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;”
Regionalized dynamic climate series for ecological climate impact research in modern controlled environment facilities
<p>Modern controlled environment facilities (CEFs) enable the simulation of dynamic microclimates in controlled ecological experiments through their technical ability to precisely control multiple environmental parameters. However, few CEF studies exploit the technical possibilities of their facilities, as climate change treatments are frequently applied by static manipulation of an inadequate number of climate change drivers, ignoring intra-annual variability and co-variation of multiple meteorological variables. We present so called Test Reference Years (TRY) that consist of typical annual cycles of temperature, relative humidity, air pressure, global radiation, photosynthetically active photon flux density, tropospheric ozone concentration and CO<sub>2</sub>. The TRYs represent possible annual cycles of a reference period (1987-2016) and the two contrasting climate scenarios RCP2.6 and RCP8.5.</p>
Distribution. Discontinuous and limited to wetland environments in SE Sudan, perhaps the Rift Valley of Ethiopia, W Kenya surrounding Lake Victoria (two locations, very rare), Uganda, Burundi, Rwanda, NW Tanzania, and NE DR Congo. Maps and distributional information here are provisional pending future research. in Bovidae
Distribution. Discontinuous and limited to wetland environments in SE Sudan, perhaps the Rift Valley of Ethiopia, W Kenya surrounding Lake Victoria (two locations, very rare), Uganda, Burundi, Rwanda, NW Tanzania, and NE DR Congo. Maps and distributional information here are provisional pending future research.
Distribution. Discontinuous and limited to wetland environments in the Congo Basin N and W of the range of the Zambezi Sitatunga in S Benin (Porto Novo), S Nigeria, Cameroon, Central African Republic, Equatorial Guinea, Gabon, Republic of the Congo, N DR Congo; also several isolated populations in W Africa (Senegal, Gambia & Guinea-Bissau), NE Nigeria and W Chad, and perhaps extreme S Ghana. Maps and distributional information here are provisional pending future research. in Bovidae
Distribution. Discontinuous and limited to wetland environments in the Congo Basin N and W of the range of the Zambezi Sitatunga in S Benin (Porto Novo), S Nigeria, Cameroon, Central African Republic, Equatorial Guinea, Gabon, Republic of the Congo, N DR Congo; also several isolated populations in W Africa (Senegal, Gambia & Guinea-Bissau), NE Nigeria and W Chad, and perhaps extreme S Ghana. Maps and distributional information here are provisional pending future research.
Distribution. Discontinuous and limited to wetland environments in two areas of S Sudan: small swamps in SW Sudan near the DR Congo border, where the type specimen was collected, and the Sudd Swamps (Bahr-el-Ghazal) of the upper White Nile. Maps and distributional information here are provisional pending future research. in Bovidae
Distribution. Discontinuous and limited to wetland environments in two areas of S Sudan: small swamps in SW Sudan near the DR Congo border, where the type specimen was collected, and the Sudd Swamps (Bahr-el-Ghazal) of the upper White Nile. Maps and distributional information here are provisional pending future research.
Data from: Elevational distribution of birds in an Eastern African montane environment as governed by temperature, precipitation, and habitat availability
<p>We conducted annual point counts of birds between 2013 and 2018 at 297 plots across habitats and elevations (2,416-4,303 m) in Volcanoes National Park, Rwanda. These data were subsequently used to determine elevational and habitat preferences via indicator analyses, and to model abundance distributions as a function of temperature, precipitation, habitat availability, and congeneric competition. Of 35 focal species, we found 20 species to be particularly associated with narrow elevational range (<300 m) and 24 species to have a strong associated with one to three habitat types. Abiotic conditions, estimated for each plot location, significantly correlated with the abundance distributions of 33 species (temperature 33, precipitation 17), and biotic factors with distributions of 31 species (habitat 30, competition 7). Temperature and habitat availability were particularly associated with upper elevational limits (31 and 26 species respectively vs. 10 and 6 at lower limits), whereas precipitation affected both limits similarly (17 lower, 16 upper), and competition had a limited role at either limit (4 lower, 3 upper). That the elevational distribution of Afromontane birds results from a species-specific combination of biotic and abiotic factors is crucial information in our effort to predict climate change effects in this region.</p>
