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1,721 results for “network data”
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
<p><strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1-S4. Legends are included under each figure.</p> <p><strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.</p>
Data files for In situ training of feedforward and recurrent convolutional memristor networks
<p>MATLAB data files for the manuscript "<em>In situ training of feedforward and recurrent convolutional memristor networks" </em>published on Nature Machine Intelligence, 2019.</p> <p>The MATLAB data file "exp_mnist.mat" consists of all experimental data on implementing the convolutional neural network with the 1-transistor 1-memristor array that is used for plotting the Figure 1 and 2 of the manuscript.</p> <p>The MATLAB data file "exp_mnistsequence.mat" consists of all experimental data on implementing the convolutional long short-term memory network on the 1-transistor 1-memristor array that is used for plotting the Figure 3 and 4 of the manuscript.</p> <p>The code that generated these data files are provided by the link within the manuscript. Alternatively, the code can be accessed via <a href="https://github.com/zhongruiwang/memristorCNN">https://github.com/zhongruiwang/memristorCNN</a>.</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Data From: Harnessing Deep Belief Networks for Selective HDAC6 Inhibitors Identification
<p>This dataset contains the results from virtual screening of SPECS library after predicting the selectivity of molecules against HDAC6 over using a Deep Belief Network model that was trained and tested by the authors.</p> <p>The docking poses of 10 molecules with best docking scores were given along with their MMGBSA scores. </p> <p>The MD trajectory files along with the analysis were also provided for the selected three molecules as well as the reference molecule (co-crystalized ligand).</p>
Data for publication "The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zürich"
<p>Please see README.md for a description of this package. </p> <p>This work was funded by the European Union's Horizon 2020 research and innovation programme, grant agreement number 101037319, named Pilot Applications in Urban Landscapes - towards integrated city observatories for greenhouse gases (PAUL) and is known as ICOS Cities.</p> <p> </p>
Data and code related to "Difficult control is related to instability in biologically inspired Boolean networks"
<p>This repository contains data and code related to the publication "Difficult control is related to instability in biologically inspired Boolean networks" by Bryan C. Daniels and Enrico Borriello.</p> <p>The python code in the `isolated_fixed_points_code` directory can be used to recreate all results in the paper. See the README.md file in the `isolated_fixed_points_code` directory for more information about how to run the code.</p> <p>The files `240916_cell_collective_ck_and_isolated_fp_data.csv`, `240916_iowa_database_ck_and_isolated_fp_data.csv`, and `240916_random_ck_and_isolated_fp_data.csv` contain data about the networks analyzed in the paper, including the number of attractors and mean control kernel size of each network.</p>
Processed Data for "Improving Gene Regulatory Network Inference using Dropout Augmentation"
<p>Here are the processed dataset that are used in the manuscript "Improving Gene Regulatory Network Inference using Dropout Augmentation"</p>
Data for: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models"
<p>This repository contains the raw data to reproduce the paper: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models". Within the .tar.gz file, you will find the directory structure described above.</p> <h2>Directory Structure</h2> <h3>`data`</h3> <p>Contains the data to reproduce all figures in the manuscript. Used primarily by the Jupyter Notebooks that plot the data from the paper.</p> <h3>`eval`</h3> <p>Contains the predicted energies according to a MACE model for the following systems and facets:<br>- covsplit (100, 111, 211, 331, 410, 711): The NN model is trained on low-coverage structures and tested on high-coverage structures for a single facet<br>- evencov (100, 111, 211, 331, 410, 711): The NN is trained on even coverages and tested on odd coverages for a single facet<br>- facet (100, 111, 211, 331, 410, 711): the NN is trained on the facet indicated by the folder name (e.g., facet-100 means that the model was trained on Cu(100)) and tested on all of the other facets.