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1,429 results for “Inventories”
Elevation Models for Reproducible Evaluation of Terrain Representation - Inventory of Renderings
<p>This is an inventory of 155 renderings from 78 publications on terrain visualization techniques. The renderings guided the selection of landform types in the elevation models that are proposed in the following article:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p> <p>Visualization techniques include colored aspect, contour lines, hypsometric tints, plan oblique relief, relief shading, rock and scree representation, and spot heights. The inventory contains information about display scale of the sample renderings, landform types, cell size, and geographic location of the digital elevation models. Also inventoried are how authors evaluated their renderings, how scale was indicated on the renderings, and whether the cell size and source of elevation data was included.</p> <p>The inventory is formatted as a single table in CSV UTF-8 and MS Excel .xlsx formats. Papers are grouped by visualization type; attributes of each rendering are stored on a single row.</p>
S13 | EUCOSMETICS | Combined Inventory of Ingredients Employed in Cosmetic Products (2000) and Revised Inventory (2006)
<p>This is the collection associated with list S13 EUCOSMETICS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p><strong>Combined Inventory of Ingredients Employed in Cosmetic Products (2000) and Revised Inventory (2006)</strong></p> <p>Merged Cosmetics <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Merged_CosmeticProducts_04052017.csv">CSV</a> (4/05/2017)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/eucosmetics">EU Cosmetics List</a></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Merged_CosmeticProducts_04052017_InChIKeys.txt">Merged Cosmetics InChIKeys</a> (4/05/2017)</p> <p>The scientific committee on cosmetic products and non-food products Intended for consumers - <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/SCCNFP038900_INCI-2000.pdf">SCCNFP/0389/00 Final</a> and Commission <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Decision_2006_257_EC.pdf">Decision 2006/257/EC</a> amending the Decision 96/335/EC. Provided by Peter von der Ohe, UBA, curated by Reza Aalizadeh, University of Athens.</p> <p>Update 12 May 2020: Added the source data from both reports (INCI-2000 and Decision 96/335/EC), including the function information and undefined structures. Update 28 May 2020: removed incomplete structural information from Decision 96/335/EC files. Update 24/7/2020: corrected one synonym reported by PubChem (GFMHHNOUDDHEOO-UHFFFAOYSA-N).</p>
Data from: "Inventory of Earth's Ice Loss and Associated Energy Uptake from 1979 to 2017"
<p>Earth’s cryosphere is a buffer to the warming of the planet and its loss must be accounted for in planetary energy budgets. Yet, even as melting ice is an evident manifestation of climate change, inventories of its energy uptake are largely lacking, based on inconsistent methods, or limited to the fraction that contributes to sea level rise. By combining recent syntheses, we undertake a systematic estimate of ice loss to show that Earth lost 40700 ± 5800 Gt of ice with a corresponding energy uptake of 13.8 ± 2.0 ZJ, from 1979 to 2017, larger than previous estimates and equivalent to the energy uptake by the deep ocean, the land and the atmosphere. The total loss is due to approximately equal contributions from Arctic sea-ice, the Antarctic and Greenland ice sheets, and glaciers. Only half of it contributed to sea level rise. From the 1980s to the 2010s, the rate of ice loss has almost tripled.</p> <p>In this HDF5 dataset, we provide cumulative annual estimates of energy uptake for three components of the cryosphere in Zetajoules (10<sup>21</sup> Joules):</p> <p>1) Antarctica<br> 2) Greenland<br> 2) Glaciers<br> 3) Sea Ice</p> <p>For 1–3, we separate energy uptake contributions for the grounded and floating components. We also provide a Matlab file with code to read the fields in the dataset.</p> <p>Python code to read the data is available at: <a href="https://github.com/sioglaciology/energy_imbalance_cryosphere">https://github.com/sioglaciology/energy_imbalance_cryosphere</a></p>
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>
Biodata Resource Inventory Dataset
<p>final_inventory_2022.csv is the result of the Biodata Resource Inventory conducted in 2022. data_dictionary.csv provides an explanation of the columns in the inventory file.</p>
High-resolution air pollution emission inventory for the Nordic countries
