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4,243 results for “seasonality”
Chemical compositions of PM1 and PM2.5 collected online at PKUERS site in Beijing during three seasons from 2016 to 2018
<p>This dataset provides the chemical composition of PM<sub>1</sub> and PM<sub>2.5 </sub>measured in urban Beijing. The sampling site is PKUERS which is located on the campus of Peking University. The dataset was collected by an online aerosol mass spectrometer covering three seasons (early autumn, winter, and spring) from 2016 to 2018.</p>
Legacy effects of canopy gaps on liana abundance 25 years later in a seasonal tropical evergreen forest in northeastern Thailand
<p><span>Liana</span><span>s </span><span>require host trees to reach and stay in the forest canopy, but as seedlings and juveniles, they benefit from canopy gaps created by treefalls</span><span>.</span><span> Here, we evaluated the relative importance of these two aspects, i.e., the availability of potential</span><span> hosts</span><span> vs. the legacy effect of past treefall gaps, on the local abundance of liana stems in a seasonal tropical evergreen forest in the Sakaerat Biosphere Reserve in northeastern Thailand. Within a 2.5-ha plot for forest dynamics monitoring, canopy height was measured in 1993 and 2018 at 5-m intervals to distinguish areas of mature (canopy height </span><span>≥</span><span> 20 m), building (10-20 m), and gap phases (< 10 m). In 2017–2018, we surveyed all liana stems </span><span>≥</span><span> 1 cm in diameter at breast height within 50 subplots (10 m × 10 m each) and recorded their diameter and the diameter of the host tree. Of a total of 445 liana individuals, 242 could be identified at least to the family level, while the others had clear morphological traits of climbing mechanisms. The number of liana stems was higher in areas that had been at the building/gap phase than those at the mature phase in 1993. When this 25-year-old legacy of past gap locations was considered, there was a positive association of local abundance between lianas and trees in areas at the mature phase in 2018. In conclusion, liana abundance reflected a long-term legacy of past treefall gaps more than 25 years earlier in this seasonal evergreen forest.</span></p>
Supporting data for: Emissions background, climate, and season determine the impacts of past and future pandemic lockdowns on atmospheric composition and climate
<p>COVID-19 pandemic responses affected atmospheric composition and climate. These effects are historically contingent, depending on the background emissions, climate, and season in which they occur. We used the GISS ModelE Earth System Model to evaluate how atmospheric and climate impacts depend on the decade and season in which lockdowns occurred. Data underlying the figures and analysis are provided as Python numpy arrays as a courtesy for peer reviewers. These data are annual means of diagnostic variables from ModelE.</p>
Seasonally Varying Vegetation Impacts on Surface Fluxes (VegFlux)
<p>VegFlux introduced observationally based, seasonal variations in vegetation and bare-soil fractions into the Met Office Unified Model (UM) and quantify impacts on dust emission and moist processes, allowing improvement of climate projections for Africa.</p> <p>In the zipped folder (VegetFlux_data.tar.gz) uploaded here, the data is grouped into five folders containing each one of the following parameters: temperature (tewnt15m), precipitation (tewnpre), latent heat (tewnlhf), sensible heat (tewnshf) and vegetation fraction (vegfrac). In each folder, we have two types of simulation, one whose file names begin with tewnfa and the other whose file names begin with tewnha. The one begining with tewnfa is the simulation using fix vegetation fraction in the unified model (UM) and the tewnha using varying vegetation fraction. The timestep is 12 hours meaning two measures per day (00h and 12h) during five months (may to september).</p>
The shrinking Great Salt Lake contributes to record high dust-on-snow deposition in the Wasatch Mountains during the 2022 snowmelt season
