Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,136
datasets available to search
ShareScore release 0.7.1
Dataset results
3,136 results for “Terrestrial”
Tree Inventories for Validating Terrestrial Lidar Measurements at Harvard Forest 2007-2014
Our objective is to improve the measurements of canopy structure and biomass of a forest stand and detect their annual changes via a ground-based laser scanning technology, also known as terrestrial lidar (TLS). A TLS instrument utilizes lasers to scan an environment, measure 3D locations of objects encountered by lasers and detect intensities of laser lights scattered by those objects back to the TLS instrument. TLS have shown abilities and is being further explored to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, aboveground biomass and other useful forest structural parameters rapidly and accurately. Three TLS instruments used in this project include: (1) the Echidna (R) Validation Instrument (EVI), built by CSIRO Australia; (2) Dual-Wavelength Echidna® Lidar (DWEL), built by Boston University, University of Massachusetts, Lowell, University of Massachusetts, Boston and CSIRO Australia; (3) Compact Biomass Lidar (CBL), built by University of Massachusetts, Boston. To validate the forest structural parameters retrieved using these TLS instruments, we set up a one-ha (100 m by 100 m) forest site and collected tree inventory data including: tree location, tree species, DBH, tree height and crown dimension since 2007 with a two-year gap of 2008 and 2009. Lidar data are available from the ORNL DAAC (http://dx.doi.org/10.3334/ORNLDAAC/1045).
Terrestrial LiDAR Scans in the CTFS-ForestGEO Plot at Harvard Forest 2021
In heavily forested and jungle environments where GPS reception is unavailable due to dense canopy cover, it is difficult to determine one's location. Currently, either visual landmarks are used, or open clearings are found where GPS reception can be reestablished. Alternatively, dead-reckoning systems that rely on Inertial Measurement Unit sensor suites can help over moderate distances, but these devices cannot retain positional accuracy over extended ranges. Creare proposes to address this problem by developing the Tree Positioning System. This technological solution will combine a metrology system for determining local tree maps, and geolocalization algorithms that perform spatial pattern matching of local tree maps against a georegistered reference tree map of the area. This system was tested by scanning trees in the ForestGEO plot at Harvard Forest in June 2021.
LTREB Terrestrial data 2012 - current
Pitfall traps measure the activity density of ground-dwelling arthropod communities; Infall traps measure the deposition of flying insects Sticky Cards measure the amount of flying insects with distance from the shoreline. Emergence traps measure the quantity of emerging chironomids
A global map of terrestrial habitat types
<p>We provide a global spatially explicit characterization of 47 (version 001) terrestrial habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme, which is widely used in ecological analyses, including for assessing species’ Area of Habitat. We produced this novel habitat map by creating a global decision tree that intersects the best currently available global data on land cover, climate and land use. The maps broaden our understanding of habitats globally, assist in constructing area of habitat (AOH) refinements and are relevant for broad-scale ecological studies and future IUCN Red List assessments. We hope that these data and outlined framework will spur further development of biodiversity-relevant habitat maps at global scales. An interactive interface helping to navigate the map can be found at on the Naturemap website ( https://explorer.naturemap.earth/map).</p> <p>Provided is the code to recreate the map (to made available soon), the global composite image at native -100m Copernicus resolution for level 1 and level 2 and layers of aggregated fractional cover (unit: [0-1] * 1000) at 1km for level 1 and level 2.</p> <p>Starting with version 004 there changemasks for the years 2016, 2017, 2018 and 2019 are supplied. Changemasks for the composite masks show the changed grid cells and their new values with earlier years being nested in later years, e.g. using the changemask for 2019 includes all changes up to 2019. For the fractional cover estimates at ~1km resolution, new fractional cover changemasks are supplied as subtraction (before - after) between the previous and current year (unit range: [-1 to 1] * 1000).</p> <p>We highlight that only changes in land cover are considered since most of the ancillary layers (e.g. pasture, forest management, climate, etc...) are static and thus not all changes in habitats can be found. We therefore recommend end users to continue using the 2015 dataset unless specific habitat updates to habitat are needed.</p> <p><strong>Citation:</strong></p> <p>Please cite the published paper and state the used version of the habitat map</p> <p>Jung, M., Dahal, P.R., Butchart, S.H.M., Donald, P.F., De Lamo, X., Lesiv, M., Kapos, V., Rondinini, C., Visconti, P., (2020). A global map of terrestrial habitat types. Sci. Data 7, 256. <a href="https://doi.org/10.1038/s41597-020-00599-8">https://doi.org/10.1038/s41597-020-00599-8</a></p>
Plant richness of the terrestrial ecoregions of the world with a mean aridity index lower than 0.65
<p>Data used to compose the <strong>Figure 1</strong> and the <strong>Table S1</strong> of the paper <strong>Biogeography of Global Drylands</strong>, by Maestre <em>et al</em>. (2021).</p>
Annual terrestrial Human Footprint dataset from 1982 to 2000
<p><a href="https://www.nature.com/articles/s41597-022-01284-8">Human footprint dataset</a> extrapolated to past periods 1982--2000. For each pixel we fit a logit-model and then extrapolate it to past years to produce assumed Human footprint prior to year 2000. This assumes simple linear trends in Human footprint.</p>
Carbon and nitrogen isotopes and concentrations in terrestrial plants from a six-year (2006-2012) fertilization experiment at the Arctic LTER, Toolik Field Station, Alaska.
