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1,444 results for “mitigation”
Forest carbon prospecting for climate change mitigation: Version 1.0
<p>This data package includes the two 1-km resolution global maps (.tif) of tropical forests between ~23.44°N and 23.44°S produced from the study: 1) investible forest carbon (in tCO<sub>2</sub>e ha<sup>-1</sup>y<sup>-1</sup>) and 2) forest carbon return-on-investment (Net Present Value in USD ha<sup>-1</sup>y<sup>-1</sup>) over a 30-year timeframe. It also includes the R script to reproduce these layers and their uncertainties. </p> <p><em><strong>Investible Forest Carbon</strong>: </em>The investible forest carbon map was produced based on the total volume of CO<sub>2</sub>e associated with the three main carbon pools in the tropics, namely aboveground carbon, belowground carbon and soil organic carbon. This is followed by the application of key Verified Carbon Standard (VCS) criteria including additionality, to determine the magnitude and areas of investible forest carbon across the tropics.</p> <p><em>Aboveground carbon.</em> A stoichiometric factor of 0.475 was applied to recent spatial data on aboveground carbon biomass to obtain carbon stock based on established carbon accounting methodologies. An uncertainty analyses was also performed to account for potential variability in stoichiometric factor. Subsequently, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO<sub>2</sub>e associated with this carbon pool.</p> <p><em>Belowground carbon</em>. Belowground carbon biomass was firstly derived by applying two allometric equations relating to root to shoot biomass to the most recent spatial dataset on aboveground carbon biomass following established carbon accounting methodologies. The two equations are:</p> <p> Belowground biomass = 0.489×aboveground biomass^0.89; and</p> <p> Belowground biomass = 0.26×aboveground biomass</p> <p>A stoichiometric factor of 0.475 was subsequently applied to the estimated belowground carbon biomass to obtain the carbon stock. An uncertainty analyses was then performed to determine the mean, minimum and maximum values for belowground carbon. Following that, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO<sub>2</sub>e associated with this carbon pool.</p> <p><em>Soil Organic Carbon</em>. Organic carbon density of the topsoil layer (0-30 cm) was obtained from the European Soil Data Centre as it represented the best data available for soil organic carbon. A conversion factor of 3.67 was subsequently applied to derive the volume of CO<sub>2</sub>e associated with this carbon pool.</p> <p><em>Applying VCS criteria</em>. The criterion of additionality is a pre-condition for carbon credits to be certified under the VCS. This implies that only the volume of forest carbon that are under imminent threat of decline or loss if left unprotected by a conservation intervention can be certified under the VCS. The volume of forest carbon under threat of loss was based on the best available data on predicted deforestation rates across the tropics (through to the year 2029), and annualized over predicted 15-year period. The estimated annual deforestation rates was then applied to the total volume of CO<sub>2</sub>e associated with tropical forests as estimated above, deriving the volume of CO<sub>2</sub>e that would be certifiable and thus investible under the VCS. In addition, a conservative 10-year decay estimate was assumed for the estimate of the belowground carbon pool, and lands that will likely not be certifiable for other reasons, including recently deforested areas (i.e. for the period of 2010-2017), a well as human settlements, were excluded. Lastly, the VCS requirement to set aside buffer credits of 20% was accounted for to consider the risk of non-permanence associated with Agriculture, Forestry and Other Land Use (AFOLU) projects.</p> <p><strong><em>Return</em>-<em>on-Investment</em></strong>. From the investible forest carbon map, the relative profitability of these areas was then modelled to produce a global forest carbon return-on-investment map based on their NPV. The NPV of returns were based on several simplifying assumptions following established values from previous studies. </p> <p><em>Cost of project establishment</em>. The cost of project establishment was estimated to be at $25 ha<sup>-1</sup>. This was based on a range of costs that are key to the development of a project, including but not limited to project design, governance and planning, enforcement, zonation, land tenure and acquisition, surveying and research. </p> <p><em>Cost for annual maintenance</em>. The cost for annual maintenance was estimated to be $10 ha<sup>-1</sup>, which included aspects such as in education and communication, monitoring, sustainable livelihoods, marketing, finance and administration.</p> <p><em>Carbon price</em>. A constant carbon price of $5.8 t<sup>-1</sup>CO­<sub>2</sub>e for the first five years was applied. This price was based on an average price of carbon for avoided deforestation projects reported recently by Forest Trends’ Ecosystem Marketplace (i.e. for the period 2006 – 2018). Subsequently, a 5% price appreciation was applied annually over a project timeframe of 30 years.</p> <p><em>Discount rate</em>. We calculated NPV of annual and accumulated profits over 30 years based on a 10% risk-adjusted discount rate. </p> <p>Further details for these datasets and their uncertainties are presented in Koh et. al. For questions or issues on the spatial data layers, please contact Yiwen Zeng (<a href="mailto:zengyiwen@nus.edu.sg">zengyiwen@nus.edu.sg</a>). </p>
