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Greenhouse gas fluxes before and after Hurricane Maria
We used several methods to estimate forest damage in the neighborhood of each focal tree in order to test whether there may be a relationship between GHG fluxes and local damage severity. First, we assigned each damage category a numeric value of damage based on % canopy damage (Light = 0%-25%, Medium = 25%-75%%, and Heavy > 75%). We then calculated three mean neighborhood damage estimates for each gas sampling location by taking 1) the mean damage for all trees > 10 cm DBH in the same 20 x 20 m quadrat as our gas measurements; 2) the mean damage for all trees within a 10 m radius of our gas measurements; and 3) the mean damage for all trees within a 20 m radius of our gas measurements. We then fit simple linear models in R to test for significant relationships between CO2, CH4, and N2O and our neighborhood damage estimates. Approximately every five yearssince 1990, stems are re-measured and their statusis assessed, and new stems are added. The lastcensus of the LFDP prior to Hurricane Marı´a wascompleted in 2016, representing pre-hurricaneconditions in this study.Beginning in January 2018, all trees at least10 cm dbh in the LFDP were surveyed to assessdamage and immediate mortality from H. Marı´a(Uriarte and others 2019). The survey recordedseveral qualitative and quantitative observations oftree damage resulting from the hurricane, such asuprooting or stem break, and type of damage tostems, tree crowns and branches. Using this information,we classified each stem at least 10 cm indbh into three damage classes: (1) no or lightdamage ( £ 25% of crown volume removed by thestorm), (2) medium damage (25–75% of crownvolume lost through a combination of branchdamage and crown break), or (3) heavy or complete(> 75% of the crown lost, stem snapped, rootbreak or tip-up) Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to
A holistic analysis of passenger travel energy and greenhouse gas intensities
<p>Dataset supporting the analysis of the journal article: Schäfer, A. & Yeh, S. A holistic analysis on passenger travel energy and GHG-intensities. <em>Nature Sustainability</em>, <strong>2020. </strong></p>
Disentangling the effects of methanogen community and environment on peatland greenhouse gas production by a reciprocal transplant experiment
<p>1. Northern peatlands consist of a mosaic of peatland types that vary spatially and temporally and differ in their methane (CH<sub>4</sub>) production. Microbial community composition and environment both potentially control the processes that release carbon from anoxic peat either as CH<sub>4</sub> or carbon dioxide (CO<sub>2</sub>), a less potent greenhouse gas than CH<sub>4</sub>. However, the respective roles of these controls remain unclear, which prevents incorporating microbes in the predictions of peatland CH<sub>4</sub> emissions.</p> <p>2. Here, a reciprocal transplant experiment was carried out to separate the influences of microbial community and environment in CH<sub>4</sub> and anaerobic CO<sub>2</sub> production. Peat from an acidic <i>Sphagnum</i> bog and a sedge fen with higher pH was enclosed in membrane bags with a pore size of 0.2 µm, preventing microbial colonization from the outside, and transplanted in the field for two months.</p> <p>3. Potential CH<sub>4</sub> production was primarily controlled by the environment. The conditions in the bog suppressed the initially higher activity of fen methanogens and reduced CH<sub>4</sub> production by 79%. Against expectations, the inhibition was not specific to acetate-using Methanotrichaceae. Reciprocal transplantation favoured Methanosarcinaceae and potentially methylotrophic methanogenesis in general. Bog methanogens, mostly hydrogenotrophic Methanoregulaceae, retained their community structure and activity in the fen with a slight increase (+37%) in CH<sub>4</sub> production.</p> <p>4. Anaerobic CO<sub>2</sub> production was controlled by both the microbial community and the environment. Transplantation led to increased CO<sub>2</sub> production in both bog (+50%) and fen peat (+57%) with distinct bacterial community, showing that the new environment directed more carbon to other anaerobic processes than methanogenesis. 5. Taken together, these results relate differences in CH<sub>4</sub> production of bogs and fens to ecophysiology of specific methanogen groups. The sensitiveness of fen methanogens to the acidic conditions in <i>Sphagnum</i> bogs can help explain the decrease of CH<sub>4</sub> emission in the typical boreal peatland succession from young fens to older bogs. Increase in anaerobic CO<sub>2</sub> vs. CH<sub>4</sub> production with transplantation shows that disturbances of boreal peatlands can activate poorly defined pathways of anaerobic decomposition.</p>
Dataset: Greenhouse Gas and Noxious Emissions from Dual Fuel Diesel and Natural Gas Heavy Goods Vehicles
