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Forest Inventory of a Calcium Amended Northern Hardwood Forest: Watershed 1, 2016, Hubbard Brook Experimental Forest
In order to evaluate the role of Ca supply in regulating the structure and function of base-poor forest and aquatic ecosystems, the Ca content of soil was increased through the application of wollastonite (CaSiO3) in October 1999. Forest inventory surveys were initiated in 1996 and repeated at 5 year intervals. This data set includes 2016 inventory measurements. The data consists of a total inventory of all trees >=10 cm diameter-at-breast-height (dbh) on the whole of the watershed (11.8 ha), as measured in each of the 200 25 m x 25 m plots. Trees >=2 to <=10 cm dbh were subsampled using a 3 meter wide strip along one edge of each 25 m x 25 m plot. With the addition of tree tags in 2006 on all trees >=10 cm dbh, tracking of individual trees is now possible. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
FIG. 16 in An inventory of the spider species of Barcelonnette (France), with taxonomic notes on Piniphantes agnellus n. comb. (Araneae, Linyphiidae)
FIG. 16. –– Number of species (black) and corresponding number of specimens (grey) for each macrohabitat.
Inventory of the sustainability methodologies, indicators and criteria of research projects funded by the European Union
<p>Based on a screening in the CORDIS database and the experience of project partners, 16 projects were selected for analyses of their contributions regarding sustainability criteria and indicators.</p>
CEDS_GBD-MAPS: Global Anthropogenic Emission Inventory of NOx, SO2, CO, NH3, NMVOCs, BC, and OC from 1970-2017
<p><strong>CEDS_GBD-MAPS: Global Anthropogenic Emission Inventory of NO<sub>x</sub>, SO<sub>2</sub>, CO, NH<sub>3</sub>, NMVOCs, BC, and OC from 1970-2017</strong></p> <p><strong>version tag: 2020_v1.0 (April 2020)</strong></p> <p>Annual anthropogenic emissions of 7 key atmospheric pollutants from 1970 - 2017, produced using the <a href="http://www.globalchange.umd.edu/ceds/">Community Emissions Data System</a>, updated for the Global Burden of Disease - Major Air Pollution Sources project (<a href="https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS">CEDS_GBD-MAPS</a>).</p> <p>Emissions are provided for NO<sub>x</sub>, SO<sub>2</sub>, CO, NH<sub>3</sub>, NMVOCs, Black Carbon (BC), and Organic Carbon (OC) from 11 anthropogenic sectors and four fuel categories as both annual country totals and global gridded emission fluxes (0.5 x 0.5 degree resolution).<br> Note: The CEDS_GBD-MAPS inventory does not include emissions from open fires or aircraft.<br> <strong>Sectors: </strong><br> 1. Agriculture (non-combustion sources only, excludes open fires)<br> 2. Energy (transformation and extraction)<br> 3. Industry (combustion and non-combustion processes)<br> 4. On-Road Transportation<br> 5. Off-Road/Non-Road Transportation (rail, domestic navigation, other)<br> 6. Residential Combustion<br> 7. Commercial Combustion<br> 8. Other Combustion<br> 9. Solvents<br> 10. Waste (disposal and handling)<br> 11. International Shipping<br> <strong>Fuel Categories:</strong><br> 1. Total Coal Combustion (hard coal + brown coal + coal coke)<br> 2. Solid Biofuel Combustion<br> 3. Liquid Fuel (light oil + heavy oil + diesel oil) plus Natural Gas Combustion<br> 4. CEDS Process Source Categories (see McDuffie, et al., (ESSD) 2020) for further details.<br> Note: Total anthropogenic emissions = the sum of fuel categories 1-4</p> <p><strong>Zip File Details:</strong><br> The CEDS_GBD-MAPS inventory is available in three different formats:<br> <br> 1. <em>CEDS_GBD-MAPS_annual_country_total_emissions_by_sector_fuel_1970-2017.zip</em></p> <ul> <li>Zip file contains 7 .csv files that each contain a complete times series (1970-2017) of total annual anthropogenic emissions of each compound from each country, as a function of 11 anthropogenic sectors and 4 fuel categories.</li> <li>Emissions are in units of kt yr<sup>-1</sup> and include NO<sub>x</sub> (as NO<sub>2</sub>), CO, SO<sub>2</sub>, NH<sub>3</sub>, total NMVOCs, BC, and OC</li> </ul> <p>2. <em>CEDS_GBD-MAPS_gridded_emissions_by_sector_fuel_[year].zip</em></p> <ul> <li>Each .zip file contains 145 netCDF files of annual anthropogenic global gridded emission fluxes, reported as a function of 11 anthropogenic sectors and 5 fuel categories (1 file per compound per fuel category, plus 1 file for the sum of all fuel categories)</li> <li>Emission fluxes are in units of kg m<sup>-2</sup> s<sup>-1</sup> and include NO<sub>x</sub> (as NO), CO, SO<sub>2</sub>, NH<sub>3</sub>, 25 speciated VOCs, BC, and OC</li> <li>Emission fluxes are provided as monthly averages and have been formatted for use in the GEOS-Chem model (<a href="http://acmg.seas.harvard.edu/geos/">http://acmg.seas.harvard.edu/geos/</a>).