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.
349
datasets available to search
ShareScore release 0.9.0
Dataset results
349 results for “Viability”
Figure 5 in Recovery of Dermatophagoides pteronyssinus (Acariformes: Astigmata: Pyroglyphidae) is influenced by vacuuming protocol, fabric type and the viability of dust mites
Figure 5 Scanning Electron Microscope image of the weave structure for fabrics used in this study. A – Cotton; B – Denim; C – Fleece.
Figure 4 in Recovery of Dermatophagoides pteronyssinus (Acariformes: Astigmata: Pyroglyphidae) is influenced by vacuuming protocol, fabric type and the viability of dust mites
Figure 4 Mean number of LM (Live Mites), DM (Dead Mites) and EM (Euthanized Mites post fabric seeding) recovered from each fabric type according to duration of sampling irrespective of fabric type and vacuuming protocol applied. "*", "**" represent values for outliers (observations outside the interquartile range), ∆ Mean value. The central line in boxes represents median value of recovered mites.
Figure 2 in Recovery of Dermatophagoides pteronyssinus (Acariformes: Astigmata: Pyroglyphidae) is influenced by vacuuming protocol, fabric type and the viability of dust mites
Figure 2 Mean number of LM (Live Mites), DM (Dead Mites) and EM (Mites euthanized post fabric seeding) recovered from each fabric type irrespective of the vacuuming protocol used and duration of vacuuming. "*", "**" represent values for outliers (outliers are observations outside the interquartile range), ∆ Mean value, and the central line in boxes represents median of recovered mites.
Figure 6 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 6 Effect of time and moisture on the emergence of flies from three soil types (sandy, sandy clay loam and clay loam).The lines in the graphs of the relation between the number of flies emerged and time (a-c) represent nonlinear models with a quadratic term [a) y = 4.12***-0.94***x+0.04x^2, b)y = 0.81*+0.5*x-0.098***x^2 and c) y = 2.47***-0.26x-0.02x^2], while the lines in the graphs of the relation between number of flies emerged and moisture (d-f) represent linear models with Poisson distributions [d) y = 0.11+0.0016x, e) y = -0.69***+0.01***x and f) y = -0.37***+0.008***x]. *:P<0.05; *** P<0.001.
Figure 5 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 5 Effect of soil depth and moisture on the number of pupae in three soil types (sandy, sandy clay loam and clay loam). The lines in the graphs of the relation between the number of pupae and depth (a-c) represent exponential models [a) y=exp(1.58-1.24***x), b) y=exp(1.42-0.21***x) and c) y=exp(1.18-0.16***x)], while the lines in the graphs of the relation between number of pupae and moisture (d-f) represent linear models with Poisson distributions [d) y = 0.56-0.0006x, e) y = 0.55-0.0017x and f) y = 0.55-0.0025x].*** P<0.001.
Figure 4 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 4 Number of pupae per centimeter for each treatment (T1 to T4 with sandy soil, T5 to T8 with sandy clay loam and T9 to T12 with clay loam) in combination with different moisture levels (0%, 30%, 60% and 90% for T1 to T4, T5 to T8 and T9 to T12, respectively). Treatments: T1 = sandy × 0% moisture, T2 = sandy x 30% moisture, T3 = sandy x 60% moisture, T4 = sandy x 90% moisture, T5 = sandy clay loam x 0% moisture, T6 = sandy clay loam x 30% moisture, T7 = sandy clay loam x 60% moisture, T8 = sandy clay loam x 90% moisture, T9 = clay loam x 0% moisture, T10 = clay loam x 30% moisture, T11 = clay loam x 60% moisture, and T12 = clay loam x 90% moisture.
Figure 3 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 3 Illustration of the steps of the experiment: A) Larvae on the soil surface; B) Containers used in the experiment; C) Removal of a 1 cm ring; D) Transfer of the soil to a plastic tray; E) Sorting and counting of the pupal cases; and F) Insects that were unable to rupture the soil layer. Photos: Eric Joel Ferreira do Amaral.
Figure 1 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 1 Representation of the steps for rearing B. carambolae: A) Oviposition container; B) Cage with adults; C) Eggs; D) Feed based on carrots in a plastic tray containing larvae; E) Paper envelope containing the plastic tray with larvae. Photos: Eric Joel Ferreira do Amaral.
