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1,721 results for “network data”
Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES)
<p>Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES). Detailed description of the files in README.txt.</p>
Supplementary table of PRIDE datasets analyzed for "FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data"
<p>Our proteomics dataset comes from The PRoteomics IDEntifications (PRIDE) database, the world’s largest data repository of mass spectrometry-based proteomics data. Specifically, we used 633 human proteomics project experiments with a total of 32,546 runs and reanalyzed them using ionbot with an FDR threshold of 0.01 [16], resulting in a total of 154,885,151 peptide spectrum matches for 18,846 proteins. Here is the full list of projects, runs, and general statistics.</p>
Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England
<p>Globally important chalk streams in England are in poor ecological health, in part due to inadequate water quality. Addressing this issue requires an understanding of the governance systems that surround water quality. The complexity and uncertainty inherent in hydrological systems has led to the emergence of integrated and adaptive forms of governance. In these multi-actor governance systems, the structure of the relationships between actors (the social network) has been shown to affect governance processes and outcomes.</p> <p>Using participatory social network analysis, we mapped and analysed the social networks for the River Test and River Itchen in Hampshire, UK, to identify actors and their roles, determine the network characteristics, and identify interventions to improve governance.</p> <p>Although the results suggest a well connected network of actors from the state, private sector and civil society, we find that decision making is not decentralised. Bureaucratic governance by central state actors dominates. However, trust in these central state actors and private actors in the networks is low, which undermines collaboration and co-ordination in the network.</p> <p>Devolving authority to local actors, building trust in the networks, and improving connections to important actors could help to improve governance outcomes for water quality.</p>
Training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series
<p>This dataset contains training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series. The data have been derived from Sentinel-5P total column carbon monoxide observations, using the offline processing stream.</p> <p><strong>Preprocessing</strong></p> <p>The following operations have been applied on the original S5P imagery:</p> <ol> <li>Images have been resampled to 0.1 by 0.1 degree spatial resolution</li> <li>Pixels with quality assessment value less than or equal to 0.5 have been set to NA</li> <li>Images have been aggregated by day of observation</li> <li>Images have been cropped to -60 to 60 degrees latitude</li> <li>Images have been devided into spatiotemporal blocks of size 128 x 128 pixels and 16 days</li> </ol> <p>Imagery has been recorded between 2021-01-01 and 2021-11-25. Notice that both the training and the validation blocks have been randomly sampled from all available blocks.</p> <p><br> <strong>Data Format and Naming Conventions</strong></p> <p>Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions:</p> <ul> <li>Files starting with <em>X</em> represent input measurements for training, where artificial gaps have been added.</li> <li>Files starting with <em>Y</em> represent true measurements without artificially added gaps (but still containing gaps in many cases).</li> <li>Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with <em>MASK</em></li> <li>Files starting with <em>VALMASK</em> contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only.</li> </ul> <p>Numbers in filenames encode spatial and temporal block indexes.</p> <p>In addition, the dataset contains prediction of the validation blocks from different models in the `predictions` directory. The subfolders contain output from different models:</p> <ul> <li>mean refers to simple block-wise mean predictions.</li> <li>timeseries refers to simple linear time series interpolation.</li> <li>gapfill refers to the method proposed in [1].</li> <li>stmra refers to the method proposed in [2].</li> <li>STpconv refers to predictions passed on an artificial neural netowork with three-dimensional partial convolutions.</li> </ul> <p><strong>References</strong></p> <p>[1] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., & Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2841-2853.</p> <p>[2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.</p>
Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs
<p class="MsoNormal"><strong>1. </strong>Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy.</p> <p class="MsoNormal"><strong>2. </strong>Here, we demonstrate the ability of deep convolutional neural networks (CNNs) to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of annual forest change (2001-2020) and the ALOS PALSAR digital surface model, to forecast deforestation (2021). We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km<sup>2</sup> tropical forest regions in the Southern Peruvian Amazon.</p> <p class="MsoNormal"><strong>3.</strong><strong> </strong><span>The networks could predict the location of future forest loss to a high degree of accuracy (F</span><sub>1 </sub><span>= 0.58-0.71). Our best performing model (3D CNN) had the highest pixel-wise accuracy (F</span><sub>1 </sub><span>= 0.71) when validated on 2020 forest loss (2014-2019 training). Visual interpretation of the mapped forecasts indicated that the network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides).</span></p> <p class="MsoNormal"><strong>4.</strong><strong> </strong>CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in harnessing the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.</p>
Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks
<p>This dataset contains the following:</p> <ul> <li><strong>data</strong>: <ul> <li><em>preprocessed</em>: hourly European weather radar data (here: <em>radar</em>) and aggregated simulation outputs (here: <em>abm</em>), combined with ERA5 reanalysis data and Voronoi tessellation details</li> <li><em>shapes: </em>geographical shapes used for plotting</li> </ul> </li> <li><strong>results</strong><em>:</em> trained models and corresponding results for both simulated data (here: <em>abm</em>) and weather radar data (here: <em>radar</em>).</li> <li><strong>figures</strong><em>: </em>final figures presented in our paper "Learning to predict spatio-temporal movement dynamics from weather radar networks" to summarize the results</li> </ul> <p>The corresponding code used to train and evaluate models is archived here: <a href="https://doi.org/10.5281/zenodo.6921595">10.5281/zenodo.6921595</a>.</p>
The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data
<p>Dataset used in the research presented in the article:</p> <p>Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" <em>Materials</em> 15, no. 10: 3727. https://doi.org/10.3390/ma15103727</p> <p>The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.</p>
Data related to "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al.
