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749 results for “large trees”
Long-term (1935-2019) tree population data from remeasurements of a large network of permanent study plots in old-growth forest, Dukes Research Natural Area, Marquette Co., MI, USA
The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 0.2-acre (~0.08 ha) permanent monitoring (CFI) plots. This package includes tree censuses for subsets of CFI plots conducted in 1935, 1948, and 1974-1980, and repeated censuses with mapped stems from 1989 to 2019. This 84-year record constitutes one of the longest repeated-measurement, permanent-plot data-sets for old-growth temperate forest.
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
Inferring whole-genome histories in large population datasets: inferred tree sequences for 1000 Genomes
<p>Tree sequences inferred for the 1000 Genomes phase 3 autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip 1kg_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("1kg_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("1kg_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project
<p>Tree sequences inferred for the SGDP autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("sgdp_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("sgdp_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK
<p>This is the updated version of building 3D model data. The revision includes appending attributes to the lod1 and lod2 shapefile and creating cityjson file for each 3D building model. All 3D building models are available in mesh (.obj), multipath shapefile, and cityjson (.json) now.</p> <p><strong>IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4).</strong></p> <p>Urban Big Data Centre of the University of Glasgow generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council. It is a large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR point clouds. </p> <p>The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.</p> <p>The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [1]-[2]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided in 5 km by 5 km tiles.</p> <p>Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[3]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles. The RMSE of Euclidean distances between each point in the point cloud to the reconstructed model was calculated to evaluate the LoD2 model construction. A table of RMSE and a note for a few problematic models are provided.</p>
FIG. 5 in Terminalia carinata Sabatier & J.Engel, sp. nov. (Combretaceae), a new large tree species from the Guiana shield revealed by re-examination of material previously identified as T. guyanensis Eichler
FIG. 5. — Terminalia guyanensis Eichler: A, fruiting branch; B, transverse section of fruit; C, inflorescences; D, longitudinal section (left) and lateral view (right) of flower; A, B, Sabatier 6331 (CAY); C, D, Sabatier & al. 6018 (P). Drawn by Laurence Ramon. Scale bars: A, B, 2 cm; C, 1 cm; D, 1 mm.
FIG. 3 in Terminalia carinata Sabatier & J.Engel, sp. nov. (Combretaceae), a new large tree species from the Guiana shield revealed by re-examination of material previously identified as T. guyanensis Eichler
FIG. 3. — Leaf abaxial surface in SEM: A-D, Terminalia carinata Sabatier & J.Engel, sp. nov.; note the hidden stomata; E, F, Terminalia guyanensis Eichler; A, B, Sabatier et al. 4891; D, E, De Granville et al. 10958; E, F, Sabatier et al. 6018. Scale bars: A, C, E, 200 µm; B, D, F, 100 µm.
FIG. 2 in Terminalia carinata Sabatier & J.Engel, sp. nov. (Combretaceae), a new large tree species from the Guiana shield revealed by re-examination of material previously identified as T. guyanensis Eichler
FIG. 2. — Terminalia carinata Sabatier & J.Engel, sp. nov.: A, B, inflorescences; C, fruiting branch (note fruit keeled on one side and flat on the other); D, stem with leaves; note: i) the typical Terminalia arrangement of leaves clustered at twig tips; and ii) the leaf margin revolute at very base; E, trunk; F, trunk slash; A, B, Mori & Gracie 18653; C, Sabatier et al. 4891 (type specimen); D, Sabatier 2309. A, B, Photographs by Carol Gracie; C, D, photographs by Daniel Sabatier; E, F, photographs by Julien Engel.
FIG. 1 in Terminalia carinata Sabatier & J.Engel, sp. nov. (Combretaceae), a new large tree species from the Guiana shield revealed by re-examination of material previously identified as T. guyanensis Eichler
FIG. 1. — Terminalia carinata Sabatier & J.Engel, sp. nov.: A, stem with leaves; B, detail of abaxial leaf surface; C, inflorescences with a young shoot of leaves; D, flower; E, longitudinal section of flower; F, three views of stamens; G, fruits; H, transverse section of fruit; A, B, Boom & Mori 2134 (CAY); C-F, Mori & Gracie 18653 (CAY); G, H, Mori & Boom 15121 (CAY). Drawn by Laurence Ramon. Scale bars: A, C, G, H, 1 cm; B, D, E, 1 mm; F, 0.5 mm.
