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407 results for “greenhouse”
Emissions Database for Global Atmospheric Research, version v4.3.2 part I Greenhouse gases
<p>The Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2, partim Greenhouse gases compiles anthropogenic emissions data for CO2, CH4 and N2O based on international statistics and emission factors. The version v4.3.2 of the EDGAR emission inventory provides global estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the year of EU’s first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol (KP)). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emission sources of the multiple gases include all human activities except the land-use, land-use change and forestry sector and are compiled following a bottom-up and IPCC-compliant approach. The dataset provides in addition to the complete timeseries 1970-2012 also annual and global gridmaps of 0.1 degree by 0.1 degree resolution for each source-sector and each year. For 2010 also 12 monthly gridmaps per source-sector are provided.</p>
Greenhouse gas profiles from the 2021 HEMERA-TWIN balloon launch
<p>The dataset contains mixing ratio observations of o long-lived greenhouse gase carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and (SF6) from the HEMERA TWIN gonodola launch from Kiruna (Sweden) on 12. August 2021. Profile data for CO2 and CH4 from AirCore sampling and CH4 data from the PICO IR laser spectrometer cover altitudes from the PBL to 32km. 14 cryogenic air samples collected between 14 and 31 km altitude have been analysed for all four gases.</p>
Greenhouse Game Study Data
<p>Anonymous pre and post-student survey data on learning statistics by playing the Greenhouse game; available at https://dataspace.sites.grinnell.edu/greenhouse1.html. The data dictionary and the code used to clean and anonymize the data are also available. </p>
IPCC Climate Zones (from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories)
<p><strong>Description</strong></p> <p>These data (re)create spatial data for the 2019 IPCC Climate Zones, shown in <em>Figure 3A.5.1</em> of <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch03_Land%20Representation.pdf">Chapter 3: Consistent Representation of Lands</a> in <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html">Volume 4: Agriculture, Forestry and Other Land Use</a> of the <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html">2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories</a>. I recreated these data because I could not readily identify the data in a spatial format online, a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p>Resolution: 0.5 arc degree</p> <p>CRS: lon/lat WGS 84</p> <p><strong>If you use these data please ensure you also cite the IPCC</strong> - Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland.</p> <p> </p> <p><strong>Methods</strong></p> <p>The data were derived using the classification scheme shown in <em>Figure 3A.5.2</em> based on the gridded Climate Research Unit (CRU) Time Series (TS) monthly climate data (<a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.3711">Harris et al., 2014</a>) for the period from 1985 to 2015 following the methods described in <em>Annex 3A.5 Default climate and soil classifications </em>of the above Chapter. All data were processed in <em>R</em> version 4.2.1, with the packages <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> (v0.4.2), <a href="https://cran.r-project.org/web/packages/lubridate/index.html"><em>lubridate</em></a> (v1.8.0), <a href="https://cran.r-project.org/web/packages/magrittr/index.html"><em>magrittr</em></a> (v2.0.3), and <a href="https://cran.r-project.org/web/packages/terra/index.html"><em>terra</em></a> (v1.6-7)<em> </em>attached. The full session info is included as a <em>.txt</em> file. As these methods are not exhaustively described in the Annex, the following assumptions were made:</p> <ul> <li><a href="http://http://dx.doi.org/10.5285/c311c7948e8a47b299f8f9c7ae6cb9af">CRU TS3.25</a> was used as the most recently published data (published on 2017-09-22) that could have been incorporated into the Refinement. Other possibilities include CRU TS3.24 (which are the first data to include 2015), or CRU TS4.00 or CRU TS4.01 (both of which were published in parallel to 3.24 and 3.25). These data were all investigated, and CRU TS3.25 produced results that were the most visually similar to the published <em>Figure 3A.5.1</em> (though non-identical).</li> <li>As the methods did not mention a preferred elevation data source, the <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> R package was used to obtain data at zoom level 2 (approx resolution of 0.15 arc degree), that was then resampled to match the 0.5-degree resolution of the CRU data. These data originally come from the <a href="https://www.ngdc.noaa.gov/mgg/global/global.html">ETOPO1 global relief model</a>.