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12,393 results for “materiality”
FIGURE 1 in Redescription of the little known hermit crab, Clibanarius clibanarius (Herbst, 1791) (Decapoda: Anomura: Diogenidae), based on material from southern India and type material
FIGURE 1. Clibanarius clibanarius (Herbst, 1791), male (sl 13.1 mm), DABFUK/AR-AN 31. A, shield and cephalic appendages, dorsal view (setae on right side omitted); B, dactylus to carpus of left pereopod 4, lateral view (setae omitted); C, thoracic sternite 6, ventral view; D, tergite of pleomere 6, dorsal view (setae omitted); E, telson, dorsal view (setae omitted); F, same, terminal margin, posterior view (setae omitted).
Supplementary material for Levin and Swoap
<p>This folder contains three files</p> <p>1) a demonstration video for deep breathing</p> <p>2) a demonstration video for alternate nostril breathing</p> <p>3) a sample data set.</p>
Supplementary material 2 from: Kirichenko N, Triberti P, Lopez-Vaamonde C (2019) New species of leaf-mining Phyllonorycter (Lepidoptera Gracillariidae) from Siberia feeding on Caragana (Fabaceae). ZooKeys 835: 17-41. https://doi.org/10.3897/zookeys.835.33166
: Explanation note: Fabaceae-feeding Phyllonorycter species involved in the study. Where relevent, genitalia preparation number is given in square brackets in the Life stage column. Both the Process ID and Sample ID codes link the record in the BOLD database and the voucher specimen from which the sequence is derived.
FIGURE 4. Leucothoe spinicarpa Abildgaard, 1789 in A redescription of Leucothoe spinicarpa (Abildgaard, 1789) based on material from the North Atlantic (Amphipoda: Leucothoidae)
FIGURE 4. Leucothoe spinicarpa Abildgaard, 1789. Male (neotype) – a: Uropod 1, b: Uropod 2, c: Uropod 3, d: Telson, e: Epimera 13. Male (late stage) – f: distal end of telson.
Supplementary materials for the spatial clustering of Europe
<p>This repository includes:</p> <p>- Three raster files (load density distribution, wind full-load hours, solar full-load hours) for Europe</p> <p>- Three shapefiles (results of the clustering)</p> <p>- Eight input files for the urbs model (2015 and 2050).</p> <p>To use the open-source model generator urbs, visit: https://github.com/tum-ens/urbs</p> <p>The three raster files can be used as inputs for the clustering script: https://github.com/tum-ens/geoclustering</p>
Supplementary material 1 from: Ney G, Schul J (2019) Epigenetic and genetic variation between two behaviorally isolated species of Neoconocephalus (Orthoptera: Tettigonioidea). Journal of Orthoptera Research 28(1): 11-19. https://doi.org/10.3897/jor.28.28888
: Explanation note: Table S1: Sample collection localities, locality coordinates, and number of each species sampled in each year (N.robustus / N.bivocatus).
Data to "Distinguishing mirror from glass: A 'big data' approach to material perception"
<p>This record contains images, models, and analysis scripts written in MATLAB.</p> <p>Tamura, Prokott, Fleming. "Distinguishing mirror from glass: A ‘big data’ approach to material perception." in preparation.</p>
InCLosure Code for Enclosures of Real Functions: Supplementary Material for Chapter "Universal Intervals: Towards a Dependency-Aware Interval Algebra"
<p>InCLosure Code for Enclosures of Real Functions: Supplementary Material for Chapter "Universal Intervals: Towards a Dependency-Aware Interval Algebra", In S. Chakraverty, editor, Mathematical Methods in Interdisciplinary Sciences. John Wiley & Sons, Hoboken, New Jersey, March, 2020. ISBN: 9781119585503.</p>
Supplementary material 1 from: Reyes-Puig C, Bittencourt-Silva G, Torres-Sánchez M, Wilkinson M, Streicher J, Maddock S, Kotharambath R, Müller H, Angiolani Larrea F, Almeida-Reinoso D, Ron S, Cisneros-Heredia D (2019) Rediscovery of the Endangered Carchi Andean Toad, Rhaebo colomai (Hoogmoed, 1985), in Ecuador, with comments on its conservation status and extinction risk. Check List 15(3): 415-419. https://doi.org/10.15560/15.3.415
Supplementary material 1 from: Reyes-Puig C, Bittencourt-Silva G, Torres-Sánchez M, Wilkinson M, Streicher J, Maddock S, Kotharambath R, Müller H, Angiolani Larrea F, Almeida-Reinoso D, Ron S, Cisneros-Heredia D (2019) Rediscovery of the Endangered Carchi Andean Toad, Rhaebo colomai (Hoogmoed, 1985), in Ecuador, with comments on its conservation status and extinction risk. Check List 15(3): 415-419. https://doi.org/10.15560/15.3.415
European demonstration program on the effect-based and chemical identification and monitoring of organic pollutants in European surface waters - Supporting Material
<p>This data contains the supporting material of the publicaiton <a href="https://www.sciencedirect.com/science/article/pii/S0048969717314365">European demonstration program on the effect-based and chemical<br> identification and monitoring of organic pollutants in European surface waters.</a></p> <p> </p>
Supplemental material to 'Quantifying the out-of-plane response of unreinforced masonry walls subjected to relative support motion'
<p>This repository contains the results of numerical simulations along with the Matlab scripts needed to reproduce the results shown in the article:</p> <blockquote> <p>Godio M, Beyer K. Quantifying the out-of-plane response of unreinforced masonry walls subjected to relative support motion. Frattura ed Integrità Strutturale (Fracture and Structural Integrity). 2019;50:194-208. <a href="https://www.fracturae.com/index.php/fis/article/view/2533">DOI:10.3221/IGF-ESIS.50.17</a></p> </blockquote> <p>Version history<br> V1: datasets added to the repository<br> V2: content PDF-file added to the repository</p>
Supplemental material to 'Evaluation of force-based and displacement-based out-of-plane seismic assessment methods for unreinforced masonry walls through refined model simulations'
