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.
2,556
datasets available to search
ShareScore release 0.7.1
Dataset results
2,556 results for “assemblages”
Figure 5 in Structure of the macrozoobenthos assemblages in the central part of the northwestern Black Sea shelf (Zernov's Phyllophora field) at the beginning of the 21 century
Figure 5. Some characteristic species in Mytilus galloprovincialis biocoenotic complex at the Zernov's Phyllophora Field. A – Mytilus galloprovincialis with a colony of Botryllus schlosseri on the shell; B – Modiolula phaseolina; C – Mytilaster lineatus; D – Abra alba; E – Papillicardium papillosum; F – Parvicardium exiguum; G – Pitar rudis; H – Anadara kagoshimensis; I – Dipolydora quadrilobata, anterior end; J – Heteromastus filiformis; K – Harmothoe imbricata; L – Alitta succinea; M – Spio decorata; N – Nephtys hombergii; O – Terebellides stroemii; P – Spirobranchus triqueter; Q – Aonides paucibranchiata; R – Amphiura stepanovi; S – Nemertea; T – Melinna palmata; U – Upogebia pusilla; V – Ciona intestinalis; W – Ascidiella aspersa; (photos A. Nadolny and N. Revkov).
Figure 3 in Structure of the macrozoobenthos assemblages in the central part of the northwestern Black Sea shelf (Zernov's Phyllophora field) at the beginning of the 21 century
Figure 3. Location of biocoenotic subcomplexes (I–IV) of the bottom macrofauna in the Zernov's Phyllophora Field water area.
Figure 4 in Structure of the macrozoobenthos assemblages in the central part of the northwestern Black Sea shelf (Zernov's Phyllophora field) at the beginning of the 21 century
Figure 4. "Pontic circalittoral biogenic detritic bottoms with dead or alive mussel beds, shell deposits, with encrusting corallines and attached foliose sciaphilic macroalgae" habitat in the Zernov's Phyllophora Field water area. The photo was taken by scuba diver Taras Getman at st. 26 (2010).
Figure 2 in Structure of the macrozoobenthos assemblages in the central part of the northwestern Black Sea shelf (Zernov's Phyllophora field) at the beginning of the 21 century
Figure 2. Hierarchical clustering (A) and MDS ordination (B) of survey stations at Zernov's Phyllophora Field
Figure 1 in Life in the extreme environment: Structure and species richness of bird assemblages on Yuzhny Island of Novaya Zemlya, Russia
Figure 1. Study region on Yuzhny Island of Novaya Zemlya. (A) Map of Novaya Zemlya. The yellow circle indicates the study region of Yuzhny Island. (B) Detailed map of the study region with the location of counting routes. The shaded area represents the study area (1). The dashed red lines indicate the counting routes (2). The dashed black line indicates the helicopter route (3).
Figure 4 in Life in the extreme environment: Structure and species richness of bird assemblages on Yuzhny Island of Novaya Zemlya, Russia
Figure 4. Relative bird species abundance (log10 scale) over habitat patches on Yuzhny Island of Novaya Zemlya. Numbers of (01) – (10) are the codes of the habitat types. Differences between assemblages were all significant (Kruskal-Wallis test: p = 0.003). Images show habitat types; numbers indicate their codes (see Table 2 for detail). (Photos: V. M. Spitsyn).
Figure 3 in Life in the extreme environment: Structure and species richness of bird assemblages on Yuzhny Island of Novaya Zemlya, Russia
Figure 3. Species diversity of bird assemblages on Yuzhny Island of Novaya Zemlya. (A) Bi-plot of detrended correspondence analysis (DCA) with supplementary variables, showing the ordination of species and environmental variables. Circles indicate bird species abundance (categorical estimations by using a logarithmic scale, see Table 3), abundances decrease with increasing distance from each point in a unimodal fashion (ter Braak and Smilauer, 2002). Data represent independent samples from various habitats (n = 10). Total variation is 2.44, supplementary variables account for 66.1% (adjusted explained variation is 23.8%). Eigenvalues (lambda) are 0.675, 0.162, 0.069, and 0.025 for first (horizontal), second (vertical), third and fourth axes, respectively. The first two axes explain 34.4% of the variation. The pseudo-canonical correlations of bird abundance and environmental variables for axes 1 and 2 are 0.77 and 0.91, respectively. For an explanation of environmental variables, see Table 4. For abbreviations of species names see Fig. 4. (B) Bi-plot of the same analysis revealing the ordination of species richness over a range of habitats and environmental variables. Circles indicate bird assemblages in primary types of habitats (size of each circle corresponds to the number of bird species). The red numbers near the circles indicate species richness. The black numbers near the circles (01–10) indicate the codes of habitat types (see Fig. 4 and Table 2 for detail).
