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163 results for “temporal variability”
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)
Figure 15 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 15. Features of intense bloom of cyanobacteria in the South Caspian in a Suomi NPP VIIRS true color image of July 29, 2018.
Figure 13 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 13. Features of intense bloom of cyanobacteria in the South Caspian in 2017 in Aqua MODIS true color images of: July 23 in the southeastern part (a); August 3 in the southern part (b); August 8 - a merged structure along the entire southern coast (c). Map of Chl-a concentration (d) is drawn from Aqua MODIS data of August 8
Figure 12 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 12. Features of intense phytoplankton bloom in the South Caspian in 2010 in Aqua MODIS true color images of July 13 (a) and August 4 (b)
Figure 8 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 8. Features of intense phytoplankton bloom in the South Caspian in 2001 in true color Terra MODIS images: in the initial period, on July 14 (a), arrows indicate the bloom area; at the peak of the bloom, on July 25 (b).
Figure 9 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 9. Features of various stages of intense cyanobacteria bloom in the South Caspian in August-September 2005 in Aqua MODIS true color images of: August 14 (a); August 24 (b); September 1 (c); September 16 (e). Map of Chl-a concentration of September 1 (d) is taken from (Soloviev 2005).
Figure 7 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 7. Schematic map of intense phytoplankton bloom areas in 2022 (green contours), built from daily Aqua MODIS data in the See the Sea information system.
Figure 11 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 11. Features of intense bloom of cyanobacteria in the South Caspian in Aqua MODIS true color image of August 20, 2009.
Figure 4 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 4. Annual maximum, mean and minimum Chl-a concentrations for the North Caspian (a), Middle Caspian (b), South Caspian (c) in the period from July 2002 to December 2022, from Aqua MODIS data.
Figure 6 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 6. Features of coastal current, vortex structures and jets along the western coast of the Caspian Sea in an Aqua MODIS image of July 26, 2022. The tracer is Chl-a of high concentration.
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.
An in-situ daily dataset for benchmarking temporal variability of groundwater recharge
<p>A newly developed benchmark dataset of groundwater recharge per unit specific yield (RpSy, n meters) at daily temporal resolution is presented. The data has been obtained through the application of the Water table Fluctuation (WTF) method at groundwater wells within the continental US. To ensure high-fidelity estimates, only wells that meet a set of stringent criteria have been considered. The RpSy dataset may serve as a benchmark for validating the temporal consistency of recharge products and daily simulation results from land surface and integrated hydrologic models.</p> <p>The resulting product is a continuous daily RpSy (n meters) time series data for 485 groundwater wells. The data files are provided in the .csv format and consist of three columns for each observation well. The first column lists the local time, while the second and third columns provide the RpSy and RpSyu (considering a groundwater depth-dependent specific yield) time series in meters per day. Additionally, a file containing site information for all the selected wells is included. It contains four columns that detail the USGS ID of the groundwater well, its latitude (Lat), longitude (Long), and screen depth (depth, in meters). The data file can be accessed in most text editors and spreadsheets.</p>
Data for: Temporal consistency and spatial variability in detection: implications for monitoring of macroinvertebrates from shallow groundwater aquifers (Subterranean Biology, 2024)
<p>Original research article: Knüsel M., Alther R., Couton M. & Altermatt F. (2024) Temporal consistency and spatial variability in detection: implications for monitoring of macroinvertebrates from shallow groundwater aquifers. Subterranean Biology 49: 139-161. <a href="https://doi.org/10.3897/subtbiol.49.132515" target="_blank" rel="noopener">https://doi.org/10.3897/subtbiol.49.132515</a></p>
Data for "Temporal and vertical variability in phytoplankton primary production and microbial community respiration in the North Pacific Subtropical Gyre"