<p>This common Nordic (Denmark, Finland, Iceland, Norway, and Sweden) air pollution emission inventory was compiled using country total emissions from national emission inventories that the countries submit to the CLRTAP. Our inventory was based on the 2016-2018 submissions. The inventory contains annual emissions for 1990, 1995, 2000, 2005, 2010, 2012 and 2014. Components included in the inventory are: particulate matter (PM10 and PM2.5), black carbon (BC), organic carbon (OC), sulphur oxides (SOx), nitrogen oxides (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOC) and ammonia (NH3). The gridding was done separately for each country, using national data and gridding methods. The emissions were harmonized to the same sector nomenclature, i.e. SNAP, and to the EEA reference grid. Spatial resolution for the inventory is 1 km × 1 km in the European grid ETRS89-LAEA (EPSG: 3035). Large point source emissions are provided with locations and stack heights included. Two modifications to the CLRTAP submissions were made: (1) road transport non-exhaust PM emissions were adjusted to better conform with Nordic traffic dust assessments; and (2) for OC emission, that are not included in the inventories, rough estimates were calculated based on expert estimates on OC/PM2.5-ratios on main SNAP level. The inventory was originally created for the NordicWelfAir-project (<a href="https://projects.au.dk/nordicwelfair">https://projects.au.dk/nordicwelfair</a>). The main aim of developing this new inventory was to provide air pollution modelers and health scientists a harmonized dataset to be used for studies on the link between air pollution exposure and negative impacts on the human health.<br>Description of the data can be found in this data article, which can be referenced when using the data: <a href="https://doi.org/10.5194/essd-16-1453-2024">https://doi.org/10.5194/essd-16-1453-2024</a>.</p>
Raw data for D1.1: Inventory of skills and competencies
<p>Raw data for the manuscript entitled: <strong>European Agrifood and Forestry Education for a Sustainable Future - Gap Analysis from an Informatics Approach</strong></p> <p><strong>Abstract</strong></p> <p><strong>Purpose: </strong>To evaluate how well European agrifood and forestry Masters program websites use vocabulary associated with the NextFood Project ‘categories of skills’.</p> <p><strong>Methodology: </strong>Web-scraping Python scripts were used to collect texts from European Masters programs websites, which were then analysed using statistical tools including Partial Least Squares Regression and contextual relation analysis. A total of fourteen countries, twenty-seven universities, 1303 European Masters programs, 3305 web-pages and almost two million words were studied using this approach.</p> <p><strong>Findings: </strong>While agrifood and forestry Masters programs used vocabulary from the NextFood Project ‘categories of skills’ in most cases equal to or more often than non-agrifood and forestry Masters programs, we found evidence for the relative underuse of words associated with networking skills, with least use among agriculture-related Masters programs. </p> <p><strong>Practical Implications: </strong>The informatic approach provides evidence that European agrifood and forestry Masters programs are for the most part following the educational paths for meeting future challenges as outlined by the NextFood Project, with the possible exception of networking skills.</p> <p><strong>Theoretical Implications: </strong>This text-based, informatic approach complements the more targeted approaches taken by the NextFood Project in studying the skilling-pathways, which involved focus-group interviews, surveys of stakeholders, interviews of individuals with expert-knowledge and literature reviews.</p> <p><strong>Originality: </strong>A text-based, web-scraping informatic approach has thus far been limited in the study of agrifood and forestry higher education, especially relative to recent advances made in the social sciences.</p>
Hypersonic Transport: 3D Emission Inventory of STRATOFLY-MR3 Fleet Operated on Brussels to Sydney Route in 2075
