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”
miRNA gene regulatory networks for 38 human tissues
<p>We reconstructed miRNA regulatory networks for 38 tissues from the Genotype-Tissue Expression project (GTEx) using two different prior networks, one obtained with target predictions from TargetScan and one with target predictions from miRanda.</p> <p>We used these networks to investigate gene expression and regulation by miRNAs across these tissues. In the RData file, we share the following objects:</p> <p>- <strong>exp</strong>: a 16,161 by 9,435 data frame including normalized expression data for each sample.</p> <p>- <strong>expTS</strong>: a 16,161 by 38 matrix including the tissue-specificity scores for each gene in each tissue.</p> <p>- <strong>netT</strong>: a 10,391,523 by 41 data frame that includes the miRNA regulatory networks. The column "miRNA" includes the name of the regulating miRNA, the column "Gene" includes the target gene (HGNC symbol), and the column "Prior" the prior regulatory network based on target predictions from TargetScan, with 1 for edges that are canonical and 0 for edges that are non-canonical. The remaining 38 columns contain the PUMA network edge weights for each of the 38 tissues.</p> <p>- <strong>netT_TS</strong>: a 10,391,523 by 38 matrix that includes the tissue-specificity scores of the miRNA regulatory networks that were modeled on the TargetScan prior. Edges are not labelled, but edge order corresponds to the edges in "netT".</p> <p>- <strong>netM</strong>: a 10,391,523 by 41 data frame that includes the miRNA regulatory networks. The column "miRNA" includes the name of the regulating miRNA, the column "Gene" includes the target gene (HGNC symbol), and the column "Prior" the prior regulatory network based on target predictions from miRanda, with 1 for edges that are canonical and 0 for edges that are non-canonical. The remaining 38 columns contain the PUMA network edge weights for each of the 38 tissues.</p> <p>- <strong>netM_TS</strong>: a 10,391,523 by 38 matrix that includes the tissue-specificity scores of the miRNA regulatory networks that were modeled on the miRanda prior. Edges are not labelled, but edge order corresponds to the edges in "netT".</p> <p>- <strong>samples</strong>: a 9,435 by 2 data frame that includes sample identifiers (matching the identifiers in "exp") and the tissue to which these samples belong.</p> <p>- <strong>mirnames</strong>: a 694 by 3 data frame that contains miRNA names of regulators and their matching target miRNA names. The first column "base_miRNA" contains the "base" miRNA, the name of the miRNA without any extensions. The second column "reg_miRNA" contains the 643 regulator miRNA, which may have -3P/-5P extensions, and which matches the miRNAs that are present as regulators in the networks. The third columns "tar_miRNA" contains the 621 target miRNAs, which may have numbered suffix extensions, and for which we have expression data available.</p>
Road network with roadscape vectors in Awaji Island, Japan
<p><strong>Summary</strong><br>This dataset contains road network with roadscape vectors in Awaji Island, Japan. The road network data is derived from OpenStreetMap, and it consists of 102,506 road nodes and 212,050 road links in the area of Awaji Island. Roadscape vectors are given to each road link.</p> <p><strong>Road network data</strong><br>A road network is a directed graph G = (V, E), where V is a road node set and E ⊆ V × V is a road link set. A road node vi ∈ V represents an intersection or an end point. A road link ek = (vi, vj) ∈ E represents a directed link from the starting node vi to the ending node vj.</p> <p><strong>Road nodes data</strong><br><code>m_road_nodes_latlng.csv</code><br>Road nodes are contained in the file m_road_nodes_latlng.csv. The number of road nodes is 102,506. Each line corresponds to one node. These lines have the following format as tab delimited: <br><code>node_id, lat, lng</code><br>Here, node_id denotes a road node ID for identifying a road node. lat and lng denote latitude and longitude, respectively.</p> <p><strong>Road links data</strong><br><code>m_road_links_nodes.csv</code><br>Road links are contained in the file m_road_links_nodes.csv. The number of road links is 212,050. Each line corresponds to one link. These lines have the following format as tab delimited: <br><code>link_id, start_node_id, end_node_id</code><br>Here, link_id denotes a road link ID for identifying a road link. start_node_id and end_node_id denote starting node ID and ending node ID, which refer to node_id in the file m_road_nodes_latlng.csv (i.e. the same node ID refers to the same road node across these files), respectively.