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407 results for “greenhouse”
Greenhouse Gas and Water Chemistry Data from Ponds in the Twin-Cities area of Minnesota, 2021
Freshwaters are significant contributors of greenhouse gases to the atmosphere, including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Small waterbodies such as ponds are now recognized to have disproportionate greenhouse gas emissions relative to their size, but recorded emissions from ponds have varied by several orders of magnitude. To assess drivers of variation in pond greenhouse gas dynamics, this study measured concentrations and emissions of CO2, CH4, and N2O across 26 ponds in Minnesota, USA during the ice-free season. The studied ponds ranged in land-use, from urban stormwater ponds to natural forested ponds. Water chemistry variables were measured with sonde profiles as well as surface water samples. Greenhouse gas emsisions of CO2 and CH4 were measured with a floating chamber at three locations on each pond, and gas concentrations of CO2, CH4, and N2O were measured using a headspace equilibrium technique in both the surface waters and bottom waters of each pond.
Greenhouse gas data of permafrost-affected reservoirs on the Qinghai-Tibet Plateau, 2016-2018
This dataset is a collation of 3-year direct measurement values of CO2, CH4 and N2O concentrations and fluxes of permafrost-affected reservoirs on the Qinghai-Tibet Plateau, along with information on location and chemical conditions of the study sites. Given the rarity and high value of this data set, it will be very valuable for the next update to global reservoir GHG flux estimates.
Data and code from: A mixture of grass-legume cover crop species may ameliorate water stress in a changing climate, a greenhouse experiment at Dickinson College in Carlisle, PA, USA, 2021.
Data and R code associated with a greenhouse study investigating the influence of water stress on growth, root traits, and biomass of rye and crimson clover seedlings grown separately or together. Data were collected in the Dr. Inge P. Stafford Greenhouse of Dickinson College (Carlisle PA, USA) in June 2021.
Dataset for: Common carp (Cyprinus carpio) invasion alters greenhouse gas emissions in shallow lakes.
Climate change and invasive species are among the most important environmental problems of this century. Freshwaters are important regulators of the global carbon cycle and a key source of atmospheric greenhouse gases. However, freshwater environments may be particularly susceptible to species invasion and adverse effects, and the consequences of altered species assemblages on greenhouse gas emissions remain poorly understood. In this study, we analyzed the impact of one of the world's most damaging invasive species, the common carp, on freshwater greenhouse gas emissions. We show that lakes with invasive carp had lower methane emissions despite increased eutrophication, contradicting the well-established assumption that methane emissions from lakes increase with nutrient levels and productivity. This is likely due to substantial depletion of the benthic environment. As invasive species spread continues, new species assemblages may therefore disrupt ecosystem functioning and diverse global cycles in unexpected ways.
Belowground foodweb biomass from moist acidic tundra nutrient addition and greenhouse plots (since 1989) sampled July 2008.
Biomass of belowground community groups (bacteria, fungi, protozoa, nematodes, rotifers, tardigrades) determined for organic and mineral soils in moist acidic tundra sampled in the moist acidic tundra nutient (N&P) addition and greenhouse plots in July 2008.
European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data
<p>The files uploaded under this doi number represent the updated data sets used in the manuscript submitted to ESSDD in its revised version entitled: "European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data" Excel files including the data behind all the manuscript figures are available for download only for review purposes. We added, as sugggested by the referees, metadata belonging to UNFCCC 2018, FAOSTAT, EDGAR v4.3.2, CAPRI and CBM.</p>
Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004
<p>Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)<sup>-1</sup> yr<sup>-1</sup>] or [g N (kg product)<sup>-1</sup> yr<sup>-1</sup>] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. </p> <p>Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector</p>
Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system
<p><span>Earthworms are known to stimulate soil greenhouse gas (GHG) emissions, but the majority of previous studies have used simplified model systems or lacked continuous high-frequency measurements. To address this, we conducted a two-year study using large lysimeters (</span><span>5 m<sup>2</sup> area and 1.5 m soil depth) </span><span>in an ecotron facility, continuously measuring ecosystem-level CO<sub>2</sub>, N<sub>2</sub>O, and H<sub>2</sub>O fluxes. We investigated the impact of endogeic and anecic earthworms on GHG emissions and ecosystem water use efficiency (WUE) in a simulated agricultural setting. Although we observed transient stimulations of carbon fluxes in the presence of earthworms, cumulative fluxes over the study indicated no significant increase in CO<sub>2</sub> emissions. Endogeic earthworms reduced N<sub>2</sub>O emissions during the wheat culture (-44.6%), but this effect was not sustained throughout the experiment. No consistent effects on ecosystem evapotranspiration or WUE were found. Our study suggests that earthworms do not significantly contribute to GHG emissions over a two-year period in experimental conditions that mimic an agricultural setting. These findings highlight the need for realistic experiments and continuous GHG measurements.</span></p>
Figure 1. A in Evaluate the efficiency of releasing two predatory species at their optimal temperature for controlling Tetranychus urticae (Acari: Tetranychidae) in a croton greenhouse
Figure 1. A timeline representing the release of predatory species P. persimilis and S. punctillum to control the twospotted mite T. urticae in a croton greenhouse.
