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9,882 results for “Scale”
Figure 11 in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 11. Species accumulation curve for tintinnid ciliates diversity in the Pacific Ocean.
Figure 13 in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 13. Species accumulation curve for tintinnid ciliates diversity in the Indian Ocean.
Prevalence of Mental Health in Relation to Religious Practices in A Contemporary Small-scale Older Amdo Tibetans
<p>This dataset includes survey and wearable device data collected from elderly Amdo Tibetans in 2023. The study explores the relationship between religious participation (such as prayer, kowtow, and pilgrimage) and both physical and mental health outcomes. Data were collected through household interviews and wearable wristbands to provide objective measures of physical activity. Participants with a history of mental health issues were excluded. The dataset provides insights into the health impacts of religious practices in a rural, subsistence-based lifestyle.</p>
VSC Strong Scaling Days 2024
<p>Run scripts and log files for strong scaling tests with ICON on the VSC5 cluster of the Vienna Scientific Cluster HPC centre</p> <p>ICON model: https://icon-model.org/, tag icon-2024.07-public, resolution R2B9 (5km), AMIP-like setup</p> <p>More information on the model setup and runs is collected in a separate gitlab project, https://gitlab.phaidra.org/climate/vsc-strongscaling-days-2024</p>
Data for "The Nature of Right-handed Polarized Ion-scale Waves In the Near-Sun Solar Wind and Extended Solar Corona: the Antisunward Fast-Magnetosonic Whistler Wave or the Sunward Ion Cyclotron Wave?" by Shi et al.
<h2>The database includes all theoretical analysis results based on the linear model.</h2> <h2>Captions:</h2> <p><strong>data_fig1.mat</strong> file is used to plot Figure 1, which is the data of the instabilities driven by the interplay of the temperature anisotropy and relative streaming speed of the proton beam component.</p> <div>x_axis: X axis data, relative streaming speed</div> <div>y_axis: Y axis data, temperature anisotropy</div> <div>gamma_max: normalized growth rate \gamma, used in figure 1a</div> <div>frequency_max: normalized frequency f in the plasma frame, used in figure 1b</div> <div>theta_max: wave propagating angle \theta in the plasma frame , used in figure 1c</div> <div>ellip_max: ellipticity \epsilon in the plasma frame, used in figure 1d</div> <div>theta_max_sc: wave propagating angle \theta in the spacecraft frame , used in figure 1e</div> <div>ellip_max_sc: ellipticity \epsilon in the spacecraft frame, used in figure 1f</div> <div> </div> <div> </div> <p><strong>data_fig2.mat </strong>file is used to plot Figure 2, which is the data of the dependence of the instability in regime I on the relative steaming speed of the proton beam component.</p> <div> sICW_x_axis1: X axis data of figure 2a, normalized k </div> <div> sICW_y_axis1: Y axis data of figure 2a, relative streaming speed</div> <div> sICW_gamma_all: normalized growth rate \gamma of sunward ICW in k and relative steaming speed space, used in figure 2a</div> <div> sICW_x_axis2: X axis data of figure 2b-2e, normalized k</div> <div> sICW_frequency: normalized frequency f of sunward ICW, used in figure 2b</div> <div> sICW_gamma: normalized growth rate \gamma of sunward ICW, used in figure 2c</div> <div> sICW_etr_b: the energy transfer rate of proton beam, used in figure 2d</div> <div> sICW_etr_c: the energy transfer rate of proton core, used in figure 2e</div> <div> </div> <div> asICW_x_axis1: X axis data of figure 2f, normalized k </div> <div> asICW_y_axis1: Y axis data of figure 2f, relative streaming speed</div> <div> asICW_gamma_all: normalized growth rate \gamma of antisunward ICW in k and relative steaming speed space, used in figure 2f</div> <div> asICW_x_axis2: X axis data of figure 2g-2j, normalized k</div> <div> asICW_frequency: normalized frequency f of antisunward ICW, used in figure 2g</div> <div> asICW_gamma: normalized growth rate \gamma of antisunward ICW, used in figure 2h</div> <div> asICW_etr_b: the energy transfer rate of proton beam, used in figure 2i</div> <div> asICW_etr_c: the energy transfer rate of proton core, used in figure 2j</div> <div> </div> <div> </div> <p><strong>data_fig3.mat</strong> file is used to plot Figure 3, which is the data of the dependence of the instability in regime I on the temperature anisotropy of the proton core component.