<p>This site contains inputs/outputs used for atmospheric backward trajectory analyses (.zip files) and snowmelt mass and energy balance modeling (all other files) in this study. The abstract of the study is below: </p> <p>Seasonal snowmelt from the Wasatch Mountains of northern Utah, USA is a primary control on water availability for the metropolitan Wasatch Front, surrounding agricultural valleys, and the Great Salt Lake (GSL). Prolonged drought, increased evaporation due to warming temperatures, and sustained agricultural and domestic water consumption have caused GSL water levels to reach record low stands in 2021 and 2022, resulting in increased exposure of dry lakebed sediment. When dust emitted from the GSL dry lakebed is deposited on the adjacent Wasatch snowpack, the snow is darkened, and snowmelt is accelerated. Regular observations of dust-on-snow (DOS) began in the Wasatch Mountains in 2009, and the 2022 season was notable for both having the most dust deposition events and the highest snowpack dust concentrations. To understand if record high DOS concentrations were linked to record low GSL levels, dust source regions for each dust event were identified through a backward trajectory model analysis combined with aerosol measurements and field observations. Backward trajectories indicated that the exposed lakebed of the GSL likely contributed 23% of total dust deposition and had the highest dust emissions per surface area. The other potential primary contributors were the Great Salt Lake Desert (45%) and the Sevier + Tule dry lakebeds (17%), both with lower per-area emissions. The impact on snowmelt, quantified by mass and energy balance modeling in the presence and absence of snow darkening by dust, was over two weeks (17 days) earlier. The impact of dust on snowmelt could have been more dramatic if the spring had been drier, but frequent snowfall buried dust layers, delaying dust-accelerated snowmelt later into the melt season.</p>
Figure 5 in Consistent seasonal polyphenism in male genitalia of three Leptidea butterfly species (Lepidoptera: Pieridae)
Figure 5. Between-species pairwise comparisons of phallus and saccus performed in form space using outlines (each representing the mean shape for the species).
Figure 2 in Consistent seasonal polyphenism in male genitalia of three Leptidea butterfly species (Lepidoptera: Pieridae)
Figure 2. Box-plots depicting the size (centroid size) of genital structures at the interspecific (species) and intraspecific (generation) level, respectively. Abbreviations: J, L. juvernica; j1 and j2, L. juvernica first and second generation; R, L. reali; r1 and r2, L. reali first and second generation; S, L. sinapis; and s1, s2 and s3, L. sinapis first, second and third generation.
Figure 1 in Consistent seasonal polyphenism in male genitalia of three Leptidea butterfly species (Lepidoptera: Pieridae)
Figure 1. Lateral view of Leptidea male genital structures: phallus (A), capsule (B), saccus (C) and uncus (D). Red circles indicate landmark locations (fixed landmarks are recognizable by the number from sliding semi-landmarks).
FIG. 2 in Host-parasite relationships between a Malagasy fruit bat (Pteropodidae) and associated bat fly (Diptera: Nycteribiidae): seasonal variation of host body condition and the possible impact of parasite abundance
FIG. 2. Body Condition Index (BCI) of R. madagascariensis in the Grotte des Chauves-souris, Parc National d'Ankarana, based on five different field sessions and separated into the different age and sex classes. AF = adult female, AM = adult male, NF = neonate female, NM = neonate male, SAF = sub-adult female, SAM = sub-adult male
FIG. 3 in Swarming behaviour, catchment area and seasonal movement patterns of the Bechstein's bats: implications for conservation
FIG. 3. Examples illustrating the recovered movement patterns between maternity colonies and swarming sites. Individuals at swarming sites LA (A) and KG (B) were recovered at multiple colonies. Likewise, individuals recovered at colonies A (C) and H (D) were caught at different swarming sites on the same night. Capture site abbreviations and maternity colony IDs correspond to those used in Fig. 1 and Table 1
FIG. 2 in Swarming behaviour, catchment area and seasonal movement patterns of the Bechstein's bats: implications for conservation
FIG. 2. Map showing the minimum catchment polygon (dark grey), and maximum range circle (light grey) of the two main swarming sites (LA, KG). Country border between Belgium and the Netherlands (irregular black line), forest fragments (irregular grey patches), swarming sites (grey pentagons) and recovered roost sites (black dots) are also indicated
