Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4,694
datasets available to search
ShareScore release 0.7.1
Dataset results
4,694 results for “data analysis”
SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data
<p>This dataset includes three high-density sleep EEG recordings of healthy participants, downsampled to 250 Hz and stored in FIF format:</p> <ol> <li>Nap recording of a young adult participant</li> <li>Overnight recording of a young adult participant</li> <li>Overnight recording of an older adult participant</li> </ol> <p>Additionally, the dataset includes three text files for each recording:</p> <ul> <li>bad_channels.txt: Indexes of noisy channels</li> <li>annotations.txt: Onset and duration of noisy temporal intervals</li> <li>staging.txt: Sleep staging vector</li> </ul> <p>The corresponding package can be found on <a href="https://github.com/NirLab-TAU/sleepeegpy">GitHub.</a></p> <p>For citation, please use:<br>Falach, R., G. Belonosov, J. F. Schmidig, M. Aderka, V. Zhelezniakov, R. Shani-Hershkovich, E. Bar, and Y. Nir. "SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data." Computers in Biology and Medicine 192 (2025): 110232.<br><a href="https://doi.org/10.1016/j.compbiomed.2025.110232" rel="nofollow">https://doi.org/10.1016/j.compbiomed.2025.110232</a></p>
Dataset and full R script used in the data analysis of the paper "Overlooked and undervalued: Peripheral pollinators in an urban network"
<p>Dataset and full R script used in the data analysis of the paper "<strong>Overlooked and undervalued: Peripheral pollinators in an urban network</strong>".</p> <p>Summary:</p> <p>Since insect pollinators are essential for their ecological and agricultural roles, their conservation should be a priority, particularly in the remnant green spaces within highly urbanised cities. To gain insight into the occurrence of interactions between plants and often overlooked pollinators, and into their requirements for persistence over time in urban green spaces, we studied flower visitor diversity associated with a remnant of native vegetation in Cordoba (Argentina), one of the largest cities in South America. We recorded 198 insect species from six orders (Hymenoptera, Diptera, Lepidoptera, Coleoptera, Thysanoptera, and Hemiptera) interacting with the flowers of 94 plant species. The plant-pollinator interaction network was significantly modular, with 178 pollinators playing a peripheral role (i.e., it has a few links inside its own module and rarely any to other modules). We focused on the life history traits of these peripheral pollinators, which are often neglected in ecological studies. We classified their requirements to complete the life cycle and to persist over time into three broad categories: floral rewards, places to reproduce, and additional resources for food and nests. The life cycle requirements of peripheral pollinators differ significantly across insect orders. Hymenoptera and Lepidoptera have distinct life history requirements while Diptera and Coleoptera overlap in resource use. The three life history categories highlight how pollinators displayed different foraging behaviour, reproductive strategies of immature and adult stages, and the requirement of additional food resources used by larvae and adults beyond flower rewards to complete their life cycles. Knowledge about the requirements of neglected pollinators is a benchmark that can help to identify where efforts need to be made to conserve and maintain their biodiversity, even in small urban green spaces.</p>
Image analysis data for Downie et al. (2025)
<p>Image analysis outputs that can be processed using code available at <a href="https://github.com/quantixed/p065p038" target="_blank" rel="noopener">https://github.com/quantixed/p065p038</a></p> <p>A preprint of the manuscript is available at <a href="https://doi.org/10.1101/2024.05.31.596797" target="_blank" rel="noopener">https://doi.org/10.1101/2024.05.31.596797</a>.</p> <ul> <li>LBR cluster analysis (plasma membrane, 3D) - <code>SJR233</code> analysis of LD317</li> <li>LBR cluster analysis (plasma membrane, 2D movie) - <code>SJR217</code> analysis of LD295</li> <li>Mitochondria-ER contact analysis from SBF-SEM - <code>SJR242</code></li> <li>Line profile comparison - <code>SJR266</code> analysis of LD237, LD239, LD352, LD365, LD360</li> <li>Thapsigargin experiment - <code>SJR265</code> analysis of LD446</li> <li>Sec61 and LBR cluster analysis (3D) under different promoters - <code>Sec61Expr</code></li> <li>LBR cluster sizes after 2h or 4h (3D) - <code>LBR_LongTerm</code></li> </ul> <p>All other data for plot recreation can be found in the GitHub repo.</p>
Training data for MaxQuant and Msstats label-free analysis in Galaxy
<p>The files serve as input and intermediate results for a MaxQuant and Msstats training on skin cancer tissues (<a href="https://doi.org/10.1016/j.matbio.2017.11.004">https://doi.org/10.1016/j.matbio.2017.11.004</a>) in the Galaxy training network (https://training.galaxyproject.org).</p> <p>Input files: human FASTA database for Maxquant. Annotation file and comparison matrix file for Msstats.</p> <p>Intermediate result files: MaxQuant protein groups, evidence and PTXQC.</p>
Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.