<p>High-resolution 3D inventories of future hypersonic transport (HST) are compiled for the year 2075, integrating the gaseous engine emissions of a fleet of 200 hydrogen-powered Mach 8 passenger aircraft*. These aircraft are operated once a day for 360 days on a reference route from Brussels (BRU) to Sydney (MYA) with either NO<sub>x</sub>-optimized (ICA**: 114 000 ft; 34.75 km) or H<sub>2</sub>O-optimized (ICA**: 107 500 ft; 32.77 km) flight profiles, derived to minimize environmental impacts in terms of total emissions. The emissions are spatially gridded at a horizontal resolution of 1° in longitude and latitude, with a vertical resolution of 1000 ft, and are temporally accumulated on an annual basis. Note that the 3D emission inventories encompass detailed data on species-specific HST emissions***, fuel burn, and total distance traveled: </p> <ul> <li>Species: NO, H<sub>2</sub>O; H<sub>2</sub></li> <li>Temporal information: 2075; annually</li> <li>Spatial information: 1° x 1° x 1000 ft</li> <li>Data Format: NetCDF</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------------------------------<br>* The hypersonic aircraft concept under consideration is the <a href="https://arc.aiaa.org/doi/abs/10.2514/6.2021-1877">STRATOFLY-MR3</a> vehicle, which was conceptually developed in <br> the framework of the <a href="https://cordis.europa.eu/project/id/769246">H2020 STRATOFLY project</a>.<br>** Initial Cruise Altitude<br>*** with a unit of kg/km<sup>3 </sup>(corrected in v0.2)</p> <p> </p>
EDGAR v5.0 emissions inventory speciated for the MOZART chemical mechanism
<p>Emission inventories need to be adapted to be used in chemical transport models (CTMs). They usually need ad-hoc preprocessing based on the chemical mechanism used in the CTM, including speciation of non-methane volatile organic compounds (NMVOCs). </p> <p><strong>Here we provide monthly <a href="https://edgar.jrc.ec.europa.eu/index.php/dataset_ap50">EDGAR v5.0 </a> global air pollutant emissions for the year 2015, speciated for the <a href="https://gmd.copernicus.org/articles/3/43/2010/">MOZART</a> chemical mechanism.</strong></p> <p><strong>The dataset is also ready to use in <a href="https://ruc.noaa.gov/wrf/wrf-chem/">WRF-Chem </a>atmospheric model with MOZART-MOSAIC options.</strong></p> <p>Emission files are provided as individual NetCDF files for each pollutant containing anthropogenic sector emissions as individual variables.</p> <p>In the folder you will find:</p> <ul> <li><strong>edgarv5_MOZART_data.tar.gz</strong>: EDGAR v5.0 monthly emissions for the year 2015 (NetCDFformat), speciated for MOZART chemical mechanism. Both total and individual sector emissions are included in each file. </li> <li><strong>edgarv5_MOZART_MOSAIC.inp</strong>: Input file for anthroemiss preprocessing tool for MOZART-MOSAIC options in WRF-Chem.</li> <li><strong>technical_note_EDGARv5_MOZART.pdf </strong>: documentation.</li> </ul> <p>These files are also ready-to be used in <a href="https://www2.acom.ucar.edu/wrf-chem/wrf-chem-tools-community">WRF-Chem anthro-emiss preprocessing tool</a> with the MOZART-MOSAIC options.</p> <p>Accompanying code for preparing the dataset can be found at repository: <a href="https://doi.org/10.5281/zenodo.6145846">https://doi.org/10.5281/zenodo.6145846</a></p> <p>For more detail, please refer to the technical documentation (technical_note_EDGARv5_MOZART.pdf).</p> <p> </p> <p> </p>
Inventory of Policy Interventions related to Energy and Transport Behaviours
<p>User behaviour is a complex issue, as it is influenced by a multitude of factors from economic status, age, education, political environment etc. The WHY project aims to identify the possible user reactions and their effect on the energy consumption due to external stimuli. In context of the WHY project these external stimuli are called “interventions”. In this chapter different approaches for policy driven interventions are discussed and presented. </p>
Inventory of tools and resources for crop diversification available for stakeholders
<p>The aim of the database is to give an overview of existing resources, tools and methods to promote crop diversification strategies (rotation, multiple cropping, intercropping) at different levels (including the value chain and territory levels). This version contains 143 resources.</p> <p>Each resource is described with a set of criteria: strategies used / described in the resource, purpose of the resource (what is an end-user doing with the resource), expected performances, area of validity, context of use, but also characteristics for use (cost, training, required time to collect data…).</p> <p>A toolbox was also designed to support end-users to navigate among this database and aims to help different type of end-users to identify interesting and adapted resources to foster crop diversification.</p> <p></p>
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
Inventory data of woody plants surveyed and measured in North Senegal (Ferlo) in 2015-2017