</li> </ul> <p> </p> <p><strong>Known discrepancies</strong></p> <ul> <li>The distribution of Tropical Wet and Tropical Moist in South America does not exactly match the original data.</li> <li>There are small discrepancies in Tropical Montane classifications (likely arising from the use of a different elevation layer). These are most noticeable in, but not restricted to, Africa.</li> <li>The classification of Boreal Dry, Polar Dry, and Polar Moist in northern Russia and (to a lesser extent) in northern Canada does not exactly match the original data.</li> <li>There are a small number of Cool Temperate Dry pixels in the UK, and Warm Temperate Dry pixels around Brittany which do not occur in the original data.</li> </ul> <p> </p> <p><strong>Disclaimer</strong></p> <p><strong>I am not affiliated with the IPCC in any way</strong>, I just needed spatial data of the Climate Zones, and could not readily identify any online. This is a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p> </p> <p><strong>File description</strong></p> <ul> <li><em>README.html</em> - ~this description file.</li> <li><em>IPCC_Climate_Zones_ts_3.25.tif</em> - the output Climate Zones map at 0.5-arc degree resolution based on the CRU TS3.25 data.</li> <li><em>IPCC_Climate_Zones_colour_map.clr </em>- a colour map file to render the output map with the same colours as in the IPCC 2019 Refinement figure.</li> <li><em>IPCC_Climate_Zones_ts_3.25.png</em> - an image file of the output Climate Zones map.</li> <li><em>ipcc_climate_zones_2019.R</em> - the script used to produce these data.</li> <li><em>session_info.txt</em> - the R session info.</li> </ul>
Datasets for greenhouse gasses emissions and removals from inventories and global models over Africa
<p>This file includes the data from Mostefaoui et al. (ESSD, under submission), for 54 countries African countries</p> <p> The data includes: </p> <p>(1) CO2 fluxes from global models - satellite inversions and Dynamic Global Vegetation Models (DGVM) -, and from a collection of national inventories for LULUCF, GFEDv4 and FAO data.</p> <p> DGVM values are the median of 14 models, consistent with the Global Carbon Budget 2020 (https://essd.copernicus.org/articles/12/3269/2020/) LULUCF UNFCCC corrected values are from Grassi <a href="https://priv-bx-myremote.tech.ec.europa.eu/preprints/essd-2022-104/,DanaInfo=.aetugDhuwm0xto76O48y,SSL+">https://essd.copernicus.org/preprints/essd-2022-104/</a> </p> <p>(2) CH4 fluxes from global models consistent with the Global Methane Budget 2020 (https://essd.copernicus.org/articles/12/1561/2020/)</p> <p>(3 N2O fluxes from global models (three inversions)</p> <p>For further methodological details, see Mostefaoui et al. (ESSD, under submission):</p> <p>Mounia Mostefaoui, Philippe Ciais, Matthew J. McGrath, Philippe Peylin, Prabir Patra. Greenhouse gasses emissions and their trends over the last three decades across Africa, ESSD (under submission)</p>
Seedling emergence and biomass data of nine dryland plant species characterizing the impact of soil residual auxin herbicide across two soil types and water pulse events on greenhouse growth; Las Cruces, New Mexico, Spring 2021.
Synthetic-auxin herbicides are often used to control woody plants and aid in grassland restoration. Seed-based restoration is common alongside herbicide applications and there may be unintended effects of these herbicides on dryland plant species at the seed and seedling stages. Additionally, abiotic conditions at the time of herbicide application may influence herbicide-soil-plant interactions. We conducted a greenhouse study to examine the effects of a common shrub-control herbicide mix and its interaction with soil type and a post-herbicide water pulse on common desert plant seeds and seedlings. In this greenhouse study, we found that a subset of species responded negatively to soil residual herbicide activity of a mixture of aminopyralid, clopyralid, and triclopyr at the seed and seedling stages. Species sensitive to soil herbicide residues were primarily shrub and forb species that are often the target species of herbicide applications for woody plant control, such as Prosopis glandulosa (honey mesquite) and Larrea tridentata (creosote bush). However, two shrub species (Atriplex canescens [four-wing saltbush] and Yucca elata [soaptree yucca]) and one perennial grass species (Digitaria californica [Arizona cottontop]), which are used in dryland restoration projects, were found to be particularly sensitive to soil residual herbicide activity. Thus, if using these herbicides to control woody plants and restore herbaceous vegetation via active seeding or relying on the in situ seed bank, considerations should be given to what species are used in the seed mix, what species are already present in the soil seed bank, and other details of the circumstances of herbicide application.
