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6,059
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Dataset results
6,059 results for “Journale”
Electronic representation of Russian journals on Earth Sciences
<p>The dataset describes the deprth of digital archives of the most authoritative Russian journal on Earth Sciences. The five figures demostrate the results of graphical processing while preparing digital archives of Geologiya i Geofizika and Zapiski Gornogo Instituta journals, as well as the forms of metadata presentation in electronic archives.</p>
Supplementary material for journal article "Challenge dose titration in a Mycobacterium bovis infection model in goats"
<p>Supplementary Figure and Table to Journal article. Figure shows daily rectal temperature of each animal after inoculation. Table 1 shows number and volume of pulmonary lesions for each animal as detected by computed tomography imaging. Table 2 gives details about scoring used at clinical examination.</p>
Dataset related to the Journal Article 'Efficiency Enhancement of Marine Propellers via Reformation of Blade Tip-Rake Distribution'
<p>This Dataset contains results related to the Graphs shown in the publication titled "Efficiency Enhancement of Marine Propellers via Reformation of Blade Tip-Rake Distribution". The results refer to open water performance curves for the benchmark propeller geometries and the models with optimal tip-rake. In the Folder we provide the data for each figure in a specific folder with the number corresponding to the number of the figure in the published version of the paper. </p>
Data archive for the peer-reviewed journal article "Links between atmospheric aerosols and sea state in the Arctic Ocean"
<p>This dataset accompanies the peer-reviewed journal article titled "Links between atmospheric aerosols and sea state in the Arctic Ocean" which was accepted for publication in the Journal of Atmospheric Environment in September 2024, https://doi.org/10.1016/j.atmosenv.2024.120844. </p> <p>This dataset contains information on sea surface properties, meteorology, and aerosol data from measurements conducted during the Arctic Century Expedition which was carried out in August and September of 2021 in the Russian Arctic region. The dataset contains the following information:</p> <p><br>1) aerosol_size_distributions.csv: The hourly averaged time-series of aerosol size distribution measurements from an aerodynamic particle sizer. Further information for this data file is provided in Meta_data_for_aerosol_size_distributions.txt.</p> <p><br>2) aerosol_composition_and_volume.csv: Time series of mass concentrations of Na+Mg (SSA proxy) and Al+Si+Ca (dust proxy) in aerosol particles collected on filters. The time-series also contains aerosol volume concentration information for the coarse and fine aerosol categories, i.e., samples with count median diameters larger than 0.99 µm and smaller than 0.99 µm, respectively. Further information for this data file is provided in Meta_data_for_aerosol_composition_and_volume.txt. </p> <p><br>3) sea_surface_elevation_time_series.pkl: a pickle file containing the sea surface elevation time-series. The sea surface elevation data was extracted from 3D-reconstructed sea surface data. The 3D reconstruction of the sea surface was achieved by processing stereoscopic images of the sea surface using the Waves Acquisition Stereo System (WASS) software (Bergamasco et al., 2017). Further information for this data file is provided in Metadata_for_sea_surface_elevation_time_series.txt.</p> <p><br>4) aerosol_meteo_wave_merged_data.csv: This file contains the time-series of merged hourly averages of aerosol number concentrations, meteorological data, environmental data, and sea surface properties. The dataset also contains the average coordinate of the research vessel and its distance to land masses throughout the expedition. The meteorological data were measured during the expedition and the original unmerged data are available in Thurnherr et al. (2024). Other environmental data, such as sea surface temperature, are obtained from the fifth generation ECMWF reanalysis for the global climate and weather (ERA5, Hersbach et al., 2023), and sea ice concentration was obtained from AMSR-2 daily satellite measurements (Copernicus Climate Change Service (C3S), 2020). Sea surface properties are extracted from time series of sea surface elevation. Further information for this data file is provided in Metadata_for_aerosol_meteo_wave_merged_data.txt.</p>
Dataset related to the Journal Article 'A deep learning method for the prediction of ship fuel consumption in real operational conditions'
<p>This dataset contains the data used to plot the graphs and create tables corresponding to the figure/table number in the published version of the paper.<br>Paper DOI:https://doi.org/10.1016/j.engappai.2023.107425</p> <p> </p>
Data accessibility in the chemical sciences: an analysis of recent practice in organic chemistry journals
