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Input GNSS time series data for Tanaka et al. (2024), JGR Solid Earth
<p>Detail explanatios are in the uploaded README file. </p>
Input data for PARASO, a circum-Antarctic fully-coupled 5-component model
<p>Input data for running the PARASO experiments.</p> <p>These files should be extracted, and the folder containing them should be referred to in the `data.cfg` Coral configuration file. See also PARASO documentation from the PARASO sources.</p> <p>The ERA5 forcings (COSMO boundary files and NEMO surface forcings) are not provided herein as they are too large, but we provide:</p> <p>- scripts for downloading and post-processing the ERA5 NEMO forcings;</p> <p>- INT2LM configuration file, with the new Antarctic geometry, to generate COSMO lateral forcings.</p> <p>A 3-month sample of ERA5 data is also available (see <strong>Forcings</strong> below).</p> <p><strong>Model description: </strong>Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, <a href="https://doi.org/10.5194/gmd-15-553-2022">10.5194/gmd-15-553-2022</a>, 2022.</p> <p><strong>Source code (no COSMO)</strong>: Pelletier, Charles, Klein, François, Zipf, Lars, Haubner, Konstanze, Mathiot, Pierre, Pattyn, Frank, Moravveji, Ehsan, & Vanden Broucke, Sam. (2021). PARASO source code (no COSMO) (v1.4.3). Zenodo. <a href="https://doi.org/10.5281/zenodo.5576201">10.5281/zenodo.5576201</a></p> <p><strong>Forcings: </strong>Pelletier, Charles, & Helsen, Samuel. (2021). PARASO ERA5 forcings (1.4.3) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5590053">10.5281/zenodo.5590053</a><br> </p> <p> </p> <p><strong>Acknowledgements</strong></p> <p><strong>ORAS5: </strong>Zuo, H, Alonso-Balmaseda, M, Mogensen, K, Tietsche, S: OCEAN5: The ECMWF Ocean Reanalysis System and its Real-Time analysis component. 2018. <a href="https://doi.org/10.21957/la2v0442">10.21957/la2v0442</a> downloaded from the <a href="https://www.cen.uni-hamburg.de/en/icdc/data/ocean/easy-init-ocean/ecmwf-oras5.html">ICDC</a> (University of Hamburg) on 01-SEP-2019. <em>(The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.)</em></p> <p><strong>BedMachine: </strong>Morlighem, M. 2020. <em>MEaSUREs BedMachine Antarctica, Version 2</em>. Ice-shelf Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/E1QL9HFQ7A8M">10.5067/E1QL9HFQ7A8M</a>. Accessed 01-DEC-2019.</p> <p>Morlighem, M., E. Rignot, T. Binder, D. D. Blankenship, R. Drews, G. Eagles, O. Eisen, F. Ferraccioli, R. Forsberg, P. Fretwell, V. Goel, J. S. Greenbaum, H. Gudmundsson, J. Guo, V. Helm, C. Hofstede, I. Howat, A. Humbert, W. Jokat, N. B. Karlsson, W. Lee, K. Matsuoka, R. Millan, J. Mouginot, J. Paden, F. Pattyn, J. L. Roberts, S. Rosier, A. Ruppel, H. Seroussi, E. C. Smith, D. Steinhage, B. Sun, M. R. van den Broeke, T. van Ommen, M. van Wessem, and D. A. Young. 2020. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet, <em>Nature Geoscience</em>. 13. 132-137. <a href="https://doi.org/10.1038/s41561-019-0510-8">10.1038/s41561-019-0510-8</a></p> <p><strong>Iceberg forcings: </strong>Jourdain, Nicolas C., Merino, Nacho, Le Sommer, Julien, Durand, Gaël, & Mathiot, Pierre. (2019). Interannual iceberg meltwater fluxes over the Southern Ocean (1.0) [Data set]. <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.3514728">10.5281/zenodo.3514728</a></p> <p>Merino N., Jourdain, N. C., Le Sommer, J., Goose, H., Mathiot, P. and Durand, G (2018). Impact of increasing Antarctic glacial freshwater release on regional sea-ice cover in the Southern Ocean. <em>Ocean Modelling</em>, 121, 76-89. <a href="https://doi.org/10.1016/j.ocemod.2017.11.009">10.1016/j.ocemod.2017.11.009</a></p>
ABSOLUT input data for an example application on the districts of Germany
