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1,429 results for “inventory”
New England Enhanced Forest Inventory
Light detection and ranging (LiDAR) has become a common tool for generating remotely sensed forest inventories. However, regional modeling of forest attributes using LiDAR has remained challenging due to varying parameters between LiDAR datasets, such as pulse density. Here we develop a regional model using a three dimensional convolutional neural network (CNN). We then apply our model to publicly available data over New England, generating maps of fourteen forest attributes at a 10 m resolution over 85 % of the region. Attributes include aboveground biomass (kg), total biomass (kg), tree count (#), percent conifer (%), basal area (m^2), mean height (m), quadratic mean diameter (cm), percent spruce/fir (%), percent white pine (%), inner bark volume (m^3), merchantable volume (m^3), and spruce/fir volume (m^3. All values correspond to the amount per pixel cell (I.E. kg of biomass found within that pixel). Map/model performance was assessed using the USFS’s FIA inventory, which constituted an independent dataset free from spatial autocorrelation. More data can be found in the following pre-print: Ayrey, E., Hayes, D. J., Kilbride, J. B., Fraver, S., Kershaw, J. A., Cook, B. D., & Weiskittel, A. R. (2019). Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Forest Inventories. bioRxiv, 580514.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC1 SET Surface Water level data from in Biscayne National Park, Florida, USA (2016-2025)
Surface water level data (m) was collected in Biscayne National Park (BISC) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2016 to 2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 1, known as BISC-SET-1 or BISC1. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC2 SET Surface Water level data from in Biscayne National Park, Florida, USA (2017-2025)
Water level data (m) was collected in Biscayne National Park (BISC) by the National Park Service - South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017-2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 2, known as BISC-SET-2 or BISC2. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - SARI SET Surface Water level data from Salt River Bay National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands.
Surface water level data (m) was collected in Salt River Bay National Historic Park and Ecological Preserve (SARI) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - Mary's Point SET Surface Water level data from Virgin Islands National Park, St. John, US Virgin Islands
Surface water level data (m) was collected in Virgin Islands National Park, Mary's Point (MARY) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - Water Creek SET Surface Water level data from Virgin Islands National Park, St. John, US Virgin Islands
Surface water level data (m) was collected in Virgin Islands National Park, Water Creek (WACR) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
The Sierra Lakes Inventory Project: Non-Native fish and community composition of lakes and ponds in the Sierra Nevada, California
The Sierra Lakes Inventory Project (SLIP) was a research endeavor that ran from 1995-2002 and has supported research and management of Sierra Nevada aquatic ecosystems and their terrestrial interfaces. We described the physical characteristics of and surveyed aquatic communities for > 8,000 lentic water bodies in the southern Sierra Nevada, including lakes, ponds, marshes, and meadows. We also created digital map layers for these water bodies when such layers did not exist. The original objective of SLIP was to describe impacts of non-native fish on lake communities, but SLIP data has subsequently enabled study of additional ecological issues, including regional amphibian declines and their impacts on communities, and impacts of non-native fish on terrestrial species. In addition, these data are being used to develop fish removal efforts to restore aquatic ecosystems and recover endangered amphibians. The SLIP data is stored in a relational database that collectively describes water bodies (e.g., depth, elevation, location), surveys (conditions, effort), and communities (including approximately 170 fish, amphibian, reptile, benthic macroinvertebrate, and zooplankton taxa).
Bonanza Creek LTER: Tree Inventory Data from 1989 to present at Core research sites in Interior Alaska
This is the data from the periodic (3-10 yr interval) tree inventory monitoring of tree growth within the vegetation control plots. In 2013 and 2014, an initial tree inventory was done on each site belonging to the Regional Site Network (RSN). Some young RSN sites had no, or very few trees. In general, inventory is every 5 years presently; the most recent collection was in 2018 and next scheduled collection is for 2023.
Hubbard Brook Experimental Forest: Watershed 1 Tree Inventory, 1996 - ongoing
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. The watershed is forested by typical northern hardwood species (sugar maple, beech and yellow birch) on the lower 90 % of its area, and by a montane boreal transition forest of red spruce, balsam fir and white birch on the highest 10%. 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 nd trees that grow into the ≥10 cm dbh size class are tagged each survey. The data consist of the diameters (dbh) of all the trees ≥10 cm dbh, live and dead, in the whole of the watershed (about 9000 individual stems) and an additional 3000-4000 saplings. 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.
