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9,882 results for “Scale”
FIGURE 86. Pterygosoma agamae Peters, 1849 in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 86. Pterygosoma agamae Peters, 1849, female in dorsal view.
FIGURE 59. Pterygosoma tuberculata Jack, 1962 in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 59. Pterygosoma tuberculata Jack, 1962; female. A, dorsal view; B, peripheral seta.
FIGURE 57. Pterygosoma gracilipalpis Jack 1962 in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 57. Pterygosoma gracilipalpis Jack 1962, female in ventral view.
FIGURE 83. Pterygosoma aculeatum Lawrence, 1936 in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 83. Pterygosoma aculeatum Lawrence, 1936, female in dorsal view.
FIGURE 78. Pterygosoma blandfordi Fajfer, 2016 in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 78. Pterygosoma blandfordi Fajfer, 2016, female in ventral view (after Fajfer 2016).
FIGURE 1. Pterygosoma mutabilis Jack, 1961, female. A in A systematic revision of the scale mite genus Pterygosoma Peters, 1849 (Acariformes: Pterygosomatidae)
FIGURE 1. Pterygosoma mutabilis Jack, 1961, female. A, dorsal view; B, palps in dorsal view.
Patch-level facilitation fosters high-Andean plant diversity at regional scales
<p>Survey of the alpine vegetation in seven mountains of the Patagonian Andes from January to March of 2017 and 2018. On each mountain, we established one study site at each of three elevations (1600, 1800 and 2000 m). Accordingly, we sampled a total of 21 alpine plant communities (i.e., seven mountains x three elevations) dominated by cushion plants. At each community, 50 individual cushion plants were haphazardly selected within an area of approx. 0.5 ha, pairing each cushion with an adjacent non-cushion or open area 50 cm away in a random direction. In order to sample a similar surface in the surrounding open area, a wire hoop was shaped to match the size of the sampled cushion that was then placed on the ground. The number and identity of all plant species were recorded at both cushion and open area plots. Given that cushion plants are roughly elliptical, microsites were defined as elliptical plots, and thus, the longer and shorter axes of each cushion were measured as an approximate estimation of its area. In total, we sampled 2100 plots (1050 cushion plants and 1050 open area plots).</p>
Data from "Large-scale CO spirals and complex kinematics associated with the T Tauri star RU Lup"
<p>Image cubes, self-calibrated visibilities, and self-calibration and imaging scripts for data from Huang et al., 2020, "Large-scale CO spirals and complex kinematics associated with the T Tauri star RU Lup," ApJ, 898, 140 (arXiv:2007.02974) </p> <p>The raw ALMA data can be found at the ALMA archive under program ID 2018.1.01201.S (PI: Jane Huang).</p> <p><strong>Scripts:</strong></p> <p>selfcal_C43-5.py, selfcal_C43-2.py, selfcal_ACA.py: self-calibration scripts for observation of RU Lup in various ALMA configurations</p> <p>imaging.py: imaging script used for all lines</p> <p>reductionutils.py can be downloaded from <a href="https://zenodo.org/record/3628656">https://zenodo.org/record/3628656</a></p> <p><strong>Measurement sets:</strong></p> <p>RULupcontinuum.ms.tgz: Continuum visibilities for "RULupallcontinuum.image.pbcor.fits" (the high-resolution ALMA continuum data can be found on the <a href="https://bulk.cv.nrao.edu/almadata/lp/DSHARP/">DSHARP</a> page)</p> <p>RULup_12CO.ms.contsub.tgz: Continuum-subtracted visibilities for the <sup>12</sup>CO J=2-1 line toward RU Lup</p> <p>RULup_12CO_nocontsub.ms.tgz: Visibilities for the <sup>12</sup>CO J=2-1 line (no continuum subtraction) toward RU Lup, used to make the 'RULup_12COpeakintensity.fits' file.