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243 results for “Coastal area”
Fig. 4 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 4. Stages of oocyte development of Anchoa marinii. A, oogonias (o) and primary growth (p) oocytes; B, cortical alveoli stage oocyte (arrow); C, yolked oocytes (arrow); D, details of a yolked oocyte (r: radiata zone; g: granulosa cells; t: teca cells); E, migration of the nucleus (n); F, hydrated oocytes; G, atretic follicle; H, post- ovulatory follicle (arrow). Scale bars: A, B, D, 20 µm; C, E, F, G, H, 70 µm.
Fig. 3 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 3. Captures per unite effort (CPUE), temperature and salinity values obtained for Anchoa marinii. Black triangles: CPUE; open squares: temperature; circles with dotted line: salinity.
Карта-схема района исследований. 1А, 1Б, 1В – станции раЗреЗа 1; 2А, 2Б, 2В – станции раЗреЗа 2; 3А, 3Б, 3В – станции раЗреЗа 3. A schematic map of the research area. 1A, 1Б, 1В – stations of line 1; 2A, 2Б, 2В – stations of line 2; 3A, 3Б, 3В – stations of line 3. in Pelagic larvae of bivalve mollusks in meroplankton in the coastal waters of Aniva Bay (southern Sakhalin, Sea of Okhotsk)
Карта-схема района исследований. 1А, 1Б, 1В – станции раЗреЗа 1; 2А, 2Б, 2В – станции раЗреЗа 2; 3А, 3Б, 3В – станции раЗреЗа 3. A schematic map of the research area. 1A, 1Б, 1В – stations of line 1; 2A, 2Б, 2В – stations of line 2; 3A, 3Б, 3В – stations of line 3.
Fig. 1 in Spatial variation of summer microphytoplankton and zooplankton communities related to environmental parameters in the coastal area of Djerba Island (Tunisia, Eastern Mediterranean) Abstract
Fig. 1: Location of sampling stations along the western and eastern coasts of Djerba Island. The grey contour lines in the maps show the position of the isobaths and the numbers in parenthesis indicate the depths of these isobaths.
Fig. 3 in Anomuran and Brachyuran Symbiotic Crabs in Coastal Areas between the Southern Ryukyu arc and the Coral Triangle
Fig. 3. Anomuran crabs recorded from the western Fan-Zai-Aou Bay in northern Taiwan. (a) Allogalathea elegans (ò, ñ), (b) Galathea tanegashimae (ò, ñ), (c) Lauriea simulata (ñ), (d) Petrolisthes virgatus (ò, ñ), (e) Petrolisthes sp. (ñ). Scale bar represents 5 mm.
Fig. 1 in Anomuran and Brachyuran Symbiotic Crabs in Coastal Areas between the Southern Ryukyu arc and the Coral Triangle
Fig. 1. Map of the sampling area (a) and locations (b) in northern Taiwan during the sampling period (April to August 2014).
Fig. 4 in Anomuran and Brachyuran Symbiotic Crabs in Coastal Areas between the Southern Ryukyu arc and the Coral Triangle
Fig. 4. Brachyuran crabs recorded from the western Fan-Zai-Aou Bay in northern Taiwan. (a) Nucia sp. (ò), (b) Echinoecus pentagonus (ò, ñ), (c) Gonatonotus nasutus (ò), (d) Permanotus purpureus (ò, ñ), (e) Pilumnus sp. (ñ), (f) Domecia glabra (ò,ñ), (g) Domecia hispida (ò, ñ), (h) Tetralia aurantistellata (ò), (i) Tetralia cinctipes (ò, ñ), (j) Tetralia glaberrima (ò, ñ), (k) Tetralia nigrolineata (ò, ñ), (l) Tetralia rubridactyla (ò, ñ), (m) Tetraloides heterodactylus (ò, ñ), (n) Tetraloides nigrifrons (ò), (o) Tetraloides nigrifrons with darker color on carapace (ò, ñ), (p) Trapezia cymodoce (ò, ñ), (q) Trapezia digitalis (ò), (r) Trapezia lutea (ò, ñ), (s) Trapezia septata (ò, ñ), (t) Trapezia serenei (ò, ñ), (u) Chlorodiella laevissima (ò), (v) Chlorodiella nigra (ò, ñ), (w) Cymo melanodactylus (ò, ñ), (x) Hapalocarcinus marsupialis (ñ), (y) Utinomiella dimorpha (ò, ñ). Scale bar represents 5 mm.
