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Figure 9 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 9. Temperature and humidity profiles at sites A, B, and C between April and December 2017. TA = temperature at site A (three loggers), TB = temperature at site B (three loggers), TC = temperature at site C (three loggers). HA, HB, HC = humidity profile at sites A, B, and C, respectively. Some missing data are due to temporal failure of some loggers. Humidity loggers stopped to function completely in the middle of October at all three sites.
Figure 7 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 7. Minimum distances (in meters, x axis) of numbered individuals (y axis: absolute numbers of individuals) from sites A and B, over all subsequent observations.
Figure 6 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 6. Minimum distances (in meters, x axis) of numbered individuals (y axis: absolute numbers of individuals) from site A and B of all observations with a ~1-week interval.
Figure 3 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 3. Snail counts at site A (above) and site B (below); juv. small = small juveniles (too small to mark); juv. dotted = dotted individuals; subadults and adults = numbered subadults/adults.
Figure 8 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 8. Sum of MDs (in meters, x axis) over all subsequent observations (over the season) of numbered individuals (y axis: absolute numbers of individuals) from sites A and B.
Figure 2. M in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 2. M. subcristata shell-size classes: (A) small juveniles (too small to mark), appr. 8 mm shell height), (B) dotted juvenile, appr. 12 mm; (C) numbered adult, appr. 20 mm.
Figure 5 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 5. Population estimated according to Jolly-Seber for site A (above), site B (below). Y axis: estimated number of individuals.
Figure 1 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 1. Study area with three study sites (A, B, C). Note that due to the perspective view site B and C appear much smaller.
Figures 4–10 in New method of estimation of the relative area of perforations on valves of centric diatoms using SEM images on the example of Minidisus vodyanitskiyi Lyakh et Bedoshvili
Figures 4–10. Steps used to measure the relative area of perforations on valve SEM images. 4. The bounded ellipse is constructed using five points, and ellipse main diameters were determined. 5. Areola foramens (yellow and blue) and inorganic flakes (pink) were manually selected. 6–8. The real convex shape of areola foramens covered by filiform siliceous growth was restored using convex hull algorithm. 9. Average diameter and distance between cribral pores were measured. 10. Cribral pores were represented by hexagons and the number of those hexagons was estimated.
Figures 1–3 in New method of estimation of the relative area of perforations on valves of centric diatoms using SEM images on the example of Minidisus vodyanitskiyi Lyakh et Bedoshvili
Figures 1–3. Inner (1) and outer views (2–3) of the valves and frustules of M. vodyanitskiyi. In the top-right corner of (3) there is a zoomed part of a valve with siliceous growth on areolae ribs.
Ignoring within-flower self-fertilization and inbreeding depression biases estimates of selection on floral traits
<p>Within-flower self-pollination should be the major source of self-fertilization in mixed-mating species that present single or few flowers simultaneously. It is also an often unmeasured source of selfing in species with many flowers open simultaneously. In self-compatible species in which pistil and stamen numbers vary, the rate of within-flower selfing should depend on the number of pistils and stamens, the timing of flowering, and the morphology of subsidiary floral traits. The intensity and direction of selection on these traits should thus also depend on the level of inbreeding depression. Here, we measured the dependence of the within-flower selfing rate on floral sex allocation, phenology, petal length, and floral stalk height in a population of the perennial herb <em>Pulsatilla alpina</em> (Ranunculaceae) in which most individuals had single flowers. We estimated inbreeding depression in the population by comparing inbreeding coefficients between parents and seed progeny using microsatellite markers. We then estimated selection on the measured traits via female reproductive success at the flower level and compared our estimates with a hypothetical scenario in which inbreeding depression was assumed to be absent. Inbreeding depression was estimated to be severe (0.95). The within-flower selfing rate varied widely among flowers and depended positively on stamen number and negatively on pistil number and flowering date, supporting the predictions of a mass-action model. The dependence of the selfing rate on the measured floral traits consistently predicted (non-linear) patterns of selection under high inbreeding depression that were distinct from those under a hypothetical scenario of no inbreeding depression.</p> <p>Synthesis: While previous research has emphasized the importance of mass-action mating on selfing among flowers of plants with large floral displays, our results demonstrate its importance for selfing within individual flowers. They also demonstrate the importance of accounting for both the selfing rate and inbreeding depression when inferring selection on floral and other traits via female fitness.</p>
Figure 6. Association between survival probability estimates with circulating T4 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
Figure 6. Association between survival probability estimates with circulating T4 (nmol/L) at the time of capture/release from the best supported model: {Φ(g + T4)}.