Data from: Drivers of neutral and adaptive differentiation in pike (Esox lucius) populations from contrasting environments
<p>Understanding how eco-evolutionary processes and environmental factors drive population differentiation and adaptation are key challenges in evolutionary biology of relevance for biodiversity protection. Differentiation requires at least partial reproductive separation, which may result from different modes of isolation such as geographic isolation (allopatry) or isolation by distance (IBD), resistance (IBR), and environment (IBE). Despite that multiple modes might jointly influence differentiation, studies that compare the relative contributions are scarce. Using RADseq, we analyse neutral and adaptive genetic diversity and structure in 11 pike (<i>Esox lucius</i>) populations along a latitudinal gradient (54.9 - 63.6°N), to investigate the relative effects of IBD, IBE and IBR, and to assess whether the effects differ between neutral and adaptive variation, or across structural levels. Patterns of neutral and adaptive variation differed, likely reflecting that they have been differently affected by stochastic and deterministic processes. The importance of the different modes of isolation differed between neutral and adaptive diversity, yet were consistent across structural levels. Neutral variation was influenced by interactions among all three modes of isolation, with IBR (seascape features) playing a central role, wheares adaptive variation was mainly influenced by IBE (environmental conditions). Taken together, this and previous studies suggest that it is common that multiple modes of isolation interactively shape patterns of genetic variation, and that their relative contributions differ among systems. To enable identification of general patterns and understand how various factors influence the relative contributions, it is important that several modes are simultaneously investigated in additional populations, species and environmental settings.</p>
Supplementary Table S1and S2 (raw data) of "Effect of salinity and water dilution on environmental DNA degradation in freshwater environments"
<p>All data, including the raw values for the qPCR experiments</p>
Cryptophytes: a keystone algal group in the rapidly changing Antarctic Peninsula marine environments - data availability
<p>This dataset contains the results of relative and absolute contributions of cryptophytes derived from the HPLC/CHEMTAX analysis. The data were collected during the late summer (February) between 2008-2018 along the Northern Antarctic Peninsula. The index of photoprotective carotenoid pigments to chlorophyll-<em>a</em> (PPC: Chl-<em>a</em>) used in the study is also presented. </p>
FIGURE 7 in Last interglacial environment of the Baikal Region (Southern Siberia, Russia) based on analysis of fossil invertebrates and plants
FIGURE 7. Scheme of environment changes during accumulation of the lower unit of the section Bely Yar II.
FIGURE 5 in Last interglacial environment of the Baikal Region (Southern Siberia, Russia) based on analysis of fossil invertebrates and plants
FIGURE 5. Plant macrofossils of the section Bely Yar II: 1, cf. Picea sp.; 2, Potentilla cf. reptans; 3 and 4, Cyperaceae gen. sp.; 5 and 6, Carex sect. Vignea sp.; 7 and 8, Polygonum sp.; 9, Polygonum persicaria; 10 and 11, Schoenoplectus tabernaemontani; 12– 15, Scirpus sp.; 16, Hippuris vulgaris; 17, Potamogeton sp.; 18, Eleocharis cf. palustris; 19, Eleocharis acicularis; 20, Eleocharis ovata; 21, Eleocharis sp.; 22, Carex sp.; 23 and 24, Bunias cochlearioides; 25, Chenopodium sp.; 26 and 27, Myriophyllum verticillatum; 28, Rumex sp. Legend: *, seed's part; m, exocarpes. Scale bar = 1mm.