</p> <p><strong>Roadscape vectors</strong><br>A roadscape vector is defined as a four-dimensional probability vector composed of four kinds of roadscape elements, rural, mountainous, waterside, and urban elements. Each element of the vector denotes the probability of including the roadscape element. Therefore, the sum of values over all elements is 1.</p> <p><strong>Road link vectors data</strong><br><code>m_road_links_vector.csv</code><br>Roadscape vectors of road links are contained in the file m_road_links_vector.csv. Each line corresponds to one link with its roadscape vector. These lines have the following format as tab delimited: <br><code>link_id, rural, mountain, water, urban</code><br>Here, link_id denotes a road link ID, which refer to link_id in the file m_road_lonks_nodes.csv (i.e. the same link ID referes to the same road link across these files). The rural, mountain, water, and urban denote rural, mountainous, waterside, urban elements in its roadscape vector, respectively.</p> <p><strong>Citation<br></strong>To make use of the dataset, please cite the following paper:<strong><br></strong></p> <ol> <li>Koji Kawamata and Kenta Oku. Roadscape-based Route Recommender System using Coarse-to-fine Route Search, In <em>Proceeding of the ACM RecSys Workshop on Recommenders in Tourism (RecTour 2018)</em>, pp.23--27, 2018.</li> <li>Koji Kawamata and Kenta Oku. Roadscape-based route recommender system using coarse-to-fine route search. Journal of Information Processing, 27, pp.392–403. https://doi.org/10.2197/ipsjjip.27.392</li> </ol> <p> </p> <p> </p>
Data-driven brain network models differentiate variability across language tasks
<p>Data and script associated with the manuscript titled "Data-driven brain network models differentiate variability<br> across language tasks". </p>
Networks of Swiss water governance issues. Studying fit between media attention and organizational activity
<p>Anonymized data and R code needed to replicate the analysis presented in the study "Networks of Swiss water governance issues. Studying fit between media attention and organizational activity" to be published in Society & Natural Resources.</p> <p>The study looks at how relations between Swiss water governance issues are portrayed in the media as compared to the way organizations involved in water governance reflect these relations in their activity.</p> <p>This is a paper output of the SNF funded project "Overlapping subsystems". Access to the complete, non-anonymized dataset is restricted.</p> <p>Study doi: tbd</p>
Unsupervised Acoustic Segmentation and Clustering using Siamese Network Embeddings
<p>The spoken term discovery system outputs and evaluations for track 2 on Zero Speech 2017. </p>
Historic Caddo Network Analysis Data
<p>Ceramic and lithic types and counts used in the Historic Caddo network analysis for northeast Texas. This dataset is incomplete, and does not include the site locations used to plot the network.</p>
Technical Report and Simulation Results for the Paper "It's About Time: On Optimal Virtual Network Embeddings under Temporal Flexibilities"
<p>Archiving the data and the technical report corresponding to our publication "It’s About Time: On Optimal Virtual Network Embeddings under Temporal Flexibilities".<br> The data was previously publicly available at https://net.t-labs.tu-berlin.de/~stefan/tvnep.html.</p>
Novel gene regulatory networks identified in response to nitro-conjugated linoleic acid in human endothelial cells
<p>A distinct transcriptome regulated by NO<sub>2-</sub>CLA was revealed in primary human coronary artery endothelial cells (HCAECs) through RNA sequencing. </p>
Data for "Efficient neural decoding of self-location with a deep recurrent network"
<p>Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the article</p> <p>"Efficient neural decoding of self-location with a deep recurrent network".</p> <p> </p> <p>This data should be used with the code found in https://github.com/NeuroCSUT/RatGPS and should be placed in the Bayesian/Data folder of the codebase.</p> <p> </p>
raw data and simulation scripts for "Exotic states in a simple network of nanoelectromechanical oscillators"
<p>Presented are raw data and simulation python scripts used to create figures from the manuscript "Exotic states in a simple network of nanoelectromechanical oscillators". Data is formatted by {time, mag_1, phase_1, mag_2, phase_2, mag_3, phase_3, mag_4, phase_4, mag_5, phase_5, mag_6, phase_6, mag_7, phase_7, mag_8, phase_8} for 17 column data files, and {time, phase_1, phase_2, phase_3, phase_4, phase_5, phase_6, phase_7, phase_8} for 9 column data. The data is organized by Figure number within manuscript and supplementary information.</p>
Supporting Information: Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography
