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407 results for “greenhouses”
Idiosyncratic phenology of greenhouse gas emissions in a Mediterranean reservoir
<p>Extreme hydrological and thermal regimes characterize the Mediterranean biome and can significantly impact the phenology of greenhouse gas (GHG) emissions in reservoirs. Our study examined the seasonal changes in GHG emissions of a shallow, eutrophic, hardwater reservoir in Spain. We observed distinctive seasonal patterns for each gas. CH<sub>4 </sub>emissions substantially increased during stratification, influenced predominantly by the rise of water temperature and gross primary production and the drop in reservoir mean depth. N<sub>2</sub>O emissions mirrored CH<sub>4</sub>'s seasonal trend, significantly correlating to water temperature, wind speed, and net primary production. Conversely, CO<sub>2 </sub>emissions decreased during stratification and displayed a quadratic, rather than a linear relationship with water temperature -an unexpected deviation from CH<sub>4</sub> and N<sub>2</sub>O emission patterns- likely associated with calcite formation coupled to photosynthesis. This investigation highlights the need to integrate these idiosyncratic patterns into GHG emissions models, enhancing the prediction of global GHG emissions in the global change era.</p>
Data and code for: Influence of atmospheric nitrogen deposition on soil greenhouse gas fluxes from forests in China and the world
<p><span>Since the industrial revolution, greenhouse gas emissions (particularly CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) caused by human activities have accelerated global climate change. To avoid catastrophic transitions in the Earth system, many countries including China have set goals to achieve “net zero emission” (or “carbon neutrality”) by mid-21<sup>st</sup> century. Forestland-related practices are among the most preferred “natural climate solutions”. However, high uncertainties remain in the greenhouse gas fluxes from forest soils, because of the limited capability to observe soil dynamics at a large spatial scale. Meanwhile, forest soil greenhouse gas fluxes are influenced by multiple anthropogenic environmental changes including enhanced atmospheric nitrogen deposition, which further complicates the interactions between forest ecosystem and the atmosphere. During the past half century, simulated nitrogen deposition (or “nitrogen addition”) experiments have been conducted in various forest sites worldwide, founding a basis for quantifying the spatially-varying responses of soil greenhouse gas flux to nitrogen deposition. </span></p> <p><span>In this research, we systematically synthesized global nitrogen addition experiment data from published literature and public databases, using which we explored the responses of the three major greenhouse gases to N input. Derived sensitivity of soil N<sub>2</sub>O emission to N deposition allowed for determining the N saturation (or limitation) status of global forests. Using process-augmented data-driven approach and random forest regression models, we estimated soil greenhouse gas budgets on regional and global levels. On the basis, we quantified the varying effects of N deposition on soil greenhouse gas fluxes in N-limited and N-saturated forests across biomes. </span><span> </span></p> <p><span>The produced global map of N-saturated forests in this research could facilitate studies on carbon and nitrogen cycles and improve forest nitrogen management. The revealed response patterns and response factors of soil greenhouse gases to N input could help improve the structure and parameters of ecosystem models. Furthermore, the localized N<sub>2</sub>O emission factors for 145 countries could be used to reduce the uncertainties in their national greenhouse gas inventories. The “process-augmented data-driven” approach could potentially bridge the gap between site-level manipulative experiments and the demand for regional greenhouse gas budgets, allowing manipulative experiments to play a more important role in global change research. </span></p>
Cryosphere Inland Water Greenhouse Gases Database (CIWD-GHG)
