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239 results for “Data-driven”
Data-driven segmentation of cortical calcium dynamics
Open the record for dataset details and reuse information.
Data-Driven Control of Converters in DC Microgrids for Bus Voltage Regulation
<p>DDC_IECON2018</p> <p>Lisette Cupelli; Marco Cupelli; Antonello Monti</p> <p>Within the context of InterFlex project under GA 731289, a Data-Driven Control (DDC) framework for converter-interfaced Distributed Generators (DGs) in a Microgrid has been developed and compared with a non-linear Control technique i.e. Linearizing State Feedback (LSF) for bus voltage regulation. For further information, please refer to the respective publication: https://ieeexplore.ieee.org/document/8592812</p>
Data-driven Household Load Flexibility Modelling: Shiftable Atomic Load
<p>This is flexibility model for shiftable atomic loads (i.e. washing machine, dryers and dish washers). The model is based on real 1-minute level measurements from real households over period of time. The details of the model are described in [R]. The model is implemented in Excel for cloth washing machines weekday consumption and flexibility scenario and all the required data is included for modelling the other equipment.</p> <p>[R] Degefa, M.Z., Sæle, H., Petersen, I. and Ahcin, P., 2018, October. Data-driven Household Load Flexibility Modelling: Shiftable Atomic Load. In <em>2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)</em> (pp. 1-6). IEEE.</p> <p><a href="https://ieeexplore.ieee.org/document/8571836">https://ieeexplore.ieee.org/document/8571836</a></p>
Dataset for Gelatinous zooplankton-mediated carbon flows in the global oceans: A data-driven modeling study
<p>Gridded dataset of gelatinous zooplankton (GZ) biomass (mg C m<sup>-3</sup>) and numeric density (individuals m<sup>-3</sup>), time-averaged, in a 1-degree grid. Data are separated by phyla: Cnidaria, Ctenophora, and Chordata (pelagic tunicates). Original data compiled as part of the Jellyfish Database Initiative Project (JeDI; Condon et al. 2015, doi:10.1575/1912/7191) and converted to carbon biomass units for Lucas et al. 2014.</p> <p>Cnidarian additions to this dataset include records from the northern California Current (Brodeur et al., 2014) and Gulf of Mexico (Robinson et al., 2015). Chordata additions include salps from the Bermuda Atlantic Time Series (BATS; Stone & Steinberg, 2014), Western Antarctic Peninsula (WAP; Steinberg et al., 2015), and Southern Ocean, from KRILLBASE (Atkinson et al., 2017). Note that we excluded the KRILLBASE records from the WAP region that to prevent double-counting. See Methods in Luo et al. (2020) for details on biometric conversions to carbon biomass.</p> <p>Data were averaged by time (season, then year), and then within each 1-degree grid cell.</p> <p> </p> <p> </p> <p>Code for the model using this dataset is available at: <a href="https://github.com/jessluo/gz_biogeochem_pub">https://github.com/jessluo/gz_biogeochem_pub</a></p> <p> </p> <p><strong>Luo, Jessica Y.</strong>, Condon, R. H., Stock, C. A., Duarte, C. M., Lucas, C. H., Pitt, K. A., & Cowen, R. K. (2020). Gelatinous zooplankton‐mediated carbon flows in the global oceans: A data‐driven modeling study. <em>Global Biogeochemical Cycles</em>, 34, e2020GB006704. <a href="https://doi.org/10.1029/2020GB006704">https://doi.org/10.1029/2020GB006704</a></p>
Dataset accompanying paper submission for "Toward data-driven generation and evaluation of model structure for integrated representations of human behavior in water resources systems"
<p>This data set accompanies code archived at DOI: <a href="https://doi.org/10.5281/zenodo.3833186">10.5281/zenodo.3833186</a>, which was used in the experiments for the paper submission "Toward data-driven generation and evaluation of model structure for integrated representations of human behavior in water resources systems"</p>
The cognitive and perceptual correlates of ideological attitudes: a data-driven approach
<p>Data and analysis scripts for: </p> <p>Zmigrod L, Eisenberg IW, Bissett PG, Robbins TW, Poldrack RA. 2021 The cognitive and perceptual correlates of ideological attitudes: a data-driven approach. Phil. Trans. R. Soc. B 20200424. https://doi.org/10.1098/rstb.2020.0424</p>
Big Data Analytics: Data-driven optimization of manufacturing companies
<p>This dataset contains the result of a survey conducted to identify the transformation of manufacturing companies by big data analytics. It contains 154 participations from employees of manufacturing companies of which 133 have completed all questions. The dataset is UTF8 encoded and available in German (original) as well as English (translated).</p>
Matlab codes implementing the XDROM+ data-driven ENSO forecast model and some analysis of it
