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15,733 results for “article”
Data set supporting journal article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020
<p>This data set contains all necessary data needed to replicate figures and analysis presented in the research article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020.</p> <p>In detail, this data set contains 1) meteorological data and half-hourly evapotranspiration rates obtained by a conventional eddy covariance set-up, a low-cost eddy covariance set-up and an energy balance eddy covariance set-up for measurement campaigns of approximately four weeks duration (*_Fluxes_Campaigns_*); 2) raw data to recalculate flux footprints for the campaigns of approximately four weeks duration (*_Campaign_Footprints_*) and for the whole year (*_Annual_Footprints_*); 3) half-hourly evapotranspiration rates obtained by a low-cost eddy covariance set-up and an energy balance eddy covariance set-up gap-filled and corrected for energy balance closure (*_Fluxes_Annual_*). The data were collected at five agroforestry systems and five monoculture agriculture systems without trees across Northern Germany. </p>
Dataset related to article "Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures"
<p>This dataset is related to the article entitled: Lipoprotein receptor loss in forebrain radial glia results in neurological deficits and severe seizures. This article is published in the Journal GLIA.</p> <p>Bres EE et al.<br> Lipoprotein receptor loss in forebrain radial glia results in<br> neurological deficits and severe seizures. Glia. 2020;1–33.</p>
Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"
<p>The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration.</p> <p>Country codes: ALPHA-3<br> Unit: Megawatt (electric) (interval average values, i.e. MWh/h)</p> <p><strong>Generation technology types</strong></p> <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> <p><strong>Consumption technology types</strong></p> <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>
Raw data for article "In Situ Synchrotron X-Ray Diffraction Characterization of Corrosion Products of a Ti-Based Metallic Glass for Implant Applications" Gostin et al 2018
<p>This repository contains raw data for the article "In Situ Synchrotron X-Ray Diffraction Characterization of Corrosion Products of a Ti-Based Metallic Glass for Implant Applications" by Gostin et al. 2018 in Advanced Healthcare Materials, 7, 1800338 (https://doi.org/10.1002/adhm.201800338).</p> <p>Most data comes from one beamtime at the Diamond synchrotron in the UK in May 2016. It consists of X-ray diffraction images taken in situ in artificial corrosion pits on a Ti-based metallic glass.</p> <p>Please see the README file for more details.</p>
Adsorption kinetics data sets, compiled from the literature. As used in the research article "A revised pseudo-second order kinetic model for adsorption, sensitive to changes in adsorbate and adsorbent concentrations"
<p>Data sets reporting experimental adsorption kinetics, compiled from the literature. These data sets were subjected to empirical analysis in the development of our revised pseudo-second order rate equation (the rPSO model) as discussed in the ChemRxiv pre-print "<a href="https://chemrxiv.org/articles/preprint/A_Revised_Pseudo-Second_Order_Kinetic_Model_for_Adsorption_Sensitive_to_Changes_in_Sorbate_and_Sorbent_Concentrations/12008799">A Revised Pseudo-Second Order Kinetic Model for Adsorption, Sensitive to Changes in Sorbate and Sorbent Concentrations</a>".</p>
Datasets and Models for Historical Newspaper Article Segmentation
<p>This record contains the datasets and models used and produced for the work reported in the paper "<em>Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers</em>" (<a href="https://infoscience.epfl.ch/record/282863?ln=en">link</a>).</p> <p>Please cite this paper if you are using the models/datasets or find it relevant to your research:</p> <pre><code>@article{barman_combining_2020, title = {{Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers}}, author = {Raphaël Barman and Maud Ehrmann and Simon Clematide and Sofia Ares Oliveira and Frédéric Kaplan}, journal= {Journal of Data Mining \& Digital Humanities}, volume= {HistoInformatics} DOI = {10.5281/zenodo.4065271}, year = {2021}, url = {https://jdmdh.episciences.org/7097}, }</code></pre> <p><br> <strong>Please note that this record contains data under different licenses.