Fig. 2 in A report of 20 unrecorded bacterial species isolated from the coastal area of Korean islands in 2022
Fig. 2. Transmission electron micrographs of cells of the strains isolated in this study. Strains: 1. IMCC43116 (0.2 μm); 2. IMCC43200 (0.2 μm); 3. IMCC43225 (0.5 μm); 4. IMCC43240 (0.2 μm); 5. IMCC43252 (0.5 μm); 6. IMCC43266 (0.2 μm); 7. IMCC43345 (0.5 μm); 8. IMCC43261 (0.5 μm); 9. IMCC43586 (0.2 μm); 10. IMCC33673 (0.2 μm); 11. IMCC33772 (0.5 μm); 12. IMCC33842 (0.2 μm); 13. IMCC43611 (0.2 μm); 14. IMCC43617 (0.5 μm); 15. IMCC43655 (0.5 μm); 16. IMCC43657 (0.5 μm); 17. IMCC43666 (0.5 μm); 18. IMCC43670 (0.5 μm); 19. IMCC33788 (0.5 μm); 20. IMCC33805 (0.2 μm). Scale bars are indicated in parenthesis after strain ID.
Fig. 1 in A report of 20 unrecorded bacterial species isolated from the coastal area of Korean islands in 2022
Fig. 1. The maximum-likelihood (ML) phylogenetic tree based on 16S rRNA gene sequences showing the relationship between the strains isolated in this study and their closest bacterial species. Bootstrap values over 70% are shown at nodes for ML, neighbor-joining (NJ), and minimum evolution (ME) methods, respectively (ML/NJ/ME; -, less than 70%). Tree was rooted with Dehalococcoides mccartyi KCTC 15142T (not shown; GenBank accession no. AX814128). Filled circles indicate that the corresponding node was also recovered in the trees reconstructed with both the NJ and ME algorithms, while open circles indicate that the corresponding node was recovered in the tree generated with only one of these algorithms. Scale bar = 0.05 substitutions per nucleotide position.
Fig. 2 in A report of 20 unrecorded bacterial species in Korea, isolated from soils of coastal areas in 2022
Fig. 2. Transmission electron micrographs of cells of the strain isolated in this study. Strains: 1, HNIBRBA4475; 2, HNIBRBA4476; 3, HNIBRBA4477; 4, HNIBRBA4478; 5, HNIBRBA4479; 6, HNIBRBA4480; 7, HNIBRBA4481; 8, HNIBRBA4482; 9, HNIBRBA4483; 10, HNIBRBA4484; 11, HNIBRBA4485; 12, HNIBRBA4486; 13, HNIBRBA4487; 14, HNIBRBA4488; 15, HNIBRBA4489; 16, HNI- BRBA4490; 17, HNIBRBA4491; 18, HNIBRBA4492; 19, HNIBRBA4493; 20, HNIBRBA4494.