<p>This is a database called Cryosphere Inland Water Greenhouse Gases Database (CIWD-GHG), which provides spatial-temporally resolved greenhouse gases (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) data from cryosphere inland waters (lakes, ponds, reservoirs, rivers, and streams). The dataset was created through a synthesis procedure.</p> <p>To compile the dataset, we conducted searches in various sources including peer-reviewed papers, dissertations, theses, and public data repositories. The searches were conducted using platforms such as Web of Science, Google Scholar, ProQuest Dissertations & Theses Global, China National Knowledge Infrastructure, Arctic Data Center, Zenodo, Environmental Data Initiative, and PANGAEA. The search string is: (methane OR CH4 OR carbon dioxide OR CO2 OR nitrous oxide OR N2O OR greenhouse*) AND (river OR stream OR lake OR pond OR reservoir) AND (Arctic* OR Tibet* OR Greenland OR Antarctic OR glacier* OR permafrost). We ensured completeness by conducting multiple searches before April 2023.</p> <p>We applied a consistent criterion for screening and selecting the searched results. Specifically, we included data on greenhouse gas concentrations or fluxes measured in inland water systems such as streams, rivers, ponds, lakes, and reservoirs that are associated with permafrost or glaciers. The study focused on the cryosphere extent, excluding sites outside this extent. Wetland ecosystems and inland waters in cold regions without glaciers or permafrost were also excluded. Additionally, floodplain lakes connected with river channels were excluded, except for those barely connected with high-closure river channels, which were included as lake sites. Gas concentration data in permanently ice-covered water bodies were not included. We also collected auxiliary data on waterbody physical and chemical characteristics, climate, and land cover to the extent possible.</p> <p>The dataset was divided into two separate files: CryoLake.xlsx and CryoRiver.xlsx. CryoLake.xlsx contained data on lakes, ponds, and reservoirs, while CryoRiver.xlsx contained data on rivers and streams. Each file consisted of four sub-tables: source table (data sources), sites table (sites information), concentration table (concentration data), and flux table (flux data). All sub-tables were linked using unique source IDs, and the sites, concentration, and flux tables were further linked using unique site IDs. The temporal resolution varied, with daily data being the shortest resolution. Sub-daily measurements were averaged to daily data, and data reported in monthly, seasonal, and annual scales were also included. Considering the difficulty in obtaining cryosphere-related GHG data, we included all available data. The spatial resolution primarily focused on the plot scale, although aggregated sites were also included. Detailed spatiotemporal information of the measured data was recorded in the data table for further analysis. Due to regional heterogeneity, we did not have a uniform standard for dividing seasons. Instead, seasons were assigned based on the site descriptions provided in each study.</p> <p>The R script file is the multilevel bootstrap method used for Inland water greenhouse gas emissions upscaling. The ziped file contains bootstrap results for further calculating zonal and monthly GHG emissions.</p>
Carbon Dioxide and Methane Flux Meta Analysis, Schaerer et al: Permafrost microbes unleashed: thaw reactors provide timely insights into greenhouse gas feedbacks for climate stewardship
<p>Meta-analysis results and workflow: <strong>Meta-Analysis-Report-V1.pdf</strong> </p> <p>raw data tables for input into meta-analysis:</p> <p><strong>co2_flux_by_layer_temp.csv</strong></p> <p><strong>co2_flux_by_layer_time.csv</strong></p> <p><strong>ch4_flux_by_layer_temp.csv</strong></p> <p><strong>ch4_flux_by_layer_time.csv</strong></p> <p><strong>co2_flux_by_headspace_temp.csv</strong></p> <p>(Data included in these tables was digitized using the R package metaDigitize)</p> <p>****</p> <p>We also attempted to summarize the raw data from 12 studies which is summarized in the <strong><em>Flux_Summary_Report </em></strong>document. we converted all units into mg C / g Soil * d (calculations are included in the <strong><em>co2_meta_analysis</em></strong> spreadsheet). For studies not reporting raw data or data tables (7/12 studies), we estimated the values from the figures manually. This typically resulted in an estimate of the mean flux of several replicates (all studies had 3-10 replicates). We filled in metadata as well as we could based on the information available in the papers, although there were many gaps. This information is summarized in the <strong><em>flux_data_compilation</em> </strong>spreadsheet.</p> <p>Studies in the raw data comparison include: Mackelprang 2011, Waldrop 2010 & 2021, Barbato 2022, Dang 2022, Muller 2018, Monteaux 2020, Dutta 2006, Lee 2012, O'Donnell 2009, Roy Chowdhury 2014, Trubl 2021.</p>
Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production
<p>This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.</p>
Andersen et al., 2018 - A UAV-based active AirCore system for measurements of greenhouse gases - Raw data
<p>Raw Picarro data sets and flight logs from the Lutjewad drone flights on September 13th 2016, along with the processed flight data, as presented in "A UAV-based active AirCore system for measurements of greenhouse gases" published in <em>Atmospheric Measurement Techniques </em>(<a href="https://doi.org/10.5194/amt-11-1-2018">https://doi.org/10.5194/amt-11-1-2018</a>)<em>. </em></p>
Dataset from: Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest Biogeosciences 2019
<p>Dataset used for the manuscript <strong>Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest</strong> in Biogesciences, 2019</p>
Soil greenhouse gas fluxes and associated parameters from forest and oil palm in the SAFE landscape
<b>Description: </b><p>Greenhouse gas fluxes measured by the static chamber method including associated environmental parameters</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126"><b>Characterising soil microbial communities and measuring associated biogeochemical fluxes</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (79))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3258117">here</a></p><p><b>Files: </b>This consists of 1 file: 3_GHG_jdrewer.xlsx</p><p><b>3_GHG_jdrewer.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>data one off field</b> (described in worksheet Data_one_off)</p><p>Description: Soil and litter parameters</p><p>Number of fields: 14</p><p>Number of data rows: 56</p><p>Fields: </p><ul><li><b>Location</b>: Location measurement was taken (Field type: Location)</li><li><b>site</b>: Location measurement was taken (Field type: ID)</li><li><b>chamber_id</b>: Chamber ID (Field type: ID)</li><li><b>landuse</b>: Land use of location (Field type: Categorical)</li><li><b>pH</b>: Soil pH (Field type: Numeric)</li><li><b>bulk_density</b>: dry weight of soil (Field type: Numeric)</li><li><b>soil_N%</b>: Percentage of soil N (Field type: Numeric)</li><li><b>soil_C%</b>: Percentage of soil C (Field type: Numeric)</li><li><b>litter_N%</b>: Percentage of leaf Nitrogen (Field type: Numeric)</li><li><b>litter_C%</b>: Percentage of leaf Carbon (Field type: Numeric)</li><li><b>C/N_soil</b>: Ratio of soil Carbon: Nitrogen (Field type: Numeric)</li><li><b>Latitude</b>: Latitude of sampling point (Field type: Latitude)</li><li><b>Longitude</b>: Longitude of sampling point (Field type: Longitude)</li><li><b>Elevation</b>: Elevation of sampling point (Field type: Numeric)</li></ul></li><li><p><b>data of repeated measures</b> (described in worksheet Data_repeated_measures)</p><p>Description: Soil greenhouse gas flux data and associated variables</p><p>Number of fields: 14</p><p>Number of data rows: 672</p><p>Fields: </p><ul><li><b>Location</b>: Location measurement was taken (Field type: Location)</li><li><b>site</b>: Location measurement was taken (Field type: ID)</li><li><b>chamber_id</b>: Chamber ID (Field type: ID)</li><li><b>landuse</b>: Land use of location (Field type: Categorical)</li><li><b>date</b>: Date the measurement was taken (Field type: Date)</li><li><b>time</b>: Time the measurement was taken (Field type: Time)</li><li><b>flux_CH4</b>: Soil CH4 flux (Field type: Numeric)</li><li><b>flux_CO2-C</b>: Soil CO2 flux (Field type: Numeric)</li><li><b>flux_N2O-N</b>: Soil N2O flux (Field type: Numeric)</li><li><b>NH4-N</b>: Soil NH4 concentration (Field type: Numeric)</li><li><b>NO3-N</b>: Soil NO3 concentration (Field type: Numeric)</li><li><b>air_temp</b>: Air temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_temp</b>: Soil temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_moisture</b>: Soil moisture around the flux chamber (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2015-01-01 to 2016-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Soil greenhouse gas fluxes along transects from oil palm to riparian forests in the SAFE landscape