Greenhouse gas fluxes and concentrations and associated habitat data in western Dane County, Wisconsin, USA, streams during the 2018 growing season
Streams are often sources of carbon dioxide (CO2) and methane (CH4), particularly in agricultural regions where sediment and organic matter inputs can be substantial. Floods are occurring more often and more intensely in southern Wisconsin, one such agricultural region, due to climate change and few studies have investigated how floods impact stream CO2 and CH4 fluxes and concentrations. I compared concentrations and fluxes of CO2 and CH4 with greater than 30 variables representing in-stream and watershed attributes at 10 sites in mixed agricultural and suburban locations in southern Wisconsin. Sampling was conducted 10 times at each site during the growing season (May-November) in 2018
2015 Drought soil biogeochemistry and greenhouse gas emissions study at El Verde
We report the effects of the severe 2015 Caribbean drought on soil moisture, oxygen (O2), temperature, phosphorus (P), iron (Fe), pH, and GHG emissions (CO2 and CH4) across a catena sensor array field outside of El Verde Research Station, Luquillo LTER, Puerto Rico. Seven sensors of each type were installed at 12 cm depth along a ridge to valley catena; the entire catena transect was replicated five times for a total of 105 sensors. Within the sensor field we also installed nine automated gas flux chambers randomly located in each topographic zone (ridge, slope and valley). Soil carbon and nitrogen, extractable phosphorus (P) pools, iron (Fe) species, and pH were sampled before and during the drought as indicators of biogeochemical conditions. 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 the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Greenhouse gas and water chemistry data from urban ponds in Madison, Wisconsin during the summer and under-ice period of 2021-2022
Stormwater ponds are common features in urbanized landscapes and can suffer from rapid oxygen depletion when thermally stratified or ice-covered. To investigate under-ice oxygen dynamics and drivers of bottom water oxygen saturation, we sampled 20 stormwater ponds in Madison, Wisconsin, USA during the summer of 2021 and winter 2022. The urban ponds ranged in age, shape, size, and depth. We repeatedly took YSI profiles of water temperature, oxygen, and specific conductance 7 times in the summer and 3 times in the winter. Water chemistry variables were collected in the surface waters, habitat surveys were conducted in the summer, and ice/snow thickness was recorded in the winter. We also measured the concentration of greenhouse gases in the surface waters as a consequence to oxygen depletion using the headspace equilibrium method.
Electron shuttling capacity and greenhouse gas production of soils for three high-elevation wetlands at Niwot Ridge, 2024.
High-elevation wetlands are important indicators of how mountain ecosystems may respond to global climate change. These wetlands also act as locations of disproportionate biogeochemical processing on the landscape, but they remain relatively understudied compared to lowland wetlands. This study aimed to characterize redox-active organic matter (RAOM) reduction, a known key control on carbon cycling in high-latitude peatland ecosystems, to better understand biogeochemical cycling in high elevation wetlands and carbon greenhouse gas production at Niwot Ridge LTER. Soils were collected from three different types of wetlands, a subalpine wetland, a periglacial solifluction lobe, and an alpine wet meadow. Samples were incubated at a common temperature in the laboratory to measure RAOM reduction, carbon dioxide production, and methane production over 63-d. This dataset reports the electron shuttling values, a measure of RAOM reduction, and the greenhouse gas production over the incubation period.
Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI)
<h2><strong>About</strong></h2><p>The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded methane GHGI) includes spatially and temporally resolved (gridded) maps of annual anthropogenic methane emissions (0.1°×0.1°) for the contiguous United States (CONUS). Total gridded methane emissions for each emission source sector are consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA <a href="https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks"><i>Inventory of U.S. Greenhouse Gas Emissions and Sinks</i></a> (U.S. GHGI). More information is available on the <a href="https://www.epa.gov/ghgemissions/gridded-methane-emissions">U.S. EPA website</a>. </p><p>This repository accompanies the peer-reviewed manuscript <a href="https://pubs.acs.org/doi/10.1021/acs.est.3c05138"><i>Maasakkers, et al., 2023</i></a>. Data in this repository are an update to the gridded GHGI version 1, previously described in <a href="https://pubs.acs.org/doi/10.1021/acs.est.6b02878"><i>Maasakkers, et al., 2016</i></a> and available on the <a href="https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions">U.S. EPA website</a>. </p><h4><strong>This repository contains two data products:</strong></h4><ol><li><strong>Gridded GHGI v2 (main product; 2 file types). </strong>Gridded annual U.S. anthropogenic methane emissions for 2012-2018 for 26 source categories (gridded GHGI). This dataset is developed to be consistent with the national U.S. GHGI published in 2020 (<i>U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 - 2020. U.S. Environmental Protection Agency, 2020, EPA 430-R-22-003, </i><a href="https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2018"><i>https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2018</i></a>).<br><br>This dataset includes 2 file types: <br>a. Annual methane emission fluxes for 26 inventory source categories. Files contain one year of emissions per source category and include a time dimension variable to make the data suitable (COARDS-compliant) for atmospheric models.<br> (Dimensions: latitude x longitude x time; units: molecules CH4 cm-2 s-1):<br><i> - Gridded_GHGI_Methane_v2_YYYY.nc</i><br><br>b. Monthly emission scaling factors for inventory source categories with strong interannual variability (see 'Data Details' below). To use these factors to calculate absolute monthly methane emission fluxes, multiply the scaling factors for each relevant source category by the corresponding emission fluxes in the annual flux files.<br> (Dimensions: latitude x longitude x month; units: dimensionless): <br> - <i>Gridded_GHGI_Methane_v2_Monthly_Scale_Factors_YYYY.nc</i><br> </li><li><strong>Gridded GHGI v2 Express Extension (1 file type).</strong> The v2 Express Extension includes gridded annual U.S. anthropogenic methane emissions for 2012-2020 for 27 source categories (one additional source category compared to the main v2 dataset above). This dataset is developed to be consistent with total methane emissions from the U.S. GHGI published in 2022 (<i>EPA (2022) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020. U.S. Environmental Protection Agency, EPA 430-R-22-003. </i><a href="https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissionsand-sinks-1990-2020"><i>https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissionsand-sinks-1990-2020</i></a><i>)</i>. <br><br><i>**Note**:</i><strong> </strong>This dataset is <strong>not</strong> a full update to the main gridded GHGI v2 product. To quickly incorporate more recent national methane emission estimates into gridded products, national methane emissions from a more recent U.S. GHGI were spatially allocated (i.e., gridded) using the annual source-specific spatial emission patterns developed for the 2012-2018 main v2 product. Emissions for years 2019 and 2020 were allocated using 2018 spatial patterns.<br><br>This dataset includes 1 file type:<br>a. Annual emission files<br> (Dimensions: latitude x longitude x time; units: molecules CH4 cm-2 s-1):<br> - <i>Express_Extension_Gridded_GHGI_Methane_v2_YYYY.nc</i></li></ol><p><i>--------------------------------------------------</i></p>
Code to reproduce the analyses of "Multiple stressors alter greenhouse gas concentrations in streams through local and distal processes"