</p> <div> sICW_x_axis1: X axis data of figure 3a, normalized k </div> <div> sICW_y_axis1: Y axis data of figure 3a, temperature anisotropy of the proton core</div> <div> sICW_gamma_all: normalized growth rate \gamma of sunward ICW in k and temperature anisotropy of the proton core space, used in figure 3a</div> <div> sICW_x_axis2: X axis data of figure 3b-3e, normalized k</div> <div> sICW_frequency: normalized frequency f of sunward ICW, used in figure 3b</div> <div> sICW_gamma: normalized growth rate \gamma of sunward ICW, used in figure 3c</div> <div> sICW_etr_b: the energy transfer rate of proton beam, used in figure 3d</div> <div> sICW_etr_c: the energy transfer rate of proton core, used in figure 3e</div> <div> </div> <div> asICW_x_axis1: X axis data of figure 3f, normalized k </div> <div> asICW_y_axis1: Y axis data of figure 3f, temperature anisotropy of the proton core</div> <div> asICW_gamma_all: normalized growth rate \gamma of antisunward ICW in k and temperature anisotropy of the proton core space, used in figure 3f</div> <div> asICW_x_axis2: X axis data of figure 3g-3j, normalized k</div> <div> asICW_frequency: normalized frequency f of antisunward ICW, used in figure 3g</div> <div> asICW_gamma: normalized growth rate \gamma of antisunward ICW, used in figure 3h</div> <div> asICW_etr_b: the energy transfer rate of proton beam, used in figure 3i</div> <div> asICW_etr_c: the energy transfer rate of proton core, used in figure 3j</div> <div> </div> <div> </div> <p><strong>data_fig4</strong>.mat file is used to plot Figure 4, which is the data of the dependence of the wave frequency on the bulk flow speed.</p> <div> x_axis: X axis of figure 4, bulk flow speed.</div> <div> y_axis1: Y axis of figure 4a, normalized relative streaming speed of regime III and V</div> <div> y_axis2: Y axis of figure 4b, normalized relative streaming speed of regime I</div> <div> fVsw_fmw: the normalized frequency distributions in spacecraft frame of antisunward fast-magnetosonic whistler waves in regimes III and V, used in figure 4a</div> <div> fVsw_sic: the normalized frequency distributions in spacecraft frame of sunward ion cyclotron waves in regimes I, used in figure 4b</div> <div> fVsw_fmw_n1: spacecraft frequency of antisunward fast-magnetosonic whistler waves on the 1.5VA bulk flow speed, used in figure 4c</div> <div> fVsw_fmw_n2: spacecraft frequency of antisunward fast-magnetosonic whistler waves on the 2.5VA bulk flow speed, used in figure 4c</div> <div> fVsw_sic_n1: spacecraft frequency of sunward ion cyclotron waves on the 0.25VA bulk flow speed, used in figure 4c</div> <div> fVsw_sic_n2: spacecraft frequency of sunward ion cyclotron waves on the 0.5VA bulk flow speed, used in figure 4c</div> <div> fVsw_sic_n3: spacecraft frequency of sunward ion cyclotron waves on the 0.75VA bulk flow speed, used in figure 4c</div>
Data and code for "High-field superconductivity from atomic-scale confinement and spin-orbit coupling at (111)LaAlO3/KTaO3 interfaces"
<p>This folder contains the raw data and scripts used to prepare the figures of the main text and supplemetary. </p>
Data from: DNA barcoding gap: reliable species identification over morphological and geographical scales
