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Code outputs and figures from "Efficient high-resolution refinement in cryo-EM with stochastic gradient descent"
<p>Code outputs and figures for the numerical experiments on preconditioned SGD for cryo-EM reconstruction for reproducing the results in the article:</p> <blockquote> <p><a title="doi" href="https://doi.org/10.1107/S205979832500511X" target="_blank" rel="noopener"><code><em>Efficient high-resolution refinement in cryo-EM with stochastic gradient descent</em>.</code></a></p> <p><em>Bogdan Toader, Marcus A. Brubaker, Roy R. Lederman</em></p> <pre>Acta Crystallographica Section D, 2025</pre> </blockquote> <p>The outputs are obtained by running the Jupyter notebooks in the <em>notebooks/preconditioned_sgd</em> directory in the GitHub repository (release v0.2):</p> <blockquote> <p><a title="github link" href="https://github.com/bogdantoader/simplecryoem">https://github.com/bogdantoader/simplecryoem</a></p> </blockquote> <div>The particle images used for these experiments can be downloaded from <a title="empiar-10076 link" href="https://www.ebi.ac.uk/empiar/EMPIAR-10076">EMPIAR-10076</a> and require inverting the contrast. The file containing the pose variables and CTF parameters is the <em>particles_file/my_particles_8.star </em>file in the attached archive.</div>
DOSE - Global dataset of reported subnational economic output
<p><strong>DOSE V2.11</strong><strong> is an update of DOSE V2. </strong>We made a few corrections and additions to the data where various users had identified gaps or inaccuracies. Please see 'DOSEV2.11_changes.pdf' for details. </p> <p><strong>DOSE – the MCC-PIK Database Of Sub-national Economic Output. </strong>DOSE v2 contains harmonised data on reported economic output for:</p> <ul> <li>1,661 sub-national regions</li> <li>across 83 countries</li> <li>from 1953 to 2020</li> <li>with sectoral detail for the agricultural, manufacturing and services sectors.</li> </ul> <p>To avoid interpolation, values were assembled from numerous statistical agencies, yearbooks and the literature and harmonised for both aggregate and sectoral output. In addition to regional economic output in local currency units (LCU) at current market prices as collected from the original data sources, DOSE contains per capita estimates in LCU and US dollars at both, current and 2015 market prices to enable comparison across time and space. Population data, market exchanges rates and deflator data used to generate them are included as well. Moreover, we provide temporally and spatially consistent data for regional boundaries, enabling matching with geo-spatial data such as climate observations. Annual temperature and precipitation data for each region are already included. Overall, DOSE provides the opportunity for detailed analyses of economic development at the subnational level, consistent with reported values.</p> <p>A peer-reviewed data descriptor with detailed documentation of the different data assembling, processing and validation steps as well as illustrative plots of the data set's coverage and examples for its application can be found here: </p> <p>L. Wenz, R.D. Carr, N. Koegel, M. Kotz, M. Kalkuhl. <a href="https://rdcu.be/dfTPH">DOSE – Global data set of reported sub-national economic output</a>. Nature Scientific Data. 2023. https://doi.org/10.1038/s41597-023-02323-8</p> <p> </p>
Outputs of the WiMMed hydrological model for Sierra Nevada (Spain). Sept2015-Aug2022
