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.
4,669
datasets available to search
ShareScore release 0.9.0
Dataset results
4,669 results for “Environment;”
Figure 1 in Herbicide response and germination behavior of two goosegrass (Eleusine indica) populations in the Australian environment
Figure 1. Effect of paraquat dose on (A) seedling survival, (B) spike number (percent of nontreated control), and (C) aboveground biomass (percent of nontreated control) of the two Australian populations (Gatton and Ingham) of Eleusine indica. Error bars represent the standard error of the mean.
Figure 5 in Herbicide response and germination behavior of two goosegrass (Eleusine indica) populations in the Australian environment
Figure 5. Effect of osmotic potential on the germination of the two Australian populations (Gatton and Ingham) of Eleusine indica incubated under alternating light/ dark for 28 d at 30/20 C.The lines represent a three-parameter sigmoid model fit to the germination data in response to concentrations of osmotic potentials.
Figure 3 in Herbicide response and germination behavior of two goosegrass (Eleusine indica) populations in the Australian environment
Figure 3. Effect of light/dark regimes on the germination of the two Australian populations (Gatton and Ingham) populations of Eleusine indica. Seeds were incubated for 28 d at alternating day/night temperatures of 30/20 C. Error bars represent the LSD at the 5% level of significance.
Figure 4 in Herbicide response and germination behavior of two goosegrass (Eleusine indica) populations in the Australian environment
Figure 4. Effect of sodium chloride concentration on the germination of the two Australian populations (Gatton and Ingham) of Eleusine indica incubated under alternating light/dark for 28 d at 30/20 C.The lines represent a three-parameter logistic model fit to the germination data in response to sodium chloride concentrations.
Figure 2 in Herbicide response and germination behavior of two goosegrass (Eleusine indica) populations in the Australian environment
Figure 2. Effect of alternating day/night temperatures (15/5 to 35/25 C) on the germination of the two Australian populations (Gatton and Ingham) of Eleusine indica. Error bars represent the LSD at the 5% level of significance.
Behavioural changes in the city: the common black garden ant defends aphids more aggressively in urban environments
<p>Data and R code to analyse changes in aphid and ant populations and behaviour along a gradient of urbanisation in Berlin, Germany. This release is associated to a publication in preparation and includes the updated R code used for publication:</p> <p>Gaber, H., Ruland, F, Jeschke, J. & Bernard-Verdier, M. (2024) Behavioural changes in the city: the common black garden ant defends aphids more aggressively in urban environments. <em>Ecology & Evolution</em> (publication details will soon be added)</p>
Figure 2 in Gyrodactylus magadiensis n. sp. (Monogenea, Gyrodactylidae) parasitising the gills of Alcolapia grahami (Perciformes, Cichlidae), a fish inhabiting the extreme environment of Lake Magadi, Kenya
Figure 2. Line drawings of the haptoral sclerites and MCO of Gyrodactylus magadiensis n. sp. from Alcolapia grahami in Lake Magadi, Kenya. (A) Haptoral sclerites with hamulus (ha), dorsal bar (db), and ventral bar (vb); (B) marginal hook; (C) male copulatory organ (MCO). Scale bars – (A) 20 µm; (B and C) 5 µm.
Figure 4 in Gyrodactylus magadiensis n. sp. (Monogenea, Gyrodactylidae) parasitising the gills of Alcolapia grahami (Perciformes, Cichlidae), a fish inhabiting the extreme environment of Lake Magadi, Kenya
Figure 4. Evolutionary history of Gyrodactylus magadiensis n. sp. based on Bayesian Inference approaches using ITS sequences for selected gyrodactylids. Statistical support for Bayesian inference (BI) and maximum likelihood (ML) methods indicated at branch nodes with posterior probabilities and bootstrap support indicated, respectively (ML/BI).
Figure 1 in Gyrodactylus magadiensis n. sp. (Monogenea, Gyrodactylidae) parasitising the gills of Alcolapia grahami (Perciformes, Cichlidae), a fish inhabiting the extreme environment of Lake Magadi, Kenya
Figure 1. Collection sites from which Alcolapia grahami were collected. (A) Silhouette of Africa showing area of study; (B) map of study area indicating countries, water bodies, and relation of Lake Magadi to Nairobi; (C) fish spring lagoon of Lake Magadi from which the fish specimens were collected.
Figure 3 in Gyrodactylus magadiensis n. sp. (Monogenea, Gyrodactylidae) parasitising the gills of Alcolapia grahami (Perciformes, Cichlidae), a fish inhabiting the extreme environment of Lake Magadi, Kenya
Figure 3. Light (LM) and scanning electron (SEM) micrographs of the haptoral sclerites and male copulatory organ (MCO) of Gyrodactylus magadiensis n. sp. (A) Haptoral sclerites with hamulus (ha), dorsal bar (db), and marginal hooks (mh), GAP (LM); (B) hamulus (ha) and dorsal bar (db) after soft tissue digestion (SEM); (C) isolated marginal hook (SEM); (D) dorsal bar (LM); (E) dorsal view of dorsal bar (SEM); (F) ventral view of dorsal bar (SEM); (G) male copulatory organ (MCO) with large central spine and six spinelets, two large and four small (LM). Scale bars – (A and B) 20 µm; (C and G) 5 µm; (D–F) 10 µm.
Figure 2 in Efficacy of low-dose EarthTec QZ treatment for the control of New Zealand mud snails Potamopyrgus antipodarum in a hatchery environment
Figure 2. Mean percent of active individuals with standard error (SE) plotted against days of treatment for three species of snail at Page Springs Hatchery.