Figure 3 in Evaluate the efficiency of releasing two predatory species at their optimal temperature for controlling Tetranychus urticae (Acari: Tetranychidae) in a croton greenhouse
Figure 3. The comparison between the population of T. urticae in predators' greenhouse and control greenhouse.
Greenhouse gas dynamics in river networks fragmented by drying and damming
<p>River fragmentation by drying and damming is occurring more frequently in the Anthropocene, yet there is limited research about its effects on greenhouse gas (GHG) fluxes. These fragmented rivers have the potential to be important sources of GHGs to the atmosphere through both similar and dissimilar mechanisms. Our objective was to review the literature of the individual and the interactive effects of fragmentation by drying and damming on GHG fluxes in river networks, identifying the magnitudes and drivers of CO<sub>2</sub>, methane (CH<sub>4</sub>), and N<sub>2</sub>O flux rates. We conducted a systematic search of studies addressing the separate and interactive effects of drying and damming on GHG fluxes from running waters. The search was primarily conducted using Web of Science for studies published from 1900 to January 2021. For the 42 studies about rivers impacted by drying, 54 about damming, and 6 about their interactive effects, we collected a suite of qualitative and quantitative information. The major proximal drivers of GHG emissions in river networks impacted by drying were sediment moisture, sediment temperature, sediment organic matter content and sediment texture. In networks impacted by damming, the major proximal drivers were water temperature, dissolved oxygen, and chlorophyll ɑ. We found research lacking in non-arid climates for drying, and on small water retention structures for damming. We propose a conceptual model where the spatial distribution of fragmentation is the principle driver of GHG fluxes at the network scale. </p>
Data from: Promoting success in thin layer sediment placement: effects of sediment grain size and amendments on salt marsh plant growth and greenhouse gas exchange
<p>Thin layer sediment placement (TLP) is a method to mitigate factors resulting in loss of elevation and severe alteration of hydrology, such as sea level rise and anthropogenic modifications, and prolong the lifespan of drowning salt marshes. However, TLP success may vary due to plant stress associated with reductions in nutrient availability and hydrologic flushing or through the creation of acid sulfate soils. This study examined the influence of sediment grain size and soil amendments on plant growth, soil and porewater characteristics, and greenhouse gas exchange for three key US salt marsh plants: <em>Spartina alterniflora, Spartina patens, </em>and <em>Salicornia pacifica. </em>We found that bioavailable nitrogen concentrations (measured as extractable NH<sub>4</sub><sup>+</sup>-N) and porewater pH and salinity were found to have an inverse relationship with grain size, while soil redox was more reducing in finer sediments. This suggests that utilizing finer sediments in TLP projects will result in a more reduced environment with higher nutrient availability, while larger grain-sized sediments will be better flushed and oxidized. We further found that grain size had a significant effect on vegetation biomass allocation and rates of gas exchange, although these effects were species-specific. We found that soil amendments (biochar and compost) did not subsidize plant growth but were associated with increases in soil respiration and methane emissions. Biochar amendments were additionally ineffective in ameliorating acid sulfate conditions. This study uncovers complex interactions between sediment type and vegetation, emphasizing limitations of soil amendments. The findings aid restoration project managers in making informed decisions regarding sediment type, target vegetation, and soil amendments for successful TLP projects.</p>
Quick scan 'Locations for highest-potential greenhouse development in the world'
<p><br>Forecast studies show an increasing demand for greenhouses worldwide, as governments encourage local, safe and sustainable food production. Climate change, scarcity of water and other key resources are adding to the trend towards greenhouses.</p> <p>This project shows a world map of the highest suitability for greenhouses, broken down by mid-tech and high-tech greenhouses. This is done by performing a quick scan, which means synthesis and application of existing knowledge and data. Of the countries with the highest potential, more detailed maps are shown.</p> <p>This paper is commissioned by the Netherlands Enterprise Agency (Rijksdienst voor Ondernemend Nederland (RVO)) and funded by the European Community. Dutch Green Delta (DGD) and some of their partners contributed with their expertise and experiential knowledge.