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3,172 results for “Publications”
Evoking the N400 Event-Related Potential (ERP) component using a publicly available novel set of sentences with semantically incongruent or congruent eggplants (endings)
<p class="MsoNoSpacing"><span>During speech comprehension, the ongoing context of a sentence is used to predict sentence outcome by limiting subsequent word likelihood. Neurophysiologically, violations of context-dependent predictions result in amplitude modulations of the N400 event-related potential (ERP) component. While N400 is widely used to measure semantic processing and integration, </span><span>no publicly-available auditory stimulus set is available to standardize approaches across the field. Here, we developed an auditory stimulus set of 442 sentences that utilized the semantic anomaly paradigm, provided cloze probability for all stimuli, and was developed for both children and adults. With 20 neurotypical adults, we validated that this set elicits robust N400's, as well as two additional semantically-related ERP components: the recognition potential (~250 ms) and the late positivity component (~600 ms). This stimulus set (<a href="https://doi.org/10.5061/dryad.9ghx3ffkg">https://doi.org/10.5061/dryad.9ghx3ffkg</a>) and the 20 high-density (128-channel) electrophysiological datasets (<a href="https://doi.org/10.5061/dryad.6wwpzgmx4">https://doi.org/10.5061/dryad.6wwpzgmx4</a>) </span><span>are made publicly available to promote data sharing and reuse. Future studies that use this stimulus set to investigate sentential semantic comprehension in both control and clinical populations may benefit from the increased comparability and reproducibility within this field of research.</span></p>
Recording of ETAPAS 4th Dissemination Event: Tools for the ethical and trustworthy adoption of Artificial Intelligence in the service of public administrations
<p>On the 12th of October 2022 the 4th ETAPAS dissemination event "Tools for the ethical and trustworthy adoption of Artificial Intelligence in the service of public administrations" took place online and on site at the Centre for Research & Technology Hellas (CERTH).</p> <p>If you missed the workshop you can find here the recording of the event, where we presented the first outputs generated by the ETAPAS Project to Greek Public Administration in order display concrete results on how AI can be ethically embedded in their activities.</p> <p>Among the topics we discussed:</p> <ul> <li>the ETAPAS Project and key tools developed for the good governance of AI;</li> <li>the chatbot Kari and the challenges using AI-based solutions presents;</li> <li>the development of a misinformation detection platform by CERTH;</li> <li><a href="https://www.pop-ai.eu/">popAI</a> and <a href="https://token-project.eu/the-project/">TOKEN</a> projects;</li> <li>strategies for a governance framework for artificial intelligence in public administration.</li> </ul> <p>Check this recording to see all the interesting presentations and discussions that emerged during the project.</p>
Corpus Criticorum (1450-1650) - Supplement 3 - A BibTeX dataset of early modern publications featuring the notion of critique on their title pages.bib
<p>A comprehensive BibTeX bibliography of early modern publications featuring the notion of Critique on their title pages formed in Zotero. The data was collected by exploiting readily-available pan-European (e.g., the <em>Universal Short Title Catalogue</em>), language-based (e.g., the Germanophone <em>VD17</em>), and national (e.g., Italy’s <em>ICCU</em>) catalogue records. The quasi-totality (217/218) of the title pages were consulted in order to perform data curation (i.e., standardisation) and assure its accuracy (i.e., verification of names, dates, title page wording, etc.). </p>
Deep potential model and datasets used in the publication "Plastic Deformation of Superionic Water Ices"
<p>The file training_data_for_deepmd.zip contains the training set and deep potential model for superionic ices used in the publication "Plastic Deformation of Superionic Water Ices". The deep potential (DP) neural-network model was generated using the DeepMD-kit package. The training set was generated using AIMD/CP2K calculations for both undeformed and deformed states of the fcc superionic phase at temperatures of 2500, 3000, 3500 and 4000 K. </p> <p> </p> <p>The file cp2k_shear.zip contains instructions to modify the CP2K code to calculate the shear vs strain deformation in continuous mode. </p> <p> </p> <p>The file lammps_dislocation.zip contains the files needed to run the dislocation mobility calculation. </p> <p> </p>
Simulation data used for publication "Seeding of equatorial plasma bubbles by vertical neutral wind" by Yokoyama et al.