<b>Description: </b><p>Riparian greenhouse gas fluxes measured by the static chamber method including associated environmental parameters and river water </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126"><b>Characterising soil microbial communities and measuring associated biogeochemical fluxes</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (79))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3258079">here</a></p><p><b>Files: </b>This consists of 1 file: 1_HJ_river_water_riparian.xlsx</p><p><b>1_HJ_river_water_riparian.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>river_water</b> (described in worksheet river_water)</p><p>Description: river water measurments</p><p>Number of fields: 17</p><p>Number of data rows: 63</p><p>Fields: </p><ul><li><b>site</b>: location sample was taken (Field type: Location)</li><li><b>location</b>: habitat (Field type: Categorical)</li><li><b>replicate</b>: water sample replicate number (Field type: Replicate)</li><li><b>sampling_occasion</b>: date of sample collection (Field type: Date)</li><li><b>date</b>: date of sample analysis (Field type: Date)</li><li><b>TDS</b>: Total Desolved Solids (Field type: Numeric)</li><li><b>pH</b>: water pH (Field type: Numeric)</li><li><b>conductivity</b>: water conductivity (Field type: Numeric)</li><li><b>Temp</b>: tempreture of river water (Field type: Numeric)</li><li><b>air_CH4</b>: air concentration of CH4 (Field type: Numeric)</li><li><b>water_CH4</b>: water concentration of CH4 (Field type: Numeric)</li><li><b>air_N2O</b>: air concentration of N2O (Field type: Numeric)</li><li><b>water_N2O</b>: water concentration of N2O (Field type: Numeric)</li><li><b>air_CO2</b>: air concentration of CO2 (Field type: Numeric)</li><li><b>water_CO2</b>: water concentration of CO2 (Field type: Numeric)</li><li><b>NH4-N</b>: concentration of NH4-N in water (Field type: Numeric)</li><li><b>NO3-N</b>: concentration of NO3-N in water (Field type: Numeric)</li></ul></li><li><p><b>data_one_off_field</b> (described in worksheet data_one_off_field)</p><p>Description: soil and littter property measurements</p><p>Number of fields: 12</p><p>Number of data rows: 48</p><p>Fields: </p><ul><li><b>Location</b>: location of chamber (Field type: Location)</li><li><b>chamber_id</b>: chamber ID (Field type: ID)</li><li><b>site</b>: Site ID (Field type: ID)</li><li><b>landuse</b>: land use type (Field type: Categorical)</li><li><b>pH</b>: soil pH (Field type: Numeric)</li><li><b>soil_N</b>: soil nitrogen content (Field type: Numeric)</li><li><b>soil_C</b>: soil carbon content (Field type: Numeric)</li><li><b>litter_N</b>: litter nitrogen content (Field type: Numeric)</li><li><b>litter_C</b>: litter carbon content (Field type: Numeric)</li><li><b>C_N</b>: soil C:N ratio (Field type: Numeric)</li><li><b>Latitude</b>: GPS co-ordinate that the sample was taken (Field type: Latitude)</li><li><b>Longitude</b>: GPS co-ordinate that the sample was taken (Field type: Longitude)</li></ul></li><li><p><b>data_repeated_measures</b> (described in worksheet data_repeated_measures)</p><p>Description: repeated soil measures</p><p>Number of fields: 16</p><p>Number of data rows: 336</p><p>Fields: </p><ul><li><b>chamber_id</b>: Chamber ID (Field type: ID)</li><li><b>site</b>: Site ID (Field type: ID)</li><li><b>landuse</b>: land use type (Field type: Categorical)</li><li><b>sampling_occasion</b>: date of sample collection (Field type: Date)</li><li><b>date</b>: date of sample analysis (Field type: Date)</li><li><b>time</b>: Time the measurement was taken (Field type: Time)</li><li><b>flux_CH4-C</b>: Soil CH4 flux (Field type: Numeric)</li><li><b>flux_CO2-C</b>: Soil CO2 flux (Field type: Numeric)</li><li><b>flux_N2O-N</b>: Soil N2O flux (Field type: Numeric)</li><li><b>NH4-N_H2O</b>: Soil NH4 concentration (Field type: Numeric)</li><li><b>NO3-N_H2O</b>: Soil NO3 concentration (Field type: Numeric)</li><li><b>NH4-N_KCl</b>: Soil NH4 concentration (Field type: Numeric)</li><li><b>NO3-N_KCl</b>: Soil NO3 concentration (Field type: Numeric)</li><li><b>air_temp</b>: Air temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_temp</b>: Soil temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_moisture</b>: Soil moisture around the flux chamber (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2016-11-01 to 2017-11-30</p><p><b>Latitudinal extent: </b>4.3960 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Greenhouse gas and energy fluxes in a boreal peatland forest after clearcutting
<p>This package contains the data used in the research article: "Greenhouse gas and energy fluxes in a boreal peatland forest after clearcutting" published in Biogeosciences journal.</p> <p>Changes in this version:</p> <p>Chamber_data.xlsx is now named Chamber_data_clearcut.xlsx. CO2 fluxes were also corrected.</p> <p>Added daily mean CO2, CH4 and N2O fluxes measured at the control site.</p> <p> </p> <p>Chamber_data_clearcut.xlsx contains the daily mean fluxes of CO2, CH4 and N2O measured with soil chambers at the clearcut site.</p> <p>Chamber_data_control.xlsx contains the daily mean fluxes of CO2, CH4 and N2O measured with soil chambers at the control site.</p> <p>EC_CO2_fluxes.xlsx contains the gapfilled 30-min mean CO2 fluxes (NEE) and its components (GPP and respiration).</p> <p>Energy_fluxes.xlsx contains the gapfilled hourly mean energy fluxes.</p> <p>Meteo_data.xlsx contains the daily means of the meteorological variables used in the study.</p>
Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa
<p><strong>Dataset Name:</strong><br><em>Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa.</em></p> <p><strong>Description:</strong><br>This dataset includes Excel sheets containing literature sources and archetypal data on Western Asian and North African countries' residential dwelling typologies. As well as Vacancy rates used and simulation results.</p> <p><strong>Files:</strong><br>The following files are included in the dataset:</p> <ul> <li> <em>[CountryName]_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> <em>[CountryName]_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li><em> VacantHouses.xlsx</em>: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> <li> <em>Resource Use Results:</em> BuildME Simulation Results</li> </ul> <p><strong>Usage:</strong><br>The dataset is intended for researching and analyzing the Western Asian and North African countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br>The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br>If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Akin, Sahin, Aida Eghbali, Chibuikem Chrysogonus Nwagwu, and Edgar Hertwich. 2024. “Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa” https://doi.org/10.5281/zenodo.13380340.</p> <p><strong>Contact:</strong><br>The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact <strong>sahin.akin@ntnu.no, the corresponding author.</strong></p>
Data from a controlled drought experiment in greenhouse conditions on saplings of Prunus spinosa
<p>Dataset with measurements and observations on saplings of Prunus spinosa in a common garden setting (three European populations are included), that were treated with water withholding in the summer of 2021 in greenhouse conditions.</p> <p>One dataset contains the data on all 274 saplings that are included in the experiment. The second dataset contains measurements and counts of stomata on a subset of 57 saplings. The two datasets can be connected by the plant ID.</p>
Global greenhouse gas reconciliation 2022
<p>In this study, we provide an update of the methodology and data used by Deng et al. <a href="https://paperpile.com/c/eukncF/EENwC/?noauthor=1">(2022)</a> in order to compare the latest national greenhouse gas inventories (NGHGIs) and atmospheric inversion models ensembles contributed by international research teams coordinated by the Global Carbon Project to support the first global stocktake of the Paris Agreement. The comparison framework uses transparent processing of the net ecosystem exchange fluxes of carbon dioxide (CO2) from inversions to provide estimates of terrestrial carbon stock changes over managed land that can be used to evaluate NGHGIs. For methane (CH4), and nitrous oxide (N2O), we separate anthropogenic emissions from natural ones from original inversion results, to make them comparable with NGHGIs. Our global harmonized NGHGIs database was updated for inventory data until Feburary 2023 by compiling data from periodical UNFCCC inventories by Annex I countries and sporadic and less detailed emissions reports by non-Annex I countries given by National Communications and Biennial Update Reports. For the inversion data, we updated results from an ensemble of 22 global inversions produced for the most recent assessments of the global budgets of CO2, CH4 and N2O coordinated by the Global Carbon Project with ancillary data. The CO2 inversion ensemble in this study goes through 2021, building on our previous report from 1990 to 2019, and includes three new satellite inversions compared to the previous study, and an improved managed land mask. As a result, although significant differences exist between the CO2 inversion estimates, both satellite and in-situ inversions over managed lands indicate that Russia and Canada had a larger land carbon sink in recent years than reported in its NGHGI, while the NGHGIs reported a significant upward trend of carbon sink in Russia but a downward trend in Canada. For CH4 and N2O, the results of the new inversion ensemble are extended to 2020. Good correlation was found between the in-situ and satellite inversions for CH4. Much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites, coordinated into a global constellation, is expected in the coming years. The methodology proposed here to compare inversion results with NGHGIs can be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges.</p>
