Find research datasets worth reusing

FRACTESUS_SCK CEN_SA508Cl3 Standard_T0_MCT_input

<div>Fractesus project. Fracture test mini-CT. Master curve input SA508 Cl.3. SCK CEN.            </div>

MC_T0TEM input_NRG_SA508CI_KJc_MCT

<div>Fractesus project. Fracture test mini-CT. Master curve input SA508 Cl.3. NRG.            </div>

MC_T0TEM input_NRG_73W_IRR_KJc_MCT

<div>Fractesus project. Fracture test mini-CT. Master curve input 73W Irradiated. NRG.           </div>

EK-CER_73Wirr_MCT_MC_T0TEM_input

<div>Fractesus project. Fracture test mini-CT. Master curve input 73W Irradiated. EK-CER.           </div>

opencc-zeroApr 2024View details →

Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (inputs, outputs, analysis)

<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt: contains raw data that are used for analysis, also conatin folder for GaMD testing.</li> </ul>     1. cMD(Classical MD simulation) analysis files :              1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.         2. Inputs and output files of caver calculations.          3. H-bond raw distance files from all simulations named run1-run5.          4. Distance files used to calculate PCA and cluster analysis.         5. Input files and input structure used to run simulations along with output restart files from each stage of production.             2. GaMD(Gaussian Accelerated MD simulation) analysis files :            1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.         2. Inputs and output files of caver calculations.          3. H-bond raw distance files from all simulations named run1-run5.          4. Distance files used to calculate PCA and cluster analysis.         5. Input files and input structure used to run simulations along with output restart files from each stage of production.     3. GaMD-testing :         1. Input file of GaMD used to run testing and output gamd.log files for multiple run of σOP 1.2 - 1.4 and σOD 2.5.     4. Initial 200ns cMD simulation files used for cluster analysis :         1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format         2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.         3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder. <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant: contains raw data that are used for analysis.</li> </ul>     1. cMD(Classical MD simulation) analysis files :              1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.         2. Inputs and output files of caver calculations.          3. H-bond raw distance files from all simulations named run1-run5.          4. Distance files used to calculate PCA and cluster analysis.         5. Input files and input structure used to run simulations along with output restart files from each stage of production.             2. GaMD(Gaussian Accelerated MD simulation) analysis files :            1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.         2. Inputs and output files of caver calculations.          3. H-bond raw distance files from all simulations named run1-run5.          4. Distance files used to calculate PCA and cluster analysis.         5. Input files and input structure used to run simulations along with output restart files from each stage of production.     3. Initial 200ns cMD simulation files used for cluster analysis :         1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format         2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.         3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder. <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant: contains raw data that are used for analysis.</li> </ul>     1. cMD(Classical MD simulation) analysis files :              1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.         2. Inputs and output files of caver calculations.          3. H-bond raw distance files from all simulations named run1-run5.          4. Distance files used to calculate PCA and cluster analysis.         5. Input files and input structure used to run simulations along with output restart files from each stage of production.             2. GaMD(Gaussian Accelerated MD simulation) analysis files :            1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.         2. Inputs and output files of caver calculations.          3. H-bond raw distance files from all simulations named run1-run5.          4. Distance files used to calculate PCA and cluster analysis.         5. Input files and input structure used to run simulations along with output restart files from each stage of production.     3. Initial 200ns cMD simulation files used for cluster analysis :         1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format         2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.         3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder. <ul> <li>03_TT_analysis.tar.gz - TransportTools: contains config file and all the raw data from all set and subset of reclustered (using in-house python script) caver calculations used for running TT.</li> </ul>     1. Caver input data for comparison between 500ns, 1 us, 2.5 us and 5us between LinB-Wt and it’s mutants.     2. TransportTools log file.     3. Main statistics result of comparative analysis. <ul> <li>04_reweighting.tar.gz: directory contains reweighted .csv files after running in-house reweighting protocol.             1. GaMD log files from each simulation of LinB-Wt and it’s mutants.     2. CSV files from TT result folder.     3. Result *.csv file contained reweighted tunnel properties in folder reweighted_filtered_new.</li> <li>05_caverdock.tar.gz: contains raw data for caverdock calculations uisng 100 best tunnels with four ligands 2-bromoethanol (be), 1,2-dibromoethane (dbe), Bromide ion (br-) and water (h2o).</li> </ul>     1. Top 100 tunnels present in tunnel folder for all three tunnels ST, p1b and p3 with subdirectory containing three variants and four ligand, whichare used for running caverdock.     2. Ligand *.pdbqt file and receptor *.pdbqt are present in each 100 tunnel folder of respective caverdock calculation.     3. Inside each variant and each ligand, there is respective result of migration analysis with energy barrier calculation of respective tunnels *energy_barriers-new.log* and further simplied *.csv files that was used for preparing figure in manuscript.