<p>The dataset includes two-dimensional simulation output used in the paper.</p> <p>"altitude.dat" and "zonal.dat" contains grid information.</p> <p>"read_n_phi_2D.pro" is an IDL file to read the dataset, with detailed description of each data.</p> <p>The original three-dimensional simulation output is too large to publish at the repository. Author (TY) is willing to share the original data.</p>
Public views on gene editing and its uses
<p>Rapid advances in genome editing, including CRISPR-Cas9 endonucleases, and their potential application in medicine and enhancement have been hotly debated by scientists and ethicists. Although a veto on germ line gene editing has been proposed<sup>1</sup>, the use of gene editing on human cells in the clinical context remains controversial, particularly for interventions aimed at enhancement<sup>2</sup>. In a report on human genome editing the US National Academy of Sciences (NAS) note that “important questions raised with respect to genome editing include how to incorporate societal values into salient clinical and policy consideration”<sup>3</sup>. We report here our research that opens a window onto what the public think.</p>
Zenodo Public Metadata Records until 17 January 2017
<p>This dataset contains metadata of all publicly available records on Zenodo (n=142,117 data records), downloaded on 17 January 2017. The data was retrieved via the Zenodo OAI-PMH interface using the R package oai (Chamberlain & Bojanowski, 2016).</p> <p>The data is provided in two formats:</p> <ol> <li>The original XML in the format OAI DataCite v3, see <em> zenodo-all-metadata-records_oai-datacite3_2017-01-17.xml</em></li> <li>Converted to a tab-separated file, see <em>zenodo-all-metadata-records_2017-01-17.tsv</em></li> </ol>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Publications per country
<p>Publications per country. Map based on longitude and latitude. The circle’s colors show each country, while their size indicates the number of articles.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Publications by year and research areas
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Data for publication "Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action".
<p>Data underlying the publication "Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action".</p> <p>Journal: Nature Communications</p> <p>Authors: <em>Matthias Mengel<sup>1*</sup></em><em>, Alexander Nauels</em><sup><em>2</em></sup><em>, Joeri Rogelj</em><sup><em>3,4</em></sup><em>, Carl-Friedrich Schleussner<sup>1,5</sup></em></p> <p>(1) Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany</p> <p>(2) Australian-German College of Climate & Energy Transitions, The University of Melbourne, Parkville, Victoria 3010, Australia</p> <p>(3) ENE Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria</p> <p>(4) Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, Zurich 8006, Switzerland</p> <p>(5) Climate Analytics, Ritterstr. 3, 10969 Berlin, Germany</p> <p>(*) email matthias.mengel@pik-potsdam.de</p> <p>Abstract:</p> <p>Sea-level rise is a major consequence of climate change that will continue long after emissions of greenhouse gases have stopped. The 2015 Paris Agreement aims at reducing climate-related risks by reducing greenhouse gas emissions to net zero and limiting global-mean temperature increase. Here we quantify the effect of these constraints on global sea-level rise until 2300 including Antarctic ice-sheet instabilities. We estimate median sea-level rise between 0.7 and 1.2m if net zero greenhouse gas emissions are sustained until 2300, varying with the pathway of emissions during this century. Temperature stabilization below 2°C is insufficient to hold median sea-level rise until 2300 below 1.5m. We find that each 5-year delay in near-term peaking of CO2 emissions increases median year-2300 sea-level rise estimates by ca. 0.2m, and extreme sea-level rise estimates at the 95th percentile by up to 1m. Our results underline the importance of near-term mitigation action for limiting long-term sea-level rise risks.</p> <p> </p> <p>Large zip files provides data. Small zip file python code for plotting and writing supplementary data.</p>
Dataset for publication "Effects of Y and Ho doping on microstructure evolution during oxidation of extraordinary stable Hf-B-Si-Y/Ho-C-N films up to 1500 °C" in Materials & Design 237, (2024), 112589.