<p>These are data for running the ABSOLUT R programs published separately on Zenodo: <a href="https://doi.org/10.5281/zenodo.4468608">10.5281/zenodo.4468608</a>.</p> <p>The data published here consist of:</p> <p><strong>absolutcontrol.dat</strong> – text file (UTF-8) with case-specific settings for program execution, may be edited by the user</p> <p><strong>crop-areas.csv</strong> – CSV table of crop areas in hectares for different crops in German administrative areas. Modified from data originally provided by the statistical offices of Germany (© Statistische Ämter des Bundes und der Länder, Deutschland, 2020) and re-distributed here in this modified form also under the terms of the Data licence Germany – attribution – version 2.0, see https://www.govdata.de/dl-de/by-2-0</p> <p><strong>districtweather.zip</strong> – zipped directories DistrictWeather and DistrictFeatures. DistrictWeather contains 401 ASCII DAT files with monthly weather variables, one per district. These have been generated using rasterized weather data from the German meteorological service (Deutscher Wetterdienst, DWD), an official digital map of administrative boundaries provided by the German Federal Agency for Cartography and Geodesy, and CLC-2012 land use data provided by Copernicus. DistrictFeatures is an empty directory to be used by the corresponding program.</p> <p><strong>climatescenarios.zip</strong> - zipped directories ClimateScenarios and ClimateScenarioFeatures. ClimateScenarios contains three subdirectories with example climate scenario realisations in the same format as DistrictWeather. ClimateScenarioFeatures is an empty directory to be used by the corresponding program.</p> <p><strong>yield-indat.csv </strong>– CSV table of crop yields in dt/ha for different crops in German administrative areas, annual values for the years 1999–2020. Modified from data originally provided by the statistical offices of Germany (© Statistische Ämter des Bundes und der Länder, Deutschland, 2021) and re-distributed here in this modified form also under the terms of the Data licence Germany – attribution – version 2.0, see https://www.govdata.de/dl-de/by-2-0</p>
Input data for use cases of TransportTools
<ul> <li>Use case I: Disclosing rare transient tunnels and their usage by water molecules in 15 simulations of DhaA dehalogenase <ul> <li>tunnel data</li> <li>water traces data</li> <li>configuration file for TransportTools</li> </ul> </li> <li> <p>Use case II: Understanding the effect of mutations by contrasting simulations of three different variants of LinB dehalogenase</p> <ul> <li>tunnel data</li> <li>water traces data</li> <li>molecular dynamics simulations with relevant water molecules only</li> <li>configuration file for TransportTools</li> </ul> </li> <li> <p>Use Case III: Inferring selectivity of transport pathways in LinB86 dehalogenase for a substrate molecule from almost 600 simulations</p> <ul> <li>tunnel data</li> <li>substrate traces data</li> <li>configuration file for TransportTools</li> </ul> </li> </ul>
Data for Water inputs across the Namib Desert: implications for dryland edaphic microbiology
<p>These are the data files required to run the analyses in Water inputs across the Namib Desert: implications for dryland edaphic microbiology.</p>
REMix model input data for the THG95/GHG95 scenario analysed within the MuSeKo project
<ul> <li>This file contains data used in the REMix energy system model in a scenario assessment for the years 2020, 2030, 2040, and 2050</li> <li>The dataset comprises techno-economic data, energy demand data, renewable energy potentials, fuel as well as emission prices, and energy infrastructure capacities</li> <li>This data is considered in the THG95/GHG95 (Treibhausgas / green house gas) scenario developed within the project MuSeKo</li> <li>This scenario comprises Germany, its neighbours as well as Italy, Norway and Sweden</li> <li>Further descriptions and data is available in the project report of MuSeKo (in German), which can be downloaded from <a href="https://elib.dlr.de/135971/">https://elib.dlr.de/135971/</a></li> </ul> <p>Version 2 provides a correction of biogas potentials in Germany</p>
Data used for manuscript "The coordination of green-brown food webs and their disruption by anthropogenic nutrient inputs"