<p>Ecosystem Services related to flood prevention, aquifer recharge and erosion prevention in SIERRA NEVADA (Spain) were quantified through the WiMMed hydrological model (Watershed Integrated Model in Mediterranean Environments; Herrero et al., 2014). WiMMed is a distributed and physically based model that combines hourly and daily meteorological data with soil hydro-physical properties and land use and land cover information to simulate water balance and flow circulation at basin scale (see Herrero et al. (2014) for details). </p><p>In this study we applied the WiMMed model considering the land use and land cover data for 2020 (according to SIPNA) and the meteorological data from Sept2015 to Aug2022 to evaluate the value of ecosystem services, following the work made by Moreno-Llorca et al. (2020). Specific parameters, expressing the influence of vegetation changes in the hydrological processes of the study area, were considered, namely on evapotranspiration, interception, infiltration, overland flow and soil erodibility. Aquifer recharge (mm/m2/year) was calculated as the total volume of water moving from the soil into the aquifer and becoming groundwater. For that, the model firstly interpolates the precipitation at the cell scale (Herrero et al., 2009), and then calculates rainfall/snowfall partition, reproduces the interception from the vegetation, calculates the snow accumulation and melting (Herrero et al., 2009), and separates surface runoff from infiltration on the ground surface. Vertical and horizontal soil water movement was reproduced by a two-layer soil approach, using Darcy-Buckingham law with Mualem-vanGenuchten parameterization (Muñoz Carpena and Ritter Rodriguez, 2005). Evapotranspiration extract water from soil using a parameterization based on potential evapotranspiration and soil water content (Herrero et al., 2014). Water percolating through the second layer of soil becomes aquifer recharge. Soil erosion prevention (T/ha/year) was calculated by considering the inverse of soil loss by water flow concentration (rill processes) and raindrop impacts (interrill processes). WiMMed uses the variation of different parameters that link soil loss, with changes in vegetation cover and land uses, as described in (Millares et al., 2019). Changes on soil erodibility were estimated from vertical distribution of root biomass, by adapting empirical models (e.g. Gale and Grigal, 1987; Jackson et al., 1996) to Mediterranean environments reported previously (Martinez- Fernandez et al., 1995). From these estimations, distributed soil erodibility was calculated from the empirical model proposed by Flanagan and Livingstone (1995). The calibration and validation of the WiMMed model in Sierra Nevada has been conducted through a series of studies that analysed each hydrological process in the area and designed and corrected each WiMMed module, pertaining to snow (Herrero et al., 2009), soil (Aguilar and Polo, 2011), baseflow (Millares, 2008; Millares et al., 2009), river flow (Pérez-Palazón et al., 2014), or soil loss and sediment transportation (Bergillos et al., 2016; Millares et al., 2020).</p><p><strong>INPUT DATA</strong></p><p>The input data used in the hydrological simulations were:</p><ul><li>Digital elevation model from national remote sensing program PNOA-LIDAR MDT02 and the topographic features calculated by WiMMed from the DEM: surface drainage system, river delineation, slope, aspect, sky view factor and horizon (sky obstruction in 8 directions).</li><li>Meteorological data from more than 50 weather stations in the area: hourly/daily rainfall (mm), hourly and daily temperature (oC), daily solar radiation (MJ/m2), average daily wind speed (m·s−1), average daily relative humidity (%), average daily barometric pressure (hPa).</li><li>Physico-chemical and hydraulic properties of the soil selected from the available spatial database performed by Rodríguez (2008), in which thematic maps were obtained for Andalusia at a 250-m resolution: hydraulic conductivity (mm·h−1), saturation and residual moisture values (mm·mm−1), air-entry matric potential (mm), retention parameter of the van Genuchten (dimensionless) and soil thickness (mm).</li><li>Land cover and land use information from SIPNA 2020.</li><li>Aquifer regions and information from hydrogeological atlas of Andalusia (ITGE-Junta de Andalucía, 1998; Castillo, 2008).