<p>This dataset gathers measurements from field inventory on woody vegetation carried in the sylvo-pastoral zone of Ferlo (Senegalese Sahel) in 2015-2016-2017. The data consist of dendrometric measurements, location and species for 3215 woody individuals (trees, bushes, and shrubs) belonging to 25 species and 11 families.</p> <p><strong>Sites </strong></p> <p>The study sites are located in the Northern Sandy Pastoral Region of Senegal around the deep wells of Widou Thiengoly (15.99° N, 15.32° W) and Tessékéré (15.85° N, 15.06° W). The vegetation formation is an open savanna, with a relatively low woody cover.</p> <p>For the <strong>field work of 2015</strong>, we applied a stratified sampling according to the topography and the distance to the studied deep wells. We inventoried 139 plots of 0.25 ha each. The center of each plot was marked by a gps point. The plots were located at increasing distances from the boreholes: 2, 3.5, 5, 7.5, 10, 12.5 and 15 km (20 plots per distance). For each distance, we randomly selected at least six plots in depressions (43 plots in total), the other plots being located on slopes or on hilltops, with a total vertical drop of several meters (96 plots in total). Whenever possible, the plots in each category of topography were distributed between the two soil types. In total, 86 plots were allocated to the ferruginous soils and 53 plots to the sub-arid brown red soils. </p> <p>In<strong> 2016, </strong>additional woody plants were surveyed within 10 circular plots with a variable radius between 27 m and 52 m, so that at least 10 individuals were counted for each plot. <strong>In 2017, </strong>woody plants were surveyed within 30 square plots of 0.25 ha each. Woody individuals were all geotagged in 2016 and 2017. </p> <p><strong>Field measurements</strong></p> <p>Adults woody plants (with a circumference superior to 10 cm at ground level) were inventoried within square plots (2015, 2017) or circular (2016). Species name was identified for all individuals and recorded following the taxonomic referential of the African Plant Database (version 3.4.0). Three types of dendrometric measurements were performed on woody plants: (i) circumference, measured at 30 cm from ground level, except for shrubs for which circumference was measured at ground level; (ii) tree height, measured by an ultrasonic hypsometer Vertex IV (Haglof Inc.) (iii) two perpendicular crown diameters. GPS points were taken (GPSMAP 62, Garmin Inc.) at the center of each plot (for 2015) and, in some cases for each individual (2016-2017).</p> <p><strong>Data structure and metadata</strong></p> <p>Data are encoded in a single file, using comma-delimited format and UTF-8 encoding. Each row describes one individual woody plant with its corresponding measurements. The following table presents the variables (columns) contained in the dataset.</p> <p>Shapefile format is also available (same data as the .csv).</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Definition</strong></p> </td> </tr> <tr> <td> <p>Tree_id</p> </td> <td> <p>-</p> </td> <td> <p>Unique identifier of the individual, with a 13 characters length. The 4 characters following the “y” indicate the year of the inventory. For instance, “tr.y2015.0034” is referring to the woody plant number 34 inventoried in 2015.</p> </td> </tr> <tr> <td> <p>Plot_id</p> </td> <td> <p>-</p> </td> <td> <p>Plot identifier</p> </td> </tr> <tr> <td> <p>Plot_area_ha</p> </td> <td> <p>ha</p> </td> <td> <p>Area of the inventoried plot</p> </td> </tr> <tr> <td> <p>Species</p> </td> <td> <p>-</p> </td> <td> <p>Genus, species, subspecies names and botanical authors</p> </td> </tr> <tr> <td> <p>Family</p> </td> <td> <p>-</p> </td> <td> <p>Family name</p> </td> </tr> <tr> <td> <p>Growth_form</p> </td> <td> <p>-</p> </td> <td> <p>Shrub, bush or tree</p> <p>Growth form expresses the extent of growth and the potential branching of the main-shoot axis. In this work, we refer to three types of growth form: shrub, bush and tree. A shrub refers to a small woody plant with a height below 2 meters and multi-stemmed. A tree designates a woody plant taller than 5 to 6 meters, generally presenting a single trunk. A bush, or a dwarf tree as in Pérez-Harguindeguy et al. (2013), is the intermediary between a shrub and a tree. Its height is usually between 2 to 6 meters and it is often multi-stemmed. Because of intra-specific traits variation, the mentioned growth form is valid for our study area.</p> </td> </tr> <tr> <td> <p>Circ30_m</p> </td> <td> <p>m</p> </td> <td> <p>Circumference measured at 30 cm from ground level. “NA” indicates missing data (for the individuals measured in 2017).