<p>This dataset contains the data underlying all figures of the paper entitled 'Greenhouse Gas and Noxious Emissions from Dual Fuel Diesel and Natural Gas Heavy Goods Vehicles'.</p>
(DATA) Adsorption Behavior of Greenhouse Gases on Carbon Nanobelts: A Semi-Empirical Tight-Binding Approach for Environmental Application
<ul> <li>Input structures.</li> <li>Scripts to generate some inputs.</li> <li>Script to run all the calculations.</li> <li>Scripts to run analysis.</li> <li>Molecular dynamics trajectories (in PDB and TRJ formats)</li> <li>Molecular dynamics animations (in MP4 format)</li> </ul>
Study on optimization effect of south roof curve of Chinese solar greenhouse
<p><span>To maximize the use of solar energy and increase the utilization area of Chinese solar greenhouse (CSG) land, a solar greenhouse radiation model was established based on previous studies, combining with the law of solar movement, meteorological data, and optical properties of materials. The model is verified by selecting the measured data of typical sunny days. The results show that the optimization of the south roof curve of the greenhouse can not only increase the land-use area in the greenhouse but also increase the solar energy captured by the solar greenhouse. And the CSG in the greenhouse, the land-use area in the greenhouse increased to 42 m<sup>2</sup>, the lighting rate of the south roof, and the lighting rate of the ground increased 15.2% and 0.78% respectively. The amount of interception in the back wall of the greenhouse was reduced by 0.67%, and that in the greenhouse was increased by 2.22%. The optimal structure CSG in Shenyang (span 9 m) is the optimal shoulder height position, obtain the best shoulder height position of the greenhouse, the level of front and bottom Angle is 0.7 m, and the height is 2 m. Combined with the actual greenhouse construction demand, the optimal curve function Y1 and Y2 of the south roof of the greenhouse are calculated. This paper verifies the structural safety of the front roof of the greenhouse after the renovation, and shows that increasing the shoulder height can also increase the structural stability of the greenhouse and strengthen the structural safety of the greenhouse.</span></p>
Hot spots and hot moments of greenhouse gas emissions in agricultural peatlands
<p>Drained agricultural peatlands occupy only 1% of agricultural land but are estimated to be responsible for approximately one-third of global cropland greenhouse gas emissions. However, recent studies show that greenhouse gas fluxes from agricultural peatlands can vary by orders of magnitude over time. The relationship between these hot moments (individual fluxes with disproportionate impact on annual budgets) of greenhouse gas emissions and individual chamber locations (i.e. hot spots with disproportionate observations of hot moments) is poorly understood but may help elucidate patterns and drivers of high greenhouse gas emissions from agricultural peatland soils. We used continuous chamber-based flux measurements across three land uses (corn, alfalfa, and pasture) to quantify the spatiotemporal patterns of soil greenhouse gas emissions from temperate agricultural peatlands in the Sacramento-San Joaquin Delta of California. We found that the location of hot spots of emissions varied over time and were not consistent across annual timescales. Hot moments of nitrous oxide (N<sub>2</sub>O) and carbon dioxide (CO<sub>2</sub>) fluxes were more evenly distributed across space than methane (CH<sub>4</sub>). In the corn system, hot moments of CH<sub>4</sub> flux were often isolated to a single location but locations were not consistent across years. Spatiotemporal variability in soil moisture, soil oxygen, and temperature helped explain patterns in N<sub>2</sub>O fluxes in the annual corn agroecosystem but was less informative for perennial alfalfa N<sub>2</sub>O fluxes or CH<sub>4</sub> fluxes across ecosystems, potentially due to insufficient spatiotemporal resolution of the associated drivers. Overall, our results do not support the concept of persistent hot spots of soil CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O emissions in these drained agricultural peatlands. Hot moments of high flux events generally varied in space and time and thus required high sample densities. Our results highlight the importance of constraining hot moments and their controls to better quantify ecosystem greenhouse gas budgets.</p>
Mapping and modelling global mobility infrastructure stocks, material flows and their embodied greenhouse gas emissions - Data
