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301 results for “bloom”
Figure 2 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 2. Features of various types of algae in the Middle Caspian in a true color image of Landsat-8 OLI of August 6, 2017. (©OceanColor Web).
Figure 3 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 3. Average monthly values of Chl-a concentration for the North Caspian (a,b), Middle Caspian (c,d) and South Caspian (e,f) in the period from July 2002 to December 2022, from Aqua MODIS data.
Figure 9. Dipole eddy evolution from August 7 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 9. Dipole eddy evolution from August 7 to August 9, 2018 in the suspended matter field from OLCI Sentinel-3A data for August 7 (a) and August 8, 2018 (b) and MSI Sentinel-2B data for August 9, 2008 (c) according Krayushkin et al. (2018).
Figure 5 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 5. Spatial variations of optical, physical, chemical, and biological parameters along the coast of the Sambia Peninsula and the Curonian Spit. The yellow background corresponds to warm waters in eddies, the blue background corresponds to cold waters, and the grey background corresponds to waters outside eddies.
Figure 4 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 4. Photosynthetic active radiation attenuation coefficient (Kd) and inverse Secchi depth (D, circles) vs. turbidity and inverse Secchi depth (squares) at stations with cyanobacteria blooms (green symbols), under the influenced of cold water stations (blue symbols) and under the influenced of warm water stations (black symbols), respectively. Turbidity is given in units according to the turbidity standard for Formazine (Formazin Turbidity Unit, ftu).
Figure 3 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 3. Sea surface temperature (a) and chlorophyll a concentration (b) on August 22 (11:30 UTC), and sea surface temperature (c) on August 23, 2018 (12:10 UTC), all from MODIS-Aqua satellite data; (d) fragment of optical satellite image (red, green, blue composite) derived from the Ocean and Land Color Instrument (OLCI) on Sentinel-3A satellite from August 23, 2018 (9:25 UTС); (e) temperature (˚C) and salinity (f) transects along the northern coast of the Sambia Peninsula and Curonian Spit on August 23, 2018.
Figure 7 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 7. Vertical distribution of chlorophyll a concentration along the coastal area of the Sambia Peninsula on August 22, 2018.
Figure 8 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 8. Fragments of optical satellite images derived from OLCI Sentinel-2 on May 3, 2019 (a) and August 28, 2022 (b).
Figure 2 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 2. Study area and locations of station in the southeastern Baltic Sea (а) Conditional symbols: Yellow circles correspond to the stations conducted on August 22, 2018. Red circles correspond to the stations conducted on August 23, 2018. White circle is Wastewater Treatment Plant (WTP) on the northern coast of Sambia Peninsula. Amber Mining Plant (AC) is indicated as an asterisk on the western coast.
Figure 1 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 1. Phytoplankton patches on the surface of the southern Baltic Sea. Fragment of a color-synthesized image of the Baltic Sea surface in the visible range from OLCI-Sentinel-3 satellite scanner data of June 27, 2018.
Figure 6 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 6. Phytoplankton biomass contribution in the upper 1 m layer (station 16 and 24 – integrated samples over euphotic depth).
Phytoplankton blooms in the Southern Ocean in high vs. low sea ice years
<p>Processing files and relevant datasets for Schlosser & Strutton (2025), 'Phytoplankton blooms in the Southern Ocean in high vs. low sea ice years', published in Elementa. Available here: <span>https://doi.org/10.1525/elementa.2024.00055</span></p>
Fig. 1 in Influence of a tropical marina on nearshore fish communities during a harmful algal bloom event
Fig. 1. Stylised maps of Singapore and Raffles Marina (inset). Dotted lines indicate areas outside Raffles Marina where fish traps were deployed.
Fig. 3. Average a in Influence of a tropical marina on nearshore fish communities during a harmful algal bloom event
Fig. 3. Average a) Species richness, b) catch abundance, and c) species diversity (Shannon Wiener index) of fish communities within and outside Raffles Marina before and after a harmful algal bloom event in February 2014 (all means ± SE). DJF-13: December 2013– February 2014; MAM-14: March 2014–May 2014; JJA-14: June 2014–August 2014; SON-14: September 2014–November 2014; DJF-14: December 2014–February 2015. Seasons that are not significantly different are denoted by the same letter (lower case – within marina; upper case – outside marina).
Fig. 2 in Influence of a tropical marina on nearshore fish communities during a harmful algal bloom event
Fig. 2. Principal coordinates analysis of fish communities within and outside Raffles Marina before the harmful algal bloom event in February 2014. The two principal coordinates explained 55.1% of total variation. Factors shown within the circle correlate with PCO1 or PCO2 with a factor of at least 0.5.
Fig. 5 in Influence of a tropical marina on nearshore fish communities during a harmful algal bloom event
Fig. 5. Principal coordinates analysis of fish communities outside Raffles Marina before and after a harmful algal bloom event in February 2014. The two principal coordinates explained 43.2% of total variation. Factors shown within the circle correlate with PCO1 or PCO2 with a factor of at least 0.5. (DJF-13: December 2013–February 2014; MAM-14: March 2014–May 2014; JJA-14: June 2014–August 2014; SON-14: September 2014– ovember 2014; DJF-14: December 2014–February 2015).
Fig. 4 in Influence of a tropical marina on nearshore fish communities during a harmful algal bloom event
Fig. 4. Principal coordinates analysis of fish community within Raffles Marina before and after a HAB event in February 2014. The two principal coordinates explained 38.1% of total variation. Factors shown within the circle correlate with PCO1 or PCO2 with a factor of at least 0.5. (DJF-13: December 2013–February 2014; MAM-14: March 2014–May 2014; JJA-14: June 2014–August 2014; SON-14: September 2014–November 2014; DJF-14: December 2014–February 2015).
Fig. 1 in Observations on the bloom-forming jellyfish Crambionella stuhlmanni (Chun, 1896) in the St Lucia Estuary, South Africa
Fig. 1. Large specimen (≈ 16 cm bell diameter) of Crambionella stuhlmanni in the shallows of Charter's Creek. (Photo Nicola K. Carrasco, 29 May 2008)
Fig. 4 in Observations on the bloom-forming jellyfish Crambionella stuhlmanni (Chun, 1896) in the St Lucia Estuary, South Africa
Fig. 4. Swarming Crambionella stuhlmanni washed up on the shore of Catalina Bay. (Photo Ricky H. Taylor, Dec. 2005)
Fig. 3 in Observations on the bloom-forming jellyfish Crambionella stuhlmanni (Chun, 1896) in the St Lucia Estuary, South Africa
Fig. 3. Salinity and relative abundance of Crambionella stuhlmanni recorded in the St Lucia Estuary from 1991 to 2000. Abundance: 0 – absent, 1 – present, 2 – abundant.
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.