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1,721 results for “network data”

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zenodo44/100

Educational transformation and network learning dataset – qualitative data from an international collaborative EU-project

We are releasing our dataset of workshop outcomes acquired from the annual consortium conferences organized by the international “NextFood” consortium. The purpose of this project is to develop new ways of educating the future sustainability leaders of the agrifood sector, making sure that the professionals (farmers, advisers, businesses, students) have the right set of skills and competences needed to tackle the sustainability challenges we face ahead. Data gathering started from May 2018 yielding considerable amount of data on achievements, challenges and action plans related to educational transformation. This dataset will be updated by the time of project finalization. This work was funded by the European Union, through the Horizon 2020 project “NextFood”, Grant agreement No. 771738.

opencc-by-4.0Dec 2021View details →

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Crowdsourced air traffic data from The OpenSky Network 2020 [CC-BY]

WARNING! This dataset is no longer updated after January 2022. Refer to the <a href="https://doi.org/10.5281/zenodo.3737101">original dataset</a> with different license terms for an up to date version. Motivation The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. License Creative Commons CC-BY The only difference with the <a href="https://doi.org/10.5281/zenodo.3737101">original dataset</a> comes from anonymised aircraft information. WARNING:This dataset is now longer updated after January 2022. The original dataset is still updated. Disclaimer The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. <ul> <li>Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted. Fields origin or destination are empty when no airport could be found.</li> <li>Aircraft information come from the OpenSky aircraft database. Fields typecode and registration are empty when the aircraft is not present in the database.</li> </ul> Description of the dataset One file per month is provided as a csv file with the following features: <ul> <li>callsign: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)</li> <li>number: the commercial number of the flight, when available (the matching with the callsign comes from public open API)</li> <li>aircraft_uid: a unique anonymised identifier for aircraft;</li> <li>typecode: the aircraft model type (when available);</li> <li>origin: a four letter code for the origin airport of the flight (when available);</li> <li>destination: a four letter code for the destination airport of the flight (when available);</li> <li>firstseen: the UTC timestamp of the first message received by the OpenSky Network;</li> <li>lastseen: the UTC timestamp of the last message received by the OpenSky Network;</li> <li>day: the UTC day of the last message received by the OpenSky Network;</li> <li>latitude_1, longitude_1, altitude_1: the first detected position of the aircraft;</li> <li>latitude_2, longitude_2, altitude_2: the last detected position of the aircraft.</li> </ul> Examples Possible visualisations and a more detailed description of the data are available at the following page: <<a href="https://traffic-viz.github.io/scenarios/covid19.html">https://traffic-viz.github.io/scenarios/covid19.html</a>> Credit Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders "Crowdsourced air traffic data from the OpenSky Network 2019–2020" Earth System Science Data 13(2), 2021 <a href="https://doi.org/10.5194/essd-13-357-2021">https://doi.org/10.5194/essd-13-357-2021</a>

opencc-by-4.0Jul 2020View details →

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Data Set for the Journal Article "Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis"

This dataset includes the XYZ structures of the centroids of all compounds found. Charge and multiplicity are given in the comment line of each XYZ file.

opencc-by-4.0Jan 2022View details →

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Generated Data for the Manuscript "Nonideality-Aware Training for Accurate and Robust Low-Power Memristive Neural Networks"

The file contains data generated and referred to in the text and the figures of the manuscript.

opencc-by-4.0Dec 2021View details →

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Local survey network and monitoring data of VLBI telescope at Metsähovi

Observations of local surveying network and monitorin at Metsähovi. More information is in file Data_description.pdf.

opencc-by-4.0Apr 2022View details →

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Data Sets for SNR Estimation in Flexible Optical Networks: Lightpath, Link, and Span Levels