</p> <p>RULup_13CO.ms.contsub.tgz: Continuum-subtracted visibilities for the <sup>13</sup>CO J=2-1 line toward RU Lup</p> <p>RULup_C18O.ms.contsub.tgz: Continuum-subtracted visibilities for the C<sup>18</sup>O J=2-1 line toward RU Lup</p> <p>RULup_DCOp.ms.contsub.tgz: Continuum-subtracted visibilities for the DCO<sup>+</sup> J=3-2 line toward RU Lup</p> <p><strong>Images: </strong></p> <p>RULup12CO.1sigcut.pbcor.mom0.fits: Integrated intensity map of <sup>12</sup>CO J=2-1 appearing in Fig. 1</p> <p>RULup12CO.pbcor.mom1.fits: <sup>12</sup>CO J=2-1 intensity-weighted velocity map appearing in Fig. 1</p> <p>RULup13CO.pbcor.mom0.fits: Integrated intensity map of <sup>13</sup>CO J=2-1 appearing in Fig. 1</p> <p>RULup13CO.pbcor.mom1.fits: <sup>13</sup>CO J=2-1 intensity-weighted velocity map appearing in Fig. 1</p> <p>RULupC18O.pbcor.mom0.fits: Integrated intensity map of C<sup>18</sup>O J=2-1 appearing in Fig. 1</p> <p>RULupC18O.pbcor.mom1.fits: C<sup>18</sup>O J=2-1 intensity-weighted velocity map appearing in Fig. 1</p> <p>RULup12CO.image.pbcor.fits: <sup>12</sup>CO J=2-1 image cube appearing in Fig. 2</p> <p>RULupC18O.image.pbcor.fits: C<sup>18</sup>O J=2-1 image cube appearing in Fig. 2</p> <p>RULup13CO.image.pbcor.fits: <sup>12</sup>CO J=2-1 image cube appearing in Fig. 3</p> <p>RULup_12COpeakintensity.fits: Peak intensity image of <sup>12</sup>CO used to make Figure 4</p> <p>RULupDCOp.pbcor.mom0.fits: Integrated intensity map of DCO<sup>+</sup> J=3-2 appearing in Fig. 10</p> <p>RULupDCOp.image.pbcor.fits: DCO<sup>+</sup> J=3-2 image cube appearing in Fig. 11</p> <p>RULupallcontinuum.image.pbcor.fits: Continuum image appearing in Fig. 12</p> <p>RULup12COinitial.mask.image.fits: <sup>12</sup>CO CLEAN mask shown in Fig. 13</p> <p> </p>
A large-scale wide-baseline light field dataset - Part II
<p>This dataset is the Part II of a large-scale, synthetic wide-baseline light field dataset (called WLF), including 16 light fields. </p> <p>Each light field provides 9x9 angular (RGB) images and ground truth disparities. This light field dataset involves the spatial resolution (1920x1080) images only.</p> <p>The dataset is originally created for training the deep learning-based models for depth estimation. You might use this dataset for other tasks if possible. </p> <p>You might also have a try to play with this dataset using our code in https://github.com/YanWQ/LLF-Net.</p>
A Large Scale PeMS Traffic Speed Dataset
<p>This repository is a traffic speed data set collected by California Freeway Performance Measurement System (PeMS). In this data set, it has 11160 time series corresponding to <strong>11160</strong> sensors/locations. In three data files (i.e., <em>pems-4w.csv</em>, <em>pems-8w.csv</em>, and <em>pems-12w.csv</em>), each time series is indeed a time series with <strong>288</strong> (5-minute) time points per day during the first <strong>4 weeks</strong> (PeMS-4W), first <strong>8 weeks</strong> (PeMS-8W), and first <strong>12 weeks</strong> (PeMS-12W) of 2018, respectively. Note that the original data was downloaded from PeMS system with 30-second traffic speed records. Our data is a subset of file <em>speed.h5</em> created by article <a href="https://arxiv.org/pdf/2004.08038.pdf">Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting</a> and its original link is available at <a href="https://bit.ly/2SsGleK">here</a>.</p> <p>Our research work on this data set is:</p> <p>Xinyu Chen, Yixian Chen, Nicolas Saunier, Lijun Sun (2021). Scalable low-rank tensor learning for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 129: 103226. [<a href="https://doi.org/10.1016/j.trc.2021.103226">DOI</a>] [<a href="https://arxiv.org/abs/2008.03194">preprint</a>] [<a href="https://doi.org/10.5281/zenodo.3939792">data</a>] [<a href="http://github.com/xinychen/tensor-learning">Python code</a>]</p>