A greenhouse experiment partially supports inferences of ecogeographic isolation from niche models of Clarkia sister species
<p><b>Premise: </b>Ecogeographic isolation, or geographic isolation caused by ecological divergence, is thought to be of primary importance in speciation, yet is difficult to demonstrate and quantify. To determine whether distributions are limited by divergent adaptation or historical contingency, the gold standard is to reciprocally transplant taxa between their geographic ranges. Alternatively, ecogeographic isolation is inferred from species distribution models and niche divergence tests based on widely available environmental and occurrence data.</p> <p><b>Methods: </b>We test for ecogeographic isolation between two sister species of California annual wildflowers, <i>Clarkia concinna</i> and <i>C. breweri</i>, with a hybrid approach. We use niche models to predict water availability as the major axis of ecological divergence and then test that with a greenhouse experiment. Specifically, we manipulate water availability in field soils for two populations of each species and predict higher fitness in conditions representing home habitats to those representing the environment of each's sister species.</p> <p><b>Key Results: </b>Water availability and soil representing <i>C. concinna</i> generally increased both species' fitness. Thus, water and soil may indeed limit <i>C. concinna</i> from colonizing the range of C. breweri, but not vice versa. We suggest that the competitive environment and pollinator availability, which are not directly captured with either approach, may be key biotic factors correlated with climate that contribute to unexplained ecogeographic isolation for <i>C. breweri</i>.</p> <p><b>Conclusions:</b> Ours is a valuable approach to assessing ecogeographic isolation, in that it balances feasibility with model validation, and our results have implications for species distribution modeling efforts geared towards predicting climate change responses.</p>
China's agricultural greenhouse gas emission intensity and its influencing factors
<p>This dataset includes the agricultural greenhouse gas emissions intensity of China and various factors that affect agricultural greenhouse gas emissions (agricultural patent intensity, agricultural per capita value added, urbanization rate, environmental investment intensity, and urban rural income gap.</p>
FIGURE 5 in A new species in the genus Phyllocoptes Nalepa (Eriophyidae) from greenhouse roses in Poland
FIGURE 5: Symptoms caused by Phyllocoptes resovius n. sp., on Rosa hybrida 'Whisky Mac' under greenhouse conditions: A – mosaic-red discolorations of young leaves; B – deformation and discoloration of newly developed shoot; C – deformation of fully developed leaf.
FIGURE 2 in A new species in the genus Phyllocoptes Nalepa (Eriophyidae) from greenhouse roses in Poland
FIGURE 2: Phyllocoptes resovius n. sp. female: LO – lateral opisthosoma; CG – coxo-genital region; em – empodium (enlarged); PL – postero-lateral habitus; IG – internal genitalia (enlarged); male: GM – genital region.
FIGURE 1 in A new species in the genus Phyllocoptes Nalepa (Eriophyidae) from greenhouse roses in Poland
FIGURE 1: Phyllocoptes resovius n. sp. female: D – dorsal habitus; L1, L2 – legs; immature: DL – dorsal larva; DN – dorsal nymph..
FIGURE 4 in A new species in the genus Phyllocoptes Nalepa (Eriophyidae) from greenhouse roses in Poland
FIGURE 4: SEM photograph of Phyllocoptes resovius n. sp. – prodorsal shield and anterior opisthosoma.
FIGURE 5 in Effects of six greenhouse cucumber cultivars on reproductive performance and life expectancy of Tetranychus turkestani (Acari: Tetranychidae)
FIGURE 5: Ward's dendrogram of six greenhouse cucumber cultivars based on reproductive parameters of Tetranychus turkestani on six greenhouses cucumber cultivars. A- favorite host plant for the reproduction of the strawberry spider mite, B- partially unpleasant group for the reproduction of this mite, B1- comparatively semi-resistant group and B2 - partly more resistant compared to cultivars in the B1 cluster.
ScienceDex guides
Understand access before you commit
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.