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9,882 results for “Scale”
A Large-scale Data Set and an Empirical Study of Docker Images Hosted on Docker Hub
<p>The data set schema, fields description, and analysis scripts are in the GitHub artifact repository.</p> <p>https://github.com/linncy/icsme2020-docker-study</p> <p> </p> <p>@inproceedings{LinICSME20,<br>author={Changyuan Lin and Sarah Nadi and Hamzeh Khazaei},<br>title={A Large-scale Data Set and an Empirical Study of Docker Images Hosted on Docker Hub},<br>booktitle={Proceedings of the 36th IEEE International Conference on Software Maintenance and Evolution (ICSME)},<br>year={2020},<br>url_Paper={https://www.dropbox.com/s/3bktcmdr7rlw1ic/LinICSME20.pdf}<br>}</p>
Data from: Endemism patterns are scale dependent
<p>Areas of endemism are important in biogeography because they capture facets of biodiversity not represented elsewhere. However, the scales at which they are relevant to research and conservation are poorly analysed. Here, we calculate weighted endemism (WE) and phylogenetic endemism (PE) separately for all birds and amphibians across the globe. We show that scale dependence is widespread for both indices and manifests across grain sizes, spatial extents and taxonomic treatments. Variations in taxonomic opinions – whether species are treated by systematic 'lumping' or 'splitting' – can profoundly affect the allocation of WE hotspots. Global patterns of PE can provide insights into complex evolutionary processes but this congruence is lost at the continental to country extents. These findings are explained by environmental heterogeneity at coarser grains, and to a far lesser extent at finer resolutions. Regardless of scale, we find widespread deficits of protection for endemism hotspots. Our study presents a framework for assessing areas for conservation that are robust to assumptions on taxonomy, spatial grain and extent.</p>
Companion for "Measuring Phenology Uncertainty with Large Scale Image Processing"
<p>This is the software and dataset companion for the paper entitled "Measuring Phenology Uncertainty with Large Scale Image Processing". Further instructions can be found in the README.org file.</p>
RadioGx: Analysis of Large-Scale Radio-Genomic Data
<p>This dataset contains an index of RadioSet objects available for download using the RadioGx R package. Each of the RSet objects are hosted in their own separate Zenodo uploads. The RadioGx R package is available for download from Bioconductor.</p>
Data of A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " 113234", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2020.113234", author = "Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic"</pre>
Extreme model exploration of a multi-scale simulation of tumor growth
<p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different simulations trajectories obtained for alternative parameter values. The parameter explored are: i) the decay rate of the TNF after it binds the cell; ii) the TNF binding rate; and iii) the TNF secretion rate by NFkB activated cells.</p> <p>The dataset includes 48 different combinations of parameters. Each simulation is stored in a folder instance_[0-9]+ which includes the PhysiBoSS standard output files (<a href="https://github.com/gletort/PhysiBoSS/wiki">https://github.com/gletort/PhysiBoSS/wiki</a>). The root folder also includes other settings, logs, and outputs as well as the binary used to run the simulation.</p>
FIGURE 124. Pterygosoma spinosa Jack, 1962, male. A in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 124. Pterygosoma spinosa Jack, 1962, male. A, idiosoma in dorsal view; B, genital cone in dorsal view, enlarged.
FIGURE 106 in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 106. Pterygosoma neumanni (Berlese 1910), protonymph. A, dorsal view, B, genital region in ventral view.
FIGURE 99. Pterygosoma indare Fajfer, 2013, female. A, tarsus I in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 99. Pterygosoma indare Fajfer, 2013, female. A, tarsus I in lateral view; B, tarsus II in dorsal view; C, gnathosoma in ventral view (after Fajfer 2013a, modified).
FIGURE 94. Pterygosoma engai Fajfer, 2013, female. A in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 94. Pterygosoma engai Fajfer, 2013, female. A, dorsal view; B, ventral view (after Fajfer 2013a).
FIGURE 92. Pterygosoma draconensis Jack, 1962. A in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 92. Pterygosoma draconensis Jack, 1962. A, female in ventral view; B, tarsus I in ventro-lateral view; C, tarsus II in lateral view.
FIGURE 90. Pterygosoma bibronii Jack 1962, female. A in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 90. Pterygosoma bibronii Jack 1962, female. A, ventral view; B, tarsus I in ventro-lateral view; C, tarsus II in dorsal view.
FIGURE 61. Pterygosoma tuberculata Jack, 1962 in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 61. Pterygosoma tuberculata Jack, 1962, female; details. A, palps in dorsal view; B, cheliceral digits in later view; C, genital region in ventral view.
FIGURE 52. Pterygosoma melanus Hirst, 1917, female. A in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 52. Pterygosoma melanus Hirst, 1917, female. A, ventral view; B, tarsus II in ventro-later view.
FIGURE 46. Pterygosoma hirsti Lawrence, 1936, female. A in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 46. Pterygosoma hirsti Lawrence, 1936, female. A, ventral view; B, part of gnathosoma in dorsal view; C, tarsi IV in lateral view.
FIGURE 71. Pterygosoma problematica Jack, 1962, female. A in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 71. Pterygosoma problematica Jack, 1962, female. A, dorsal view; B, shape of pseudanal setae ps1.
FIGURE 76. Pterygosoma singularis Jack, 1961, female. A in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 76. Pterygosoma singularis Jack, 1961, female. A, ventral view; B, gnathosoma in dorsal view.
FIGURE 73. Pterygosoma phillipsi Jack, 1962 in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 73. Pterygosoma phillipsi Jack, 1962; female. A, idiosoma in dorsal view; B, anterior seta; C, fixed cheliceral digit in lateral view; D, peripheral seta.
FIGURE 41. Pterygosoma benguellae Jack, 1962, female. A in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 41. Pterygosoma benguellae Jack, 1962, female. A, dorsal view; B, antero-lateral seta; C, pseudanal seta ps3; D, medial peripheral seta.
FIGURE 21. Pterygosoma balochistani Fajfer, 2016 in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 21. Pterygosoma balochistani Fajfer, 2016, larva in dorsal view (after Fajfer 2016, modified).
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.