<p>This repository contains the results of discrete element simulations used to study the response of vertically-spanning unreinforced masonry walls under different wall configurations. The simulations are run using the software package UDEC 6.0 (<a href="https://www.itascacg.com/">Itasca</a>). The dataset has served as basis for the study conducted in the following paper:</p> <blockquote> <p>Godio M, Beyer K. Evaluation of force-based and displacement-based out-of-plane seismic assessmentmethods for unreinforced masonrywalls through refined model simulations. Earthquake Engng Struct Dyn. 2019;48:454-475. <a href="http://doi.org/10.1002/eqe.3144">DOI:10.1002/eqe.3144</a></p> </blockquote> <p>Version history:<br> V1: datasets '01_UDEC_VALIDATION' and '02_UDEC_RESULTS are added<br> V2: minor modifications to the datasets: deletion of unwanted temporary files from the datasets<br> V3: '00_CONTENT' PDF-file is added </p>
Interpretable semi-supervised population prediction and disaggregation using ancillary data - Supplementary Material
<p>Interpretable semi-supervised population prediction and disaggregation using ancillary data - Supplementary Material</p> <p>(details about the datasets + access to datasets + access to source code)</p>
Supplementary material 5 from: Cabezas MP, Ros M, Santos AM, Martínez-Laiz G, Xavier R, Montelli L, Hoffman R, Fersi A, Dauvin JC, Guerra-García JM (2019) Unravelling the origin and introduction pattern of the tropical species Paracaprella pusilla Mayer, 1890 (Crustacea, Amphipoda, Caprellidae) in temperate European waters: first molecular insights from a spatial and temporal perspective. NeoBiota 47: 43-80. https://doi.org/10.3897/neobiota.47.32408
: Explanation note: A Phylogenetic tree of nuclear 28S rRNA. Unfortunately, this gene could not be amplified in P.tenuis species. In P.pusilla, only two haplotypes were detected, differing only by the presence of an indel. B Phylogenetic tree of nuclear ribosomal internal transcribed spacer (ITS). No variation was observed among P.pusilla sequences. Trees were rooted with Caprelladanilevskii and Caprellaliparotensis. Values at the nodes correspond to ML bootstrap support and Bayesian posterior probabilities, respectively.
Fig. S7 in Supplementary Material for The global tree restoration potential
Fig. S7. Global forest restoration potential. The global potential forest cover is illustrated on (A), representing an area of 8.7 billion hectares of forest cover. Forests are defined as pixels with a forest cover ≥ 10%. The global potential forest cover available for restoration is illustrated in (B) using cropland from Globcover and in (C) using Cropland from Fritz and colleagues (2015). These are calculated from the global potential forest cover (A) subtracting existing forest cover and removing agricultural and urban areas. This global tree restoration potential represents an area of 1.8 billion hectares of forest (Globcover; Table S2) or of 1.7 billion hectares of forest (Fritz and colleagues (2015); Table S2).
Fig. S4. K in Supplementary Material for The global tree restoration potential
Fig. S4. K-fold cross-validation (A) procedure; (B) density plot and (C) boxplots of observed versus predicted tree cover estimates. See Methods for detailed description of panel a.
Fig. S1 in Supplementary Material for The global tree restoration potential
Fig. S1. Observed tree cover across the world's protected areas. Spatial distribution of the 0.5 hectare plots located in protected areas (9), for which we photo-interpreted tree cover using very high spatial resolution images. Small captions represent the different forest types in protected areas as seen from very high spatial resolution images, including boreal, dry, temperate and tropical forests.
Fig. S3 in Supplementary Material for The global tree restoration potential
Fig. S3. Tree cover distribution. Histogram illustrating the relative frequency of tree cover, distributed by bins of 10 %. The U-shaped distribution shows a dominance of 0 and 100% of tree cover in the world, when tree cover is photo-interpreted at very high spatial resolution in protected areas, on 0.5-hectare plots and independently of model-based approaches.
Fig. S6 in Supplementary Material for The global tree restoration potential
Fig. S6. Uncertainty map of the prediction of potential tree cover. The uncertainty is calculated at the pixel-level as the standard deviation among the k tree cover layers predicted from the k models developed from the k-fold cross-validation. The bimodal distribution of the standard deviation is illustrated within caption (A), showing a peak at 0% and another at 7% of standard deviation in tree cover. The resulting map (A) shows higher uncertainty in regions with intermediate potential tree cover and low uncertainty in regions with low (e.g. desert) or high (rainforest) tree cover levels. One exception remains, with higher levels of uncertainty in tropical wet forests of Central Africa (C) vs. other tropical wet forests (B).
Fig. S5. Uncertainty from k in Supplementary Material for The global tree restoration potential
Fig. S5. Uncertainty from k-fold cross validation. The uncertainty is expressed as the standard deviation of the tree cover predicted from the k potential tree cover layers computed during the k-fold crossvalidation. (A) Summary of the procedure. (B) Uncertainty (standard deviation) vs. mean predicted tree cover at the pixel level. The relationship shows that the level of uncertainty is greater at intermediate tree cover classes, reaching 15% of tree cover variation at 50% of the predicted potential tree cover.
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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.