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761 results for “impact assessment”
Data to 'The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0'
<p>This dataset includes the AWARE2.0 characterization factors as documented in the article "The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0" (<a href="https://www.doi.org/10.1111/jiec.70023" target="_blank" rel="noopener">DOI: 10.1111/jiec.70023</a>). When using the dataset in your own work, please cite the article and provide reference to this zenodo repository.</p> <p>For importing the country-level characterization factors into LCA software, please see the AWARE2.0 implementations (openLCA, SimaPro, brightway2) in IMPACT World+, version 2.1: <a title="IMPACT World+ version 2.1" href="https://doi.org/10.5281/zenodo.14041258">https://doi.org/10.5281/zenodo.14041258</a></p> <h3>Content</h3> <p><strong>- native resolution (monthly, watershed scale):</strong></p> <ul> <li><strong>AWARE20_Native_CFs_geospatial.gpkg</strong>: Geospatial file containing the AWARE2.0 basins as polygons with associated monthly and annual CFs</li> <li><strong>AWARE20_Native_CFs_geospatial.kmz</strong>: Version of <em>AWARE20_Native_CFs_geospatial.gpkg </em>for GoogleEarth</li> <li><strong>AWARE20_Native_CFs.xlsx</strong>: AWARE2.0 CFs on basin level (monthly and annual) and associated water consumption used for weighting</li> <li><strong>AWARE20_Intermediate_Variables.xlsx</strong>: Intermediate Variables from the calculation of the AWARE2.0 CFs, such as the longterm average natural and actual water availability, the AMDs, the EFRs, etc.</li> <li><strong>figures_AWARE_AWARE20_comparison_all_basins.zip</strong>: Figures comparing CFs, AMDs, Natural and Actual Availability, EWRs, and EFR coefficients between AWARE and AWARE2.0, for each of the 8149 basins individually. Consult these figures for a visual impression of how and why CFs might have changed between AWARE and AWARE2.0.</li> </ul> <p><strong>- spatiotemporal aggregations:</strong></p> <ul> <li><strong>AWARE20_Countries_and_Regions.xlsx</strong>: AWARE2.0 CFs aggregated according to geography definitions of GLAM and ecoinvent <a href="https://geography.ecoinvent.org/#version-2-5-ecoinvent-3-10" target="_blank" rel="noopener">(version 2.5, applicable to ecoinvent 3.10) </a></li> <li><strong>AWARE20_Subnational_Resolution.xlsx</strong>: AWARE2.0 CFs aggregated to subnational resolution, using the GADM dataset version 4.1 (<a href="https://gadm.org/old_versions.html" target="_blank" rel="noopener">https://gadm.org/old_versions.html</a>)</li> <li><strong>AWARE20_Crop_Specific.xlsx</strong>: AWARE2.0 CFs aggregated according to geography definitions of ecoinvent <a href="https://geography.ecoinvent.org/#version-2-5-ecoinvent-3-10" target="_blank" rel="noopener">(version 2.5, applicable to ecoinvent 3.10)</a>, using crop-specific irrigation water consumption for 27 crop classes as spatiotemporal weights. See readme sheet in Excel file for more information.</li> </ul> <p> </p> <h3><strong>Changes:</strong></h3> <ul> <li>v1.0.1: <ul> <li>addition of crop-specific spatiotemporal aggregations (AWARE20_Crop_Specific.xlsx)</li> </ul> </li> <li>v1.0.0 (corresponding to published article): <ul> <li>update of readme sheets with appropriate references to corresponding article</li> <li>update of reference "Müller Schmied et al. (2024)"</li> <li>added file: AWARE20_Subnational_Resolution.xlsx</li> </ul> </li> <li> v0.0.3: <ul> <li>use bug-fixed WaterGAP2.2e data from Sept 2023</li> <li>added country and subnational aggregations</li> <li>changed "NoData" to "NotDefined" in the tables</li> <li>added gridcell pHWC to intermediate variables</li> <li>corrected table of water consumption data without post-processing in "Intermediate_Variables"</li> </ul> </li> </ul> <p> </p> <h3><strong>Caveats:</strong></h3> <ul> <li>Spatial CF aggregations for treaties: <ul> <li>Due to the creation date of the data set, the <strong>BRICS aggregations </strong>in<strong> </strong><em>AWARE20_Countries_and_Regions.xlsx</em> do not include the states that joined after 2023. In <em>AWARE20_Crop_Specific.xlsx</em>, the 10-member BRICS is labeled BRICS+.</li> </ul> </li> </ul>
A large ensemble of CMIP6-based transient climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments often require a large ensemble of local-scale transient climate scenarios. Each ensemble member represents plausible long weather series at a local scale. The climate projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. Moreover, very few projections are usually available for each GCM due to a high computational cost. An alternative approach involves employing a stochastic weather generator to produce a large number of transient scenarios based on the climate projections from GCMs. In a current dataset, transient climate scenarios were generated using the LARS-WG weather generator, based on climate projections from GCMs from the CMIP6 ensemble across 26 representative sites throughout the UK. Each transient scenario spans the period from 2020 to 2090. At each site, 100 transient scenarios were generated for two emission scenarios (SSP2-4.5 and SSP5-8.5) and five selected GCMs from CMIP6 (ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR, and MRI-ESM2-0). The choice of GCMs were based on their performance over northern Europe and their climate sensitivity. The use of a subset of GCMs substantially reduces computational time required for impact assessment, while allowing to quantify uncertainties in impacts related to uncertain future climate. The dataset can be used with impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare, when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</p>
CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments require local-scale climate scenarios. The climate change projections from <span>Global Climate Models (GCMs) </span>are difficult to use at local scale due to their <span>coarse spatial and temporal resolution. </span><span>It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of </span>realisations <span>to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A</span><span> <span>dataset of future climate change scenarios was therefore generated at </span></span><span>26 representative sites across the UK</span><span> based on the latest </span><span>CMIP6 multi-model ensemble </span><span>downscaled to local-scale by using a </span><span>stochastic weather generator LARS-WG 7.0. The data set provides </span><span>1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (</span><span>Shared Socioeconomic Pathways - SSPs <em>viz</em>. </span>SSP2-4.5 and <span>SSP5-8.5)</span><span>.</span><span> </span><span>A total of </span>15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. <span>LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. </span>Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, <em>viz</em>.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs.<span> <span>The 1000 years of </span></span>realisations <span>of daily weather for the baseline as well as future climate change scenarios are helpful for estimating </span>seasonality and<span> inter-annual variation, and for detecting short, </span>low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset <span>can be used as an input to climate change impact models in various fields, including, </span><span>land and water resources, agriculture and food production, </span>ecology and epidemiology, and <span>human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</span></p>
Macroeconomic assessment of Climate Change Impacts
<p>Macroeconomic assessment of impacts on: Agriculture, Fishery, Forestry, Sea level rise, Riverine floods, Transport, Energy supply, Energy demand, Labour productivity, plus compounded assessment of all impacts</p>
Hydrodynamic field data near Galveston, Texas wetland edges to help assess storm impacts and erosion
<p>Water free surface elevation measurements via submerged pressure transducers along transects near Galveston Bay wetland edges</p>
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
Impact of medical radionuclide discharges on people and the environment: scenario data used in the non-human biota impact assessment
<p>This dataset contains the input data for the D-DAT model: activity concentrations in water for the simulated Molse Nete scenario. It also contains the dynamic model-calculated activity concentrations in sediment and the non-human biota. These are the primary data upon which the dose calculations werte performed, and they can be used to reproduce these calculations. The related preprint article is also given in this repository: https://zenodo.org/records/10488393.</p> <p> </p> <p> </p> <p> </p>
Life Cycle Impact Assessment method for ozone depletion based on WMO 2022
<p>This dataset provides the most recent <a>characterization factors</a> for ozone depletion based on the latest ozone depletion potentials from the 2022 World Meteorological Organization (WMO) scientific assessment. The dataset is formatted for easy import into life cycle assessment (LCA) software such as Brightway, the Activity Browser, and SimaPro. The characterization factors are available for both 100-year and infinite time horizons.</p> <p>When using the dataset, please cite the folllowing publication:</p> <p>van den Oever, A. E.M., Puricelli, S., Costa, D., Thonemann, N., Lavigne Philippot, M., Messagie, M., Dataset with updated ozone depletion characterization factors for life cycle impact assessment, Data in Brief (in press), 2024, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.111103" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.111103</a></p>
Sampling sites where ecological indices were used to assess the impact of different environmental stressors in aquatic environments in Argentina
Dataset is a compilation of all sampling sites of articles where ecological indices were used to assess the impact of different environmental stressors in aquatic environments from Argentina. Points of this dataset were extracted from 78 papers published between 1996 and 2018. We selected articles that use ecological indices to analyze some local environmental problematics or stressors. Using the type of index from each article we performed the kml file, which contained the categorized sampling sites by different symbols according to the ecological index: physico-chemical, biological, geomorphological and multimetric. We carried out a map (shapefile) with all the sampling sites referenced to the ecoregions of Argentina proposed by Burkart (1999).