These data sets have been generated based on the analytic models [1,2] to estimate signal to the noise ratio (SNR) for spans, links, and lightpaths of a Flexible Optical Network (FON) over standard single-mode fiber (SSMF). For PM-BPSK and PM-QPSK modulation format levels, equation 41-43 [1], and for PM-8-64QAM modulation format levels, equation 7.32 [2], are applied. [1] P. Poggiolini, G. Bosco, A. Carena, V. Curri, Y. Jiang and F. Forghieri, "The GN-Model of Fiber Non-Linear Propagation and its Applications," in Journal of Lightwave Technology, vol. 32, no. 4, pp. 694-721, Feb.15, 2014, DOI:  10.1109/JLT.2013.2295208. [2]  P. Poggiolini, Y. Jiang, A. Carena and F. Forghieri, "Analytical modeling of the impact of fiber non-linear propagation on coherent systems and networks" in Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks, New York, NY, USA:Wiley, pp. 247-310, 2016.

opencc-by-4.0May 2022View details →

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The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - B. Data for 2020 - 2026 - Covid scenario

opencc-by-4.0Nov 2021View details →

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The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - A. Code and data for 2016-2019

The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries. This repository includes data for years from 2016 to 2019 (hist scenario) and the corresponding labels. Data for years 2020 to 2026 are stored in the corresponding repositories: <ul> <li>covid: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></li> <li>counterfactual: <a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></li> </ul> Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>. From 2020 to 2026, the dataset includes two diverging scenarios. The covid scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The counterfactual scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as hist. The Projections folder includes the generated tables for years from 2016 to 2019 (hist scenario) and the corresponding labels. The Sources folder contains the data records from the IFS and WEO databases. The Method data contains the data files used to generate the tables with the SPIN method and the following Python scripts: <ul> <li>SPIN_covid19_MRIO_files_preparation.py generates the data files from the source data.</li> <li>SPIN_covid19_RMRIO runs.py is the command to run the SPIN method and generate the dataset.</li> <li>figures.py is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.</li> </ul> All tables are labelled in 2015 US$ and valued in basic prices.

opencc-by-4.0Nov 2021View details →

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The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - C. Data for 2020 - 2026 - Counterfactual scenario

opencc-by-4.0Nov 2021View details →

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Code and data accompanying Palmeirim et al. (2022) Emergent properties of species-habitat networks in an insular forest landscape. Science Advances

Dataset containing species distribution in insular forest fragments at Balbina and full R code for analyses and figures. For deatails, please see the original publication: "Emergent properties of species-habitat networks in an insular forest landscape". Ana Filipa Palmeirim, Carine Emer, Maíra Benchimol, Danielle Storck-Tonon, Anderson S. Bueno, Carlos A. Peres. Science Advances (2022). 10.1126/sciadv.abm0397.

opencc-by-4.0Dec 2021View details →

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Accurately modeling biased random walks on weighted networks using node2vec+ - Additional data

Human gene interaction network data used to reproduce gene classification experiments https://github.com/krishnanlab/node2vecplus_benchmarks

opencc-by-4.0Aug 2022View details →

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Data for conductance-based simulations of "Cortical oscillations support sampling-based computations in spiking neural networks"

This repository contains the full data generated by the conductance-based simulations described in: <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009753">Cortical oscillations support sampling-based computations in spiking neural networks</a>. The code is accessible via <a href="https://doi.org/10.5281/zenodo.5512526.">this repository</a>.

opencc-by-4.0Sep 2022View details →

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Supplementary Data for NIPS Publication: Protein Interface Prediction using Graph Convolutional Networks.