opencc-by-4.0May 2024View details →

FRACTESUS_SCK CEN_73WIrr_T0_MCT_ASTMdesign_Input

<div>Fractesus project. Fracture test mini-CT. Master curve input 73W Irradiated. SCK CEN.           </div>

opencc-by-4.0May 2024View details →

Optimized summary-statistic-based single-cell meta-analysis. Input files

This dataset contains information about the input files used in the Optimized summary-statistic-based single-cell meta-analysis research project.

Datasets for input and output of INFORM Severity-based SMAA study of resource allocation in humanitarian aid and disaster management under climatic losses and damages

The landscape of climate change and extreme events will remain a wicked problem for equitable and forward-looking resource prioritisation. The question of how to couple climate and multi-risk information remains. IPCC has considered that multi-criteria decision analysis (MCDA) can help. We use stochastic multi-attribute analysis (SMAA), a variant of MCDA, to compute prioritisations of climatic losses & damages (l&d) for fragile countries with a humanitarian response plan. SMAA is combined with the INFORM Severity index, measuring the status of crises and disasters, and preferences gathered from stakeholders (e.g., United Nations, European Union, World Bank, the research and public sector, civil society). <ul> <li>Dataset S1. XLS-file with all the input data compiled from sources, concurrent data manipulation, and descriptions of steps taken until ready for the SMAA.</li> <li>Dataset S2. XLS-file with results of the SMAA for all weight schemes and concurrent analysis, such as sensitivity heat mapping, correlations, regressions, and Tukey mean-difference plot.</li> </ul>

Pose Selector Workflow - Structure Input Files for Machine Learning (Set 2)

Second set of structure input files for the docking poses of the remaining 2022 protein-ligand complexes. Together with the structure files and absolute binding free energy (ABFE) estimates shared in 10.5281/zenodo.11397017, this data can be used to train a machine-learning (ML) model predicting the ABFE of binding poses of protein-ligand complexes. More background on the workflow generating the structure files and ABFE estimates is provided in 10.5281/zenodo.11397017.

Pose Selector Workflow - Docking Poses, Absolute Binding Free Energy Estimates and Structure Input Files for Machine Learning

The Pose Selector (PS) workflow calculates absolute binding free energies (ABFEs) for binding poses of protein-ligand complexes. First, it converts the binding poses (both docking poses as well as experimentally observed ligand binding poses), which are provided as a combination of protein PDB file and ligand MOL2 file, into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks and repair steps. Next, the PS workflow post-processes and analyses the last frame of the resulting eight 100 ps trajectories per binding pose with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the ABFE estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions. For the dataset published here, the PS workflow was run on docking poses generated for the PDBbind 2020 dataset (http://www.pdbbind.org.cn/index.php), shared in dockingPosesPDBBind2020.tar.gz. This entry and its partner entry 10.5281/zenodo.11397486 also share the intial coordinates used in the MD simulations of >800,000 docking poses of 4022 protein-ligand complexes (structureFiles_dockingPoses1.tar.gz in this entry and structureFiles_dockingPoses2.tar.gz in 10.5281/zenodo.11397486) and of the experimental ligand binding pose of 4549 complexes (structureFiles_experimentalStructures.tar.gz) as well as the corresponding ABFE estimates (absoluteBindingFreeEnergyEstimates.tar.gz). The MD simulations were run on the LUMI and MeluXina supercomputers while the implicit-solvent calculations were carried out on Galileo (Cineca). The README file describes the structure of the shared data in more detail and points out how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates as well as how to use the data provided in this entry to train a machine-learning model predicting the ABFE of binding poses of protein-ligand complexes. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Pose-Selector-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).