Data and code for the analysis in "Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland"
<p>Data and code used for the analysis in <em>Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland</em> (Lemaitre et al., Swiss Medial Weekly 2020).</p>
Tool for the environmental and economic impact assessment of industrial recycling routes for lithium-ion traction batteries
<p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).</p> <p> </p> <p>Please send your inquiries regarding the tool to s.bloemeke@tu-braunschweig.de.</p>
Data for Mellado et al. The impacts of marking on bats: mark-recapture models for assessing injury rates and tag loss. Journal of Mammalogy. 103:100-110. DOI:10.1093/jmammal/gyab153
<p>Data sets used in Mellado et al. The impacts of marking on bats: mark-recapture models for assessing injury rates and tag loss. Journal of Mammalogy. 103:100-110. (https://doi.org/10.1093/jmammal/gyab153)</p> <p>File Descriptions:</p> <p>CapHistTagLoss.txt - Capture histories for <em>Carollia perspicillata</em> identifying if individual was captured with both tags (B), arm bands (A), collar (C), not captured (0) or not monitored (dot). Covariates included are Sex, Forearm Length and Scaled Mass Index.<br> CaptHistTagInj.txt - Capture histories for <em>Carollia perspicillata</em> identifying if individual was captured with no lesions from arm band (A), minor injury (I), major injury (M), not captured (0) or not monitored (dot). Covariates included are Sex, Forearm Length and Scaled Mass Index.<br> LesionOccurrence.txt - Censored time-to-event data for survival analysis. Recorded events were the occurrence of lesions of any type due to arm bands.<br> RingCondition.txt - Censored time-to-event data for survival analysis. Recorded events were the occurrence of damage to arm bands.<br> SMI.txt - Longitudinal data for individual <em>Carollia perspicillata</em> Scaled Mass Index, identifying individual records, the occurrence of lesions, sex, month, year</p> <p> </p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
Relevance of "Building BioData.pt" indicators identified in ESFRI (E), OECD (O) and RI-PATHS (R) impact assessment frameworks
<p>The relevance of the indicators maintained by the "Building BioData.pt" project was assessed against the objectives of different organizations/initiatives: 1) Strategic objectives of BioData.pt; 2) Objectives of the Portuguese Roadmap for Research Infrastructures; 3) Objectives of ELIXIR; 4) Objectives of EOSC; 5) Sustainable Development Goals of the United Nations.</p>
Supporting model output for article "Assessing the potential impact of river chemistry on Arctic coastal production"
<p>The following is a summary of processed model output data from a series of HiLAT model runs.<br> A description of the model runs and visualization of model output and analysis can be found in the<br> accompanying manuscript.</p> <p><br> Gibson G. A., Elliott, S., Piliouras, A. Clement Kinney, J., Jeffery, N. (2022) Assessing the potential<br> impact of river nitrate on coastal production in the Arctic. Frontiers in Marine Science: Coastal Ocean<br> Processes.</p> <p><br> This work was supported by the Regional and Global Model Analysis (RGMA) program of the US<br> Department of Energy’s Office of Science as a contribution to the HiLAT project. Additional support for<br> this project was provided by the National Science Foundation, under award #173886.<br> </p> <p><strong>River Nutrient Forcing</strong></p> <p>The experiments involved modifying the nutrient concentrations in the river nutrient forcing files.<br> The river nutrient files are specified during model setup. For use in the HiLAT model, GNEWS annual<br> river nutrient inputs were partitioned into twelve monthly forcing values. The nearest ocean grid point<br> to each of the GNEWS river mouth locations was identified and then, as with the runoff, the nutrient<br> inputs for each river basin were spatially mapped to surface ocean model grid cells, which are 10 meters<br> thick, such that the spatial pattern of river nutrient dispersion follows river water inputs to the oceans.</p> <p>The experiments were:<br> i) The baseline model simulation: The 12 monthly values for each grid cell were constant in time.<br> <strong>river_nutrients_GNEWS2000_gx1v6.nc</strong><br> ii) an experiment in which baseline Arctic River Nitrogen (NO 3 and NH 4 ) concentrations were doubled.<br> <strong>river_nutrients_GNEWS2000_gx1v6_x2Arctic.nc</strong><br> iii) an experiment in which baseline Arctic River Nitrogen (DON and DIN) concentrations were scaled to<br> the river volume discharge contained in <strong>runoff.daitren.iaf.20120419.nc</strong><br> <strong>river_nutrients_GNEWS2000_gx1v6_scaled_climatology.nc</strong><br> iv) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by two months.