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.
268
datasets available to search
ShareScore release 0.7.1
Dataset results
268 results for “Mercury”
Fig. 1 in Mercury and stable isotopes ( N and C) as tracers during the ontogeny of Trichiurus lepturus
Fig. 1. Northern Rio de Janeiro, in south-eastern Brazil. The sampling area where the Trichiurus lepturus specimens were collected is marked with a dashed polygon.
Fig. 2 in Mercury and stable isotopes ( N and C) as tracers during the ontogeny of Trichiurus lepturus
Fig. 2. Length (cm), weight (g), total mercury concentration (THg) in dry and wet weight basis and isotopic signatures (δ15N and δ13C) of sub-adult and adult specimens of Trichiurus lepturus, considering dry season, rainy season, and all sampling periods. Data is shown as a mean and standard deviation. The scale for length, weight, and THg is different for the two ontogenetic phases.
Fig. 3 in Total mercury in the fish Trichiurus lepturus from a tropical estuary in relation to length, weight, and season
Fig. 3. Correlations (n = 104). a. Log weight (g) in respect to total length (cm); b. Log (mgHg-T.kg-1 in the muscle) in relation to total length (cm); c. Log (mgHg-T.kg-1) in relation to log weight (g) of Trichiurus lepturus from the Goiana Estuary from November 2005 to January 2007. All plots show a 95% confidence interval.
Fig. 2 in Total mercury in the fish Trichiurus lepturus from a tropical estuary in relation to length, weight, and season
Fig. 2. Rainfall in the study area from a historic (1961 to 1990) data set (solid line) and from 2005-2007 (bars). Source: http:// www.inmet.gov.br - meteorological station Recife-82.898).
Fig. 5 in Total mercury in the fish Trichiurus lepturus from a tropical estuary in relation to length, weight, and season
Fig. 5. Correlation (n = 81) between rainfall (mm) and log (µgHg-T.kg-1 in the muscle) of Trichiurus lepturus from the Goiana Estuary (1 = dry season 1 - November and December 2005, January 2006; 2 = end of the rainy season - August to October 2006; 3 = dry season 2 - November and December 2006, January 2007).
Fig. 1 in Isotopic profile and mercury concentration in fish of the lower portion of the rio Paraíba do Sul watershed, southeastern Brazil
Fig. 1. Lower portion of the rio Paraíba do Sul watershed with the distribution of the five sampling points: rio Paraíba do Sul in Itaocara (ITA), São Sebastião do Paraíba (SSP) and São Fidélis (SFI); rio Pomba in Baltazar (RPO); rio Dois Rios in Guarani (RDR).
Fig. 4 in Isotopic profile and mercury concentration in fish of the lower portion of the rio Paraíba do Sul watershed, southeastern Brazil
Fig. 4. Relationship between Hg concentrations and δ15N values of the trophic guilds of fish of the lower portion of the rio Paraíba do Sul watershed.
Fig. 3 in Isotopic profile and mercury concentration in fish of the lower portion of the rio Paraíba do Sul watershed, southeastern Brazil
Fig. 3. Isotopic niche areas of the trophic guilds of fish of the lower portion of the rio Paraíba do Sul watershed. Values in ‰2 indicate the corrected standard ellipse areas (SEAcs).
Fig. 2 in Isotopic profile and mercury concentration in fish of the lower portion of the rio Paraíba do Sul watershed, southeastern Brazil
Fig. 2. δ13C and δ15N isotopic signatures of the trophic guilds of fish of the lower portion of the rio Paraíba do Sul watershed. Bars represent standard deviations.
FIGURE 7 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 7 | Relationships between body size (standard length, mm) and total Hg of fishes commonly used for consumption. Top panel (non-mined) contains three species that were common at mined sites (bottom panel). Relationships were estimated for five species that are commonly found and consumed, and for what a class structure was observed. Symbols represent individual fish. Clear diamonds = frugivores [1 sp.: Myloplus asterias (n = 19)], dark triangles = carnivores [1 sp.: Serrasalmus eigenmanni (n = 14)], green circles = piscivores [(3 spp.: S. rhombeus (n = 24), Ageneiosus ucayalensis (n = 2), Hoplias malabaricus (n = 1)].
