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Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data
<p>This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023).</p> <p>First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized.</p> <ul> <li><strong>Cl Concentrations - Wang et al. (2021).zip:</strong> Original Cl concentrations field from Wang et al. (2021). </li> <li><strong>CH4 Fluxes - Saunois et al. (2020).zip: </strong>Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).</li> </ul> <p>Second, we store the processed input data generated for the purpose of our study.</p> <ul> <li><strong>CH4 Fluxes - LMDz9696.zip:</strong> Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>d13C Signatures - LMDz9696.zip:</strong> δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>dD Signatures - LMDz9696.zip:</strong> δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>OH O1D Concentrations - LMDz9696-INCA.zip:</strong> OH and O1D monthly concentrations simulated with LMDz-INCA.</li> <li><strong>Masks regions.zip</strong>: Masks for the regions used for the input data and the analysis.</li> </ul> <p> </p>
R scripts, input and output data for: Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs
<p>An important part of infectious disease management is predicting factors that influence disease outbreaks, such as <em>R</em>, the number of secondary infections arising from an infected individual. Estimating <em>R</em> is particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculated <em>R</em> for <em>Bacillus anthracis</em> infections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations, and simulation models, we show that <em>R</em> is spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within two years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction (<em>R</em><1) to explosive outbreaks (<em>R</em>>10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host-pathogens interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical.</p>
Energy input, habitat heterogeneity, and host specificity on avian haemosporidian diversity at continental scales
<p>The correct identification of biotic and abiotic drivers affecting parasite diversity and assemblage composition at different spatial scales is crucial for understanding how pathogen distribution responds to anthropogenic disturbance and climate change. Here, we used a database of avian haemosporidian parasites to identify such drivers and their effect on the taxonomic and phylogenetic diversity of genera Plasmodium, Haemoproteus, and Leucocytozoon from three zoogeographic regions. We explored how parasite diversity is related to energy input (i.e., temperature, precipitation, and potential evapotranspiration [PET]), to habitat heterogeneity (i.e., climatic seasonality, vegetation density, ecosystem heterogeneity, human disturbance, and host richness), and to a novel assemblage-level metric related to parasite niche overlap (degree of generalism). We found that the relative importance of the predictors differed between the three studied parasite genera and across diversity metrics. Among the most consistent predictors, host richness was positively related to the taxonomic diversity of the three genera. Energy input and human footprint explained the phylogenetic diversity of Haemoproteus. Finally, the degree of generalism explained the diversity of Plasmodium and Leucocytozoon. Our results suggest that different dimensions of haemosporidian diversity are shaped by energy input, host heterogeneity, and assembly processes related to parasite resource use within local parasite assemblages.</p>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></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>
SLIIDERS: Sea Level Impacts Input Dataset by Elevation, Region, and Scenario