Hubbard Brook Experimental Forest: Watershed 5 Tree Inventory, 1982 - ongoing
A whole-tree harvest was conducted during the dormant season of 1983-1984 in order to assess ecosystem response to whole-tree logging operations. Pre-harvest forest inventory surveys were conducted in 1982 on the whole of the watershed. Post-harvest surveys were conducted in 1990, 1994 and every 5 years thereafter. This data set includes data for 1982 – 2019 surveys. The hydrology has been monitored since 1962 and stream water chemistry monitored since 1963. In 1982, before the clearcut, the watershed was forested by typical northern hardwood species (sugar maple, beech and yellow birch) on the lower 85 % of its area and by a montane boreal transition forest of red spruce, balsam fir and white birch on the highest 15%. 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.
Hubbard Brook Experimental Forest: Watershed 6 Tree Inventory, 1965 - ongoing
The watershed is forested by typical northern hardwood species (sugar maple, beech and yellow birch) on the lower 90% of its area and by a montane boreal transition forest of red spruce, balsam fir and white birch on the highest 10%. Forest inventory surveys were initiated in 1965, repeated in 1977, and repeated at 5 year intervals after that. This data set includes all inventories from 1965 to 2022 (11 surveys). The inventory consists of a total inventory of all trees ≥10 cm diameter-at-breast-height (dbh) (over 11,000 individual stems overtime) on the whole of the watershed (13.23 ha, 549−792 m in elevation), as measured in each of the 208 grid cells (= plots; 25 m x 25 m, 625 m2). 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. While the specifics of the inventory design varied between watersheds and over time, the core measurements were consistent. Differences between exact inventory methods over time are detailed in the Methods. The surveys include 6000 – 7000 live trees and another 2000-3000 dead standing trees. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES) and funded largely through the Long-term Ecological Research (LTER) program through NSF since 1988. 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.
Zooplankton sample inventory for Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018
This dataset provides an inventory of physical samples collected from zooplankton bongo and/or ring net tows conducted during the Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018. The NES-LTER transect, located south of Martha’s Vineyard, Massachusetts, comprises standard stations L1-L11 and extends 150 km offshore. Dedicated NES-LTER cruises target all four seasons: winter, spring, summer, and fall. Samples are collected via oblique tows with a 61-cm bongo net fitted with 150- and 335-micron mesh nets, as well as a ring net with a 20-micron mesh net. During earlier spring and fall cruises along the NES-LTER Transect, in collaboration with the Ocean Observatories Initiative, samples were collected through vertical tows using a ring net with a 150-micron mesh. Samples collected are distributed among various laboratories for DNA metabarcoding, stable isotopes analysis, and morphological identification.
Inventory maps of hazardous geological processes_Transcarpathia, Ukraine
<p>Under the ImProDiReT Project running at Regional Transcarpathia level an Inventory maps of the hazardous geological processes’ manifestations for the Transcarpathia (landslides, mudflows, flooding and flash floods, karst) have been created.</p>
LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots
<p>In forest inventory, trees are usually measured by handheld instruments; among the most relevant are calipers, inclinometers, ultrasonic devices, and laser range finders. Traditional forest inventory is nowadays redesigned, since modern laser scanner technology became available. Laser scanner generate massive data in the form of 3D point clouds. Novel methodology is currently developed to provide estimates of the tree positions, stem diameters, and tree heights from these 3D point clouds. This dataset was made publicly accessible to test new software routines for the automatic measurement of forest trees using laser scanner data. Benchmark studies with performance tests of different algorithms are welcome. The dataset contains co-registered raw 3D point-cloud data collected on 20 forest inventory sample plots in Austria. The data was collected by two different laser scanning systems: (i) a mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK), and (ii) a static terrestrial laser scanner (TLS) (Focus3D X330, Faro Technologies Inc., Lake Mary, FL, USA). The data also contains digital terrain models (DTM), field measurements as reference data (“ground-truth”), and the output of recent software routines for the automatic tree detection and the automatic stem diameter measurement.</p>