<p>This is Data for "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al., 2022</p> <p>This repository contains:</p> <p>Supplementary Dataset Tables 1-4 (Supplementary_Dataset_Tables_1-4.xlsx). They contain DE genes and GO terms from snRNA-seq and visium analysis.</p> <p>Single nuclei and Visium spatial transcriptomics sequencing data (snRNA-seq_and_spatial_transcriptomics.zip) containing raw count matrices of snRNA-seq samples; Conos object with aligned snRNA-seq samples; snRNA-seq single nuclei cell subtype annotations; raw count matrices of Visium spatial transcriptomics samples; Visium spatial transcriptomics manual histological cortical layer annotations; and 10x Genomics spaceranger count pipeline output for Visium spatial transcriptomics data.</p> <p>Histological images of H&E stained Visium spatial transcriptomics samples mounted on visium slide capture area (visium_sample_images.zip)</p>
Preserved Specimen Records with Still Images Registered Across Biodiversity Data Networks in Period 2019-2022 hash://sha256/da7450941e7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86
<p>Natural History Collections keep physical specimen of organisms. Some of these collections provide digital catalogs. These digital catalogs may be registered with, and tracked by, digital registries like Global Biodiversity Information Facility (GBIF, https://gbif.org), Integrated Digitized Biocollections (iDigBio, https://idigbio.org), and the Biological Collection Access Service (BioCASe, https://biocase.org).</p> <p>This publication reports on the availability of registered preserved specimen records with at least one associated image (i.e. StillImage) over period 2019/2022. </p> <p>For a description of the methods, see:</p> <p>Jorrit Poelen. (2022). jhpoelen/specimen-image-index: 0.0.1 (0.0.1). Zenodo. https://doi.org/10.5281/zenodo.7032668</p> <p>The associated provenance log identifiers include:</p> <p>hash://sha256/251fa349c051bbda370decb7e5e58960d702add59f6e131ebf7c960d0f93b417<br> hash://sha256/261177a96185166f1c301beacf7350abff03d1b5710be6bfd8c4aff9caffef12<br> hash://sha256/8ff46ae6a30bf9647df0294b92434a83784626b3f8c37163db3edefb049daead<br> hash://sha256/9a41d2583f0b8169ffdd44fb2d3a5e057eba4a10e5d9193d0c6e9dcf07c3119e<br> hash://sha256/b9864a749112cad2fe19e62bf5d8bad580a7036d363d16d81d5c16be325fa0fd<br> hash://sha256/da7450941e7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86</p> <p>where the hash://sha256/da7450941e7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86 is cryptographically linked to the others. </p> <p>For completeness, the associated provenance logs are included using their hex hashes as file attached to this publication:</p> <p>251fa349c051bbda370decb7e5e58960d702add59f6e131ebf7c960d0f93b417<br> 261177a96185166f1c301beacf7350abff03d1b5710be6bfd8c4aff9caffef12<br> 8ff46ae6a30bf9647df0294b92434a83784626b3f8c37163db3edefb049daead<br> 9a41d2583f0b8169ffdd44fb2d3a5e057eba4a10e5d9193d0c6e9dcf07c3119e<br> b9864a749112cad2fe19e62bf5d8bad580a7036d363d16d81d5c16be325fa0fd<br> da7450941e7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86</p> <p>Other files include, suffixed by the first four hex characters of their associated provenance log id:</p> <p># this file <br> README</p> <p># identifiers of tracked content captured in associated provenance logs.<br> content_251f.tsv<br> content_2611.tsv<br> content_8ff4.tsv<br> content_9a41.tsv<br> content_b986.tsv<br> content_da74.tsv</p> <p># identifiers of tracked content said to contain multimedia<br> content-with-multimedia_251f.tsv<br> content-with-multimedia_2611.tsv<br> content-with-multimedia_8ff4.tsv<br> content-with-multimedia_9a41.tsv<br> content-with-multimedia_b986.tsv<br> content-with-multimedia_da74.tsv</p> <p># identifiers of tracked content said to contain multimedia and still images<br> content-with-still-images_251f.tsv<br> content-with-still-images_2611.tsv<br> content-with-still-images_8ff4.tsv<br> content-with-still-images_9a41.tsv<br> content-with-still-images_b986.tsv<br> content-with-still-images_da74.tsv</p> <p># identifiers of tracked content said to contain multimedia, still images, and