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.
7,081
datasets available to search
ShareScore release 0.7.1
Dataset results
7,081 results for “Habitats”
Figure 3. CCA showing the relationship between 16 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 3. CCA showing the relationship between 16 species (red triangles) and 9 environmental variables (red arrows). See Tables 2 and 4 for an explanation of abbreviations and variables.
Figure 2 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 2. Jaccard's coefficient similarity dendrograms showing the faunal similarity among the 12 sampling sites (based on presence/absence of species) and clustering relationships among the 16 ostracod species. (Species codes are given in Table 3.)
Fig. 1 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Fig. 1. Map of the eight studied coastal lagoons. Selected sampling sites at Tuzla Lake 1 (St-1), Tuzla Lake 2 (St-2), Tuzla Lake 3 (St-3), Taz (St-4), Işık (St-5), Dalyan (St-7, 8, and 9), Kuvalak (St-10), and Taşaltı (St-11 and 12) were used for comparisons of the lagoons. The sampling sites are indicated by red circles; the red arrows show the direction of water currents.
Figure 1. A in Habitat description of the rare orchid Didymoplexis verrucosa for more effective conservation
Figure 1. A, Location of the province of KwaZulu-Natal within South Africa; B, northern KwaZulu-Natal showing the very limited extent of remaining forest ecosystems shaded in black with degree grid lines for spatial reference; C, study area shaded in grey south of the coastal town of Mtunzini with quarter degree grid lines for spatial reference.
Figure 2 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 2. Vegetation composition of Barking deer habitat in Murree-Kotli Sattian-Kahuta National Park.
Figure 4 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 4. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) with different surrounding habitats (sugarcane + sesame, monoculture, sesame) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 3 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 3. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) at different crop developmental stages (crop phenology) of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 2 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 2. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 1 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 1. Percent numbers of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during 2018 at Layyah, Punjab, Pakistan.
Figure 2 in Changes in the feeding behavior and habitat use of the desert hedgehog Paraechinus aethiopicus (Ehrenberg 1832, Eulipotyphla: Erinaceidae), in Saudi Arabia
Figure 2. Relationship between values of the trophic niche breadth in the four seasons during period from February 2015 and October 2019 in five study sites in Saudi Arabia.
FIGURE 8 in The Sea Slug Phanerophthalmus luteus (Gastropoda: Opisthobranchia) and its Habitat and Ecology at the Marine Jellyfish Lake (Ongeim'l Tketau), Palau, Western Pacific Ocean
FIGURE 8. Central and Western Pacific showing the distribution of Phanerophthalmus luteus (black stars) and the location of Jellyfish Lake, Palau (large gray star). Modified from distribution map for P. luteus in Austin, Gosliner, and Malaquias (2018, fig. 23).
FIGURE 5 in The Sea Slug Phanerophthalmus luteus (Gastropoda: Opisthobranchia) and its Habitat and Ecology at the Marine Jellyfish Lake (Ongeim'l Tketau), Palau, Western Pacific Ocean
FIGURE 5. Hydrography of Jellyfish Lake. The water column is divided into an oxic and an anoxic zone by a bacterial plate that creates a chemo- and thermocline. The bacteria absorb all the light and digest most of the vegetation (except larger branches). No foraminifera or animals are known to live below the bacterial plate due to the absence of oxygen in the water column. Phanerophthalmus luteus is restricted to the upper 3 to 10 m in the oxygenated part of the water column; they are most abundant between 4.5 and 7.6 m. Figure modified from Venkateswaran et al. (1993) by adding the depth distribution of P. luteus.
FIGURE 4 in The Sea Slug Phanerophthalmus luteus (Gastropoda: Opisthobranchia) and its Habitat and Ecology at the Marine Jellyfish Lake (Ongeim'l Tketau), Palau, Western Pacific Ocean
FIGURE 4. Bathymetry of Jellyfish Lake, Mecherchar Island. The gray line with arrows indicates the area in the lake of our marine survey to 10 m deep for sea slugs along the north and east sides of the lake. Black circles indicate the transect and collecting stations for foraminifera used to estimate the depth distribution of Phanerophthalmus luteus. Map and transect from Lipps and Langer 1999.
FIGURE 3 in The Sea Slug Phanerophthalmus luteus (Gastropoda: Opisthobranchia) and its Habitat and Ecology at the Marine Jellyfish Lake (Ongeim'l Tketau), Palau, Western Pacific Ocean
FIGURE 3. Vegetation of Jellyfish Lake, Mecherchar Island. A. The lake, slightly less than 400 m long, is in a hole at least 230 m deep (150 to 200 m from the top of the hole to the Lake's surface and 30 m to the bottom of the lake) in the Miocene limestone. North is at the top of the image. B. Dense terrestrial vegetation, including mangroves at the lake edges, hangs over the lake. The surrounding vegetation contributes organic debris to the lake. C. Bottom of the lake from 0 to 13 m is covered with plant debris and algal growth. Photograph is at 2 m deep looking down slope. D. One of many logs that have fallen into the lake and are now inhabited by a wide variety of algae and animals including P. luteus. View is down the log from a depth of about 0.5 m. Credits: A. Aerial photograph courtesy of Dr. Pat Colin. B.-D. Photographs by Jere H. Lipps, 2013.