<p>This is the BEST forecast model of large scale features of ENSO as of today, beating (Zhao et al. Nature 2024).</p> <p>This archive is supplementary to a comment article concerning (Zhao et al. Nature 2024) intended as a "Matters Arising" piece to be submitted to Nature (https://www.researchsquare.com/article/rs-5336072/v1). Given that i criticise also the handling editor and 3 reviewers of (Zhao et al. Nature 2024) calling their incompetence out, do not be surprised if you have to look for the paper in some other journal instead. Oh well, integrity is above all else, no?! On that note, may I interest you in a bit of sci-fi? https://www.linkedin.com/pulse/crime-punishment-bit-differently-tamas-bodai-g4cvf/?trackingId=WdQkSNjgSuyxlyWVLRonrw%3D%3D</p>
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
<p>This repository contains materials for the paper, "Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties", published in <a href="https://onlinelibrary.wiley.com/doi/10.1002/minf.202300249">Molecular Informatics.</a></p> <p>`data.zip` contains vocabulary and dataset files for identifying chemical vocabularies and key chemical words associated with protein ligand binding. </p> <p>`results.zip` comprises outputs specific to vocabularies and datasets, as well as various related statistics.</p> <p> </p> <p> </p>
Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be
<p>Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be</p>
Data-Driven Discovery of Carbonyl Organic Electrode Molecules: Machine Learning and Experiment
<p>Prediction model of organic molecular electrode</p>
Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach - Data Supplement
<p>This contribution contains data and analysis scripts for the manuscript "Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach" by Lenin Del Rio Amador, Mathieu Boudreault and David A. Carozza.</p>
Data supporting article: "Data-Driven Discovery of the Origins of UV Absorption in Alpha-3C Protein"
<p>The uploaded folder contains data and a python notebook containing code used to plot the figures presented in the article "Data-Driven Discovery of UV Absorption in Alpha-3C Protein"</p>
Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
<p>Here are the datasets for the publication named "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation" published in Nature Communications. Experimental cycling data for three commercial 18650 type batteries (Dataset_1:NCA battery, Dataset_2:NCM battery, and Dataset_3:NCM+NCA_battery) are given, where each csv file corresponds to one cell cycling data. The cells are named as CY<em>X-Y_Z</em>-#<em>N</em> according to their cycling conditions. <em>X</em> means the temperature, <em>Y_Z</em> represents the charge_discharge current rate, #<em>N</em> is the cell tag. Each csv file has 9 columns, including cycle time ('time/s'), controlled voltage and current ('control/V/mA'), battery voltage ('Ecell/V'), applied current ('<I>/mA'), charge or discharge electricity ('Q discharge/mA.h' and 'Q discharge/mA.h'), controlled voltage or current ('control/V', 'control/mA' and ), and cycle number ('cycle number'). In the impedance data, one representative cell from each cycling condition is chosen for the discussion in the main text. More detailed descriptions can be found in the zip file.</p>
Data from: A new data-driven mathematical model dissociates attractiveness from sexual dimorphism of human faces
<p>Human facial attractiveness is evaluated by using multiple cues. Among others, sexual dimorphism (i.e. masculinity for male faces/femininity for female faces) is an influential factor of perceived attractiveness. Since facial attractiveness is judged by incorporating sexually dimorphic traits as well as other cues, it is theoretically possible to dissociate sexual dimorphism from facial attractiveness. This study tested this by using a data-driven mathematical modelling approach. We first analysed the correlation between perceived masculinity/femininity and attractiveness ratings for 400 computer-generated male and female faces (Experiment 1) and found positive correlations between perceived femininity and attractiveness for both male and female faces. Using these results, we manipulated a set of faces along the attractiveness dimension while controlling for sexual dimorphism by orthogonalisation with data-driven mathematical models (Experiment 2). Our results revealed that perceived attractiveness and sexual dimorphism are dissociable, suggesting that there are as yet unidentified facial cues other than sexual dimorphism that contribute to facial attractiveness. Future<span> studies can investigate the true preference of sexual dimorphism or the genuine effects of attractiveness by using well-controlled facial stimuli like those </span><span>that this study</span><span> generated. </span><span>The</span> findings will be of benefit to the further understanding of what makes a face attractive.</p>