specimen<br> content-with-still-images-and-specimen_251f.tsv<br> content-with-still-images-and-specimen_2611.tsv<br> content-with-still-images-and-specimen_8ff4.tsv<br> content-with-still-images-and-specimen_9a41.tsv<br> content-with-still-images-and-specimen_b986.tsv<br> content-with-still-images-and-specimen_da74.tsv</p> <p># reported scientificName associated with preserved specimen record with image(s)<br> content-name-image_251f.tsv.gz<br> content-name-image_2611.tsv.gz<br> content-name-image_8ff4.tsv.gz<br> content-name-image_9a41.tsv.gz<br> content-name-image_b986.tsv.gz<br> content-name-image_da74.tsv.gz</p> <p># names associated with preserved specimen with images grouped in categories Plantae, Insecta and Mammalia</p> <p>plantae_insecta_or_mammalia_image_251f.tsv.gz<br> plantae_insecta_or_mammalia_image_2611.tsv.gz<br> plantae_insecta_or_mammalia_image_8ff4.tsv.gz<br> plantae_insecta_or_mammalia_image_9a41.tsv.gz<br> plantae_insecta_or_mammalia_image_b986.tsv.gz<br> plantae_insecta_or_mammalia_image_da74.tsv.gz</p> <p># associated discussions</p> <p>github_bio-guoda_preston_issues_168-2022-08-29.txt<br> github_jhpoelen_specimen-image-index_issues_1-2022-08-29.txt</p> <p># associated processing scripts</p> <p>make-all.sh<br> make.sh<br> has-multimedia.sh<br> extract-still-image-records.sh<br> has-still-image.sh<br> extract-preserved-specimen-records.sh<br> has-specimen.sh<br> create-named-specimen-with-image-table.sh</p>
Evaluation of Phosphoproteomics data-driven signalling network inference
<p><strong>Data</strong></p> <p>Each processed data set and its adjacency matrices representing gold standard networks are stored in one specific folder. The folders have the data set name.</p> <p>The process of obtaining the pairwise results and the evaluation metrics are divided into two main parts:</p> <p><strong>1. Pairwise results</strong></p> <ul> <li>First, all the data set folders have to be placed in one main folder.</li> <li>Then run the script: <em>RunAll_Matrices.R</em></li> <li>This script: <ul> <li>Goes through each data set folder: <ul> <li>reads the data set</li> <li>calculates for each method the resulting matrix</li> <li>Save in the data folder one file with all resulting matrices: file type RData</li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>2. Evaluation </strong></p> <ul> <li>After this first step, each data set folder has a file that contains all the resulting adjacency matrices.</li> <li>The second step is to run the script RunAll_Golds.R</li> <li>This script <ul> <li>Goes through each folder: <ul> <li>reads the data set</li> <li>reads the result adjacency matrices from RData files</li> <li>reads the gold networks</li> <li>Calculates all evaluation metrics for each result and each gold network type.</li> </ul> </li> </ul> </li> </ul>
Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network''
<p>This dataset contains the modified MAMEBUS source code, configuration files for the MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.</p>
Analyses, data and figures related to: "Connecting ships: Using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and visualise wood use patterns"
<p>Analyses, data and figures related to: "Connecting ships: using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and to visualise patterns of wood use" by Ronald M. Visser (Saxion University of Applied Sciences, Deventer, the Netherlands) and Yardeni Vorst (Vorst wood research, Zaandam, the Netherlands) submitted to the International Journal of Wood Culture</p>
Structure, function, and control of the musculoskeletal network - Data
<p>Supplementary data for: Structure, function, and control of the musculoskeletal network</p> <p>Table S8: The assigned homunculus categories and data driven community assignments of muscles.</p> <p>Table S9: The hypergraph of muscles and bones from the Hosford muscle tables used in the main text.</p> <p>Table S10: The hypergraph of muscles and bones from Grant's atlas used in the supplementary text.</p> <p> </p> <p>Data for Figures:</p> <p>2e</p> <p>3a, 3b</p> <p>4b, 4c, 4d</p> <p>S4a-h</p> <p>S5</p> <p>S6a, S6b</p> <p>S7a, S7b</p> <p>S8</p> <p>S9</p> <p>S10</p> <p>S11</p> <p>S12</p> <p> </p>
Data Set for the Journal Article "SCINE - Software for Chemical Interaction Networks"