CEDAR 2024 workshop simulation inputs

Open the record for dataset details and reuse information.

FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept - Input Data

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.  <div>Not all variables were used in the final analysis <table> <tbody> <tr> <td>               NetCDF File</td> <td>     Variable </td> <td> Used/not Used </td> <td> Source/Reference </td> </tr> <tr> <td> burned_area.nc </td> <td> Burned area </td> <td> As training data </td> <td>MCD64A1/ Giglio et al. (2018)</td> </tr> <tr> <td> burned_area_nat_veg.nc </td> <td> Burned area in natural vegetation </td> <td> As training data </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> burned_area_non_nat_veg.nc </td> <td> Burned area in non natural vegetation </td> <td> As training data </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td>  tas_max.nc </td> <td>  Maximum Temperature </td> <td> Used </td> <td> ISIMIP3a FRIELER et al. (2023) </td> </tr> <tr> <td> precip.nc </td> <td> Precipitation </td> <td> Used </td> </tr> <tr> <td> vpd.nc </td> <td> Vapor pressure deficit </td> <td> Not Used </td> </tr> <tr> <td> rhumid.nc </td> <td>  Relative Humidity  </td> <td> Not Used </td> </tr> <tr> <td> consec_dry_days.nc</td> <td> Consecutive number of dry days  </td> <td>Not Used</td> </tr> <tr> <td> soilM.nc </td> <td> Soil  Moisture </td> <td> Not Used </td> <td> JULES-ES </td> </tr> <tr> <td> lightn.nc    </td> <td>  Lightning </td> <td> Not Used </td> <td>  ISIMIP3a FRIELER et al. (2023) </td> </tr> <tr> <td> popDen.nc </td> <td>  Population density </td> <td> Not Used </td> </tr> <tr> <td> road_density.nc </td> <td> Road density </td> <td>Used</td> <td>  GRIP global (MEIJER et al., 2018) </td> </tr> <tr> <td> cveg.nc </td> <td> Vegetation carbon </td> <td> Not Used </td> <td> JULES-ES </td> </tr> <tr> <td> csoil.nc </td> <td> Carbon in dead vegetation </td> <td>Used</td> <td> JULES-ES </td> </tr> <tr> <td> forest.nc </td> <td>   Forest </td> <td> Used </td> <td>  MAPBIOMAS, 2022 </td> </tr> <tr> <td> grassland.nc </td> <td>   Grassland </td> <td> Not Used </td> </tr> <tr> <td> savanna.nc </td> <td>   Savanna </td> <td> Not Used </td> </tr> <tr> <td> cropland.nc </td> <td>   Cropland </td> <td> Not Used </td> </tr> <tr> <td> pasture.nc </td> <td>   Pasture </td> <td> Used </td> </tr> <tr> <td> np.nc </td> <td> Number of patches  </td> <td> Not Used </td> <td> Calculated from MAPBIOMAS, 2022 </td> </tr> <tr> <td> ed.nc  </td> <td>Edge density</td> <td>Used</td> </tr> </tbody> </table> </div>

opencc-by-4.0May 2024View details →

Supporting data for "Granularity of model input data impacts estimates of carbon storage in soils"

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.   In this repository are the downscaled climate input data that drive the RothC model, as well as the model outputs for each GCM.