<br> <strong>river_nutrients_GNEWS2000_gx1v6_shifted2m_climatology.nc</strong><br> v) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by a month and<br> doubled in concentration.<br> <strong>river_nutrients_GNEWS2000_gx1v6_shifted_climatology_x2.nc</strong></p> <p>River nutrient fluxes are in units of nmol/cm2/s<br> Only concentrations within the domain TLONG>=60 &TLONG <=340 & TLAT >=60 were modified in<br> concentration/timing.</p> <p><br> Variables of interest:<br> din_riv_flux: dissolved inorganic nitrogen river flux<br> don_riv_flux: dissolved organic nitrogen river flux</p> <p>Each of the experiments is described in detail in Gibson et al (2022).</p> <p>---------------------------------------------<br> There are multiple versions of most output file types, corresponding to the river nutrient experiments that<br> were conducted.</p> <p><br> Many variables in the output files are <strong>regional averages</strong> where model regions are indicated by a number<br> *note - for aesthetics, the numbering used in the model output files differs slightly from the numbering<br> used in the accompanying manuscript. The numbers assigned in the analysis files aligns with the numbers<br> assigned to regions within the region mask provided in the grid file.</p> <p><strong>Grid File/region masks</strong><br> gx1v6_polar_mask_coast.5.22.20c.nc This file is an updated version of the standard grid file. It has been<br> updated to include the addition of a coastal Arctic region variable ‘Arctic_Coast_Mask’ which indicates<br> which grid cells are in the coastal regions used in the analysis and the Arctic_Region variable which<br> indicates which grid cells are in the broader regions.</p> <p><br> Variables contained in this file are:<br> Arctic_Coast_Mask: contains values 0-9 indicating which (if any) coastal region a grid cell is in<br> Arctic_Region Mask: contains values 0-11 indicating which (if any) region a grid cell is in</p> <p><br> TLAT: latitude of grid cell<br> TLONG: longitude of grid cell<br> TAREA: Area of grid cell<br> HT: Bathymetry of grid cell</p> <p> </p><table> <tbody> <tr> <td> </td> <td> <p><strong>Arctic_Region (seas)</strong></p> </td> <td> <p><strong>Arctic_Coast_Mask </strong><strong>(coast)</strong></p> </td> </tr> <tr> <td> <p><strong>Bering Sea</strong></p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p><strong>Chukchi Sea</strong></p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p><strong>East Siberian Sea</strong></p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p><strong>Laptev Sea</strong></p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p><strong>Beaufort Sea</strong></p> </td> <td> <p>5</p> </td> <td> <p>5</p> </td> </tr> <tr> <td> <p><strong>Barents Sea</strong></p> </td> <td> <p>6</p> </td> <td> <p>6</p> </td> </tr> <tr> <td> <p><strong>Canadian Basin</strong></p> </td> <td> <p>7</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Eurasian Basin</strong></p> </td> <td> <p>8</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Nordic Seas</strong></p> </td> <td> <p>9</p> </td> <td> <p>7</p> </td> </tr> <tr> <td> <p><strong>Labrador Sea</strong></p> </td> <td> <p>10</p> </td> <td> <p>8</p> </td> </tr> <tr> <td> <p><strong>Kara Sea</strong></p> </td> <td> <p>11</p> </td> <td> <p>9</p> </td> </tr> </tbody> </table> --------------<p></p> <p> </p><p>Model outputs that were analyzed in the manuscript are contained in three different kinds of output file.<br> For each file type a file exists for each river nutrient experiment.</p> <p></p> <p>The following series of files contains variables related to the particulate organic carbon flux to the<br> sediment, demineralization and remineralization rates.<br> bgc_T62_gx1GIF_nut-riv-BASELINE-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv_2XDC-coast_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-seas_region-sed-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-seas_region-sed-137-157.nc</p> <p><br> Variables contained in these are:<br> POCTOSED_AVG* : Particulate organic carbon flux to sediment<br> PONTOSED_AVG* : Particulate organic nitrogen flux to sediment<br> SEDDENITRIF_AVG* : Sediment denitrification rate<br> POC_PROD_AVG* : Production of Particulate organic carbon<br> POC_FLUX_AVG* : Particulate organic carbon flux into layer/cell<br> DON_REMIN_AVG* : Dissolved Organic Nitrogen remineralization rate<br> DOC_REMIN_AVG* : Dissolved Organic Carbon remineralization rate<br> DIAT_N_LIM_AVG* : Diatom nitrogen limitation<br> DIAT_N_LIM_AVG* : Diatom nitrogen limitation<br> DIAT_P_LIM_AVG* : Diatom phosphorous limitation<br> DIAT_FE_LIM_AVG* : Diatom iron limitation<br> DIAT_LIGHT_LIM_AVG*: Diatom light limitation<br> SP_N_LIM_AVG* : Small phytoplankton nitrogen limitation<br> SP_P_LIM_AVG* : Small phytoplankton phosphorous limitation<br> SP_FE_LIM_AVG* : Small phytoplankton iron limitation<br> SP_LIGHT_LIM_AVG* : Small phytoplankton light limitation<br> Where * represents the coastal region number.