The data set describes stable carbon and nitrogen isotopes and carbon and nitrogen concentrations from an August 2012 pluck of a fertilization experiment begun in 2006. Fertilization was with nitrogen (N) and phosphorus (P). Fertilization levels included control, F2, F5, and F10, with F2 corresponding to yearly additions of 2 g/m2 N and 1 g/m2 P, F5 corresponding to yearly additions of 5 g/m2 N and 2.5 g/m2 P, and F10 corresponding to yearly additions of 10 g/m2 N and 5 g/m2 P. After harvest, plants were separated by species and then by tissue. Tissues were then dried, ground and analyzed for stable isotopes and concentrations at the University of New Hampshire stable isotope laboratory.
Point cloud data from terrestrial laser scanning for stem volume modelling of Scots pine trees
<p>Stem volume is a key forest inventory attribute characterizing growth and yield of individual trees and forest stands. Three-dimensional information from terrestrial laser scanning (TLS) can be used to reconstruct tree stems and provide information on stem volume as well as stem shape. We collected diameter at breast height and height information with traditional field measurements as well as preprocessed TLS point cloud data on 230 Scots pine trees (<em>Pinus sylvestris L.</em>) from southern Finland. The data set here includes three-dimensional information on Scots pine tree stems derived from TLS point clouds. The usage of this data set can include, but is not limited to, development of point cloud processing algorithms for single tree stem reconstruction and investigations of of stem volume modelling for Scot pine. </p> <p>This data set includes two files: Scots_pines.txt includes DBH and height information based on field measurements from the 230 Scots pine trees. File includes the following columns: treeID, DBH, and h, where DBH is presented in cm and h (i.e. tree height) in m. Stem_points.zip, on the other hand, includes 230 laz-files where figure in the name of the laz-file refers to the tree ID in Scots_pines.txt-file. Laz-files include three columns that describe x, y, and z, coordinates (in meters) of stem points in a local coordinate system extracted from the normalized TLS point clouds (i.e. z coordinate describes height above ground).</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>
Simulation of the SLR Space Segment Evolution to Improve the Realization of Terrestrial Reference Frames and Determination of Low-Degree Gravity Field Parameters
<p>These are data obtained from simulation studies of the development of the space segment of the SLR technique. Detailed information can be found in Najder et al. (2025). Najder, J., Sośnica, K., Zajdel, R., & Kur, T. (2025). Simulation of the SLR space segment evolution to improve the realization of terrestrial reference frames and determination of low-degree gravity field parameters. <em>Journal of Geodesy</em>, <em>99</em>(6), 46. https://doi.org/10.1007/s00190-025-01971-5</p>
Supplementary Material for "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City"
<p>This data repository is for the publication "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City" and contains all R scripts and data files to reproduce results as well as all supplementary tables and figures.</p>
habitatmap_terr: the interpreted, terrestrial part of habitatmap_stdized
<p>The data source <code>habitatmap_terr</code> is the further interpreted, terrestrial part of '<a href="https://doi.org/10.5281/zenodo.3355192">habitatmap_stdized</a>' (which, in turn, was derived from the raw data source '<a href="https://doi.org/10.5281/zenodo.3354381">habitatmap</a>'). It is a GeoPackage that contains:</p> <ul> <li> <p><code>habitatmap_terr_polygons</code>: a spatial polygon layer in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>);</p> </li> <li> <p><code>habitatmap_terr_types</code>: a table with the types that occur in each polygon.</p> </li> </ul> <p>This version of <code>habitatmap_terr</code> was derived from version '<code>habitatmap_stdized_2023_v1</code>' as follows:</p> <ul> <li> <p>it excludes all polygons that are most probably aquatic habitat or RIB. These are the polygons for which <strong>all</strong> habitat or RIB types are aquatic. In the process, a distinction was also made between <code>2190_a</code> and <code>2190_overig</code>. There is no exclusion of aquatic types when these coexist with terrestrial types in the same polygon;</p> </li> <li> <p>it excludes types which most probably are <em>no</em> habitat or RIB at all. Those are the types where <code>code_orig</code> contains <code>"bos"</code> or is equal to <code>"6510,gh"</code> or <code>"9120,gh"</code>;</p> </li> <li> <p>it translates several main type codes into a corresponding subtype which they almost always represent: <code>6410</code> -> <code>6410_mo</code>, <code>6430</code> -> <code>6430_hf</code>, <code>6510</code> -> <code>6510_hu</code>, <code>7140</code> -> <code>7140_meso</code>, <code>9130</code> -> <code>9130_end</code>;</p> </li> <li> <p>it distinguishes types <code>rbbhfl</code> and <code>rbbhf</code>.