</p> <p>The total area of covered crops is very difficult to indicate because there are no clear definitions and hence no uniform data. This study estimates approximately 700,000 hectares of protected horticulture worldwide, of which approximately 53,000 hectares are high-tech greenhouses. This is in line with other literature sources. China provides the greatest uncertainty in data. </p> <p>Based on the analysis of area suitability, USA is the country with the highest relative score for high-tech, followed by France, Germany, UK and Ukraine. For mid-tech, the USA and France are also the countries with the highest relative score, followed by India, Libya, and Brazil.<br>Based on the highest market opportunities for greenhouses explored for the production and sales of tomato, the top 5 countries are Germany, the Netherlands, France, the USA, and Spain.<br>Based on the presence of existing greenhouses, Mexico is the country with the highest surface for high-tech, followed by the Netherlands, Turkey, Belgium and Germany. For mid-tech surfaces the top 5 countries are China, Turkey, Spain, Republic of Korea, and Egypt.</p> <p>Based on the combination of the 3 analyses above, the top 10 countries with the strongest expected growth in high-tech greenhouses are: the USA, France, Spain, Germany, Poland, the Netherlands, Italy, Japan, Turkey, and China. When we differentiate these countries in 3 categories we identify:<br>• Emerging countries: the USA, Poland, Italy, Saudi Arabia, and the UK.<br>• Conversion countries from mid-tech to high-tech greenhouses: Spain, France, China, Japan, India, and South Korea.<br>• Countries that already have areas of high-tech greenhouses: Germany, the Netherlands, Turkey, Belgium, and Mexico.</p> <p>The whitepaper, PowerPoint, all maps, and PowerBI datafiles generated by this project can be downloaded below. </p>
Figure 2 in Susceptibility of the sweet pepper (Capsicum annuum L.) to the infestation of Tetranychus urticae (Acari: Tetranychidae) and the different insect pests under greenhouse conditions in Ismailia, Egypt
Figure 2. The interaction effects of seasons and cultivars on the Chl., Car., total protein and phenol contents (A) and the activity of the antioxidant enzymes (B) of the two sweet pepper cultivars during the two growing seasons 2021–22.
Figure 1 in Susceptibility of the sweet pepper (Capsicum annuum L.) to the infestation of Tetranychus urticae (Acari: Tetranychidae) and the different insect pests under greenhouse conditions in Ismailia, Egypt
Figure 1. Monthly abundance of total TSSM (A), associated insect pest (B), and predator (C) numbers on the two sweet pepper cultivars during the two growing seasons 2021–22.
Structural breaks in greenhouse gas emissions for OECD countries in 1995-2022 and 37 sectors
<p>The quantitative assessment of policy instruments aimed at climate change mitigation requires the rigorous identification of abnormal changes in greenhouse gas emissions. We present a new dataset of robust level changes in greenhouse gas emissions that cannot be explained by aggregate socioeconomic fluctuations. Modern methods of structural break identification based on two-way fixed effects models are employed to estimate the size of significant level changes in emissions. The resulting dataset spans information for all OECD countries and all 37 IPCC sectors, ranging from 1995 to 2022. The data unveils large differences in abnormal changes in emissions across gases, countries and sectors, as well as over time. Our resulting data can be applied to a broad range of research questions, including the analysis of the comparative efficacy of policy instruments to mitigate climate change.</p>
Source data to create the figures of the study "Rising greenhouse gas emissions embodied in the global bioeconomy supply chain" using REX3 with new GHG extension including LULUCF
<p>This repository contains the source data to create the figures of the study <a href="https://doi.org/10.1038/s43247-025-02144-0">Rising greenhouse gas emissions embodied in the global bioeconomy supply chain</a> published in <em>Communications Earth & Environment</em>. The results were calculated with the REX3 database in Version 3.2 of this repository and the GHG extension and matlab codes in Version 3.4 of this repository.</p> <p>Figure 1, and 3–5 were created in Rstudio with the attached Rcode <em>Bioeconomy_GHG_sankeys.R</em></p> <p>Figure 2 was created in tableau with an <a href="https://public.tableau.com/app/profile/livia.cabernard/vizzes">interactive data visualizer</a> that allows to zoom into the global bioeconomy supply chain.</p>
Joint Communication and Sensing: a Proof of Concept and Datasets for Greenhouse Monitoring using LoRaWAN