Resilient and sustainable Permeable Pavements For urban Flood Mitigation
<p>Permeable pavements are more sustainable option towards climate change as they maintain the hydrological cycle. However, they suffer from problems such as lower strength and integrity. In this project, this issue is addressed in three main stages: a. development of high viscosity bitumen; b. evaluating different types of additives to be used in porous asphalt mixtures (surface course of permeable pavements); c. Development of a multi-criteria tool to implement the permeable pavement system using GIS software. </p>
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change (public)
<p><strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>This repository contains</strong>:</p> <ol> <li>Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1a_f_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong><driver>_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. The folder also contains the output regrowth models seen in Figure 1 in the format "<strong>regrowth_model_<driver_threshold>.RData" </strong>where driver_threshold refers to the driving variable name and the associated threshold limit for the given driver.</li> <li>Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. </li> <li>Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_plot.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files are the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. </li> <li>Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong><REGION>-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The code takes data in AGB and converts to AGC. The folder also contains the output regrowth models seen in Figure 3 in the format <strong>"regrowth_model_<region_disturbance_type>.RData" </strong>where region_disturbance refers to the region and the type of disturbance experienced. </li> <li> Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_plot.R" in the code repository (see below). </li> </ol> <p><strong>Code: </strong>The corresponding code mentioned here can be access here: <a href="https://github.com/heinrichTrees/secondary-forest-regrowth-amazon-public">heinrichTrees/secondary-forest-regrowth-amazon-public (github.com)</a></p> <p><strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper as well as the DOI of this repository. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Global agricultural land use scenarios for estimating the potential of forest regeneration for climate mitigation to 2050
<p>The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use.</p> <p>The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions.</p> <p>The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)".</p> <p>Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information.</p> <p>This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.</p>
Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension (Supporting data)
<p>This data accompanies the paper "Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension", published in Computers & Fluids.</p>
Land use-based Adaptation and Mitigation Solution (LAMS) suitability maps final version
<div>These maps provide the suitable area (maximum available area) for the potential implementation of the specific proposed LAMS. The suitability map includes 4 classes: not suitable (0), least suitable (1), moderately suitable (2) and most suitable (3). Maps for 13 different LAMS (from the LAMS catalogue V1-1) have been developed for the six RethinkAction case studies, when possible. This v2 includes the metadata.</div> <div>The codes and the names of the LAMS are the following:</div> <div>LAMS03-EstGra: Establishment (conversion to) of permanent grassland</div> <div>LAMS14-SpaPla: Spatial planning for the sustainable deployment of energy on land</div> <div>LAMS15-PhoPla: Photovoltaic plants</div> <div>LAMS21-AgrPla: Agrovoltaic farms</div> <div>LAMS22-IncFor: Increased portion of forests included under protected areas</div> <div>LAMS23-RefAff: Reforestation/afforestation</div> <div>LAMS31-UrbSpr: Limiting urban sprawl</div> <div>LAMS32-GreUrb: Establishment and maintenance of green urban ecosystems</div> <div>LAMS44-IncCul: Increase in cultivated area</div> <div>LAMS49-FloSol: Floating solar photovoltaic panels in water bodies</div> <div>LAMS50-SolPan: Solar panels in rooftops/buildings</div> <div>LAMS55-WatHar: Water harvesting: collect and store rain water in reservoirs</div> <div>LAMS59-LanMan: Land management of solar photovoltaic systems land</div>
