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.
648
datasets available to search
ShareScore release 0.9.0
Dataset results
648 results for “uncertainties”
Characterizing uncertainty in climate impact projections: a case study with seven marine species on the North American continental shelf
<p>Projections of climate change impacts on living resources are being conducted frequently, and the goal is often to inform policy. Species projections will be more useful if uncertainty is effectively quantified. However, few studies have comprehensively characterized the projection uncertainty arising from greenhouse gas scenarios, Earth system models, and both structural and parameter uncertainty in species distribution modeling. Here we conducted 8964 unique 21st century projections for shifts in suitable habitat for seven economically important marine species including American lobster, Pacific halibut, Pacific ocean perch, and summer flounder. For all species, both the Earth system model used to simulate future temperatures and the niche modeling approach used to represent species distributions were important sources of uncertainty, while variation associated with parameter values in niche models was minor. Greenhouse gas emissions scenario contributed to uncertainty for projections at the century scale. The characteristics of projection uncertainty differed among species and also varied spatially, which underscores the need for improved multi-model approaches with a suite of Earth system models and niche models forming the basis for uncertainty around projected impacts. Ensemble projections show the potential for major shifts in future distributions. Therefore, rigorous future projections are important for informing climate adaptation efforts.</p>
Uncertainty in times of COVID-19: Raw survey data
<p>Data from a survey of consumer expectations</p> <p>From April 24, 2020, through June 22, 2020, Fabian Lange and Lars Vilhuber conducted the survey "Uncertainty in COVID-19 times". The survey is a single-question survey focusing on people's anticipation about social distancing rules and firm closures during the 2020 COVID-19 health crisis.</p>
Towards a Quantitative Model to Deal with Uncertainty Management in Software Projects
<p>The evolution of thinking in project management has raised interest in areas not yet explored by researchers and practitioners of project management, including the management of uncertainties associated with risk management. The correct risk and uncertainty management in software projects can represent a competitive differential for the software development industry. Despite the increasing use of uncertainty management strategies, many projects still fail. Some recent studies show that the current techniques used to manage uncertainties organize the project's known information, but give little or no indication of the unknown information or uncertainties associated with the project. These techniques do not take into account the impact of existing dependency and interdependence relationships between the various sources of uncertainties in the project. This work will apply Action Research to develop a model with a focus on uncertainty quantification techniques. This work aims to present a model with a focus on uncertainty quantification techniques that take into account the relationships of dependencies and interdependence that exist between the sources of risks and uncertainties in software projects and as a result, contribute with the advance of state of the art in the practice of risk and uncertainty management in project software.</p>
Data from: Quantifying uncertainty due to fission-fusion dynamics as a component of social complexity
Groups of animals (including humans) may show flexible grouping patterns, in which temporary aggregations or subgroups come together and split, changing composition over short temporal scales, i.e. fission and fusion). A high degree of fission-fusion dynamics may constrain the regulation of social relationships, introducing uncertainty in interactions between group members. Here we use Shannon's entropy to quantify the predictability of subgroup composition for three species known to differ in the way their subgroups come together and split over time: spider monkeys (Ateles geoffroyi), chimpanzees (Pan troglodytes) and geladas (Theropithecus gelada). We formulate a random expectation of entropy that considers subgroup size variation and sample size, against which the observed entropy in subgroup composition can be compared. Using the theory of set partitioning, we also develop a method to estimate the number of subgroups that the group is likely to be divided into, based on the composition and size of single focal subgroups. Our results indicate that Shannon's entropy and the estimated number of subgroups present at a given time provide quantitative metrics of uncertainty in the social environment (within which social relationships must be regulated) for groups with different degrees of fission-fusion dynamics. These metrics also represent an indirect quantification of the cognitive challenges posed by socially dynamic environments. Overall, our novel methodological approach provides new insight for understanding the evolution of social complexity and the mechanisms to cope with the uncertainty that results from fission-fusion dynamics.
Data from: Predicting ecological responses in a changing ocean: the effects of future climate uncertainty
Predicting how species will respond to climate change is a growing field in marine ecology, yet knowledge of how to incorporate the uncertainty from future climate data into these predictions remains a significant challenge. To help overcome it, this review separates climate uncertainty into its three components (scenario uncertainty, model uncertainty, and internal model variability) and identifies four criteria that constitute a thorough interpretation of an ecological response to climate change in relation to these parts (awareness, access, incorporation, communication). Through a literature review, the extent to which the marine ecology community has addressed these criteria in their predictions was assessed. Despite a high awareness of climate uncertainty, articles favoured the most severe emission scenario, and only a subset of climate models were used as input into ecological analyses. In the case of sea surface temperature, these models can have projections unrepresentative against a larger ensemble mean. Moreover, 91% of studies failed to incorporate the internal variability of a climate model into results. We explored the influence that the choice of emission scenario, climate model, and model realisation can have when predicting the future distribution of the pelagic fish, Electrona antarctica. Future distributions were highly influenced by the choice of climate model, and in some cases, internal variability was important in determining the direction and severity of the distribution change. Increased clarity and availability of processed climate data would facilitate more comprehensive explorations of climate uncertainty, and increase in the quality and standard of marine prediction studies.