Fig. 1 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila
Fig. 1. Methodology used to predict morning emergence time of Gambelia sila. Emergence was predicted as the time of day immediately preceding a distinct upward slope in the lizard's T b (triangles and dotted line) based on the assumption that it would take several minutes for the radio transmitter to heat in the sun. The rising Tb was also typically associated with the departure from the burrow physical model temperatures (diamonds and long-dashed line) and the approach of the open (sun) physical model temperatures (squares and short-dashed line). In each case, the predicted time was then compared to the observed emergence time when the lizard's head first appeared outside its burrow. The average difference between observed and predicted emergence times was 11 minutes and 37 seconds.
Fig. 4 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila
Fig. 4. Proportions of correctly predicted observations of microhabitat use of Gambelia sila using temperature-based activity estimation based on physical model temperatures. Lizard microhabitat use was predicted correctly most often when they were in the open, but overall microhabitat use was not accurately predicted with TBAE in this heliothermic lizard.
Fig. 3 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila
Fig. 3. Temperature-based activity estimation resulted in accurate prediction of above-ground activity by Gambelia sila more often than accurate prediction of below-ground (burrow) occupation. Using air temperature (T air) was superior to using physical model temperatures when predicting below-ground occupation. For both methods, ~93% of observations predicted to be above ground were correct, whereas 62% (using T air) and 51% (using physical models) were correct for below-ground predictions.
Fig. 2 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila
Fig. 2. Proportions of correct predictions using air temperature to predict surface activity versus below-ground refuge use of Gambelia sila. This method resulted in accurate predictions 64–76% of the time among the various temperature differentials shown on the x-axis. Predictions were maximized (76% correct) using the criterion that lizards are above ground when their body temperatures (T b) are at least 6 °C above the air temperature (T ).
Data and Code to Accompany: "On-Body Textile Hysteresis Estimation for Personalized Physical Human-Robot Interaction"
<p>Data files, Matlab code, and figures to accompany "On-Body Textile Hysteresis Estimation for Personalized Physical Human-Robot Interaction" (submitted for peer-review on 7/20/2024).</p>
Dataset: effect estimates obtained from randomised and non-randomised studies
<p>This dataset provides study-level information on matched sets of published randomised and non-randomised studies answering the same clinical questions. The dataset includes 346 meta-analyses, and data on over 2,700 unique studies.</p> <p>The dataset includes the following information (definitions and coding are provided in the spreadsheet):</p> <ul> <li>IDs for clinical topics and source meta-analyses</li> <li>IDs for individual studies</li> <li>Brief descriptions of clinical topics</li> <li>Therapeutic area (ATC Code first level)</li> <li>Topic narrowness categorical</li> <li>Indicators for high-quality source meta-analyses (Cochrane review; top journal)</li> <li>Time period</li> <li>Comparator type</li> <li>Outcome type</li> <li>Meta-analysis-level median risk of bias for NRS and RCT</li> <li>Publication year of individual studies</li> <li>Risk of Bias in individual studies</li> <li>Standardised effect estimates (logOR) and variance for each study</li> <li>Summary logOR and variance for NRS and RCT, respectively, within each meta-analysis for the full sample</li> </ul> <p>The protocol for this study was registered on PROSPERO: </p> <p>Maximilian Salcher-Konrad. Comparison of treatment effects in randomised vs. non-randomised studies and the role of analytical methods to control for confounding: a meta-epidemiological study. PROSPERO 2018 CRD42018062204 Available from: <a href="https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018062204">https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018062204</a></p>
Fig. 3. The motor performance was estimated using a in Abstrαct
Fig. 3. The motor performance was estimated using a negative geotactic assay. The HD models (A, B) and the SCA1 models (C, D) were tested at 7 days (A, C) and 14 days (B, D). Each Gal4 driver tested is listed along the X-axis. ''-'' on the X-axis corresponds to ''no driver'' controls. The Y-axis represents the time to reach 5 cm (seconds). Each bar represents the mean ¡ SE. Single or double asterisks indicate significant differences (p,0.05 or p,0.01, respectively). The numbers of flies tested in each experiment (between 7 and 20) are listed in S1 Table. For each of the disease models that expressed Htt-103Q or ATXN1-82Q we tested about 20 flies. Because the short Q controls showed stable performance we only about 8 flies for each group. The results of the statistical analyses are listed in S4 Table. doi:10.1371/journal.pone.0116567.g003
Fig. 5 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 5. Histograms of emergence counts from the time-series emergence traps. Count bars for each day are subdivided by individual trap.
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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.