<p>Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. </p><p>The data in this repository show the material stocks contained in global mobility infrastructure networks at the country-level and mapped at 5arcmins, as well as country-level estimates of material flows for maintenance, replacement and expansion of those infrastructures, and the associated GHG emissions from materials production. This repository contains all data as shown in figures of the article, including the GeoTIFF files for figure 3, and the supplementary data file containing full country-level results.</p><p><strong>Data</strong><br>This dataset includes the following data:</p><ul><li>Global maps of material stocks in mobility infrastructure networks at 5 arcmins, separate for all roads, all rail-based infrastructure, as well as in total and per capita</li><li>Global country-level material stock estimates for mobility infrastructures</li><li>Global country-level estimates of material flows and associated GHG emissions for materials production</li><li>Material intensity in mass per area of road (kg/m²) per road type</li><li>Material intensity in mass per area of railway track (kg/m²) per railway type</li><li>Material intensity in mass per area (kg/m²) per bridges and tunnels</li></ul><p>Material intensity factors are available for iron and steel, concrete, asphalt, aggregate (sand & gravel), timber, and other.</p><p><strong>Further information</strong><br>This dataset complements the following scientific article:</p><p>Wiedenhofer, Dominik, André Baumgart, Sarah Matej, Doris Virág, Gerald Kalt, Maud Lanau, Danielle Densley Tingley, u. a. "Mapping and Modelling Global Mobility Infrastructure Stocks, Material Flows and Their Embodied Greenhouse Gas Emissions". <i>Journal of Cleaner Production</i>, November 2023, 139742. <a href="https://doi.org/10.1016/j.jclepro.2023.139742">https://doi.org/10.1016/j.jclepro.2023.139742</a>.</p><p>For further information please see the publication. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <i>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</i></p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
Diel greenhouse gas emissions demonstrate a strong response to vegetation patch types in a freshwater wetland
<p>Data supporting submitted research paper. "All_flux_variables.csv" includes all plot data and is organized by the date/time of sample, campaign number, and sample location in rows and data collected as headers in the columns. "Flux_tower_data.csv" are meterological variables include in data analysis collected by a flux tower on sight. All other files are time series data for water pH (n = 2), water temperature (n = 3-5). Refer to the "metadata.csv" for units and descriptions of data in files. </p>
Greenhouse gas fluxes at a agricultural peatland in Southern Finland
<p>Greenhouse gas fluxes were measured during summer and fall of 2024 in an extensively managed agricultural peatland in Holonsuo, Lahti, Finland (61.0025 °N, 25.8214 °E). Greenhouse gas fluxes on four measurement plots were monitored along with soil temperature and water table level. </p> <p>The study site is a peatland drained for agricultural purposes (peat field). Total of four measurement plots were founded at the site. The plots were located diagonally between two ditches. Measurements were carried out approximately every 3-4 weeks from June to September 2024 (Fig1). During the measurement period the field was not used for cultivation. Portable LI-COR Trace Gas Analyzers were the used measurement devices; TG10 for CO<sub>2</sub> and CH<sub>4</sub> and TG20 for N<sub>2</sub>O. Greenhouse gas fluxes were measured using a dark chamber. Volume of the used chamber was 24,16 dm<sup>3</sup> and the chamber was equipped with a fan. Before setting the chamber on the measurement plot the vegetation was cut short each time (approx. 5 cm height). Soil temperature at 5 and 30 cm depth as well as water table level were also measured simultaneously with greenhouse gas measurements.</p> <p>The data set ("Holonsuo_ghg_data.csv") contains the measurement date, water table level (cm below ground), soil temperature at 5 and 30 cm (°C) and greenhouse gas fluxes g m<sup>-2</sup> h<sup>-1</sup>. In Fig1 fluxes are presented as averages of each measurement date (unit mg m<sup>-2</sup> h<sup>-1</sup>).</p>
Landsat-derived annual maps of agricultural greenhouse in Shandong province, China from 1989 to 2018
<p>Using 8,450 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual maps of agricultural greenhouse (AG) in Shandong province, China from 1989 to 2018. Two types of reference datasets, including AG and Non-AG, were labeled via visual inspection of high-resolution imagery available in Google Earth or Landsat imagery based on a 10 km grid sampling structure. The mapping window for each year was selected based on the vegetation growth and the phenological information. Classification for each year was carried out initially based on the random forest classifier after the feature optimization. A temporal consistency correction algorithm based on classification probability was then proposed to the classified AG maps for further improvement.</p>
Carbon sequestration of a forested wetland receiving nutrient inputs - soil, tree and greenhouse gas data
