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1,079 results for “source data”
Source Data for Manuscript: "Retrievals Applied To A Decision Tree Framework Can Characterize Earth-like Exoplanet Analogs"
<p>This dataset accompanies the manuscript entitled: "Retrievals Applied To A Decision Tree Framework Can Characterize Earth-like Exoplanet Analogs", which was accepted for publication in the Planetary Science Journal. Included are the source files for all figures included in the paper.</p>
Code and Source Data for "Knowledge-Guided Machine Learning can improve C cycle quantification in agroecosystems"
<p>Datasets for code and Source Data for the study "Knowledge-Guided Machine Learning can improve C cycle quantification in agroecosystems" https://doi.org/10.1038/s41467-023-43860-5. All files belong to Licheng Liu and Zhenong Jin at University of Minnesota. deposit_code_v2.zip contains packaged codes and sample runs for KGML-ag-Carbon training, validation and implementations. Source Data.zip contains data for generating the figures inside the study. </p> <p>Note: We used Pytorch 1.6.0 (<a href="https://pytorch.org/get-started/previous-versions/">https://pytorch.org/get-started/previous-versions/</a>, last access: 21 Oct 2023) and Python 3.7.11 (<a href="https://www.python.org/downloads/release/python-3711/">https://www.python.org/downloads/release/python-3711/</a>, last access: 21 Oct 2023) as the programming environment for model development. Statistical analysis, such as linear regression, was conducted using Statsmodels 0.14.0 (<a href="https://github.com/statsmodels/statsmodels/">https://github.com/statsmodels/statsmodels/</a>, last access: 21 Oct 2023) In order to use a GPU to speed-up the training process, we installed the CUDA Toolkit 10.1.243 (<a href="https://developer.nvidia.com/cuda-toolkit">https://developer.nvidia.com/cuda-toolkit</a>, last access: 21 Oct 2023). </p> <p><strong>To use the full kgml_lib function, please create a new environment with the same python and libs above.</strong></p>
Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China"
<p>Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China".</p><p>Transformations among the parameters of the moment tensor model refer to the code package from Tape and Tape (https://github.com/carltape/mtbeach/; https://github.com/carltape/surfacevel2strain; Tape and Tape, 2009, 2012, 2013, 2015).</p><p>Tape, C., P. Muse, M. Simons, D. Dong, and F. Webb (2009). Multiscale estimation of GPS velocity fields, Geophys. J. Int. 179, no.2, 945-971, doi: 10.1111/j.1365-246X.2009.04337.x.</p><p>Tape, W., and C. Tape (2012). A geometric setting for moment tensors, Geophys. J. Int. 190, no. 1, 476–498, doi: 10.1111/j.1365-246X.2012.05491.x.</p><p>Tape, W., and C. Tape (2013). The classical model for moment tensors, Geophys. J. Int. 195, no. 3, 1701–1720, doi: 10.1093/gji/ggt302.</p><p>Tape, W., and C. Tape (2015). A uniform parametrization of moment tensors, Geophys. J. Int. 202, no. 3, 2074–2081, doi: 10.1093/gji/ggv262.</p>
Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes input data to run the OnStove Nepal model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All result files and figures can be downloaded from the permanent repository <a href="https://doi.org/10.5281/zenodo.10643983">https://doi.org/10.5281/zenodo.10643983</a>.</p> <p>The "<strong>GIS_input_data/</strong>" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "<strong>1. Data</strong>"<strong> </strong>folder in your project. </p> <p>The "<strong>Scenario_inputs/</strong>" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the <strong>supplementary material</strong> of the related publication in the link <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>. To run the model extract the scenario data inside your "<strong>2. Scenario inputs</strong>"<strong> </strong>folder in your project. </p>
Supporting Data Sources
<p>The file contains supporting data sources for the research paper entitled "<em>QUANTITATIVE EVALUATION OF SUSTAINABLE MARKETING EFFECTIVENESS: A POLISH CASE STUDY" </em>submitted to a selected scientific journal for a prospective publication.</p>
Reproducibility code and data - Understanding cetacean habitats in the Eastern Caribbean: a study combining data from multiple sources