<p>This record includes the Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (SLIIDERS) dataset. It also includes source code to generate this product as well as necessary inputs that are not available for download elsewhere. Both the dataset and the source code are consistent with version 1.2. <strong>Note</strong>: The version associated with <a href="https://gmd.copernicus.org/articles/16/4331/2023/">Depsky et al., 2023</a> is v1.1.</p> <p>The zipped SLIIDERS Zarr store can be downloaded and accessed locally or can be directly accessed via code similar to the following:</p> <pre><code>from fsspec.implementations.zip import ZipFileSystem import xarray as xr xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())</code></pre> <p><strong>File Inventory</strong></p> <p><em>Products</em></p> <ul> <li><strong>sliiders-v1.2.zarr.zip</strong>: SLIIDERS. A global dataset containing 18 socioeconomic variables, reflecting present day socioeconomic and geophysical characteristics of 11,980 coastal regions and projecting capital stock, GDP, and population growth trajectories through 2100 for five SSPs and two economic growth models. These variables are used as inputs to the pyCIAM modeling platform detailed in Depsky et al. 2023.</li> <li><strong>sliiders-v1.2.nc</strong>: Same as the original SLIIDERS dataset, but in netcdf format.</li> </ul> <p><em>Inputs</em></p> <p>All provided inputs are manually created or adjusted points used to create the coastline segments of SLIIDERS:</p> <ul> <li><strong>ciam_segment_pts_manual_adds.parquet</strong>: A list of segment points manually added to those that come from the extreme sea level model CoDEC (<a href="https://doi.org/10.5281/zenodo.3660926">Muis et al. 2020</a>)</li> <li><strong>gtsm_stations_ciam_ne_coastline_snapped.parquet:</strong> Stations from CoDEC snapped to coastlines from <a href="https://www.naturalearthdata.com/downloads/10m-physical-vectors/">Natural Earth</a></li> <li><strong>gtsm_stations_eur_tothin.parquet</strong>: A list of European points in CoDEC to thin. CoDEC provides ~10km resolution in Europe and ~50km elsewhere. For consistency, SLIIDERS uses ~50km spacing for its coastal segments globally.</li> </ul> <p><em>Source Code</em></p> <ul> <li><strong>sliiders-1.2.zip</strong>: The source code used to generate SLIIDERS v1.1. See READMEs within this code for more details. This is consistent with release v1.2 of the code maintained on github at <a href="https://github.com/ClimateImpactLab/SLIIDERS">https://github.com/ClimateImpactLab/SLIIDERS</a></li> </ul>
Simulation input scripts for figures in the article "Experimental parameters for plasma wakefield acceleration in a narrow plasma column"
<p>Simulation input scripts for figures in the article "Experimental parameters for plasma wakefield acceleration in a narrow plasma column"</p> <p>Alter line "my_constants.dx_dz= 10e-6" to chage the misalignment degree</p>
Accompanying data for the paper "Robustness of the Data-Driven Identification algorithm with incomplete input data"
<h2>Links</h2> <ul> <li>isSupplementTo <em>publication-article</em> <a href="https://doi.org/10.46298/jtcam.12590">https://doi.org/10.46298/jtcam.12590</a></li> <li>isNewVersionOf <em>dataset</em> <a href="../records/10090469">https://zenodo.org/records/10090469</a></li> </ul> <h2>Authors</h2> <ul> <li><strong>Leygue, Adrien</strong>, Ecole Centrale de Nantes, ORCID: <a href="https://orcid.org/0000-0003-0714-822X">0000-0003-0714-822X</a></li> </ul> <h2>Language</h2> <ul> <li>English</li> </ul> <h2>License</h2> <ul> <li>Creative Commons Attribution 4.0</li> </ul> <h2>Funding sources</h2> <ul> <li>This work was performed by using HPC resources of Centrale Nantes Supercomputing Center on the cluster Liger, granted and identified D1705030 by the High Performance Computing Institute(ICI).</li> </ul> <h2>Data structure and information</h2> <p>Synthetic data used in the case study (section 3) of the paper.</p> <p>The data in XDMF (Milou.xdmf ) + hdf5 (Milou.hdf5) format comprises:</p> <ol> <li>The 2D computational mesh with triangular linear elements</li> <li>The nodal Forces for all loading steps (nodal quantity)</li> <li>The displacement for all loading steps (nodal quantity)</li> <li>Cauchy stress fields for all loading steps (cell quantity)</li> </ol>