<br>- full: the model was trained on all facets and all coverages<br>- slopes (various versions and configurations): the models were trained with different body-order correlation (v) for the Cu(711) facet and tested only on the Cu(711) facet<br>- Rh111: Energies for the Rh(111) + CHOH + CO systems.</p> <h3>`mcmc`</h3> <p>Contains the data for MCMC (Markov Chain Monte Carlo) evaluations for two systems: Cu and Rh<br>- copper-mcmc-public.tar.gz<br>- rhodium-mcmc-public.tar.gz</p> <h3>`models`</h3> <p>Contains the weights and parameters of the best-performing MACE models trained in this work, as selected by the validation loss:</p> <p>File formats: `.model` and `_swa.model` relate to the first-stage of training and the second-stage of training.</p> <h3>`pyscripts`</h3> <p>Python scripts to perform the MCMC sampling given the custom configuration file `sample_cfg.json`.</p> <h3>`scripts`</h3> <p>Shell scripts for evaluation and training the MACE models, along with the hyperparameters used in doing so.</p> <p>- Evaluation scripts (eval-*.sh)<br>- Training scripts (train-*.sh)</p> <h3>`train`</h3> <p>Training, validation, and testing data for all Cu and Rh facets in this work, according to the naming scheme described above.</p> <p>- Rh111<br>- covsplit<br>- evencov<br>- facet<br>- full<br>- slopes</p>
Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed
<p>Environmental and AIS data collected during the H2020 project EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network (LOON) testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy)</p> <p>Temperature measured across the water column in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) Nov 12, 19-20, 23-24 - 2020<br> ii) Dec 1-4, 14-20 - 2020<br> iii) Jan 12-13, 15, 18-24, 27-28 - 2021</p> <p><br> Meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) Nov 12, 19-20, 23-24 - 2020<br> ii) Dec 1-4, 14-20 - 2020<br> iii) Jan 12-13, 15, 18-24, 27-28 - 2021</p> <p><br> Sound velocity measured close to the surface (SVP1) and close to the sea bottom (SVP2) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) Nov 12, 19-20, 23-24 - 2020<br> ii) Dec 1-4, 14-20 - 2020<br> iii) Jan 12-13, 15, 18-24, 27-28 - 2021</p> <p>SVP2 data missing for Dec 14-20 (2020) and Jan 24, 27-28 (2021).</p> <p>Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy). The dataset includes AIS data for:<br> i) Nov 12, 19-20, 23-24 - 2020<br> ii) Dec 1-4, 14-20 - 2020<br> iii) Jan 12-13, 15, 18-24, 27-28 - 2021<br> </p> <p>For reference, see: "Environmental data collected on the CMRE LOON tested during the EUMR project: dataset description", Petroccia, Roberto; Zappa, Giovanni; Cimino, Giampaolo; Grati, Alberto; Alves, João. CMRE-DA-2021-001. July 2021, available at https://www.cmre.nato.int/research/publications/latest-techreports/1638-cmre-da-2021-001</p>
ZIRFs: zero-inflated random forests for estimating gene regulatory networks from single cell RNA-seq data (assessment of predictive accuracy and VIM stability)
<p>We developed a zero-inflated random forests (ZIRFs) algorithm to produce a metric of connection strength between regulator genes and target genes. This file contains SCENIC results for the aorta and diaphragm tissue data sets from the Tabula Muris Consortium results. SCENIC is a genetic regulatory network analysis published by Aibar et al. (2017). The purpose of the data sets and R source code are described by README files in each directory.</p>
Data related to: Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection, Norman et al. (2021)
<p>This dataset contains intra-cranial EEG recordings and analysis code related to the paper: "Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection" by Norman et al. (https://doi.org/10.1016/j.neuron.2021.06.020)<br> The study investigates the role of hippocampal ripples in the human brain during retrieval of recent and remote autobiographical memories and semantic facts. The intracranial recordings underwent standard preprocessing as described in the paper and were stored in EEGLAB datasets. The analysis code that accompanies the dataset implements the main analyses described in the paper.</p> <p>The dataset includes the following zip files:</p> <ul> <li>iEEG data and main analysis code: <ul> <li>“Norman_et_al_2021_iEEG_data_and_code_1.zip" </li> <li>“Norman_et_al_2021_iEEG_data_and_code_2.zip" </li> </ul> </li> <li>Patients' anatomical data: <ul> <li>“Norman_et_al_2021_Freesurfer.zip”</li> </ul> </li> <li>Additional toolboxes (developed by others): <ul> <li>“MATLAB_toolboxes.zip”</li> </ul> </li> </ul> <p>The code is written primarily in Matlab (version R2018b) and runs on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM. Matlab's Signal Processing Toolbox is required, as well as EEGLAB, Unfold toolbox, and several other open-source toolboxes.</p>
Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient - Data and code
<p>Dataset and code used in the article "Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient", by A. Fisogni et al., published in Landscape and Urban Planning (2022, 226:104512, <a href="https://www.sciencedirect.com/science/article/pii/S016920462200161X?via%3Dihub">https://doi.org/10.1016/j.landurbplan.2022.104512</a>)</p>
ELIXIR-CONVERGE - Survey of benefits of an Data Management expert network
<p>The aim of this short survey to was to gauge the perceived benefits of having established a network of Research Data Management professionals across the ELIXIR nodes, as part of the ELIXIR-CONVERGE project. </p> <p>Survey responses to the following questions were collected between 17 May and 11 July 2022 after an open invitation to the <a href="https://elixir-europe.org/about-us/how-funded/eu-projects/converge/wp1/dm-network">ELIXIR Data Management Network</a>:</p> <ul> <li>Is the ELIXIR Data Management Network providing you with any benefit? </li> <li>What benefits?</li> <li>Ideas for more ways of working?</li> <li>Are you associated with an ELIXIR node?</li> <li>What is your role?</li> </ul> <p>Included are survey responses raw data, and a pdf that summarises the responses.</p> <p> </p>
Supplemental data for "Computational screening of chemically active metal center in coordinated dipyridyl tetrazine network"
<p>Atomic coordinates of structures used in N. Ud Din, D. Le, T. S. Rahman "Computational screening of chemically active metal center in coordinated dipyridyl tetrazine network", J. Phys.: Condens. Matter .(2023). DOI: 10.1088/1361-648X/acb8f3</p>
Pore network data for Heletz sandstones
<p>CT-scan image of Heletz sandstone and extracted network data from CT-scan images for Heletz sandstone.</p>
Data from: CoAct Citizen Science chatbot explores social support networks in mental health based on lived experiences
<p>A data set on lived experiences in the context of social support in mental health, created within a Citizen Social Science project. </p> <p><br> Societies around the world increasingly encounter wicked and complex problems, such as those related to mental health, environmental justice, and youth employment. <strong>CoAct as a EU-funded global effort</strong> addresses these problems by deploying Citizen Social Science. </p> <p> </p> <p><strong>Citizen Social Science</strong> is understood here as participatory research co-designed and directly driven by citizen groups sharing a social concern. This methodology wants to give citizen groups an equal ‘seat at the table’ through <strong>active participation in research</strong>, from the design to the interpretation of the results and their transformation into concrete actions. Citizens thus act as <strong>co-researchers</strong> and are recognised as in-the-field competent experts. </p> <p> </p> <p>In Barcelona, a group of <strong>32 co-researchers</strong> work together with the OpenSystems group, Universitat de Barcelona, the Catalan Federation of Mental Health (Federació Salut Mental Catalunya), and with the help of many others on a better understanding of informal <strong>social support networks in mental health</strong> in the project <em>CoActuem per la Salut Mental</em> (lit. “We act together for mental health”). The co-researchers, who are either persons with a personal history of mental health problems or are family members of the latter, contributed their <strong>personal experiences related to social support</strong> in the form of <strong>222 micro-stories</strong>, each shorter than 400 characters, and most accompanied by an illustration by Pau Badia.</p> <p> </p> <p>Those micro-stories form the heart of the first co-created Citizen Science chatbot, the code of which is open on <a href="https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git">https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git</a> . The <strong>Telegram chatbot</strong> sends them to participants <strong>on a daily basis over the course of a year</strong> and asks them either, whether they and/ or their close surrounding lived this experience, too (stories of type C), or, how they would or would have reacted in the presented situation (stories of type T). The answers of each participant can be contrasted with the individual participants’ answer to a 32-questions <strong>socio-demographic survey</strong>. Further, the timing of the messages is included to