Data from: EMG versus torque control of human-machine systems: equalizing control signal variability does not equalize error or uncertainty
In this paper we asked the question: if we artificially raise the variability of torque control signals to match that of EMG, do subjects make similar errors and have similar uncertainty about their movements? We answered this question using two experiments in which subjects used three different control signals: torque, torque+noise, and EMG. First, we measured error on a simple target-hitting task in which subjects received visual feedback only at the end of their movements. We found that even when the signal-to-noise ratio was equal across EMG and torque+noise control signals, EMG resulted in larger errors. Second, we quantified uncertainty by measuring the just-noticeable difference of a visual perturbation. We found that for equal errors, EMG resulted in higher movement uncertainty than both torque and torque+noise. The differences suggest that performance and confidence are influenced by more than just the noisiness of the control signal, and suggest that other factors, such as the user's ability to incorporate feedback and develop accurate internal models, also have significant impacts on the performance and confidence of a person's actions. We theorize that users have difficulty distinguishing between random and systematic errors for EMG control, and future work should examine in more detail the types of errors made with EMG control.
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations
Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. First, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. We developed a hidden Markov (also known as multievent) capture–recapture model to estimate prevalence in free‐ranging populations accounting for imperfect detectability and uncertainty in individual's classification. We carried out a simulation study to compare naive and model‐based estimates of prevalence and assess the performance of our model under different sampling scenarios. We then illustrate our method with a real‐world case study of estimating the prevalence of wolf (Canis lupus) and dog (Canis lupus familiaris) hybrids in a wolf population in northern Italy. We showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. Model‐based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. We also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. Our results underline the importance of a model‐based approach to obtain unbiased estimates of prevalence of different population segments. Our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals).
Data from: The impact of reconstruction methods, phylogenetic uncertainty and branch lengths on inference of chromosome number evolution in American daisies (Melampodium, Asteraceae)
Chromosome number change (polyploidy and dysploidy) plays an important role in plant diversification and speciation. Investigating chromosome number evolution commonly entails ancestral state reconstruction performed within a phylogenetic framework, which is, however, prone to uncertainty, whose effects on evolutionary inferences are insufficiently understood. Using the chromosomally diverse plant genus Melampodium (Asteraceae) as model group, we assess the impact of reconstruction method (maximum parsimony, maximum likelihood, Bayesian methods), branch length model (phylograms versus chronograms) and phylogenetic uncertainty (topological and branch length uncertainty) on the inference of chromosome number evolution. We also address the suitability of the maximum clade credibility (MCC) tree as single representative topology for chromosome number reconstruction. Each of the listed factors causes considerable incongruence among chromosome number reconstructions. Discrepancies between inferences on the MCC tree from those made by integrating over a set of trees are moderate for ancestral chromosome numbers, but severe for the difference of chromosome gains and losses, a measure of the directionality of dysploidy. Therefore, reliance on single trees, such as the MCC tree, is strongly discouraged and model averaging, taking both phylogenetic and model uncertainty into account, is recommended. For studying chromosome number evolution, dedicated models implemented in the program ChromEvol and ordered maximum parsimony may be most appropriate. Chromosome number evolution in Melampodium follows a pattern of bidirectional dysploidy (starting from x = 11 to x = 9 and x = 14, respectively) with no prevailing direction.
Data from: Conservation under uncertainty: optimal network protection strategies for worst-case disturbance events
1. Conservation goals are ideally set after a thorough understanding of potential threats; however, predicting future spatial patterns of threats, such as disturbance, remains challenging. Here, we develop a novel extension of network fortification-interdiction models (NFIM) that deals with uncertainty in future spatial patterns of disturbance by optimally selecting sites that will best mitigate a worst-case scenario for a given magnitude of disturbance. 2. This approach uses information on between-patch movement probabilities and patch-specific survival, which can be estimated from mark-recapture data, to optimize life expectancy. Optimization occurs in three interrelated stages: protection, followed by disturbance, and then assessment. 3. We applied the modeling approach to two mark-recapture data sets: Roseate terns Sterna dougallii in the northeastern United States and the Everglade snail kite Rostrhamus sociabilis plumbeus in Florida. We contrasted the results to a more conventional approach of protecting sites that maximize connectivity (by minimizing the distances among protected sites) and a bi-objective model that maximizes connectivity and the number of individuals under protection. 4. Protecting sites that best mitigate future worst-case disturbance scenarios consistently resulted in higher predicted life expectancies than protecting patches that minimize dispersal distance. Predicted life expectancy was similar between NFIM and the biobjective model for the small roseate tern network, yet, the NFIM predicted higher life expectancy than any of the scenarios in the bi-objective model in the snail kite network. 5. Synthesis and applications. This application of interdiction models prescribed a combination of patches for protection that results in the least possible decrease in life expectancy. Our analyses of the snail kite and roseate tern networks suggest that managing to protect these prescribed patches by the network fortification interdiction model (i.e. protecting against the worst-case disturbance scenario) is more beneficial than managing patches that minimize dispersal distance or maximize the number of individuals under protection if the conservation goal is to ensure the long term persistence of a species.