<p>This R project reproduces the analyses carried out in the article entitled “Modelling cetacean habitats in the Eastern Caribbean: a study combining data from multiple sources” submitted to PCI Ecology. Analyses involve two steps (modelling 1: exploratory; and modelling 2: inferential).</p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 4 relative to Figure 7 – ArhGEF11 CRISPR interference
<p><span>Raw image files (TIFF format), corresponding 2D-cartographies (_2Dmap.tiff files) and metadata files for 2D-cartographies (.xml files, readable with the opensource software Icy), relative to <strong>Figure 7B </strong>and<strong> Figure 7 - Figure Supplement 6</strong> (see <strong>Materials and Methods — Morphological and morphometric analysis of aortic and hemogenic cells</strong>).</span></p> <p><span>The source data comprises for each 48 - 55 hpf <em>Tg(kdrl:eGFP-JAM3b; kdrl:nls-mKate2)</em> zebrafish embryo 3 z-stack and 2D cartographies (segments 1 to 3) encompassing the whole length of the aorta, for control condition (n = 2 individuals) and morpholino splicing interference condition (n = 2 individuals). For z-stacks of both control and morphant conditions, two fluorescence channels were acquired, corresponding to the nuclear mKate2 expressed in endothelial cells and the eGFP-JAMs signal localized at the intercellular junctions of endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm. 2D-cartographies were obtained using the Icy plugin “TubeSkinner”, and the semi-manual segmentation of all aortic cells can be uploaded from the corresponding metadata file on the 2D-cartographies using the load ROI function of Icy.</span></p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 3 relative to Figure 7 – ArhGEF11 morpholino splicing interference
<p><span>Raw image files (TIFF format), corresponding 2D-cartographies (_2Dmap.tiff files) and metadata files for 2D-cartographies (.xml files, readable with the opensource software Icy), relative to <strong>Figure 7A </strong>and<strong> Figure 7 - Figure Supplement 5</strong> (see <strong>Materials and Methods — Morphological and morphometric analysis of aortic and hemogenic cells</strong>).</span></p> <p><span>The source data comprises for each 48 - 55 hpf <em>Tg(kdrl:eGFP-JAM2a; kdrl:nls-mKate2)</em> zebrafish embryo 3 z-stack and 2D cartographies (segments 1 to 3) encompassing the whole length of the aorta, for control condition (n = 2 individuals) and morpholino splicing interference condition (n = 3 individuals). For z-stacks of both control and morphant conditions, two fluorescence channels were acquired, corresponding to the nuclear mKate2 expressed in endothelial cells and the eGFP-JAMs signal localized at the intercellular junctions of endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm. 2D-cartographies were obtained using the Icy plugin “TubeSkinner”, and the semi-manual segmentation of all aortic cells can be uploaded from the corresponding metadata file on the 2D-cartographies using the load ROI function of Icy.</span></p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 1 relative to Figure 3
<p><span>Raw image files (TIFF format) relative <strong>to Figure 3</strong> (see <strong>Materials and Methods — Dt-runx1 phenotype analysis – cell count</strong>).</span></p> <p><span>The source data comprises for each 52 - 55 hpf zebrafish embryo 3 z-stack (segments 1 to 3) encompassing the whole length of the aorta, for control condition (<em>Tg(Kdrl:Gal4;UAS:RFP), </em>n = 3 individuals) and mutant condition (<em>Tg(kdrl:Gal4;UAS:RFP;4xNR:dt-runx1-eGFP), </em>n = 7 individuals). For control condition, one fluorescence channel was acquired, corresponding to the cytoplasmic RFP expressed in endothelial cells. For mutant condition, two fluorescence channels were acquired, corresponding first to the cytoplasmic RFP expressed in endothelial cells using the same reporter as for the control condition, and second the cleaved cytoplasmic GFP reporting the expression of our dt-runx1 mutant construct in endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm.</span></p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 2 relative to Figure 4