</li></ul><p><strong>OUTPUT DATA</strong></p><p>The results contained in this database are raster files in UTM ETRS89 30S, with a spatial resolution of 30x30 meters, for the whole SIerra Nevada. The raster files are Esri-ASCII ArcGIS (.asc) grids with 3846 columns (X) and 2099 rows (Y). There are different time scales for each variable. The prefix of the file indicates this time scale, namely "Ano" for annual maps, "mes" for monthly maps and "Tot" for the whole simulation. The suffix indicates the variable of interest:</p><ul><li>Pre: Accumulated precipitation (solid + liquid) in mm</li><li>T_m: Mean temperature in ºC</li><li>P_n: Accumulated snowfall in mm</li><li>ErT: Accumulated total erosion (rill + interrill) in kg/m2</li><li>ET0: Accumulated potential evapotranspiration in mm</li><li>EvC: Accumulated real evaporation from canopy (intercepted precipitation) in mm</li><li>EvN:Accumulated real sublimation from snow in mm</li><li>EvS: Accumulated real evapotranspiration ration from soil in mm</li><li>Exp: Accumulated direct runoff in mm</li><li>Fus: Accumulated snowmelt in mm</li><li>HSol1: Instantaneous soil moisture in surface layer 1 (upper 25 cm) in mm</li><li>HSol2: Instantaneous soil moisture in deep layer 2 in mm</li><li>Inf: Accumulated infiltration from surface into soil in mm</li><li>Per: Accumulated aquifer recharge (from soil to groundwater) in mm</li><li>Qlat: Accumulated lateral flow (horizontal movement of water between cells) in mm</li><li>Tmn: Minimum temperature in ºC</li><li>Tmx: Maximum temperature in ºC</li></ul><p>There are also some other grid files (Tot_XXX.asc) related to the initial and final conditions of the state variables or internal conditions of the model.</p><p><strong>References</strong></p><p>Aguilar, C., Polo, M.J., 2011. Generating reference evapotranspiration surfaces from the Hargreaves equation at watershed scale. Hydrol. Earth Syst. Sci. 15, 2495–2508. doi: 10.5194/hess-15-2495-2011.</p><p>Bergillos, R.J., Rodríguez-Delgado, C., Millares, A., Ortega-Sánchez, M., Losada, M.A., 2016. Impact of river regulation on a Mediterranean delta: assessment of managed versus unmanaged scenarios. Water Resour. Res. 52 (7), 5132–5148.</p><p>Castillo, A. 2008. Manantiales de Andalucía. Agencia Andaluza del agua, Consejería de Medio Ambiente, Junta de Andalucía, Sevilla, 410 pp.</p><p>Herrero, J., Polo, M.J., Moñino, A., Losada, M.A., 2009. An energy balance snowmelt model in a Mediterranean site. J. Hydrol. 371 (1-4), 98–107.</p><p>Herrero, J., Millares, A., Aguilar, C., Egüen, M., Losada, M.A., 2014. Coupling spatial and time scales in the hydrological modelling of mediterranean regions: WiMMed, in: CUNY Academic Works. In: Presented at the International Conference on Hydroinformatics, p. 8. ITGE-Junta de Andalucía: Atlas Hidrogeológico de Andalucía. Madrid, 216 pp., ISBN: 84-7840-351-5, available at: http: //aguas.igme.es/igme/publica/libros1 HR/libro110/lib110.htm, last access: 18 March 2012, 1998</p><p>Millares, A., 2008. Integración del caudal base en un modelo distribuido de cuenca. Estudio de las aportaciones subterráneas en ríos de montaña. University of Granada.</p><p>Millares, A., Polo, M.J., Losada, M.A., 2009. The hydrological response of baseflow in fractured mountain areas. Hydrol. Earth Syst. Sci. 13 (1261–1271), 2009.</p><p>Millares, A., Díez-Minguito, M., Moñino, A., 2019. Evaluating gullying effects on modeling erosive responses at basin scale. Environ. Modell. Software 111, 61–71. Millares, A., Herrero, J., Bermúdez, M., Leiva, J.F., Cantalejo, M., 2020. Long-term modelling of soil loss and fluvial transport processes in a mountainous semi-arid basin, southern Spain, in: River Flow 2020 - Twentieth International Conference on Fluvial Hydraulic. Delf, Netherlands.</p><p>Moreno-Llorca, R., Vaz, A. S., Herrero, J., Millares, A., Bonet-García, F. J., & Alcaraz-Segura, D. 2020. Multi-scale evolution of ecosystem services' supply in Sierra Nevada (Spain): An assessment over the last half-century. <i>Ecosystem Services</i>, <i>46</i>, 101204.