<p><span><span><span><span><span><span><span><span><span><span><span>Here we describe a pilot wetland carbon project located 30 km west of New Orleans where measurements were taken in 2013 and 2018, and applied to the carbon offset methodology, "Restoration of Degraded Deltaic Wetlands of the Mississippi Delta" ("the ACR Methodology") published by the American Carbon Registry (ACR). Baseline emissions were modeled using values derived from scientific literature. Results indicate net sequestration rate of 619,727 tons carbon dioxide equivalent (CO<sub>2</sub>e) over the 40 year project duration, which equates to 16,527 t CO2-e/yr, if wetland greenhouse gases (GHGs) are included, and 200,143 t CO<sub>2</sub>e over 40 years, or 5,003 t CO2-e/yr, if wetland greenhouse gasses were conservatively omitted. A kriging exercise was carried out that modeled the tree and soil pools, which resulted in net sequestration of 723,375 t CO2-e over 40 years (annual mean 18,084 t CO2-e/yr) with greenhouse gases, and 262,472 t CO2-e over 40 years (annual mean rate 6,560 t CO2-e/yr) if greenhouse gases were omitted. Unfortunately, the project was withdrawn, prohibiting the issuance and eventual transaction of carbon credits, due to very large uncertainty estimates mostly associated with GHG emissions and the kriging approach as in situ sampling could not be conducted as required by the methodology.</span></span></span></span></span></span></span></span></span></span></span></p>
Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery
<p><strong>Power Generation Data Set</strong></p> <p>This data set contains imaging data acquired by ESA's Sentinel-2<br> Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates <br> provided by the European Pollutant Release and Transfer Register [2]. The images<br> contain scenes of power stations, some of which are actively<br> emitting smoke plumes.</p> <p>This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images.</p> <p><br> <strong>Description</strong><br> </p> <p>Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground)<br> .</p> <p>This repository contains a total of 2131 images. This<br> repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation<br> labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images.</p> <p><br> <strong>Content</strong></p> <p>The following files are contained in this repository:</p> <ul> <li>README.md - this file</li> <li>images.zip [2.0GB] - contains 2131 GeoTIFF images</li> <li>segmentation_labels.zip [1.5MB] - contains 2131 JSON files</li> <li>labels.csv [310KB] - contains additional labels for each image: <ul> <li>Generation output rate [4],[5]</li> <li>Country</li> <li>Type of fired fuel</li> <li>Latitude and longitude of the power plant</li> <li>Concurrent weather information (temperature, humidity and wind vector)</li> </ul> </li> </ul> <p> </p> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p> Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,<br> "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",<br> Tackling Climate Change with Machine Learning workshop at NeurIPS 2021.</p> <p>Please refer to this publication for additional information on the data set.</p> <p>The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation.</p> <p> </p> <p><br> <strong>Author</strong></p> <p>Joëlle Hanna</p> <p>University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch</p> <p><br> <strong>References</strong><br> </p> <p>[1]: https://earth.esa.int/web/sentinel/missions/sentinel-2<br> [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial<br> [3]: https://labelstud.io/<br> [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show<br> [5]: https://doi.org/10.5281/zenodo.3574566</p>
Data and code for "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"
<p>For the files and data associated with the Yuan et al. 2022 "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"</p> <p>Description: Data/code used in energy-economic impacts and health impacts analysis.</p> <p>Directory contents:</p> <p><strong>Energy Economic Impacts</strong></p> <ul> <li><strong>Code </strong>used for producing figures and data tables <ul> <li>'paperFigs_March2022.Rmd' contains the R code used for data analysis and visualization in the paper. (<em>The code runs with R v4.0.0, RStudio v1.4.1106, and the following packages: scales_1.1.1, ggpubr_0.4.0, cowplot_1.1.0, readxl_1.3.1, here_0.1, forcats_0.5.0, stringr_1.4.0, dplyr_1.0.4, purrr_0.3.4, readr_1.3.1, tidyr_1.1.0, tibble_3.0.6, ggplot2_3.3.4, and tidyverse_1.3.0.</em>)</li> <li>'ERL_Figure4.py' contains the Python code used for generating Figure 4 in the paper</li> </ul> </li> <li><strong>Table</strong>: data tables for figures in the paper and supplementary materials</li> <li><strong>Figure</strong>: figures in the paper and supplementary materials</li> <li><strong>Data</strong>: USREP-ReEDS results and data from other sources <ul> <li>'rrpt_subset.csv' contains the portions of the ReEDS output from February 26, 2021 that are necessary to create the figures in the paper.