Public IST:Forecasting input files
<p>Input files for Galaxy Clustering and Weak Lensing to reproduce the forecasts of the Euclid IST:Forecasting.</p>
AMIRIS demand response workflow input
<p>This upload contains the <strong>input data</strong> necessary to run a workflow applying the agent-based power market model <strong><a href="https://gitlab.com/dlr-ve/esy/amiris/amiris">AMIRIS</a> </strong>in order to study the impact of power tariffs design on <strong>demand response</strong> profitability.</p> <h2>Usage</h2> <p>The data has to be copied into the "./inputs/data/" folder of the <a href="https://github.com/jokochems/demand_response_analyses_workflow"><em>demand response analyses workflow</em></a> and unpacked there. See the description of the workflow on the dependencies how to execute the model.</p> <p>Important note: You will need a version of AMIRIS that is not yet open source and contains the demand response implementation. Feel free to contact the author of this data set in order to request it. Also, you will need a solver, such as Gurobi or CPLEX for instance.</p> <h2>Background</h2> <p>Data has been obtained from previous <a href="https://github.com/pommes-public/pommesinvest"><em>pommesinvest </em></a>model runs.</p> <p>It has been put together by executing a data <a href="https://github.com/pommes-public/pommesevaluation/blob/main/amiris_converter.ipynb">converter script</a> from the <a href="https://github.com/pommes-public/pommesevaluation/">pommesevaluation </a>repository that compiles the inputs into a format understood by AMIRIS, thus allowing for a soft model coupling (sequential execution) with full input harmonization.</p>
pommesinvest input data
<p>This upload contains the <strong>input data</strong> necessary to run the <strong>fundamental power market model </strong><a href="https://github.com/pommes-public/pommesinvest"><strong>pommesinvest</strong></a>.</p> <h2>Usage</h2> <p>The data has to be copied into the "./inputs" folder of <em>pommesinvest </em>and unpacked there. See the description of <em>pommesinvest </em>on how to execute the model.</p> <h2>Background</h2> <p>Data has been complied by executing <a href="https://github.com/pommes-public/pommesdata">pommesdata</a> which is the associated data preparation routine resp. its <a href="https://github.com/pommes-public/pommesdata/blob/dev/pommesdata/data_preparation.ipynb">main script</a> with default settings.</p>
Input GNSS time series data for Tanaka et al. (2024), JGR Solid Earth
<p>Detail explanatios are in the uploaded README file. </p>
Model Inputs and Results - The role of coal plant retrofitting strategies in decarbonizing India's power system
<p>These files are the model inputs and results for the submission based on GenX version v0.3.6 - The role of coal plant retrofitting strategies in decarbonizing India’s power system</p>
Biogeochemical river inputs for global ocean models (RivR2O)
<h2><strong>1. General Description</strong></h2> <p>The global biogeochemical riverine export dataset (RivR2O) uploaded here is a synthesis product for yearly means of preindustrial C, N and P exports to the ocean and their historical evolutions, which are ready-to-use for global ocean models. They will serve as biogeochemical river inputs in the River-2-Ocean Model Intercomparison Study (R2OMIP). The files cover >10000 global catchments which can be read as lists with coordinates, or as gridded netcdf files (0.25°X0.25°). They cover the compounds DIC, DOC, POC, DIP and DIN. The assumed pre-industrial era is assumed to be pre-1900, whereas historical data will cover 1901-2020. </p> <p>Please site the dataset as: </p> <p>Lacroix, F., Liu, M., Ma, M., Resplandy, L., Beusen, A., Hauck, J., Lennartz, S., Li, Y., Tian, H., & Regnier, P. (2024). Biogeochemical river inputs