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3,172 results for “Publications”
grasshoppermouse/placekmysore2015publicdata: Initial public release
<p>Initial public release of an R data package from a study of pregnancy diet in Mysore India 2015.</p> <p>To install in R:</p> <p># install.packages("devtools")<br> devtools::install_github("grasshoppermouse/placekmysore2015publicdata")</p>
Explaining non-adoption of electronic government services by citizens. A study among non-users of public e-services in Latvia
<p>This data was collected as part of the H2020 Citadel project, http://www.citadel-h2020.eu, project no. 726755. The objective was to analyse citizen motives for not using electronic government services. Using interviews among users of Citizens´ Service Centres in Latvia, the data is used to analyses the motives of citizens not to use electronic government services but to rely on non-electronic equivalents or on in-person assistance. Findings and fieldwork details are available in D2.1 of this project.</p>
Dataset to accompany publication "Distinguishing Inner and Outer-Sphere Hot Electron Transfer in Au/p-GaN Photocathodes"
<p>This dataset accompanies the publication "Distinguishing Inner and Outer-Sphere Hot Electron Transfer in Au/p-GaN Photocathodes" published in Nano Letters. The data can be used to reproduce the original plots in figures 2-4 in the main text and all original plots in figures S1-S13 in the supporting information. All files are in .xlsx and easily readable. <br>The abstract for the associated paper is as follows:<br>Exploring nonequilibrium hot carriers from plasmonic metal nanostructures is a dynamic field in optoelectronics, with applications including photochemical reactions for solar fuel generation. The hot carrier injection mechanism and the reaction rate are highly impacted by the metal/molecule interaction. However, determining the primary type of the reaction and thus the injection mechanism of hot carriers has remained elusive. In this work, we reveal an electron injection mechanism deviating from a purely outer-sphere process for the reduction of ferricyanide redox molecule in a gold/p-type gallium nitride (Au/p-GaN) photocathode system. Combining our experimental approach with ab-initio simulations, we discover that an efficient inner-sphere transfer of low-energy electrons leads to an enhancement in the photocathode device performance in the interband regime. These findings provide important mechanistic insights, showing our methodology as a powerful tool for analyzing and engineering hot-carrier-driven processes in plasmonic photocatalytic systems and optoelectronic devices.</p>
Data accompanying publication: "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction"
<p>Embeddings and raw files to complement the paper "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction". The embeddings should be all the data needed for full reproducibility of the results published. The GitHub repo GeneralDFT (https://github.com/moleculebits/GeneralDFT) contains the python scripts required to make use of the data, along with some basic plotting functionalities.</p>
Data and code for the publication of a surge-specific DEM workflow on ASTER DEMs.
<p>This repository is associated to the publication submitted with the title "Glacier surge monitoring from temporally dense elevation time series: application to an ASTER dataset over the Karakoram region".<br>It contains elevation change maps produced by the workflow, the Python script of the workflow, and vector outlines used in the study.</p> <p>_______________________________<br>Content of the data repository:</p> <p>1) Elevation change maps (raster <em>**.tif</em> files)<br> Regional maps of the elevation changes over 3 years periods, interpolated results in metre (m).<br> Name: <em>dh_[date1]_[date2].tif</em><br> <br>2) Surge-affected areas (vector <em>**.gpkg </em>files)<br> Surge-affected areas drawn manually of four selected glacier surges, divided into reservoir and receiving areas.<br> <br>3) Python script <em>dem_processing_publi.py</em><br> Script with the implementation of the workflow presented in this study.</p>
PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing
<p>We introduce <strong>PDMX</strong>: a <strong>P</strong>ublic <strong>D</strong>omain <strong>M</strong>usic<strong>X</strong>ML dataset for symbolic music processing. Refer to our <a title="PDMX Paper" href="https://arxiv.org/abs/2409.10831" target="_blank" rel="noopener">paper</a> for more information, and our <a title="PDMX GitHub Repository" href="https://github.com/pnlong/PDMX/" target="_blank" rel="noopener">GitHub repository</a> for any code-related details. Please cite both our paper and <a href="https://arxiv.org/abs/2410.02084" target="_blank" rel="noopener">our collaborators' paper</a> if you use this dataset (see our GitHub for more information).</p> <p>Upon further use of the PDMX dataset, we discovered a discrepancy between the public-facing copyright metadata on the <a href="https://musescore.com/">MuseScore website</a> and the internal copyright data of the MuseScore files themselves, which affected 31,221 (12.29% of) songs. We have decided to proceed with the former given its public visibility on Musescore (i.e. this is what the MuseScore website presents its users with). We have noted files with conflicting internal licenses in the <em><strong>license_conflict</strong></em> column of PDMX. We recommend using the <em><strong>no_license_conflict</strong></em> subset of PDMX (which still includes 222,856 songs) moving forward.</p> <p>Additionally, for each song in PDMX, we not only provide the <em>MusicRender</em> and metadata JSON files, but we also try to include the associated compressed MusicXML (MXL), sheet music (PDF), and MIDI (MID) files when available. Due to the corruption of 42 of the original MuseScore files, these songs lack those associated files (since they could not be converted to those formats) and only include the <em>MusicRender</em> and metadata JSON files. The <em><strong>all_valid</strong></em> subset of PDMX describes the songs where all associated files are valid.</p>
Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"
<p><span><span><span>This dataset contains supplementary code, images and models for the publication „Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.</span></span></span></p> <p> </p> <p><span><span><span>The content will be updated and additionally linked to the corresponding git repositories.</span></span></span></p>
Earth.Org.UK (EOU) public data snapshot
<p>Snapshot of public data file set from EOU including energy and IoT data.</p> <p>NOTE: there are some third-party data and other files present which may not be covered by CC0: these should be called out in local README files and similar.</p>
MIrreM Public Database on Irregular Migration Stock Estimates
<p>The <em>MIrreM </em><em>Public Database on Irregular Migration Stock Estimates</em> (the Database) provides an inventory and critical appraisal of country-level estimates of irregular migration stocks in 13 European countries, the United States and Canada for the period 2008 to 2023. It is a deliverable of the MIrreM project, which is a follow-up to Clandestino. Clandestino covered the period 2000-2008.</p> <p>Users of the Database are advised to consult the following <strong>companion documents:</strong></p> <ul> <li>The README File (version 3), which can be accessed alongside the Database, and contains contextual and technical information about the Database. </li> <li>Discussion of the context, the underlying concepts, and the methodology used in the data collection and quality assessment: Vargas-Silva, C., Leerkes A., Kierans, D., Siruno, L. and Kraler, A. (2025). Tools for collecting information on irregular migration estimates and indicators. <em>Open Research Europe 5:176. <a href="https://doi.org/10.12688/openreseurope.20695.1" target="_blank" rel="noopener noreferrer">https://doi.org/10.12688/openreseurope.20695.1</a></em></li> <li>Analysis of the stock estimates: Kierans, D. and Vargas-Silva, C. (2024). <em>The Irregular Migrant Population of Europe</em>. MIrreM Working Paper No. 11. Krems: University for Continuing Education Krems (Danube University Krems). <a href="https://doi.org/10.5281/zenodo.13857073">https://doi.org/10.5281/zenodo.13857073 </a></li> </ul> <p>Furthermore, users of the Database are notified of a ‘sister’ database of the MIrreM project, which captures and assesses estimates and indicators of irregular migration <em>flows</em> over the same period, the <em>MIrreM </em><em>Public Database on Irregular Migration Flow Estimates and Indicators </em>and accompanying analysis:</p> <ul> <li>Siruno, L., Leerkes, A., Badre, A., Bircan, T., Brunovská, E., Cacciapaglia, M., Carvalho, J., Cassain, L., Cyrus, N., Desmond, A., Fihel, A., Finotelli, C., Ghio, D., Hendow, M., Heylin, R., Jauhiainen, J.S., Jovanovic, K., Kierans, D., Mohan, S.S., Nikolova, M., Oruc, N., Ramos, M.P.G., Rössl, L., Sağiroğlu, A.Z., Santos, S., Schütze, T., & Sohst, R.R. (2024) <em>MIrreM Public Database on Irregular Migration Flow Estimates and Indicators.</em> Krems: University for Continuing Education Krems (Danube University Krems). <a href="https://doi.org/10.5281/zenodo.10813413">https://doi.org/10.5281/zenodo.10813413</a>. </li> <li>Siruno, L., Leerkes, A., Hendow, M. & Brunovksá, E. (2024) <em>Working Paper on Irregular Migration Flows</em>. MIrreM Working Paper No. 9. Krems: University for Continuing Education Krems (Danube University Krems). <a href="../records/10702228">https://zenodo.org/records/10702228</a></li> </ul>