ImpactX+MADX input and output files for a thin-kick model of the FNAL Booster

Input and output files for a thin-kick model of the Fermilab Booster ring in MAD-X (expressed in SXF format), together with a Python script to parse and run ImpactX using the SXF lattice file.

SVAFotate core GRCh38 input BED including gnomADv4.1

An updated input BED file for use with SVAFotate including the SV data from gnomAD v4.1. This core file is for use with GRCh38 as all included data was derived from GRCh38 alignments (no liftovers included).

Input data for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019.

This dataset provides input data (fluxes, background concentrations, and observations) for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019 using chemical transport models (CTMs). While some components of the dataset are available in other repositories, this compilation serves to 1) streamline the data collection process for other users and 2) bypass the need to perform data aggregation. Here is a description of each dataset: cams73_latest_co2_conc_surface_inst_2019*.nc CO2 mole fractions from the CAMS global inversion-optimised product v20r2 (Chevallier et al., 2010). The data are provided at a resolution of 3.75° in longitude and 1.9° in latitude, with a 3-hourly temporal resolution.  monitor_CO2_CIF_2019.nc <div> <div> <div> <div> <div> <div> Observed CO2 atmospheric mixing ratios in Europe, compiled in version V8 of the ICOS GlobalView Obspack (ICOS RI et al., 2023), include continuous measurements from 58 stations across Europe, incorporating both ICOS and non-ICOS facilities. The original dataset has been aggregated and adapted to match the format of the monitor files used in the Community Inversion Framework (CIF; Berchet et al., 2021). </div> </div> </div> </div> </div> </div> EDGARv4.3_BP2021_CO2_EU2_2019.nc Anthropogenic CO2 fluxes (European, hourly) obtained from EDGAR-v4.2 and BP. The anthropogenic CO2 emissions are based on the spatial distribution from the EDGAR-v4.2 inventory, national and annual budgets from British Petroleum (BP) statistics, and hourly temporal profiles derived using the COFFEE approach (Steinbach et al., 2011, available on the ICOS Carbon Portal). This data is provided at a 0.1° × 0.1° horizontal resolution and hourly temporal resolution. FG2.TRENDY11.ORC3.S3.3H_NBP_resp_2019.nc NBP CO2 fluxes (global, 3-hourly) obtained from ORCHIDEE simulations.  The ORCHIDEE-TRENDY simulation is conducted as part of the TRENDY model intercomparison project (e.g., Sitch et al., 2015; Friedlingstein et al., 2022). This simulation uses inputs provided by the project, including the CRUERA atmospheric climate forcing (global, 6-hourly, 0.5-degree resolution), LUH2 land-use change dataset, global atmospheric CO2 concentration data, and nitrogen fertilizer input datasets. All TRENDY simulations adhere to a standardized protocol: a model spin-up phase using recycled forcing data from 1901-1920, with other inputs from 1700, continues until the model's carbon pools reach equilibrium (340 years of spin-up for ORCHIDEE). This is followed by a transient simulation from 1700-1900, varying CO2 and land-use data while recycling climate forcing, and a historical simulation from 1901-2020 with all data inputs varied. FR2.ORC3v7267.CRUERA3.NBP_3H.2019.nc NBP CO2 fluxes (Europe, 3-hourly) obtained from ORCHIDEE simulations.  