</strong><br> <br> <strong>1. DATA</strong></p> <ul> <li><strong>Annotations (json files)</strong>: JSON files contains image annotations, with one file per newspaper containing region annotations (label and coordinates) in VIA format. The following licenses apply: <ul> <li> luxwort.json: those annotations are under a <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">CC0 1.0 license</a>. Please refer to the right statement specified for each image in the file.</li> <li>GDL.json, IMP.json and JDG.json: those annotations are under a <a href="https://creativecommons.org/licenses/by-sa/4.0/legalcode">CC BY-SA 4.0 license</a>.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Image files: </strong>The archive images.zip contains the Swiss titles image files (GDL, IMP, JDG) used for the experiments described in the paper. Those images are under copyright (property of the journal <em>Le Temps </em>and of <em>ArcInfo</em>) and can be used <em>for academic research or educational purposes only</em>. Redistribution, publication or commercial use are not permitted. These terms of use are similar to the following right statement: <a href="http://rightsstatements.org/vocab/InC-EDU/1.0/">http://rightsstatements.org/vocab/InC-EDU/1.0/</a></li> </ul> <p> </p> <p><strong>2. MODELS</strong></p> <p>Some of the best models are released under a <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> license (they are also available as assets of the current Github <a href="https://github.com/dhlab-epfl/dhSegment-text/releases/tag/0.1">release</a>).</p> <ul> <li><strong>JDG_flair-FT</strong>: this model was trained on JDG using french Flair and FastText embeddings. It is able to predict the four classes presented in the paper (<code>Serial</code>, <code>Weather</code>, <code>Death notice</code> and <code>Stocks</code>).</li> <li><strong>Luxwort_obituary_flair-bpemb</strong>: this model was trained on Luxwort using multilingual Flair and Byte-pair embeddings. It is able to predict the <code>Death notice</code> class.</li> <li><strong>Luxwort_obituary_flair-FT_indomain</strong>: this model was trained on Luxwort using in-domain Flair and FastText embeddings (trained on Luxwort data). It is also able to predict the <code>Death notice</code> class.</li> </ul> <p>Those models can be used to predict probabilities on new images using the same code as in the original <a href="https://github.com/dhlab-epfl/dhSegment">dhSegment</a> repository. One needs to adjust three parameters to the <code>predict</code> function: 1) <code>embeddings_path</code> (the path to the embeddings list), 2) <code>embeddings_map_path</code>(the path to the compressed embedding map), and 3) <code>embeddings_dim</code> (the size of the embeddings).</p> <p>Please refer to the paper for further information or contact us.</p> <p> </p> <p><strong>3. CODE: </strong></p> <p><a href="https://github.com/dhlab-epfl/dhSegment-text">https://github.com/dhlab-epfl/dhSegment-text</a></p> <p><br> <strong>4. ACKNOWLEDGEMENTS</strong><br> We warmly thank the journal <a href="https://letemps.ch">Le Temps</a> (owner of <em>La Gazette de Lausanne</em> and the <em>Journal de Genève</em>) and the group <a href="https://www.arcinfo.ch/">ArcInfo</a> (owner of <em>L'Impartial</em>) for accepting to share the related datasets for academic purposes. We also thank the <a href="https://bnl.public.lu/fr.html">National Library of Luxembourg</a> for its support with all steps related to the <em>Luxemburger Wort</em> annotation release.<br> This work was realized in the context of the <a href="https://impresso-project.ch"><em>impresso</em> - Media Monitoring of the Past</a> project and supported by the Swiss National Science Foundation under grant CR- SII5_173719.<br> <br> <strong>5. CONTACT</strong><br> Maud Ehrmann (EPFL-DHLAB)<br> Simon Clematide (UZH)</p>
Base images for the article "Optimization of a frosting process for soda lime silicate glass based on phosphoric acid"