<p>This data archive contains all data and software described and used in the following publication:</p> <p>Thomas Weymuth, Jan P. Unsleber, Paul L. Türtscher, Miguel Steiner, Jan-Grimo Sobez, Charlotte H.<br>Müller, Maximilian Mörchen, Veronika Klasovita, Stephanie A. Grimmel, Marco Eckhoff, Katja-Sophia<br>Csizi, Francesco Bosia, Moritz Bensberg, Markus Reiher, "SCINE --- Software for Chemical Interaction<br>Networks", in preparation.</p> <p>The directory structure is as follows:</p> <ul> <li>software: contains all software used for the example exploration <ul> <li>requirements.txt: lists all Python packages needed to create the virtual environment with which the exploration has been carried out; the virtual environment was created with Python 3.6.8.</li> <li>start.py: script to initialize the database with the reactants</li> <li>step_1.py: script to create the first set of reaction trials; after having set up the trials, execute the script "start.py" with the option "continue"</li> <li>step_2.py: script to create the second set of reaction trials; after having set up the trials, execute the script "start.py" with the option "continue"</li> <li>puffin_1.3.0.sif: Singularity image containing a full Puffin instance (version 1.3.0) to execute all calculations of the exploration</li> <li>submit_container.sh: script to submit the Puffin image to the queueing system</li> </ul> </li> <li>data: contains a complete dump of the database created during the example exploration; additionally, this directory contains a script called "import.sh" which can be used to reimport the data into a MongoDB instance</li> </ul>
Assessing Pairwise Ecological Association Inference using a novel Ecological Network Inference Simulation-Validation Framework - Data Repository
<p>Accompanying data for manuscript entitled "<span>A novel Network Inference Simulation-Validation Framework for Assessment of Ecological Network Inference Performance</span>" whose submission is imminent.</p>
A Global Gridded Municipal Water Withdrawal Estimation Method Using Aggregated Data and Artificial Neural Network
<p>Global gridded municipal water withdrawal estimations for the following WST paper.</p> <p>Jiabao Yan, Shaofeng Jia; A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network. <em>Water Science Technology</em>, 2023; 87 (1): 251–274. <a href="https://doi.org/10.2166/wst.2022.399" target="_blank" rel="noopener">https://doi.org/10.2166/wst.2022.399</a></p> <p>The representative year of the data is 2015, and the unit of the data is in millimeters (mm).</p>
Data from: High foraging fidelity and plant-pollinator network dominance of non-native honeybees (Apis mellifera) in the Ecuadorian Andes
<p>These data reflect the floral visitor survey and mark-recapture efforts used in the 2022 study, "High Foraging Fidelity and Plant‑Pollinator Network Dominance of Non‑native Honeybees (<em>Apis mellifera</em>) in the Ecuadorian Andes"</p>
Polyconvex inelastic Constitutive Artificial Neural Networks: Source code and data
<p>This dataset contains the source code of the polyconvex extension of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:</p> <p>Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl.<em> Polyconvex inelastic Constitutive Artificial Neural Networks.</em></p> <p> </p> <p><strong>Results:</strong> Discovering a model for the polymer VHB 4910 subjected to cyclic loading</p> <p>Here, we investigate the ability of the polyconvex iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.</p> <p>The experimental data are taken from the literature:</p> <p>Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. <em>Computational Materials Science</em>, <em>59</em>, 65-74.</p> <p><a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a></p>
Numerical methods for neurobiological networks - Accompanying Data
<p>Data supporting the following tutorial</p> <p>https://github.com/danieleavitabile/numerical-analysis-mathematical-neuroscience</p>
Data supporting the manuscript "Human adolescent brain network development is different for paralimbic versus neocortical zones"
<p>This data supports the manuscript "Human adolescent brain network development is different for paralimbic versus neocortical zones" by Dorfschmidt et al. All code used in the analysis can be found here: https://github.com/LenaDorfschmidt/morphometric-similarity-adolescence/</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.