<p>Supporting Information README</p> <p>2018-Jan-17</p> <p>"Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography"</p> <p>Yanhui Dong, Yunmei Fu, Tian-Chyi Jim Yeh, Yu-Li Wang, Yuanyuan Zha, Liheng Wang, Yonghong Hao</p> <p>This file contains the supplementary data for this manuscript, including the locations and properties of fracture networks, the locations of observation wells and validation wells, the water head used in inverse model and validation tests, as well as the executive file used in the inverse model.</p>
Does phenology explain plant-pollinator interactions at different latitudes? An assessment of its explanatory power in plant-hoverfly networks in French calcareous grasslands
<p>These are the data supporting the results in the paper entitled: <strong>"</strong>Does phenology explain plant-pollinator interactions at different latitudes? An assessment of its explanatory power in plant-hoverfly networks in French calcareous grasslands"</p> <p><strong>Authors: </strong>Natasha de Manincor¹*, Nina Hautekeete¹, Yves Piquot¹, Bertrand Schatz², Cédric Vanappelghem³, François Massol¹<sup>,4</sup></p>
Dataset from research: Origin, characteristics, predominance and conceptual networks of eponyms in bibliometric literature
<p>Dataset in a xlsx file used by Juan-Carlos Valderrama-Zurián & collaborators in the research “Origin, characteristics, predominance and conceptual networks of eponyms in bibliometric literature”.</p>
Input data and scripts for "Spatial conservation prioritization for the East Asian islands: a balanced representation of multi-taxon biogeography in a protected area network"
<p>This release contains the input files 'input_data.zip' for the spatial conservation prioritization analysis by Zonation software, which are conducted in Lehtomäki et al. Input data includes biodiversity features (species distribution maps from vascular plants, mammals, birds, reptiles, amphibians, and freshwater fishes), habitat condition map (human influence index), priority mask information (the categorized protected area distribution) and the Japanese prefecture polygons in GeoTiff format, and the list of species attributes for conservation weighting in CSV format. Note that endangered rare species have been excluded from this dataset, though they were reflected in the output files. The Zonation setting files and R scripts for pre- and post analyses are included in 'japan-zsetup-1.0.zip' and also placed at GitHub : https://github.com/cbig/japan-zsetup</p> <p>The output files (priority score maps and removal curves) from the original Zonation analyses are summarized in 'output_from_original.data.zip'</p>
FIGURE 6. Trimma haplogroups associated with T. wangunui from a Neighbour Joining Network derived from a in A new species of Trimma (Pisces; Gobiidae) from the Western Pacific Ocean
FIGURE 6. Trimma haplogroups associated with T. wangunui from a Neighbour Joining Network derived from a BOLD analysis of the COI gene with 835 samples (R. Winterbottom).
Data and results for Cochrane systematic review and network meta-analysis on hepatorenal syndrome
<p>This contains the data and the raw results for the Cochrane systematic review and network meta-analysis on hepatorenal syndrome (https://doi.org/10.1002/14651858.CD013103). Please unzip the file and read the instructions before using the data.</p>
Data and results for Cochrane systematic review and network meta-analysis on treatment of spontaneous bacterial peritonitis
<p>This contains the data and the raw results for the Cochrane systematic review and network meta-analysis on the treatment of spontaneous bacterial peritonitis (https://doi.org/10.1002/14651858.CD013120). Please unzip the file and read the instructions before using the data.</p>
Macroscopic Fundamental Diagram Based Discrete Transportation Network Design
<p>Simulation data for the research paper named "Macroscopic Fundamental Diagram Based Discrete Transportation Network Design ".</p>
Optimization of Gang-operated On-load Tap Changers in Multi-conductor Radial Networks: Formulation and Convex Relaxation
<p>DSS files for case studies in CIGRE conference paper</p> <p>Only the secondary of the regulator can tap between 0.9 and 1.1, this is not specified in the DSS files themselves</p>
Datasets for "T. Murovič, A. Trost, Massively Parallel Combinational Binary Neural Networks for Edge Processing, Elektrotehniški vestnik, vol. 86, no. 1-2, pp. 47-53, 2019"
<p>Zipped datasets files for replicating results of: "T. Murovič, A. Trost, Massively Parallel Combinational Binary Neural Networks for Edge Processing, Elektrotehniški vestnik, vol. 86, no. 1-2, pp. 47-53, 2019"</p> <p>DATASETS WERE NOT CREATED BY US. DATASETS ARE WORKS OF RESEARCHERS AND PAPERS REFERENCED IN "T. Murovič, A. Trost, Massively Parallel Combinational Binary Neural Networks for Edge Processing, Elektrotehniški vestnik, vol. 86, no. 1-2, pp. 47-53, 2019"</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.