<p><strong>Data availability</strong>. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. </p><ul><li>SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).</li></ul>
Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning
<div> <div> <p>Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.</p> <p>In the paper "Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on <strong>two experimental datasets</strong> based on silicon photomultipliers (SiPM) as main transducers:</p> <ol> <li>In the toy experiment, we collect data from a pair of SiPM sensors from a common laser source. The neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. </li> <li>In the electromagnetic calorimeter experiment, we collect data from an eight-channel calorimeter module. Several neural network models (Fully-Connected, Convolutional Neural Network and Long Short Term Memory) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. </li> </ol> <p>In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely. <strong>The dataset in this repository serves as a basis for similar researches on timing performance of SiPM-based nuclear detectors, and on application of neural networks to typical signals of nuclear radiation detectors.</strong></p> </div> </div>
A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning: Supplemental data
<p>This dataset includes the crater map and equatorial crater depths and diameters presented in the paper: A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning.</p>
Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"
<p>This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories. </p>
Data for sensitivity analysis
<p>To explore how the height of hydrofracture zone changes with parameter, use all the eight independent parameters, namely (1) Biot-Willis poro-elastic constant, (2), Poisson ratio of the strata, (3) bulk density of the rock, (4) tensile strength of the rock, (5) maximum overpressure in the deep-seated reservoir, (6) rock permeability, (7) permeability growth rate in the fracture, (8) bottom radius of the hydrofracture zone.</p><p>The sensitivity analysis simulated 90 scenarios using values of the above-mentioned parameters. Each independent variable in this dataset is populated with randomly generated numbers.</p>
FIG. 1 in Analysis of Genomic Sequence Data Reveals the Origin and Evolutionary Separation of Hawaiian Hoary Bat Populations
FIG. 1.—Map of the Hawaiian Islands with collection sitesfor Hawaiian hoary bat tissues used inthis study. Sites with n> 1 are denoted with an asterisk.
FIG. 2 in Analysis of Genomic Sequence Data Reveals the Origin and Evolutionary Separation of Hawaiian Hoary Bat Populations
FIG. 2.—PCA result plot showing clustering of individual bats from four Hawaiian Islands using 21,808,031 SNPs. Sample information included in supplementary table S4, Supplementary Material online.
FIG. 4 in Analysis of Genomic Sequence Data Reveals the Origin and Evolutionary Separation of Hawaiian Hoary Bat Populations
FIG. 4.—SNAPP-based phylogenetic tree inference. (A) The maximum clade credibility or consensus tree, showing approximate divergence of hoary bats across the Hawaiian archipelago. The axis on the bottom of the figure corresponds to million years before present (Ma), using the emergence of Hawai'i (~0.43 Ma) as a calibration point (95% confidence intervals were given in square brackets). (B) The drawing of all sampled trees showing all ingroup nodes were supported by maximum posterior probabilities (1.00).
Beneath the Antarctic sea-ice: Fine-scale analysis of Weddell seal (Leptonychotes weddellii) behaviour and predator-prey interactions, using micro-sonar data in Terre Adélie
<p>In this study, we tried to assess:<br>i) whether and how female Weddell seals feed (frequency, depth, duration) during lactation,<br>ii) what is their utilization of a limited foraging area (benthic or pelagic dives) as they are spatially constrained by the presence of their pup, and<br>iii) how can we characterize their foraging dives and the approach/catching phases using new tools providing a more detailed description of their behaviour.<br>Sonar tags were deployed on three breeding female Weddell seals in Terre Adélie (East Antarctica) in November 2019, to study animals' movements and dives at high resolution (3D acceleration, magnetometry, time and depth and GPS location), as well as information on prey and predator-prey interactions using acoustic data.</p>
Handling of Personal Data by Smart Home Equipment: an Exploratory Analysis in the Context of LGPD
<p>This dataset provides data about an exploratory research that analyzed the Privacy and Security Policies and the Instruction Manuals of 59 home automation equipment for Smart Home in order to verify which personal data was handled and how these documents were providing information about processes performed in personal data. The analysis was conducted with a quantitative approach followed by a qualitative analysis, using content analysis.</p>
agroBRIDGES Multi-Actor Framework Engagement Data Analysis