<p>Raw dataset of the optimization of a frosting process for soda lime silicate glass based on phosphoric acid.</p> <p><strong>Naming scheme:</strong></p> <ul> <li>Images starting with <strong>HGr</strong> are frosted using the industrial process. These files represent the reference frosting.</li> <li>Images starting with <strong>HG</strong> are frosted manually following the industrial process.</li> <li>In all other images, the solution concentrations within the preliminary bath are noted als follows: <ul> <li><strong>[c<sub>H3PO4</sub>]-[c<sub>NH4HF2</sub>]_[specimen]_[position].jpg</strong></li> <li>For example 10-2_e_1.jpg: This specimen was treated with a preliminary bath with 10 M-% H<sub>3</sub>PO<sub>4 </sub>and 20 g/L NH<sub>4</sub>HF<sub>2</sub>. It originates from the fith specimen (e) and is the first image of this series.</li> </ul> </li> </ul> <p> </p>
Le concept d'article de données
<p>Réalisée pour l'<a href="https://scienceouverte.univ-lorraine.fr/donnees-de-la-recherche-ul/donnees-de-la-recherche/">atelier de la donnée ADOC Lorraine</a>, cette illustration synthétise le concept d'article de données (angl. <i>data paper</i>). Créant des liens entre les articles de recherche et les données déposées dans un entrepôt adapté, l'article de données permet aux chercheurs et aux chercheuses d'améliorer la qualité de leur recherche, en accord avec les principes de la science ouverte et du partage de données.</p>
Article de données et cycle de vie des données
<p>Réalisée pour l'<a href="https://scienceouverte.univ-lorraine.fr/donnees-de-la-recherche-ul/donnees-de-la-recherche/">atelier de la donnée ADOC Lorraine</a>, cette illustration met en valeur la place d'un article de données (angl. <i>data paper</i>) lors de l'étape de diffusion du cycle de vie des données de la recherche.</p>
Numbers and shares of Open Access Journals in Sociology charging publication fees (article processing charges APCs)
<p>On the eleventh of June 2014 the Directory of Open Access Journals DOAJ listed 109 sociology journals. For these journals the information on publication charges was wrong in more than 10 % of the cases. Focusing on the eleven journals that - according to the DOAJ - used (always or conditionally) APCs the situation is even worse: 10 of these journals (90,9 %) were categorized wrongly.</p> <p>In fact only three out of these 109 journals (2,75 %) are charging their authors. The files contain corrected information for the 109 journals including the sums charged by the journals.</p>
Indexed scientific articles for the seven journals listed on the Design Society website and all papers indexed for the DESIGN and ICED conferences
<p>Includes all articles indexed by Scopus® for the seven journals listed on the Design Society website and all papers indexed for DESIGN and ICED (accessed on 05/Nov/2016).</p> <p>The full search query used to extract the articles is:</p> <p>"( SRCTITLE ( "Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM" OR "Journal of Engineering Design" OR "Design Studies" OR "Research in Engineering Design" OR "CoDesign" OR "Journal of Design Research" OR "Design Science Journal" OR "The International Journal of Design Creativity and Innovation" OR "International Conference on Engineering Design" OR "INTERNATIONAL DESIGN CONFERENCE" ) ) AND ( "data collection" OR "data acquisition" OR "data source" OR database OR "empirical data" OR "empirical grounding" OR interview OR documents OR "data logs" OR "case study" OR observation OR experiment* OR "empirical finding*" OR "empirical result*" ) AND ( EXCLUDE ( EXACTSRCTITLE , "Hardware Software Codesign Proceedings Of The International Workshop" ) OR EXCLUDE ( EXACTSRCTITLE , "Journal Of Engineering Design And Technology" ) OR EXCLUDE ( EXACTSRCTITLE , "Research In Engineering Design Theory Applications And Concurrent Engineering" ) OR EXCLUDE ( EXACTSRCTITLE , "Chinese Journal Of Engineering Design" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2005 International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 12 Proceedings Of The 10th ACM International Conference On Hardware Software Codesign And System Synthesis Co Located With Esweek" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2006 Proceedings Of The 4th International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2007 International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Embedded Systems Week 2008 Proceedings Of The 6th IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2008" ) OR EXCLUDE ( EXACTSRCTITLE , "Second IEEE ACM IFIP International Conference On Hardware Software Codesign And Systems Synthesis Codes Isss 2004" ) OR EXCLUDE ( EXACTSRCTITLE , "Embedded Systems Week 2011 Esweek 2011 