<p>Data used for manuscript "The coordination of green-brown food webs and their disruption by anthropogenic nutrient inputs".</p> <p>This includes estimations of various properties of food webs, such as stocks of compartments, fluxes between compartments, and conversion efficiencies.</p>
Model input and output data of the FlexMex model comparison
<p>This data collection includes the input and output data of the FlexMex model experiment (grant number: 03ET4077A-H) funded by the German Federal Ministry for Economic Affairs and Energy (BMWi). The aim of the FlexMex project is to better understand the interrelationships of modelling approaches and model results in the mapping and analysis of technical-structural flexibilities in future electricity systems.</p> <p>The data are separated in the two subfolders InputData and OutputData. For the input data, a distinction is made between scalar and time series data.</p> <p>Please find additional information on the models, test cases and analysis in the ReadMe and the publications cited there.</p>
Input data for ERIC values calculation for RAA process
<p>For each area (in total 9 areas of SEE) and for each week (in total 2 weeks of 2021) used in final demonstration of CROSSBOW TC1.1.1 one input file is prepared – in total 18 excel files (*.xlsx).</p> <p>Each file is consisted of 4 sheets:</p> <ul> <li>LOAD – load forecast for 168 timestamps of the given week;</li> <li>DISP_GEN_THERMAL – unit capacity and Forced Outage Rate of production for dispatchable thermal units;</li> <li>DISP_GEN_HYDRO – unit capacity and Forced Outage Rate of production for dispatchable hydro units;</li> <li>NONDISP_GEN – forecasted values for Photovoltaic, Wind, Run of River, Combined Heat and Power and Biomass non-dispatchable units for 168 timestamps of the given week.</li> </ul> <p>Sampling period: Week 9 and Week 10 of 2021</p>
Flee Input Data Collection
<p>This is a collection of FabFlee input data files, collected on 3-5-2022. For more information on how to use these files, please refer to flee.readthedocs.io.</p>
Input data and Supplementary Results for "Turnover in life-strategies recapitulates marine microbial succession colonizing model particles"
<p><strong>README</strong></p> <p>This page contains processed input data used for downstream analysis and some Supplementary Results for the paper:</p> <p>Pascual-García, A., Schwartzman, J., Enke, T.N., Iffland-Stettner, A., Cordero, O.X., Bonhoeffer, S., Turnover in life-strategies recapitulates marine microbial succession colonizing model particles (2022).</p> <p> </p> <p><strong>Input data</strong></p> <p> </p> <ul> <li> <p>File <em>“count_table.ESV.biom”</em>: Table containing the abundance of each Exact Sequence Variant (ESV) in the different samples (biom format).</p> </li> <li> <p>File <em>“count-table_</em><em>metagenomes</em><em>_KEGGs.L3.spf”</em>. Table containing the abundances of genes found in the shotgun metagenomics experiments annotated in KEGG and then aggregated into classes according to the finest classification in KEGG's hierarchy (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“count-table_</em><em>PICRUST2</em><em>_KEGGs.L3.spf</em>”. Table containing the abundances of genes predicted with PICRUSt v2. These genes were annotated in KEGG and aggregated into classes according to the finest hierarchy in KEGG (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“count-table_Isolates_KEGGs.L3.spf</em>”. Table containing the abundances of genes found in the isolates genomes that were annotated in KEGG and aggregated into classes according to the finest classification in KEGG's hierarchy (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“samples_metadata.tsv”</em>. Metadata table describing the samples.</p> </li> <li> <p>File <em>“isolates_metadata.tsv”.</em> Metadata table describing the isolates, it includes shallow phylogenetic levels and a categorical identifier describing the environmental preference estimated for the ESV having a 100% sequence identity with a ZINB-GLM.</p> </li> <li> <p>File <em>“sequences.ESV.</em><em>fasta</em><em>”.