<p>Streams are significant contributors of greenhouse gases (GHG) to the atmosphere, and the increasing number of stressors degrading freshwaters may exacerbate this process, posing a threat to climatic stability. However, it is unclear whether the influence of multiple stressors on GHG concentrations in streams results from increases of in-situ metabolism (i.e., local processes) or from changes in upstream and terrestrial GHG production (i.e., distal processes). Here, we hypothesize that the mechanisms controlling multiple stressor effects vary between <span>carbon dioxide (</span>CO<sub>2</sub>) and <span>methane (</span>CH<sub>4</sub>), with the latter being more influenced by changes in local stream metabolism, and the former mainly responding to distal processes. To test this hypothesis, we measured stream metabolism and the concentrations of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) and CH<sub>4</sub> (<em>p</em>CH<sub>4</sub>) in 50 stream sites that encompass gradients of <span>nutrient enrichment, oxygen depletion, thermal stress, riparian degradation and discharge</span>. Our results indicate that these stressors had additive effects on stream metabolism and GHG concentrations, with stressor interactions explaining limited variance. Nutrient enrichment was associated with higher stream heterotrophy and <em>p</em>CO<sub>2</sub>, whereas <em>p</em>CH<sub>4</sub> increased with oxygen depletion and water temperature. Discharge was positively linked to primary production, respiration and heterotrophy but correlated negatively with <em>p</em>CO<sub>2.</sub> Our models indicate that CO<sub>2</sub>-equivalent concentrations can more than double in streams that experience high nutrient enrichment and oxygen depletion, as compared to those with oligotrophic and oxic conditions. Structural equation models revealed that the effects of nutrient enrichment and discharge on <em>p</em>CO<sub>2</sub> were related to distal processes rather than local metabolism. In contrast, <em>p</em>CH<sub>4</sub> responses to nutrient enrichment, discharge and temperature were related to both local metabolism and distal processes. Collectively, our study illustrates <span>potential climatic feedbacks resulting from freshwater degradation and </span>provides insight into the processes mediating stressor impacts on the production of GHG in streams.</p>
Soil greenhouse gas emissions (CO2 and N2O) data and metadata derived from H2020 Diverfarming project
<p>Soil greenhouse gas emissions (CO<sub>2</sub> and N<sub>2</sub>O) data and metadata of an almond crop diversified with <em>Thymus hyemalis </em>(diversification 1) and with<em> Capparis spinosa </em>(diversification 2). This data comes from WP5 "Environmental impact and delivery of ecosystem services by crop diversification", derived from H2020 Diverfarming project. This workpackage has been designed to provide sound and robust scientific understanding of the benefits and drawbacks of the tailored diversified cropping systems for improvement of the environmental quality and delivery of ecosystem services in each pedoclimatic region. http://www.diverfarming.eu</p>
N2O raw data from static greenhouse gas chamber measurements
<p>This dataset contains N<sub>2</sub>O concentration measurements of a 2 years measurement campaign for greenhouse gas fluxes from agricultural soils.</p> <p>The format of the data is ready to be fed into the gasfluxes R package on CRAN to calculate fluxes for each individual chamber measurement (identical IDs are referred to one single measurement, the ID contains the measurement day, treatment and replicate).</p> <p>The data is originally published in Krauss et al. 2017 and further used for improvements of the flux calculation procedure in Hüppi et a. 2018 (see references)</p>
Thomas_et_al_2023_[updated]_The_potential_of_hay_for_graminoid_introduction_in_the_restoration_of_subtropical_grasslands_results_from_a_greenhouse_experiment
<p>Data set from a greenhouse experiment where we aimed to assess the effect of three harvest dates of hay and two amounts of dry hay in the emergency of seedlings, to application in ecological restoration of Campos Sulinos grasslands, South Brazil.</p>
Greenhouse gas emissions (lifecycle) of each compared vehicle, Tesla 3 (283 HP), and Infiniti Q50 (300 HP).