Proceedings Of The 9th IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 11" ) OR EXCLUDE ( EXACTSRCTITLE , "2010 IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2010" ) OR EXCLUDE ( EXACTSRCTITLE , "2013 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2013" ) OR EXCLUDE ( EXACTSRCTITLE , "2014 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2014" ) OR EXCLUDE ( EXACTSRCTITLE , "2015 ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2015" ) OR EXCLUDE ( EXACTSRCTITLE , "8th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2010" ) ) AND ( EXCLUDE ( EXACTSRCTITLE , "2015 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2015" ) OR EXCLUDE ( EXACTSRCTITLE , "9th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2011" ) OR EXCLUDE ( EXACTSRCTITLE , "11th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2013" ) OR EXCLUDE ( EXACTSRCTITLE , "10th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2012" ) OR EXCLUDE ( EXACTSRCTITLE , "A Practical Introduction To Hardware Software Codesign" ) )"</p> <p>The keywords used in the article "DATA-DRIVEN ENGINEERING DESIGN RESEARCH: OPPORTUNITIES USING OPEN DATA" are:</p> <p>"data collection" OR "data acquisition" OR "data source" OR database OR "empirical data" OR "empirical grounding" OR interview OR documents OR "data logs" OR "case study" OR observation OR experiment* OR "empirical finding*" OR "empirical result*"</p>
Data set to article "Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink"
<p>The file Data_Marxetal2014_AB.csv contains the data to the paper<br> Marx, S., Hansen-Goos, O., Thrun, M., & Einhäuser, W. (2014). Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink. Journal of Vision, 14(14):4, 1-18, http://www.journalofvision.org/content/14/14/4, doi:10.1167/14.14.4.<br> as comma-separated value (csv) file</p> <p>Each row contains the data of one trial, represented by the following columns</p> <p>1 - number of the line<br> 2 - subject ID<br> 3 - experiment number<br> 4 - color condition (1: gray inverted, 2: gray original, 3: color inverted, 4: color original)<br> 5 - number of targets<br> 6 - SOA in ms<br> 7 - serial position of first target (0 if absent)<br> 8 - serial position of second target (0 if absent)<br> 9 - category of first target (1: feline, 2: avian, 3: ungulate, 4: canine)<br> 10 - category of second target (1: feline, 2: avian, 3: ungulate, 4: canine)<br> 11 - response to "How many animals?" (detection)<br> 12 - response to first category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)<br> 13 - response to second category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)</p>
Data set for article Veto, P., Einhäuser, W., & Troje, N.F. (2017). Biological motion distorts size perception. Scientific Reports, 7, 42576.
<p>In this data set you find 3 files containing data from Experiments 1, 2 & 3 of Veto P, Einhauser W & Troje NF (2017) Biological motion distorts size perception. Scientific Reports, 7, 42576; doi: 10.1038/srep42576</p> <p><br> The data are freely available for academic use only. If you use these data for a publication, please cite the aforementioned article.<br> If you have any questions regarding the data, please do not hesitate to contact Peter Veto at vettop@gmail.com</p> <p>Each row of the files contain data from one trial.<br> Columns:</p> <p>Experiment 1<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Target orientation (1: Upright; -1: Inverted)<br> 5 - Stimulus width<br> 6 - Stimulus height<br> 7 - Response width<br> 8 - Response height</p> <p>Experiment 2<br> 1-8 Same as Experiment 1<br> 9 - Condition: dynamic (1) or static (2) target</p> <p>Experiment 3<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Walker orientation<br> (1: upper walker upright, lower walker inverted;<br> 2: upper walker inverted, lower walker upright)<br> 5 - Condition<br> (1: upper target larger (21%) than lower target;<br> 2: upper target larger (10.5%) than lower target;<br> 3: target sizes are identical;<br> 4: lower target larger (10.5%) than upper target;<br> 5: lower target larger (21%) than upper target)<br> 6 - Inter stimulus interval (from end of walker presentation to onset of target circles)<br> (1: 17ms; 2: 100ms)<br> 7 - Response<br> (1: upper target was larger;<br> 2: lower target was larger)</p>
Dataset supplementing the article Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129.