Consequences of cross-season demographic correlations for population viability
<p class="MsoNormal"><span>Demographic correlations are pervasive in wildlife populations and can represent important secondary drivers of population growth. Empirical evidence suggests that correlations are in general positive for long-lived species, but little is known about the degree of variation among spatially segregated populations of the same species in relation to environmental conditions. We assessed the relative importance of two cross-season correlations in survival and productivity, for three Atlantic puffin</span><span> (</span><span><em>Fratercula</em> <em>arctica</em></span><span>) </span><span>populations with contrasting population trajectories and non-overlapping year-round distributions. The two correlations reflected either a relationship between adult survival prior to breeding on productivity or a relationship between productivity and adult survival in the subsequent year. Demographic rates and their correlations were estimated with an integrated population model, and their respective contributions to variation in population growth were calculated using a transient life table response experiment. For all three populations, demographic correlations were positive at both time lags, although their strength differed. Given the difference in year-round distributions of these populations, this variation in the strength of population-level demographic correlations points to environmental conditions as an important driver of demographic variation through life-history constraints. Consequently, the contributions of variances and correlations in demographic rates to population growth rates differed among puffin populations, which has implications for – particularly small – populations' viability under environmental change as positive correlations tend to reduce the stochastic population growth rate.</span></p>
Data used in: Assessing the resilience and viability of communities in a changing environment
<p>In order to assess community resilience, we propose to analyse how variation in overall abundance of individuals affect the number of species. We define community senstivity as the ratio between rate of change in log expected number of individuals in the community and the rate of change in the log expected number of species. Second, we define community resistance as the proportional reduction in expected number of individuals that the community can sustain before expecting to lose one species. To illustrate these concepts we use four datasets of bird communities in European deciduous forests.</p> <p>We estimated the total variance of the species abundance distribution in order to calculate the community sensitivity and resistance. We found large differences in species heterogeneity and species-specific response to environmental fluctuations, the two major components of the total variance.</p> <p>The datasets are collected from various previously published sources, see references below and in the paper. The code for importing and analysing the raw data as done in the paper can be found here:<span class="ng-binding"> <a href="https://doi.org/10.5281/zenodo.8252471">https://doi.org/10.5281/zenodo.8252471</a>, but we also provide better-structured datasets for easier applications to others.<br></span></p>
The raw microarray data and the differential expression analysis results from "Manipulating the growth environment through co-culture to enhance stress tolerance and viability of probiotic strains in the gastrointestinal tract".
<p>The signal data for each spot were subsequently quantified by using Feature Extraction software (Agilent Technologies).M1.txt to M5.txt: monoculture; C1.txt to C5.txt: co-culture; P1.txt to P5.txt: pH-controlled monoculture. The differential expression analysis results were obtained by using limma.</p>
Consequences of cross-season demographic correlations for population viability
Open the record for dataset details and reuse information.
Data: Applying stochastic and Bayesian integral projection modeling to amphibian population viability analysis
Open the record for dataset details and reuse information.
Data used in: Assessing the resilience and viability of communities in a changing environment
Open the record for dataset details and reuse information.
Data and R-scripts from: Multiple stressors: negative effects of nest predation on the viability of a threatened gull in different environmental conditions
Open the record for dataset details and reuse information.
Data from: Population viability of the orchid Gymnadenia conopsea increases with population size but is not related to genetic diversity
Open the record for dataset details and reuse information.
Data for: An integrated population model and population viability assessment for the southern population of a data-poor species
Open the record for dataset details and reuse information.
Black spruce seed viability from seed rain traps in the 2004 burns off the Steese, Taylor, and Dalton Highways, collected in 2005-2007.
This dataset contains viability estimates of black spruce seed collected in traps at 39 intensive study sites within the 2004 burns of interior Alaska. Data are presented as raw data from trap collections at each site. All sites were in communities dominated by black spruce before burning, and were located 100 to 600 m from the road, beyond the range of any visible road effects and away from any signs of other human disturbance.
Modeling Viability of Avian Populations in the Southern Appalachians: Potential impacts of Climate Change from 2002 to 2004
There is a general consensus that the global climate has slowly warmed (0.6 degrees C) over the past 100 years and that this trend will continue at an accelerated rate over the next 100 years (1 degree to 6 degrees C) (Kattenburg et al. 1996). Aside from the mean annual increase in temperature, the frequency of weather extremes, such as heat waves, drought, and tropical storms are projected to increase across North America over the next century (Easterling et al. 2000). Past changes in temperature and precipitation have been accompanied by changes in insect and vertebrate distributions from both tropical and temperate environments (Parmesan 1996, Pounds et. Al 1999). We have developed bird-habitat models that allow us to predict the occurrences of species in specific forest types within and across national forests within the Blue Ridge physiographic province with a high degree of accuracy (Linder et al. submitted). These models categorize habitat as unsuitable, marginal or high quality as determined by the rate of occupancy over five years of point count data. We will validate our occurrence models with reproductive data. We hypothesize that reproductive success will correspond to predicted habitat quality. While stochastic events may obscure patterns in the short-term, long-term reproductive trends should reflect site quality.
Archaeological mitogenomes illuminate the historical ecology of sea otters (Enhydra lutris) and the viability of reintroduction
<p class="CxSpFirst">Genetic analyses are an important contribution to wildlife reintroductions, particularly in the modern context of extirpations and ecological destruction. To address the complex historical ecology of the sea otter (<i>Enhydra lutris</i>) and its failed 1970s reintroduction to coastal Oregon, we compared mitochondrial genomes of pre-extirpation Oregon sea otters to extant and historical populations across the range. We sequenced the first complete ancient mitogenomes from archaeological Oregon sea otter dentine and historical sea otter dental calculus. Archaeological Oregon sea otters (N=20) represent ten haplotypes, which cluster with haplotypes from Alaska, Washington, and British Columbia, and exhibit a clear division from California haplotypes. Our results suggest that extant northern populations are appropriate for future reintroduction efforts. This project demonstrates the feasibility of mitogenome capture and sequencing from non-human dental calculus and the diverse applications of ancient DNA analyses to pressing ecological and conservation topics and the management of at-risk/extirpated species.</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.