</em> File containing the Exact Sequence Variants fasta.</p> </li> </ul> <p><strong>Supplementary Materials</strong></p> <p> </p> <ul> <li> <p>File <em>“qiime2_visualizations.zip”</em>. A file containing visualizations compatible with the qiime2 viewer (simply drag and drop the file in <a href="https://view.qiime2.org/">https://view.qiime2.org/</a>) for each sample or combination of samples, labelled as `$substrate.$medium.$replicate`, where `$medium = {Beads, Seawater}` and `$replicate = {A,B,C}`. If the label is not present for one field, it means that all samples are aggregated for that field e.g.:</p> <ul> <li> <p>“<em>count_table.ESV.Chitosan.Beads.A.bar-plots.</em><em>qzv”</em> Contains the replicate experiment A for communities on the synthetic beads in chitosan.</p> </li> <li> <p>“<em>count_table.ESV.Chitosan.bar-plots.</em><em>qzv”</em> Contains all samples in chitosan (both seawater communities and the three replicates of communities on the beads).</p> </li> </ul> </li> <li> <p>File <em>“README.odt”</em>. This readme in libreoffice format.</p> </li> <li> <p>File <em>“</em><em>Genome_deposition_information.xlsx”. </em> NCBI identifiers for the isolates’ genomes.</p> </li> <li> <p>File "barcodes_to_samples_MGRAST.xlsx". Contains the barcodes of each sample and its metadata as it was deposited in MG-RAST. In a second tab, there is a subset of samples with a low number of reads that MG-RAST analyzed together, generating a single entry (termed "mixed").</p> </li> <li> <p>Access to raw an processed metagenomes and analysis are provided through MG-RAST following [this link](<a href="https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp85635">https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp85635</a>).</p> <ul> <li> <p>As of May 30th, 2022, there are two issues with the dataset in MG-RAST which are out of our scope to solve. We will report any update here. The first problem is related to the entry TCCTGAGC-GTAAGGAG-s_2_, which does not load in MG-RAST. These are very low samples and were discarded in most analyses. In addition, you will find in the metadata 17 metagenomes that do not belong to our project.</p> </li> </ul> </li> </ul>
The input data set includes 729 objects (patients) and 39 variables (clinical qualitative and quantitative descriptors).
<p>For reliable data treatment and interpretation qualitative descriptors were omitted and only numerical clinical indicators were included in the data matrix. Finally, the data set dimension was [729 x 18].</p> <p> The data were treated by hierarchical cluster analysis and factor analysis. The major goal of the data mining was to reach statistically significant partitioning of the objects and variables into similarity patterns (clusters) which helps to better understand the data structure, to assess the meaning of the partitioning achieved, thus promoting the evaluation of the health status of the patients and the role of specific descriptors for the formation of the partitioning patterns.</p> <p>3D classification Python tool.</p>
[Demo Input Data] for SCAFE: a software suite for analysis of transcribed cis-regulatory elements in single cells
<p>This archive (input.tar.gz) contains the demo data for SCAFE v1.0.0 (on <a href="https://doi.org/10.5281/zenodo.7023163">Zenodo</a> or <a href="https://github.com/chung-lab/SCAFE/releases/tag/v1.0.0">Github</a>)</p> <p><em>SCAFE</em> (Single Cell Analysis of Five-prime Ends) provides an end-to-end solution for processing of single cell 5’end RNA-seq data. It takes a read alignment file (*.bam) from single-cell RNA-5’end-sequencing (e.g. 10xGenomics Chromimum®), precisely maps the cDNA 5'ends (i.e. transcription start sites, TSS), filters for the artefacts and identifies genuine TSS clusters using logistic regression. Based on the TSS clusters, it defines transcribed cis-regulatory elements (tCRE) and annotated them to gene models. It then counts the UMI in tCRE in single cells and returns a tCRE UMI/cellbarcode matrix ready for downstream analyses, e.g. cell-type clustering, linking promoters to enhancers by co-activity <em>etc</em>.</p> <p>For details on installation, usage and test run on demo data, visit <a href="https://github.com/chung-lab/SCAFE">https://github.com/chung-lab/SCAFE</a></p>