</p> </li> </ul> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/abf596e/src/generate_habitatmap_terr">'n2khab-preprocessing' at commit abf596e</a> for its creation from the <code>habitatmap_stdized</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>Attributes of <code>habitatmap_terr_polygons</code>:</p> <ul> <li> <p><code>polygon_id</code></p> </li> <li> <p><code>description_orig</code>: polygon description based on the original type codes in the <code>habitatmap</code> data source</p> </li> <li> <p><code>description</code>: based on <code>description_orig</code> but with the interpreted type codes</p> </li> <li> <p><code>source</code>: states where <code>description</code> comes from: either <code>habitatmap_stdized</code> or <code>habitatmap_stdized + interpretation</code></p> </li> </ul> <p>Attributes of <code>habitatmap_terr_types</code>:</p> <ul> <li> <p><code>polygon_id</code></p> </li> <li> <p><code>type</code>: the interpreted habitat or RIB type.</p> </li> <li> <p><code>certain</code>: <code>TRUE</code> when the type is certain and <code>FALSE</code> when the type is uncertain.</p> </li> <li> <p><code>code_orig</code>: original type code in raw <code>habitatmap</code>.</p> </li> <li> <p><code>phab</code>: proportion of polygon covered by type, as a percentage.</p> </li> <li> <p><code>source</code>: states where <code>type</code> comes from: either <code>habitatmap_stdized</code> or <code>habitatmap_stdized + interpretation</code></p> </li> </ul>
LTER-Italy terrestrial sites map
<p>Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) freshwater sites are evidenced. The colours of the dots correspond to the terrestrial ecosystem (brown) typologies. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org/">https://deims.org</a>).</p>
Water in the terrestrial planet-forming zone of the PDS 70 disk
<p>This release includes the portion of the JWST-MIRI MRS spectrum of the PDS 70 disk analysed in the paper by Perotti et al. (2023). The original observational data are part of the Guaranteed Time Observation (GTO) program 1282 (PI: Th. Henning) with observation number 66 and will become public on 2 August, 2023 on the MAST database (https://archive.stsci.edu/). This release contains:<br> <br> 1) the full rebinned (4.9-22.5 μm) JWST-MIRI MRS spectrum of PDS 70 presented in Fig. 2 of Perotti et al. (2023);<br> 2) the JWST-MIRI MRS spectrum of PDS 70 in the 6.78-7.36 μm region used for the water line analysis shown in Fig. 3 of Perotti et al. (2023);<br> 3) the Spitzer-IRS low-resolution spectrum observed as part of the Spitzer-IRS GTO program 40679 (PI: G. Rieke) shown in Fig.1 of Perotti et al. (2023). </p> <p>The first dataset consists of one .csv file (1_PDS70_fig2_MIRI_Perotti23.csv) which contains the the 4.9-22.5 μm JWST-MIRI MRS spectrum of PDS 70 presented in Fig. 2 of Perotti et al. (2023). The spectrum is rebinned by averaging 15 spectral points and assign errors σ to the rebinned spectral points assuming a normal error distribution with equal weights for each individual spectral element.<br> <br> The second dataset consists of one .dat file and one python script. One .dat file contains the JWST-MIRI spectrum of PDS 70 in the 6.78-7.36 μm region shown in Fig. 3 of Perotti et al. (2023) where the brightest water emission lines are observed (2_PDS70_fig3_MIRI_Perotti23.dat). The JWST-MIRI continuum-subtracted spectrum and the best-fit water LTE slab model are included. The Python script used to reproduce Figure 3 of Perotti et al. (2023) is also provided (2_script_plot_fig3.py). <br> <br> The third dataset consists of one .dat file (3_PDS70_fig1_IRS_Perotti23.dat) which represents the Spitzer-IRS low-resolution spectrum of PDS 70 shown in Figure 1 of Perotti et al. (2023).</p>
Comparison of terrestrial versus aquatic decomposition rates of logs at the Andrews Experimental Forest, 1985 to 2015
The data collected from this study describe the decomposition of small logs (20-30 cm diameter, 2 m length) in a stream channel to those on an adjacent upland site at the H. J. Andrews Experimental Forest. The stream is a 3rd order above the junction of Lookout Creek and Mack Creek. Three species of trees are being examined: Douglas-fir, western hemlock, and red alder. Data collection started in 1985 and is scheduled to continue to 2050. Periodically a subset of logs is resampled to determine changes in volume, bark cover, density, and nutrient stores. The last set of samples was collected in 2005. Logs ranging in diameter between 20 and 30 cm of a length of 2 m were cut out of live trees of the three species. Logs were placed by hand along a skid road at the terrestrial site. A cable system was used to place log randomly along a stream reach. The location of logs in the stream is noted when they are sampled. The length and diameter as well as bark cover of each sampled log is noted at the time of sampling (td01701). Six cross-sections are removed with a chainsaw. The thickness of the tissue types is noted (inner bark, outer bark, sapwood, and heartwood) and are described in td01702. Samples of each tissue type are taken to determine their moisture content (water mass/dry mass) and density (dry mass/green volume). Density is derived from dry mass and volume as determined via dimensional measurements. Dimensional data, volumes, masses, density, and moisture content are documented in the td01703 table. The volume of logs and tissue types, the total mass, and proportional mass of the tissue types as well as moisture contents is derived from the data in the other data tables and is stored in the td01704 table.