<p>Dataset for publication "Effects of Y and Ho doping on microstructure evolution during oxidation of extraordinary stable Hf-B-Si-Y/Ho-C-N films up to 1500 °C" in Materials & Design Volume 237, January 2024, 112589, DOI 10.1016/j.matdes.2023.112589.</p> <p>XRD patterns, SAED patterns, EDS spectra, TEM images, HRTEM images.</p>
OpenPack: Public multi-modal dataset for packaging work recognition in logistics domain
<p><strong>OpenPack</strong> is an open-access logistics dataset for human activity recognition, which contains human movement and package information from 16 subjects in four scenarios. Human movement information is subdivided into three types of data, acceleration, physiological, and depth-sensing. The package information includes the size and number of items included in each packaging job. </p> <p>In the "Humanware laboratory" at IST Osaka University, with the supervision of industrial engineers, an experiment to mimic logistic center labor was designed. 12 workers with previous packaging experience and 4 without experience performed a set of packaging tasks according to an instruction manual from a real-life logistics center. During the different scenarios, subjects were recorded while performing packing operations using Lidar, Kinect, and Realsense depth sensors while wearing 4 ATR IMU devices and 2 Empatica E4 wearable sensors. Besides sensor data, this dataset contains timestamp information collected from the hand terminal used to register product, packet, and address label codes as well as package details that can be useful to relate operations to specific packages.</p> <p>The 4 different scenarios include; sequential packing, worker-decided sequence changes, pre-ordered item packing, and time-sensitive stressors. Each of the subjects performed 20 packing jobs in 5 work sessions for a total of 100 packing jobs. <strong>53+</strong> hours of packaging operations have been labeled into 10 global operation classes and 16 sub-action classes for this dataset. Action classes are not unique to each operation but may only appear in one or two operations. </p> <p>You can find information on how to use this dataset at: <a href="https://open-pack.github.io/">https://open-pack.github.io/</a>. For details on how this dataset was collected please check the following publication "OpenPack: A Large-Scale Dataset for Recognizing Packaging Works in IoT-Enabled Logistic Environments" <a href="https://doi.ieeecomputersociety.org/10.1109/PerCom59722.2024.10494448">10.1109/PerCom59722.2024.10494448</a>.</p> <p> </p> <p><strong>Full Dataset</strong></p> <p>In this repository, the data and label files are contained in separate files for each worker. Each worker's file contains; IMU, E4, 2d keypoint, 3d keypoint, annotation, and system-related<em> </em>data<em>.</em></p> <p><em><strong>Preprocessed Dataset (IMU with operation and action Labels)</strong></em></p> <p>We have received many comments that it was difficult to combine multiple workers' IMU and annotation data. Therefore, we have created several CSV files containing the four IMU's sensor data and the operation labels in a single file. These files are now included as "imu-with-operation-action-labels.zip". </p> <p><em><strong>Preprocessed Dataset (Kinect 2D and 3D keypoint data with operation and action Labels)</strong></em></p> <p>We have received several requests for a preprocessed dataset containing only specific types of keypoint data with its assigned operation and action labels. Two new preprocessed files have been added for 2D and 3D keypoint data extracted from the frontal view Kinect camera. These files are:</p> <p>"<a href="11059235" target="_blank" rel="noopener noreferrer">kinect-2d-kpt-with-operation-action-labels.zip</a>", and</p> <p>"<a href="11059235" target="_blank" rel="noopener noreferrer">kinect-3d-kpt-with-operation-action-labels.zip</a>".</p> <p> </p> <p>Work is continuously being done to update and improve this dataset. When downloading and using this dataset please verify that the version is up to date with the latest release. The latest release <strong>[1.1.0]</strong> was uploaded on 24/04/2024. </p> <p><strong><em>Changes LOG:</em></strong></p> <ul> <li>v1.0.0: Add tutorial preprocessed dataset for IMU data with operation labels.</li> <li>v1.1.0: Update preprocessed datasets. (Include Kinect 2d and 3d keypoint data with Operation and action labels)</li> </ul> <p> </p> <p><strong>We hosted an activity recognition competition using this dataset (OpenPack v0.3.x) awarded at a PerCom 2023 Workshop! The task was very simple: Recognize 10 work operations from the OpenPack dataset. You can refer to this website for coding materials relevant to this dataset. </strong><a href="https://open-pack.github.io/challenge2022"><strong>https://open-pack.github.io/challenge2022</strong></a></p>
