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9,882 results for “Scale”
FIGURE 3 in Scale surface microstructure and scale size in the tooth-carp genus Aphanius (Teleostei, Cyprinodontidae) from endorheic basins in Southwest Iran
FIGURE 3. Scale surface morphology of the studied species. A–I: Aphanius sophiae from the Kor River subsystem (A—Safashahr, B—Ghadamgah, C—Beyza, D—Bandeamir, E—Kharameh) and from the Tashk and Bakhtegan Lakes subsystem (F—Gomban, G—Tashk, H—Bakhtegan, I—Gol). J–M: A. farsicus from the Maharlu Lake Basin (J—Dobaneh, K—Pirbanoo, L—Babunak, M—Barmeshoor). N: A. pluristriatus from Zarjan in the Mond River Basin. Arrows indicate secondary radii (A), primary radii (K), and tertiary radii (E, J, N). Dashed lines in J show the area of rostral, lateral and caudal fields. Scale bar: 200 µm.
Figure 7. A in Sleeping with the enemy: unravelling the symbiotic relationships between the scale worm Neopolynoe chondrocladiae (Annelida: Polynoidae) and its carnivorous sponge hosts
Figure 7. A, ASV richness (Shannon index) using rarefied counts for all the samples in this study. Samples are differentiated by species, tissue type and replicate. B, ASV richness (Shannon index) using rarefied counts for Chondrocladia robertballardi. Samples are differentiated by tissue type and replicate. C, non-metric multidimensional scaling (nMDS) ordination of microbiome similarity among samples in this study. D, nMDS ordination of microbiome similarity among samples of C. robertballardi, after data correction for sampling site effect.
Figure 2. A–F in Sleeping with the enemy: unravelling the symbiotic relationships between the scale worm Neopolynoe chondrocladiae (Annelida: Polynoidae) and its carnivorous sponge hosts
Figure 2. A–F, faecal pellet analysis of Neopolynoe chondrocladiae from station CS-BT6 (specimen ID CS-BT6-602-B2, light microscopy). A, general view of the polynoid; white arrow indicates the faecal pellet. B, general view of the pellet. C, detail of a pereipod fragment (possibly from the isopod genus Astacilla) within the pellet. D, detail of an appendage of an unidentified crustacean within the pellet. E, aggregation of microsclere isochaelae (white arrow) and scattered anchorate isochaelae (black arrow) within the pellet. F, detail of anchorate isochaela (black arrow in E). G, H, SEM micrographs of a fragment of Chondrocladia robertballardi (specimen ID CS-ECOMARG). G, calanoid copepod (possibly genus Calanus) attached to the anterior part to the axis of C. robertballardi. H, detail of one of the appendages of the copepod surrounded by isochaelae (white arrows).
Figure 1. A in Sleeping with the enemy: unravelling the symbiotic relationships between the scale worm Neopolynoe chondrocladiae (Annelida: Polynoidae) and its carnivorous sponge hosts
Figure 1. A, map of the sampling sites in the Atlantic and Indian Oceans. B, map of the sampling sites in the Cantabrian Sea (Atlantic Ocean). See Table 1 for details on sampling sites.
Figure 4. A in Sleeping with the enemy: unravelling the symbiotic relationships between the scale worm Neopolynoe chondrocladiae (Annelida: Polynoidae) and its carnivorous sponge hosts
Figure 4. A, histological section of an elytron of Neopolynoe chondrocladiae from station CS-BT6 (specimen ID CS-BT6- 602-B2) presenting some microtubercles externally. B, detail of the mid-part of the elytron showing internally calixshaped photocytes (arrowed) arranged along the ventral side of the elytron. C, confocal microscopy picture of an elytron of N. chondrocladiae, ventral view. Brighter autofluorescent cells surrounding the elytrophore scar correspond to photocytes. Absorption spectrum showing a peak at 530 nm. D, confocal microscopy picture of an elytron of Neopolynoe acanellae, ventral view. Brighter autofluorescent cells to the right of the elytrophore scar correspond to photocytes. Absorption spectrum showing a peak at 530 nm. E, confocal microscopy picture of an elytron of Robertianella synopththalma, ventral view. Brighter autofluorescent cells around the elytrophore scar correspond to photocytes. Absorption spectrum showing a peak at 530 nm.
Figure 6. Heatmap showing the top 200 in Sleeping with the enemy: unravelling the symbiotic relationships between the scale worm Neopolynoe chondrocladiae (Annelida: Polynoidae) and its carnivorous sponge hosts
Figure 6. Heatmap showing the top 200 most abundant ASVs for each sample. The colour range (0 to 4) represents the log10 transformation of the rarefied counts.