</li> <li>'urpt_subset.csv' contains the portions of the USREP output from February 26, 2021 that are necessary to create the figures in the paper.</li> <li>'urpt_welfare_subset.csv' contains more detailed USREP welfare output from February 26, 2021.</li> <li>'cooper_pop_proj.csv' contains U.S. population projections from the University of Virginia Weldon Cooper Center for Public Service published in 2018.</li> <li>'carbon_price_comparison.csv' contains data from other recent carbon pricing studies, as described in supplementary materials G.</li> </ul> </li> </ul> <p><strong>Health Impacts</strong></p> <ul> <li><strong>analysis</strong>: <ul> <li><strong>lib</strong>: annotated code library, which loads raw data from the root data folder and conducts health impacts analysis</li> <li><strong>data</strong>: outputs <ul> <li><strong>inmap</strong>: spatial inputs/outputs for inmap</li> <li><strong>working</strong>: intermediate procssed output files</li> <li><strong>final</strong>: final health impacts results</li> </ul> </li> </ul> </li> <li><strong>data</strong>: raw data used in analysis <ul> <li><strong>working</strong>: processed intermediate raw data for faster loading in R</li> </ul> </li> </ul>
A comprehensive and synthetic dataset for global, regional and national greenhouse gas emissions by sector 1970-2018 with an extension to 2019
<p>Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO<sub>2</sub> emissions from fossil fuel combustion and industry (FFI), CH<sub>4</sub> emissions, N<sub>2</sub>O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO<sub>2</sub> emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.</p>
Life-cycle greenhouse gas emissions in power generation using palm kernel shell
<p>Although the Japanese feed-in tariff was introduced to expand renewable energy, leading to the expansion of palm kernel shell (PKS) use, the greenhouse gas (GHG) emission reduction effect is evaluated using the limited life-cycle of PKS, focusing on processes after PKS generation point. Therefore, this study aimed to elucidate the life-cycle GHG emissions of power generation using PKS. We targeted two PKS-firing power plants as these are the first two instances of the use of PKS in power plants in Japan. A system boundary was established to cover palm plantation management in Indonesia and Malaysia, as both power plants import PKS from these countries. The GHG emissions were derived from land-use change, palm plantation, oil extraction, PKS transportation, and power plants. Six scenarios were examined for the emissions based on the type of land-use change and the existence of biogas capture in oil extraction. CO<sub>2</sub> emissions from PKS combustion were also calculated by assuming that carbon neutrality was lost because of cultivation abandonment. The GHG emissions in one scenario, where the plantations were replanted and continuously managed and no biogas capture implemented in oil extraction, exhibited an average of 0.134 kg-CO<sub>2</sub>eq/kWh reduction in a plant in Kyushu District, and 0.043 kg-CO<sub>2</sub>eq/kWh reduction in a plant in Shikoku District for liquid natural gas-fired steam power generation, respectively. More than 65% of life-cycle GHG emissions originate from biogas generated during oil extraction; thus, biogas capture is an effective strategy to reduce current emissions. In contrast, in the case of accompanying land-use change or collapse of carbon neutrality, the emissions considerably exceeded those of fossil fuels. These findings indicated that the FIT fails to consider the risk of increased emissions or further substantial emission reductions. Therefore, the feasibility of FIT application to PKS needs to be re-established by evaluating the entire PKS life-cycle. </p>
Agroforestry carbon stocks and greenhouse gas emission rates in central Alberta, Canada
<p>Agroforestry systems (AFS) contribute to carbon (C) sequestration and reduction in greenhouse gas emissions from agricultural lands. However, previously understudied differences among AFS may underestimate their climate change mitigation potential. In this 3-year field study, we assessed various C stocks and greenhouse gas emissions across two common AFS (hedgerows and shelterbelts) and their component land uses: perennial vegetated areas with and without trees (woodland and grassland, respectively), newly planted saplings in grassland, and adjacent annual cropland in central Alberta, Canada. Between 2018 and 2020 (~April–October), nitrous oxide emissions were 89% lower under perennial vegetation relative to the cropland (0.02 and 0.18 g N m−2 year−1, respectively). In 2020, heterotrophic respiration in the woodland was 53% lower in shelterbelts relative to hedgerows (279 and 600 g C m−2 year−1, respectively). Within the woodland, deadwood C stock was particularly important in hedgerows (35 Mg C ha−1 or 7% of ecosystem C) relative to shelterbelts (2 Mg C ha−1 or < 1% of ecosystem C), and likely affected C cycling differences between the woodland types by enhancing