<div> <p>Data is the analysis of the data outputs of 240 randomly selected research papers from 12 top-ranked journals published in early 2023. We investigate author compliance with recommended (but not compulsory) data policies, whether there is evidence to suggest that authors apply FAIR data guidance in their data publishing, and if the existence of specific recommendations for publishing NMR data by some journals encourages compliance. Files in the data package have been provided in both human and machine-readable forms. The main dataset is available in the Excel file Data worksheet.XLSX, the contents of which can also be found in Main_dataset.CSV, Data_types.CSV, and Article_selection.CSV with explanations of the variable coding used in the studies in Variable_names.CSV, Codes.CSV, and FAIR_variable_coding.CSV. The R code used for the article selection can be found in Article_selection.R. Data about article types from the journals that contain original research data is in Article_types.CSV. Data collected for analysis in our sister paper[4] can be found in Extended_Adherence.CSV, Extended_Crystallography.CSV, Extended_DAS.CSV, Extended_File_Types.CSV, and Extended_Submission_Process.CSV. A full list of files in the data package and a short description for each is given in README.TXT.</p> </div>
Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics
<p>Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics.</p> <p>Including the simulation input parameter file and the necessary output data to plot each figure in the article. </p>
Article fit data for selected journals (2015-2019)
<p>This dataset contains metadata and article fit scores for articles published in selected scholarly journals from 2015-2019 (inclusive), as well as a random sample of articles assigned to random journal IDs, and their fit scores therein. Article metadata is drawn from OpenAlex using the openalexR package ca. April 2024.</p>
Coral growth data for the research article "Reconstruction of long-term sub-lethal effects of warming on a temperate coral in a climate change hotspot" in Journal of Animal Ecology
<p>This repository contains the coral growth data files used to generate the results for the following article:</p> <p> </p> <p>MJ. Vergotti, JP. D’Olivo, T. Brachert, P. Capdevila, J. Garrabou, C. Linares, P. Spreter, DK. Kersting (2024) Reconstruction of long-term sub-lethal effects of warming on a temperate coral in a climate change hot-spot. <em>Journal of Animal Ecology</em>. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.14225</p> <p> </p> <p><strong>Abstract: </strong>The impact of warming on zooxanthellate corals is widespread, from tropical to temperate seas, with their associated mortalities causing global concern. The temperate coral <em>Cladocora caespitosa</em> is the only zooxanthellate coral with reef-building capacity in the Mediterranean Sea, a climate change hotspot with warming rates triple the global average. Over the past two decades, <em>C. caespitosa</em> populations have suffered severe mortality events associated with marine heatwaves (MHWs). However, with monitoring efforts beginning, at best, in the 2000s, the occurrence of MHWs before to that period, as well as the sub-lethal effects of these events remain poorly understood. Here we use sclerochronology to reconstruct the histories of past stress events and long-term sub-lethal effects on <em>C. caespitosa</em> in three locations within the NW Mediterranean Sea, each with different environmental conditions. Skeletal extension, density and calcification rates were compared to the <em>in situ</em> seawater temperature of each site to assess their relationship. Additionally, we assessed the occurrence of skeletal growth anomalies to reconstruct stress events between 1991 and 2021, a period that encompasses the onset and evolution of warming-related mass mortality events in the NW Mediterranean Sea. Our results reveal a positive association between calcification and temperature, following a latitudinal temperature gradient. However, the evolution of the likelihood distribution of growth rates in the warmest site (Columbretes Islands) since the 1990s indicates a decrease in linear extension and calcification rates during the most recent years. With the increase in the frequency of MHWs and growth anomalies during the last decade, this decline suggests a recurrence in physiological stress events. These results unravel information on the long-term impacts of warming on coral growth and highlight the potential of applying sclerochronology to reconstruct sub-lethal effects of warming using <em>C. caespitosa</em>. </p> <p> </p> <p> </p> <p><strong>Funding</strong>: This research is supported by the Horizon 2020 program of research and innovation of the European Union under the MaCoBioS grant agreement, by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project no. 401447620) and by the Spanish Ministry of Science, Innovation and Universities under the project UndResCoral (project no. PID2022-137539OA-C22). D.K.K. was supported by a Ramon y Cajal postdoctoral grant funded by the Ministry of Science and Innovation (PEICTI 2021–2023; grant no. RYC2021-033576-I). C.L. acknowledges the support by ICREA Academia. J.G. acknowledges the grant “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S) funded by AEI 10.13039/501100011033.</p>
Intermediate data products for: Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar (Zippel et al. 2021, Journal of Atmospheric and Oceanic Technology)