FIG. 4 in Terminalia carinata Sabatier & J.Engel, sp. nov. (Combretaceae), a new large tree species from the Guiana shield revealed by re-examination of material previously identified as T. guyanensis Eichler
FIG. 4. — Distribution of Terminalia guyanensis Eichler and Terminalia carinata Sabatier & J.Engel, sp. nov.: (·) T. guyanensis and (▲) T. carinata Sabatier & J.Engel, sp. nov., herbarium specimens; () T. guyanensis and () T. carinata Sabatier & J.Engel, sp. nov., observations (no voucher) from the GUYADIV and GUYAFOR networks (Engel 2015).
FIG. 6 in Terminalia carinata Sabatier & J.Engel, sp. nov. (Combretaceae), a new large tree species from the Guiana shield revealed by re-examination of material previously identified as T. guyanensis Eichler
FIG. 6. — Terminalia guyanensis Eichler: A, fruiting branch; B, inflorescences; C, trunk; D, bark with a machete slash; A, Sabatier & Molino 5682 (CAY); B-D, Sabatier et al. 6018 (P). A-D, Photographs by Daniel Sabatier.
I-MAESTRO data: 42 million trees from three large European landscapes in France, Poland and Slovenia
<p>Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89,000 ha), Poland (Milicz forest district - 21,000 ha) and Slovenia (Snežnik forest - 4,700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100,000 ha including about 64,000 ha of forest and consist of more than 42 million trees of 51 different species.</p><p>For each virtual landscape we provide a table (in .csv format) with the following columns:<br>- cellID25: the unique ID of each 25x25 m² cell<br>- sp: species latin names<br>- n: number of trees. n is an integer >= 1, meaning that a specific set of species "sp", diameter "dbh" and height "h" can be present multiple times in a cell.<br>- dbh: tree diameter at breast height (cm)<br>- h: tree height (m)</p><p>We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible. The coordinate reference systems are EPSG: 2154 for the Bauges, EPSG: 2180 for Milicz, and EPSG: 3912 for Sneznik.</p><p>The v2.0.0 presents the algorithm in its final state.</p><p>Finally, we provide a proof of how our algorithm makes it possible to reach the total BA and the BA proportion of broadleaf trees provided by the ALS mapping using the alpha correction coefficient and how it maintains the Dg ratios observed on the field plots between the different species (see algorithm presented in the associated Open Research Europe article).</p><p>Below is an example of R code that opens the datasets and creates a tree density map.</p><p>------------------------------------------------------------<br># load package</p><p>library(terra)</p><p>library(dplyr)</p><p> </p><p># set work directory</p><p>setwd() # define path to the I-MAESTRO_data folder</p><p> </p><p># load tree data</p><p>tree <- read.csv2('./sneznik/sneznik_trees.csv', sep = ',')</p><p> </p><p># load spatial data</p><p>cellID <- rast('./sneznik/sneznik_cellID25.asc')</p><p> </p><p># set coordinate reference system</p><p># Bauges:</p><p># crs(cellID) <- "epsg:2154"</p><p># Milicz:</p><p># crs(cellID) <- "epsg:2180"</p><p># Sneznik:</p><p># crs(cellID) <- "epsg:3912"</p><p> </p><p># convert raster into dataframe</p><p>cellIDdf <- as.data.frame(cellID)</p><p>colnames(cellIDdf) <- 'cellID25'</p><p> </p><p># calculate tree density from tree dataframe</p><p>dens <- tree %>% group_by(cellID25) %>% summarise(n = sum(n))</p><p> </p><p># merge the two dataframes</p><p>dens <- left_join(cellIDdf, dens, join_by(cellID25))</p><p> </p><p># add density to raster</p><p>cellID$dens <- dens$n</p><p> </p><p># plot density map</p><p>plot(cellID$dens)</p>
Cornus florida (Cornaceae) - bark - of a large tree
Image of Cornus florida (Cornaceae) - bark - of a large tree
Quercus macrocarpa (Fagaceae) - bark - of a large tree
Image of Quercus macrocarpa (Fagaceae) - bark - of a large tree
Sequoiadendron giganteum (Cupressaceae) - bark - of a large tree
Image of Sequoiadendron giganteum (Cupressaceae) - bark - of a large tree
Quercus phellos (Fagaceae) - bark - of a large tree
Image of Quercus phellos (Fagaceae) - bark - of a large tree
Quercus phellos (Fagaceae) - bark - of a large tree
Image of Quercus phellos (Fagaceae) - bark - of a large tree
Quercus imbricaria (Fagaceae) - bark - of a large tree
Image of Quercus imbricaria (Fagaceae) - bark - of a large tree
Quercus palustris (Fagaceae) - bark - of a large tree
Image of Quercus palustris (Fagaceae) - bark - of a large tree
Quercus palustris (Fagaceae) - bark - of a large tree
Image of Quercus palustris (Fagaceae) - bark - of a large tree
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.