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450 results for “Spatio-temporal”
Figure 1 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 1. Map of the Rio de Janeiro state coast highlighting the 12 sampling stations in Araruama lagoon.
Figure 5 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 5. Cladocera Assemblage of Araruama Lagoon from January 2010 to December 2013. Stations 11 and 12 with different scales. E. spinifera (black and white lines arranged laterally); P. tergestina (black with small white spots);P. avitostris (vertical black and white lines); P. polyphemoides (chess pattern); P. sckmackeri (black and white lines waved horizontally).
Figure 3 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 3. BoxPolt of temperature presented from means and standard deviation, spatial variation (A) and temporal variation (B).
Fig. 4 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 4. Models of Cyathocotyle bushiensis (Cb) and Sphaeridiotrema spp. (Sg) metacercarial prevalence (prev) and intensity (int) in the waterbodies we studied in northern Minnesota during 2011‾2013; a) East Winnibigoshish index area, b) West Winnibigoshish index area, c) Lower Twin Lake, d) Crow Wing River, e) White Earth ponds, f) Shell River. Depth_cm is water depth at the sampling location. Dist.scaup is the minimum Euclidean distance between a given waypoint and the nearest point sampled under a raft of scaup in either the same season, or up to two seasons prior in that same year. Log.abund is the log transformed snail abundance at a sampling point. Size.mean is the mean snail size at a sampling point. Year2012 and Year2013 are comparisons between samples collected in 2011 vs 2012 and 2011 vs 2013, respectively.
Fig. 3 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 3. Average intensity of (a) Cyathocotyle bushiensis (Cb) and (b) Sphaeridiotrema spp. (Sg) metacercariae in each of the waterbodies studied in northcentral Minnesota during nine seasons in 2011‾2013 with 95% confidence intervals. Note that waterbody specific y-axis scales are used to highlight differences within a waterbody.
Fig. 2 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 2. Average prevalence of (a) Cyathocotyle bushiensis (Cb) and (b) Sphaeridiotrema spp. (Sg) metacercariae in each of the waterbodies studied in northcentral Minnesota during nine seasons in 2011‾2013 with 95% confidence intervals.
Fig. 1 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 1. Map of study area in northcentral Minnesota depicting the study lakes with county boundaries, within the state and USA.
Dataset _ Bombus pauloensis telemetry: Spatio-temporal use of the environment and floral resources.
<p>These data includes the GPS points collected from the tracked bees (<em>Bombus pauloensis</em> queens) that were used in the analysis of the home ranges and kernal density, data were usesd in the the MCP and the LUC analysis and pollen collected from the queens.</p>
Figure 5 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 5 Canonical correlation analysis (CCA) ordering diagram between environmental factors and Anopheles species in the pre (a) and post-construction (b) phases of the Jirau hydroelectric plant: Relative Humidity of the air (R. H%); Temp (Temperature ° C); Subtitle: Anopheles albit – An. albitarsis; Anopheles argyrit – An. argyritarsis; Anopheles benar – An. benarrochi; Anopheles braz – An. braziliensis; Anopheles darl – An. darlingi; Anopheles evan – An.evansae; Anopheles mattog – An. mattogrossensis; Anopheles mediop – An. mediopunctatus; Anopheles osw – An. oswaldoi; Anopheles per – An. peryassui; Anopheles rang – An. rangeli; Anopheles trian – An. triannulatus.
Figure 3 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 3 Density of Anopheles species (x) in the sampled months (January to August) before (a) and after (March to October) the construction (b) of the Jirau hydroelectric w plant, in Rondônia, Brazil.
Figure 1 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 1 Sampling points of anophelines in the area covered by the Jirau hydroelectric plant, in the stretch between the locations of Jaci Paraná and Abunã (squares), in the pre (black) and post-construction (gray) phases.
Figure 2 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 2 Housing types (a-d) spatial variation of Anopheles darlingi before (e) and after (f) the construction of the Jirau hydroelectric plant, in Rondônia, Brazil. Subtitle: AB – Abunã; JHP – Jirau Hydroeletric Plant; JP – Jaci Paraná; NMP – Nova Mutum Paraná.
Figure 22 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 22. Outlines of 8 dipole structures that preceded all intense algal blooms in the central part of the South Caspian during the study period from 1999 to 2022. Different colors mark different years.
Figure 20 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 20 shows interannual variability of monthly averaged cloudiness for June, July, August and September in 2002-2022. One can see that all bloom events have occurred when monthly averaged cloudiness was in the range of 0.1-0.57. In general, absence of clouds should be favorable for algal bloom due to high level of insolation, but we can point to years 2006, 2012, 2014, 2016, and 2022 when monthly averaged cloudiness in August was less than 0.3 and no algal blooms were identified.
Figure 21 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 21. Interannual variability of photosynthetically active radiation (Einstein/m2day) in June, July, August and September (2002-2022) in the southern part of the South Caspian. Red circles mark cases of intense algal bloom.
Figure 20 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 20. Interannual variability of cloudiness in June, July, August and September (2002-2022) in the southern part of the South Caspian. Red circles mark cases of intense algal bloom.
Figure 17 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 17. Interannual variability of SST (0C) in June, July, August and September (2002-2022) in the southern part of the South Caspian. Red circles mark cases of intense algal bloom.
Figure 19 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 19 shows interannual variability of monthly averaged wind speed for June, July, August and September in 2000-2022. We can see that all bloom events occurred when monthly averaged wind speed was in the range of 4.3-5.0 m/s. In general, low wind speed should be favorable for algal bloom due to absence of wind-wave mixing, but the problem is that this area of the Caspian is the calmest area of the sea (Rahimi et al. 2022). Only twice, in September 2016 and 2019, wind speed reached 5.5-5.75 m/s (Figure 19). Seemingly, every year should be favorable for algal bloom, but that is not true. Moreover, in August 2003, 2006, 2007, 2014, and 2016, wind speed was less than 4.3 m/s and no algal blooms were recorded in these years.
Figure 16 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 16. Features of intense bloom of cyanobacteria in the South Caspian in 2021: Aqua MODIS true color image of July 4 (a); map of Chl-a concentration of July 4 (b); Sentinel-2A MSI image of July 21 (c).
Figure 14 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 14. Features of intense bloom of cyanobacteria at the border of the Middle and South Caspian on August 8, 2017 (Aqua MODIS true color image)
ScienceDex guides
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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.