EOMORES earth observation and in situ data of water quality in lakes and coastal areas - year 1
<p>EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters.</p> <p>The current data set is a sample of the data generated within the first project year (2017), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area.<br> Data sets of the second (2018) and third (2019) year of EOMORES will also be submitted.</p> <p>The following is included:<br> - Estonia lakes and coast: in situ data 2017<br> - Finland: links to repositories of EO data<br> - Italy Trasimeno: sample of EO data 2017<br> - Lithuania Curonian Lagoon: sample of EO data 2017<br> - Netherlands Lake Markermeer: sample of EO data 2017<br> - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017<br> - UK Scotland: in situ data Loch Leven and Loch Lomond 2017<br> - UK WCO Sentinel2A match ups: Western Channel Observatory match ups with Sentinel-2 satellite 2016, 2017</p> <p>http://eomores-h2020.eu</p>
High resolution Sea Surface Wind retrieval over coastal Protected Areas by means of Sentinel-1 data
<p><br> The algorithm used, i.e. SARWIND LG-Mod ver. v4.01 (see reference below), is aimed at producing the Sea Surface Wind (SSW), i.e. Speed and Direction, from a single co-polarized (VV or HH) SAR image. We used EW (Extended Wide) and IW (Interferometric Wide) Swath Mode GRD (Ground Range, Multi-Look, Detected) HR (High Resolution) Sentinel-1 images, with pixel spacings of 40m x 40m and 10m x 10m (azimuth x range) respectively. Associated auxiliary products were obtained from ESA SNAP 5.0 release. SSW fields were provided for the two coastal Protected Areas (PAs) named Camargue and Wadden Sea.</p> <p>Each output folder of the SARWIND LG-Mod results contains useful plots and the estimated SSW field, provided in the file 'SAR_Sigma0_pp_decimationL2P2Tn_gradientOptSobel_LGMod_Results.txt' (pp = VV or HH; n = smoothing/decimation level), which is in the sub-folder 'LG-Mod_Theoretical_Results/Results_MEdegTHxx.xxx_Fisher (where xx.xxx is the final threshold applied). This txt file reports the following 19 columns:</p> <p><br> 1) LAT; 2) LON; 3) AZI; 4) RNG; [Location of the centre of the processed AOI]</p> <p>5) REF_U; 6) REF_V; 7) REF_W; 8) REF_D; [ECMWF reference wind, as U/V components and speed/direction]</p> <p>9) SAR_U; 10) SAR_V; 11) SAR_W; 12) SAR_D; [SARWIND LG-Mod wind estimates, as U/V components and speed/direction]</p> <p>Both REF_D and SAR_D are wind directions (expressed in degrees) with respect to the geographic North (0°=North, 90°=East, 180°=South, 270°=West), that the wind is blowing to.<br> Both REF_W and SAR_W are wind speeds (expressed in m/s).<br> Regarding REF_U/SAR_U and REF_V/SAR_V, note that a positive U component represents wind blowing to the East; a positive V component represents wind blowing to the North.</p> <p>13) SceneCentre_TrueHeading_FF; [Mean angle formed between the geographical South-North direction and the SAR azimuth direction (wrt the centre of the SAR Full-Frame image)]</p> <p>SceneCentre_TrueHeading_FF is a positive clockwise angle. In particular: SceneCentre_TrueHeading_FF is in ]180,360[ [deg].<br> Thus:<br> Descending Pass <-> SceneCentre_TrueHeading_FF is in ]180,270[ [deg]<br> Ascending Pass <-> SceneCentre_TrueHeading_FF is in ]270,360[ [deg]</p> <p>14) ROI_Npoints_UnUsablePointsMasked; [Number of samples used for each SARWIND LG-Mod wind estimation]</p> <p>15) MeanIncAng; 16) MeanNRCS; [Mean incident angle (expressed in degrees) and NRCS of the ROI]</p> <p>17) MeanResultantLength; 18) Alpha2_Est; [Fisher's formula parameters]</p> <p>19) MEdeg [Margin of Error, i.e. accuracy of each wind direction estimate, between 0° and 45°]</p> <p>The accuracy MEdeg is given by the semi-width of the confidence interval, with a confidence level (1-α) fixed, which is assigned to the wind direction estimate. Consequently, lower MEdeg values correspond to better estimates. And, if MEdeg == 45°, wind estimates must be discharged.</p> <p><br> Finally, note also that you can cut an entire row when [SAR_U SAR_V SAR_W SAR_D] == [NaN NaN NaN NaN] (typically, this happens for 'land pixels').