</p> </td> </tr> <tr> <td> <p>Height_m</p> </td> <td> <p>m</p> </td> <td> <p>Tree height. “NA” indicates missing data (for the individuals measured in 2017).</p> </td> </tr> <tr> <td> <p>Dcrown1_m</p> </td> <td> <p>m</p> </td> <td> <p>First diameter of the crown</p> </td> </tr> <tr> <td> <p>Dcrown2_m</p> </td> <td> <p>m</p> </td> <td> <p>Second diameter of the crown (perpendicular to the first diameter)</p> </td> </tr> <tr> <td> <p>Geoloc_method</p> </td> <td> <p>-</p> </td> <td> <p>Geolocation method; indicates if it is the center of the plot which was geolocated (“geoloc.plot”, for individuals in 2015) or the woody plant (“geoloc.tree”, for 2016-2017).</p> </td> </tr> <tr> <td> <p>Lat_dd</p> </td> <td> <p>Decimal degrees</p> </td> <td> <p>North latitude of the plot if the geoloc_method == “geoloc.plot” and of the woody plant if the geoloc_method == “geoloc.tree”.</p> </td> </tr> <tr> <td> <p>Long_dd</p> </td> <td> <p>Decimal degrees</p> </td> <td> <p>West longitude of the plot if the geoloc_method == “geoloc.plot” and of the woody plant if the geoloc_method == “geloc.tree”.</p> </td> </tr> <tr> <td> <p>Topography</p> </td> <td> <p>-</p> </td> <td> <p>Local topography of the plot. Indicates if the plot is located within a depression (lowland) or on a hilltop.</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td> <p>-</p> </td> <td> <p>Date of the survey: dd-mm-yy</p> </td> </tr> </tbody> </table> <p> </p>
Baseline map of 137Cs inventories in reference soil sites at the continental scales of South America
<p>This dataset contains the baseline map of <sup>137</sup>Cs inventories in reference soil sites (Bq m<sup>-2</sup>, decay-corrected to 2020) estimated by Partial Least Square Regression (PLSR) with a spatial resolution of 2 km at the continental scale of South America, as well as the prediction uncertainties of the baseline map (coefficient of variation, %).<br> Details information regarding this dataset can be found in the original publication:<br> Mapping the spatial distribution of global <sup>137</sup>Cs fallout in soils of South America as a baseline for Earth Science studies, Earth-Science Reviews, Volume 214, 2021, 103542, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2021.103542.</p>
High temporal and spatial resolution emission inventory for maritime shipping emissions on the North Sea and Baltic Sea (2015)
<p>A temporally and spatially highly resolved emission inventory for the North Sea and Baltic Sea for the year 2015, created with current emission factors and ship activity data. The emissions inventory is available as 396 csv files, one for each day in 2015 and December 2014, grouped as monthly archives. </p> <p><strong>Note that due to the underlying ship activity data and the geographic boundaries, the time index in the <em>Datetime </em>column in the <em>ship_emissions_YYYYMMDD.csv</em> files is not equidistant.</strong> For example, since vessels leave the geographic area and reenter later, no data is available for the time the vessel is not within the area.</p> <p>The underlying model source code is available on Github, with a release of the associated version on Zenodo: [](https://doi.org/10.5281/zenodo.6951672)</p> <p> </p>
CLDF dataset with phoneme inventories from the "Journal of the IPA", aggregated by Baird et al. (2021)
<p>Cite the source of the dataset as:</p> <blockquote> <p>Baird, L., Evans, N., & Greenhill, S. J. (2021). Blowing in the wind: Using 'North Wind and the Sun' texts to sample phoneme inventories. Journal of the International Phonetic Association, 1–42. doi:10.1017/s002510032000033x</p> </blockquote>
Data from : Tree inventory data from permanent plots in French forest reserves
<p>We present a dataset resulting from the first round of a national monitoring program of forest reserves. It contains 9538 permanent plots, distributed across 111 study sites in mainland France (including Corsica). Notably focusing on dead wood measurement, this protocol has primarily been applied in strict forest reserves and special nature reserves (sensu Bollmann et Braunisch 2013), with 68% (6494) of the plots being currently located in strict forest reserves (unmanaged) and 24,7% (2363 plots) in forests unmanaged for at least 50 years. Sites cover a large variety of ecological conditions, from lowland to subalpine forests, but with an underrepresentation of Mediterranean forests (Table 1). The protocol assesses all the stages of a tree's life cycle, from seedling to decomposed lying dead wood. On each plot, a combination of three sampling techniques was used: (i) fixed area inventory for regeneration, standing dead trees, living trees and coarse woody debris (CWD) with diameter over 30 cm, (ii) transect lines for CWD with diameter < 30 cm, and (iii) fixed angle plot method for living trees with a diameter at breast height (DBH) > 30 cm (using a relascopic angle of 3%). Measurements include: exact tree location (azimuth, distance), species, diameter(s), tree-related microhabitats, decay stage and bark cover, seedling cover. With the ongoing climate change, the program network can also provide important information to monitor changes in forest ecosystems. It can also be used as forest management monitoring or conservation status assessment.</p>