<p><span>Raw image files (TIFF format) and segmented 3D images (.ims, Imaris proprietary files) relative to <strong>Figure 4</strong> and <strong>Figure 4 Figure Supplement 3</strong> (see <strong>Materials and Methods — RNAscope image analysis – Pard3</strong>).</span></p> <p><span>The source data comprises for each 52 - 55 hpf zebrafish embryos 2 z-stack (segments 1 to 2) encompassing the whole length of the aorta, for control condition (<em>Tg(Kdrl:eGFP), </em>n = 7 individuals) and mutant condition (<em>Tg(kdrl:Gal4; 4xNR:dt-runx1-eGFP), </em>n = 12 individuals). For both control and mutant conditions, two fluorescence channels are displayed, corresponding to the cytoplasmic GFP expressed in endothelial cells (in green) and the RNAscope signal (OPAL-570, in magenta). Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.4 µm. The .ims files contain the 3D rendering of the z-stacks as well as the segmentations of Pard3ba mRNA RNAscope spots (in magenta), in the aorta (Spots 1 Selection) or outside (Spots 1), as well as the segmentation of endothelial cells (green) (Cells 1) and hemogenic endothelial cells (Cells 1 Cell export).</span></p>
Source data for "Halving the North Sea's offshore wind energy carbon footprint"
<p>This dataset provides source data for the paper "Halving the North Sea’s offshore wind energy carbon footprint". It contains basic geographical factors, including wind speed, water depth, and distance from shore, and environmental impact intensities, including steel, Cu, and Al use, climate change, marine ecotoxicity, and marine eutrophication impacts. For more details, please refer to https://pubs.acs.org/doi/full/10.1021/acs.est.2c02183 and https://www.sciencedirect.com/science/article/pii/S1364032122004993. </p>
Data Sources for Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis
<p><strong>Dataset Name:</strong><br><em>Literature Data, Archetype Parameter Sheets, and Schedules for the publication, named Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis</em>.</p> <p><strong>Description:</strong><br>This dataset includes Excel sheets containing literature sources, archetypal data, and schedules for Nigerian residential dwelling typologies.</p> <p><strong>Files:</strong><br>The following files are included in the dataset:</p> <ul> <li> Nigeria<em>_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> Nigeria<em>_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li> Nigeria<em>_Schedules.xlsx:</em> Excel sheet containing the operation schedules compiled from literature sources and reorganized by expert consensus and given in Designbuilder input format.</li> </ul> <p><strong>Usage:</strong><br>The dataset is intended for researching and analyzing the Nigerian residential buildings. The literature sources included in the Nigeria_LiteratureSources.xlsx and Nigeria_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br>The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br>If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Chibuikem Chrysogonus Nwagwu, Sahin Akin, and Edgar G. Hertwich. 2024. “Data Sources for Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis” https://doi.org/10.5281/zenodo.10995123</p> <p><strong>Contact:</strong><br>The archetypes' energy models (DesignBuilder or IDF files) as well as full material and energy use result sheets can be provided on request. If you have any questions or comments about the dataset, please contact <strong>chibuikem.nwagwu@sintef.no, the corresponding author.</strong></p>
Multiwavelength Constraints on the Origin of a Nearby Repeating Fast Radio Burst Source in a Globular Cluster (Public Data Release)
<p>This Zenodo dataset contains the data for radio bursts B1-B9 from FRB 20200120E, as described in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) (see: https://doi.org/10.1038/s41550-024-02386-6).</p> <p>The following data products are included:</p> <ul> <li>Channelized total intensity (Stokes I) data containing radio bursts B1-B5 from FRB 20200120E, recorded using the Effelsberg radio telescope during Pinpointing Repeating CHIME Sources with the EVN (PRECISE) VLBI observations. These data have a time resolution of 8 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b1_8us_burst_data.npy</li> <li>frb20200120e_b2_8us_burst_data.npy</li> <li>frb20200120e_b3_8us_burst_data.npy</li> <li>frb20200120e_b4_8us_burst_data.npy</li> <li>frb20200120e_b5_8us_burst_data.npy</li> </ul> </li> <li>Channelized total intensity (Stokes I) data containing radio bursts B6-B9 from FRB 20200120E, recorded using the Effelsberg radio telescope. These data have a time resolution of 64 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b6_64us_burst_data.npz</li> <li>frb20200120e_b7_64us_burst_data.npz</li> <li>frb20200120e_b8_64us_burst_data.npz</li> <li>frb20200120e_b9_64us_burst_data.npz</li> </ul> </li> <li>Frequency-summed total intensity (Stokes I) burst profiles of radio burst B4. The frequency range and time resolution of the data are listed below. These data were used in Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).