SEM images of gold palladium coated epoxy casts. Scale = 0.60 mm in Late Middle Eocene bats from the Creechbarrow Limestone Formation, Dorset, southern England with description of a new species of Archaeonycteris (Chiroptera: Archaeonycteridae)
SEM images of gold palladium coated epoxy casts. Scale = 0.60 mm
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs rat dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html">http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>) and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (drug matrix dataset)
<p>This data was generated by Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics [Internet]. 2006 Oct;7(7):1025–1044. Available from: http://dx.doi.org/10.2217/14622416.7.7.1025 PMID: 17054413. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="https://cran.r-project.org/web/packages/ToxicoGx/">cran.r-project.org/web/packages/ToxicoGx</a>) and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p> <p> </p> <p> </p>
Desert-like badlands and surrounding (semi-)dry grasslands of Central Germany promote small-scale phenotypic and genetic differentiation in Thymus praecox
Environmental heterogeneity among sites can generate phenotypic and genetic variation facilitating differentiation and microevolution of plant populations. Badlands are desert-like, predominantly vegetation-poor habitats often embedded in (semi-)dry grasslands. The desert-like conditions of badlands demand extreme adaptation of plants, i.e., phenotypic modifications in short-term and/or natural adaptation in long-term. However, detailed knowledge is missing about both plant phenotypic and genetic differentiation in this unique and widely occurring habitat type. The present study focused on the largest known badlands systems in Central Europe located in the 'Drei Gleichen' region, a designated nature conservation area in Central Germany. Locations were suitable for this study in terms of having co-occurring badlands and (semi-)dry grassland habitats (sites) occupied by the pioneer plant Thymus praecox. Here, we studied the environmental preferences, morphological and functional trait variation, and genetic variation using microsatellite markers of T. praecox. Results revealed significant, mainly site-dependent environmental, phenotypic and genetic differentiation. In general, individuals in badlands are shorter in height, have lower patch sizes (length x width), relative growth rates, and smaller stomata. The PCA additionally unveiled slightly increased leaf robustness, trichome density, decreased stomatal conductance, fewer females, and earlier phenology in badlands. We interpret differentiation patterns as adaptive responses to light, temperature, drought and nutrient stress conditions supported by reviewed literature. Genetic differentiation was strongest between local badlands and grassland sites, and clearly weaker among locations and between sites (in total) as indicated by GST, AMOVA, PCoA and population structure. Our study supports the importance of small-scale microhabitat conditions as a driver of microevolutionary processes, and the population´s need for sufficient phenotypic plasticity and genetic resources to deal with environmental changes. We demonstrated that badlands are an appropriate model system for testing plant response to extreme habitats and that more research is needed on these fascinating landscapes.