</li> <li>Example: ALD2-em-liquid-fuel-plus-natural-gas_CEDS_1970.nc inside the CEDS_GBD-MAPS_gridded_emissions_by_sector_fuel_1970.zip file provides monthly emission fluxes in 1970 for the subVOC ALD2 that result from the combustion of liquid fuel and natural gas in each of the 11 source sectors.</li> </ul> <p>3. <em>CEDS_GBD-MAPS_[compound]_gridded_total_anthro_emissions_by_sector_input4CMIP_1970-2017.zip</em></p> <ul> <li><em>compound = [BC_OC], [CO_NOx_SO2_NH3], [speciated_NMVOCs_01-04], [speciated_NMVOCs_05-08], [speciated_NMVOCs_09-14], [speciated_NMVOCs_15-18], [speciated_NMVOCs_19-22], or [speciated_NMVOCs_23-25]</em></li> <li>Each .zip file contains between 2 - 4 netCDF files (1 per compound) of anthropogenic global gridded emission fluxes from 1970-2017, as a function of 11 anthropogenic sectors only (no disaggregation of fuel categories)</li> <li>netCDF files follow the CEDS CMIP6 gridded emissions format. More information available at: <br> <a href="http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/">http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/</a></li> <li>Emission fluxes are in units of kg m<sup>-2</sup> s<sup>-1</sup> and include NO<sub>x</sub> (as NO<sub>2</sub>), CO, SO<sub>2</sub>, NH<sub>3</sub>, 25 speciated VOCs, BC, and OC</li> <li>Emission fluxes are provides as monthly averages</li> <li>Note: Zip files are group by compound only as a means to reduce the zipped file sizes. The file format for each compound is the same. </li> </ul> <p> </p> <p><strong>*Additional data details are provided in the README.txt file*</strong></p> <p> </p> <p>*Version 2020_v1.0 of this dataset was produced to accompany the following manuscript:<br> McDuffie, E. E., S. J. Smith, P. O'Rourke, K. Tibrewal, C. Venkataraman, E. A. Marais, B. Zheng, M. Crippa, M. Brauer, R. V. Martin, <strong>A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel- specific sources (1970- 2017): An application of the Community Emissions Data System (CEDS)</strong>, <em>Earth System Science Data, Submitted</em></p>
The GIS database of Woodnat project for the inventory and monitoring of walnut plantation in Italy and Spain
<p>The database contains 95 selected walnut plantations monitored with the financings of the H2020 WOODnat project in Italy and Spain and georeferenced in WGS84 reference system (EPSG 4326). For each plantation, stationary, cultural and climatic data are available; on a sample of 30 trees for each plot data of growth, wood quality and sanitary conditions are available. These data can be exploited to assess potential wood volume obtainable and quality of raw material, and to identify the weaknesses and errors, strengths and opportunities of the experiences conducted to plan future plantings with greater awareness.</p>
Data Release: Aurorasaurus Science Products Inventory & Survey Results (2014-2019)
<p>Aurorasaurus is an eight-year-old project: the first and only citizen science initiative that tracks auroras around the world via reports on our website, mobile apps, and social media. (See Kosar et al., (2018). Aurorasaurus Real-Time Citizen Science Aurora Data (Version v1.0) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.1255196">http://doi.org/10.5281/zenodo.1255196</a>.)</p> <p>At the American Geophysical Union Fall Meeting 2019, MacDonald and Brandt (2019) gave a talk entitled "Towards developing appropriate and diverse metrics for citizen science – a case study." In the presentation, they examined our 2015 user survey, used more recent metrics to assess Aurorasaurus' current status, and began to lay a framework for their next round of evaluation. </p> <p>In order to frame the process, MacDonald and Brandt (2019) utilized the Science Products and Data Practices inventories created by Wiggins et al., (2018). The authors constructed lists of items and practices that should be present in citizen science projects. While the Science Products and Data Practices inventories are excellent for quantitative analysis, MacDonald and Brandt (2019) wanted to include qualitative analysis of past evaluation as well. To that end, they informally and retrospectively mapped Aurorasaurus' 2015 survey questions to the BASIK framework.</p> <p>Aurorasaurus is publishing this dataset as a case study for how the Wiggins et al. framework can be applied and combined with other systems.</p>