allow for a broader analysis. </p> <p> </p> <p>The chatbot is still running, hence this data set will still be updated. For further information on the project <strong>CoAct</strong>, see <a href="https://coactproject.eu/">https://coactproject.eu/</a>. For further details on the co-creation process and purpose of the chatbot <strong>CoActuem per la Salut Mental</strong>, take a look on <a href="https://coactuem.ub.edu/">https://coactuem.ub.edu/</a>. Please direct your questions regarding the data set to <strong>coactuem[at]ub.edu</strong>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The CoAct project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 873048. We especially thank the co-researchers for the passion and time invested.</p>
ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'
<p>ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'</p>
Network data and script accompanying the paper "Operationalizing anthropological theory: four techniques to simplify networks of co-occurring ethnographic codes"
<p>This repository accompanies the paper<a href="https://rdcu.be/dbuhi"> "Operationalizing anthropological theory: four techniques to simplify networks of co-occurring ethnographic codes"</a>, by Cottica et al. It contains:</p> <ol> <li>A data file, containing networks of co-occurrence of ethnographic codes from three ethnographies. Data are pseudonymized (see the paper for details).</li> <li>A script that, when run on the data, produces simplified versions of each network. Simplifications follow four different techniques, described in the paper. Each technique relies on a tuning parameter, so that, for each network and each techniques, the script produces several simplified networks, each one associated with a unique value of the tuning parameter.</li> </ol> <p>The data file format is that of a Tulip perspective. To open, download Tulip (https://tulip.labri.fr), launch it and open the file from within the Tulip GUI.</p> <p>The script file is in Python. To run, open it from within the Tulip IDE first.</p> <p> </p>
Network Data of the District Heating System for the city of Sønderborg from 2016-2019
<p>The data set contains measurement data for heat load, as well as feed and return flow temperatures, from seven plants for the years 2016-2019 with a 15-minute time resolution. The heating plants belong to the district heating systems of Sønderborg, Denmark.</p>
Geochemical data of bottom sediments from a network of drainage canals located in the low-lying coastal area of Ravenna, Italy.
<p>This dataset contains all raw geochemical data of bottom sediments from a network of drainage canals located in the low-lying coastal area of Ravenna. The dataset is divided in three separated excel worksheets: </p> <p>- <strong>Focus Area</strong>. Sediment composition of the 21 sediment samples collected in 2022 in the Focus Area. Refer to Figs. 1 and 2 in the manuscript Giambastiani et al., 2024 for the sample locations. Listed are also other information related to sampling, such as depositional facies (BR: beach ridge deposits; IF: Interfluvial floodplain deposits), distance from the sea, altimetry, amount of fertilizer applied based on the land use, and EC of drainage water. <br>The sediment samples were collected in March 2022 along the drainage system of the lowlying coastal aquifer of Ravenna (Italy) by the authors.</p> <p>- <strong>LRC, Land Reclamation Consortium</strong>. PTEs composition of the sediment samples of the Land Reclamation Consortium dataset. Refer to Fig. 1 and 2 in the manuscript Giambastiani et al., 2024 for the location. Listed are also other information related to sampling, such as distance from the sea, altimetry, and amount of fertilizer applied based on the land use. <br>The sediment samples were collected since 2010 along the drainage system of the lowlying coastal aquifer of Ravenna (Italy) by The Land Reclamation Consortium of Romagna (Italy). No other uses apart from scientific purpose is allowed without notice to the authors.</p> <p>- <strong>Wells</strong>. Physical and chemical groundwater parameters of 4 wells localted within the Focus Area. Refer to Fig.2 in the manuscript Giambastiani et al., 2024 for the location. <br>Data were collected during previous studies by Greggio et al. (2020) and reprocessed to obtain vertical profiles of EC, pH, Eh, and chemical concentrations along the coastal aquifer depth.</p> <p>More informations regarding the source, ownership, collection methodologies and analytical techniques are in Giambastiani et al., 2024.</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.