Dataset of "Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes"
<p>Dataset of the article <em>Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes </em>(<a href="https://doi.org/10.1016/j.expthermflusci.2022.110647">https://doi.org/10.1016/j.expthermflusci.2022.110647</a>). A data-driven method is applied to combine non-time-resolved velocity field and fast probe data, and achieve time-resolved velocity and pressure field.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/Data-driven-pressure-estimation-with-EPOD.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
Data from: Building communities of teaching practice and data-driven open education resources with NEON faculty mentoring networks
<p>With the growing availability and accessibility of big data in ecology, we face an urgent need to train the next generation of scientists in data science practices and tools. One of the biggest barriers for implementing a data-driven curriculum in undergraduate classrooms is the lack of training and support for educators to develop their own skills and time to incorporate these principles into existing courses or develop new ones. Alongside the research goals of the National Ecological Observatory Network (NEON), providing education and training are key components for building a community of scientists and users equipped to utilize large-scale ecological and environmental data. To address this need, the NEON Data Education Fellows program formed as a collaborative Faculty Mentoring Network (FMN) between scientists from NEON and university faculty interested in using NEON data and resources in their ecology classrooms. Like other FMNs, this group has two main goals: 1) to provide tools, resources, and support for faculty interested in developing data-driven curriculum, and (2) to make teaching materials that have been implemented and tested in the classroom available as open educational resources for other educators. We hosted this program using an open education and collaboration platform from the Quantitative Undergraduate Biology Education and Synthesis (QUBES) project. Here, we share lessons learned from facilitating five FMN cohorts and emphasize the successes, pitfalls, and opportunities for developing open education resources through community-driven collaborations.</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>
Towards transferable data-driven models to predict urban pluvial flood water depth in Berlin, Germany
<p>The attached files include the predictive features and the water depth from 2D hydrodynamic simulations that were used to train data driven models to predict water depth in Berlin.</p>
Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
<p> </p> <div> <div><a href="https://arxiv.org/search/cs?searchtype=author&query=Tello,+A">Andres Tello*</a><em>, </em><a href="https://arxiv.org/search/cs?searchtype=author&query=Truong,+H">Huy Truong*</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lazovik,+A">Alexander Lazovik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Degeler,+V">Victoria Degeler</a>. Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks. Engineering Proceedings. 2024; 69(1):50. <a href="https://doi.org/10.3390/engproc2024069050">https://doi.org/10.3390/engproc2024069050</a></div> <br> <div>(*) Both authors contributed equally.<br><br></div> <h2>Update</h2> <div>(04/09/2024): Citation is updated.<br>We have added headers for CSVs and auxiliary data (duration time, edge list, ordered names.. ) in the configuration file (JSON format). As such, corresponding INP files can be omitted when working with this version. <br>The EXN network has been included in this version, so the total number of processed networks is 11.<br>For more details, please read ZENODO_README.md.</div> <h2>Contact</h2> <div>For dataset-related questions: <a href="mailto:h.c.truong@rug.nl" target="_blank" rel="noopener">Huy Truong</a></div> <br> <div>For data acquisition: <a href="mailto:a.tello@rug.nl" target="_blank" rel="noopener">Andres Tello</a></div> <br> <div>If you use this dataset, please cite:</div> <blockquote>@article{tello2024largescale,<br> AUTHOR = {Tello, Andrés and Truong, Huy and Lazovik, Alexander and Degeler, Victoria},<br> TITLE = {Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks},<br> JOURNAL = {Engineering Proceedings},<br> VOLUME = {69},<br> YEAR = {2024},<br> NUMBER = {1},<br> ARTICLE-NUMBER = {50},<br> URL = {https://www.mdpi.com/2673-4591/69/1/50},<br> ISSN = {2673-4591},<br> DOI = {10.3390/engproc2024069050}<br>}</blockquote> </div>
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.