Figure 4 in River degradation impacts fish assemblages in Kosovo's Ibër basin
Figure 4. Left: CCA results between physico-chemical variables and species densities. Right: CCA results between anthropogenic pressures and species densities.
Figure 1 in River degradation impacts fish assemblages in Kosovo's Ibër basin
Figure 1. Site locations on the Ibër and its tributaries in northern Kosovo; inset map shows locations on the Ibër's main western stem, upstream of the city of Mitrovica. Minor inset map (upper left) shows the position of Kosovo in the Western Balkans (data about sampling sites presented in Table 2).
Figure 3 in River degradation impacts fish assemblages in Kosovo's Ibër basin
Figure 3. Left: MDS for sampling sites of Ibër river, regarding the physico-chemical variables. Red square: very polluted; Brown triangle: Polluted; Orange triangle: Less polluted. Right: MDS for sampling sites of Ibër river, regarding the anthropogenic pressures as recorded in the on-site protocol assessment.
Figure 2 in River degradation impacts fish assemblages in Kosovo's Ibër basin
Figure 2. Fish specimens photographed in the field aquarium during the survey: A. Squalius cephalus, B. Cobitis elongatoides, C. Phoxinus sp., D. Gobio obtusirostris, E. Rutilus rutilus, F. Salmo cf. trutta, G. Sabanejewia balcanica, H. Chondrostoma nasus, I. Romanogobio uranoscopus, and J. Barbus balcanicus. Photographs by Stamatis Zogaris.
Figure 3 in Gastropod assemblages in the harsh environment of Mediterranean Dinaric karst intermittent rivers
Figure 3. Redundancy analysis (RDA) ordination biplot showing the relationships between freshwater gastropods (blue arrow symbols) and environmental variables (red arrow symbols) in four intermittent rivers in the Mediterranean, Croatia. Environmental variables: Water temperature (°C); Concentration of dissolved oxygen in water (O mg L−1); pH; Conductivity (μS cm−1); Water velocity (m s−1); Concentration of ortho-phosphates in 2 water (mg P L−1), Chemical oxygen demand (mg O L-1), Alkalinity (mg CaCO L-1).
Figure 2 in Gastropod assemblages in the harsh environment of Mediterranean Dinaric karst intermittent rivers
Figure 2. Freshwater gastropod assemblage metrics in four intermittent rivers in the Mediterranean, Croatia: a) taxa richness and b) log-transformed abundance shown as mean with standard deviation (SD).
Figure 1 in Gastropod assemblages in the harsh environment of Mediterranean Dinaric karst intermittent rivers
Figure 1. Map of the study area with photographs showing the examples of sampling sites at the four studied Mediterranean intermittent karst rivers, Croatia.
Figure 3 in Water quality assessment in an irrigation pond based on adult caddisfly (Insecta: Trichoptera) assemblages
Figure 3. Canonical Correspondence Analysis (CCA) showing correlation between caddisflies species and physicochemical variables. Abbreviations for taxonomy are shown in Table 2.
Figure 2 in Water quality assessment in an irrigation pond based on adult caddisfly (Insecta: Trichoptera) assemblages
Figure 2. The total number of species and individuals caught at an irrigation pond in the Kasetsart University, Thailand.
Figure 3 in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure 3. Distribution of phytoplankton groups in the study area identified as a result of cluster analysis. Green – Co_1, Blue – Co_2, Red – Co_3.
Figure 2 in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure 2. Similarity dendrogram demonstrating the phytoplankton groups identified based on the quantitative characteristics of communities at stations (relative abundance of species).
Figure 5 in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure 5. MDS diagram showing groups of environmental conditions identified based on similarity in the distribution of concentrations of the primary nutrients and temperature at stations.
Figure 1 in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure 1. Scheme of stations in the study area. 1 – The river part of the research area, 2 – the kultuk zone, and 3 – the sea part of the avandelta.
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.