FIGURE 3 in Last interglacial environment of the Baikal Region (Southern Siberia, Russia) based on analysis of fossil invertebrates and plants
FIGURE 3. Macro remains of insects and other invertebrates from Bely Yar II: 1, Agonum (Europhilus) sp., elytron, sample BYA-0.97-1.01; 2, Bembidion bimaculatum, pronotum sample BYA-0.85-0.97; 3, Bembidion sp., head, sample BYA-1.01-1.1; 4, B. mannerheimi, pronotum, sample BYA-0.97-1.01; 5, B. semipunctatum, pronotum, sample BYA-1.3-1.4; 6, Poecilus ravus, pronotum, sample BYA-0.15-0.23; 7 and 8, Agabus infuscatus, pronotum and top of elytron, sample BYA-0.15-0.23; 9, Agabus sp., metasternum, sample BYA-1.01-1.1; 10, Agabus congener, pronotum, sample BYA-0.97-1.01; 11 and 12, A. sturmii, elytra, sample BYA-0.15-0.23; 13 and 14, Helophorus (Rhopalohelophorus) sp., head and top of elytron, sample BYA-1.01-1.1; 15– 17, Hydrobius fuscipes, head and top of elytron, BYA-0.15-0.23, elytron sample BYA-0.97-1.01; 18, Limnebius glabriventris, elytron, sample BYA-1.01-1.1; 19 and 20, Ochthebius sp., elytron, sample BYA-0.97-1.01, top of elytron, sample BYA-0.97- 1.01; 21, Catops alpinus, pronotum, sample BYA-1.3-1.4; 22, Agathidium laevigatum, elytron sample BYA-0.15-0.23; 23, Stenus bimaculatus? head, sample BYA-0.15-0.23; 24–26, Stenus sp., meso-metasternum and elytron, sample BYA-0.15-0.23, pronotum, sample BYA-1.1-1.2; 27, Atheta (Dimetrota) sp.?, pronotum, sample BYA-0.15-0.23; 28, Tachinus jacuticus, pronotum, sample BYA-0.97-1.01; 29 and 30, Philonthus sp.1, head, sample BYA-1.01-1.1, pronotum, sample BYA-1.1-1.2; 31–33, Philonthus sp. 2, pronotum, sample BYA-1.1-1.2, metasternum, sample BYA-0.15-0.23, elytron, sample BYA-1.3-1.4; 34, Ochthephilus sp., elytron, sample BYA-1.01-1.1; 35, Xylodromus depressus, elytron sample, BYA-1.3-1.4; 36, Aleocharinae gen. indet., elytron, sample BYA-0.15-0.23; 37, Aphodius depressus, elytron sample BYA-0.85-0.97; 38, Aphodius sp., legs, sample BYA-0.97-1.01; 39, Curimopsis cyclolepidia, elytron, sample BYA-1.35-1.45; 40, Olibrus affinis, elytron, sample BYA-1.01-1.1; 41, Heterocerus fossor, pronotum, sample BYA-1.01-1.1; 42, Negastrius pulchellus, elytron, sample BYA-1.4-1.5; 43, Anthicus ater, pronotum, sample BYA-1.1-1.2; 44 and 45, Chrysolina sp., head and fragment of elytron, sample BYA-0.15-0.23; 46, Donacia sparganii, fragment of elytron, sample BYA-0.15-0.23; 47, Plateumaris sp., top of elytron, sample BYA-0.97-1.01; 48, Donaciinae gen. indet. (Donacia or Plateumaris), head, sample BYA-0.15-0.23; 49, Carphoborus sp., top of elytron sample BYA-1.01-1.1; 50 and 51, Thryogenes nereis, pronotum, elytron, sample BYA-1.01-1.1; 52, Phytobius leucogaster, elytron, sample BYA-0.15-0.23; 53 and 54, Bagous longitarsis, head, elytron sample BYA-1.01-1.1; 55, Bagous sp., abdomen, sample BYA-0.15-0.23; 56, Tournotaris bimaculatus, elytron sample BYA-0.15-0.23; 57–59, Notaris aethiops, head, pronotum, elytron, sample BYA-0.15-0.23; 60, Myrmica sp., head, sample BYA-1.01-1.1; 61, Camponotus sp., mandible, sample BYA-1.1-1.2; 62, Ichneumonoidea gen. indet., head, sample BYA-1.01-1.1; 63 and 64, Sigara sp., fragments of elytra, sample BYA-1.1-1.2, BYA-1.01-1.1; 65, Microvelia sp., pronotum and abdomen, sample BYA-0.15-0.23; 66, Salda sp., pronotum, sample BYA-0.15-0.23; 67, Gerris lacustris, scutellum, sample BYA-0.15-0.23; 68, Trichoptera gen. indet., frontoclypeus of larvae head, sample BYA-0.15-0.23; 69–71—Diptera gen. indet., puparia, samples BYA-1.2-1.3, BYA-1.1-1.2; 72—Eisenia nordenskioldi?, cocoon, sample BYA-0.15-0.23. Scale bar = 1 mm.