Aquatic and terrestrial insect activity phenology with trap collections at the Andrews Experimental Forest, 2009-2014
This study was designed to evaluate the influence of microclimatic heterogeneity, associated with complex terrain, on phenology and to evaluate potential trophic responses to scenarios of climate change, disturbance and land use. We focus on a simplified model trophic system involving vascular plants, terrestrial and aquatic insects, and migratory neotropical and resident birds. The model trophic system is interesting because the phenologies of different components in the model system are independent (cued by various abiotic drivers) and dependent (due to trophic interactions), potentially leading to complex system behaviors. Plant and poiklothermic animal (ex. invertebrates) phenologies are highly temperature dependent. Phenologies of terrestrial plants and invertebrates would therefore likely exhibit wide spatial and temporal variation across the landscape in response to temperature variation associated with elevational differences, cold air drainages patterns, and temperature inversions. Aquatic invertebrate phenologies are also tied to temperature, but stream temperatures are influenced by different factors than those driving air temperatures and they may be less sensitive to complex terrain. For the invertebrate part of this study, we are examining spring-time (April through June) flying (terrestrial and adult aquatic) insect activity and adult aquatic insect emergence across a range of sites in the HJ Andrews Experimental Forest. Flying insect activity will be assessed using malaise traps deployed at 16 sites ranging from 450m to over 1300m in elevation, and with a variety of forest stand ages and slope aspects. Emerging aquatic insects will be collected with emergence traps in six 1st to 2nd order streams ranging from 450m to 1000m in elevation, and differing in water source (spring vs run-off) and surrounding forest age. Insects from malaise traps will be identified to varying levels from order to genus, depending on the group and available keys. Adult aquat
Measurements of Coarse Woody Debris %C and %N at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC.
Coarse woody debris (CWD) plays a critical role in nutrient retention and cycling, including the cycling and retention of carbon and nitrogen. However, comparison studies of CWD in different forest types and elevation gradients in the southern Appalachian Mountains are lacking. We measured CWD in five different forest communities/elevations at Coweeta Hydrologic Lab. A subsample of CWD in each plot was measured for percent C and percent N, as well as for cations.
Coarse Woody Debris Cations Measurements at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC.
Coarse woody debris (CWD) plays a critical role in nutrient retention and cycling, including the cycling and retention of carbon and nitrogen. However, comparison studies of CWD in different forest types and elevation gradients in the southern Appalachian Mountains are lacking. We measured CWD in five different forest communities/elevations at Coweeta Hydrologic Lab. A subsample of CWD in each plot was measured for percent C and percent N, as well as for cations.
Consequences of non-random tree species loss on litter mass loss, nutrient dynamics, carbon cycling, and decomposer communities across a terrestrial-aquatic interface at Coweeta Hydrologic Lab, Otto, NC
Although litter decomposition is a fundamental ecological process, most of our understanding comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss. The focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. Data were analysed using a statistical approach that first looks for additive identity effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and or composition.
Terrestrial-Stream Biodiversity Litter Processing Datasets from Watershed 20 within the Coweeta Hydrologic Laboratory
Although litter decomposition is a fundamental ecological process, most of our understandings comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss -- the focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. This study was conducted at the Coweeta Hydrologic Laboratory in Watershed 20 on Ball Creek that drains into Coweeta Creek, a tributary of the Little Tennessee River. Data were analyzed using a statistical approach that first looks for additive identiy effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and/ or composition. This approach addresses questions key to understanding the potential effects of species loss on ecosystem processes. If additive effects dominate, the consequences for decomposition dynamics will be predictable based on our knowledge of individual species, but not statistically predictable if non-additive effects dominate.
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.