Storm Alex Landslide Inventory
<p>The storm Alex that in 2020 hit the Mediterranean Alps represented and extreme meteorological event triggering devastating floods and landslides in both Italy and France, with severe consequences for people and anthropic settlements. After the Storm Alex, a detailed inventory of rainfall-induced hillslope instability processes was prepared by means of the visual interpretation of VHR satellite imagery in two adjacent mountain catchments of the Liguria Region (northern Italy) impacted by intense rainfall. The inventory map included a total of 302 features classified in debris slide (214), debris slides/debris flow (79) and channelized flow erosion (9). </p>
Solar Asset Mapper: A continuously-updated global inventory of solar energy facilities built with satellite data and machine learning
<p><strong>TransitionZero’s Solar Asset Mapper is a global, satellite-derived dataset of utility-scale solar farms generated with a combination of machine learning and human annotation. Our Q1 2024 dataset contains the location and shape of 63,616 assets, along with estimated capacities. We estimate the construction date for over 80% of these assets. The dataset contains over 19,100 square kilometres of solar farms across 183 countries, with a total estimated capacity of 711 GW.</strong></p> <p>Download the dataset, read the explainer and explore our polygon browser UI at <a href="https://www.transitionzero.org/products/solar-asset-mapper" target="_blank" rel="noopener">TransitionZero.org.</a></p> <p><a href="https://blog.transitionzero.org/hubfs/Data%20Products/TZ-SAM/tz-sam-scientific-methodology-Q12024.pdf" target="_blank" rel="noopener">Download our methodology paper here </a></p> <h1><strong>1. Dataset Description</strong></h1> <p>We publish six files.</p> <ul> <li><em>analysis_polygons.gpkg:</em> our “analysis-ready” dataset containing geometries, capacity estimates and construction date estimates.</li> <li><em>analysis_polygons.csv:</em> a version of analysis_polygons.gpkg containing a central latitude and longitude in place of a geometry, to allow parsing without geospatial software.</li> <li><em>sources.csv</em>: a table mapping the IDs of our analysis-ready dataset to the raw geometries that make them up.</li> <li><em>raw_polygons.gpkg:</em> the raw geometries used to compose analysis_polygons.gpkg.</li> <li><em>TZ Solar Asset Mapper Q1 2024.xlsx</em>: an Excel formatted version of the analysis_polygons.csv file.</li> <li><em>tz-sam_scientific_data.pdf</em>: A pre-print aricle that explains the methodology in detail.</li> </ul> <h2><strong>1.1 Analysis-level datasets</strong></h2> <p>Our analysis-level dataset comprises our most complete view of global asset-level solar installations, incorporating our own detections as well as known solar farm geometries from other datasets.</p> <p>The geospatial dataset contains the following fields:</p> <ul> <li>id: unique ID for the asset</li> <li>geometry: Polygon or MultiPolygon defining the asset</li> <li>capacity_mw: estimated capacity of the asset in megawatts</li> <li>constructed_before: upper bound for construction date (estimated date of the image in which the solar plant was first seen in a constructed state)</li> <li>constructed_after: lower bound for construction date (estimated date of the image in which construction began for the solar plant)</li> </ul> <p>The CSV version replaces the Geometry column with:</p> <ul> <li>latitude: the latitude of the centroid of the asset</li> <li>longitude: the longitude of the centroid of the asset</li> <li>country: administrative country name</li> </ul> <h2><strong>1.2 Raw datasets and sources</strong></h2> <p>The analysis-level datasets hide some complexity in the underlying data that we expose in the <em>raw_polygons</em> and <em>sources</em> file.</p> <ul> <li>We produce new sets of polygons for each run. Often these overlap, sometimes in complicated ways.