Data from: Habitat loss and thermal tolerances influence the sensitivity of resident bird populations to winter weather at regional scales
<p>1. Climate change and habitat loss pose the greatest contemporary threats to biodiversity, but their impacts on populations largely vary across species. These differential responses could be caused by complex interactions between landscape and climate change and species-specific sensitivities.</p> <p>2. Understanding the factors that determine which species are most vulnerable to the synergistic effects of climate change and habitat loss is a high conservation priority. Here, we ask (a) whether and to what extent land cover moderates the impacts of winter weather on population dynamics of wintering birds, and (b) what role species' physiology might play in modifying their responses to changing weather conditions.</p> <p>3. To address these questions, we used thousands of observations collected by citizen scientists participating in Project FeederWatch to build dynamic occupancy models for 14 species of wintering birds.</p> <p>4. Populations of wintering birds were more dynamic, having higher rates of local extinction and colonization, in more forested landscapes during extreme cold – presumably enabling them to better track resources. However, urban areas appeared to provide refuge for some species, as demonstrated by increased local colonization during the harshest winter weather. Lastly, we found that species-specific differences in thermal tolerances strongly influenced occupancy dynamics such that species that are less cold-tolerant were more likely to go locally extinct at colder sites and during colder periods throughout winter.</p> <p>5. Together, our results suggest species that are less cold-tolerant and populations occupying less forested landscapes are most vulnerable to extreme winter weather. 11-Jun-2020</p>
Large-scale RNAi screening uncovers new therapeutic targets in the human parasite Schistosoma mansoni
<p><span><span><span><span><span><span><span><span><span><span><span>Schistosomes kill 250,000 people every year. Treatment of schistosomiasis relies on a single drug (praziquantel). Unfortunately, a scarcity of molecular tools has hindered the discovery of new drug targets. Here, we describe a large-scale RNA interference screen in adult Schistosoma mansoni examining the function of 2,216 genes. We discovered 250 genes with phenotypes affecting neuromuscular function, tissue integrity, stem cell maintenance, and parasite survival. Leveraging these data, we prioritize compounds with activity against the parasites and uncover a pair of protein kinases (TAO and STK25) that cooperate to maintain muscle-specific mRNA transcription. Loss of either of these kinases results in paralysis and worm death in a mammalian host. These studies will expedite therapeutic development and invigorate studies of these neglected parasites. Large-scale RNAi screening uncovers new therapeutic targets in the human parasite Schistosoma mansoni.</span></span></span></span></span></span></span></span></span></span></span></p> <p> </p>
Can Precursory Moment Release Scale With Earthquake Magnitude? A View From the Laboratory
<p>This is the data repository for '</p> <p>Can Precursory Moment Release Scale With Earthquake Magnitude? A View From the Laboratory'</p> <p> </p> <p>This is the ReadMe file corresponding to the study entitled: "Can Precursory moment release scale with earthquake magnitude? A view from the laboratory."<br> By M. Acosta, F. X. Passelègue, A. Schubnel, R.Madariaga & M. Violay.<br> This study has been published in the Journal Geophysical Research Letters in November 2019. https://doi.org/10.1029/2019GL084744<br> This Read-Me file has been last edited on 2019-12-11<br> This readme file describes the data repository and supplementary files accompanying the above publication. For any further queries please contact mateo.acosta@epfl.ch<br> The following files are included:<br> --- Regarding Figure 1.<br> 1) "Acosta_et_al_2019_Figure1Data.xlsx" This is the processed data from the experiments described in Figure1 of the article.In this .xlsx File, each sheet corresponds to one figure panel as follows: <br> Fig.1a: Shear stress vs Slip Dry ExperimentColumn A: Dry experiment slip in (mm) ; Column B: Dry experiment near fault shear stress in (MPa);<br> Fig.1b: Shear stress, Slip, and AE's Vs time to mainshock Dry ExperimentColumn A: Dry experiment time in (s); Column B: Dry experiment slip in (mm) ; Column C: Dry experiment near fault shear stress in (MPa); Column D: Insert time (for Acoustic emissions) ; Column E: Acoustic emissions.