Supplementary tables for publication "A reference-free algorithm discovers regulation in the plant transcriptome"
<p>Supplementary tables for publication "A reference-free algorithm discovers regulation in the plant transcriptome" (doi: https://doi.org/10.1101/2024.05.23.595613)</p> <p>Table A: complete list of significant anchors and associated genes from analysis of sorghum dataset</p> <p>Table B: complete list of significant anchors and associated genes from analysis of maize dataset</p> <p>Table C: complete list of significant anchors and associated genes from analysis of Arabidopsis P/Fe dataset</p> <p>Table D: complete list of significant anchors and associated genes from analysis of Arabidopsis FLOE1 dataset</p> <p>arabidopsis_floe1_ALL_anchors_satc_truncated.txt: data from the Arabidopsis FLOE1 dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>arabidopsis_pfe_ALL_anchors_satc_truncated.txt: data from the Arabidopsis P/Fe dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>maize_pollen_ALL_anchors_satc_truncated.txt: data from the maize dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>sorghum_drought_ALL_anchors_satc_truncated.txt: data from the sorghum dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample.</p> <p>cryptic_splicing_anchors.tsv: list of anchors described in Supplementary Information section of the article that are examples of cryptic splicing. Columns are dataset name, gene name/ID, anchor sequence, target 1 sequence, and target 2 sequence. </p>
Europe-wide public preferences for plankton-based ecosystem services and marine biodiversity from a series of Deliberative Monetary Valuation workshops
<p>The data were collected as part of the Horizon Europe project BIOcean5D – Marine Biodiversity Assessment and Prediction Across Spatial, Temporal and Human Scales. The aim of the study was to elicit public preferences for marine biodiversity and ecosystem services, with a particular focus on plankton. This dataset comprises responses from a series of Deliberative Monetary Valuation workshops held across Europe, along with the corresponding supplementary material. The responses encompass a range of data, including socio-demographic information, personal characteristics, beliefs, prior knowledge, preferences derived from a Discrete Choice Experiment, and the associated motivations of each respondent. In total, 15 workshops were conducted between October 2023 and February 2024 in the following locations: Poland (Poznan and Sopot), Italy (Padova and Chioggia), the Basque Country (Vitoria-Gasteiz and Bilbao), Germany (Bremerhaven and Hannover), and France (Rennes and Brest).</p>
Human pan-body age- and sex-specific molecular phenomena inferred from public transcriptome data using machine learning - Data
<p>Expression data used in manuscript <i>Human pan-body age- and sex-specific molecular phenomena inferred from public transcriptome data using machine learning</i></p>
Dataset related to Figure 2, publication: E. Fasci et al 2023 J. Phys.: Conf. Ser.2439 012017
<p>Data have been acquired by means of a comb-calibrated frequency-stabilized cavity ring-down spectrometer. They represent the absorption spectrum of the 3_(2,2)→2_(2,1) H_2O line, belonging to the nu_1 + nu_3 band, recorded under a continuous flux of high-purity N_2, while keeping the pressure to the constant value of 4 kPa.</p><p>This work received funding from the EURAMET-EMPIR program cofinanced by the participating states and from the European Union's Horizon 2020 research and innovation program through the PROMETH2O project, coordinated by Vito Fernicola.</p>
Dataset of the publication Phys. Chem. Chem. Phys., 2023,25, 23614-23625
<p>The experimental work was partially done within the project PROMETH2O (EMPIR 20IND06), which received funding from the EMPIR programme co-financed by the Participating States and from the European Unions Horizon 2020 research and innovation programme.</p>
Data on digital perfomance of public sector in South-East Asia