</li> <li>We cluster together overlapping and nearby geometries from both our detections and external sources. Currently these sources are:</li> <li>Large solar farms scraped from OpenStreetMap (OSM)</li> <li>Validated geometries from <a href="../records/5005868">Kruitwagen et. al., A global inventory of solar photovoltaic generating units</a>.</li> </ul> <p>Each cluster comprises one row in the analysis-level dataset. In order to enable tracking raw detections from run to run, as well as to provide detailed sourcing information, we provide all of these raw polygons, along with a source file that lists all of the raw polygons contained in each analysis-level polygon.</p> <p>raw_polygons.gpkg contains the following fields:</p> <ul> <li>id: ID of the raw source polygon</li> <li>geometry<strong>: </strong>Polygon or MultiPolygon defining the asset</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <p>Sources.csv contains the following fields:</p> <ul> <li>cluster_id: ID of the corresponding item in the analysis-level dataset</li> <li>source_id: ID of the raw source polygon</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <h2><strong>1.3 Caveats and limitations</strong></h2> <h3><strong>1.3.1 Capacity Estimates</strong></h3> <p>While we have made every effort to remove false positives from the published dataset, some will remain due to the difficulty of manually validating detections in 10-metre satellite imagery. To estimate false positive prevalence throughout the data a subset of approximately 2000 detections were selected at random from our positively labelled solar assets. Each of these were validated through a higher degree of scrutiny utilising high-resolution imagery. This analysis yielded an expected rate of false positives of around 1%.</p> <h3>1.3.2 Plant Shapes</h3> <p>Our plant outlines are not perfect. They will occasionally be much smaller or larger than the underlying plant. Our tests show that on average, these effects average out.</p> <h3>1.3.3 Capacity Updates</h3> <p>Our capacity estimation model should produce relatively unbiased country-level aggregates, since it is trained to learn the typical ground coverage ratio of plants by country. The model has no way to distinguish between a very dense and a very sparse (e.g. dual-axis-tracking) plant in the same country. Plants with unusually high or low ground coverage ratios will not have accurate capacity estimates.</p> <h3>1.3.4 Construction Date Estimates</h3> <p>We are not able to directly estimate the construction date of a plant. We estimate an upper bound (the date of the image in which the plant was first seen in constructed state) and a lower bound (the date of the image in which the plant was last seen in an unconstructed state). For plants that were constructed before the launch date of Sentinel-2 in 2017, we produce only an upper bound.</p> <p>We leave it to consumers of the data to interpret these bounds and/or estimate likely grid connection dates.</p> <p><strong>2. Attribution</strong></p> <p>TZ-SAM is made available under a Creative Commons Attribution Non-Commercial 4.0 International License (CC-BY-NC-4.0). Attribution to TransitionZero is required. You must also clearly indicate if you have made any changes to the TZ-SAM dataset and what these are. Please refer to the suggested citation formats:</p> <ul> <li>“TransitionZero Solar Asset Mapper, TransitionZero, May 2024 release.”</li> <li>“TZ-SAM, TransitionZero, May 2024 release.”</li> <li>“TransitionZero (2024) Solar Asset Mapper.”</li> </ul>
SOS-Water D3.1 Data inventory
<p>The <strong>SOS-Water D3.1 Data inventory</strong> was created as part of the SOS-Water project (<a href="https://sos-water.eu">sos-water.eu</a>, <a href="https://doi.org/10.3030/101059264" target="_blank" rel="noopener noreferrer">10.3030/101059264</a>). The inventory provides an overview of all available water-related remote-sensing datasets relevant for use in the project and their key dataset attributes. It includes relevant services from Copernicus (CGLS, CLMS, C3S), the ESA CCI (lakes, land cover, snow, soil moisture), EUMETSAT (H-SAF), and other national and international organizations (e.g., NASA, NOAA, USGS, FAO). The accompanying report "<em><strong>D3.1 - Data inventory and EO data needs for water resources monitoring</strong></em>" is accessible over the <a href="https://ec.europa.eu/info/funding-tenders/opportunities/grants/docs/080166e5f9defeca/Attachment_0.pdf">EU Funding & Tenders portal</a>), highlighting mismatches between water resource challenges and the availability of EO/in-situ data.</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.