<p>We compared two vehicles with similar horsepower, Tesla 3 (283 HP), and Infiniti Q50 (300 HP). The CO_2 emissions for these vehicles were: </p> <ul> <li> <p>for the model Tesla 3, CO_2 emissions were 161.8 gCO_2 eq/mile, including 31.8 gCO_2 eq/mile in vehicle production, 25 gCO_2 eq/mile in battery production, and 105 gCO_2 eq/mile in electricity production.</p> </li> <li> <p>for the model Infiniti Q50, CO_2 emissions were 503.8 gCO_2 eq/mile , including vehicle production 40.5 gCO_2 eq/mile, fuel production 91.3 gCO_2 eq/mile, in-service combustion 372 gCO_2 eq/mile.</p> </li> </ul>
Country greenhouse gas emissions from the non-renewable fraction of woodfuel used in households
<p><strong>Country greenhouse gas emissions from the non-renewable fraction of woodfuel used in households</strong></p> <p> </p> <p><strong>Data Structure</strong></p> <p>The data is structured as a tabular data with attributes: AreaName, ISO3, ItemName, ElementName, Year, Value, Unit.</p> <p><strong>Attributes (Columns)</strong></p> <p>Attributes in the data are defined as below:</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Descriptions</strong></p> </td> </tr> <tr> <td> <p><strong>AreaName</strong></p> </td> <td> <p>characterizes all countries including world and regional aggregates</p> </td> </tr> <tr> <td> <p><strong>ISO3</strong></p> </td> <td> <p>represents three letter ISO3 country codes (not all regional aggregates have ISO3 country codes)</p> </td> </tr> <tr> <td> <p><strong>ItemName</strong></p> </td> <td> <p>represents all items covered in the data</p> </td> </tr> <tr> <td> <p><strong>ElementName</strong></p> </td> <td> <p>represents all gases covered in the data</p> </td> </tr> <tr> <td> <p><strong>Year</strong></p> </td> <td> <p>period covered by the data</p> </td> </tr> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p>represents the emissions value</p> </td> </tr> <tr> <td> <p><strong>Unit</strong></p> </td> <td> <p>Unit of measurement (in this data emissions are measured in kilotonnes)</p> </td> </tr> </tbody> </table> <p> </p>
PixelCropRobot dataset: images of vegetables crops in different phenological stages taken in greenhouses
<p><em>Dataset created under the PixelCropRobot project, developed by FCUP, INESC TEC and FEUP.</em></p> <p><strong>Dataset folder:</strong></p> <blockquote> <p>This folder contains the images of each species in two formats (3456 × 4608 pixels and 864 × 1152 pixels), the annotations of the 864 × 1152 px. images, in Pascal VOC (.xml) and YOLO (.txt) formats and also a set of Python scripts useful for managing the dataset.</p> </blockquote> <p>The aim was to capture images of eight crops selected taking into account the length of the crop cycle (annual), the intensity of agricultural practices (mainly weed removal) and the low impact of pests and diseases.</p> <p>The images were captured using a smartphone (Huawei Mate 10 Lite), with 16 megapixels (MP) resolution (3456 × 4608 px.), in Professional mode (no flash, continuous autofocus, automatic ISO and shutter speed). Image collection took place at different hours of the day, with variable lighting conditions.</p> <p>The images are divided as follows (in parenthesis are the classes):</p> <ul> <li>Arugula - 312 (coty, minus9, plus9)</li> <li>Carrot - 533 (coty, smallleaves, carrot)</li> <li>Coriander - 321 (coty, smallleaves, coriander)</li> <li>Lettuce - 1426 (coty, minus9, plus9, ready)</li> <li>Radish - 494 (coty, smallleaves, bigleaves, root)</li> <li>Spinach - 270 (spinach, big)</li> <li>Swiss chard - 454 (coty, chard)</li> <li>Turnip - 313 (coty, smallleaves, turnip)</li> </ul> <p>To standardise the dataset, each image was renamed according to the corresponding EPPO (European and Mediterranean Plant Protection Organization) code and the date of creation of that image. The size of each image was also reduced four times (to 864 × 1152 pixels) to facilitate processing. For example, an image of lettuce captured on June 22 presents the name as follows: LACSA_Jun_22_x_864_1152.jpg.</p>
Regional greenhouse gas net emission intensities by land cover category in Finland