Figure 3. A in Sleeping with the enemy: unravelling the symbiotic relationships between the scale worm Neopolynoe chondrocladiae (Annelida: Polynoidae) and its carnivorous sponge hosts
Figure 3. A, microCT scan capture of C. virgata (NHMUK 1890.4.10.6) and its symbiont Neopolynoe chondrocladiae. Full video of the scan available at https://youtu.be/I7woszSZHEk B, average angle of the parapodia with respect to the body axis of N. chondrocladiae along its body. Measurements were grouped in anterior, mid-anterior, mid-posterior and posterior regions. Inset showing an example of how angles were measured using virtual sections at every chaetiger.
Figure 8 in Sleeping with the enemy: unravelling the symbiotic relationships between the scale worm Neopolynoe chondrocladiae (Annelida: Polynoidae) and its carnivorous sponge hosts
Figure 8. Stable isotopic values (mean and standard deviation of δ15N and δ13C) of the different hosts (Acanella arbuscula and Chondrocladia robertballardi) and annelids (Neopolynoe acanellae and N. chondrocladiae) collected in this study. Zooplankton values correspond to a mixture of species of crustaceans (copepods, ostracods, amphipods, and cumaceans) and chaetognaths.
Data associated with studies on wind and phoretic dispersal of crapemyrtle bark scale
<p>These are data associated with studies on the wind-mediated and phoretic dispersal of crapemyrtle bark scale.</p>
Data for : Ecological associations distribution modelling of marine plankton at global scale
<p>Git repository : https://gitlab.univ-nantes.fr/combi-ls2n/adm</p>
PM100: A Job Power Consumption Dataset of a Large-Scale HPC System
<p>The dataset is a collection of jobs extracted from the job_table data of M100 (<a href="https://doi.org/10.5281/zenodo.7588815">https://doi.org/10.5281/zenodo.7588815</a>), a collection of data extracted from a Tier-0 supercomputer hosted at CINECA (Marconi100, <a href="https://www.hpc.cineca.it/hardware/marconi100">https://www.hpc.cineca.it/hardware/marconi100</a>). The original job data present in M100 are filtered out by considering only the jobs running exclusively on the resources. Each job entry included in PM100 contains the power consumption of the job recorded at Node level, CPU level and Memory level. The final dataset contains 231116 jobs, executed on Marconi100 between May and October 2020. </p><p>The dataset is stored as a parquet file, where each entry contains the information on a job execution. </p><p>The structure of the data, as well as the code to generate them, is contained in the official GitHub repository of the project: <a href="https://github.com/francescoantici/PM100-data/">https://github.com/francescoantici/PM100-data/</a>.</p>
[Dataset] Evaluation of the Empirical Scaling Factor of Joule Heating Rates in TIE-GCM with EISCAT Measurements
<p>Dataset used for "Evaluation of the Empirical Scaling Factor of Joule Heating Rates in TIE-GCM with EISCAT Measurements"</p>
Code to run the analyses of "Forest storm resilience depends on the interplay between functional composition and climate - insights from European-scale simulations" by Barrere et al. (2024).
<p>Repository containing the code to run the model and statistical analyses of the paper "Forest storm resilience depends on the interplay between functional composition and climate - insights from European-scale simulations" by Julien Barrere, Björn Reineking, Maxime Jeaunatre and Georges Kunstler, accepted by Functional Ecology in 2024.</p><p> </p><p>The code requires prior installation of the <a href="https://github.com/gowachin/matreex">matreex</a> R package, developped by Maxime Jeaunatre (INRAE), and of the ```targets``` package. The data folder, required to run the code, can be made available upon request to julienbarrere3@gmail.com</p><p> </p><p>Once the packages are installed and the data folder is placer in the main folder, just run ```targets::tar_make()``` from R and the script will download the other packages required and run the analyses.</p><p> </p><p>A version of this code is also available in the github <a href="https://github.com/jbarrere3/FunDiv_ipm">repository</a></p><p> </p><p> </p>
A large-scale artificial forest tree population for sampling and estimation methods simulations
<p>The dataset utilized in the Simulation Study section of the article is generated for a 10 km x 10 km area. This artificial dataset is constructed by populating polygons with trees sourced from the NFI database, representing the stands of the associated Vosges polygon. The aim is to create a sizable dataset that closely resembles a real forest by incorporating authentic information. As the Vosges dataset consists of a set of polygons, the artificial dataset is formed using voronoi polygons, each randomly linked to a Vosges dataset polygon. The Vosges dataset contributes basal area information to the voronoi polygons. These voronoi polygons are then filled with trees from the NFI database, corresponding to the stands of the Vosges polygon. The dataset comprises 1,174,227 trees, each with specified attributes such as circumference at a height of 130cm, top height, volume, species, and status (dead/alive).<br> </p>
Arctic Tectonics and Volcanism: a multi-scale, multidisciplinary educational approach (Petrel and GPlates data package)
<p>The dataset includes pre-loaded and integrated data sets used in teaching the AGx51 course on Arctic Tectonics and Volcanism at the University Centre in Svalbard. </p><p>The dataset includes:</p><ul><li>a Petrel 2020 project with pre-loaded cultural, map, subsurface and borehole data from Svalbard</li><li>a GPlates project with relevant circum-Arctic datasets, exercises and plate tectonic models</li></ul><p>The data package is related to a manuscript currently in review for Geoscience Communication. </p>
Low-Temperature Vacuum Evaporation of Ammonia from Pig Slurry at Laboratory and Pilot-Plant Scale (Data sets)
Open the record for dataset details and reuse information.