<p>This repository contains some of the intermediate data products needed to reproduce the results in the <em>Journal of Atmospheric and Oceanic Technology</em> article "Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar" by S.F. Zippel, J. T. Farrar, C. J. Zappa, U. Miller, L. St. Laurent, T. Ijichi, R. A. Weller, L. McRaven, S. Nylund, and D. Le Bel. Specifically, this material should allow reproduction of Figures 3, 5-7, 12 and 13. Reproduction of Figures 8-11 also requires data from associated glider deployments nearr the SPURS-1 mooring, which may be requested from co-author L. St. Laurent.</p> <p>Code to do the analysis and make the plots is here: https://github.com/zippelsf/MooredTurbulenceMeasurements</p> <p>Matlab data files:</p> <p>(1) 677404_burst1865.mat</p> <p>Single-burst data used for the example spectral fit in Figure 7. The burst was collected during the SPURS-1 project at 21.5m depth. The data collection and processing methods are described in detail in Section 2. </p> <p>(2) 811604_burst0510.mat (Single-burst data used in the unwrapping example, Figure 5)</p> <p>(3) 8116_dissipation_timeseries.mat (Used for associated ancillary data in Figure 6)</p> <p>(4) 913411_burst2879.mat (Single-burst data, used for ancillary data to make Figure 3).</p> <p>(5) BuoyancyFlux_b.mat</p> <p>Ocean buoyancy flux estimates for SPURS-2 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(6) BuoyancyFlux_c.mat</p> <p>Ocean buoyancy flux estimates for SPURS-1 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(7) SPURS1_dissipation_grid_v1d.mat</p> <p>Gridded TKE dissipation rates for SPURS-1 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 8-13. Dissipation rates also available on NASA's PODAAC.</p> <p>(8) spurs1_met_1hr.mat (Processed met data from SPURS-1 mooring. Also available on WHOI's UOP website.)</p> <p>(9) SPURS2_dissipation_grid_v1c.mat</p> <p>Gridded TKE dissipation rates for SPURS-2 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 12. Dissipation rates also available on NASA's PODAAC.</p>
List of overlay journals
<p>This is a listing of known overlay journals that organise peer-review on top of preprints.</p> <p>In this dataset are the following attributes of each journal:</p> <p>* Name of journal</p> <p>* ISSN of journal (if it has one)</p> <p>* the preprint server or repository that manuscripts are placed-on for review by the overlay journal</p> <p>* the discipline the journal covers</p> <p>* the URL of the journal</p> <p>* the number of articles the journal published in 2020</p> <p>* the year the journal commenced publishing</p> <p>* and a few other notes...</p>
Dataset for "Beating 1 Sievert: Optimal Radiation Shielding of Astronauts on a Mission to Mars" publication in Space Weather journal
<p>Datasets in .fig Matlab format and figures in .jpg format published in Space Weather journal</p> <p>effectiveDoseRF.mat contains the effective dose "response functions" and an example (how2useDoseResponceFunctions.m) of how to use them to assess GCR dose.</p>
Age-dependent extreme event exposure - data accompanying journal publication
<p>This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science</p> <p> </p> <p>Input fields:</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/isimip.zip">isimip.zip</a>: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data.</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/GMT_50pc_manualoutput_4pathways.xlsx">GMT_50pc_manualoutput_4pathways.xlsx</a>: Global mean temperature anomaly trajectories from the IPCC SR15</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/wcde_data.xlsx">wcde_data.xlsx</a>: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer.</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx">WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx</a>: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme</p> <p> </p> <p>Intermediate files *only use if you're interested in reproducing the results*:</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/workspaces.zip">workspaces.zip</a>: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis.</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_isimip.mat">mw_isimip.mat</a>: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation)</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_countries.mat">mw_countries.mat</a>: information on the countries used in the analysis (e.g. border polygon coordinates)</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_exposure.mat">mw_exposure.mat</a>: age-dependent exposure computed from the ISIMIP and population data</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_exposure_pic.mat">mw_exposure_pic.mat</a>: pre-industrial control age-dependent exposure computed from the ISIMIP and population data</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_exposure_pic_coldwaves.mat">mw_exposure_pic_coldwaves.mat</a>: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data</p> <p> </p> <p> </p> <p>Output of the analysis:</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_output.mat">mw_output.mat</a>: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.</p> <p> </p> <p> </p>
Replication package for technical lag analysis for the JSEP journal.