The ORCHIDEE-VERIFY simulation is performed as part of the VERIFY project over the European region. This simulation is driven by the CRUERA dataset, which is derived from the ERA5-Land dataset (originally global, 1-hourly, at 0.1-degree resolution), transformed to the VERIFY region of interest (35°N to 73°N, 25°W to 45°E, 3-hourly, at 0.125-degree resolution), and re-aligned with the CRU observation dataset (for air temperature, shortwave radiation, humidity, and precipitation). The Hilda+ dataset is used for land use, and the EMEP model outputs are used for nitrogen inputs. The VERIFY simulation follows the general protocol used in the TRENDY project. FR2.ORC3v7267.CRUERA3.hetero_resp_3H.2019.nc Heterotrophic respiration CO2 fluxes (Europe, 3-hourly) obtained from ORCHIDEE simulations as described in the previous section. Becker_coastal_fluxes_RF_v2021_2_2019.nc Ocean CO2 fluxes (Europe, daily).  The ocean fluxes come from a hybrid product combining the University of Bergen coastal ocean flux estimate and the Rödenbeck global ocean estimate (Rödenbeck et al., 2014). This data is provided at a 0.125° × 0.125° horizontal resolution and at a daily temporal resolution.   References    Berchet, A., Sollum, E., Pison, I., Thompson, R. L., Thanwerdas, J., Fortems-Cheiney, A., Peet, J. C. A. v., Potier, E., Chevallier, F., Broquet, G., and Berchet, A.: The Community Inversion Framework: codes and documentation, https://doi.org/10.5281/zenodo.6304912, 2022 Chevallier, F., Ciais, P., Conway, T. J., Aalto, T., Anderson, B. E., Bousquet, P., Brunke, E. G., Ciattaglia, L., Esaki, Y., Fröhlich, M., Gomez, A., Gomez-Pelaez, A. J., Haszpra, L., Krummel, P. B., Langenfelds, R. L., Leuenberger, M., Machida, T., Maignan, F., Matsueda, H., Morguí, J. A., Mukai, H., Nakazawa, T., Peylin, P., Ramonet, M., Rivier, L., Sawa, Y., Schmidt, M., Steele, L. P., Vay, S. A., Vermeulen, A. T., Wofsy, S., and Worthy, D.: CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements, Journal of Geophysical Research: Atmospheres, 115, https://doi.org/10.1029/2010JD013887, 2010 Friedlingstein, P., O’Sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I. T., Olsen, A., Peters, G. P.,Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Alkama, R., Arneth, A., Arora,V. K., Bates, N. R., Becker, M., Bellouin, N., Bittig, H. C., Bopp, L., Chevallier, F., Chini, L. P., Cronin, M., Evans, W., Falk, S., Feely, R. A., Gasser, T., Gehlen, M., Gkritzalis, T., Gloege, L., Grassi, G., Gruber, N., Gürses, O., Harris, I., Hefner, M., Houghton, R. A.,Hurtt, G. C., Iida, Y., Ilyina, T., Jain, A. K., Jersild, A., Kadono, K., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken,J. I., Landschützer, P., Lefèvre, N., Lindsay, K., Liu, J., Liu, Z., Marland, G., Mayot, N., McGrath, M. J., Metzl, N., Monacci, N. M.,Munro, D. R., Nakaoka, S.