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.
118
datasets available to search
ShareScore release 0.9.0
Dataset results
118 results for “nearshore”
Fig. 8 in Mollusc biodiversity in late Holocene nearshore environments of the Caspian Sea: A baseline for the current biodiversity crisis
Fig. 8. Reconstruction of Turali Bay, c. 2300 calyr BP. The palaeocoastline is approximatly based on the estimated lake level reconstructed for the time of deposition.
Fig. 5 in Mollusc biodiversity in late Holocene nearshore environments of the Caspian Sea: A baseline for the current biodiversity crisis
Fig. 5. Late Holocene Novocaspian bivalve species from Turali, with indication of sample and collection number. LV displayed on the left, RV on the right. A–B Cerastoderma sp. A [non C. rhomboides (Lamarck, 1819)] A M0215, RGM.961896 B M0215, RGM.962391 C–D Cerastoderma glaucum (Bruguière, 1789) C M0216, RGM.962390 D M0216, RGM.962389 E–F Didacna baeri (Grimm, 1877) E M0204, RGM.962379 F M0204, RGM.962380 G–H Didacna eichwaldi (Krynicki, 1837) G M02-05, RGM.961900 H M02-05, RGM.961900 I–J Didacna parallela (Bogachev, 1932) I M0202, RGM.962383 J M0202, RGM.962384 K–L Didacna protracta (Eichwald, 1841) K M0202, RGM.962386 L M0222, RGM.962385 M–N Didacna pyramidata (Grimm, 1877) M M0202, RGM.962387 N M0222, RGM.962388 O–P Didacna trigonoides (Pallas, 1771) O M0215, RGM.962378 P M0215, RGM.962377. Q–R Didacna barbotdemarnii (Grimm, 1877) Q M0222, RGM.962420 R M0221, RGM.962421. Scale bars 5 mm.
Fig. 9 in Mollusc biodiversity in late Holocene nearshore environments of the Caspian Sea: A baseline for the current biodiversity crisis
Fig. 9. Box core residue (sample M0267, c. 2 km off shore Turali at a water depth of 9.4 m) separated into the dark Novocaspian (left) and light 20th Century (right) fractions defined in the text. Largest shell c 1.5 cm across.
Fig. 4 in Mollusc biodiversity in late Holocene nearshore environments of the Caspian Sea: A baseline for the current biodiversity crisis
Fig. 4. Rarefaction curves of the Turali samples with 95% confidence interval and extrapolated species richness for double sample size.
Fig. 1 in Mollusc biodiversity in late Holocene nearshore environments of the Caspian Sea: A baseline for the current biodiversity crisis
Fig. 1. Geographic context of study site. (a) Map of the Caspian Sea. Source bathymetry: Kostianoy et al. (2005): Fig. 1 (p. 7). (b) Location of the outcrops treated in this paper. Tu1-Tu5 represent outcrops Turali 1-5. Brown ridges are Holocene Novocaspian beach barriers (see Kroonenberg et al., 2007). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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. 7 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 7. Results from LDA analysis showing (a) the distribution and overlap of groups of clusters (ellipsoid) and (b) the contribution of parameters to F1 and F2.
Fig. 4 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 4. Boxplots showing (a) abundance (log10-transformed) and (b) diversity index (H′ index) of fish samples in each habitat. Note: The same letter(s) in each box indicates values that are not significantly different when applying the Duncan's post-test, p-value> 0.05.
Fig. 5 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 5. Nodal diagram showing species and sample groups and abundance (log10-transformed) of fish samples per cluster.
Fig. 1 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 1. Location of the sampling habitats at Had Khanom Mu Ko Thale Tai National Park, Thailand. Note: the sampling sites; ▲ seagrass beds; ■ mudflats; ● sandy beaches; ★ mangroves.
Measurements of nearshore waves through coherent arrays of free-drifting wave buoys
<p>Surface gravity wave breaking occurs along coastlines in complex spatial and temporal patterns that significantly impact erosion, scalar transport, and flooding. Numerical models are used to predict these processes, but many models lack sufficient evaluation with observations during storm events. To fill the need for more nearshore wave measurements during extreme conditions, we deployed coherent arrays of small-scale, free-drifting wave buoys named microSWIFTs. The result is a large dataset covering a range of conditions. The microSWIFT is a small wave buoy with a GPS module, and Inertial Measurement Unit (IMU) used to directly measure the buoy's global position, horizontal velocities, rotation rates, accelerations, and heading. We use an Attitude and Heading Reference System (AHRS), 9 degrees-of-freedom Kalman filter to rotate the measured accelerations from the reference frame of the buoy to the Earth reference frame. We then use the corrected accelerations to compute the vertical velocity and sea surface elevation. The measurements were collected over a 27-day field experiment in October 2021 at the US Army Corps of Engineers Field Research Facility in Duck, NC. The microSWIFTs were deployed as a series of coherent arrays. They all sampled simultaneously with a common time reference, leading to a robust spatial and temporal dataset during each deployment. We evaluate wave spectral energy density estimates from individual microSWIFTs by comparing them with a nearby acoustic waves and currents (AWAC) sensor. We also compare significant wave height estimates from the coherent arrays with the nearby AWAC estimates. A zero crossing algorithm is applied to each buoy time series of sea surface elevation to extract realizations of measured surface gravity waves, yielding 116,307 wave realizations throughout the experiment. These measurements spanned offshore significant wave heights ranging from 0.5 meters to 3 meters and peak wave periods ranging from 5 to 15 seconds over the entire experiment. </p>
Measurements of nearshore waves through coherent arrays of free-drifting wave buoys
Open the record for dataset details and reuse information.
Testing biodiversity-ecosystem function relations in nearshore marine sediments
Open the record for dataset details and reuse information.
Semi-quantitative microcystin concentration measured in surface water samples collected by the Citizen-Led Environmental Observatory (CLEO) from multiple nearshore sites in Lake Lillinonah, Connecticut, USA, 2015-2018
Included in this data package are cyanobacterial toxin data from the Citizen-Led Environmental Observatory (CLEO), a volunteer water quality monitoring program run by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University at Lake Lillinonah, Connecticut, USA. The program has been operational since 2008 (toxin data available form 2015-2018). Trained volunteer monitors collect routine surface water samples from multiple nearshore sites twice a month from Memorial Day through Labor Day. In addition, spot samples are taken whenever a significant algae bloom is present at the collection site. These samples are analyzed for microcystin concentrations. Routine water samples are also analyzed for levels of the total nitrogen and phosphorus concentrations. Additionally, CLEO volunteers collect data on water temperature, Secchi disk depth, water color, presence of floating woody debris, recreation potential, trash, particle type and surface scum every 1-3 days during the same period. These data are available in EDI packages EDI567 (general water quality) and EDI568 (nutrients).
SBC LTER: Ocean: Diel nearshore water profiles (CTD and chemistry) during stratified conditions
These data describe diel fluctuations in nutrient and physical measurements observed in the nearshore region of the Santa Barbara Channel (Goleta Bay) during the summer of 2018. Sampling took place over three, 36-hour campaigns in August 2018 and encompasses neap, midway, and spring tide conditions. Data are contained in two tables: 1) a time series of physical measurements (temperature, salinity, etc.) measured over time at three different depths, 2) a time series of chemical and biological measurements (ammonium, chlorophyll a, etc.) measured over time at three different depths.
Fig. 1 in Distribution Of Small Cetaceans In The Nearshore Waters Of Sarawak, East Malaysia
Fig. 1. Three main study areas in Sarawak.
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.