<p>This ALOHA_GOP&R.xslx data set provides measurements of biological rates conducted between April 2015 and July 2020 at different depths in the euphotic zone at or in the vicinity of Station ALOHA (22° 45' N, 158° W), the long-term sampling site of the Hawaii Ocean Time-series (HOT) program, within the North Pacific Subtropical Gyre. </p> <p>The file ALOHA_GOP&R.xslx contains incubation-based measurements of gross oxygen production and community respiration that were measured in the same incubation bottles by applying the <sup>18</sup>O-water method and tracking net changes in oxygen to argon ratios during dawn to dusk in situ incubations, following Ferrón et al. (2016). The samples were measured using membrane inlet mass spectrometry. Rates were measured at 6 depths within the euphotic zone: 5, 25, 45, 75, 100, 125 m, except in a few occasions in which there were no measurements made at 125 m.</p> <p>Data description</p> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Description</p> </td> <td> <p>Units</p> </td> </tr> <tr> <td> <p>Date </p> </td> <td> <p>Date of sampling and start of incubation (UTC -10 hours)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Cruise ID</p> </td> <td> <p>Cruise identification</p> </td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Latitude</p> </td> <td> <p>Latitude</p> </td> <td> <p>degrees N</p> </td> </tr> <tr> <td> <p>Longitude</p> </td> <td> <p>Longitude</p> </td> <td> <p>degrees E</p> </td> </tr> <tr> <td>Stn ALOHA </td> <td>Whether the data are from Station ALOHA (yes/no)</td> <td> </td> </tr> <tr> <td>IncT</td> <td> <p>Incubation time</p> </td> <td> <p>hours</p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td>Nominal depth of sampling and incubation </td> <td> <p>meters</p> </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>CR</td> <td>Estimate of community respiration </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td> <p>Flag GOP</p> </td> <td>Flag identification for gross oxygen production (good=1,questionable=2)</td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Flag CR</p> </td> <td> <p>Flag identification for community respiration (good=1,questionable=2)</p> </td> <td> <p>#</p> </td> </tr> </tbody> </table> <p> </p> <p>The Light-dark_ALOHA_rates.xlsx file contains metabolic rates measured by the ligth-dark oxygen method between June 2005 and June 2007 at different depths in the euphotic zone at Station ALOHA (22° 45' N, 158° W). Rates of net community production, communnity respiration, and gross oxygen production were measured at 6 depths within the euphotic zone (5, 25, 45, 75, 100, 125 m) in dawn to dawn incubations, following Williams et al. (2004). </p> <p>Data description</p> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Description</p> </td> <td> <p>Units</p> </td> </tr> <tr> <td> <p>HOT</p> </td> <td>HOT cruise number</td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td>Date of sampling and start of incubation (UTC -10)</td> <td> <p> </p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td>Nominal depth of sampling and incubation </td> <td> <p>meters</p> </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production, average of 8 replicates</td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>GOP SE</td> <td>Gross oxygen production standard error </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>CR</td> <td> <p>Dark community respiration, average of 8 replicates</p> </td> <td> <p>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></p> </td> </tr> <tr> <td> <p>CR SE</p> </td> <td>Dark community respiration standard error</td> <td> <p>meters</p> </td> </tr> <tr> <td>NCP</td> <td>Net community production,average of 8 replicates </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>NCP SE</td> <td>Net community production standard error </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> </tbody> </table> <p> </p>
Fig. 3 in Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances
Fig. 3. Principal Components Analysis of the environmental variables' matrixes recorded in the upper Uruguay River between October 2001 and March 2004. Sampling sites: LIG: Ligeiro, ULIG: Uruguay-Ligeiro, CH: Chapecó and UCH: Uruguay-Chapecó. Reproductive Periods: RP1: First Reproductive Period, RP2: Second Reproductive Period and RP3: Third Reproductive Period.
Fig. 2 in Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances
Fig. 2. Abundance of fish larvae in different stages of development recorded in the sampling sites of the upper Uruguay River from October 2001 to March 2004. Larval development stages: LY = Larval Yolk; PF = Pre-flexion; FL = Flexion and FP = Post-flexion. Sampling sites: LIG: Ligeiro, ULIG: Uruguay-Ligeiro, CH: Chapecó and UCH: Uruguay- Chapecó. Reproductive Periods: RP1: First Reproductive Period, RP2: Second Reproductive Period and RP3: Third Reproductive Period.
Fig. 1 in Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances
Fig. 1. Location of the sampling sites in the upper Uruguay River in southern Brazil. Samplings sites: LIG: Ligeiro, ULIG: Uruguay-Ligeiro, CH: Chapecó, UCH: Uruguay-Chapecó.
Fig. 3 in Temporal distribution of ichthyoplankton in the Ivinhema River (Mato Grosso do Sul State/ Brazil): Influence of environmental variables
Fig. 3. Average fish larva abundance of the ten most important taxa captured in the Ivinhema River (Mato Grosso do Sul State, Brazil) during the period between April 2005 and March 2006.
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.