Data set and code supporting Marshall et al., "An inventory of online reptile images"
<p>Data set and code supporting: MARSHALL, B.M., FREED, P., VITT, L.J., BERNARDO, P., VOGEL, G., LOTZKAT, S., FRANZEN, M., HALLERMANN, J., SAGE, R.D., BUSH, B. and DUARTE, M.R., 2020. An inventory of online reptile images. <em>Zootaxa</em>, <em>4896</em>(2), pp.251-264. DOI:<a href="https://doi.org/10.11646/zootaxa.4896.2.6">10.11646/zootaxa.4896.2.6</a></p> <p>Data includes: </p> <ul> <li>Supplementary Table 1. List of all species and the number of photos in each of the 6 repositories: "SuppData1_Species_Photo_Count_Table_2020-08-04_no_syn.csv"</li> <li>Supplementary Table 2. List of species without photo in any of the 6 repositories: "SuppData2_Species_no_photos.csv"</li> <li>Supplementary Table 3. Per country summary data of number of species present and number with images: "SuppData3_Country_species_counts.csv"</li> <li>Reptile Database species checklist: "reptile_checklist_2020_04.csv"</li> <li>Reptile Database species synonyms used in second Wikimedia search: "reptile names 2019 syno.csv"</li> </ul> <p>Code includes:</p> <ul> <li>R code used to retrieve Flickr photograph metadata: "SuppCode1_Flickr_search.R"</li> <li>R code used to retrieve Wikimedia photograph metadata: "SuppCode2_Wikimedia_query.R"</li> <li>R code used to retrieve HerpMapper photograph metadata: "SuppCode3_HerpMapper_search.R"</li> <li>R code used to generate figures: "SuppCode4_Figure Generation.R"</li> </ul> <p>Also includes Zootaxa supplementary table.</p> <p> </p>
LAUTx - Individual Tree Point Clouds From Austrian Forest Inventory Plots
<p>This dataset contains manually segmented tree point clouds from Personal Laser Scanning (PLS) data, and additionally automatic segmented trees from the same point clouds. The raw point cloud data has been published in LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots (<a href="https://doi.org/10.5281/zenodo.3698956">https://doi.org/10.5281/zenodo.3698956</a>) and six of those plots were processed for this data. Purpose of this data is to serve as benchmarking for automatic tree segmentation algorithms.</p>
MARCSI - Inventory of Marine Citizen Science Initiatives and the FAIRness of the data they produce
<p>Inventory (data set) of Marine Citizen Science Intiatives collected and described in the publication entitled "Past and present marine citizen science around the globe: a cumulative inventory of initiatives and data produced" co-authored by Uta Wehn, Ane Bilbao, Luke Somerwill, Torsten Linders, Joan Maso, Stephen Parkinson, Christina Semasingha,<sup> </sup>Sasha Woods.</p>
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
Near Pan-Svalbard cryospheric hazards inventory (SvalCryo)
<p>We present a comprehensive inventory of thaw slumps (TS) and thermo-erosion gullies (TEG) on the Svalbard Archipelago. We used the most recent orthophotos (0.5 x 0.5 m pixel size) acquired in 2009-2011 from the Web Map Services (WMS) of the Norwegian Polar Institute. TS and TEG were identified and digitised on-screen as polygons in the ETRS_1989_UTM_Zone_33N coordinate reference system. <span>TS and TEG were identified based on their morphology, digitised on-screen (maximum zoom was 1:1000) as polygons, and then individually quality checked in the GIS environment. This process was repeated twice, to avoid any bias in feature(s) mapping, first by a geomorphologist (first author) and then by an Arctic geologist (second author). The cryospheric inventory of the 14 regions (Andre<span>é</span> Land, Dickson Land, James I Land, Nordenski<span>ö</span>ld Land, Bünsow Land, Olav V Land, Sabine Land, Nathorst Land, Heer Land, Wedel Jarlsberg Land, Torell Land, S<span>ørkapp Land, </span>Barents<span>øya and Edgeøya) </span>totalises 8491 polygons, out of which 3679 are TS and 4812 are TEG. Within the attribute tables, there are eight columns comprising details about each polygon/feature, as follows: FID (ID showing the total number of polygons), Shape (Polygon), ID (each polygon from each region has associated an ID for both TS and TEG), Area (sq. m), Perimeter (m), MaxDistanc (calculated between two points along the polygon perimeter), Elongation (calculated as the maximum distance divided by the square root of the area), Region (the name of the region that the feature belongs to).</span></p>
Accompanying material to the Inventory of opportunities and bottlenecks in policy to facilitate the adoption of soil-improving techniques
<p>Inventory of policies at EU and country level for the inventory and analysis of bottlenecks and opportunities in sectoral and environmental policies to facilitate the adoption of Soil-Improving Cropping Systems (SICS).</p>
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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.