The philosophical basis, and utility of DNA barcoding has been a subject of numerous debates. While most literature embraces it, some studies continue to question its use in dipterans, butterflies, and marine gastropods. Here, we explore the utility of DNA barcoding in identifying spider species that vary in taxonomic affiliation, morphological diagnosibility and geographic distribution. Our first test searched for a "barcoding gap" by comparing intra- and interspecific means, medians and overlap in more than 75,000 computed Kimura 2 parameter (K2P) genetic distances in three families. Our second test compared K2P distances of congeneric species with high versus low morphological distinctness in 20 genera of 11 families. Our third test explored the effect of enlarging geographical sampling area at a continental scale on genetic variability in DNA barcodes within 20 species of nine families. Our results generally point towards a high utility of DNA barcodes in identifying spider species. However, the size of the barcoding gap strongly depends on taxonomic groups and practices. It is becoming critical to define the barcoding gap statistically more consistently, and to document its variation over taxonomic scales. Our results support models of independent patterns of morphological and molecular evolution by showing that DNA barcodes are effective in species identification regardless of their morphological diagnosibility. We also show that DNA barcodes represent an effective tool for identifying spider species over geographic scales, yet their variation contains useful biogeographic information.
Data from: Concurrent temporal channels for auditory processing: oscillatory neural entrainment reveals segregation of function at different scales
Natural sounds convey perceptually relevant information over multiple timescales, and the necessary extraction of multi-timescale information requires the auditory system to work over distinct ranges. The simplest hypothesis suggests that temporal modulations are encoded in an equivalent manner within a reasonable intermediate range. We show that the human auditory system selectively and preferentially tracks acoustic dynamics concurrently at 2 timescales corresponding to the neurophysiological theta band (4–7 Hz) and gamma band ranges (31–45 Hz) but, contrary to expectation, not at the timescale corresponding to alpha (8–12 Hz), which has also been found to be related to auditory perception. Listeners heard synthetic acoustic stimuli with temporally modulated structures at 3 timescales (approximately 190-, approximately 100-, and approximately 30-ms modulation periods) and identified the stimuli while undergoing magnetoencephalography recording. There was strong intertrial phase coherence in the theta band for stimuli of all modulation rates and in the gamma band for stimuli with corresponding modulation rates. The alpha band did not respond in a similar manner. Classification analyses also revealed that oscillatory phase reliably tracked temporal dynamics but not equivalently across rates. Finally, mutual information analyses quantifying the relation between phase and cochlear-scaled correlations also showed preferential processing in 2 distinct regimes, with the alpha range again yielding different patterns. The results support the hypothesis that the human auditory system employs (at least) a 2-timescale processing mode, in which lower and higher perceptual sampling scales are segregated by an intermediate temporal regime in the alpha band that likely reflects different underlying computations.
Data from: Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance
Although activation/deactivation of specific brain regions have been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here we investigated time-varying functional connectivity patterns across the human brain in periods of 30-40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memo ry encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.
Figure 8. Messelornis nearctica, BHI 1294, scale bar 20 in New Birds from the Lower Eocene Green River Formation, North America
Figure 8. Messelornis nearctica, BHI 1294, scale bar 20 mm.
Figure 5. Messelornis nearctica, USNM 336269, scale bar 20 in New Birds from the Lower Eocene Green River Formation, North America
Figure 5. Messelornis nearctica, USNM 336269, scale bar 20 mm.
Fig. 6 in Acropyga and Azteca Ants (Hymenoptera: Formicidae) with Scale Insects (Sternorrhyncha: Coccoidea): 20 Million Years of Intimate Symbiosis
Fig. 6. Mealybug (detail) in amber (Harvard collection piece, AMNH DR14403).