<br> <ul> <li>frb20200120e_b4_8us_1254-1510mhz_burst_profile.npz; (frequency range, time resolution) = (1254-1510 MHz, 8 μs)</li> <li>frb20200120e_b4_1us_1302-1478mhz_burst_profile.npy; (frequency range, time resolution) = (1302-1478 MHz, 1 μs)</li> <li>frb20200120e_b4_31.25ns_1398-1414mhz_burst_profile.npy; (frequency range, time resolution) = (1398-1414 MHz, 31.25 ns)</li> </ul> </li> </ul> <p>We also provide the following Python code containing functions that can be used to load and plot the radio data. The plots generated by this code are similar to those shown in Figure 1 and Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).</p> <ul> <li>plot_frb20200120e_radio_data_pearlman+2024_nature_astronomy.py</li> </ul> <p>The X-ray data (from <em>NICER</em>, <em>XMM-Newton</em>, <em>Chandra</em>, and <em>NuSTAR</em>) used in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) are publicly available and can be accessed through NASA's High Energy Astrophysics Science Archive Research Center (HEASARC) archive.</p> <p>If the data or Python code included in this Zenodo repository are used, please include the following two citations in your work:</p> <ol> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster. <em>Nature Astronomy</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.1038/s41550-024-02386-6</a></li> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster (public data release). <em>Zenodo</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.5281/zenodo.13359005</a></li> </ol> <p>If you have questions about the contents of this Zenodo repository, please contact the lead author: Dr. Aaron B. Pearlman (<a href="mailto:aaron.b.pearlman@physics.mcgill.ca">aaron.b.pearlman@physics.mcgill.ca</a>)</p>
Resources of IncRML: Incremental Knowledge Graph Construction from Heterogeneous Data Sources
<h2>IncRML resources</h2> <p>This Zenodo dataset contains all the resources of the paper 'IncRML: Incremental Knowledge Graph Construction from Heterogeneous Data Sources' submitted to the Semantic Web Journal's Special Issue on Knowledge Graph Construction. This resource aims to make the paper experiments fully reproducible through our <a href="https://github.com/kg-construct/exectool" target="_blank" rel="noopener">experiment tool</a> written in Python which was already used before in the <a href="https://doi.org/10.5281/zenodo.7837289" target="_blank" rel="noopener">Knowledge Graph Construction Challenge by the ESWC 2023 Workshop on Knowledge Graph Construction</a>. The exact Java JAR file of the RMLMapper (rmlmapper.jar) is also provided in this dataset which was used to execute the experiments. This JAR file was executed with Java OpenJDK 11.0.20.1 on Ubuntu 22.04.1 LTS (Linux 5.15.0-53-generic). Each experiment was executed 5 times and the median values are reported together with the standard deviation of the measurements.</p> <h2>Datasets</h2> <p>We provide both dataset dumps of the GTFS-Madrid-Benchmark and of real-life use cases from Open Data in Belgium.<br>GTFS-Madrid-Benchmark dumps are used to analyze the impact on execution time and resources, while the real-life use cases aim to verify the approach on different types of datasets since the GTFS-Madrid-Benchmark is a single type of dataset which does not advertise changes at all.