These data sets can be used to re-run the experiments from our paper, Protein Interface Prediction using Graph Convolutional Networks. The data are derived from protein complexes in the docking benchmark dataset v. 5.0. Each file is a python tuple that has been saved using cPickle and compressed using gzip. Links: Paper: https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks Poster: https://zenodo.org/record/1134154 Code: https://github.com/fouticus/pipgcn   File Descriptions: train.cpkl.gz and test.cpkl.gz have the data formatted for neighborhood based graph convolutions. The diffc_ files are the same data formatted for the diffusion convolutional neural networks that we compare against.  train.cpkl.gz is a tuple of length 2: <ul> <li>element 0 is a list of length 175 containing the PDB codes from the docking benchmark dataset</li> <li>element 1 is a list of length 175 containing features for each protein. Each element is a dictionary containing the following keys: <ul> <li>r_vertex: vertex (residue) features for the receptor. numpy array of shape (x, 70) where x is the number of residues in the receptor and 70 is the number of features.</li> <li>l_vertex: vertex (residue) features for the ligand. analogous to above, with shape (y, 70) where y is the number of residues in the ligand.</li> <li>complex_code: PDB code of the complex. matches the list of codes described above.</li> <li>l_edge: edge features for the neighborhood around each residue in the ligand. numpy array of shape (y, 20, 2) where y is defined as above. the second dimension is the edges to the 20 nearest neighboring residues, ordered by decreasing distance. The third dimension allows for two features per edge. </li> <li>r_edge: edge features for the neighborhood around each residue in the receptor. numpy array of shape (x, 20, 2) where x is as above. </li> <li>l_hood_indices: the index of the 20 closest residues to each residue, ordered by decreasing distance. numpy array of shape (y, 20, 1). "Index" means which row in l_vertex gives the vertex features for the closest neighbor, second closest neighbor, etc. </li> <li>r_hood_indices: analogous to above, shape (x, 20, 1).</li> <li>label: 1 or -1 label for each residue pair. numpy array of shape (x*y, 3). Each row looks like (i, j, k) where i is the index of the ligand residue, j is the index of the receptor residue, and k is either -1 (negative example) or 1 (positive example).</li> </ul> </li> </ul> test.cpkl.gz matches the structure of train.cpkl.gz except it has the test set of 55 complexes.  Descriptions of the vertex and edge features can be found in Appendix A of  <a href="https://mountainscholar.org/handle/10217/185661">this.</a> diffc_g2_p2_train.cpkl.gz is a tuple of length 2: <ul> <li>element 0 is a list of the same 175 PDB codes as above. </li> <li>element 1 is a list of features for the 175 complexes. Each element is a dictionary of features with these keys: <ul> <li>r_vertex, l_vertex, complex_code, label: these are the same as described above. </li> <li>'r_power_series': Stacked diffusion matrices which are powers of the similarity matrix used in the DCNN method. numpy array of shape (x, 2, x) where x is the number of receptor residues. the middle dimension 2 indicates how many "hops" is used for that diffusion (1 vs. 2). In other words, element (i, 0, j) is the similarity after 1 hops between residues i and j. element (i, 1, j) is the similarity after 2 hops. See DCNN paper for details.</li> <li>'l_power_series': same as above but for the ligand. shape is (y, 2, y).</li> </ul> </li> </ul> diffc_g2_p2_test.cpkl.gz is the same as diffc_g2_p2_train.cpkl.gz but for the 55 test complexes. diff_g2_p5_train.cpkl.gz and diff_g2_p5_test.cpkl.gz are the same as the p2 version above, except that the diffusion matrices have shape (x, 5, x) and (y, 5, y) because one of our comparisons against the DCNN model uses 5 hops instead of just 2.    Note: these files were pickled with Python 2.7. If you're unpickling with Python 3.x you might have to specify encoding as 'latin1'.    Please direct any questions to: <ul> <li>Alex Fout (fout@colostate.edu)</li> <li>Jonathon Byrd (jonbyrd@colostate.edu)</li> <li>Basir Shariat (basir@cs.colostate.edu</li> <li>Asa Ben-Hur (asa@cs.colostate.edu)</li> </ul>

opencc-by-sa-4.0Dec 2017View details →

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Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus: optogenetical stimulation data