<p>The multi-actor framework employed in agroBRIDGES foresees the creation of a regional Multi-Actor Platform (MAP) in the 12 focal European regions and countries of the project (Beacon Regions) as well as a Stakeholder Reference Group (SRG) at European level with a view to engaging stakeholders in the project’s activities.</p> <p>In this way, this dataset contains the engagement analysis of the MAP and the SRG members that have been selected as active participants in the activities carried out in the frame of the <a href="https://www.agrobridges.eu/">agroBRIDGES</a> H2020 project, focused on building bridges between producers and consumers, thanks to the <a href="https://www.agrobridges.eu/toolbox/">agroBRIDGES Toolbox</a> developed and other supporting activities organized throughout the project. To achieve project objectives, results from MAPs and SRG management have been tracked during the first half of the project, and will continue to be monitored until the end of the project, in collaboration with all Beacon Region Leaders and the SRG Manager.</p> <p>In this first round of monitoring, with respect to the SRG, 20 stakeholders have been engaged, of which approximately 50% have had a high engagement, 30% a medium engagement, and 20% a low-to-none engagement. As part of the planned engagement analysis and based on the data provided by each of the Beacon Region Leaders, the initial and long-term number of MAP members, as well as the evolution in their level of engagement, have been monitored., getting a total of 179 MAP members currently engaged in the project. In a first evaluation, high engagement was generally achieved in approximately 80% of cases, with less engagement from some educational bodies and consumers.</p> <p>In the second round of monitoring, the MAPs were expanded to a total of 199 stakeholders in 12 countries, while the SRG synthesis remained unchanged. The activities of the project supported higher engagement of the MAPs, while collaboration with the SRG continued in an ad hoc basis, as pan-European activities were less frequently organised than regional ones.</p> <ul> <li>The countries involved are: <ul> <li>Denmark</li> <li>Finland</li> <li>France</li> <li>Greece</li> <li>Ireland</li> <li>Italy</li> <li>Latvia</li> <li>Lithuania</li> <li>Netherlands</li> <li>Poland</li> <li>Spain</li> <li>Turkey</li> </ul> </li> </ul> <p>The dataset contains:</p> <ul> <li><strong>agroBRIDGES_StakeholderEngagement_2022.12.27_v1</strong>: Spreadsheet in .xlsx format, containing a table with all MAP members, classified by type of organization, country, and including the level of engagement of each of them.</li> <li><strong>agroBRIDGES_StakeholderEngagement_2022.12.27_v1</strong>: Spreadsheet in .xlsx format, containing a table with all MAP members, classified by type of organization, country, and including the level of engagement of each of them.</li> <li><strong>agroBRIDGES_StakeholderEngagement_2023.12.19_v2</strong>: Spreadsheet in .xlsx format, containing a table with all MAP members, classified by type of organization, country, and including the level of engagement of each of them. The data is updated up to December 2023.</li> <li><strong>agroBRIDGES_MAP-Engagement_2023.12.19_v2:</strong> Spreadsheet in .xlsx format, where all MAP Engagement data are collected, classified by country. This file also includes a tab with indicators of how the level of engagement has been assigned. The data is updated up to December 2023.</li> <li><strong>agroBRIDGES_SRG-Engagement_2022.12.27_v1</strong>: Spreadsheet in .xlsx format, where all data concerning SRG Engagement is collected. This file also includes a tab with indicators of how the level of engagement has been assigned.</li> </ul>
Data behind The ALCHEMI atlas: principal component analysis reveals starburst evolution in NGC 253
<p>This depository is for additional files of the PCA paper using the ALCHEMI survey.</p> <p>std_datalist.csv: This is a csv file that includes standardized intensities for all the transitions/continua.</p> <p>pca_alchemi_corrmatrix.py: This is a python file to plot a correlation matrix of standardized intensities. It displays a transition pair when you hover the cursor on the matrix element. It uses std_datalist.csv.</p>
Data for sensitivity analysis of Hübler, M., M. Wiese, M. Braun and J. Damster (2023): The distributional effects of CO2 pricing at home and at the border on German income groups
<p>This dataset contains the output files used in the sensitivity analysis of the computable general equilibrium (CGE) model developed in Hübler et al. (2023). Each folder is labeled with the respective set of sector-level elasticity of substitution parameters considered in the analysis: elasticities between domestically produced versus imported goods (esubd), Armington elasticities (esubm) and elasticities between production factors (esubva).</p> <p>For each set of parameters, we generate 1000 random draws from a +-10 % interval around each of the sector-specific elasticities, resulting in 1000 sets of sectoral parameter values. Each .xlsx output file located in a dedicated subfolder corresponds to a model run with a specific set of parameter values. In addition, we conduct sensitivity analyses of two individual parameters, namely the CO2 target (CO2factor) considered in our policy scenarios and the elasticity of substitution in consumption (esub_cons).</p> <p>The sensitivity analysis is carried out using the <a href="https://snakemake.readthedocs.io/en/stable/">Snakeflow</a> workflow management system, and R code for generating parameter spaces and processing the output files is available on <a href="https://github.com/mariuslbraun/climate-trade-distribution-sensitivity">GitHub</a>.</p>