<p>The goal of these LoRaWAN based greenhouse monitoring datasets, is to provide the global research community with a benchmark tool to evaluate different techniques for precision agriculture in large greenhouse environments. An identical collection methodology was used for both of the two datasets over the same tomato crop: during a period of five months, respectively. Together with temperature and humidity values, network information such as receiving time of the message and Received Signal Strength Indicator (RSSI) were stored in the greenhouse monitoring datasets:</p> <ul> <li><strong>Greenhouse-1.csv</strong> <ul> <li>Data from 27 sensors denoted as AF 16-42 with an average of 19687 LoRaWAN messages per sensor from April till August 2020, obtained in the greenhouse for tomato crop in Belgium.</li> </ul> </li> <li><strong>Greenhouse-2.csv</strong> <ul> <li>Data from 19 sensors denoted as AF 49-67 with an average of 19009 LoRaWAN messages per sensor from July till November 2020, obtained in the other greenhouse for tomato crop in the Netherlands.</li> </ul> </li> <li><strong>Greenhouse-1-Transformed-Data.csv</strong> <ul> <li>Mean temperature, humidity, and RSSI values along with plant height for the same period.</li> </ul> </li> </ul> <p>Both the greenhouses, had no LoRaWAN connectivity, so individual gateway were installed for both locations. For Greenhouse-1 data, sensors were switched on in a room on 10<sup>th</sup> of April and brought to the greenhouse chamber on 17<sup>th</sup> April at 06:38 am for sensing. It would be crucial to accordingly use data set, considering the above time period.</p> <p>The collection methodology of datasets, and first results of a joint communication and sensing proof-of-concept are documented in the journal paper : https://www.mdpi.com/1424-8220/22/4/1326.</p> <p> </p>
Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
<p><strong>Description</strong></p> <p>This dataset contains remote sensing data from the ESA Copernicus missions Sentinel-2 and Sentinel-5P (tropsopheric NO2 column density) in the 2018-2020 timespan. The satellite measurements each cover ~3100 locations in Europe and ~100 on the US Westcoast, each with a size of 1.2x1.2km. The locations are selected such that each measurement is centered at the location of an air quality measurement station on the ground (from the European Environment Agency or the US Environmental Protection Agency, measuring NO2). This makes it possible to analyze spatiotemporally aligned remote sensing and ground-based measurements.</p> <p> The 13 Sentinel-2 bands are upsampled (bilinear) to 10m resolution and cropped to 120x120 pixel. For some locations multiple Sentinel-2 images are available. The images are stored as binary numpy `.npy` files organized into directories based on their locations. </p> <p>The Sentinel-5P data was pre-processed by mapping the measurements from consecutive satellite overpasses onto a common rectangular grid of 0.05×0.05◦(∼5×5km) across Europe. To harmonize the Sentinel-2 (10m to 60m, upscaled to 10m) and Sentinel-5P (5×3.5km, rescaled to 5×5km) imaging resolutions, the Sentinel-5P data is linearly interpolated to 10m resolution and cropped to 120×120 pixel around the locations of interest. Additionally, all measurements with a QA flag (qa_value) below 75 were discarded, following ESA recommendations. The Sentinel-5P data are stored as `.netcdf` file, organized by location. For each location, three such files are available, containing averaged Sentinel-5P measurements at different temporal frequencies (2018-2020, quarterly, monthly).</p> <p>The <p>samples_{frequency}_{area}.csv</p> files provide a list of observations with the corresponding file paths to a (cloud-free) Sentinel-2 image, the Sentinel-5P measurement, and the average NO2 concentration measurement by the EEA or EPA ground station. These files can be used for easy data-loading.</p> <p><strong>Content</strong></p> <p>The data is organized into the following files:</p> <ul> <li>README.md - this file</li> <li>sentinel-2-eea.tar.gz [33.1GB]</li> <li>sentinel-5p-eea.tar.gz [80.1GB]</li> <li>samples_2018_2020_eea.csv </li> <li>samples_quarterly_eea.csv</li> <li>samples_monthly_eea.csv</li> <li>sentinel-2-epa.tar.gz [0.15GB]</li> <li>sentinel-5p-epa.tar.gz [1.8GB]</li> <li>samples_2018_2020_epa.csv</li> <li>samples_quarterly_epa.csv</li> <li>samples_monthly_epa.csv</li> </ul> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p><em>Scheibenreif, L., Mommert, M., Borth, D., "</em>Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space<em>", Tackling Climate Change with Machine Learning workshop at ICML 2021.</em></p> <p>Please refer to this publication for additional information on the data set.</p> <p>This data set contains modified Copernicus Sentinel data acquired in 2018-2020, processed by ESA.</p> <p> </p> <p><strong>Responsible Author</strong></p> <p>Linus Scheibenreif<br> University of St. Gallen, Institute of Computer Science<br> Chair Artificial Intelligence and Machine Learning<br> linus.scheibenreif ( at ) unisg.ch</p>
Microbial iron(III) reduction during palsa collapse promotes greenhouse gas emissions before complete permafrost thaw
<p>Data associated with publication "Microbial iron(III) reduction during palsa collapse promotes greenhouse gas emissions before complete permafrost thaw". The data contained within this data set is arranged according to the main text and the supplementary information of this publication.</p> <p><strong>Background information</strong></p> <p>Field site: Stordalen mire, Abisko, Sweden (68 22ʹ N, 19 03ʹ E)</p> <p>Thaw stages: Palsa, bog and fen</p> <p>Type of samples: Gas samples, porewater samples, soil core samples</p>
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.