</p> <h3>Benchmarks</h3> <ul> <li>GTFS-Madrid-Benchmark: change types with fixed data size and amount of changes: additions-only, modifications-only, deletions-only (11 versions)</li> <li>GTFS-Madrid-Benchmark: amount of changes with fixed data size: 0%, 25%, 50%, 75%, and 100% changes (11 versions)</li> <li>GTFS-Madrid-Benchmark: data size with fixed amount of changes: scales 1, 10, 100 (11 versions)</li> </ul> <h3>Real-world datasets</h3> <ul> <li>Traffic control center Vlaams Verkeerscentrum (Belgium): traffic board messages data (1 day, 28760 versions)</li> <li>Meteorological institute KMI (Belgium): weather sensor data (1 day, 144 versions)</li> <li>Public transport agency NMBS (Belgium): train schedule data (1 week, 7 versions)</li> <li>Public transport agency De Lijn (Belgium): busses schedule data (1 week, 7 versions)</li> <li>Bike-sharing company BlueBike (Belgium): bike-sharing availability data (1 day, 1440 versions)</li> <li>Bike-sharing company JCDecaux (EU): bike-sharing availability data (1 day, 1440 versions)</li> <li>OpenStreetMap (World): geographical map data (1 day, 1440 versions)</li> </ul> <h3>Ingestion</h3> <p>Real-world datasets LDES output was converted into SPARQL UPDATE queries and executed against Virtuoso to have an estimate for non-LDES clients how incremental generation impacted ingestion into triplestores.</p> <h2>Remarks</h2> <ol> <li>The first version of each dataset is always used as a baseline. All next versions are applied as an update on the existing version. The reported results are only focusing on the updates since these are the actual incremental generation.</li> <li>GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz datasets are not uploaded as GTFS-Madrid-Benchmark scale 100 because both share the same parameters (50% changes, scale 100). Please use GTFS-Scale-100-{ALL, CHANGE}.tar.xz for GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz</li> <li>All datasets are compressed with XZ and provided as a TAR archive, be aware that you need sufficient space to decompress these archives! 2 TB of free space is advised to decompress all benchmarks and use cases. The expected output is provided as a ZIP file in each TAR archive, decompressing these requires even more space (4 TB).</li> </ol> <h2>Reproducing</h2> <p>By using our <a href="https://github.com/kg-construct/exectool" target="_blank" rel="noopener">experiment tool</a>, you can easily reproduce the experiments as followed:</p> <ol> <li>Download one of the TAR.XZ archives and unpack them.</li> <li>Clone the GitHub repository of our experiment tool and install the Python dependencies with '<em>pip install -r requirements.txt'.</em></li> <li>Download the rmlmapper.jar JAR file from this Zenodo dataset and place it inside the experiment tool root folder.</li> <li>Execute the tool by running: '<em>./exectool --root=/path/to/the/root/of/the/tarxz/archive --runs=5 run</em>'. The argument '<em>--runs=5</em>' is used to perform the experiment 5 times.</li> <li>Once executed, you can generate the statistics by running: '<em>./exectool --root=/path/to/the/root/of/the/tarxz/archive stats</em>'.</li> </ol> <h2>Testcases</h2> <p>Testcases to verify the integration of RML and LDES with IncRML, see <a href="https://doi.org/10.5281/zenodo.10171394">https://doi.org/10.5281/zenodo.10171394</a></p>
Topical-siRNA-therapy-1: Source data
<p>This repository contains the source data for the scientific article:</p> <p>article title: "Topical siRNA Therapy of Diabetic-Like Wound Healing"</p> <p>author: "Eva Neuhoferova & Marek Kindermann et al"</p> <p>date: "2024-19-12"</p> <p>description: "Functional and biocompatible wound dressing developed to enable a controlled release of a traceable vector loaded with the antisense siRNA against MMP-9 in the wound"</p> <ul> <li>primary data source: <a href="https://github.com/KindermannMarek/Topical-siRNA-therapy-1">https://github.com/KindermannMarek/Topical-siRNA-therapy-1</a></li> <li>the "README.md" file contains a description of the files stored in the repository</li> </ul>
Source data to create the figures of the study "Rising greenhouse gas emissions embodied in the global bioeconomy supply chain" using REX3 with new GHG extension including LULUCF
<p>This repository contains the source data to create the figures of the study <a href="https://doi.org/10.1038/s43247-025-02144-0">Rising greenhouse gas emissions embodied in the global bioeconomy supply chain</a> published in <em>Communications Earth & Environment</em>. The results were calculated with the REX3 database in Version 3.2 of this repository and the GHG extension and matlab codes in Version 3.4 of this repository.</p> <p>Figure 1, and 3–5 were created in Rstudio with the attached Rcode <em>Bioeconomy_GHG_sankeys.R</em></p> <p>Figure 2 was created in tableau with an <a href="https://public.tableau.com/app/profile/livia.cabernard/vizzes">interactive data visualizer</a> that allows to zoom into the global bioeconomy supply chain.</p>