<p>Data on digital perfomance of public sector in South-East Asia includes variables affecting process of digitalization in public sector in South-East Asian countries. </p>
Dataset used in the publication entitled "Multi-Point Method using Effective Demodulation and Decomposition Techniques allowing Identification of Disturbing Loads in Power Grids"
<p>Dataset obtained from experimental research carried out in the prepared laboratory setup and information on the performed numerical simulation studies. Based on the dataset, the proposed new method of identification of sources of voltage fluctuation has been validated in the publication: Kuwałek P., Wiczyński G., Multi-Point Method using Effective Demodulation and Decomposition Techniques allowing Identification of Disturbing Loads in Power Grids. The description of the prepared laboratory setup is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
SPIDER - Lumbar spine segmentation in MR images: a dataset and a public benchmark
<p>This is a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 studies of 218 patients with a history of low back pain. The data was collected from four different hospitals. There is an additional hidden test set, not available here, used in the accompanying SPIDER challenge on spider.grand-challenge.org. We share this data to encourage wider participation and collaboration in the field of spine segmentation, and ultimately improve the diagnostic value of lumbar spine MRI.</p> <p>Which MRI studies are assigned to the training and validation sets can be found in the overview file. This file also provides the biological sex for all patients and the age for the patients for which this was available. It also includes a number of scanner and acquisition parameters for each individual MRI study. The dataset also comes with radiological gradings found in a separate file for the following degenerative changes:</p> <p>1.    Modic changes (type I, II or III)</p> <p>2.    Upper and lower endplate changes / Schmorl nodes (binary)</p> <p>3.    Spondylolisthesis (binary)</p> <p>4.    Disc herniation (binary)</p> <p>5.    Disc narrowing (binary)</p> <p>6.    Disc bulging (binary)</p> <p>7.    Pfirrman grade (grade 1 to 5). </p> <p>All radiological gradings are provided per IVD level.</p> <div>This dataset, and the associated public benchmark, are described in this paper: <a href="https://www.nature.com/articles/s41597-024-03090-w" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03090-w</a></div> <div>The public segmenation challenge can be found here: <a href="https://spider.grand-challenge.org/" target="_blank" rel="noopener">https://spider.grand-challenge.org/</a></div> <div> </div> <div>When using this dataset, please cite this dataset with the correct DOI, and also cite the afformentioned paper.</div>
Code and data for publication "Assessing carbon cycle projections from complex and simple models under SSP scenarios" published in "Climatic Change"
<p>Data and scripts for the article "Assessing carbon cycle projections from complex and simple models under SSP scenarios" by I. Melnikova, P. Ciais, O. Boucher and K. Tanaka was accepted for publication in Climatic Change (https://doi.org/10.1007/s10584-023-03639-5)</p><p> </p><p>We use bash, CDO, and python.</p><p>SSP2.xlsx contains preprocessed annual estimates of climate and carbon cycle variables from ESMs and SCMs used in the paper.</p><p>Two bash scripts contain preprocessing cdo commands for ESM output.s SCMs were preprocessed directly in python.</p><p>Jupyter notebook (python) contains preprocessing of data and plotting of all figures of the manuscript. The folder "additional" contains some more Excel files needed to run Jupyter-Notebook. Please adapt the folder names.</p><p>If you have any questions, please contact the corresponding author Irina MELNIKOVA at melnikova . irina@nies.go.jp</p><p> </p>
Data for the publication "Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model" - part 1
<p>The data is split into two datasets, for each to be smaller than 50 GB.</p>
Dataset for publication "Deposition of Sn-Zr-Se precursor by thermal evaporation and PLD for the synthesis of SnZrSe3 thin films"
<p>This dataset entails various structural material data that was used to provide additional evidence for arguments presented in publication "Deposition of Sn-Zr-Se precursor by thermal evaporation and PLD for the synthesis of SnZrSe3 thin films". </p><p>Mainly data consists of: SEM, XRD, Raman, Auger and TGA raw data.</p><p>Summary of results is provided in Extended_data.pdf file </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.