Data from: Multi-taxon inventory reveals highly consistent biodiversity responses to ecospace variation
Amidst the global biodiversity crisis, identifying general principles for variation in biodiversity remains a key challenge. Scientific consensus is limited to a few macroecological rules, such as species richness increasing with area, which provide limited guidance for conservation. In fact, few agreed ecological principles apply at the scale of sites or reserve management, partly because most community-level studies are restricted to single habitat types and species groups. We used the recently proposed ecospace framework and a comprehensive data set for aggregating environmental variation to predict multi-taxon diversity. We studied richness of plants, fungi, and arthropods in 130 sites representing the major terrestrial habitat types in Denmark. We found the abiotic environment (ecospace position) to be pivotal for the richness of primary producers (vascular plants, mosses, and lichens) and, more surprisingly, little support for ecospace continuity as a driver. A peak in richness at intermediate productivity adds new empirical evidence to a long-standing debate over biodiversity responses to productivity. Finally, we discovered a dominant and positive response of fungi and insect richness to organic matter accumulation and diversification (ecospace expansion). Two simple models of producer and consumer richness accounted for 77 % of the variation in multi-taxon species richness suggesting a significant potential for generalization beyond individual species responses. Our study widens the traditional conservation focus on vegetation and vertebrate populations unravelling the importance of diversification of carbon resources for diverse heterotrophs, such as fungi and insects.
Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories 1959-2018
<p>GCP-GridFED (version 2019.1) is a gridded fossil emissions dataset that is consistent with the national CO<sub>2</sub> emissions reported by the Global Carbon Project (GCP). GCP-GridFEDv2019.1 provides monthly fossil CO<sub>2 </sub>emissions for the period 1959-2018 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO<sub>2</sub> emitted by the oxidation of oil, coal and natural gas, with mixed international bunker fuels considered separately, as well as for the calcination of limestone during cement production. GCP-GridFED also includes gridded uncertainties in CO<sub>2 </sub>emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O<sub>2</sub> uptake based on oxidative ratios for oil, coal and natural gas.</p> <p>GCP-GridFED was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR; version 4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2019 global carbon budget (GCB-NAE) for the years 1959-2018 (Friedlingstein et al., 2019).</p> <p>The data description article is under review.</p>
Figure 2 in Improved local inventory and regional contextualization for anuran (Amphibia) diversity assessment at an endangered habitat in southeastern Brazil
Figure 2. Rarefaction curves based on Jackknife I species-richness estimator for records of adults, tadpoles and all life stages pooled for four canga lakes at the Quadrilátero Ferrífero region, southeastern Brazil.
EPA Emissions Inventory 2014
<p>This dataset contains data from the <a href="https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-nei-data">EPA National Emissions Inventory from 2014</a>, separated by sector, in shapefile format. Each line within each file contains the amount of each pollutant (VOC, NOx, SOx, NH3, or PM2.5) in micrograms per second and the coordinates for which that emission is located (X,Y).</p>
Multi-temporal Landslide Inventory for the Far-Western region of Nepal
<p>The Multi-Temporal Landslide Inventory for the Far-Western region of Nepal datasets comprises 26350 different landslide events digitize in form of polygons from Google Earth satellite imagery interpretation. In Google earth has been used for interpretation 93 different sources for 79 different time slices between 2002 and 2018. The maximum scale of interpretation used is 1:1000, meanwhile the scale of digitalization was constant between 1:800 and 1:2000, resulting in a final visualization scale of 1:1000. All landslides in the inventory have been classified between deep-seated and shallow types (attribute field "Depth") by visual interpretation which have been later corroborated with calculations of the elevation differences within the surface