<br> Fig.1c: Fault coupling Vs time to mainshock Dry experimentColumn A: time to mainshock; Columns B-K: Fault coupling for events 1-10 respectively<br> Fig.1d: Shear stress vs Slip Pf=1MPa ExperimentColumn A: Pf=1MPa experiment slip in (mm) ; Column B: Pf=1MPa experiment near fault shear stress in (MPa);<br> Fig.1e: Shear stress, Slip, and AE's Vs time to mainshock Pf=1MPa ExperimentColumn A: Pf=1MPa experiment time in (s); Column B: Pf=1MPa experiment slip in (mm) ; Column C: Pf=1MPa experiment near fault shear stress in (MPa); Column D: Insert time (for Acoustic emissions) ; Column E: Acoustic emissions.<br> Fig.1f: Fault coupling Vs time to mainshock Pf=1MPa experiment.Column A: time to mainshock; Columns B-F: Fault coupling for events 1-5 respectively.<br> <br> <br> --- Regarding Figures 2 and 3 ""Acosta_et_al_2019_Figure2and3Data.xlsx"""The data file contains an extended data table that allows to reproduce all the Figures in the article. All the other information is contained in the article or supplementary material.</p>
Strong Scaling Tools 1 Al-Zn-Mg-Cu case study
<p>All data, results, and scripts from Al-Zn-Mg-Cu high-throughput case study.</p> <p><strong>*.zip</strong> are the specimen-specific PARAPROBE results and POS rawdata.</p> <p><strong>*.pdf</strong> are the PDF reports that were created with the automation of paraprobe-autoreporter.</p> <p><strong>src.zip</strong> contains the source code of the paraprobe-autoreporter Python classes with which the results were computed. This source code is meant as a documentation of the results only!<br> <strong>Instead, all users should always use the latest versions of the PARAPROBE tools from the GitLab repository https://gitlab.mpcdf.mpg.de/mpie-aptfim-toolbox/paraprobe</strong><br> <strong>https://paraprobe-toolbox.readthedocs.org</strong></p>
Harrison's rule scales up to entire parasite assemblages but is determined by environmental factors
<p>Harrison's rule states that parasite body size and the body size of their hosts tend to be positively correlated. After it was proposed a century ago, a number of studies have investigated this trend, but the support level has varied greatly between parasite/host associations. Moreover, while the rule has been tested at the individual species level, we still lack knowledge on whether Harrison's rule holds at the scale of parasite and host communities.</p> <p>Here, we mapped flea (parasites) and rodent (hosts) body sizes across Mongolia and asked whether Harrison's rule holds for parasite/host assemblages (i.e., whether a parasite's average body size in a locality is positively correlated with its host's average body size). In addition, we attempted to disentangle complex relationships between flea size, host size, and environmental factors by testing alternative hypotheses for the determinants of fleas' body size variation.</p> <p>We gathered occurrence data for fleas and rodents from 2370 sites across Mongolia, constructed incidence matrices for both taxa, and calculated the average body sizes of fleas and their hosts over half-degree cells. Then, we applied a path analysis, accounting for spatial autocorrelation, trying to disentangle the drivers of the correlation between parasite and host body sizes.</p> <p>We found a strong positive correlation between average flea and host size across assemblages. Surprisingly though, we found that environmental factors simultaneously affected the body sizes of both fleas and hosts in the same direction, leading to a most likely deceptive correlation between parasite and host size across assemblages.</p> <p>We suggest that environmental factors may, to a great extent, reflect the environmental conditions inside the hosts' burrows where fleas develop and attain their adult body size, thus influencing their larval growth. Similarly, rodent body size is strongly influenced by air temperature, in the direction predicted by Bergmann's rule. If our findings are valid in other host-parasite associations, this may explain the dissenting results of both support and lack thereof for Harrison's rule.</p>
Data for "Fine-scale tundra vegetation patterns are strongly related to winter thermal conditions"
<p>This page links to the data and code associated with the publication Niittynen et al. (In Press). "Fine-scale tundra vegetation patterns are strongly related to winter thermal conditions".</p> <p>The dataset contains fine-scale microclimatic, soil and species (vascular plant, bryophyte and lichen) data from three Arctic areas.The environmental data and analyses are describes in the linked publication.</p>
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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.