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Specialization indexes of countries for 254 subject categories, by input and by output
<p>This is a Supplementary Material of the manuscript "<strong>Measuring and interpreting the differences of the nations’ scientific specialization indexes by output and by input</strong>". The file contains complete data on 199 countries in 254 subject categories.</p>
Input dataset for SANIFS configuration
<p>Dataset used as input and forcing for the SANIFS forecasting system described as use case of the parallel version of the SHYFEM model. The reference of the parallel implementation of the SHYFEM model is https://doi.org/10.5281/zenodo.5596734</p>
Pretrained Models for Paper "Variable-Input DeepONets for Operator Learning"
<p>This repository contains all trained models which were used to compute the results presented in the paper "Variable-Input DeepONets for Operator Learning".</p>
Simulation Input Data for "Atomic Origins of Biomass Recalcitrance in Organic Solvents"
<p>This is the reduced data behind an upcoming manuscript investigating lignocellulosic interactions in plant secondary wall, when exposed to different organic solvent pretreatment. The data is taken directly from the directory structure that contains both the simulation and analysis, with excluded trajectory files and intermediate products to fit within the zenodo upload limit. The tar command used to generate this tarball was:</p> <pre><code class="language-bash">tar -zcvf lignincelluloseindustrialsolvent.tar.gz --exclude="*BAK" --exclude="*#" --exclude="*xtc" --exclude="*gro" --exclude="*log" --exclude="*[0-9].out" --exclude="*npz" --exclude="*pkl" --exclude="*npy" --exclude="*png" --exclude="*bmim*" --exclude="*old" --exclude="*dcd" --exclude="*tmp" --exclude="*xst" --exclude="*edr" --exclude="*txt" --exclude="*state_prev.cpt" --exclude="*ppm" --exclude="Simulations" FaceDifferences</code></pre> <p>Within the FaceDifferences directory, there are 2 primary subdirectories:</p> <ul> <li><strong>Build </strong>contains the scripts and files to build the individual lignin cellulose in organic solvent molecular systems.</li> <li><strong>NewSolventSimulations</strong> contains the all-atom MD simulation inputs and the analysis scripts (subdirectory <strong>Analysis</strong>)</li> </ul>
Model and input files for Iyer & Ou, et al. 2022 (Ratcheting of climate pledges needed to limit peak global warming )
<p>The GCAM model (GCAMv5.3 NDC) and input files used to conduct Iyer & Ou, et al. 2022 (Ratcheting of climate pledges needed to limit peak global warming )</p> <ol> <li>The model needs to be compiled using third-party libraries (see https://jgcri.github.io/gcam-doc/gcam-build.html). Source code has been included in the GCAMv5.3_NDC/csv</li> <li>Default GCAM input files have been included in GCAMv5.3_NDC/input/gcamdata/xml</li> <li>Additional input files for NDC scenarios have been included in GCAMv5.3_NDC/input/NDC_Ratchet_policy</li> <li>A sample configuration_NDC_sample.xml has been included in GCAMv5.3_NDC/exe with detailed setup instruction</li> </ol>
Input data for Bayesian and information theoretic model selection and similarity analysis
<p>This data serves as input to the codes found in the following repository https://github.com/MariaFMoralesOreamuno/Bayesian_Information_theoretic_model_selection.git</p> <p> </p>
Thermochronology data in Ebro basin and model input parameters for computing cooling histories
<p>Two files (word and excel) containing Table DR1 that refer to the model input parameters and Table DR2 with details of the (U-Th-Sm)/He analyses. </p>
MIROC4-ACTM: Model setup, input and output data for CH4 LETKF (Bisht et al., GMD-D, 2022)
<p>Details in :</p> <p>Bisht, J. S. H., Patra, P. K., Takigawa, M., Sekiya, T., Kanaya, Y., Saitoh, N., and Miyazaki, K.: Estimation of CH<sub>4</sub> emission based on advanced 4D-LETKF assimilation system, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-719, 2022.</p>
Input
<p>The input files of FVCOM. </p>
Input files for WRF simulations Across the the iCMV
<p>The west-east terrain cross-section along ~150km of the northern part of Israel has a coastal-plain—mountain—valley structure; hereafter denoted iCMV. The boundary layer height (BLH) evolution mechanism across the iCMV has not yet been fully understood. We use Weather Research and Forecasting (WRF) simulations and ceilometer measurements to decipher the iCMV BLH evolution mechanism during the late summertime period of 5-14 September 2017, where the daily maximum BLH at the mountainous city of Jerusalem (JrM; 800m above sea-level) varies by ~1000m. We first verify the BLH, simulated by 5 model configurations (with 4 different BL schemes). The RMSE for the 3 best configurations are around 160 and 200m for the coastal and JrM areas, respectively. An analysis of the simulated daily BL evolution reveals a general mechanism. In the early morning, the up-slope flows and synoptic forcing interact to induce a surface flow convergence (SFC) zone. During a westerly (easterly) synoptic flow, the SFC is induced east (west) of JrM. Assisted by the inland propagating sea breeze front, the SFC is advected eastwards during the afternoon, until it collapses at the valley bottom. The SFC zone is accompanied by a <a name="_Hlk110946754"></a>substantial positive vertical-wind column and a locally-elevated BLH. The more western the morning-time SFC is, the more likely the elevated BLH will pass during midday through JrM, with a higher daily maximum BLH. The exact dependence of the BLH evolution and the associated temperature and humidity variations upon the synoptic regime require further study.</p>