-I., Niwa, Y., O’Brien, K., Ono, T., Palmer, P. I., Pan, N., Pierrot, D., Pocock, K., Poulter, B., Resplandy, L.,Robertson, E., Rödenbeck, C., Rodriguez, C., Rosan, T. M., Schwinger, J., Séférian, R., Shutler, J. D., Skjelvan, I., Steinhoff, T., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tanhua, T., Tans, P. P., Tian, X., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf,G. R., Walker, A. P., Wanninkhof, R., Whitehead, C., Willstrand Wranne, A., Wright, R., Yuan, W., Yue, C., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2022, Earth System Science Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022,https://essd.copernicus.org/articles/14/4811/2022/, publisher: Copernicus GmbH, 2022 ICOS RI, Bergamaschi, P., Colomb, A., De Mazière, M., Emmenegger, L., Kubistin, D., Lehner, I., Lehtinen, K., Lund Myhre, C., Marek, M., Platt, S. M., Plaß-Dülmer, C., Schmidt, M., Apadula, F., Arnold, S., Blanc, P.-E., Brunner, D., Chen, H., Chmura, L., Conil, S., Couret, C., Cristofanelli, P., Delmotte, M., Forster, G., Frumau, A., Gheusi, F., Hammer, S., Haszpra, L., Heliasz, M., Henne, S., Hoheisel, A., Kneuer, T., Laurila, T., Leskinen, A., Leuenberger, M., Levin, I., Lindauer, M., Lopez, M., Lunder, C., Mammarella, I., Manca, G., Manning, A., Marklund, P., Martin, D., Meinhardt, F., Müller-Williams, J., Necki, J., O’Doherty, S., Ottosson-Löfvenius, M., Philippon, C., Piacentino, S., Pitt, J., Ramonet, M., Rivas-Soriano, P., Scheeren, B., Schumacher, M., Sha, M. K., Spain, G., Steinbacher, M., Sørensen, L. L., Vermeulen, A., Vítková, G., Xueref-Remy, I., di Sarra, A., Conen, F., Kazan, V., Roulet, Y.-A., Biermann, T., Heltai, D., Hensen, A., Hermansen, O., Komínková, K., Laurent, O., Levula, J., Pichon, J.-M., Smith, P., Stanley, K., Trisolino, P., ICOS Carbon Portal, ICOS Atmosphere Thematic Centre, ICOS Flask And Calibration Laboratory, and ICOS Central Radiocarbon Laboratory: European Obspack compilation of atmospheric carbon dioxide data from ICOS and non-ICOS European stations for the period 1972-2023; obspack_co2_466_GLOBALVIEWplus_v8.0_2023-04-26, https://doi.org/10.18160/CEC4-CAGK, 2023 Rödenbeck, C., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., Cassar, N., Reum, F., Keeling, R. F., and Heimann, M.: Interannual sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme, Biogeosciences, 11, 4599–4613, https://doi.org/10.5194/bg-11-4599-2014, 2014 Sitch, S., Friedlingstein, P., Gruber, N., Jones, S. D., Murray-Tortarolo, G., Ahlström, A., Doney, S. C., Graven, H., Heinze, C., Huntingford,C., Levis, S., Levy, P. E., Lomas, M., Poulter, B., Viovy, N., Zaehle, S., Zeng, N., Arneth, A., Bonan, G., Bopp, L., Canadell, J. G.,Chevallier, F., Ciais, P., Ellis, R., Gloor, M., Peylin, P., Piao, S. L., Le Quéré, C., Smith, B., Zhu, Z., and Myneni, R.: Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://bg.copernicus.org/articles/12/653/2015/, publisher: Copernicus GmbH, 2015. Steinbach, J., Gerbig, C., Rödenbeck, C., Karstens, U., Minejima, C., and Mukai, H.: The CO2 release and Oxygen uptake from Fossil Fuel Emission Estimate (COFFEE) dataset: effects from varying oxidative ratios, Atmospheric Chemistry and Physics, 11, 6855–6870,1160 https://doi.org/10.5194/acp-11-6855-2011, 2011