This dataset contains 2-photon calcium imaging data from the paper 'Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus'. This is the calcium imaging data from CA1 pyramidal cells and interneurons, including both spontaneous activity and activity in response to optogenetic stimulation. Data organization This dataset contains all the data related to the all-optical part of the paper and was analyzed using the code from the <a href="https://gitlab.com/cossartlab/bocchio-vorobyev-et-al-2023/-/tree/main/Optogenetical%20stimulation?ref_type=heads">lab repository</a>. The original calcium imaging movies are excluded due to size limitations. Further information Please email vorobev[a t]phystech.edu if you need further information on the data or if you wish to access the raw calcium imaging movies (not uploaded here due to storage limitations).

opencc-by-4.0Jun 2024View details →

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Annotated Data in Spanish for Toxicity and Insults in Digital Social Networks

This repository contains data sets and materials for a gold standard elaboration on toxicity and incivility in the digital sphere based on human coding to benchmark algorithmic classification tasks with transformers and LLMs. The labelling progress is 62%. We are labelling two samples of novel datasets of political digital interactions on Twitter (rebranded as X). The first set comprises almost 5 million data points from three Latin American protest events: (a) protests against the coronavirus and judicial reform measures in Argentina during August 2020; (b) protests against education budget cuts in Brazil in May 2019; and (c) the social outburst in Chile stemming from protests against the underground fare hike in October 2019. We are focusing on interactions in Spanish to elaborate a gold standard for digital interactions in this language, therefore, we prioritise Argentinian and Chilean data. The second set contains more than 31 million messages and more than 9 million interactions between 2010 and 2022, covering the election of members of the first Constitutional Convention in Chile, the drafting process and the referendum in which the proposal was rejected. This project is generously funded by the OpenAI Academic Programme, 2024 FAE-UDP Research Grant, and partially by the St Hilda's College Muriel Wise Fund at the University of Oxford. The <a href="https://training-datalab.com/">Training Data Lab</a> research group also logistically supports this project.

opencc-by-4.0Jun 2024View details →

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Network Data on Key Brussels Think Tanks

This is anoynmized network on ties between experts and prominent Brussels think tanks dealing with economic policy, including gender, profession, nationality, PhD, and educational training. Data collated by Ramona Coman, Université libre de Bruxelles, for the ENLIGHTEN project (H2020 #649456).

opencc-by-4.0Mar 2018View details →

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Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software

Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.

opencc-by-4.0Jan 2017View details →

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Data for: Tang et al., Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. bioRxiv 2018.

Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations. Paper: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: <a href="https://doi.org/10.1101/454793">https://doi.org/10.1101/454793</a>. Details: A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows: <ol> <li>Development (Phases I-II). 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).</li> <li>Hold-out (Phase III). 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.</li> <li>CERAD-like hold-out. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.</li> </ol> Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented. Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files. Slide source and preparation: All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification. Code: Please visit <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a>

opencc-by-4.0Oct 2018View details →

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District heating network data for the city of Flensburg from 2014-2016

The data package contains flow temperatures and the overall heat load for the district heating network of Flensburg, Germany for the years 2014-2016.

opencc-by-sa-4.0Jan 2019View details →

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TwitCID: a Collection of Data Sets for Studies on Information Diffusion on Social Networks

The TwitCID collection consists of five Twitter datasets which were extracted from the 1 percent of tweets from Twitter API.  The Firstweek and Secondweek data set were collected during the first week and second week of January 2017 while the Iphone, Gucci and Galaxy data sets were collected from 21 September 2015 to 31 May 2017 using the keywords “iphone”, “gucci” and “galaxys” respectively.   We publish these datasets on behalf of our academic institution – IRIT, France and for the sole purpose of non-commercial research under the license CC BY-NC-SA (Attribution-NonCommercial-ShareAlike). In accordance with Twitter's Terms of Service, we only provide identifiers of tweets. In order to collect the actual tweets in JSON, you could use the script Collect_JSONtweets.py attached. If you would like to use this collection, please cite our paper:  Hoang, T. B. N., Mothe, J., & Baillon, M. (2019, September). TwitCID: a collection of data sets for studies on information diffusion on social networks. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 88-100). Springer, Cham.

opencc-by-4.0Jun 2019View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record