Simulation input files and analysis scripts for "Optimal bond-constraint topology for molecular dynamics simulations of cholesterol"
<p>Simulation input files and analysis scripts for "Optimal bond-constraint topology for molecular dynamics simulations of cholesterol".</p> <p>See ... for details.</p>
Input data for manuscript "SNR-based GNSS reflectometry for coastal sea-level altimetry – Results from the first IAG inter-comparison campaign"
<p>GNSS data collected at station GTGU for one year (2015.5-2016.5) and nearby tide gauge data.</p> <p>Note: the antenna position in the header file, expressed in global Cartesian coordinates, corresponds inadvertently to integer values of latitude, longitude, and altitude (57.0°, 11.0°, 0.0 m); non-truncated values are as follows: 57.3929549°, 11.9134886°, 40.420 m.</p>
Human placental MeRIP-seq data-Input
<p>MeRIP-seq for huamn placental tissues from different gestational age. Early, first trimester; Middle, second trimester; Late, third trimester. </p>
2. MC_T0TEM input_Framatome_ANP5
<div>Fractesus project. Fracture test mini-CT. Master curve input ANP-5. Framatome.</div> <div> <div> <p> </p> </div> </div>
Input data for predicted sedimentary environments on the Norwegian continental margin
<p>Input and output data relating to R workflow for predicting sedimentary environments on the Norwegian continental margin (https://github.com/diesing-ngu/SedEnv). The following files are included:</p><p><strong>SedEnv_4km_MaxCombArea_point_20230622.shp</strong> - Point shapefile of the response data (substrate type). Note that these data points were derived from mapped products and are not sample points as such. </p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>mud_2023-06-30.tif </strong>- Georeferenced TIFF-file of predicted mud content. Used as an additional predctor.</p>
Input data for predicted sediment accumulation rates on the Norwegian continental margin
<p>Input data relating to R workflow for predicting sediment accumulation rates on the Norwegian continental margin (https://github.com/diesing-ngu/SedRates). The following files are included:</p><p><strong>norway_sar_2023-08-28.csv</strong> - Data on sediment accumulation rates from the <a href="https://doi.org/10.5194/essd-15-4105-2023">MOSAIC v2.0</a> database</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>mud_2023-06-30.tif </strong>- Georeferenced TIFF-file of predicted mud content. Used as an additional predctor.</p><p><strong>SedEnv3_probabilities_2023-07-01.tif </strong>- Georeferenced Tiff-file of the prediction probabilities of the predicted sedimentary environments. Used as additional predctors.</p><p><strong>SedEnv3_max_probabilities_2023-07-01.tif </strong>- Georeferenced Tiff-file of the maximum probabilities, i.e., the probability of the class that was mapped.</p><p><strong>SedEnv3_AOA_2023-07-01.tif </strong>- Georeferenced Tiff-file of the area of applicability of the model <a href="https://doi.org/10.1111/2041-210X.13650">(Meyer & Pebesma, 2021)</a></p>
Input data for predicted dry bulk densities on the Norwegian continental margin
<p>Input data relating to R workflow for predicting dry bulk densities on the Norwegian continental margin (https://github.com/diesing-ngu/DBD). The following files are included:</p><p><strong>DBD_2023-07-21.csv </strong>- Data on dry bulk densities in surface sediments</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>GrainSizeReg_folk8_probabilities_2023-06-28.tif</strong> - Georeferenced TIFF-file of the prediction probabilities of the predicted substrate classes. Used as additional predctors.</p>
Input data for predicted substrate types on the Norwegian continental margin
<p>Input data relating to R workflow for predicting substrate types on the Norwegian continental margin (https://github.com/diesing-ngu/GrainSizeReg). The following files are included:</p><p><strong>AoI_Harris_mod </strong>- Polygon shapefile delimiting the area of interest</p><p><strong>GrainSize_4km_MaxCombArea_folk8_point_20230628</strong> - Point shapefile of the response data (substrate type). Note that these data points were derived from mapped products and are not sample points as such. </p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p>
Input data for predicted organic carbon content on the Norwegian continental margin
<p>Input data relating to R workflow for predicting organic carbon content on the Norwegian continental margin (https://github.com/diesing-ngu/TOC). The following files are included:</p><p><strong>mosaic_2023-04-21.csv</strong> - Data on organic carbon content in surface sediments from the <a href="https://doi.org/10.5194/essd-15-4105-2023">MOSAIC v2.0</a> database</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>mud_2023-06-30.tif </strong>- Georeferenced TIFF-file of predicted mud content. Used as an additional predctor.</p>
Input data for predicted mud content on the Norwegian continental margin
<p>Input data relating to R workflow for predicting mud content on the Norwegian continental margin (https://github.com/diesing-ngu/GSMgrids). The following files are included:</p><p><strong>gsm_data.csv</strong> - Data on gravel, sand and mud of surface sediments</p><p><strong>MGObsPkt_150_160_170.shp </strong>- Point shapefile of categorical samples</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>GrainSizeReg_folk8_probabilities_2023-06-28.tif</strong> - Georeferenced TIFF-file of the prediction probabilities of the predicted substrate classes. Used as additional predctors.</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.