for global ocean models (RivR2O) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13799103" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13799103</a></p> <h3><strong>1.1. Preindustrial inputs and their transformations</strong></h3> <p>The files are for preindustrial river inputs can be downloaded as netcdf (<strong>r2o_riverinputs_preindustrial.nc</strong>), or as catchment lists (DIC,DOC,POC,DIN: <strong>riverexports_list_CN.csv</strong> , DIP: <strong>riverexports_list_P.csv</strong>) with given coordinates. They quantify yearly means for every catchment without a significant anthropogenic perturbation. They were constructed in the following ways:</p> <p><strong>DI</strong><strong>C, DOC, POC</strong></p> <p>Preindustrial DIC, DOC and POC were obtained by subtracting the estimated anthropogenic perturbations for every catchment, which were determined for the 1901-2020 time period by Tian et al. (2023), from the synthesis of present-day exports by Liu et al. (2024). We further accounted for a net DOC source in the tropics (+0.07 Pg C yr-1), and a source in the Southern Hemisphere (+0.01 Pg C yr-1) from estuaries and coastal vegetated ecosystems (including submerged) based on Regnier et al. (2022). Note that in the study, Northern Hemisphere lateral transfers of DOC due to estuaries and coastal vegetation are estimated to approximately zero. A fraction of POC was also removed from the dataset due to models misrepresenting burial on shelf and the remaining fraction (recycled POC) should be added to the semi-refractory DOC pool (see protocol). DIC inputs from groundwater discharge (0.016 Pg C yr-1) were distributed globally homogeneously at every river mouth. Globally, this then amounts to a total of 0.51 Pg C yr-1 of DIC, 0.35 Pg C yr-1 of DOC and 0.095 Pg C yr-1 of POC of available C export to the ocean over the preindustrial time period. </p> <p><strong>DIN </strong></p> <p>The DIN product averages over three river N exports models (ORCHIDEE-NLAT: Ma et al., in review; DLEM: Yang et al., 2015; Tian, pers. Com., IMAGE-GNM: Beusen et al., 2015, 2016) for every catchment. The resulting preindustrial DIN load to the ocean is 11 Tg N yr-1. In addition, labile DON is accounted here as DIN (9 Tg N yr-1) based on the ratio C:N of 2583:103 from labile DOC given above (See R2O MIP protocol). This in total amounts to 20 Tg DIN yr-1 inputs to the ocean in the dataset.</p> <p><strong>DIP</strong></p> <p>The DIP product averages catchment estimates from IMAGE-GNM (Beusen et al., 2016) and Lacroix et al. (2020). The resulting preindustrial DIP load to the ocean is 2.28 Tg P yr-1. In addition, we account for labile DOP as DIP here (0.19 Tg P yr-1) based on the C:P ratio of 2583:1 (See R2O MIP protocol). This in total amounts to 2.47 Tg DIP yr-1 inputs to the ocean in the dataset.</p> <h3><strong>1.2. Anthropogenic Perturbation (1901-2024)</strong></h3> <p>The river input files from 1901 can be downloaded as a zip file (<a href="https://zenodo.org/api/records/14266183/draft/files/r2o_river_inputs_1901_2024.zip/content" target="_blank" rel="noopener noreferrer">r2o_river_inputs_1901_2024.zip</a>), which contains a netcdf files for every year of the time series (1901-2024) as rivr2o_riverinputs_{year}.nc. E.g. for 1901 -> rivr2o_riverinputs_{year}.nc </p> <p><strong>DI</strong><strong>C, DOC, POC</strong></p> <p>Preindustrial DIC, DOC and POC were obtained by interpolating linearly the estimated anthropogenic perturbations for every catchment, which were determined for the 1901-2024 time period by Tian et al. (2023), to the present-day exports by Liu et al. (2024). Based on Regnier et al. (2022), we assumed no lateral transfers of DOC due to estuaries and coastal vegetation for the present day. The same fraction of POC was also removed from the dataset due to models misrepresenting burial on shelf and the remaining fraction (recycled POC) should be added to the semi-refractory DOC pool (see protocol). DIC inputs from groundwater discharge (0.016 Pg C yr-1) were distributed globally homogeneously at every river mouth. Globally, this then amounts to a total of 0.53 Pg C yr-1 of DIC, 0.30 Pg C yr-1 of DOC and 0.12 Pg C yr-1 of POC of available C export to the ocean over the 2011-2020 period.