FIGURE 6 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 6 | Mean Hg concentrations (dry weight) in sediments and instream biota separated by trophic groups (producers and consumers) between mined and non-mined sites in the Mazaruni River, Guyana. Species were grouped together based on their trophic guild. Polynomial trendline: y = 0.05x2 - 0.22x + 0.28, coefficient of determination R² = 0.98.
FIGURE 2 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 2 | Locations of sampling sites in the middle Mazaruni River drainage in A. Guyana within B. South America. Sampling sites included location in the C. Main channel of the Mazaruni River (mined) and three main tributaries including Eping River (non-mined), Puterang and Kurupung rivers (mined).
FIGURE 5 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 5 | Biomagnification of Hg in the Mazaruni River, Guyana, represented as the log total Hg concentrations plotted against nitrogen (δ15N) isotope ratios. Species were grouped together based on their trophic guild. Each symbol represents the average of all species within each trophic guild. The dashed horizontal line indicates the reference limit of 0.5 µg/g by WHO guideline for Hg in fish consumed by humans.
FIGURE 1 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 1 | Conceptual model of how Hg enters riverine food webs of the Mazaruni River, Guyana. First, artisanal gold mining operations installed in the river use a suction dredge to reach gold contained in bottom sediments. These mining operations use mercury for gold amalgamation of which the majority is lost to the atmosphere or river. Once in the river, Hg can be methylated by microbes into the toxic Methylmercury (MeHg) and be assimilated rapidly by aquatic biota. Hg concentration bioaccumulates and biomagnifies in the trophic food chain. Fishes at higher trophic levels [piscivores and carnivores (e.g., piranhas, aimaras, and catfishes)] are important items in the diet of local communities and can become a direct source of Hg uptake via direct consumption.
FIGURE 4 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 4 | Mean nitrogen (δ15N) and carbon (δ13C) isotope ratios of fish species collected from mined (right panel) and non-mined (left panel) sites in the Mazaruni River, Guyana. Species were grouped together based on their trophic guild. Each symbol represents the average of all species within each trophic guild. Numbers represent the basal resources collected at surveyed sites (1 = bryophyte, 2 = benthic algae, 3 = aquatic macrophytes). The abbreviations for the fish trophic guilds: Algivore/ Detritivore (Alg/Det), Insectivore (Insec), Herbivore (Herb), Omnivore (Omni), Carnivore (Carn), Piscivore (Pisc), as well as one shrimp (Macrobrachium).
FIGURE 3 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 3 | Sampling locations on the middle Mazaruni River: A. Main channel of the upper Eping River a small, black water tributary of the Mazaruni River. This river appeared more pristine and less impacted by gold mining activities; B. Sandy, shallow habitat sampled in middle channel of the upper Eping River; C. A gold dredge in the middle of the Kurupung River, on the right side is evidence of soil removal that resulted from mining activities; D. Deforestation (to establish mining stations) along the Kurupung River upstream, left bank from the confluence with the Mazaruni River; E-F. Main channel of the Mazaruni River at Olive Creek. At this location, the main channel of Mazaruni River is highly impacted by gold mining activities and gold dredges and 'tailing' beaches are commonly observed; G-H. The main channel of the lower Puterang River before the confluence with the Mazaruni River. At these locations, the Puterang River carries down heavily silted sediments that are spilled into the Mazaruni River.