Data and source code for Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
<p>We present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.</p>
Data associated with the publication "Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements"
<p>This dataset refers to the publication "Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements". https://doi.org/10.5194/acp-2021-16. </p>
Source data for the publication "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time", Nature Communications, 2021
<p>The archives contain the raw data used to generate the transient absorption spectra for uridine (Figure 1) and 5-methyluridine (Figure 2) presented in the main paper, as well as the trajectory plots and auxiliary spectra presented in the Supplementary Information of the paper "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time" authored by R. Borrego-Varillas et al. published in Nature Communications, 2021. Specifically:</p> <p><strong>URD</strong>: folder with raw data from the uridine trajectories (56 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p><strong>5mURD</strong>: folder with raw data from the 5-methyluridine trajectories (57 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p>The raw data of each trajectory is inside a folder named <em>geom_XXX</em> where <em>XXX</em> stands for a 3-digit label of the trajectory. The trajectories have been selected out of a pool of 500 trajectories according to the S0-S1 vertical gap so that only trajectories whose energy gap falls under the envelope of the pulse are selected</p> <p><strong>URD</strong>: 003 005 006 011 015 023 039 040 054 056 060 083 098 104 112 114 116 121 122 147 152 158 161 171 173 175 177 186 189 200 204 211 219 223 225 232 234 235 236 246 251 252 257 259 265 268 271 272 279 286 287 289 305 313 318 336</p> <p><strong>5mURD</strong>: 010 044 045 048 052 057 065 074 085 094 097 099 100 105 110 112 113 121 131 137 138 140 144 145 159 164 170 179 182 183 184 186 189 199 203 205 209 214 219 220 221 239 243 250 251 273 284 290 295 301 302 320 325 327 328 333 334</p> <p>In each geom_XXX folder there are following files:</p> <p><strong>S1-S<em>Y</em>.dat</strong>: ASCII files () in which the individual columns correspond to </p> <p>col1: time [fs] </p> <p>col2: transition energy of state S<em>Y</em> with respect to S1 [cm-1] where S0 is the ground state</p> <p>col3-5: X, Y and Z components of the transition dipole moment between S1 and S<em>Y</em> [a.u.]</p> <p>col6: magnitude of the transition dipole moment between S1 and S<em>Y</em> [a.u.] </p> <p>col7: angle between transition dipole moment at time t and t=0 [deg]</p> <p>Note that in URD S1-S0.dat contains in most cases about 500 data points (0-500 fs), in 5mURD S1-S0.dat contains 1000 data points (0-1000 fs) except for a few cases in which the trajectories were interrupted earlier. This data has been used to simulate the stimulated emission before the hopping event and the hot ground state photoinduced absorption after hopping. S1-S<em>Y</em>.dat () contain only data points until the hopping event which have been used to simulate the excited state photoinduced absorption.</p> <p>The spectra reported in the main article (Figs 1 & 2) as well as in the SI can be reproduced following eq. 13-18 in the Supplementary Information.</p> <p> </p> <p><strong>HighMediumLayer_traj.xyz.zip</strong>: archived Cartesian coordinates of the High Layer (nucleobase) and Medium Layer (sugar and waters within 5 Å distance from nucleobase) along the dynamics</p> <p>Note that due to the different number of waters in each trajectory the size of the Medium layer (and thus the size of the system) may vary from trajectory to trajectory.</p> <p>Note that due to the different duration of each trajectory the number of geometries may vary from trajectory to trajectory.</p> <p><strong>LowLayer.xyz:</strong> Cartesian coordinates of the Low Layer (waters > 5 Å from the nucleobase); the coordinates of these waters are kept fixed along the trajectory.</p> <p>The coordinates of High, Medium and Low layers can be used to reproduce the QMMM calculations (energies, gradients and transition dipole moments along each trajectory) with the official COBRAMM release (<a href="https://gitlab.com/cobrammgroup/cobramm.git">https://gitlab.com/cobrammgroup/cobramm.git</a>) following the parameters provided in Supplementary Note 2 of the Supplementary Information.</p>
Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains
<p>Dec 15, 2021</p> <p> </p> <p><strong>Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains</strong></p> <p> </p> <p>Carlos M. Carrillo and Francisco Muñoz-Arriola</p> <p> </p> <p><strong>Motivation</strong></p> <p>The data presented here was used to investigate the uskills of precipitation in the US northern Great Plains, and it can be cited as described below. The original data for producing this data is from the Climate Forecast System (CFS) retrospective reanalysis and reforecast as well as precipitation data from the Climate Prediction Center (CPC) from the National Oceanic and Atmospheric Administration (NOAA). Also, gridded data is from the North American Regional Reanalysis (NARR) from the National Centers for Environmental Prediction (NCEP).</p> <p> </p> <p><strong>License </strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p><strong>Description of the dataset</strong></p> <p>Files are provided with the following features:</p> <p><strong>List of cases: </strong></p> <p> files.0.00.dy.txt</p> <p><strong>Low-level jet (or the GP-LLJ index)</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> LLJ_pc_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> from CFS models,</p> <p> eof1.v850.cfs.1982-2009.dy.tar</p> <p> pc1.v850.cfs.1982-2009.dy.tar</p> <p> from NARR model,</p> <p> pc1.vwnd.narr.1982-2009.tar</p> <p><strong>The geopotential height (or CGT index): </strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> Z200_mode_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar</p> <p> xt-reco-z200.Full.z200.narr.1982-2009.bin.tar</p> <p><strong>Precipitation at the US Great Plains:</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/clim/yrcases</p> <p>Master file: <strong>prec_corr_cfs_1D_pdf_full.m</strong></p> <p>With input data:</p> <p> prec.cfs.MW.1982-2009.tar</p> <p> prec.cpc.MW.1982-2009.tar</p> <p><strong>Correlation patterns:</strong></p> <p> Precipitation: PREC.NGP.corr.txt</p> <p> LLJ: LLJ.pcs.corr.narr.pdf.txt</p> <p> Z200: Z200.pcs.corr.narr.pdf.txt</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p><strong>Description of the dataset</strong></p> <p>Files are provided with the following features:</p> <p><strong>List of cases: </strong></p> <p> files.0.00.dy.txt</p> <p><strong>Low-level jet (or the GP-LLJ index)</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> LLJ_pc_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> from CFS models,</p> <p><strong> </strong>eof1.v850.cfs.1982-2009.dy.tar</p> <p> pc1.v850.cfs.1982-2009.dy.tar</p> <p> from NARR model,</p> <p> pc1.vwnd.narr.1982-2009.tar</p> <p><strong>The geopotential height (or CGT index): </strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> Z200_mode_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar</p> <p> xt-reco-z200.Full.z200.narr.1982-2009.bin.tar</p> <p><strong>Precipitation at the US Great Plains:</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/clim/yrcases</p> <p>Master file: <strong>prec_corr_cfs_1D_pdf_full.m</strong></p> <p>With input data:</p> <p> prec.cfs.MW.1982-2009.tar</p> <p> prec.cpc.MW.1982-2009.tar</p> <p><strong>Correlation patterns:</strong></p> <p> Precipitation: PREC.NGP.corr.txt</p> <p> LLJ: LLJ.pcs.corr.narr.pdf.txt</p> <p> Z200: Z200.pcs.corr.narr.pdf.txt</p> <p><strong>Credit</strong></p> <p>Carlos M. Carrillo and Francisco Muñoz-Arriola, 2021: “Sources of Subseasonal Predictability of Rainfall in the Northern Great Plains”, <em>Journal of Applied Meteorology and Climatology</em>. In review.</p> <p><strong>Grant funding</strong></p> <p>This research was funded by the U.S. Geological Survey (USGS), the U.S. Department of Agriculture (USDA), the Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska-Lincoln (UNL), and the UNL’s Layman Award.</p>
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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.
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DANDI Archive for NWB datasets
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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.