FIGURE 4. Small invertebrates from Bely Yar II in Last interglacial environment of the Baikal Region (Southern Siberia, Russia) based on analysis of fossil invertebrates and plants
FIGURE 4. Small invertebrates from Bely Yar II: SEM photos (A–L) of Branchiopoda (Crustacea) and optical (M–O) photos of Crustacea and Turbellaria remains, samples BYA 1.01-1.1 (A, B, F–N), BYA-1.4-1.5. (C and D), BYA-0.15-0.23 (O). A, Ephippium of Daphnia (Daphnia) longispina group (Daphniidae), general view. B, Its dorsal portion. C, Ephippium of D. (Ctenodaphnia) magna (Daphniidae), general view. D, Its posterior portion (the presence of scales is a diagnostic character of this species). E, Its anterior projection. F, Ephippium of Simocephalus sp. (Daphniidae), general view. G and H, Its sculpture. I, Valve of Chydorus cf. sphaericus (Chydoridae). J, Its ventral portion, inner view. K, Valve of Alona sp. (Chydoridae). L, Its posterior portion, inner view. M, Ephippium of Ceriodaphnia sp. (Daphniidae). N, Distal portion of mandible of a tadpole shrimp (Triopsidae, Notostraca). O, Turbellaria eggs (Platyhelminthes).
A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set
<p>It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. In this study, we developed a data-driven approach termed <u>E</u>nvironmental <u>C</u>ovariate Search Affecting <u>G</u>enetic <u>C</u>orrelations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (<i>Glycine max</i>) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when they were involved. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields. This dataset provides the data used in this study and supplementary tables cited in the manuscript.</p>
Natural variation further increases resilience of sorghum bred for chronically drought-prone environments
<p>Moisture stress is one of the major constraints for crop production in African Sahel. Here, we explore the potential to use natural genetic variation to build on the inherent drought tolerance of an elite sorghum cultivar (Teshale) bred for Ethiopian conditions including chronic drought, evaluating a backcross nested-association mapping population using 12 diverse founder lines crossed with Teshale under three drought-prone environments in Ethiopia. All twelve populations averaged higher head exsertion and lower leaf senescence than the recurrent parent in the two highest-stress environments, reflecting new drought resilience mechanisms from the donors. 154 QTLs were detected for eight drought responsive traits – the validity of these were supported in that 113 (73.4%) overlapped with QTLs previously detected for the same traits, concentrated in regions previously associated with 'stay-green' traits. Allele effects show that some favorable alleles are already present in the Ethiopian cultivar, however the exotic donors offer rich scope for increasing drought resilience. Using model-selected SNPs associated with eight traits in this study and three in a companion study, phenotypic prediction accuracies for grain yield were equivalent to genome-wide SNPs and were significantly better than random SNPs, indicating that these studied traits are predictive of sorghum grain yield.</p>
Humanization of wildlife gut microbiota in urban environments
<p>Urbanization is rapidly altering Earth's environments, demanding investigations of the impacts on resident wildlife. Here, we show that urban populations of coyotes (Canis latrans) and crested anole lizards (Anolis cristatellus) acquire gut microbiota constituents found in humans, including the gut bacterial lineages most significantly associated with urbanization in humans (e.g., Bacteroides). Comparisons of urban and rural wildlife and human populations revealed significant convergence of the gut microbiota among urban host populations. Remarkably, all microbial lineages found in humans that were overrepresented in urban wildlife relative to rural wildlife were also overrepresented in urban humans relative to rural humans. These results indicate parallel effects of urbanization on human and wildlife gut microbiota and suggest spillover of bacteria from humans into wildlife in cities</p>
Kin recognition in Drosophila: Rearing environment and relatedness can modulate gut microbiota and cuticular hydrocarbon odour profiles
<p>From inbreeding avoidance to kin-selected cooperation, social behaviours are frequently reliant on kin recognition. However, kin recognition mechanisms are costly to evolve and currently not very well understood. Recent evidence suggests that, by altering their host's odour, gut and other host-associated microorganisms may provide a promising avenue for understanding kin recognition. In Drosophila melanogaster, kin recognition can mediate mate choice, sexual conflict and larval competition/cooperation, underscoring its important functional role. As is commonly the case, kin recognition in this species depends on both familiarity (i.e. shared rearing environment) and relatedness, and seems to rely mainly on body odours determined by cuticular hydrocarbons. Here, we investigated the degree to which larval rearing environment and relatedness (full-sibs vs. unrelated) determine co-variation between gut microbiota and cuticular hydrocarbons in D. melanogaster. We found that rearing environment strongly determined both microbiota and cuticular hydrocarbon composition, but that these effects were independent from each other. In contrast, relatedness did not influence microbiota composition, but had a strong influence on microbiota diversity, which in turn covaried significantly with cuticular hydrocarbon composition. Our results show that, in D. melanogaster, odours may convey information about both familiarity and relatedness via an interaction between: a) direct effects of the rearing environment on cuticular hydrocarbons and b) indirect effects of relatedness on cuticular hydrocarbons via gut microbiota diversity.</p>
How well does molecular simulation reproduce environment-specific conformations of the intrinsically disordered peptides PLP, TP2 and ONEG?