</p> <p><strong>DIN </strong></p> <p>The DIN product averages over three river N exports models (ORCHIDEE-NLAT: Ma et al., in review; DLEM: Yang et al., 2015; Tian, pers. Com., IMAGE-GNM: Beusen et al., 2015, 2016) for every catchment. The total amounts to 30.03 Tg DIN yr-1 inputs to the ocean in the dataset for the 2011-2020 average (including inputs from labile DON).</p> <p><strong>DIP</strong></p> <p>The DIP product averages catchment estimates from IMAGE-GNM (Beusen et al., 2016) and Lacroix et al. (2020). This in total amounts to 4.92 Tg DIP yr-1 inputs to the ocean in the dataset.</p> <h2><strong>2. Use for modelers within the </strong><strong>R2O MIP </strong></h2> <p>We only briefly describe most important information on how to apply the river input data here and refer to the official R2O MIP protocol for more detail on our general simulation guidelines.</p> <ul> <li>We firstly recommend the addition of a terrestrial dissolved organic carbon pools in the ocean models: tDOC semi-labile (DOC_sl). Their only source should be that of the terrestrial inputs given here, it should be degraded with a first order constant of k_sl = 1 / 1.5yr (based on Hansell et al., 2012). The other tDOC compound given in the dataset, tDOC labile (tdoc_l), is assumed to be rapidly degraded and should therefore be added to the ocean model DIC pool.</li> <li>The inputs should be added to the closest ocean model grid points where the ocean model has freshwater inputs. Note that the inputs are given as 10^6 C/N/P per year, and this should be taken into account in the addition of the inputs at the model timestep. We recommend scaling the inputs to the seasonality of the freshwater inputs.</li> <li>The inputs from the riverine files should be added to the corresponding pool based on the following table:</li> <li> <table> <tbody> <tr> <td> <p>River Input</p> <p>(as named in <a href="../api/records/13684982/draft/files/rivr2o_riverinputs_preindustrial.nc/content" target="_blank" rel="noopener noreferrer">rivr2o_riverinputs_preindustrial.nc</a>)</p> </td> <td> <p>Global Load (preindustrial)</p> </td> <td> <p>Global Load </p> <p>(2011-2020 Mean)</p> </td> <td> <p>Ocean Model Pool</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>DIC -></p> </td> <td> <p>0.51 Pg C yr-1</p> </td> <td> <p>0.53 Pg C yr-1</p> </td> <td> <p>DIC & Alkalinity (see protocol)</p> </td> </tr> <tr> <td> <p>DOC_l -></p> </td> <td> <p>0.19 Pg C yr-1</p> </td> <td> <p>0.21 Pg C yr-1</p> </td> <td> <p>DIC</p> </td> </tr> <tr> <td> <p>DOC_sl -></p> </td> <td> <p>0.16 Pg C yr-1</p> </td> <td> <p>0.09 Pg C yr-1</p> </td> <td> <p>DOC_sl (new ocean model pool) and associated DON and DOP</p> </td> </tr> <tr> <td> <p>POC -></p> </td> <td> <p>0.095 Pg C yr-1</p> </td> <td> <p>0.12 Pg C yr-1</p> </td> <td> <p>marine DOC and associated nutrients (DON, DOP, see protocol)</p> </td> </tr> <tr> <td> <p>DIP -></p> </td> <td> <p>2.47 Tg P yr-1</p> </td> <td> <p>4.92 Tg P yr-1</p> </td> <td> <p>DIP / Phosphate</p> </td> </tr> <tr> <td> <p>DIN -></p> </td> <td> <p>20 Tg N yr-1</p> </td> <td> <p>30.03 Tg N yr-1</p> </td> <td> <p>DIN / Nitrate</p> </td> </tr> </tbody> </table> </li> </ul> <h2> </h2> <h2><strong>3. References</strong></h2> <p>Beusen, A. H. W., L. P. H. Van Beek, A. F. Bouwman, J. M. Mogollón, and J. J. Middelburg. Coupling Global Models for Hydrology and Nutrient Loading to Simulate Nitrogen and Phosphorus Retention in Surface Water-description of IMAGE–GNM and Analysis of Performance. Geoscientific Model Development, 8, no. 12 (2015): 