Data for "Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry"
<p>Neural and FEM E-field simulation data generated for Aberra AS, Wang R, Grill WM, Peterchev AV. (2023). "Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry". <i>Brain Stimulation</i>. Dataset includes:</p><ul><li><i>cell_data/ </i>- Coordinates and morphology information for all model neurons</li><li><i>nrn_sim_data/</i> - Polarization data from NEURON simulations for all 25 model neurons included in the study, either in response to uniform E-field or tDCS.</li><li><i>layer_data/ - </i>surface meshes used for placing and orienting neuron models and corresponding sampling grids for CNNs</li><li><i>simnibs/</i> - E-field simulation and head mesh data generated within SimNIBS simulation environment</li></ul><p>To generate figures using these data, use the matlab code stored in the tDCSsim_Aberra2023 repository (https://github.com/Aman-A/tDCSsim_Aberra2023) </p>
FIGURES 5–8. Brontostoma bahiensis, male. 5–7, scale bars 5.0 in A new species of Brontostoma Kirkaldy, description of the male of Brontostoma bahiensis Gil-Santana, Costa & Marques and notes on Brontostoma doughertyae Gil-Santana et al. (Hemiptera: Heteroptera: Reduviidae: Ectrichodiinae)
FIGURES 5–8. Brontostoma bahiensis, male. 5–7, scale bars 5.0 mm, 5, dorsal view, 6, ventral view, 7, dorsal view, except distal portion of body and hind legs; 8, head, lateral view, scale bar 0.5 mm.
A tunable family of CAAC-ruthenium olefin metathesis catalysts modularly derived from a large-scale produced ibuprofen intermediate
<p>Data confirming the structure of the new compounds obtained within the project, published in <br><i><strong>Chem. Sci.</strong></i>, 2023,<strong>14</strong>, 10744-10755</p><p>The research was supported by the National Science Centre, Poland (OPUS grant DEC-2017/27/B/ST5/02563).</p>
Large-scale green grabbing for wind and solar PV development in Brazil
<h2>Large-scale green grabbing for wind and solar PV development in Brazil</h2><p>This repository contains the R code and parts of the data used for the analysis in the paper "Large-scale green grabbing for wind and solar PV development in Brazil" by Michael Klingler, Nadia Amelie, Jamie Rickman, and Johannes Schmidt, available as <a href="https://eartharxiv.org/repository/view/5824/">pre-print</a>.</p><p>Due to data sharing limitations, we cannot provide all data in the repository. Partly this data is not available publically at all (i.e. Bloomberg data, data by the instituto socio ambiental), partly the data has to be downloaded manually (CAR).</p><p>We still provide a repository which at least allows to understand the procedures we used during the analysis.</p><h3>Land tenure data set</h3><p>The procedures used to form our final land tenure data set can be found in land-tenure-data/processing.txt It is a mix of analyses in Python and in QGis.</p><h3>Analysis of land tenure data and park ownership/investment information</h3><p>The R-code to analyze the owernship relationships between windpark areas and investors/owners can be found in src/. All required libraries will install automatically.</p><p>The first two scripts cannot be executed due to data limitations. They create the sankey diagrams linking park areas to onwers and investors:</p><p>- 1.1-figures-results-1-wind.R</p><p>- 1.2-figures-results-1-solar.R</p><p>These three scripts are used to analyze the land tenure types prevailing on parks and comparing them to random areas. They should run with the provided data sets:</p><p>- 2-random-sampling-areas.R</p><p>- 3-intersection-parks-land-tenure.R</p><p>- 4-figures-land-tenure.R</p><p>This script validates our data against an independent data source. However, it cannot be run as it needs the proprietary Bloomberg database:</p><p>- 5-validation.R</p>
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.