Analysis of public transport in Vienna with Co2 emissions in Austria's households
<p>This repository serves as a backup and longterm storage for the datasets and plots resulting from the analysis on means of transport in Vienna in the time period 2010 - 2021 together with the Co2 emissions of Austria's households.</p>
Data publication: Virtual experiments for steel fiber reinforced high performance concrete (HPC)
<p>This data set contains all necessary inputs for the virtual experiments using an ellipsodal RVE for steel fiber reinforced high performance concrete (HPC), including discretization data, boundary conditions, material parameters and numerical results. The discretization is realized in terms of the finite element method. </p>
Data for a publication "The role of the preparation route on microstructure and mechanical properties of AlCoCrFeNi high entropy alloy"
<p>A dataset containing data for the published article "The role of the preparation route on microstructure and mechanical properties of AlCoCrFeNi high entropy alloy".</p> <p> </p> <p>For more details, please read the <strong>README Description of data and analysis.txt</strong> file.</p> <p> </p> <p> </p>
Public Understanding of Common Data Concepts
<p><span>This dataset reports on a survey on awareness amongst the European public of common data concepts, terms and principles. </span></p> <p><span>Elements of this survey were utilised in the following publication - </span></p> <p><span>O’Grady, M., Mangina, E. Citizen scientists—practices, observations, and experience. <em>Humanit Soc Sci Commun</em> <strong>11</strong>, 469 (2024). https://doi.org/10.1057/s41599-024-02966-x</span></p>
Data for publication: A pipeline for in-depth analysis of DNA virus populations by profiling the low abundant virus variants and partial genomic components
<p>Raw and processed sequence data from Oxford Nanopore and BGI short read sequencing platforms used in the publication: "A pipeline for in-depth analysis of DNA virus populations by profiling the low abundant virus variants and partial genomic components".</p>
Dataset for publication "Operando Observation of (Bi)carbonate Precipitation during Electrochemical CO2 Reduction in Strongly Acidic Electrolytes"
<p>Broad topic: electrochemical reduction of CO2 using Ag/Cu-based gas diffusion electrodes in neutral and acid electrolyte, operando characterisation using synchrotron radiation (wide-angle X-ray scattering), (bi)carbonate precipitation.</p> <p>Data is devided in subfolders named after the figure of the paper.</p> <p>Raw data, processed data, and Origin/PowerPoint files are all contained in the subfolders.</p> <p>Synchrotron raw data are linked to the experiment numbers MA5874 and INHC1776 at ESRF</p> <p>A subfolder corresponding to a sample contains: data from a potentiostat, gas chromatograms, recording of flow, pressure and temperature, tables of calculated Faradaic efficiency (FE), png image of the FE vs t, zipped raw files.</p> <p>.json file was created using a yadg scheme (https://dgbowl.github.io/yadg/master/index.html), and data was processed by a dgpost scheme (<a href="https://pypi.org/project/dgpost/">https://dgbowl.github.io/dgpost/master/index.html</a>)</p> <p>Synchrotron data are analysized using this python library https://github.com/EmpaEconversion/Twaxs</p>
Neutrino asymmetry evolution + BBN | Public grids
<h3>Datasets associated to “Constraints on primordial lepton asymmetries with full neutrino transport”, J. Froustey and C. Pitrou [2405.06509].</h3> <p>We solve the neutrino Quantum Kinetic Equations with the full collision term in the range of temperatures [25 MeV, 0.006 MeV], in order to get the evolution of neutrino distributions. We explore a range of primordial neutrino asymmetries, whose evolution results from a complicated interplay between the Hamiltonian terms (in particular, the self-interaction mean-field) and collisions. The output of this neutrino calculation is used in the Big Bang nucleosynthesis code <em>PRIMAT</em> to determine the primordial abundances obtained in this cosmological scenario.</p> <p> </p> <p>The datasets are respectively:</p> <ul> <li><code>NEVO_PRIMAT_grid_equalxi.csv</code>, used in Section IV.A ;</li> <li><code>NEVO_PRIMAT_grid_xiav_xie.csv</code>, used in Section IV.B ;</li> <li><code>NEVO_PRIMAT_grid_ximu_xitau.csv</code>, used in Section IV.C ;</li> <li><code>NEVO_PRIMAT_grid_xiav_xie_zoom.csv</code>, used in Section V ;</li> <li><code>NEVO_PRIMAT_grid_3D.csv</code>, an additional grid exploring the full 3D parameter space (xi_e,xi_mu,xi_tau).