239 filtered particles, 10 tilts per particle</li> </ul>
Ground-state dataset "Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states"
<h1>2D QUANTUM EDWARDS-ANDERSON GROUND-STATE DATASET:</h1> <p>The dataset contains coupling and energy data for 50 instances of a 2D quantum Edwards-Anderson model at Gamma (transverse field) = 1.8, featuring N=LxL=100 spins on a square lattice of side-length L=10 with periodic boundary conditions. The couplings are sampled from a Gaussian distribution with zero mean and unit variance.<br>The dataset consists of two text files containing coupling values and the corresponding ground-state energies.</p> <h2>Coupling Data (`coup_dataset.txt`)</h2> <p>The file `coup_dataset.txt` contains fifty sets of coupling data. Each set consists of three columns representing the indices `i`, `j`, and the coupling value `J_ij`, respectively.<br>The spin indices range from 1 to 100, ordered progressively by rows. Each set of coupling data is separated by two empty lines.</p> <h2>Energy Data (eng_dataset.txt)</h2> <p>The file `eng_dataset.txt` contains fifty rows of energy data corresponding to the coupling sets in `coup_dataset.txt`. Each row contains two columns representing the energy value and its associated statistical error-bar, rounded to the fifth decimal digit.</p>
Citation network dataset covering the work of RP Millar and its citing literature
<p>The following describes the citation network datasets that underpins the manuscript “A career in numbers: a citation network analysis of the work of RP Millar and his contribution to GnRH research” [1].</p> <p><strong>Data collection</strong></p> <p>We retrieved data from the Web of Science Core Collection under the University of Edinburgh’s subscription in January 2024. We sought to retrieve all indexed papers of Professor Robert P. Millar (RPM). We searched the following <em>AU = (Millar, R)</em>, and then retrieved records that corresponded to his WoS profile (n=428 records) and an additional 49 paper that were authored by Robert but had not been included in his WoS record – validating the records against a CV of his published works.</p> <p>We retrieved the full citation history as record by Web of Science to these papers from other indexed records. The 477 RPM papers had been cited 21,677 times by 11,138 documents by date of retrieval, and removing self-citations left 19,256 citations by 10,719 documents. We then retrieved all metadata from WoS concerning the 477 RPM papers and the 10,719 citation papers, resulting in a dataset covering 11,196 documents.</p> <p><strong>Citation network dataset</strong></p> <p>We constructed a citation network dataset by parsing data from each paper’s full bibliography consisting of:</p> <p>i. ‘Edge-list’ that records citation links from a citing to a cited document. This is constructed by assigning unique IDs to each retrieved paper and to every unique reference string contained in their bibliographies. The edge list is composed of a ‘Source’ column that contains the ID of the <em>citing</em> document and a ‘Target’ column containing the IDs of its citations, with one record per row. Given that we were only interested in citations between the WoS retrieved documents, we discarded any reference string that represented a document outwith our search.</p> <p>ii. ‘Node-attribute list’ that contains the ID, with relevant metadata contained in adjacent columns to identify documents, including authors, title of publication, journal, year of publication. We also parsed into this dataset the WoS full citation count for each paper and the total number of references in the bibliographies of each paper.</p> <p>This results in a dataset containing 11,196 nodes and 115,834 edges between nodes. We removed a total of 67 papers for which metadata was incomplete and/or corrupted. We further focussed on the largest interconnected component, removing nodes with no connections (isolates) or smaller components that were detached from the main network. We excluded papers <10 references to remove meeting abstracts and other minor journal items, and papers not published in English. This resulted in a final dataset containing 10,901 nodes and 113,742 edges, and it is this dataset that we share as it is the basis for the analyses within the paper.</p> <p><strong>Description of dataset variables</strong></p> <p><strong>‘RPM_Edgelist.csv’</strong> is a comma-separate values file that consists of all 113,742 citations between the 10,901 documents of the citation network analysed in the manuscript. The columns refer to:</p> <ul> <li>‘<em>Source</em>’, the unique identifier for the <em>citing </em>document</li> <li>‘<em>Target’</em>, the unique identifier for the <em>cited</em> document</li> <li>‘<em>Syr</em>’, the year of publication of the <em>citing</em> document</li> <li>‘<em>Tyr</em>’, the year of publication of the <em>cited</em> document</li> <li>‘<em>SC</em>’, the cluster ID of the <em>citing</em> document</li> <li>‘<em>TC</em>’, the cluster ID of the <em>cited </em>document</li> </ul> <p><strong>‘RPM_Nodelist.csv’</strong> is a comma-separate values file that consists of the 10,901 documents of the citation network analysed in the manuscript. The columns refer to:</p> <ul> <li>‘<em>Id</em>’, the unique ID assigned to a document that corresponds with the edgelist</li> <li>‘<em>Reference string</em>’, the reference string of the document</li> <li>‘<em>WoS ID</em>’, the unique accession number assigned to a document by the Web of Science. These can be used to query WoS to find further data on all papers via the ‘UT= ’ field tag.