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4,243 results for “seasonality”
FIGURE 7 in A relict new species of Oreobates (Anura, Strabomantidae) from the Seasonally Dry Tropical Forests of Minas Gerais, Brazil, and its implication to the biogeography of the genus and that of South American Dry Forests
FIGURE 7. Map of the current known distribution of Oreobates remotus sp.nov.
Figure 3. Chlorophyll-a in First observation and seasonal dynamics of the new invasive planktonic copepod Oithona davisae Ferrari and Orsi, 1984 along the southern Black Sea (Anatolian Coast)
Figure 3. Chlorophyll-a distributions during the sampling period.
Figure 2 in First observation and seasonal dynamics of the new invasive planktonic copepod Oithona davisae Ferrari and Orsi, 1984 along the southern Black Sea (Anatolian Coast)
Figure 2. The average monthly sea surface temperature in 2007 and 2015 (°C).
HIWIND Observation of Summer Season Polar Cap Thermospheric Winds
The dataset includes 1) balloon-borne instrument HIWIND observed thermospheric winds in the polar cap, and 2) TIEGCM simulation of the polar cap electron density and thermospheric winds with normal soft electron precipitation and more soft electron precipitation. The dataset will be used in a publication with the same title.
Fig. 3 in Biology and Seasonality of Fulcidax monstrosa (F.) (Chrysomelidae: Chlamisinae)
Fig. 3. Frequency distribution of the larval instars of F. monstrosa showing four instars.
Fig. 1 in Biology and Seasonality of Fulcidax monstrosa (F.) (Chrysomelidae: Chlamisinae)
Fig. 1. Restinga de Jurubatiba National Park with the study area marked.
FIGURE 9 in A new collared lizard (Tropidurus: Tropiduridae) endemic to the Western Bolivian Andes and its implications for seasonally dry tropical forests
FIGURE 9. Boxplots showing variation in scale counts among Tropidurus chromatops, T. etheridgei, and T. azurduyae.
Data from GLM analyses of seasonal inversions
<p>see info at: https://github.com/Jcbnunez/Cville-Seasonality-2016-2019</p>
Figure 3. Chlorophyll-a in First observation and seasonal dynamics of the new invasive planktonic copepod Oithona davisae Ferrari and Orsi, 1984 along the southern Black Sea (Anatolian Coast)
Figure 3. Chlorophyll-a distributions during the sampling period.
Figure 2 in First observation and seasonal dynamics of the new invasive planktonic copepod Oithona davisae Ferrari and Orsi, 1984 along the southern Black Sea (Anatolian Coast)
Figure 2. The average monthly sea surface temperature in 2007 and 2015 (°C).
Fig. 3 in Two new seasonal killifishes of the Austrolebias adloffi group from the Lagoa dos Patos basin, southern Brazil (Cyprinodontiformes: Aplocheilidae)
Fig. 3. Austrolebias pelotapes, topotype, male, not preserved (photograph by Matheus V. Volcan).
FIGURE 4 in Lonchocarpus verticillatus (Leguminosae-Papilionoideae): A new species from Seasonally Dry Tropical Forest in Colombia
FIGURE 4. Distribution map of Lonchocarpus verticillatus.
How is copepod functional diversity shaped by 2015-2016 El Niño and seasonal water masses in a coastal ecosystem of Southwest Atlantic?