Input data of GridPath model for South America's MERCOSUR sub-region
<p><strong>This repository contains input data of the GridPath model for South America’s MERCOSUR sub-region, presented in the forthcoming paper entitled “Exploring sustainable electricity system development pathways in South America’s MERCOSUR sub-region” by the same authors. These data can be used in conjunction with the GridPath model (version v0.8.0), available at <a href="https://github.com/blue-marble/gridpath](https://github.com/blue-marble/gridpath)">https://github.com/blue-marble/gridpath</a>. Description of the key input data and the scenarios presented in the paper are provided in the <a href="https://docs.google.com/spreadsheets/d/1l7ImQtKHpYTPp9cMmTGHCdJB-wJvq7klwrwKmrtCrqw/edit?usp=sharing">Readme file</a>.</strong></p>
Data published in manuscript "Highest methane concentrations in an Arctic River linked to local terrestrial inputs"
<p>This data is published in the manuscript:</p> <p>Castro-Morales, K., Canning, A., Arzberger, S., Overholt, W.A., Küsel, K., Kolle, O., Göckede, M., Zimov, N. and Körtzinger, A. (2022). Highest methane concentrations in an Arctic River linked to local terrestrial inputs. <em>Biogeosciences.</em> XX, XXX-XXX. https://doi.org/10.5194/bg-XX-XXX-2022.</p> <p>The data contains the water properties, the dissolved gas concentrations and flux densities at 1-min resolution corresponding to two transects in the Kolyma River main channel and two tributaries (Ambolikha and Leonid). The data was collected between 15 and 17 June, 2019.<strong> </strong></p> <p>This folder contains four data files and the file "README_Data_access_Castro-Morales_etal_CH4_Kolyma_River.txt" provides more details on the data.</p> <p> </p> <p> </p>
Example input files and Fe foil data for FEFF EXAFS simulations of Fe
<p>Feff input file containing Fe atomic positions, needed to run FEFF simulations of the EXAFS of Fe. The Demeter XAS analysis program files are also included (free software) : http://bruceravel.github.io/demeter/#about. These files can be used to simulate EXAFS of Fe using the FEFF software. This simulation is a building block for a future enhancement of the SIMEX (Simulation of Experiments) platform : https://github.com/eucall-software/simex_platform</p>
Supplementary Materials "Isolated proton bunch acceleration by a petawatt laser pulse": 3D3V Input Files and Plot Data Figure 3
<p><strong>PIConGPU Simulation Input + Code</strong></p> <p>3D3V simulations are based on a pre-release of PIConGPU 0.2.0 [1]</p> <p>Directory: hilz-darmstadt-2707-3D<br> - branch with all applied & backported patches<br> - input files: code/examples/SphereDarmstadt/</p> <p>[1] DOI:10.5281/zenodo.168390</p> <p> </p> <p><strong>Data</strong></p> <p>data behind simulation plots in Fig 2c and Fig 3.</p>
Input data for: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes input data used in the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard.</strong> Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>For this article, data have been processed with a R/GRASS script. The development version of this script is available on GitHub at https://github.com/ghislainv/deforestation-maps-Mada. The last release of this script is archived on Zenodo: [DOI: 10.5281/zenodo.1118484].</p>
FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept - Input Data