<p>This dataset supplements the publication<br> Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129. doi: 10.1016/j.visres.2017.02.001</p> <p>Use is free for scientific purposes, provided the aforementioned reference is appropriately cited.<br> Description of files<br> - conditionsByObserver.csv<br> contains for each of the 16 observers the color and grating direction that had been coupled to either the low-pitch or the high-pitch tone<br> column 1: observer number<br> column 2: color associated with low-pitch tone<br> column 3: color associated with high-pitch tone<br> column 4: drift direction associated with low-pitch tone<br> column 5: drift direction associated with high-pitch tone</p> <p>- conditionsByObserver.mat contains the same information as matlab variables (as four vectors/cell arrays with one entry per observer)</p> <p>- toneByBlockAndTrial.csv<br> contains the conditions for all 18 rivalry trials (6 rivalry blocks with 3 trials each) for each observer<br> column 1: observer number<br> column 2: block number<br> column 3: trial number<br> column 4: tone (low [pitch], high [pitch], none) played in this trial<br> Note that due to a technical error for observer #16, block 6 was presented first, followed by 1,2,3,4,5; for all other observers blocks were used in the order given (1,2,3,4,5,6).</p> <p>- toneByBlockAndTrial.mat contains the same information as a 16x6x3 matrix named toneByBlockAndTrial ; tones are coded numerically (1-low pitch,2-high pitch,3-none)</p> <p>- eyeTraces.mat contains three cell arrays of dimensions 16x6x3 (observer x rivalry block x rivalry trial) called xEye, oknGain, and timeSinceTrialStart;</p> <p>o each entry of xEye contains the horizontal eye position for<br> the respective trial in eye-tracker coordinates (which correspond to screen pixels, except that (1/1) is the upper right rather than the upper left and values increase from right to left due to the setup configuration)</p> <p>o oknGain contains the gain computed from these eye positions.</p> <p>o timeSinceTrialStart contains the time in seconds since onset of the trial</p> <p><br> For all variables, the sampling rate is 500 Hz, in eye-tracker coordinates the speed of the grating is 240 units/ms. Blinks were removed from both eye-data variables, fast-phases were removed from the gain data. Removed data were set to NaN in eye-data variables.</p> <p>- Matlab functions figure1d.m, figure 2.m, figure3.m and figure4.m compute raw versions of the aforementioned paper's figures from the datafiles to exemplify their usage.</p> <p>[Note: In the originally published version of the article, the first two means and their standard errors of section 3.3 were stated incorrectly. All figures and statistical analyses are based on the correct data].</p>
Datasets to article "Selection history alters attentional filter settings persistently and beyond top-down control"
<p>Single-Subject Behavioral and ERP mean amplitude data for Experiments 1 to 3.</p>
Moore grantee list of articles
<p>Data collected on publications authored by grantees of the Gordon and Betty Moore Foundation from 2001 - 2017. Data are based on self reporting from grantees and Crossref FundRef search. <strong>These data are not intended to represent a complete list of publications from grantees.</strong> This data was used to understand the potential impacts of an open access policy under consideration at the Moore Foundation. </p>
Dataset and software for support of the article "To Chaos or Not To Chaos: going into the resilience of the ecosystem".
<p>The CAVeg model, as well as its dependencies, and the CAVeg data files with the configurations for the two and three ecophysiological types, including the data needed to perform the Lyapunov exponents calculations with the CRAN-R DChaos package are available in this dataset.</p>
The data behind the ApJ article "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters"
<p>Data from "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters", <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230601088L/abstract">NASA ADS</a></p><p>inner_cluster_data.csv and outer_cluster_data.csv include the SALT3 mB, x1, and c parameter values, distance moduli and Hubble residuals (with _1 referring to Figure 9 and _2 referring to Figure 10), outlier designation from MCMC procedure, host cluster, host cluster redshift (with Hubble diagram version converted to frame of CMB), host cluster r500, projected separation from cluster center, NED Host galaxy name, photometrically-derived estimate for host mass, host or SN redshift used in analysis, and the Host SFR category (Q: quiescent, SF: star-forming, GV: green valley) for our cluster SNe Ia.</p><p>sf_field.csv and quiescent_field.csv contain SALT parameter values, distance moduli and Hubble residuals (from Figure 10), host galaxy sSFR and mass measurements, and host redshifts (all spectroscopic, also with Hubble diagram converted values) for SNe Ia in our field samples.</p><p>full_cluster.csv contains the data from the table in the appendix of the paper.</p><p>The inner_cluster_/outer_cluster_mcmc_samples.csv files contain the samples needed to reproduce the corner plot for Figure 10.</p><p>The Python scripts recreate the figures from the paper given the above data. The details for which columns and constraints needed to reproduce the figures are included in these files.</p>
Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023).
<p>Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023), DOI 10.1038/s41467-023-41718-4, include images, data used for generate that images, input files, converged potential files used for the calculations on SPR-KKR package 8.6. and raw data files.</p>
Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in Dec 2023.
<p>Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in NHESS journal in Dec 2023.</p> <p>For further technical details read the file READMEdata in the zipfile. The script MAKEFIG remakes all the figures from the data.</p> <p>For scientific details read the associated article preprint on the NHESS egusphere website.</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.