<p>The methods related to the data published herein are described in detail in the associated publications (Holmberg et al. 2023, Junttila et al. 2023). This file describes the datasets and the data preparation steps. The aim of this data publication is to provide regional assessments of the role of land cover in greenhouse gas emissions in Finland. The results in the publications are reported for the large administrative divisions, the NUTS 3 regions of mainland Finland (Statistics Finland 2023a). While limited by the accuracy of the methods and source data involved, these data can also be used for more local assessments, e.g., at the scale of municipalities. The data represent a temporal snapshot of land cover. Except for the soil maps, rivers and lakes, all land cover data are from the period 2015-2020 and are based on registry data or remote sensing.</p> <p><strong>Data description</strong></p> <p><em>Data format.</em> The data are distributed as GeoTiff raster files, which can be read using most GIS-software.</p> <p><em>Units and definitions</em>. The land cover net emission intensities are shared as raster data with a 250m-by-250m resolution in the ETRS-TM35FIN projected coordinate system. Negative values correspond to sinks (only sinks of C/CO<sub>2</sub> considered). The emission intensities are reported as total emission intensities in carbon dioxide equivalents (gCO<sub>2</sub>-eq m<sup>-2</sup>) based on the 100-year global warming potential as reported in the IPCC 5<sup>th</sup> assessment report (Myhre et al. 2013, p. 73). All cells which do not include emissions from the corresponding land use are classified as <code class="language-sql">NULL</code>s or <em>no data</em>, which should be taken into account if combining raster layers. Where the source data report emission coefficients in the amount of the main element (e.g. C for CO<sub>2</sub> or N for N<sub>2</sub>O) they have been converted to the amounts of the corresponding gas using the standard atomic weights of the relevant atoms (C: 12.011, O: 15.999, N: 14.007, H: 1.008) before conversion to carbon dioxide equivalents. See the related publication for the values of the emission coefficients used and further methodological details (Holmberg et al. 2023).</p> <p><em>Data processing</em>. Data processing for the production of the 250m-by-250m emission intensity raster maps was conducted using GRASS GIS 8.2 (GRASS Development Team, 2022).</p> <p>Land cover emissions derived from vector data (rivers, lakes, agricultural land) were rasterized at a resolution of 1m<sup>2</sup> with the emission intensity as the raster cell value. For rivers, linear features representing rivers having a width of 2 to 5 meters were converted first to areal features by creating a buffer of 1.75 meters to represent an average width of 3.5 meters (see <em>Rivers</em> below). The buffer was created <em>without caps</em> so that the total length of the linear segments was not changed. The buffered river features were merged with the areal features removing the potentially overlapping parts.</p> <p>For all source raster data, the data were available at a 16m-by-16m meter resolution. Emission intensities were aggregated to 250m-by-250m by first summing over the original raster cells intersecting with each aggregate cell while accounting for the proportion of each cell overlapping with the aggregated cell and then multiplying by the area of the original cell. The resulting raster values were divided by the total area of the aggregate cell to acquire average emission intensities. Hence, rasters including a lower proportion of the corresponding land use have lower emission intensities.</p> <p><strong>Thematic layers</strong></p> <p><em>Cropland</em>. CO<sub>2</sub> emissions from cropland were estimated for mineral soils and organic soils separately using emission coefficients from the national greenhouse gas inventory report for 2023. Averaged emission coefficients for the years 2010–2020 for southern and northern Finland were used for mineral soils (Statistics Finland 2023b, Table 3_App_6j). For organic soils separate emission coefficients were used for annual and perennial crops (IPCC 2014, Table 2.1). Cropland and crop data were acquired from the Finnish Food Authority’s Land parcel register for year 2020. Soils were classified into mineral and organic soils by intersecting the field parcels with the soil body layer of the Finnish soil database (Lilja et al. 2006, Lilja et al. 2017).</p> <p>Data files:</p> <ul> <li>Net missions from cropland on mineral soils: <code>cropland_mineral_250m_250m_mean.tif</code></li> <li>Net missions from cropland on organic soils: <code>cropland_organic_250m_250m_mean.tif</code></li> </ul> <p><em>Forests</em>. The net emissions from forests are estimated as the balance of carbon sequestration due to gross primary production of trees and understory vegetation and carbon loss due to harvested biomass, and emission from decomposition of harvest residues, litter, and soil organic matter. Forest productivity is modelled using the process-based forest growth model PREBAS (Minunno et al. 2016, 2019, Junttila et al. 2023, Mäkelä et al. 2023). The initial state for the forest model for the three main forestry species Scots pine, Norway spruce, and Silver birch is derived from the multi-source national forest inventory (<a href="http://kartta.luke.fi/">MS-NFI; version 2015</a>) and harvesting intensities are modelled on the basis of the Finnish national statistics (National Resources Institute Finland 2023). The PREBAS forest net emissions represent annual averages for the period 2017–2025.