Data from: Analysis of a cusped helicon plasma thruster discharge
<p><strong>Data from: </strong>Analysis of a cusped helicon plasma thruster discharge</p> <p>- Authors: Pedro Jimenez, Jiewei Zhou, Jaume Navarro, Pablo Fajardo, Mario Merino, Eduardo Ahedo</p> <p>- Contact email: pejimene@ing.uc3m.es</p> <p>- Date: 2024-02-08</p> <p>- Keywords: electric propulsion, electrodeless plasma thruster, helicon plasma thruster, plasma simulation</p> <p>- Version: 1.1</p> <p>- License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/">Open Data Commons Attribution License</a></p> <p> </p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the journal article:</p> <p><a href="https://iopscience.iop.org/article/10.1088/1361-6595/ad01da/meta">Analysis of a cusped helicon plasma thruster discharge (Plasma Sources Science and Technology)</a></p> <p> </p> <p><strong>Dataset description</strong></p> <p>The data in this repository has been extracted from the experiments and simulations as described in the manuscript.</p> <p>For further information on the setup for the simulation please refer to the article.</p> <p> </p> <p><strong>Data files</strong></p> <p>The data files are in comma separated .csv format. Many programming languages provide functionalities to load such fields.</p> <p>The files are organised following the order of the figures in the article. Therefore each file contains a different sized array. </p> <p><em>1D plots: </em>Each line is given as 3 colums, the first one corresponds to the abscissa coordinate (X data), the second one to the ordinate (Y data) and the last one to the error (+/- value in the same units as Y data).</p> <p><em>2D contours: </em>All data is provided in a structured mesh. 3 matrices are concatenated in the column dimension. The first two matrices correspond to the Z,R coordinates in a <a href="https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html">meshgrid</a> format, as customary in Matlab and NumPy. The last matrix corresponds to the field values. A description of the dimensions (extension in columns) of the matrices is provided as a header in the first row. Example: For a field represented in a 100(z) x 50 (r) mesh. The csv will have 50+1(header) rows and 3x100 = 300 columns.</p> <p> </p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The prefered means of citation is to reference the publication asociated to the jounal article with DOI: <a href="https://doi.org/10.1088/1361-6595/ad01da">10.1088/1361-6595/ad01da</a></p> <p>The BibTex is also provided for the sake of convinience:</p> <p>@article{jimenez2023analysis,</p> <p> title={Analysis of a cusped helicon plasma thruster discharge},</p> <p> author={Jim{\'e}nez, Pedro and Zhou, Jiewei and Navarro, Jaume and Fajardo, Pablo and Merino, Mario and Ahedo, Eduardo},</p> <p> journal={Plasma Sources Science and Technology},</p> <p> volume={32},</p> <p> number={10},</p> <p> pages={105013},</p> <p> year={2023},</p> <p> publisher={IOP Publishing}</p> <p>}</p> <p>Optionally the dataset can be cited by referencing the DOI: <a href="https://doi.org/10.5281/zenodo.8154866">10.5281/zenodo.8154866</a></p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This dataset was created by the <a href="https://erc-zarathustra.uc3m.es/">ERC-ZARATHUSTRA </a> project.</p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466).</p> <p>Initial support for the activities leading to work came from the HIPATIA project, funded by the European Union’s Horizon 2020 Research and Innovation Program (grant agreement No 870542).</p>
Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes input data to run the OnStove Nepal model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All result files and figures can be downloaded from the permanent repository <a href="https://doi.org/10.5281/zenodo.10643983">https://doi.org/10.5281/zenodo.10643983</a>.</p> <p>The "<strong>GIS_input_data/</strong>" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "<strong>1. Data</strong>"<strong> </strong>folder in your project. </p> <p>The "<strong>Scenario_inputs/</strong>" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the <strong>supplementary material</strong> of the related publication in the link <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>. To run the model extract the scenario data inside your "<strong>2. Scenario inputs</strong>"<strong> </strong>folder in your project. </p>
Data-Driven Identification and Analysis of Waiting Times in Business Processes: A Systematic Literature Review
<p>Supplementary Material for Systematic Literature Review titled "Data-Driven Identification and Analysis of Waiting Times in<br> Business Processes: A Systematic Literature Review"</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.