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4,243 results for “seasonality”
Fig. 2 in Predicting seasonal infection of eyeworm (Oxyspirura petrowi) and caecal worm (Aulonocephalus pennula) in northern bobwhite quail (Colinus virginianus) of the Rolling Plains Ecoregion of Texas, USA
Fig. 2. Scatterplot of predicted eyeworm reproduction with temperature 60 days prior to collection date with upper and lower 95% confidence intervals.
Fig. 1 in Predicting seasonal infection of eyeworm (Oxyspirura petrowi) and caecal worm (Aulonocephalus pennula) in northern bobwhite quail (Colinus virginianus) of the Rolling Plains Ecoregion of Texas, USA
Fig. 1. Contour and scatterplot of relationships between temperature and precipitation on parasite worm burdens and egg shedding. a) Predicted caecal worm intensity against temperature and precipitation contour plot. b) Scatterplot of predicted caecal worm reproduction against precipitation. d) Predicted eyeworm reproduction against temperature and precipitation contour plot.
Fig. 3. Seasonal detection rate for E. uekii, type B in Surveillance of Eimeria species in wild Japanese rock ptarmigans, Lagopus muta japonica, and insight into parasitic seasonal life cycle at timberline regions of the Japanese Alps
Fig. 3. Seasonal detection rate for E. uekii, type B, and mixed Eimeria spp. oocyst infection in both adults and chicks in 2006 and 2007. Numbers in parentheses below months indicate the total number of fecal samples analyzed. Data for the number of chicks (23 in 2006 and 11 in 2007) were only available for August.
Fig. 1 in Surveillance of Eimeria species in wild Japanese rock ptarmigans, Lagopus muta japonica, and insight into parasitic seasonal life cycle at timberline regions of the Japanese Alps
Fig. 1. Seasonal prevalence of Eimeria spp. infection in Japanese rock ptarmigans from April to November in 2006 and 2007. (a) and (b) show the prevalence of infection in adult birds and chicks, respectively. Numbers above bars indicate the total number of fecal samples analyzed.
Fig. 2 in Surveillance of Eimeria species in wild Japanese rock ptarmigans, Lagopus muta japonica, and insight into parasitic seasonal life cycle at timberline regions of the Japanese Alps
Fig. 2. Photomicrograph of eimerian oocysts detected in the feces of Japanese rock ptarmigans; (a) E. uekii and (b) type B. The scale bar indicates 20 μm.
Fig. 6 in Surveillance of Eimeria species in wild Japanese rock ptarmigans, Lagopus muta japonica, and insight into parasitic seasonal life cycle at timberline regions of the Japanese Alps
Fig. 6. Average monthly environmental temperatures on the windward and leeward slopes of Mt. Tateyama from 2006 to 2007. The temperatures on the windward slopes were not measured in April. The table below the graph shows monthly maximum and minimum temperatures.
Fig. 5 in Surveillance of Eimeria species in wild Japanese rock ptarmigans, Lagopus muta japonica, and insight into parasitic seasonal life cycle at timberline regions of the Japanese Alps
Fig. 5. Sporulation rate for Eimeria spp. (mainly E. uekii) after incubation at different temperatures for 48 h. Dark bars indicate sporulation rates of> 85% after incubation for 24 h.
NNLM Discovery Podcast Seasons 1 and 2 overview
<p>Infographic that gives an overview of the NNLM Discovery podcast Seasons 1 and 2.</p> <p>This resource is supported by the National Library of Medicine of the National Institutes of Health under Award Number U24LM013751<strong>.</strong> The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p>
Fig. 4 in Molecular profiling of 18S rRNA reveals seasonal variation and diversity of diatoms community in the Han River, South Korea
Fig. 4. Principal component analysis (PCA) biplot showing the seasonal variation of (A) all the diatom OTU reads, (B) most frequent diatom OTU detected, sampled in March (spring), June (summer), September (autumn), and December (winter). Calculated based on the number of OTU reads in each sample. Each dot represents diatom OTU recovered in this study.
Fig. 2 in Molecular profiling of 18S rRNA reveals seasonal variation and diversity of diatoms community in the Han River, South Korea
Fig. 2. Rarefaction curves representing the numbers of Operational Taxonomic Units (OTUs) of diatoms vs. the number of tags sampled from pyrosequencing data.
Fig. 1 in Molecular profiling of 18S rRNA reveals seasonal variation and diversity of diatoms community in the Han River, South Korea
Fig. 1. Seasonal variation in water temperature and DO (A), pH and conductivity (B) and TN and TP (C), and cell counts and Chla (D) at the Seongsan Bridge of Han River, Korea.
Fig. 3 in Molecular profiling of 18S rRNA reveals seasonal variation and diversity of diatoms community in the Han River, South Korea
Fig. 3. (A) Proportion of each eukaryotic taxon (eukaryote, phytoplankton, and diatom), (B) relative abundance of phytoplankton, and (C) relative abundance of diatom taxa. These data were calculated by using 18S rRNA pyrosequencing reads. Taxonomic identity of "others" represents taxa with less than 1% composition of total reads.