4045–67. <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5194%2Fgmd-8-4045-2015&data=05%7C02%7CPierre.Regnier%40ulb.be%7Cbc3e3fa0c09249529ae508dccffc9a65%7C30a5145e75bd4212bb028ff9c0ea4ae9%7C0%7C0%7C638613931025683992%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=%2FAYnNpSwHioFv4igr0eRpwUW8HuFBDkY%2B9OmS8NpYZU%3D&reserved=0%22%20\o%20%22URL%20d%E2%80%99origine%C2%A0:%20https://doi.org/10.5194/gmd-8-4045-2015%20%20Cliquez%20pour%20suivre%20le%20lien." target="_blank" rel="noreferrer noopener">https://doi.org/10.5194/gmd-8-4045-2015</a>. </p> <p>Beusen, A. H. W., Bouwman, A. F., Van Beek, L. P. H., Mogollón, J. M., and Middelburg, J. J.: Global riverine N and P transport to ocean increased during the 20th century despite increased retention along the aquatic continuum, Biogeosciences, 13, 2441–2451, https://doi.org/10.5194/bg-13-2441-2016, 2016.</p> <p>Hansell, D. A., C. A. Carlson, and R. Schlitzer (2012), Net removal of major marine dissolved organic carbon fractions in the subsurface ocean, <em>Global Biogeochem. Cycles</em>, 26, GB1016, doi:<a title="Link to external resource: 10.1029/2011GB004069" href="https://doi.org/10.1029/2011GB004069" target="_blank" rel="noopener">10.1029/2011GB004069</a>.</p> <p>Lacroix, F., Ilyina, T., and Hartmann, J.: Oceanic CO<sub>2</sub> outgassing and biological production hotspots induced by pre-industrial river loads of nutrients and carbon in a global modeling approach, Biogeosciences, 17, 55–88, https://doi.org/10.5194/bg-17-55-2020, 2020.</p> <p>Liu et al. (2024). Global riverine land-to-ocean carbon export constrained by observations and multi-model assessment, Nature Geoscience, <a href="https://www.nature.com/articles/s41561-024-01524-z" target="_blank" rel="noopener">https://www.nature.com/articles/s41561-024-01524-z</a></p> <p>Ma, M., Zhang, H., Lauerwald, R., Ciais, P., and Regnier, P.: Estimating lateral nitrogen transfer through the global river network using a land surface model, Earth Syst. Dynam. Discuss. [preprint], <a href="https://doi.org/10.5194/esd-2024-29" target="_blank" rel="noopener">https://doi.org/10.5194/esd-2024-29</a>, in review, 2024.</p> <p>Regnier, P., Resplandy, L., Najjar, R.G. <em>et al.</em> The land-to-ocean loops of the global carbon cycle. <em>Nature</em> <strong>603</strong>, 401–410 (2022). https://doi.org/10.1038/s41586-021-04339-9</p> <p>Tian, H., Yao, Y., Li, Y., Shi, H., Pan, S., Najjar, R. G., et al. (2023). Increased terrestrial carbon export and CO<sub>2</sub> evasion from global inland waters since the preindustrial era. <em>Global Biogeochemical Cycles</em>, 37, e2023GB007776. <a href="https://doi.org/10.1029/2023GB007776">https://doi.org/10.1029/2023GB007776</a></p> <p>Yang, Qichun, Hanqin Tian, Marjorie A. M. Friedrichs, Charles S. Hopkinson, Chaoqun Lu, and Raymond G. Najjar.: Increased Nitrogen Export from Eastern North America to the Atlantic Ocean Due to Climatic and Anthropogenic Changes during 1901–2008. Biogeosciences,120, no. 6 (2015): 1046–68. <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1002%2F2014JG002763&data=05%7C02%7CPierre.Regnier%40ulb.be%7Cbc3e3fa0c09249529ae508dccffc9a65%7C30a5145e75bd4212bb028ff9c0ea4ae9%7C0%7C0%7C638613931025698138%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=cWTUjbft9Qkg5NOABA0WvTEQ9%2B8kl9GP78JOQJ9K074%3D&reserved=0%22%20\o%20%22URL%20d%E2%80%99origine%C2%A0:%20https://doi.org/10.1002/2014JG002763%20%20Cliquez%20pour%20suivre%20le%20lien." target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/2014JG002763</a>. </p> <p> </p> <h2><strong>4. Version Log</strong></h2> <p>v1 -> pre-industrial river inputs with coastal vegetation and burial transformations</p> <p>v2 -> Groundwater DIC discharge was added.</p> <p>v3-> Bugfixes for groundwater discharge and blue carbon inputs.</p> <p>v4 -> Corrected index with list <strong>riverexports_list_CN.csv </strong>for DIN inputs</p> <p>v5 -> corrected tDOC splits according to R2O-MIP protocol</p> <p>v8 -> Added submerged coastal vegetation fluxes to tDOC_semilabile</p> <p>v9 -> labile DON and labile DOP are added to the DIP and DON pools (based on C:N:P ratio of 2583:106:1)</p> <p>v10 -> slight correction in the labile DOM C:N:P ratio (C:N:P = 2583:103:1)</p> <p>v11 -> correction of labile DOM C:N:P ratio in list files</p> <p>v12 -> Addition of anthropogenic time series for 1901-2024</p> <p>v13 -> Corrected unit mistake in historical timeseries for DIN (10^3 magnitude too large)</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>