opencc-by-4.0Feb 2024View details →

Climate model and proxy input data for PaleoDA South America reconstruction

This repository contains  input data needed to run the paleoclimate reconstruction code for  "A continental reconstruction of hydroclimatic variability in South America during the past 2000 years", submitted to Climate of the Past in February 2024 [https://egusphere.copernicus.org/preprints/2024/egusphere-2024-545/].  The Github repository is located here: https://github.com/mchoblet/paleoda_sa/tree/main Structure: model_data: One File for each Model (GISS, CCSM (isoGSM), CESM, ECHAM5, iHADCM3) and variable (prec,tsurf,d18O, SPEI). Monthly resolution. proxy_data: One File for each proxy record type (Trees and corals contain a separate file for annual and djf linear regression parameters, the proxy data as such is the same). The data has yearly resolution, and thus also contains NaNs for when a year is not covered by a proxy. Note, that these time series are resampled to a regular resolution in the multi-time scale PaleoDA code. Climate Model Data: The original data can be found in https://zenodo.org/records/6610684. The data in this repository here has been slightly modified and regridded for easier processing by the reconstruction algorithm.  When using the data here, please also cite https://zenodo.org/records/6610684 and the publication  "Investigating stable oxygen and carbon isotopic variability in speleothem records over the last millennium using multiple isotope-enabled climate models", by  <div>Janica C. Bühler, Josefine Axelsson, Franziska A. Lechleitner, Jens Fohlmeister, Allegra N. LeGrande, Madhavan Midhun, Jesper Sjolte, Martin Werner, Kei Yoshimura, and Kira Rehfeld (https://cp.copernicus.org/articles/18/1625/2022/cp-18-1625-2022.html)</div> Climate Proxy Data: A regional proxy record subselection for South America. See References in Appendix A Choblet et al. (https://egusphere.copernicus.org/preprints/2024/egusphere-2024-545/). The DOI of each record is stored as Metadata. How were these files created? The steps are documented in the the Github repository https://github.com/mchoblet/paleoda_sa/tree/main (data_preprocessing). The SPEI drought index has ben computed from modeled precipitation and temperature using Thornthwaite's method (using the Climate Indices package, https://github.com/monocongo/climate_indices). Manuscript revision in July 2024: <ul> <li>Added historical documentary indices time series and the Puyehue lake record. For technical reasons in the PaleoDA algorithm, it is kept apart from the other lake records. The reconstruction code on Github has been updated for including these datasets.</li> </ul>         <div> </div>

opencc-by-4.0Jul 2024View details →

Input data and results of the RECC v2.5 model for the transformation scenarios of the global building stock

This dataset contains the input data and core results of the RECC v2.5 model for the transformation scenarios of the global building stock. For details abou the RECC model, see DOI <a href="https://doi.org/10.1111/jiec.13023" target="_blank" rel="noopener">https://doi.org/10.1111/jiec.13023</a> and the RECC model landing page: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc" target="_blank" rel="noopener">https://www.industrialecology.uni-freiburg.de/odym-recc</a> The following data are included in this dataset: <ul> <li>The entire model input database (120 model parameters)</li> <li>The parameters for the sensitivity analysis (8 parameters)</li> <li>The 70 folders with the core results</li> <li>The master classification file RECC_Classifications_Master_V2.0.xlsx</li> <li>The model config file RECC_Config.xlsx</li> <li>The list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The result compilation and exporting configuration file RECCv2.5_EXPORT_Combine_Select.xlsx</li> <li>The main result summary file (extracted from the 70 result folders) Results_Extracted_RECCv2.5_10Regs_sep.xlsx</li> <li>The result summary file for comparison with the CRAFT model timber supply RECCv2.5_10Regs_CRAFT_Coupling_SHARE.xlsx</li> <li>The results of the sensitivity analysis: Results_Extracted_RECCv2.5_10Regs_Sensitivity_sep.xlsx</li> </ul> Note that the result folders of the sensitivity analysis are not archived here (too little information in relation to the data volume). They can be requested from the author. The results can also be recreated by running the RECC model with the sensitivity analysis parameters. The model itself is available as Python code from <a href="https://github.com/IndEcol/RECC-ODYM" target="_blank" rel="noopener">https://github.com/IndEcol/RECC-ODYM</a>

opencc-by-4.0Jul 2024View details →

DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: input files (2 of 3)

DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes the files necessary for nudging the simulated temperature and salinity towards Copernicus GLORYS12V1 reanalysis values in a simulation from 1 September to 31 December 2013. The remaining input files for this period are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a> and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>. Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for phyiscs-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request. Sample output files (from the physics and biogeochemistry modules) are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a> and <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</a>.