<p>Figure S1: a) El Niño-Southern Oscillation episodes. Index values (Oceanic Niño Index - ONI) of +0.5 or higher indicate El Niño; values of -0.5 or lower indicate La Niña (dotted line). Transparent gray shade represents the period of interest (2014-2016), b) Pixel contour plots show satellite-based sea surface temperature monthly means in the Arvoredo MPA surroundings. Data visualization standard plots from the zooplankton time series adopted by SCOR WG125 (Mackas et al., 2012) and performed at http://www.st.nmfs.noaa.gov/copepod/.</p><p>Table S1 – Taxa code and functional traits of copepod species during the summer and winter of 2014, 2015, and 2016 in the Arvoredo MPA surroundings.</p><p>Table S2 – Total and mean abundance (ind. m-3), standard deviation (SD), relative abundance (RA %), and frequency of occurrence (FO %) of copepod species during the summer and winter of 2014, 2015, and 2016 in the Arvoredo MPA surroundings.</p><p> </p>
Epigenetic diversity underlying seasonal and annual variations in brown planthopper (BPH) populations as revealed by methylation-sensitive restriction assay
<p>Raw data files for the image analysis.</p>
Slower changes in vegetation phenology than precipitation seasonality in the dry tropics
Open the record for dataset details and reuse information.
Figure 8-11 from: Younis EM, Al-Asgah NA, Abdel-Warith A-WA, Gabr MH, Shamlol FS (2020) Analysis of reproductive biology and spawning season of the pink ear emperor Lethrinus lentjan, from marine ecosystem. Zoologia 37: 1-10. https://doi.org/10.3897/zoologia.37.e48475
Figure 8-11 Histological sections in the ovary of L. lantjan showing the maturation stages of female: (8) immature; (9) early maturation; (10) maturity; (11) spawning. (AO) atretic oocytes, (CY) coalesced secondary oocytes, (EF) empty follicles, (HY) hydrated oocytes, (LP) late perinucleolar, (PP) pre-perinucleolar, (PY) primary yolk vesicle oocytes, (SY) secondary yolk vesicle oocytes.
Figure 5-7 from: Younis EM, Al-Asgah NA, Abdel-Warith A-WA, Gabr MH, Shamlol FS (2020) Analysis of reproductive biology and spawning season of the pink ear emperor Lethrinus lentjan, from marine ecosystem. Zoologia 37: 1-10. https://doi.org/10.3897/zoologia.37.e48475
Figure 5-7 Histological sections in the testis of L. lantjan showing the maturation stages of male: (5) immature; (6) maturity; (7) spawning. (Ps) primary spermatocytes, (Ss) secondary spermatocytes, (S) sperm, (St) spermatids, (Sg) spermatogonia, (Sc& S) sperm cells and sperms outside the seminal vesicles
BSC Post-processed Sub-seasonal Climate Forecast for vineyard management
<p>The Climate Services Team at the Barcelona Supercomputing Center has deployed a climate service for vineyard management in the context of the vitiGEOSS project. This dataset results from post-processing, i.e. by downscaling, calibrating and assessing, the subeasonal climate prediction system NCEP-CFSv2.</p> <p>Probabilistic predictions have as output several solutions (ensemble members) to account for forecast uncertainty. The forecast information is conveyed as probabilities, in this case as the probabilities of occurrence of three categories or terciles (below normal, normal and above normal). The categories are defined based on the terciles of the model climatology distribution over a period in the past. Additional information regarding the probability of occurrence of extremes is also provided, considered as the probability of not reaching the 10th percentile or surpassing the 90th percentile of the model climatology distribution. The skill scores provide information on the forecast quality (fair Ranked Probability Skill Score for the tercile categories and fair Brier Skill Score for the probabilities of extremes). A positive skill score indicates that the prediction is good (better than using average past conditions) in the long term, while a negative skill score indicates a prediction is not beating the climatological forecast.</p> <ul> <li> <p>Prediction system: National Centers for Environmental Prediction (NCEP) CFSv2, post-processed by BSC (create a lagged ensemble, downscaling and calibration).</p> </li> <li> <p>Issue frequency: Weekly (Initialization every Thursday, post-processed prediction every Friday).