Two Source Energy Balance Model Inputs and Outputs from Drone Surveys at Majadas de Tietar in May 2021
<p><strong>MONSOON PROJECT SURVEY DATA OUTPUTS: Majadas de Tietar Tree-Grass Savanna Ecosystem 05/05/2021-20/05/2021</strong></p> <p>Here we make available high resolution (0.82 cm) energy and water flux maps from unmanned aerial system (UAS) data collected using a Micasense Altum in May 2021. We use the Two Source Energy Balance Model (via pyTSEB) and include model inputs and outputs. We use the Priestley Taylor (TSEB hereafter) and Dual Time Difference (DTD hereafter) methods in pyTSEB, the details of which can be found here pyTSEB https://pytseb.readthedocs.io/en/latest/index.html. The data collection method largely follows https://www.mdpi.com/2072-4292/13/7/1286, however a new paper detailing these surveys in Majadas is under review (as of November 2021). </p> <p>This upload includes the following gridded datasets:</p> <p><strong>Model inputs</strong></p> <p>Zipfiles are named according to their collection date (<strong>DDMMYYYY.7z</strong>). Within each zipfile are the datasets corresponding to different flight times UTC +2 (<strong>hhmm_DDMMYY</strong>). Within each survey folder are rasters with descriptive filenames using the following format:</p> <p><em>Product type_Resolution_survey area_date_flight time.tif</em></p> <p>The following prefixes denote the Product types:</p> <ul> <li>CHM_... = Canopy Height Model (m)</li> <li>GFrac2_... = Green Fraction (0-1)</li> <li>MSpec_... = Raw multispectral dataset from Altum (Blue, Green, Red, NIR, Rededge, LWIR)</li> <li>TEmpK_... = Radiometric Surface Temperature (empirical calibration, K)</li> <li>TRawK_... = Radiometric Surface Temperature (no calibration, K)</li> <li>LST2_... = Radiometric Surface Temperature (calibrated using methods outlined here https://www.mdpi.com/2072-4292/12/7/1075, K)</li> <li>Grass_... = grass vegetation mask</li> <li>Tree_... = tree vegetation mask</li> </ul> <p>We also supply the config files used to generate TSEB and DTD. To run these you will need to edit the filepaths according to your own system. </p> <p><strong>Model Outputs</strong></p> <p><strong>Majadas_TSEB_EMP_outputs.7z</strong> = Two Source Energy Balance (pyTSEB) model outputs (using the Priestley-Taylor method), using radiometric temperature datasets calibrated empirically. </p> <p><strong>Majadas_DTD_EMP_outputs.7z</strong> = TSEB Dual Time Difference model outputs (from pyTSEB) using radiometric temperature datasets calibrated empirically. </p> <p><strong>DTD_ET.7z</strong> = Evapotranspiration rasters (calculated using DTD latent heat data) in g m<sup>-2</sup> s<sup>-1</sup></p> <p><strong>File names are descriptive</strong>:</p> <p><em>Model type_radiometric temperature method_vegetation type_survey area_date_flighttime.tif</em></p> <p>Model type = DTD or TSEB</p> <ul> <li>Radiometric temperature method = always empirical calibration here</li> <li>vegetation type = grass, tree, or merge (which is both tree and grass)</li> <li>Survey area = N (north, or Nitrogen fertiliser treatment), C (central, or Control fertiliser treatment), S (south, or Nitrogen and Phosphorus fertiliser treatment)</li> <li>date = in DDMMYY format</li> <li>flight time = takeoff time for the drone (hhmm) (UTC+2)</li> </ul> <p>To find the exact local time of survey times, please see the table in flight_data3.csv</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>
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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.
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.