Figure 1 in Daily activity rhythm of the African stingless bee Hypotrigona gribodoi (Hymenoptera: Meliponini) in the dry season, with notes on nest structure and colony composition
Figure 1. Numbers of bees departing from the nest (black) and returning throughout daylight hours. Returning bees are separated into those without (white) and with (gray) loaded pollen baskets
Axial diffusion of respired CO2 confounds stem respiration estimates during the dormant season
<p>Efflux-based estimates of stem respiration in oak trees during the dormant season were biased by axial diffusion of locally respired CO<sub>2</sub>. Light-induced axial CO<sub>2 </sub>diffusion along the stem due to woody tissue photosynthesis may lead to equivocal estimates of stem respiratory coefficients during the dormant season, which are generally used to estimate maintenance respiration throughout the year.</p>
Data for 'Improved predictability of the Indian Ocean Dipole using seasonally modulated ENSO forcing forecasts'
<p>Abstract of the associated paper: Despite recent progress in seasonal forecast development, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic-dynamical model (SDM) to predict the IOD using seasonally modulated El Niño-Southern Oscillation (ENSO) forcing together with a seasonal modulation of the Indian Ocean coupled ocean-atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System Version 2 and the SINTEX system. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal-to-noise ratio of the system. Therefore, potential future ENSO improvements in models should also translate to more skillful IOD predictions.</p>
Supplementary data S2 to: Global sinusoidal seasonality in precipitation isotopes
<p>Supplementary Materials to:</p> <p>Allen, S. T., Jasechko, S., Berghuijs, W. R., Welker, J. M., Goldsmith, G. R., and Kirchner, J. W.: Global sinusoidal seasonality in precipitation isotopes, Hydrol. Earth Syst. Sci. 2019. https://doi.org/10.5194/hess-2019-61.</p> <p>Please cite the above manuscript when using these resources. These files are all global maps at 5-minute resolution, spanning all longitudes and latitudes from 60 degrees south to 90 degrees north. See the methods section of the manuscript for more details. </p> <p> </p> <p>Precipitation δ<sup>18</sup>O amplitude (‰)</p> <p>Precipitation δ<sup>18</sup>O phase (day of peak value, in days from the summer solstice)</p> <p>Precipitation δ<sup>18</sup>O offset (‰)</p> <p>Precipitation δ<sup>2</sup>H amplitude (‰)</p> <p>Precipitation δ<sup>2</sup>H phase (day of peak value, in days from the summer solstice)</p> <p>Precipitation δ<sup>2</sup>H offset (‰)</p> <p>Precipitation amount amplitude (mm month<sup>-1</sup>)</p> <p>Precipitation amount phase (day of peak value, in days from the summer solstice)</p> <p>Precipitation amount offset (mm month<sup>-1</sup>)</p>
Supporting data: The seasonal origins of streamwater in Switzerland
<p>These data support the analysis conducted in "The seasonal origins of streamwater in Switzerland", accepted for publication in Geophysical Research Letters (<a href="https://doi.org/10.1029/2019GL084552">https://doi.org/10.1029/2019GL084552</a>) by Scott T. Allen, Jana von Freyberg, Markus Weiler, Gregory R. Goldsmith, James W. Kirchner. Information on the study sites, sampling procedures, and analysis can be found in the main text and supporting information files. Here we provide the data used:</p> <p><strong>Data Table S1.</strong> Annual precipitation and discharge isotope values, precipitation sine fit parameters, and typical isotope ratios of summer and winter precipitation. </p> <p><strong>Data Table S2.</strong> Streamwater grab sample SOI<sub>q</sub> values with matched daily discharge rates. See the Methods section for further details. </p>
Final dataset used in our paper "Phenological shifts alter the seasonal structure of pollinator assemblages in Europe"
<p>To build this dataset we merged records from 15 sources of data, listed in Extended Table 1. The way we mergre this database is described in the method part of the paper.</p> <p>Columns descriptor:</p> <p>Latitude and Longitude : WGS 84 coordinates</p> <p>Jday: Julian day of the record</p> <p>Species_mode: Species names and phenology mode (1,2,... or NA if the phenology is unimodal)</p> <p>Order: taxonomic order of the species</p> <p>Altitude: altitude got from spatial coordinates</p> <p>Source: Source of the data</p>
Figure 12 in Soldier flies (Diptera: Stratiomyidae) on semideciduous seasonal forest fragments, with a list of species for São Paulo State, Brazil, and two new records of species for the country
Figure 12 Map with the distribution of all 92 unique coordinates from the stratiomyid dataset throughout the Atlantic Forest remnants in the state of São Paulo, Brazil.
Figure 5 in Soldier flies (Diptera: Stratiomyidae) on semideciduous seasonal forest fragments, with a list of species for São Paulo State, Brazil, and two new records of species for the country
Figure 5 Stratiomyids from the Reserva Ecológica e Biológica Augusto Ruschi, Sertãozinho, São Paulo, Brazil. (a) Manotes sp. 1, male; (b) Manotes sp. 1, female; (c) Panacris lucida Gerstaecker, 1857, male; (d) Popanomyia sp. 1, female; (e) Psephiocera sp. 1, female; (f) Psephiocera sp. 2, male; (g) Strobilaspsis sp. 1, female; (h) Raphiocera sp. 1, male; (i) Acrochaeta ruschii Fachin & Amorim, 2015, female; (j) Merosargus brunneus Lindner, 1933, male; (k) M. cingulatus Schiner, 1868, male; (l) M. golbachi James, 1971 in James & McFadden, 1971, male. Scale bar, 1 mm.
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.