Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
245
datasets available to search
ShareScore release 0.9.0
Dataset results
245 results for “seasonal pattern”
Map 17 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 17. Distribution map for Oniscus asellus.
Map 26 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 26. Distribution map for Armadillidium vulgare.
Map 22 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 22. Distribution map for Armadillidium nasatum.
Figure 7 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Figure 7. Corrected number of observations per two-month period for Haplophthalmus mengii (N = 87).
Map 7 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 7. Distribution map for Haplophthalmus montivagus.
Fig. 6 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Fig. 6. Corrected number of observations per two-month period for Haplophthalmus danicus (N = 163).
Map 5 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 5. Distribution map for Haplophthalmus danicus.
Fig. 3 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Fig. 3. Corrected number of observations per two-month period for Ligia oceanica (N = 13).
Fig. 5 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Fig. 5. Corrected number of observations per two-month period for Androniscus dentiger (N = 141).
Map 4 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 4. Distribution map for Androniscus dentiger.
Map 3 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 3. Distribution map for Ligidium hypnorum.
Map 1 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 1. The ecological regions in Belgium.
Map 2 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 2. Distribution map for Ligia oceanica.
Fig. 4 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Fig. 4. Corrected number of observations per two-month period for Ligidium hypnorum (N = 345).
Energy-water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperms species richness in China
<p>Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large-scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human-induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human-induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km<sup>2 </sup>spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (i) all species, (ii) endemic species, and (iii) non-endemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (i) energy-water, (ii) climatic seasonality, (iii) habitat heterogeneity, and (iv) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy-water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat-heterogeneity has a better influence on non-endemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.</p>
Fig. 3 in Gape size influences seasonal patterns of piscivore diets in three Neotropical rivers
Fig. 3. Relationship between standard length and gape width for the four predator species.
Data from: Zoning has little impact on the seasonal diel activity and distribution patterns of wild boar (Sus scrofa) in an UNESCO Biosphere Reserve
<p><span><span><span><span><span><span><span><span><span><span><span><span><span><span><span>Understanding the spatio‐temporal distribution of ungulates is important for effective wildlife management, particularly for economically and ecologically important species such as wild boar (</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><em>Sus scrofa</em><span><span><span><span><span><span><span><span><span><span><span><span><span><span><span>). Wild boars are generally considered to exhibit substantial behavioral flexibility, but it is unclear how their behavior varies across different conservation management regimes and levels of human pressure. To analyze if and how wild boars adjust their space use or their temporal niche, we surveyed wild boars across the core and buffer zones (collectively referred to as the conservation zone) and the transition zone of a biosphere reserve. These zones represent low and high levels of human pressure, respectively. Specifically, we employed a network of 53 camera traps distributed in the Schaalsee UNESCO Biosphere Reserve over a 14‐month period (19,062 trap nights) and estimated circadian activity patterns, diel activity levels, and occupancy of wild boars in both zones. To account for differences in environmental conditions and day length, we estimated these parameters separately for seven 2‐month periods. Our results showed that the wild boars were primarily nocturnal, with diurnal activity occurring dominantly during the summer months. The diel activity patterns in the two zones were very similar overall, although the wild boars were slightly less active in the transition zone than in the conservation zone. Diel activity levels also varied seasonally, ranging from 7.5 to 11.0</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span> <span><span><span><span><span><span><span><span><span><span><span><span><span><span><span>h day</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><sup>−1</sup><span><span><span><span><span><span><span><span><span><span><span><span><span><span><span>, and scaled positively with the length of the night (</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><em>R</em><sup>2</sup> <span><span><span><span><span><span><span><span><span><span><span><span><span><span><span>=</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span> <span><span><span><span><span><span><span><span><span><span><span><span><span><span><span>0.66–0.67). Seasonal occupancy estimates were exceptionally high (point estimates ranged from 0.65 to 0.99) and similar across zones, suggesting that the wild boars used most of the biosphere reserve. Overall, this result suggests that different conservation management regimes (in this case, the zoning of a biosphere reserve) have little impact on wild boar behavior. This finding is relevant for wildlife management in protected areas where possibly high wild boar densities could interfere with conservation goals within these areas and those of agricultural land use in their vicinity.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></p>
Soil microbial relative resource limitation exhibited contrasting seasonal patterns along an elevational gradient in Yulong snow mountain
<p>1. Microbial relative resource limitations represented by enzyme stoichiometry reflect the relationship between microbial nutrient requirements and nutrient status in soil, but the issue on whether alterations in environments along elevational gradients affect the magnitude of microbial relative resource limitations remains unresolved.</p> <p>2. Here, we examined seasonal patterns in microbial relative carbon (C) and phosphorus (P) limitations indicated by vector lengths and angles using relative proportional enzymatic activities and key controlling factors along an elevational gradient in the Yulong Snow Mountain. We also analyzed the relationships between microbial metabolic processes and microclimates (i.e., soil moisture and temperature), soil properties (i.e., pH and soil texture), and microbial attributes (i.e., microbial biomass and fungal: bacterial ratio).</p> <p>3. We found that soil microbial relative C limitation decreased with increasing elevations, with lower levels observed in the dry season than in the wet season. In contrast, soil microbial relative P limitation varied significantly with elevations, with linearly increasing trends in wet seasons but unimodal trends in dry seasons. Meanwhile, we found higher relative C limitation in the coniferous forest but higher relative P limitation in the broad-leaved forest. Notably, soil microbial relative C limitation was primarily affected by the soil microenvironment (i.e., soil temperature) and substrate quantity (i.e., the ratio of soil dissolved organic C to available P), whereas soil microbial relative P limitation could be alleviated by increasing microbial relative C limitation combined with increasing soil pH. Additionally, the significant linear pattern of the C use efficacy with elevations was only observed in the wet seasons, which was directly influenced by soil microclimates and microbial relative C limitation.</p> <p>4. Overall, our results provided important information for better understanding the essential role of microbial processes in the regulation of C and P cycling in vulnerable subtropical mountain ecosystems.</p>
SIF and CLM5 outpfile files to support Kunik et al "Satellite-based solar-induced fluorescence tracks seasonal and elevational patterns of photosynthesis in California's Sierra Nevada mountains"
<p>These files contain 0.04° monthly sampled TROPOMI SIF, corrected for length of day and topography ("SIFdc_dem") over the Sierra Nevada region of California, along with Community Land Model (CLM) v5.0 point and regional simulation output. CLM5.0 simulations with prognostic vegetation state (CLM5.0-BGC, files begninning with "clm5_") and with satellite phenology (CLM5.0-SP, files beginning with "clm5_SP_") are provided. </p>
Population dynamics and seasonal migration patterns of Spodoptera exigua in northern China based on 11 years of monitoring data
<p>The beet armyworm, <em>Spodoptera exigua </em>(Hübner) is an important migratory pest worldwide that has caused serious economic losses in the main crop-producing areas of China. To effectively monitor and control this pest, it is necessary to investigate its interannual and seasonal migration patterns in northern China. In this study, we weekly monitored the population dynamics of <em>S. exigua</em> using sex pheromone traps in Shenyang, Liaoning Province from 2012 to 2022 and simulated the migration trajectories using the HYSPLIT model. Overall, the migration numbers varied significantly among years, with large migrations in 2018 and 2020 that resulted in a total catch of more than 2000 individuals. </p>
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.