<br> ----------------------------------------</p> <p><br> The following series of files contains primary production for the small and large phytoplankton groups<br> and the zooplankton biomass.</p> <p>Coastal regional averages – based on regions marked in the Arctic_Coast_Mask variable<br> bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_riv-BASELINE-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-ACM-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2XDC-region-prod-ACM-137-157.nc</p> <p>Regional seas averages – based on regions marked in the Arctic_Region variable<br> bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_riv-BASELINE-region-prod-seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-seas-137-157.nc</p> <p>Variables contained in these files are:<br> TAREA_SUM* – total area of the region<br> PPSP_REGSUM* – sum of primary production by small phytoplankton in a region<br> PPDIAT_REGSUM*– sum of primary production by diatoms in a region<br> ZOOC_AVG*– sum of zooplankton biomass in a region</p> <p>----------------------------------------<br> The following series of files contains ice associated variables and mixed layer nutrients</p> <p>bgc_T62_gx1GIF_nut_riv-2xArcticN-ice_coastal-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2XDC-ice_coastal -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2xArcticN-ice_seas-137-157.nc<br> bgc_T62_gx1GIF_nut-riv-mon-clim-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-BASELINE-ice_seas -137-157.nc<br> bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_seas-137-157.nc</p> <p>HI_REGAVG* : Regional averaged ice depth<br> HS_REGAVG* : Regional averaged snow depth<br> ICEAREA_REGSUM* : Regional sum ice area<br> ICEVOL_REGSUM*: Regional sum volume area<br> MLAM_REGAVG* : Regional average ammonium concentration in mixed layer<br> MLNIT_REGAVG* : Regional average nitrate concentration in mixed layer<br> PP_REGAVG* : Regional average primary production (ice algae)<br> PP_REGSUM* : Regional total primary production (ice algae)<br> TAREA_SUM* : Total area of region<br> TIME : time</p>
Data, plotting scripts, and figures for "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames" by Aaron J. Fillo, Peter E. Hamlington, and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
Assessing tropospheric turbulence impact on VGOS telescope placement in the Indian subcontinent for the estimation of Earth Orientation Parameters
<p>The dataset accompanying this study is composed of three distinct files, each integral to the research conducted. The file, named 'Simulated Data', includes the results derived from the simulations performed within this investigation. This file serves as a repository of the computed outcomes. The file, named 'Data and Code', includes the MATLAB scripts utilized to compute the Cn value from the Zenith Wet Delay (ZWD). Additionally, this file encompasses the relevant datasets for both wind speed and ZWD at various station locations. The third file contains the geographical coordinates of the Indian stations that were used in the study. Together, these files constitute a complete dataset that supports the study’s objectives and verification of the findings. </p>
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
Dataset annexed to: "Testing ethical impact assessment for nano risk governance"
<p>To test the ethical impact assessment methodology guidelines and tools adapting CEN Workshop Agreement part 2 CWA 17145-2:2017 (E)) to support risk governance of nanomaterials, in the RiskGONE project (https://riskgone.eu/), feedback from stakeholders was requested. This dataset includes the responses of participants in several online meetings. The dataset is linked to the paper "Testing ethical impact assessment for nano risk governance".</p>
Repository: Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment
<p>This repository contains the input data, codes and results of the model developed in the paper "Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment" published in the Journal of Industrial Ecology (2020) by Alexandre Milovanoff, I. Daniel Posen, Heather L. MacLean.</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.