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187
datasets available to search
ShareScore release 0.9.0
Dataset results
187 results for “digital imaging”
Microhabitat selection of meadow and steppe vipers enlightened by digital photography and image processing to describe grassland vegetation structure
<p>Dataset</p> <ol> <li> <p>Understanding animals’ selection of microhabitats is important in both ecology and biodiversity conservation. However, there is no generally accepted methodology for the characterisation of microhabitats, especially for vegetation structure.</p> </li> <li> <p>We studied microhabitat selection of <em>Vipera</em> snakes by comparing grassland vegetation structure between viper occurrence points and random points in three grassland ecosystems: <em>V. graeca</em> in mountain meadows of Albania, <em>V. renardi</em> in loess steppes of Ukraine, and <em>V. ursinii</em> in sand grasslands in Hungary. We quantified vegetation structure in an objective manner by automated processing of images taken of the vegetation against a vegetation profile board under standardised conditions. We developed an R script for automatic calculation of four vegetation structure variables derived from raster data obtained in the images: leaf area (LA), height of closed vegetation (HCV), maximum height of vegetation (MHC), and foliage height diversity (FHD).</p> </li> <li> <p>Generalized linear mixed models revealed that snake occurrence was positively related to HCV in <em>V. graeca</em>, to LA in <em>V. renardi</em> and to LA and MHC in <em>V. ursinii</em>, and negatively to to HCV in <em>V. ursinii</em>.</p> </li> <li> <p>Our results demonstrate that vegetation structure variables derived from automated image processing significantly influence viper microhabitat selection. Our method minimises the risk of subjectivity in measuring vegetation structure, allows upscaling if neighbouring pixels are combined, and is suitable for comparison of or extrapolation across different grasslands, vegetation types or ecosystems.</p> </li> </ol>
Digital Imaging Versus Ophthalmoscopy
ClinicalTrials.gov study NCT05282147. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Digital Analysis of Ultrasonographic Images in Children With Wry Neck
ClinicalTrials.gov study NCT03266224. IPD Sharing: NO. Countries: 0. Publications: 11.
Efficacy and Safety of AEYE-DS Software Device for Automated Detection of Diabetic Retinopathy From Digital Fundus Images
ClinicalTrials.gov study NCT04612868. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Applicability of 3D Printing and 3D Digital Image Reconstruction in the Planning of Complex Liver Surgery (LIV3DPRINT).
ClinicalTrials.gov study NCT03416387. IPD Sharing: YES. Countries: 1. Publications: 0.
Evaluation of the Efficacy of Digital Breast Tomosynthesis Imaging
ClinicalTrials.gov study NCT01373671. IPD Sharing: NO. Countries: 1. Publications: 0.
Image Evaluation of Philips Philips MammoDiagnost DR Full Field Digital Mammography System (FFDM)
ClinicalTrials.gov study NCT00999596. IPD Sharing: Not stated. Countries: 2. Publications: 0.
Assessment of Digital Imaging as a Tool for Diagnosing Psoriasis, Hand Rashes and Unusual Moles
ClinicalTrials.gov study NCT00005781. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Molecular Breast Imaging and Digital Breast Tomosynthesis in Screening Patients With Dense Breast Tissue
ClinicalTrials.gov study NCT03220893. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Carestream Digital Radiography Long Length Imaging Software Data Collection Protocol
ClinicalTrials.gov study NCT01592435. IPD Sharing: NO. Countries: 1. Publications: 0.
Digital Particle Image Velocimetry (DPIV) data on a hovering hawkmoth to determine the strength of the vortex loop
Open the record for dataset details and reuse information.
Digital image correlation (DIC) measurement of contact stiffness
Open the record for dataset details and reuse information.
Figure 5 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 5 Workflow of image masking using front- and back-light information.
Figure 4 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 4 Workflow of EDOF-calculation. For a detailed description, see Suppl. material 1: S2.
Figure 18 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 18 Interactive, textured 3D-model of Anoplotrupes stercorosus (21 mm body size).
Figure 16 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 16 Interactive 3D-model of Helicodonta obvoluta (9 mm shell diameter).
Figure 15 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 15 Interactive 3D-model of Prosopocoilus savagei (23 mm body size).
Figure 14 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 14 Interactive 3D-model of Pogonocherus hispidus (6 mm body size).
Figure 13 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 13 Interactive 3D-model of Oscinella frit (1.5 mm body size).
Figure 1 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 1 Schematic setup (A) and image (B) of the Darmstadt Insect Scanner DISC3D.
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.