</li> <li>‘<em>Authors</em>’, all authors formatted by full last name and initials</li> <li>‘<em># of authors’</em>, number of authors</li> <li>‘<em>Title</em>’, title of document</li> <li>‘<em>Publication year</em>’, publication year of document</li> <li>‘<em>Document type</em>’, document type defined by WoS (e.g. article, review, etc.)</li> <li>‘<em>Total references</em>’, total number of references within a documents bibliography as recorded by WoS</li> <li>‘<em>Total WoS citations</em>’, total number of citations recorded to a document from other documents indexed in the Web of Science</li> <li>‘<em>Indegree</em>’, total number of within network citations (i.e. counting only citations from other papers retrieved by our query)</li> <li>‘<em>Outdegree</em>’, total number of within network references (i.e. counting only reference to other papers retrieved by our query)</li> <li>‘<em>Degree</em>’, total number of node connections (i.e. indegree + outdegree)</li> <li>‘<em>Class</em>’, variable used to distinguish between RPM’s publications (‘RPM’) and the citing documents (‘CITE’)</li> <li>‘<em>Cluster</em>’, provides the cluster membership number as discussed within the manuscript. This was established via modularity maximisation via the Leiden algorithm (Res 1; Q=0.67 | 25 clusters).</li> </ul> <p><strong>References</strong></p> <p>[1] Leng, R. I., Leng. G. (Under review). A career in numbers: a citation network analysis of the work of RP Millar and his contribution to GnRH research. <em>J. Neuroendocrinol</em></p> <p>All bibliographic data included in this study are derived originally from Clarivate™ (Web of Science™) and downloaded in January 2024. © Clarivate 2024. All rights reserved. </p>
Other Supplementary Material for 'Myosin-I Synergizes with Arp2/3 Complex to Enhance Pushing Forces of Branched Actin Networks' by Xu et al.
<div> <p>Other Supplementary Material for "Myosin-I Synergizes with Arp2/3 Complex to Enhance Pushing Forces of Branched Actin Networks" by Xu, Rutkowski, Rebowski, Boczkowska, Pollard, Dominguez, Vavylonis, and Ostap, bioRxiv, <a href="https://doi.org/10.1101/2024.02.09.579714" rel="nofollow">https://doi.org/10.1101/2024.02.09.579714</a> </p> <p> </p> </div>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 2
<p>LENS dataset 2024-04 Part 2</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset (CSV) - 202405
<p>LENS dataset 2024-05 (CSV format)</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202405 - Part 3
<p>LENS dataset 2024-05 Part 3</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset (CSV) - 202404
<p>LENS dataset 2024-04 (CSV format)</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202405 - Part 2
<p>LENS dataset 2024-05 Part 2</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 1
<p>LENS dataset 2024-04 Part 1</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202405 - Part 1
<p>LENS dataset 2024-05 Part 1</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 3
<p>LENS dataset 2024-04 Part 3</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202404 - Part 4
<p>LENS dataset 2024-04 Part 4</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
Dataset for: Asphalt pavement crack detection based on convolutional neural network and infrared thermography
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement crack detection based on convolutional neural network and infrared thermography." IEEE Transactions on Intelligent Transportation Systems 23, no. 11 (2022): 22145-22155. https://doi.org/10.1109/TITS.2022.3142393. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (binary images)</li> </ul>
Research Compendium for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive is the Reproducible Research Compendium for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques.</p> <p>This compendium includes all files related to MARGE associated with the manuscript.</p> <p>NN model files are split into smaller files for convenience, given their sizes. To recombine the files, do, e.g., <br> cat cnn_weights_NH-LW.h5* > cnn_weights_NH-LW.h5</p> <p>User interested in running MARGE will need to clone the GitHub repo (https://github.com/exosports/MARGE), apply the patch file to checksum fc95b3c, organize the relevant files into directories as listed in the configuration files (Zenodo does not support organizing files into directory structures) and calculate the number of training, validation, and test cases to be stored in the relevant input file specified in the configuration file. MARGE is under the Reproducible Research Software License (https://planets.ucf.edu/resources/reproducible-research/software-license/). For more details on MARGE, see the User Manual on GitHub.</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.