Digital Elevation Models from Planetary Flyby Images of Mercury and the Moon with Shape and Albedo from Shading
<p>Supplemantary material to Krüll, I., Wohlfarth, K., Tenthoff, M., Wöhler, C., Galluzzi, V., Wright, J., Benkhoff, J., and Zender, J.: Shape and Albedo from Shading with Planetary Flyby Images of Mercury and the Moon, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-247, https://doi.org/10.5194/epsc2024-247, 2024.</p> <p><strong>Abstract</strong></p> <p>Surface reconstruction of planetary bodies such as the Moon and Mercury is crucial for geomorphological analysis, reflectance normalization, thermal modeling, rover landing site planning, and outreach activities. Stereo algorithms and Shape-and-Albedo-from-Shading (SAfS) are well-established methods for planetary 3D reconstruction. SAfS refines the surface slopes of a stereo Digital Elevation Model (DEM) and typically yields 3D models at image resolution. This approach is well-validated for scientifically calibrated instruments that observe the planetary body under favorable conditions. This work applied the SAfS algorithm to more challenging planetary flyby images acquired with uncalibrated off-the-shelf cameras. We investigated three scenarios: a fly-by image of the Moon captured by a GoPro during the Artemis I mission, a fly-by image of Mercury which was obtained with a monitoring camera during BepiColombo’s third flyby, and a telescope image taken in Wetter, Germany. We qualitatively and quantitatively assessed the algorithm's performance. The results of the two flyby images indicate that, despite the challenging conditions, the SAfS algorithm could reconstruct the surface up to image resolution and increase the level of detail of the input DEM. The reconstructed DEM of the telescope image is the one with the lowest resolution. All in all, our flyby-derived DEMs are accurate. They provide excellent outreach products, as demonstrated by ESA's BepiColombo flyby movie: https://www.esa.int/Science_Exploration/Space_Science/BepiColombo/BepiColombo_s_third_Mercury_flyby_the_movie</p> <p><strong>Dataset<br></strong></p> <p>We applied the SAfS algorithm to different Regions of Interest (ROIs) in the flyby and telescope images. The ROIs are marked in Artemis_Flyby_ROIs.png, Bepicolombo_Flyby3_ROIs.png and Moon_Telescope_ROIs.png, respectively. For each ROI a DEM is provided centered on the latitude and longitude (0-360, positive east) in the filename. Furthermore a Red/ Blue Stereo anaglyph of the original image was created with the SAfS DEM (for this purpose the height has been exaggerated).</p> <p> </p>
Data and code for the publication: "Unexpected anthropogenic emission decreases explain recent atmospheric mercury concentration declines"
<p>A. Feinberg, Aug 2024</p> <p>arifeinberg@gmail.com</p> <p> </p> <p>Essential data and code for the publication: Feinberg et al. : Unexpected anthropogenic emission decreases are required to explain recent atmospheric mercury concentration declines</p> <p> </p> <p>The directories include:</p> <p>1) analysis<strong>_</strong>plotting<strong>_</strong>scripts/ - all analysis scripts used to analyze observations, produce input data, and plot figures for paper</p> <p>2) GC<strong>_</strong>code/ - Archived GEOS-Chem code used to simulate the runs in this paper</p> <p>3) GC<strong>_</strong>data/ - GEOS-Chem simulation data and run scripts can be found here for the following runs:</p> <p>BASE - run2021</p> <p>BASE+LEG - run2022</p> <p>DEC<strong>_</strong>LEG<strong>_</strong>ONLY - run2024</p> <p>ZHANG23 - run2025</p> <p>DEC<strong>_</strong>ANT<strong>_</strong>NH - run2026</p> <p> </p> <p>4) input<strong>_</strong>data/ - input data used to run GEOS-Chem</p> <p> </p> <p>Please refer to other README.md files within sub-directories and contact me for any questions.</p>
Fig. 3 in Mercury distribution in different tissues and trophic levels of fish from a tropical reservoir, Brazil
Fig. 3. Mean concentrations and ratios of mercury in different tissues of omnivorous (a), carnivorous (b), and detritivorous fishes (c) collected from Vigário Reservoir. Error bars represent one standard deviation of the mean.
Fig. 1 in Mercury distribution in different tissues and trophic levels of fish from a tropical reservoir, Brazil
Fig. 1. Map of Vigário reservoir, showing its drainage basin (Piraí river, Paraíba do Sul river and Santana reservoir). Black arrows indicate the water flow. (Source: Gomes et al., 2008).
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.