</p><p>Muñoz Carpena, R., Ritter Rodriguez, A., 2005. Hidrología Agroforestal. Mundiprensa.</p><p>Pérez-Palazón, M. J., Pimentel, R., Herrero, J., & Polo-Gómez, M. J. 2014. Analysis of snow spatial and temporary variability through the study of terrestrial photography in the Trevelez river valley. In <i>Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI</i> (Vol. 9239, pp. 358-368). SPIE.</p><p>Rodríguez, J. A. 2008. Sistema de Inferencia Espacial de Propiedades Físico-Químicas e Hidráulicas de los Suelos de Andalucía. Herramienta de Apoyo a la Simulación de Procesos Agro-Hidrológicos a Escala Regional. Informe Final. Empresa Pública Desarrollo Agrario y Pesquero, Consejería de Agricultura y Pesca, Sevilla.</p><p> </p><p> </p>
The metadata of books submitted as research outputs to annual Lithuanian research assessments from 2008 to 2020
<p>This dataset contains ISBNs and below described metadata of 5199 books which Lithuanian universities and research institutions submitted as their best research outputs to get state funding after annual research evaluations from 2008 to 2020. The thorough explanation how this dataset was created provided in a preprint: Dagiene, Eleonora. 2023. "The Challenge of Assessing Academic Books: The UK and Lithuanian Cases Through the ISBN Lens." SocArXiv. <a href="https://doi.org/10.31235/osf.io/qpwxn">https://doi.org/10.31235/osf.io/qpwxn</a> </p><p>This dataset contains ISBNs and their metadata, gathered from the Global Register of Publishers <a href="https://grp.isbn-international.org/">https://grp.isbn-international.org/</a></p><p><strong>Description of data columns and remarks:</strong></p><p>ISBNs – unique identifiers – at least one author of the book identified by the ISBNs is affiliated with Lithuanian institution;</p><p>output_types – ['edited volumes', 'authored books'] – types of books eligible for research assessment;</p><p>years – 2008-2020 – book publishing years as reported by submitting institutions;</p><p>unified_title – titles of publishers – parent publishers (e.g. Springer Nature, Informa (Taylor & Francis)). Lithuanian institutions kept in Lithuanian;</p><p>primary_occupations_main – 'academic publishers', 'universities', 'intermediaries', 'other publishers', 'not publishers' – main types of institutions/companies which registered particular ISBNs;</p><p>primary_occupations_detailed – 'academic publishers', 'university presses', 'university departments', 'self-publishing', 'publishing services', 'specialised publishers', 'general publishers', 'societies', 'not publishing', 'research institutions', 'governmental institutions', 'libraries', 'museums', 'non-governmental organisations' – just more types of institutions/companies;</p><p>country – country name – countries where ISBNs were assigned, e.g. books authored or co-authored by Lithuanian researchers issued in 53 countries worldwide;</p><p>sciences – 'natural sciences', 'social sciences', 'agricultural sciences', 'technological sciences', 'medical and health sciences', 'humanities' – as brunches of sciences categorised by the Research Council of Lithuania.</p>
SURFEX-MEGAN model outputs
<p>The provided data represent the output of the different simulations performed in the submitted GMD paper "Evaluation of isoprene emissions from the coupled model SURFEX-MEGAN2.1"</p>
SAM_BOMEX_OUTPUT
<p>This is a simulation result for the paper titled: Life Cycle Evolution of Inhomogeneous Mixing in Shallow Cumulus Clouds in JGR: Atmosphere. </p><p>Information for the publication will be added after the review process.</p><p>Content: </p><ol><li><a href="https://zenodo.org/api/records/10211789/draft/files/SAM_profile.tar/content">SAM_profile.tar</a> is a group of profile data (.nc format) for all 12 cases with different boundary layer humidity and aerosol concentrations to make Fig.2. Descriptions of the variables are included.