Data for: Environmental complexity mitigates the demographic impact of sexual selection
<p>Sexual selection and the evolution of costly mating strategies can negatively impact population demography and adaptive potential. While laboratory studies have documented outcomes stemming from these processes, theory suggests that the demographic impact of sexual selection is contingent on the environment and therefore may have been overestimated in simple laboratory settings. Here we find support for this claim. We exposed copies of beetle lines, previously evolved with or without sexual selection, to a 10-generation heatwave while maintaining half of them in a simple environment and the other half in a complex environment. Populations with an evolutionary history of sexual selection maintained larger sizes and more stable growth rates in complex (relative to simple) environments, an effect not seen in populations that evolved without sexual selection. These results have implications for evolutionary forecasting and suggest that the demographic impact of sexual selection in natural populations might be lower than predicted.</p>
Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News
<p>Data supporting "Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News". <br><br></p> <p> If you use this dataset in your own research, please cite this paper:</p> <p>```<br>@misc{abels2024mitigating,<br> title={Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News}, <br> author={Axel Abels and Elias Fernandez Domingos and Ann Nowé and Tom Lenaerts},<br> year={2024},<br> eprint={2403.08829},<br> archivePrefix={arXiv},<br> primaryClass={cs.HC}<br>}<br>```</p> <p> </p> <table> <tbody> <tr> <td><strong>column name</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>treatment</td> <td>identifier for the set of headlines presented to the participant</td> </tr> <tr> <td>trial</td> <td>trial/round in which the headline was presented </td> </tr> <tr> <td>arm</td> <td>which "arm" the headline was presented as (0=left, 1=middle, 2=right)</td> </tr> <tr> <td>advice</td> <td>the participant's response (0=very unlikely, 0.25=unlikely, 0.5=undecided, 0.75=likely, 1=very likely)</td> </tr> <tr> <td>genuine</td> <td>whether the headline was genuine (1) or altered (0)</td> </tr> <tr> <td>headline</td> <td>the headline as shown to the participant</td> </tr> <tr> <td>original</td> <td>the headline before a possible alteration</td> </tr> <tr> <td>expert_id</td> <td>participant's identifier</td> </tr> <tr> <td>sentiment</td> <td>whether the headline reported a negative (-1) or positive (1) outcome</td> </tr> <tr> <td>expert:ethnicity</td> <td>the participant's ethnicity</td> </tr> <tr> <td>expert:sex</td> <td>the participant's sex</td> </tr> <tr> <td>expert:age</td> <td>the participant's age</td> </tr> <tr> <td>outcome:white, outcome:black, outcome:young, outcome:old, outcome:male, outcome:female</td> <td>whether the headline reported a negative (-1) or positive (1) or neutral (0) outcome for the specified group</td> </tr> <tr> <td>trial_time</td> <td>how long the participant took to respond to the trial/round</td> </tr> </tbody> </table> <p><strong>abstract</strong><br>Individual and social biases undermine the effectiveness of human advisers by inducing judgment errors which can disadvantage protected groups. In this paper, we study the influence these biases can have in the pervasive problem of fake news by evaluating human participants' capacity to identify false headlines. By focusing on headlines involving sensitive characteristics, we gather a comprehensive dataset to explore how human responses are shaped by their biases. Our analysis reveals recurring individual biases and their permeation into collective decisions. We show that demographic factors, headline categories, and the manner in which information is presented significantly influence errors in human judgment. We then use our collected data as a benchmark problem on which we evaluate the efficacy of adaptive aggregation algorithms. In addition to their improved accuracy, our results highlight the interactions between the emergence of collective intelligence and the mitigation of participant biases. </p>
Data and code in support of "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower"
<p>This dataset contains all the data and processing needed to produce results and figures reported in the manuscript "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower".</p> <p> </p> <p>The README file guides through the material available to support replication of the results and figures.</p>
Characterization and morphometry of prone and affected watersheds by hydro-geomorphological processes in the Serra do Mar Mountain Range, southeastern Brazil: foundation for planning and mitigation actions.