</p> <p>% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % REFERENCES: %<br> % %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % The algorithm SARWIND LG-Mod is based on the Ph.D thesis below: %<br> % %<br> % [1] Rana, Fabio Michele (2016) "Exploitation of Satellite %<br> % Synthetic Aperture Radar Data for Geophysical Parameters Retrieval over %<br> % Land and Ocean". Unpublished Ph.D thesis. Politecnico di Bari. %<br> % %<br> % Some applications of the method are described in the following papers: %<br> % %<br> % [2] Fabio M. Rana, Maria Adamo, Guido Pasquariello, Giacomo De Carolis, %<br> % and Sandra Morelli, "LG-Mod: A Modified Local Gradient (LG) Method to %<br> % Retrieve SAR Sea Surface Wind Directions in Marine Coastal Areas," %<br> % Journal of Sensors, vol. 2016, Article ID 9565208, 7 pages, 2016. %<br> % doi:10.1155/2016/9565208. %<br> % %<br> % [3] Rana, F. M., Adamo, M., & Blanda, P. (2018, July). %<br> % LG-Mod Multi-Scale Approach for Sar Sea Surface Wind Directions %<br> % Retrieval. In IGARSS 2018-2018 IEEE International Geoscience and Remote %<br> % Sensing Symposium (pp. 3216-3219). IEEE. %<br> % %<br> % [4] Rana, F. M., Adamo, M., Lucas, R., & Blonda, P. (2019). Sea surface %<br> % wind retrieval in coastal areas by means of Sentinel-1 and numerical %<br> % weather prediction model data. Remote Sensing of Environment, 225, %<br> % 379-391. %<br> % %<br> % Suggestions and comments are always welcome. %<br> % Thanks in advance, %<br> % Fabio Michele Rana %<br> % %<br> % MOB: (+39) 3804114171 %<br> % E-MAILS: fabiomichele.rana@gmail.com; fabiomichele.rana@iia.cnr.it %<br> % %<br> % SKYPE: fabiomichelerana %<br> % %<br> % SARWIND_LG-Mod_v4.01, 2014-2019 %<br> % Author: Fabio M. Rana %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> </p>
Fig. 2 in Houseflies speaking for the conservation of natural areas: a broad sampling of Muscidae (Diptera) on coastal plains of the Pampa biome, Southern Brazil
Fig. 2. Sample coverage curves based on the number of individuals of Muscidae per region in the Coastal Plain (CPPB) of Rio Grande do Sul, Brazil. The grey shaded area represents the confidence intervals. (a) region 1; (b) region 2; (c) region 3; (d) region 4; (e) region 5; (f) CPPB total.
Fig. 3 in Houseflies speaking for the conservation of natural areas: a broad sampling of Muscidae (Diptera) on coastal plains of the Pampa biome, Southern Brazil
Fig. 3. Compositional differences between Muscidae (Insecta, Diptera) assemblages of all five regions of the Coastal Plain of Pampa Biome (Rio Grande do Sul, Brazil) shown by the unweighted pair-group method using arithmetic averages (UPGMA) cluster analysis.
Figure 3 in Effect of land cover on biodiversity and composition of a soil macrofauna community in a reclaimed coastal area at Yancheng, China
Figure 3. The dendrogram of cluster analysis on soil macrofauna from different habitats with Bray–Curtis similarity by paired groups method (A: Uncultivated land; B: Bulrush land; C: Wheat farm; D: Poplar forest; E: Metasequoia forest).
Figure 2 in Effect of land cover on biodiversity and composition of a soil macrofauna community in a reclaimed coastal area at Yancheng, China
Figure 2. One-way ANOVA on taxonomic richness and abundance, Margalef 's richness index (R) and Shannon-Weaver diversity index (H') among different habitats (Mean ± SE). The means with different scripts are significantly different by SNK test, α = 0.05.
Figure 3 in Impact of dike age on biodiversity and functional composition of soil macrofaunal communities in poplar forests in a reclaimed coastal area
Figure 3. PCoA ordinal configuration of soil macrofaunal communities from different habitats by Euclidean distance similarity index. In the code of the samples, the prefix means the code of the habitat, and the suffix means the number of the sample.
Figure 2 in Impact of dike age on biodiversity and functional composition of soil macrofaunal communities in poplar forests in a reclaimed coastal area
Figure 2. One-way ANOVA of taxonomic richness and abundance (A) and Margalef 's richness index R and Shannon– Weaver diversity index H' (B) across different habitats (mean ± SE). Means with different scripts are significantly different by Dunnett's T3 test (A) and LSD test (B), α = 0.05.
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.
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.