FIGURE 7 in The Sea Slug Phanerophthalmus luteus (Gastropoda: Opisthobranchia) and its Habitat and Ecology at the Marine Jellyfish Lake (Ongeim'l Tketau), Palau, Western Pacific Ocean
FIGURE 7. Egg masses (more or less spherical to oblong white objects) of Phanerophthalmus luteus attached to filamentous and other algae on a slope in Jellyfish Lake. Photograph taken November 16, 2009, courtesy of Lori J. Bell.
Data and Code from: Wildfire influences species assemblage and habitat utilisation of boreal wildlife after more than a decade in northern Sweden
<p>Data and Code supporting the analyses presented in: Fredriksson, Cromsigt & Hofmeester - Wildfire influences species assemblage and habitat utilisation of boreal wildlife after more than a decade in northern Sweden as published in Wildlife Biology</p> <p><strong>Abstract</strong></p> <p><span>Fires can strongly change the vegetation structure and the availability of resources for wildlife, but fire suppression has long affected the natural role of fire in shaping boreal ecosystems in northern Europe. Recently, wildfires have increased in frequency, possibly due to global warming. In contrast to the boreal systems in North America, there have been few studies on responses of wildlife to wildfires in northern Europe. Based on the findings from North America, we predict that responses of wildlife to wildfire vary among wildlife species: where mammalian herbivores, such as moose (<em>Alces alces</em>) and mountain hare (<em>Lepus timidus</em>), will be attracted to burnt areas following an increase in food availability, other species, such as reindeer (<em>Rangifer tarandus</em>), are negatively impacted due to fire reducing their preferred food. We then tested our predictions by contrasting wildlife utilization of sites that burnt by wildfire in 2006 with nearby unburnt control sites in three areas in northern Sweden. To measure wildlife utilization, we used 72 camera traps, equally divided between the burnt and control sites, with two placement strategies: random and on wildlife trails. The cameras recorded 27 mammal and bird species during summer 2018. Species assemblage differed between burnt and control sites. Fieldfare (<em>Turdus pilaris</em>) used burnt sites more than control sites, while pine marten (<em>Martes martes</em>) and western capercaillie (<em>Tetrao urogallus</em>) used control sites more than burnt sites. We however did not find support for a positive effect of past forest fires on any of the observed wild mammals. We discuss how, due to the impact of forestry, forage-rich habitat may not be as limiting in Scandinavia as in the North-American context, potentially leading to recently burnt sites being less attractive to herbivores such as moose.</span></p>
Data and code to reproduce: Host and parasite intervality in differentially human-modified habitats
<p>Data and code in:</p> <p>Llopis-Belenguer, Feijen, Morand, Chaisiri, Ribas and Jokela (2024) Host and parasite intervality in differentially human-modified habitats. Oikos. DOI: 10.1111/oik.10446</p>
Fig. 2 in Flexibility is everything: prey capture throughout the seasonal habitat switches in the smooth newt Lissotriton vulgaris
Fig. 2 Frame shots showing the four feeding modes in the smooth newt. In the aquatic stage: a suction feeding under water and b jaw prehension on land. In the terrestrial stage: c suction feeding under water and d tongue prehension on land. The prey (maggot) is indicated by the arrow.
Fig. 1 Landmarks used for the kinematic analyses. 1 upper jaw tip, 2 lower jaw tip, 3 in Flexibility is everything: prey capture throughout the seasonal habitat switches in the smooth newt Lissotriton vulgaris
Fig. 1 Landmarks used for the kinematic analyses. 1 upper jaw tip, 2 lower jaw tip, 3 hyoid (throat), 4 jaw joint, 5 nape, 6 dorsal trunk reference, 7 tongue tip (only digitized when visible)
Fig. 4 in Flexibility is everything: prey capture throughout the seasonal habitat switches in the smooth newt Lissotriton vulgaris
Fig. 4 Scatter plot of the first two principal components. Principal component 1 (PC1) and principal component 2 (PC2) are derived from the 12 kinematic variables to illustrate the relationship among kinematic patterns for the four feeding modes coded by symbols and the ten individuals coded by color. Each data point represents one feeding event, and the ellipses indicate 95 % confidence interval in the four feeding modes. P@1 explains 57 % and P@2 explains 15.5 % of the total variance. See Table 3 for complete loadings of each principal component
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.