</p> </li> <li> <p>Lead times: weeks 1 to 4 (e.g. For a forecast issued on Friday 4th November, forecasts will be weekly averages starting the following Monday-Thursday and the 4 following weeks (e.g. week 1 will be 8th-15th November). The initialization date is indicated in the name of each file (e.g. 20211104). </p> </li> <li> <p>Variables: mean, minimum and maximum 2 m temperature, accumulated precipitation, and incoming solar radiation.</p> </li> <li> <p>Ensemble size: 48 members</p> </li> <li> <p>Postprocessing: Create a lagged ensemble of 48 ensemble members, downscaling from the original (1°x 1°) resolution to 0.1°x 0.1° for the three domains and weekly calibration with variance inflation. </p> </li> <li> <p>Spatial coverage of the domains: </p> </li> <ul> <li> <p>Catalonia region is indicated by ‘cat’ and covers latitudes [10 N, 44 N], and longitudes [1 W, 4 E]. The latitude indices range [1:41], and the longitude indices range [1:51].</p> </li> <li> <p>Douro region is indicated by ‘douro’ and covers latitudes [40 N, 43N ] and longitudes [9 W, 6 W]. The latitude indices range [1:31], and the longitude indices range [1:31].</p> </li> <li> <p>Campana region is indicated by ‘campania’ and covers latitudes [39 N, 43 N] and longitudes [13 E,17.3 E]. The latitude indices range [1:41], and the longitude indices range [1:44]. </p> </li> </ul> </ul> <p>The specific latitude and longitude indices to extract the predictions corresponding to each vitiGEOSS site are indicated in Table 2.</p> <ul> <li> <p>Forecast probabilities</p> </li> </ul> <p>E.g t2_campania_prob_20211104.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘prob’ and the initialization date of the forecasts (Always a Thursday).</p> <p>It contains the forecast probabilities in (%) of each tercile category below normal (prob_bn), normal (prob_n) and above normal (prob_an) and the probability of lower extreme (prob_bp10) and the probability of upper extreme (prob_ap90). The latitude, longitude and lead time (weeks 1 to 4) can be selected.</p> <ul> <li> <p>Forecast ensemble members</p> </li> </ul> <p> E.g. t2_campania_20211104.ncml</p> <p>The file name contains the name of the variable, domain and initialization date of the forecasts (Always a Thursday).</p> <p>It contains the 48 absolute values of the forecast variables in their corresponding units (see Table 2). The latitude, longitude and lead time (weeks 1 to 4) can be selected.</p> <ul> <li> <p>Category limits</p> </li> </ul> <p>E.g. t2_campania_percentiles_week44.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘percentiles’ and the month for which the category limits apply. </p> <p>It contains the limits of the predicted categories ( below normal, normal and above normal). These categories are defined with respect to a period in the past. The 33rd, 66th percentiles (p33 and p66) divide the model climatological distribution into 3 equiprobable categories. The 33rd percentile is the boundary between below-normal and normal, and the 66th percentile is the boundary between the normal and above-normal categories. The 10th and 90th percentiles, which define the threshold for the lower and upper extreme conditions, are also provided (p10 and p90). It should be noted that the definition of the categories is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p> <ul> <li> <p>Skill scores</p> </li> </ul> <p>E.g t2_campania_skill_week44.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘skill’ and the week of the year for which the skill scores apply. </p> <p>It contains the measures of forecast quality, the fair Ranked probability score for terciles (rpss) and the fair Brier Skill Score for lower and upper extremes (bsp10 and bsp90). It should be noted that the skill level is specific to each location (latitude and longitude), initialization and lead time (valid week).</p>
Urban impervious surface expansion intensifies heavy rainfall in Beijing's rainy season
Open the record for dataset details and reuse information.
Supplementary material 1 from: Armijos-Ojeda D, Székely D, Székely P, Cogălniceanu D, Cisneros-Heredia DF, Ordóñez-Delgado L, Escudero A, Espinosa CI (2021) Amphibians of the equatorial seasonally dry forests of Ecuador and Peru. ZooKeys 1063: 23-48. https://doi.org/10.3897/zookeys.1063.69580
Tables S1, S2
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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.