Fig. 5 in Identification of the stages of ovarian maturation of the Argentine hake Merluccius hubbsi Marini, 1933 (Teleostei: Merlucciidae): advantages and disadvantages of the use of the macroscopic and microscopic scales
Fig. 5. Merluccius hubbsi: oocyte with lipidic and proteic vitellogenesis (1.5mm length field).
Fig. 18. A. Loreal scale single. B in Phylogenetic relationships based on morphological data and taxonomy of the genus Salvadora Baird & Girard, 1853 (Reptilia, Colubridae)
Fig. 18. A. Loreal scale single. B. Loreal normally divided.
LMHLD: A Large-scale Multi-source High-resolution Landslide Dataset for Landslide Detection based on Deep Learning
<p>LMHLD collects remote sensing images of five satellite sensors in seven areas of the world: Wenchuan, China (2008); Rio de Janeiro, Brazil (2011); Gorkha, Nepal (2015); Jiuzhaigou, China (2015); Taiwan, China (2018); Hokkaido, Japan (2018); Emilia-Romagna, Italy (2023), a total of 25,365 patches by setting different patch sizes according to different landslide scale.</p> <p>Specifically, patch sizes vary across different areas: 32 for image segmentation in Emilia-Romagna, Italy and Gorkha, Nepal; 64 in Rio de Janeiro, Brazil; 128 in Jiuzhaigou, China and Hokkaido, Japan; and 224 in Wenchuan and Taiwan, China. All the above patches constitute LMHLD, a large-scale multi-source high-resolution heterogeneous landslide dataset.</p> <p><strong><em>LMHLD.rar</em></strong> includes all the Train, Validation, Test data used for the experiments.</p> <p>If you use our data, please cite our work published in <strong><em>IEEE Transactions on Geoscience and Remote Sensing</em></strong>. Or if you have any other questions about LMHLD, please contact us: <strong>guan.ting.liu2000@gmail.com</strong>.</p> <p><strong>Paper:</strong> Liu G, Wang Y, Chen X, et al. LMHLD: A Large-scale Multi-Source High-Resolution Landslide Dataset for Landslide Detection based on Deep Learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025. (<strong>DOI: </strong><a href="https://doi.org/10.1109/TGRS.2025.3619062" target="_blank" rel="noopener">10.1109/TGRS.2025.3619062</a>)</p>
Enabling global-scale nucleic acid repositories through versatile, scalable biochemical selection from room-temperature archives
Open the record for dataset details and reuse information.
74 Block--63k or 50k scale Maps of South Asia
<p>53 maps of the Survey of India covering block 74</p>
Figure 9 from: Dones R, Evans G (2011) A new species of armored scale, Mycetaspis ailynaomi (Hemiptera, Diaspididae, Aspidiotinae), associated with Mammea americana L. (Malpighiales, Calophyllaceae) from Puerto Rico. ZooKeys 108: 1-10. https://doi.org/10.3897/zookeys.108.1214
Figure 9 - Mycetaspis bezerrai female a pygidium b habitus (after Arruda 1972).
Figure 5 from: Dones R, Evans G (2011) A new species of armored scale, Mycetaspis ailynaomi (Hemiptera, Diaspididae, Aspidiotinae), associated with Mammea americana L. (Malpighiales, Calophyllaceae) from Puerto Rico. ZooKeys 108: 1-10. https://doi.org/10.3897/zookeys.108.1214
Figure 5 - Mycetaspis ailynaomi, habitus of holotype female.
Figure 6 from: Dones R, Evans G (2011) A new species of armored scale, Mycetaspis ailynaomi (Hemiptera, Diaspididae, Aspidiotinae), associated with Mammea americana L. (Malpighiales, Calophyllaceae) from Puerto Rico. ZooKeys 108: 1-10. https://doi.org/10.3897/zookeys.108.1214
Figure 6 - Mycetaspis ailynaomi, detail of pygidial lobes of holotype female.
ScienceDex guides
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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.