<p>Data: shapefile, tables, and kmz files. </p> <ol> <li>SHAPEFILES</li> </ol> <p>- Dataset with watersheds mapped in the Serra do Mar Paulista Region in the follow cities:</p> <ul> <li>Ubatuba (Abbvr. WU)</li> <li>Caraguatatuba (Abbvr. WC)</li> <li>São Sebastião (Abbvr. WSS)</li> <li>Bertioga (Abbvr. WB)</li> <li>Santos (Abbvr. WS)</li> <li>Praia Grande (Abbvr. WPG)</li> <li>Cubatão (Abbvr. WCUB)</li> <li>São Vicente (Abbvr. WSV)</li> <li>Itanhaém (Abbvr. WITA)</li> <li>Peruíbe (Abbvr. WPERU)</li> <li>Iguape (Abbvr. WIGUA)</li> <li>Itariri (Abbvr. WITR)</li> <li>Pedro de Toledo (Abbvr. WPDT)</li> <li>Iporanga (Abbvr. WIPORA)</li> <li>Apiaí (Abbvr. WAPI)</li> <li>Itaoca Abbvr. WITAO)</li> </ul> <p>- Each shapefile contain information about altitude (min., max, and mean), area (km²), and length (km). </p> <p>- Debris-flow Inventory shapefile.</p> <p> 2. TABLES</p> <ul> <li>Tables for the watersheds mapped in each cities also contain information about the morphometric parameters (melton ratio, basin relief, and relief ratio).</li> <li>Debris-flow inventory information. </li> </ul> <p> </p>
Data: Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality
<p><strong>The repository contains the data supporting the findings of the study: <em>Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality</em></strong></p> <p><strong>Abstract:</strong></p> <p>Close-to-nature forestry (CNF) has a long tradition in European Alpine forest management, playing a crucial role in ensuring the continuous provision of biodiversity and forest ecosystem services, including protection against natural hazards. However, climate change is causing huge uncertainties about the future applicability of CNF in the Alpine region. The question arises as to whether current CNF practices are still suitable for adapting forests to climate change impacts while also meeting the increasing societal demands regarding Alpine forests, including their potential contribution to climate change mitigation.</p> <p>To answer this question, we simulated forest development using the ForClim forest model at two Alpine study sites, together representing a large biogeographic gradient from high-elevation inner Alpine forests (Switzerland) to lower-elevation south-eastern Alpine forests (Slovenia). The simulations considered three climate scenarios (historical climate, SSP2‑4.5 and SSP5-8.5) and six alternative management strategies, including both current CNF management practices and climate-adapted versions. Using a multi-criteria decision analysis framework, we assessed the joint impacts of climate and management on biodiversity and key ecosystem services of the investigated regions, including carbon sequestration (CS) inside and outside the forest ecosystem boundary. </p> <p>The joint effects of climate change and CNF varied, both among and within the study sites along the biogeographical gradient. While CS was more resistant to climate change under current CNF at the south-eastern Alpine site, it was more sensitive at the inner Alpine site, where CS potentials decreased at lower elevations. This adverse effect could be partly mitigated by fostering the use of climate-adapted tree species. However, current CNF and adaptations of it did not meet multiple management objectives equally well: while protection from gravitation hazards and timber production also benefited from this silvicultural practice, biodiversity benefited from CNF variants with low-intensity or no management. </p> <p>In conclusion, CNF has a high potential to continue fulfilling its crucial role in European Alpine forests. A differentiated approach will be needed in the future, however, to identify forest stands where adaptive measures are required, especially at sites particularly vulnerable to climate change. In combination with less intensively managed or unmanaged areas, CNF provides a management portfolio that will help European Alpine forests to meet the demands of future society.</p> <p><strong>Data:</strong></p> <p>There is one folder for each case study, including: </p> <ul> <li>simulated biodiverstiy and ecosystem service indicators</li> <li>forest stand metadata</li> <li>normlized utility values for indicators</li> <li>partial utility values for biodiversity and ecosystem service groups</li> </ul> <p>This study was conducted as part of the <strong>ONEforest project</strong>, which received funding from the <strong>European Union's Horizon 2020</strong> research and innovation programme under the <strong>grant agreement Nº 101000406</strong>.</p>
Mitigation of urbanisation effects on aquatic ecosystems by synchronous ecological restoration