</li><li><a href="https://zenodo.org/api/records/10211789/draft/files/prof.tar/content">prof.tar</a> is a processed set of profiles from individual clouds for all cases used to make Figs.1a, 7, 8, 9, and 10. the number after the file name prof indicates: (1, in-cloud, 2, environmental shell, 3: mixing region, 4: cloud core, and 5: cloud edge.)</li><li><a href="https://zenodo.org/api/records/10211789/draft/files/all_cloud_volume_data.zip/content">all_cloud_volume_data.zip</a> is the Python Dataframe of total cloud average data to make Figs. 1c, 4, 5, and 6. Each row represents an individually detected cloud, and each column shows the total cloud volume-averaged properties of the respective name.</li></ol>
phenology data of lake mixing, and stratification based on the CESM2-LE output
<p>The phenology data of lake mixing, and stratification calculated from the daily output of CESM2 large ensemble. For more information about this dataset, please refer to the manuscript entitled 'Projected changes in the phenology of stratification and overturning in ice-covered lakes of the Northern Hemisphere (Lei Huang et al)'. For more information about CESM2 large ensemble, please refer to https://www.cesm.ucar.edu/community-projects/lens2. </p>
EAMv1 simulation output for evaluating two aerosol process coupling schemes
<p>EAMv1 is version 1 of the atmosphere component of the Energy Exascale Earth System Model (E3SM, <a href="https://github.com/E3SM-Project/E3SM">https://github.com/E3SM-Project/E3SM</a>). This upload contains processed output from transient simulations conducted using the F20TRC5-CMIP6 compset. The EAMv1 source code used in the simulations can be found in <a href="https://doi.org/10.5281/zenodo.7995850">Zenodo record 7995850</a>. Scripts used to conduct and analyze the simulations can be found in <a href="https://doi.org/10.5281/zenodo.10371316">Zenodo record 10371316</a>. </p> <p>The following tarballs contain annually averaged simulation output:</p> <ul> <li>cflx_1_L30_F20TRC5-CMIP6.tar.gz</li> <li>cflx_1_L72_F20TRC5-CMIP6.tar.gz</li> <li>cflx_2_L30_F20TRC5-CMIP6.tar.gz</li> <li>cflx_2_L72_F20TRC5-CMIP6.tar.gz</li> </ul> <p>The following split zip archives contain 6-hourly simulation output:</p> <ul> <li>cflx_1_L30_F20TRC5-CMIP6_6h.z*</li> <li>cflx_2_L72_F20TRC5-CMIP6_6h.z*</li> </ul>
Compressed EAMv1 simulation output for evaluating two aerosol process coupling schemes
<p>EAMv1 is version 1 of the atmosphere component of the Energy Exascale Earth System Model (E3SM, <a href="https://github.com/E3SM-Project/E3SM">https://github.com/E3SM-Project/E3SM</a>). This upload contains processed output from transient simulations conducted using the F20TRC5-CMIP6 compset. The EAMv1 source code used in the simulations can be found in <a href="https://doi.org/10.5281/zenodo.7995850">Zenodo record 7995850</a>. Scripts used to conduct and analyze the simulations can be found in <a href="https://doi.org/10.5281/zenodo.10371316">Zenodo record 10371316</a>. </p> <p>The following tarballs contain annually averaged simulation output:</p> <ul> <li>cflx_1_L30_F20TRC5-CMIP6.tar.gz</li> <li>cflx_1_L72_F20TRC5-CMIP6.tar.gz</li> <li>cflx_2_L30_F20TRC5-CMIP6.tar.gz</li> <li>cflx_2_L72_F20TRC5-CMIP6.tar.gz</li> </ul> <p>The following split archives contain 6-hourly simulation output:</p> <ul> <li>cflx_1_L30_F20TRC5-CMIP6_6h.tar.gz.*</li> <li>cflx_2_L72_F20TRC5-CMIP6_6h.tar.gz.*</li> </ul> <p>The split archives can be recombined and untarred using, e.g., the following commands: </p> <pre><code>${casename}="cflx_1_L30_F20TRC5-CMIP6_6h" cat ${casename}.tar.gz.* | tar xzvf -</code></pre> <p>A copy of the uncompressed and unsplit files can be found through <a href="https://portal.nersc.gov/project/m4359/huiwan/cflx/EAMv1_output_202311_for_Zenodo/">NERSC's web portal</a>.</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>
Model output for "Geometric amplification and suppression of ice-shelf basal melt in West Antarctica"
