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.
28,952
datasets available to search
ShareScore release 0.7.1
Dataset results
28,952 results for “Distributed”
Figure 8 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 8: Length frequency distribution of Coregonus spp among lakes. Data from deep-set vertical nets (set deeper than 5 m). The number of fish is adjusted for the number of vertical net batteries deployed in the lake. Vertical axis is truncated at 0.3 fish per vertical net battery to focus on the occurrence of the larger fish. Arrows indicate the approximate length of fish at which permitted nets for commercial fisheries become efficient. There was no commercial fishing in lakes Chalain, Saint-Point, Remoray and Rousses at the time of Projet Lac sampling. Lakes Biel and Sarnen are excluded as the different mesh sizes used in the Projet Lac sampling of these lakes influences the fish length frequency distribution, meaning that it is not possible to directly compare them to the other lakes.
Figure 21 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 21: Significant correlations between whole-lake average biomass of large fish caught in Projet Lac vertical nets and corresponding yields of (the sum of) commercial and recreational fisheries in kilograms per hectare per year in large and deep lakes (average depth> 50 m) for Coregonus spp (left; p-value = 0.03, R2 = 0.51) and Perca fluviatilis (right; p-value = 0.053, R2 = 0.43). Maggiore, Lugano and Garda are not included, as reliable data on recreational fishing catches were only partially available.
Figure 20 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 20: Whole-lake average number of fish of Coregonus spp per vertical net battery compared to total phosphorus concentration in large and deep lakes (average depth> 50 m). First panel shows the relationship for all Coregonus caught in the lake. The other panels shows the relationship when only fish larger than the size thresholds shown at the top of the panel are included (length measured from snout to the tip of the tail). Note that the horizontal axis is on a log scale. Dashed red lines indicate statistically significant relationships (from left to right: p-value = 0.0001, R2 = 0.79; p-value = 0.008, R2 = 0.52; p-value = 0.87, R2 = 0.003; p-value = 0.412, R2 = 0.09). Shaded regions show thresholds for total phosphorus of 10 μg / L and 5 μg / L.
Figure 3 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 3: Identification in the field based on colour, meristics (e.g. fin ray counts) and morphology.
Figure 24 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 24: Relationship of the maximum total phosphorus concentration experienced by each lake versus the proportion of Coregonus species lost in each lake (left) and the genetic differentiation (global Fst) among the post-eutrophication Coregonus species within those lakes that retain native Coregonus species (right). Modified from [42].
Figure 5 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 5: Number of lakes where each fish species was recorded in Projet Lac as native, endemic, non-native or exotic. Thirty-five lakes were surveyed as part of Projet Lac.
Figure 7 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 7: Whole-lake community composition based on CPUE in vertical nets. Upper panel shows volume-weighted aver- age number of fish per 100 m2 net area. Lower panel shows volume-weighted average biomass per 100 m2 net area. Figure includes only lakes surveyed by the standard vertical net protocol. Lakes Sarnen and Biel were sampled with a modified protocol and are excluded here. The smallest lakes Bret and Bonlieu are also excluded. Note that sequence of lakes along the X axis differs between the panels.
Figure 11 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 11: Similarity of the native fish species assemblages among lakes sampled by Projet Lac depicted by hierarchical cluster analysis (Sørensen index based on presence/absence of taxa; complete linkage). Lakes joined by shorter branches share a higher proportion of their fish species. Colours indicate river catchments: red = Rhine, green = Rhone, orange = Po, blue = Danube. See Figure 82 for an exploration of factors driving differences in the fish communities among lakes within catchments.
Figure 15 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 15: Depth distribution of fish abundance by species (individuals per unit effort) in benthic habitats to 50 m deep according to CEN benthic nets.
Figure 26 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 26: Depth distribution of catch per unit effort (CPUE; horizontal axis) for native fish species living in the deeper zones of the deep, northern perialpine lakes: Coregonus spp (yellow), Salvelinus spp (red), Cottus spp (grey/brown) and Lota lota (light grey). CPUE is the average of catches in deep-set vertical nets and benthic CEN nets. CPUE is square-root transformed to increase the visibility of the smaller values in the profundal zone. Note that the scale of the horizontal axis (CPUE) differs among the lakes. Whereas Lota lota has pelagic eggs and larvae and does hence not have to recruit locally, all other species recruit locally. Boxes at the bottom of the figure show the total phosphorus of the lake at the time of Projet Lac sampling (upper value) and the maximum measured total phosphorus value that had been experienced by the lake in the past (lower value in bold). The panels for Constance and Zurich show the data for the deeper lake in each of the pairs (i.e. Upper Constance and Lower Zurich).
Figure 23 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 23: Fish biomass (CEN gillnets) in the shallow sunlit zone near the lake floor to around 12 m was higher in lakes with more phosphorus. In the deeper parts of the lakes (below 50 m), benthic fish biomass was highest in the lakes with very low phosphorus. Dashed lines are shown for statistically significant relationships (surface: p-value = 0.003, R2 = 0.74; middle: p-value = 0.63, R2 = 0.02, deep: p-value = 0.014, R2 = 0.467). Horizontal axis is displayed on a log scale.
