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1,777 results for “under bark”
Quercus lobata (Fagaceae) - bark - of a small tree or small branch
Image of Quercus lobata (Fagaceae) - bark - of a small tree or small branch
Quercus lobata (Fagaceae) - bark - of a small tree or small branch
Image of Quercus lobata (Fagaceae) - bark - of a small tree or small branch
Quercus lobata (Fagaceae) - bark - of a large tree
Image of Quercus lobata (Fagaceae) - bark - of a large tree
Quercus lobata (Fagaceae) - bark - of a large tree
Image of Quercus lobata (Fagaceae) - bark - of a large tree
Quercus lobata (Fagaceae) - bark - of a large tree
Image of Quercus lobata (Fagaceae) - bark - of a large tree
Quercus lobata (Fagaceae) - bark - of a large tree
Image of Quercus lobata (Fagaceae) - bark - of a large tree
Quercus lobata (Fagaceae) - bark - of a large tree
Image of Quercus lobata (Fagaceae) - bark - of a large tree
Quercus lobata (Fagaceae) - bark - of a large tree
Image of Quercus lobata (Fagaceae) - bark - of a large tree
Quercus lobata (Fagaceae) - bark - of a large tree
Image of Quercus lobata (Fagaceae) - bark - of a large tree
Quercus acutissima (Fagaceae) - bark - of a medium tree or large branch
Image of Quercus acutissima (Fagaceae) - bark - of a medium tree or large branch
Quercus montana (Fagaceae) - bark
Image of Quercus montana (Fagaceae) - bark
Lake Superior bluff toes and crests from Wisconsin Point to Bark Point, 2009 and 2019
<p>This repository contains shapefiles of bluff toe and crest points delineated from LiDAR data collected in the years 2009 and 2019 for the Wisconsin Lake Superior coast from Wisconsin Point to Bark Point. The shapefiles have a horizontal coordinate reference system of EPSG:32615 and their names describe the year and feature they represent. Additional details regarding the creation of these points can found in the accompanying publication (https://doi.org/10.1016/j.jglr.2024.102366).</p>
Decomposition of bark beetle-attacked trees after mortality varies across forests
<p>Data are from a 2 year experiment examining differences in decomposition processes between bark beetle-attacked trees and trees not attacked by bark beetles in three sites spanning a broad geographic area. Specifically, in Honduras, and Mississippi and Arizona, USA, we felled one recently bark beetle-attacked and one apparently healthy conspecific tree at each site that was cut into 120 experimental logs. Logs of each tree (attacked or unattacked) were assigned one of three metal mesh covering treatments: 1) fully covered to exclude all macroinvertebrates, 2) covered from above to exclude secondary bark beetle colonization, 3) no cover to allow all detrital food web organisms. Half of all logs at each site was collected after 1 and 2 years and the density loss, insect visual damage rating, and abundance of termites, ants, and beetles was measured.</p>
Bark beetle predictive model results (FirEUrisk)
<p>This datasets contains three products developed within the FirEUrisk project. <strong>FirEUrisk</strong> is funded by the European Union as part of the Horizon 2020 framework program (Grant Agreement No. 101003890). It is focused on evaluating and promoting an integrated science-based strategy to improve forest fire risk assessments in Europe</p> <p><strong>FirEUrisk_BarkBeetleAttackPredic_50m_DE.CZ.PL_20240126_V01.tif</strong></p> <p>This product shows the probability of a bark beetle (<em>Ips typographus</em>) attack between September 2020 and October 2021. The assessment includes a set of risk factors that predispose forests to pest infestations and wildfires propagation for the FirEUrisk Central-Eastern Europe Pilot Site. This technological solution aims to identify areas prone to a bark beetle infestation using Earth Observation data and machine learning techniques. The geospatial results are provided in raster format (.tiff) to help managers optimise their resources and mitigate the negative effects of plagues. The results indicate the probability of a bark beetle attack, being ranged from 0 to 100.</p> <p> <br><strong>FirEUrisk_BarkBeetleDamage_10m_DE.CZ.PL_20240126_V01.tif</strong></p> <p>This product contains an estimate of the real bark beetle damage between September 2020 and October 2021. This estimate was derived from Sentinel-2 imagery and was used as target to train the predictive model.</p> <p> <br><strong>FirEUrisk_SpruceClassification_10m_DE.CZ.PL_20240126_V01.tif</strong></p> <p>This product contains a classification of spruce <em>(Picea abies) </em>area for September 2020. It was derived from Sentinel-2 imagery and used as input for the estimation of bark beetle damage and the predictive model.</p>
Figure 5 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 5. Slopes of occupied locations of Barking deer habitat.
Figure 1 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 1. Distribution of Barking deer in study area.
Figure 7 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 7. Aspects of occupied locations of Barking Deer in study area.
Figure 3 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 3. Plant species recorded in summer from habitat of Barking deer.
Figure 6 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 6. Coarse topography / habitat characteristics at occupied locations of Barking deer.
Figure 4 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 4. Plant species recorded in winter from habitat of Barking deer.
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.