<p>Simulation trajectory dataset to accompany publication:</p> <p>Reid, L.M., Guzzetti, I., Svensson, T., Carlsson, A-C., Su, W., Leek, T., von Sydow, L., Czechtizky, W., Miljak, M., Verma, C., De Maria, L., Essex, J.W., Chem. Sci., 2022, DOI: 10.1039/d1sc03496k</p> <p> </p>
Theoretical insights into the antiradical activity and copper-catalyzed oxidative damage of mexidol in the physiological environment
<p>This dataset contains data from the calculations described in the paper: "Quan V. Vo*, Nguyen Thi Hoa, <span class="fontstyle01"><span>Mai Van Bay, </span></span>Adam Mechler, Theoretical insights into the antiradical activity and copper-catalyzed oxidative damage of mexidol in the physiological environment. Royal Society Open Science. 2021.</p> <p>In this study, the hydroperoxyl radical scavenging activity of<b> MD </b>was studied to establish baseline antioxidant activity, followed by an investigation of the effect of<b> MD </b>on the <span class="fontstyle01"><span>copper-catalyzed oxidative damage in</span></span> <span class="fontstyle01"><span>biological systems, using computational methods. It was found that</span></span><span class="fontstyle01"><span><b> MD </b></span></span><span class="fontstyle01"><span>exhibits moderate </span></span>radical scavenging activity against HOO<sup>·</sup> in physiological environments. The activity in aqueous solution is defined by the single electron transfer mechanism of the anion state; however in nonpolar environments the formal hydrogen transfer pathway dominates at the O3-H bond. It was also confirmed that<b> MD </b><span class="fontstyle01"><span>can chelate Cu (II), forming complexes that are much harder to reduce than free Cu (II):</span></span><span class="fontstyle01"><span><b> MD </b></span></span><span class="fontstyle01"><span>chelation completely quenches the Cu (II) reduction by ascorbic acid and suppresses the rate of reduction reaction by O<sub>2</sub></span></span><span class="fontstyle01"><span><sup>·</sup></span></span><span class="fontstyle01"><span><sup>-</sup></span></span><span class="fontstyle01"><span> that are the main reductants of Cu (II) in biological environments. Therefore,</span></span><span class="fontstyle01"><span><b> MD </b></span></span><span class="fontstyle01"><span>exerts its anti- HO</span></span><span class="fontstyle01"><span><sup>·</sup></span></span><span class="fontstyle01"><span><sup> </sup></span></span><span class="fontstyle01"><span>activity primarily as an OIL-1 inhibitor. </span></span></p>
Supplementary materials for "Testing Software's Changing Features with Environment-Driven Abstraction Identification"
<p><strong>Supplementary materials of the paper entitled:</strong></p> <p><strong>“Testing Software’s Changing Features with Environment-Driven Abstraction Identification”</strong></p>
Data from: The war of corals: patterns, drivers, and implications of changing coral competitive performances across reef environments
<p>Amidst global environmental changes, predicting species responses to future environments is a critical challenge for preserving biodiversity and associated human benefits. We explored the original idea that coral competitive performances, the ability of corals to preempt ecological space on the reef through territorial warfare, serve as indicators of species' ecological niches and environmental windows, and therefore, responses to future environments. Our surveys indicated that coral performances varied with taxonomic-identity, size, and position along environmental gradients, highlighting complex interplays between life-history, warfare-strategy, and niche segregation. Our results forewarn that growing alterations of coastal environments may trigger shifts in coral dominance, with decline of major reef-building taxa like acroporids, and underscore the importance of restraining human impacts for coastal resilience. Our empirical approach untangles the complexity of species' battle-like interactions and can help identify winners and losers in various communities caught in the interplay between ecological niches, environmental windows, and global changes.</p>
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.