Figure 14 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 14: Whole-lake average fish biomass per unit net area in vertical nets was lower in deeper lakes due to their proportionally larger volume of less productive habitat. Note that vertical and horizontal axes are on a log scale. Lakes that have returned from a period of eutrophic conditions with hypoxia in the hypolimnion in at least part of the lake to meso- or oligotropic conditions are indicated in blue, while yellow points indicate re-oligotrophied lakes that have lost profundal fish species.
Figure 10 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 10: Uniqueness of the fish communities of all lakes and catchments. Uniqueness index for each lake was calculated as the sum of the inverse of the number of lakes where each species in the lake was recorded.
Figure 17 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 17: Habitat associations of fishes in the littoral zone in late summer/autumn based on sampling in 28 perialpine lakes. Grey lines indicate that the species was recorded (electrofishing and shallow-set vertical nets) more often in this littoral habitat than in other habitats. The thickness of the line reflects how much more frequently than random the species was recorded in the habitat. Associations were averaged among lakes and shown only where the association was positive in more than half of the lakes in which a species was recorded. Only fish species recorded in the littoral zone of at least three lakes are shown. Three species were recorded in at least three lakes, but had no clear habitat association (Carassius gibelio, Rhodeus amarus, Telestes muticellus). Lineages of Barbatula spp and forms of Perca fluviatilis could unfortunately not be differentiated in the analysis. Inflows and outflows are excluded to focus on the lacustrine habitats. Note that some of these species may have their strongest associations with other habitats outside the littoral (e.g. the sublittoral, profundal or pelagic), but such habitat occurrences could not be included in this analysis. See [47] for more information on the calculation of habitat association.
Figure 6 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 6: Species-abundance distributions (SADs) for each of 35 perialpine lakes and 1 lowland lake. These SADs result from combining partial SADs obtained by sampling with the CEN netting protocol, the VERT netting protocol and the electrofishing protocol (for all partial SADs see Appendix B Figure 69). Abundances are log2-transformed. Normal distributions are indicated by a thin line in each plot. The qualitative fit to the expected distribution is indicated by coloured circles: dark green = good fit, light green = modest fit, orange = poor fit, red = very poor fit. Colour of bars indicates drainage systems: green = Rhone, red = Rhine, orange = Po, blue = Danube. Note the systematic difference between Rhine lakes (9 good, 3 modest, 4 poor, 1 very poor) and Po lakes (0 good, 1 modest, 2 poor, 6 very poor). This difference cannot be due to differences in sampling effort because Maggiore and Lugano were among the best sampled lakes, but both have very poor fits to the expected distributions. Lake Aulnes is a lowland lake in the southern Rhone drainage that we sampled but did not otherwise consider in this report.
Figure 1 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 1: Map of lakes surveyed by Projet Lac with major river networks and catchments indicated by background colour. Note that the Aare-Rhine includes the subcatchments of the Reuss and Limat rivers. Data source: Federal Office of Topography swisstopo 2020.
Figure 19 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 19: Opposing relationships with total phosphorus for the biomass of the two most common fish taxa among the large and deep lakes (average depth> 50 m). Data are whole-lake average biomass (in grams) of fish per vertical net battery. Note that the horizontal axis is on a log scale. Regression statistics for Coregonus are p-value = 0.005, R2 = 0.57 and perch are p-value = 0.004, R2 = 0.58. Shaded regions show thresholds for total phosphorus of 10 μg / L and 5 μg / L.
Figure 22 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 22: Whole-lake average number of European perch (Perca fluviatilis) per vertical net battery compared to total phosphorus concentration in large and deep lakes (average depth> 50 m). The left panel shows the relationship for all perch caught in the lake (p-value = 0.022, R2 = 0.42). The right panel shows the relationship for only perch larger than 20 cm (length from snout to the tip of the tail; p-value = 0.003, R2 = 0.6). Note that the horizontal axis is on a log scale. Dashed red lines indicate statistically significant relationships.
Fig. 5 in Benstonea Callm. & Buerki (Pandanaceae): characterization, circumscription, and distribution of a new genus of screw-pines, with a synopsis of accepted species
Fig. 5. – Infructescences and details of stigmas of species of Benstonea Callm. & Buerki. A. Benstonea parva (Ridl.) Callm. & Buerki; B. Benstonea pectinata (Martelli) Callm. & Buerki; C. Benstonea rupestris (. C. Stone) Callm. & Buerki; D. Benstonea thomissophylla (. C. Stone) Callm. & Buerki. [Photos: M. W. Callmander]
Fig. 6 in Benstonea Callm. & Buerki (Pandanaceae): characterization, circumscription, and distribution of a new genus of screw-pines, with a synopsis of accepted species
Fig. 6. – Infructescence of Benstonea thurstonii (C. H. Wright) Callm. & Buerki with details of stigmas in frame. [Photo: M. W. Callmander]
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.