FIG. 4 in Daily and seasonal variation in non-acoustic communicative behaviors of male greater short-nosed fruit bats (Cynopterus sphinx)
FIG. 4. Seasonal variation in duration and frequency of A — scent marking, B — wing flapping and C — open wing gesture behaviors. Mean ± SEM of frequency and duration varying between observed months (from January to December 2012). Mean ± SEM of number of attempts and duration was calculated from seven observation sessions for each month
FIG. 2 in Daily and seasonal variation in non-acoustic communicative behaviors of male greater short-nosed fruit bats (Cynopterus sphinx)
FIG. 2. Inter-individual variation in the mean frequency of A — scent marking, B — wing flapping and C — open wing gesture behavior between mating and non-mating seasons. Data shown as the mean of number of attempts (± SEM) made by focal bats between two mating and two non mating seasons. Each data point represents individual focal bat (Animal ID — A to F)
FIG. 3 in Daily and seasonal variation in non-acoustic communicative behaviors of male greater short-nosed fruit bats (Cynopterus sphinx)
FIG. 3. Daily variation in duration and frequency of A — scent marking, B — wing flapping and C — open wing gesture behaviors. Mean ± SEM of frequency and duration between observation sessions (one hour time interval). Mean ± SEM number of attempts and duration of each attempt were calculated for each observation session across 12 months (between January and December 2012) for all focal bats
FIG. 1 in Daily and seasonal variation in non-acoustic communicative behaviors of male greater short-nosed fruit bats (Cynopterus sphinx)
FIG. 1. Non-acoustic communicative displays of male C. sphinx. A — male bat scent marking the interior of palm leaves with its saliva during night time. Circled areas in the picture shows scent marked part of the leaf. B — Tagged male bat co-roosting with females (untagged) in the day roost and displaying open wing gesture during morning hours in the mating season
FIG. 1 in Swarming behaviour, catchment area and seasonal movement patterns of the Bechstein's bats: implications for conservation
FIG. 1. Map of the Belgium and adjacent countries (inset top right) indicating the location of study area. Within the study are (main figure), sampled swarming sites and recovered roost sites are indicated (grey pentagons and black circles, respectively). Forest fragments are shaded according to age (recent: light grey; ancient: dark grey). Capture site abbreviations correspond to those used in Table 1
FIG. 1 in Host-parasite relationships between a Malagasy fruit bat (Pteropodidae) and associated bat fly (Diptera: Nycteribiidae): seasonal variation of host body condition and the possible impact of parasite abundance
FIG. 1. Location map of the study site, Grotte des Chauves-souris, in the Parc National d'Ankarana, northern Madagascar
Figure 4 in Use of remote cameras to evaluate ocelot (Leopardus pardalis) population parameters in seasonal tropical dry forests of central-western Mexico
Figure 4: Relationship between estimated ocelot density and precipitation in tropical rain forests (TRF) and tropical seasonal ecosystems (TSE). Ocelot density in tropical rain forest was the closest to show a significant increase with annual precipitation (R2 = 0.2463, p = 0.071).
Figure 3 in Use of remote cameras to evaluate ocelot (Leopardus pardalis) population parameters in seasonal tropical dry forests of central-western Mexico
Figure 3: Estimated ocelot density in tropical rainforest sites (TRF) and tropical seasonal ecosystems (TSE). Thick horizontal lines correspond to median values. The upper and lower extremes of the boxes correspond to the first and third quartiles, whiskers correspond to 1.5 times the interquartile range of the data and empty circles are outliers.
Figure 2 in Use of remote cameras to evaluate ocelot (Leopardus pardalis) population parameters in seasonal tropical dry forests of central-western Mexico
Figure 2: Examples of markings employed for individual recognition of ocelots. (A) and (B) Photographic recapture of same individual in the locality of El Naranjal. (C) and (D) Different individuals recorded in the locality of Playa del Venado. The oval indicates an example of a set of unique spot and stripes patterns employed for individual identification.
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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.