</p> <p>CO<sub>2</sub> emissions from decomposition on mineral soils are estimated with the soil carbon model YASSO07 (Liski et al. 2005, Tuomi et al. 2009). On drained peatlands, in addition to CO<sub>2</sub> emissions due to peat and litter decomposition, the soil emissions include the CH<sub>4</sub> and N<sub>2</sub>O emissions. The net emissions due to CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from drained peatland (Ojanen et al. 2010, Ojanen and Minkkinen 2019, Minkkinen et al. 2020, Junttila et al. 2023) are calculated using emission coefficients for nutrient rich sites (herb-rich and blueberry type), and nutrient poor sites (lingonberry, dwarf-shrub, and lichen type).</p> <p>Data files:</p> <ul> <li>Net missions from forest on mineral soils: <code>forest_mineral_250m_250m_mean.tif</code></li> <li>Net missions from forest on organic soils: <code>forest_organic_250m_250m_mean.tif</code></li> </ul> <p><em>Lakes</em>. Emissions of CO<sub>2</sub> and CH<sub>4</sub> were estimated for lakes using size dependent emission coefficients. The lakes were classified into five size classes with emission coefficients for CO<sub>2</sub> evasion (Kortelainen et al. 2006), CH<sub>4</sub> diffusion (Juutinen et al. 2009) and ebullition (Bastviken et al. 2004) as well as the CH<sub>4</sub> emissions due to the macrophytes <em>Phragmites australis</em> and <em>Equisetum fluviatile</em> (Juutinen et al. 2003, Bergström et al. 2007, 2011). The lake date was from the <a href="https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000">Shoreline10</a> data by the Finnish Environment Institute.</p> <p>Data files:</p> <ul> <li>Net emissions from lakes: <code>lakes_250m_250m_mean.tif</code></li> </ul> <p><em>Rivers</em>. CO<sub>2</sub> emissions from rivers were estimated using emission coefficients based on the width of the stream. The width dependent emission coefficients were derived from stream order specific emission coefficients of Swedish rivers (Humborg et al. 2010) by classifying the rivers into width groups and with the emission coefficients chosen based on the stream order specific coefficient of corresponding average width. The river emissions were calculated from the <a href="https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000">Shoreline10</a> data by the Finnish Environment Institute which represents rivers wider than 5 m as areal features, and rivers < 5 m wide as linear features. For rivers < 5m wide, an average width of 3.5 m was assumed.</p> <p>Data files:</p> <ul> <li>Net emssions from rivers: <code>rivers_250m_250m_mean.tif</code></li> </ul> <p><em>Undrained mires</em>. Total net emissions were estimated for undrained mires in Finland using average emission coefficients for CH<sub>4</sub> (Minkkinen and Ojanen 2013), CO<sub>2</sub> (Sallantaus 1994 , Turunen et al. 2002), and N<sub>2</sub>O (Minkkinen et al. 2020). The emission coefficients represent the long term accumulation of carbon as well as the emission of CH<sub>4</sub> and from N<sub>2</sub>O peatland. Peatland sites were extracted from the multi-source national forest inventory (<a href="http://kartta.luke.fi/">MS-NFI; version 2019</a>; see also Mäkisara et al. 2022) and undrained mires were delineated using data provided by the Natural Resources Institute Finland. The undrained mires were classified into four classes using the MS-NFI data: 1) productive forested mires, 2) sedge fens, 3) other open and sparsely treed fens and 4) ombrotrophic bogs, which mainly differ in their emission coefficients for methane (Minkkinen and Ojanen 2013).</p> <p>Data files:</p> <ul> <li>Net missions from undrained mires: <code>undrained_mires_250m_250m_mean.tif</code></li> </ul>
Machine learning methods for gap-filling in greenhouse gas emissions databases
<p>Datasets for use with code related to "Machine learning methods for gap-filling in greenhouse gas emissions databases" manuscript submitted to the Journal of Industrial Ecology. Code for using the datasets can be found at <a href="https://github.com/luke-scot/ml-ghg-databases">https://github.com/luke-scot/ml-ghg-databases</a>.</p>
ScienceDex guides
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.