<p>This repository contains driving data used by training and evaluation of FLAME in the "FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept" paper. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over Brazil. </p> <div>Not all variables were used in the final analysis<br> <table> <tbody> <tr> <td><strong> NetCDF File</strong></td> <td> <p><strong> Variable</strong></p> </td> <td> <p><strong>Used/not Used</strong></p> </td> <td> <p><strong>Source/Reference</strong></p> </td> </tr> <tr> <td> <p>burned_area.nc</p> </td> <td> <p>Burned area</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018)</td> </tr> <tr> <td> <p>burned_area_nat_veg.nc</p> </td> <td> <p>Burned area in natural vegetation</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> <p>burned_area_non_nat_veg.nc</p> </td> <td> <p>Burned area in non natural vegetation</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> <p> tas_max.nc</p> </td> <td> <p> Maximum Temperature</p> </td> <td> <p>Used</p> </td> <td><br><br> <p>ISIMIP3a</p> <p>FRIELER et al. (2023)</p> </td> </tr> <tr> <td> <p>precip.nc</p> </td> <td> <p>Precipitation</p> </td> <td> <p>Used</p> </td> </tr> <tr> <td> <p>vpd.nc</p> </td> <td> <p>Vapor pressure deficit</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>rhumid.nc</p> </td> <td> <p> Relative Humidity </p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td><br>consec_dry_days.nc</td> <td><br> <p>Consecutive number of dry days </p> </td> <td>Not Used</td> </tr> <tr> <td> <p>soilM.nc</p> </td> <td> <p>Soil Moisture</p> </td> <td> <p>Not Used</p> </td> <td> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>lightn.nc </p> </td> <td> <p> Lightning</p> </td> <td> <p>Not Used</p> </td> <td><br> <p> ISIMIP3a</p> <p>FRIELER et al. (2023)</p> </td> </tr> <tr> <td> <p>popDen.nc</p> </td> <td> <p> Population density</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>road_density.nc</p> </td> <td> <p>Road density</p> </td> <td>Used</td> <td> <p> GRIP global</p> <p>(MEIJER et al., 2018)</p> </td> </tr> <tr> <td> <p>cveg.nc</p> </td> <td> <p>Vegetation carbon</p> </td> <td> <p>Not Used</p> </td> <td><br> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>csoil.nc</p> </td> <td> <p>Carbon in dead vegetation</p> </td> <td>Used</td> <td><br> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>forest.nc</p> </td> <td> <p> Forest</p> </td> <td> <p>Used</p> </td> <td><br><br><br> <p> MAPBIOMAS, 2022</p> </td> </tr> <tr> <td> <p>grassland.nc</p> </td> <td> <p> Grassland</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>savanna.nc</p> </td> <td> <p> Savanna</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>cropland.nc</p> </td> <td> <p> Cropland</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>pasture.nc</p> </td> <td> <p> Pasture</p> </td> <td> <p>Used</p> </td> </tr> <tr> <td> <p>np.nc</p> </td> <td> <p>Number of patches </p> </td> <td> <p>Not Used</p> </td> <td><br><br> <p>Calculated from MAPBIOMAS,<br>2022</p> <br><br></td> </tr> <tr> <td> <p>ed.nc </p> </td> <td>Edge density</td> <td>Used</td> </tr> </tbody> </table> </div> <p> </p>
Supporting data for "Granularity of model input data impacts estimates of carbon storage in soils"
<p>The exchange of carbon between the soil and the atmosphere is an important factor in climate change. Soil organic carbon (SOC) storage is sensitive to land management, soil properties, and climatic conditions, and these data serve as key inputs to computer models projecting SOC change. Farmland has been identified as a sink for atmospheric carbon, and we have previously estimated the potential for SOC sequestration in agricultural soils in Vermont, USA using the Rothamsted Carbon Model. However, fine spatial-scale (high granularity) input data are not always available, which can limit the skill of SOC projections. For example, climate projections are often only available at scales of 10s to 100s of km2. To overcome this, we use a climate projection dataset downscaled to <1 km2 (~18,000 cells). We compare SOC from runs forced by high granularity input data to runs forced by aggregated data averaged over the 11,690 km2 study region. We spin up and run the model individually for each cell in the fine-scale runs and for the region in the aggregated runs factorially over three agricultural land uses and four Global Climate Models. </p> <p>In this repository are the downscaled climate input data that drive the RothC model, as well as the model outputs for each GCM.</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.