<p>Ecosystem degradation and biodiversity loss have been caused by economic booms in developing countries over recent decades. In response, ecosystem restoration projects have been advanced in some countries but the effectiveness of different approaches and indicators at large spatio-temporal scales (i.e., whole catchments) remains poorly understood. Our datasets with a diverse array of 440 aquatic restoration projects including wastewater treatment, constructed wetlands, plant/algae salvage, and dredging of contaminated sediments implemented and maintained from 2007 to 2017 across more than 2000km2 of the northwest Taihu basin (Yixing, China). Synchronized investigations of water quality and invertebrate communities were conducted before and after restoration. Our datasets showed that even though there was rapid urbanization at this time, nutrient concentrations (NH<sub>4</sub><sup>+</sup>-N, TN, TP) and biological indices of benthic invertebrates (taxonomic richness, Shannon diversity, sensitive taxon density) improved significantly across most of the study area. Improvements were associated with the type of restoration project, with projects targeting pollution sources leading to the clearest ecosystem responses compared with those remediating pollution sinks. However, in some locations, the recovery of biotic communities appears to lag behind nutrients (e.g. nitrogen and phosphorus), likely reflecting long-distance re-colonization routes for invertebrates given the level of pre-restoration degradation of the catchment.</p>
juan-duenas/NHESS: Soil conditioner mixtures as an agricultural management alternative to mitigate drought impacts: a proof-of-concept.
<p>The dataset and the R script have been enhanced and corrected, respectively. The main figures of the associated publication have been added in two different qualities.</p> <p>This data is associated to a paper that will appear in an special issue of the journal Natural Hazards and Earth System Sciences. https://nhess.copernicus.org/articles/special_issue1295.html</p>
The key role of production efficiency changes in livestock methane emission mitigation
<p>This dataset contains the R code, the input data, the parameters used, and the updated livestock methane emission for the period 1961-2023 using methods from Chang, J., Peng, S., Yin, Y., Ciais, P., Havlik, P., Herrero, M. (2021). The key role of production efficiency changes in livestock methane emission mitigation. AGU Advances, 2, e2021AV000391. DOI: https://doi. org/10.1029/2021AV000391 </p> <p>1. R code: Chang_et_al_Global_Livestock_CH4_Assessment_1961_2023.R<br>2. Input data and parameters: Data.zip (statistics on historical livestock numbers and production need to be downloaded from FAOSTAT (http://www.fao.org/faostat/en/)<br>3. Results on global livestock methane emissions during 1961-2023 were presented in the Global_Results.xlsx<br>4. Results on livestock methane emissions from enteric fermentation and manure management during the period 1961-2023 in each country/area were shown in the folder named Country_Results: Files are organized as "Country_[XX]CH4_[YY]_[ZZ].csv" where XX indicate emission from enteric fermentation (EF) or manure management (MM); YY indicates method used for the estimates; and ZZ indicates livestock categories.<br>5. Results on gridded livestock methane emissions at a resolution of 5 arc-min using the IPCC Mixed Tier 1 and Tier 2 (2019MT) and Tier 1 (2019T1) methods following the 2019 refinement to the 2006 IPCC guidelines for National Greenhouse Gas Inventories (Vol. 4) (IPCC, 2019): Livestock_CH4_map_5arcmin_1961_2023_2019MT_2019T1.nc4</p> <p>Please contact: Dr. Jinfeng Chang (changjf@zju.edu.cn) for any question on the dataset.</p>
Data for "Do electric vehicles mitigate urban heat? The case of a tropical city"
<p>This dataset contains the underlying data used in the publication "Do electric vehicles mitigate urban heat? The case of a tropical city", which is under review in <em>Front. Environ. Sci. .</em></p> <p>The dataset includes two folders:</p> <p>1. <strong>data</strong> <br> Include COSMO-DCEP-BEP model inputs and output needed to reproduce the results in the manuscript (NetCDF). </p> <p>2. <strong>script</strong><br> Include post-processing scripts used to generate the figures in the manuscript (Jupiter Python 3 Notebook).</p> <p><em> </em></p>
Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security
<p>Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)</p>
Raw Data for the article: Donor Preconditioning with Inhaled Sevoflurane Mitigates the Effects of Ischemia-Reperfusion Injury in a Swine Model of Lung Transplantation