of rupture area of the landslides</p> <p>The dataset comprises 4 different shapefiles:</p> <ul> <li><strong>"LandslideInventory_FarWesternNepal_Pol.shp"</strong>: Shapefile with 26350 Polygon features that bound completely the “zone of depletion” and partially the “zone of accumulation” of each identified landslide. Including completely the surface of rupture and more or less partially the depositional zone of the landslides. Landslide</li> <li><strong>"LandslideInventory_FarWesternNepal_Points.shp"</strong>: Shapefile with 25639 Point features that approximately correspond with the center of the surface of rupture area, the point location within each landslide has ben extracted automatically with GIS tools using ALOS PALSAR (12.5 m) DEM. </li> <li><strong>"LandslideInventory_FarWesternNepal_Points_Dated1992_2018.shp"</strong>: Shapefile with 8778 Point features for landslides in the inventory that have been dated within the period 1992-2018 (attribute field "Year". The dating of the landslides has been perform automatically by an own new toolbox in ArcGIS that compare annual Landsat (4-5, 7 and 8), to find sudden vegetation changes within the areas of the digitized landsldies. The tool has an accuracy of 83% to detect annual dates of activation or reactivations of the inventoried landslides. </li> <li><strong>"LandslideInventory_FarWesternNepal_AOI.shp"</strong>: Shapefile with the Polygon boundary of the landslide inventory Area of Interpretation.</li> </ul> <p>All shapefiles are in a UTM projected coordinate system UTM44N (WGS84).</p> <p> </p> <p>This research was funded by the UK Natural Environment Research Council (NERC) and Department for International Development (DFID) as project NE/P000452/1 (LandslideEVO) under the Science for Humanitarian Emergencies and Resilience (SHEAR) program.</p> <p> </p>
DRAFT EU-level policy inventory (Database 3)
<p>SoilCare is taking a very focused approach to improving soil conservation, namely through looking at agricultural practices, and more specifically <strong>S</strong>oil<strong> I</strong>mproving<strong> C</strong>ropping <strong>S</strong>ystems<strong> (SICS)</strong>. A policy mapping exercise was carried out to identify those EU-level policies, which currently shape agricultural practices and may thus influence the uptake of SICS.This Excel Workbook lists and describes all policies reviewed during the scoping exercise and details those selected for in-depth analysis. </p>
Global inventory of moraine-dammed GLOFs (1900-2020)
<p>A new version of global inventory of moraine-dammed GLOFs between 1900 and 2020</p>
India Flood Inventory-Impacts (IFI-Impacts) [1967-2023]: A multi-source national geospatial database to facilitate comprehensive flood research
<p>This repository hosts the India Flood Inventory with Impacts (IFI-Impacts) database. It contains flood event data sourced from the Indian Meteorological Department from 1967-2023. It has undergone extensive manual digitization, cleaning, and includes new information to make it suitable for computational research in hydroclimate.</p> <p>v4.0: Development of District Flood Severity Index (DFSI)</p> <p>v3.0: India Flood Inventory (IFI) 1967-2023. Updated with local government codes (LGD) for state and district. </p> <p>v1.0: India Flood Inventory (IFI) 1967-2016.</p> <p>v2.0: India Flood Inventory (IFI) 1967-2023. With impacts and district flooded area.</p> <p><strong>REFERENCES</strong></p> <p>Saharia, M., Jain, A., Baishya, R.R., Haobam, S., Sreejith, O.P., Pai, D.S., Rafieeinasab, A., 2021. India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research. Nat Hazards. <a href="https://doi.org/10.1007/s11069-021-04698-6">https://doi.org/10.1007/s11069-021-04698-6</a></p> <div> <div>Saharia, M., Jain, S.K., Prakash, V., Malik, H., Sreejith, O.P., Joshi, D., 2025. A district-level flood severity index for flood management in India. Nat Hazards. <a href="https://doi.org/10.1007/s11069-025-07493-9">https://doi.org/10.1007/s11069-025-07493-9</a></div> </div>
A JWST inventory of protoplanetary disk ices. The edge-on protoplanetary disk HH 48 NE, seen with the Ice Age ERS program
<p>JWST NIRSpec G395H spectrum for HH 48 NE edge-on disk, as analyzed in Sturm et al. (2023). DOI: 10.1051/0004-6361/202347512</p>
Presence and proportion of plants in biodiversity inventories conducted by undergraduate students enrolled in animal-related courses
<p>Dataset on biodiversity inventories conducted by 110 undergraduate students enrolled in animal-related courses using the iNaturalist platform.</p>