</li> </ul> <p>Each dataset has the same format, with 21 columns identified in the header of each file:</p> <ol> <li>xit_av, average of the initial xitilde (= xi + xi^3/pi^2)</li> <li>xit_e - xit_av, initial difference between the e flavor xitilde and the average</li> <li>(xit_mu - xit_tau)/2, also called \tilde{Delta}, difference between the mu and tau flavor initial asymmetries</li> <li>xi_e, reduced initial chemical potential of nu_e (opposite one for nu_ebar)</li> <li>xi_mu</li> <li>xi_tau</li> <li>N_eff, effective number of neutrino species after decoupling (i.e., for T = 0.006 MeV)</li> <li>Y_He4, primordial helium-4 abundance obtained from PRIMAT (with omega_baryon = 0.02242)</li> <li>D/H, primordial deuterium abundance obtained from PRIMAT (with omega_baryon = 0.02242)</li> <li>d[ln(Y_He4)]/d[ln(omega_b)] = dY, relative derivative such that Y_He4(omega) = Y_He4^ref * (1+dY*(omega-omega^ref)/omega^ref)</li> <li>d[ln(D/H)]/d[ln(omega_b)], same for D/H</li> <li>eta^f_e, final electron flavor asymmetry, defined as eta_e = (n_nue - n_nuebar)/Tcm^3</li> <li>eta^f_mu, final muon flavor asymmetry</li> <li>eta^f_tau, final tau flavor asymmetry</li> <li>eta^f_1, final asymmetry of the nu_1 mass eigenstate</li> <li>eta^f_2, final asymmetry of the nu_2 mass eigenstate</li> <li>eta^f_3, final asymmetry of the nu_3 mass eigenstate. <em>These "mass asymmetries" are determined using the fact that the final density matrix is diagonal in the mass basis, which allows to relate {eta_alpha}_(flavor) to {eta_i}_(mass). Note that we take the mean values of each mixing parameter from the <a href="https://pdg.lbl.gov/2023/tables/rpp2023-sum-leptons.pdf" target="_blank" rel="noopener">Particle Data Group (2023)</a>, neglecting the CP-phase.</em></li> <li>rho^f_e, final comoving energy density of nu_e + nu_ebar</li> <li>rho^f_mu, final comoving energy density of nu_mu + nu_mubar</li> <li>rho^f_tau, final comoving energy density of nu_tau + nu_taubar</li> <li>z^f, final dimensionless photon temperature (z = T_gamma/T_cm)</li> </ol>
Data and code for 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality
<p>This repository contains data and code for</p> <p>Lento B., Segas E., Leconte V., Doat E., Danion F., Péteri R., Benois-Pineau J., de Rugy A. (2024). <strong>3D-</strong><strong>ARM</strong><strong>-Gaze</strong><strong>: a </strong><strong>public </strong><strong>dataset of </strong><strong>3D </strong><strong>A</strong><strong>rm </strong><strong>R</strong><strong>eaching </strong><strong>M</strong><strong>ovements</strong><strong> </strong><strong>with Gaze information</strong><strong> </strong><strong>in </strong><strong>virtual reality</strong><strong>. </strong>doi:</p> <p>It contains a dataset <strong>(DBAS22_DataOnline </strong>folder) of natural arm movements together with visual and gaze information when reaching objects in a wide reachable space from a precisely controlled, comfortably seated posture. More details could be find in the link publication (see Related identifiers section).</p> <p>The <strong>DBAS22_DocOnline</strong> folder contains all the documentation files. The <strong>MainDataExplained </strong>file lists and describes the variables recorded during the experimental phases. In the <strong>SummaryOfFiles </strong>document, you will find descriptions for all the files within the <strong>DBAS22_DataOnline</strong> folder, and at the bottom, there is also a file tree that illustrates the file structure. The <strong>DBAS22FilesWorkflow </strong>document offers an overview of the workflow of experimental file creation during the experiment.</p> <p>The <strong>DBAS22_CodeOnline</strong> folder contains all the scripts to perform data analysis, listed and described in the files <strong>CodeExplanations </strong>and <strong>DependenciesRelations</strong>. The <strong>GuideInstall </strong>file contains information needed to run the Python code files.</p> <p>The <strong>DBAS22_CodeOnline</strong> folder also contains the DataPlayer Unity project. Instructions for running the project are provided in the <strong>DataPlayerGuide </strong>file and SupplementaryVideo2 (see Related identifiers section for more details). The folder <strong>DBAS22_DataPlayer_StandAloneApp </strong>contains the standalone version of the DataPlayer, which doesn't require any software installation.</p> <p>The <strong>DBAS22_VideoOnline</strong> folder contains all the videos. </p>
ScienceDex guides
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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.