<p>Primary graft dysfunction (PGD) and ischemia-reperfusion injury (IRI) occur in up to 30% of patients undergoing lung transplantation and may impact on the clinical outcome. Several strategies for the prevention and treatment of PGD have been proposed, but with limited use in clinical practice. In this study, we investigate the potential application of sevoflurane (SEV) preconditioning to mitigate IRI after lung transplantation. The study included two groups of swines (preconditioned and not preconditioned with SEV) undergoing left lung transplantation after 24-hour of cold ischemia. Recipients' data was collected for 6 hours after reperfusion. Outcome analysis included assessment of ventilatory, hemodynamic, and hemogasanalytic parameters, evaluation of cellularity and cytokines in BAL samples, and histological analysis of tissue samples. Hemogasanalytic, hemodynamic, and respiratory parameters were significantly favorable, and the histological score showed less inflammatory and fibrotic injury in animals receiving SEV treatment. BAL cellular and cytokine profiling showed an anti-inflammatory pattern in animals receiving SEV compared to controls. In a swine model of lung transplantation after prolonged cold ischemia, SEV showed to mitigate the adverse effects of ischemia/reperfusion and to improve animal survival. Given the low cost and easy applicability, the administration of SEV in lung donors may be more extensively explored in clinical practice.</p>
Dataset accompanying Riesch et al. 2022. Grazing by wild red deer can mitigate nutrient enrichment in protected semi-natural open habitats. Oecologia
<p>This repository contains the data set on nutrient fluxes through wild red deer used by Riesch et al. 2022 in an article puplished in <em>Oecologia</em> (accepted 2022-05-01).</p> <p>Metadata are provided in the first excel worksheet. For further details please see the original article (DOI: 10.1007/s00442-022-05182-z).</p>
How flexible, slit and rigid barriers mitigate two-phase geophysical mass flows: a numerical appraisal
<p>The supplementary videos SV1 and SV2 (presented in Figures 4 and 5) show geophysical flows impacting flexible, slit, and rigid barriers via pile-up mode and runup mode, respectively.</p> <p>The measurement data used for comparison in Figure 7 from 14 centrifuge tests with bouldery and the boulder-debris mixture flows (Song et al., 2018b, 2019), and dry granular flow against open-type dams (Choi et al., 2020) were published open access in their articles.</p> <p>The unique dataset, comprising normalized data obtained from analytical models (Li et al., 2021; Song et al., 2021a), empirical relations (Cui et al., 2015), experiments (Armanini et al., 2020; Choi et al., 2020; Cui et al., 2015; Hu et al., 2020; Song et al., 2021a, 2021b; Tiberghien et al., 2007; Vicari et al., 2021), field events (Hübl et al., 2009), and practical design values (Armanini, 1997; Hungr et al., 1984; Kwan & Cheung, 2012; Wendeler, 2016), is plotted in Figure 10 for comparison.</p> <p>All these articles can be found in the PDF file entitled “References in Figures 7 and 10”.</p>
Alternative Covid-19 mitigation measures in school classrooms: Analysis using an agent-based model of SARS-CoV-2 transmission
<p>The SARS-CoV-2 epidemic continues to have major impacts on children's education, with schools required to implement infection control measures that have led to long periods of absence and classroom closures. We have developed an agent-based epidemiological model of SARS-CoV-2 transmission that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties; the basis of our approach is a contact model constructed using random networks, informed by structured expert judgment. The effectiveness of mitigation strategies is considered in terms of effectiveness at suppressing infection outbreaks and limiting pupil absence. Covid-19 infections in schools in the UK in Autumn 2020 are re-examined and the model used for forecasting infection levels in autumn 2021, as the more infectious Delta-variant was emerging and school transmission was thought likely to play a major role in an incipient new wave of the epidemic. Our results are in good agreement with available data and indicate that testing-based surveillance of infections in the classroom population with isolation of positive cases is a more effective mitigation measure than bubble quarantine both for reducing transmission in primary schools and for avoiding pupil absence, even accounting for the insensitivity of self-administered tests. Bubble quarantine entails large numbers of pupils being absent from school, with only a modest impact on classroom infection levels. However, maintaining a reduced contact rate within the classroom can have a major beneficial impact on managing Covid-19 in school settings.</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.