Uncertainties from the UNFCCC National Inventory Reports (submission 2017)
<p><strong>Summary:</strong></p> <p>This data repository contains the uncertainties of the national greenhouse-gas inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC). We extracted the data from the National Inventory Reports (NIR) submitted in 2017 covering the emission from 2015. We use the data in our study on "Estimating the uncertainty of the greenhouse gas extensions in Multi-Regional Input-Output analysis" submitted to the Journal of Earth System Science Data (ESSD): <a href="https://essd.copernicus.org/preprints/essd-2023-473/">https://essd.copernicus.org/preprints/essd-2023-473/</a></p> <p><strong>Background:</strong></p> <p>NIRs are only available in pdf-format which makes accessing them from computer impossible. Against this background, we extracted the uncertainty tables from the Annex of the NIR pdf documents in a semi-automated way using a set of Python and R scripts. To bring the uncertainty into a common format, manual data cleaning and adjustments were necessary due to different structuring and processing of uncertainty data by the parties. </p> <p><strong>Data: </strong></p> <p>We brought the data into the format provided in the IPCC 2006 guidelines (Volume 1, Chapter 3). The guidelines distinguish two approaches to uncertainty quantification, tier 1 based on analytical error propagation, and tier 2 based on Monte-Carlo simulations. For each, tier 1 and tier 2 uncertainties, the IPCC 2006 guidelines provide a distinct table template, a screenshot of which can be found in this repository under <a href="../api/records/10037714/draft/files/IPCC2006_table3-2/content">IPCC2006_table3-2</a> and <a href="../api/records/10037714/draft/files/IPCC2006_table3-3/content">IPCC2006_table3-3</a>.</p> <p>Accordingly we provide two different data sets: </p> <ul> <li><a href="../api/records/10037714/draft/files/tier1.csv/content">tier1.csv</a> containing the Tier 1 uncertainties structured according to Table 3.2 of the IPCC 2006 guidelines (see <a href="../api/records/10037714/draft/files/IPCC2006_table3-2/content">IPCC2006_table3-2</a>)</li> <li><a href="../api/records/10037714/draft/files/tier2.csv/content">tier2.csv </a>containing the Tier 2 uncertainties structured according to Table 3.2 of the IPCC 2006 guidelines (see <a href="../api/records/10037714/draft/files/IPCC2006_table3-3/content">IPCC2006_table3-3</a>)</li> </ul> <p>Compared to the table templates from the IPCC 2006 guidelines we added three identifying columns to each dataset: </p> <ul> <li><strong>party</strong>: Name of the party</li> <li><strong>year</strong>: Inventory year (2015 for all items)</li> <li><strong>LULUCF</strong>: if emissions from Land use, land-use change, and forestry (LULUCF) are included (<em>incl</em>) or excluded (<em>excl</em>) in the inventory. Background: parties often publish two versions of the uncertainty table: One including emissions from Land use, land-use change, and forestry (LULUCF), one excluding.</li> </ul> <p>Moreover, we split the column <strong>A </strong>into two columns <strong>category </strong>and <strong>classification </strong>and renamed the original column <strong>A </strong>into <strong>A_raw</strong>. </p> <p> </p>
FIG. 44 in An inventory of Bramble sharks Echinorhinus brucus (Bonnaterre, 1788) (Elasmobranchii, Echinorhinidae) in natural history collections worldwide for conservation status assessment
FIG. 44. — Echinorhinus brucus (Bonnaterre, 1788) in Venezuelan (Cumaná) collections: A-D, TNEC-MT (Entry 233).
FIG. 43 in An inventory of Bramble sharks Echinorhinus brucus (Bonnaterre, 1788) (Elasmobranchii, Echinorhinidae) in natural history collections worldwide for conservation status assessment
FIG. 43. — Echinorhinus brucus (Bonnaterre, 1788) in United States (Gainesville, Cambridge, Raleigh, New Port Richey and Washington D.C.) collections: A, B, UF103000 (Entry 223); B, UF 103001 (Entry 224); C, MCZ 39633 (Entry 225); D-H, NCSM 44134 (Entry 226); I, coll. PMH223-9 (Entry 228); J, coll. PMH223- 10 (Entry 229); K, USNM RAD107112-001 (Entry 232).
FIG. 42 in An inventory of Bramble sharks Echinorhinus brucus (Bonnaterre, 1788) (Elasmobranchii, Echinorhinidae) in natural history collections worldwide for conservation status assessment
FIG. 42. — Echinorhinus brucus (Bonnaterre, 1788) in United Kingdom (Penzance, London and Cambridge) collections (continuation): A, PZNAS unregistered (Entry 216); B, RCSL 1311Ba (Entry 220); C-F, UMZC CH50.1/1 (Entry 222).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.