<h2><strong>Content description<br></strong></h2> <p>This data repository contains Úa-MITgcm model output in support of the manuscript 'Geometric amplification and suppression of ice-shelf basal melt in West Antarctica' (De Rydt and Naughten, 2024).</p> <p>In the paper, results from 4 experiments with the coupled ice-ocean model Úa-MITgcm are presented. The experiments are <em>ref_melt</em>, <em>hi_melt</em>, <em>av_melt</em> and <em>var_melt</em> (see Table 1 in De Rydt and Naughten, 2024). Original MITgcm and Úa output files at the start, middle and end of each experiment are available in the .zip-files with corresponding experiment name. Data for other timestamps are available upon request from the authors.</p> <p>The structure of each file is as follows:</p> <table> <tbody> <tr> <td>exp_name.zip/</td> <td>yyyymm/</td> <td> MITgcm/</td> <td> bathymetry.shice</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>data.diagnostics</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>output.nc</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>pload.mdjwf</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>shelfice_topo.bin</td> </tr> <tr> <td> </td> <td> </td> <td>Ua/</td> <td>DataForMIT.mat</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>NewMeltrate.mat</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>UaDefaultRun_yyyymm_dd.mat</td> </tr> </tbody> </table> <p><br><code>yyyymm</code> refers to the start month of the coupling timestep. Note that while the initial state of the ice sheet has been optimized to represent its present-day configuration, the future evolution of the ocean and ice sheet is driven by idealized forcings. The results should not be treated as projections, and the timestamps correspond to model time (in years), not real time.</p> <p><strong>MITgcm diagnostics</strong> (temperature, salinity, velocities and ice-shelf freshwater flux) are stored in <code>output.nc</code> and provided as an average value over the 1-month coupling time interval. The <code>bathymetry.shice</code>, <code>data</code>, <code>data.diagnostics</code>, <code>pload.mdjwf </code>and <code>shelfice_topo.bin</code> files are MITgcm input files for ocean bathymetry, general model parameters, diagnostic model parameters, intial pressure loading under the ice shelf and ice-shelf draft for the corresponding coupling timestep, respectively.</p> <p><strong>Úa data</strong> is provided in <code>UaDefaultRun_yyyymm_dd.mat</code> as a snapshot at the end of the coupling interval (<code>yyyymm_dd</code>). The other files, <code>DataForMIT.mat </code>and <code>NewMeltrate.mat</code> are ice-shelf geometry and MITgcm melt rates at the start of the coupling interval, respectively.</p> <h2><strong>Other resources</strong></h2> <p>The Úa-MITgcm source code and custom Úa-MITgcm configuration files for the experiments are available <a href="https://github.com/knaughten/UaMITgcm/tree/archer2">here</a> and <a href="https://github.com/knaughten/UaMITgcm/tree/archer2/example/ASE_999">here.</a> Scripts to analyse and plot the data are available <a href="https://github.com/janderydt/uamitgcm_ASE">here</a>.</p> <h2><strong>References</strong></h2> <p>De Rydt, J. and Naughten, K.: Geometric amplification and suppression of ice-shelf basal melt in West Antarctica, The Cryosphere, 18, 1863–1888, https://doi.org/10.5194/tc-18-1863-2024, 2024.</p>
CESM2 land output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2015-2100. The files are labelled according to the experiments they were generated from (base, MF (Max Forest) and No LULCC). Output fields are as follows:</p> <p>discharge_plus_runoff: surface water availability (river discharge plus surface runoff), units m^-3 s^-1</p> <p>EFLX_LH_TOT: total latent heat flux from land to atmosphere, units W m^-2</p> <p>QFLX_EVAP_TOT: total evapotranspiration (canopy evaporation plus canopy transpiration plus soil evaporation), units kg m^-2 s^-1</p> <p>SOILLIQ: soil liquid water content, units kg m^-2</p> <p>SW_surface_albedo: surface albedo, units fraction</p> <p>TSA: 2m air temperature, units K</p> <p>VEGWP: vegetation water potential, units m</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the Max Forest scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the base scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the No LULCC scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