soil labile C and microbial biomass in hedgerows. Deadwood C stock was positively correlated with annual heterotrophic respiration and total (to ~100 cm depth) soil organic C, water-soluble organic C, and microbial biomass C. Total ecosystem C was 1.90–2.55 times greater within the woodland than all other land uses, with 176, 234, 237, and 449 Mg C ha−1 found in the cropland, grassland, planted saplings treatment, and woodland, respectively. Shelterbelt and hedgerow woodlands contained 2.09 and 3.03 times more C, respectively, than adjacent cropland. Our findings emphasize the importance of AFS for fostering C sequestration and reducing greenhouse gas emissions and, in particular, retaining hedgerows (legacy woodland) and their associated deadwood across temperate agroecosystems to help mitigate climate change.</p>
Data for meta-analysis of the soil greenhouse gas emissions
<p><span>Exploring the </span><span>responses of greenhouse gases (GHGs) emissions to land use conversion or reversion is significant for taking effective land use measures to alleviate global warming.</span> <span>A global meta-analysis was conducted to analyze the responses of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions to land use conversion or reversion, and determine their temporal evolution, driving factors and potential mechanisms. Our results showed that CH4 and N2O responded positively to land use conversion while CO2 responded negatively to the changes from natural herb and secondary forest to plantation. By comparison, CH4 responded negatively to land use reversion and N2O also showed negative response to the reversion from agricultural land to forest. The conversion of land use weakened the function of natural forest and grassland as CH4 sink and the artificial nitrogen (N) addition for plantation increased N source for N2O release from soil, while the reversion of land use could alleviate them to some degree. Besides, soil carbon would impact CO2 emission for a long time after land use conversion, and secondary forest reached the methane uptake level similar to that of primary forest after over 40 years. N2O responses had negative relationships with time interval under the conversions from forest to plantation, secondary forest and pasture. In addition, meta-regression indicated that CH4 had correlations with several environmental variables, and carbon-nitrogen ratio had contrary relationships with N2O emission responses to land use conversion and reversion.</span> <span>And the importance of driving factors displayed that CO2, CH4 and </span><span>N2O</span><span> response to land use conversion and reversion were easily affected by NH4+ and soil moisture, </span><span>mean annual temperature</span><span> and NO3-, total nitrogen and </span><span>mean annual temperature</span><span>, respectively.</span> <span>This study would provide enlightenment for scientific land management and reducing of GHG emissions.</span></p>
Estimating net carbon balances and greenhouse gas radiative balances of potato and pea crops on a conventional farm in western Canada (Flux and meteorological data)
<p>Data accompanying the paper titled as "Estimating net carbon and greenhouse gas balances of potato and pea crops on a conventional farm in western Canada". Data includes measurements from eddy covariance, chamber, and meteorological sensors. Measurements were mainly conducted in 2018 and 2019, please refer to the paper for the detailed information.</p>
Dataset: Acorn weight as determinant of germination in red and white oaks: evidences from a common-garden greenhouse experiment
<p>This repository contains the files associated with the following article:</p> <p>Sánchez-Montes de Oca EJ, EI Badano, LE Silva-Alvarado, J Flores, F Barragán-Torres & JA Flores-Cano. Acorn weight as determinant of germination in red and white oaks: evidences from a common-garden greenhouse experiment. Annals of Forest Science, 75, article 12. <a href="https://doi.org/10.1007/s13595-018-0693-y">https://doi.org/10.1007/s13595-018-0693-y</a></p> <p>Both datasets are provided in Microsoft Excel format. The first dataset (2015 data-Relationships acorn fresh weight-germination) contains information about the phylogenetic section to which each oak species included in the study belong to, the fresh weights of acorns after they were soaked and their respective germination responses (100 acorns per oak species). These data were used to assess whether acorn weight influences germination across and within species. The second dataset (2017 data-Relationships acorn dry-fresh weights) also contains the phylogenetic section to which each oak species belongs to, indicating the dry biomass of acorns, their fresh weight after soaking, and their percent water content (100 acorns per oak species). These data were used to assess how dry biomass of acorns influences their fresh weight and percent water content after soaking.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.