<p>This is the replication package for our article "A Formal Framework for Measuring Technical Lag in Component Repositories --- and its Application to npm" submitted for the JSEP journal in 2018.</p> <p>This replication package requires Python 3.5+ to be installed, and all the dependencies listed in ``requirements.txt``.<br> They can be automatically installed using ``pip install -r requirements.txt``. <br> These experiment were executed on a Linux Ubuntu OS.</p> <p>To obtain the analysis used in the paper, one should execute ``jupyter notebook`` at the root of this replication package, and open the notebook contained in ``notebooks``.</p> <p>This replication package contains three folders (i.e scripts, notebooks and data), each folder has a README with a description of what it contains.</p> <p>The list of all npm package releases and Github repositories with their dependencies was download from the last available dataset of libraries.io: https://zenodo.org/record/1196312</p> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license.<br> The source code is under the GNU General Public License.</p> <p><br> For any more information about the details of these experiments, please contact: <strong><a href="mailto:ahmed.zerouali73@gmail.com">ahmed.zerouali73@gmail.com</a></strong></p>
List of data journals
<p>This document describes a dataset that aggregates information about 135 data journals.<br> Data journals focus on the publication of data papers -- a specialized publication type describing datasets, their collection and reuse potential that is peer-reviewed, citable and indexed.<br> This dataset includes a comprehensive list of data journals that was compiled by aggregating existing sources, as well as an overview of these sources.</p> <p> </p> <p>The list is continually updated on GitHub, where additional information on data journals (URLs of data journal homepages) is provided: <a href="https://github.com/MaxiKi/data-journals">https://github.com/MaxiKi/data-journals</a></p>
Coverage of DOAJ journals' citations through OpenCitations - Result DataSet
<p>The dataset contains: </p> <ul> <li><strong>by_journal.json</strong>: a file containing all information extracted by Open Citations about DOAJ journals divide by year and journal name. Inside the file, the metadata about the journal are: <ul> <li>ISSN</li> <li>EISSN</li> <li>number of articles overall in the journal</li> <li>subject(s) </li> <li>number of citations received</li> <li>number of citations done</li> <li>ratio between citations done and received</li> <li>number of citations received from DOAJ journals</li> <li>number of citations done to DOAJ journals</li> <li>ratio between citations done to and received from DOAJ journals.</li> </ul> </li> </ul> <ul> <li><strong>normal.json</strong>: a file containing all information extracted from Open Citations about DOAJ journals divided only by year. Inside the file, the data by year are: <ul> <li>number of citations received.</li> <li>number of citations done.</li> <li>ratio between citations done and received.</li> <li>number of self-citations made by DOAJ inside Open Citations.</li> <li>ratio between the self-citation and the total citations received and done by DOAJ.</li> </ul> </li> </ul> <ul> <li><strong>errors.json</strong>: a file containing the count of all errors obtained from computations. Inside the file: <ul> <li>errors about records that don't have any specified date (null dates).</li> <li>errors about records that have impossible dates (wrong dates).</li> <li>errors about articles that don't have any specified Dois.</li> <li>errors about Open Citations records that don't have any Dois in the citing or cited fields.</li> </ul> </li> <li><strong>DOAJ_metrics.json</strong>: a file containing metrics about DOAJ and Open Citations, obtained by computations. Inside the file are these fields: <ul> <li>number of journals with dois.</li> <li>number of articles which have been processed during computations.</li> <li>number of used Dois. All dois (with no repetition) which are used for the adding journal operation.</li> <li>number of repeated Dois. All dois which are repeated inside the same or in another journal.</li> <li>number of accepted Dois. All articles (with repetition) which have both a defined journal and a defined doi.</li> </ul> </li> </ul> <p> </p>
Dataset supplementing the journal article describing the pyfastspm Python package
<p>This dataset represents a fast scanning tunneling microscopy image sequence, acquired with the FAST SPM module, showing a reduced Fe3O4(001) surface observed at 657 K during oxygen exposure. This is used as example dataset in the journal article describing the pyfastspm Python package (SoftwareX 21 (2023) 101269, Figure 3 and 4, Supporting Information).</p>
Data of hybrid vesicles fusion for Nano Letters' journal article
<p>Dataset to accompany the manuscript "Thermoplasmonic induced vesicle fusion for investigating membrane protein phase affinity"</p>
Datasets supplementing journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy" in Nanoscale 2022
<p>Datasets supporting the Nanoscale journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy".</p> <p>NAP-XPS.zip: Near-ambient pressure X-ray photoelectron spectroscopy, Figures 2, 3. (NEP 101007417)</p> <p>STM.zip: Scanning tunneling microscopy, Figure 5a, inset. (NEP 101007417)</p> <p>TPD.zip: Temperature programmed desorption, Figure 1a.</p> <p>UHV-XPS.zip: X-ray photoelectron spectroscopy, Figure 1b,c.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007417, having benefited from the access provided by by ALBA in Barcelona (Spain) and CNR-IOM in Trieste (Italy) within the framework of the NFFA-Europe Pilot Transnational Access Activity, proposal ID075.</p>
data for journal article 'Nernst-Ettingshausen effect in thin Pt and W films at low temperatures'
<p>This dataset contains the supporting information for the journal article 'Nernst-Ettingshausen effect in thin Pt and W films at low temperatures'.</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.