Model output and parameters from Deutloff et al. (2025)
<p>This dataset contains the model output of the coupled and the uncoupled ensemble used in Deutloff et al. (2025) (Folder: model output). It also stores all the parameters needed to reproduce the model runs (Folder: Params, Calibration) and the input data for the model (RCMIP_simplified). </p>
Wrapper Impact Workloads and BSC Slurm Simulator Output of Static Traces based on Data from LUMI Supercomputer
<p>This dataset contains the workloads, with the workflow added to them, and the results of the simulations of the static trace utilizing LUMI fitted data carried out using <a href="https://ieeexplore.ieee.org/abstract/document/8641556">BSC's SLURM Simulator</a>.</p> <p>It is organized in two folders: workloads and results. In the first, we find a folder per experiment, which is a different randomly generated workload file. Within each experiment we find a folder per fair share inidicating the target platform, the workflow it was based on, and the characteristics of the tracked job: number of cores and runtime. The results folder follows the same scheme but with a file extension of ".trace".</p>
AWESOME CGE scenarios and modelling outputs
<p>The primary goal of this analysis was to study the impacts of climate change on food security given the potential supply of alternative water sources: desalination and reused (treated) water within the WEFE nexus in the Mediterranean Sea Basin. Accordingly, the first step of the analysis is to explicitly introduce desalination and treated water into the global CGE model and database. Using the standard GTAP model (Corong, Hertel, McDougall, Tsigas, & van der Mensbrugghe, 2017) and GTAP10A dataset (Aguiar, Chepeliev, Corong, McDougall, & & van der Mensbrugghe, 2019), the water sector was divided into three main categories using the SplitCom application (Horridge, 2008) and including (a) natural water that refers to distribution of water, (b) desalinated water that refers mostly to seawater desalination and (c) treated water that refers primarily to wastewater and brackish water treatment. The resulting modelling framework is used as the basis of the second step, that assesses the externalities associated with the different water sources incorporated in the model.</p> <p>To introduce alternative water sources in the CGE, several assumptions were made. First, desalinated water is produced from the abundant resource – seawater and therefore the scarcity value that is attributed to depletable natural resources is not applicable. Accordingly, desalinated water production can be treated as a sector disaggregated from the existing water sector in the GTAP database. For example, the economic activities of the desalination sector were reported as part of the water sector in input-output tables (United Nations, 2008).</p>
1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence.<br>The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka.</p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation,<br>n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.</p>
Interim model output of estimated bare peat/erosion density in Aberdeenshire and Angus at 1m resolution.
<p>This dataset contains the estimated unit density of peat erosion accross Aberdeenshire and Angus at 1m resolution. These outputs are derived from high resolution aerial imagery using the PeatNet pipeline for predicting the location and extent of peatland degradation features. This output was created to estimate the particulate organic carbon (POC) emissions from eroding peat based on multiple scenarios.</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.