Fig. 2 in Resource use by the facultative lepidophage Roeboides affinis (Günther, 1868): a comparison of size classes, seasons and environment types related to impoundment
Fig. 2. Medians of percentage volume of scales consumed by Roeboides affinis in the upper Tocantins river, from 1995 to 2000, in: (a) different environmental types (Lotic, n = 38, vs. Lentic, n = 52); (b) seasons (Dry, n = 25, vs. Wet, n = 68); (c) size classes 1 (n = 12), 2 (n = 38), 3 (n = 26), 4 (n = 9) and 5 (n = 5) and (d) different post-impoundment phases (Filling, n = 9, vs. Operation, n = 2) in lentic sites.
Fig. 1 in Resource use by the facultative lepidophage Roeboides affinis (Günther, 1868): a comparison of size classes, seasons and environment types related to impoundment
Fig. 1. CA ordination of 155 individuals of Roeboides affinis of two groups formed by five size classes (Group1, n = 33, and Group 2, n = 22) according to the consumption of food items (volumetric proportions) in the upper Tocantins River in two environment types (Lotic, n = 61, vs. Lentic, n = 94) related to its impoundment by the Serra da Mesa Hydroelectric Dam, from 1995 to 2000.
Fig. 15. D. latissimus larva, Fig. 16. Dead D in Methodologicalaspects Of Study On Biologyand Development Cycles Of Dytiscus Latissimus (Coleoptera: Dytiscidae) In Laboratory Environment. Spring-Summer Period
Fig. 15. D. latissimus larva, Fig. 16. Dead D.latissimus (male) died during pupation couple of hours after metamorphosis
Fig. 9. Young D in Methodologicalaspects Of Study On Biologyand Development Cycles Of Dytiscus Latissimus (Coleoptera: Dytiscidae) In Laboratory Environment. Spring-Summer Period
Fig. 9. Young D.latissimus imago. Female couple of hours after metamorphosis.: A – immediately after metamorphosis, B – in 6 hours, C – in 24 hours (integuments gained their normal colour).
Fig.6. Instar II larva attacking a in Methodologicalaspects Of Study On Biologyand Development Cycles Of Dytiscus Latissimus (Coleoptera: Dytiscidae) In Laboratory Environment. Spring-Summer Period
Fig.6. Instar II larva attacking a caddis larva (with Fig.7. D. latissimus instar III larva exuvium on its left)
Fig.5. D in Methodologicalaspects Of Study On Biologyand Development Cycles Of Dytiscus Latissimus (Coleoptera: Dytiscidae) In Laboratory Environment. Spring-Summer Period
Fig.5. D. latissimus egg While keeping the larvae one has to also laying inside the stem of consider the fact, that these larvae have an Caltha palustris
Data for: Environment-dependent relationships between corticosterone and energy expenditure during reproduction: insights from seabirds in the context of climate change
<p>We studied the relationship between baseline levels of the steroid hormone corticosterone and daily energy expenditure (DEE) in the little auk (<em>Alle alle</em>), an Arctic sea bird that is experiencing mounting energetic challenges due to climate change. We specifically investigated the hypothesis that there might be environment-dependent relationships between baseline corticosterone, DEE, time activity budgets, diving behavior and fitness-related traits (chick provisioning rate, adult body condition). Furthermore, we also examined whether mercury (Hg) contamination might interfere with corticosterone production, and hence potentially the capacity to upregulate DEE. In addition, we performed a phylogenetically controlled analysis across breeding seabird species to assess the relationship between baseline corticosterone and DEE, which we estimated via <span>a model derived from a phylogenetically controlled meta-analysis, </span><span>available within a <span>web-based app (‘Seabird FMR Calculator’, </span></span><span><a href="https://ruthedunn.shinyapps.io/seabird_fmr_calculator/"><span>https://ruthedunn.shinyapps.io/seabird_fmr_calculator/</span></a></span><span>) (Dunn et al. 2018). These datasets contain information on corticosterone levels, DEE, TABs and Hg in little auks, and the data used in our phylogenetically controlled analysis. Please see the READ me file for details.</span></p>
Figure 6 in Herbicide response and germination behavior of two goosegrass (Eleusine indica) populations in the Australian environment
Figure 6. Effect of burial depth on the seedling emergence of the two Australian populations (Gatton and Ingham) of Eleusine indica. The lines represent a gaussian model fit to the emergence data obtained at different seed burial depths.
FIGURE 6 in The Pannonian Basin System northern margin paleogeography, climate, and depositional environments in the time range during MMCT (Central Paratethys, Novohrad-Nógrád Basin, Slovakia)
FIGURE 6. Photoplate of the Plášťovce and Hámor sections: A-C. Plášťovce section. A) lat. 48°9'23.53"N, lon. 18°57'26.67"E, fine grained tuffites. B) Lat. 48° 9'14.61", lon. 18°56'47.80", clast supported through-cross bedded gravels overlying fine grained tuffites. C) Lat. 48°9'28.24"N, lon. 18°57'28.60"E, main quarry wall exposing fine to coarse grained sand, clast supported massive gravels. D-G. Hámor section, lat. 48°12'51.66"N, lon. 19°31'35.74"E, D) Overall view of the outcropping sand. E-G) Alteration of through cross-bedded low-angle laminated sands and clast supported gravels with sandy matrix.
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.