Data from: Quantifying demographic uncertainty: Bayesian methods for integral projection models (IPMs)
Integral projection models (IPMs) are a powerful and popular approach to modeling population dynamics. Generalized linear models form the statistical backbone of an IPM. These models are typically fit using a frequentist approach. We suggest that hierarchical Bayesian statistical approaches offer important advantages over frequentist methods for building and interpreting IPMs, especially given the hierarchical nature of most demographic studies. Using a stochastic IPM for a desert cactus based on a 10-year study as a worked example, we highlight the application of a Bayesian approach for translating uncertainty in the vital rates (e.g., growth, survival, fertility) to uncertainty in population-level quantities derived from them (e.g., population growth rate). The best-fit demographic model, which would have been difficult to fit under a frequentist framework, allowed for spatial and temporal variation in vital rates and correlated responses to temporal variation across vital rates. The corresponding posterior probability distribution for the stochastic population growth rate (λS) indicated that, if current vital rates continue, the study population will decline with nearly 100% probability. Interestingly, less-supported candidate models that did not include spatial variance and vital rate correlations gave similar estimates of λS. This occurred because the best-fitting model did a much better job of fitting vital rates to which the population growth rate was weakly sensitive. The cactus case study highlights several advantages of Bayesian approaches to IPM modeling, including that they: (1) provide a natural fit to demographic data, which are often collected in a hierarchical fashion (e.g., with random variance corresponding to temporal and spatial heterogeneity); (2) seamlessly combine multiple data sets or experiments; (3) readily incorporate covariance between vital rates; and, (4) easily integrate prior information, which may be particularly important for species of conservation concern where data availability may be limited. However, constructing a Bayesian IPM will often require the custom development of a statistical model tailored to the peculiarities of the sampling design and species considered; there may be circumstances under which simpler methods are adequate. Overall, Bayesian approaches provide a statistically sound way to get more information out of hard-won data, the goal of most demographic research endeavors.
Data from: Uncertainties in the value and opportunity costs of pollination services
Pollination is an ecosystem service that directly contributes to agricultural production, and can therefore provide a strong incentive to conserve natural habitats that support pollinator populations. However, we have yet to provide consistent and convincing pollination service valuations to effectively slow the conversion of natural habitats. We use coffee in Kodagu, India, to illustrate the uncertainties involved in estimating costs and benefits of pollination services. First, we fully account for the benefits obtained by coffee agroforests that are attributable to pollination from wild bees nesting in forest habitats. Second, we compare these benefits to the opportunity cost of conserving forest habitats and forgoing conversion to coffee production. Throughout, we systematically quantify the uncertainties in our accounting exercise and identify the parameters that contribute most to uncertainty in pollination service valuation. We find the value of pollination services provided by one hectare of forest to be 25% lower than the profits obtained from converting that same surface to coffee production using average values for all parameters. However, our results show this value is not robust to moderate uncertainty in parameter values, particularly that driven by variability in pollinator density. Synthesis and applications. Our findings emphasise the need to develop robust estimates of both value and opportunity costs of pollination services that take into account landscape and management variables. Our analysis contributes to strengthening pollination service arguments used to help stakeholders make informed decisions on land‐use and conservation practices.
Data from: Accounting for observation processes across multiple levels of uncertainty improves inference of species distributions and guides adaptive sampling of environmental DNA
Understanding factors that influence observation processes is critical for accurate assessment of underlying ecological processes. When indirect methods of detection, such as environmental DNA, are used to determine species presence, additional levels of uncertainty from observation processes need to be accounted for. We conducted a field trial to evaluate observation processes of a terrestrial invasive species (wild pigs- Sus scrofa) from DNA in water bodies. We used a multi-scale occupancy analysis to estimate different levels of observation processes (detection, p): the probability DNA is available per sample (θ), the probability of capturing DNA per extraction (γ), and the probability of amplification per qPCR run (δ). We selected four sites for each of three water body types and collected 10 samples per water body during two months (September and October 2016) in central Texas. Our methodology can be used to guide sampling adaptively to minimize costs while improving inference of species distributions. Using a removal sampling approach was more efficient than pooling samples, and was unbiased. Availability of DNA varied by month, was considerably higher when water pH was near neutral, and was higher in ephemeral streams relative to wildlife guzzlers and ponds. To achieve a cumulative detection probability greater than 90% (including availability, capture, and amplification), future studies should collect 20 water samples per site, conduct at least 2 extractions per sample, and conduct 5 qPCR replicates per extraction. Accounting for multiple levels of uncertainty of observation processes improved estimation of the ecological processes and provided guidance for future sampling designs.
Data from: Accounting for uncertainty in gene tree estimation: summary-coalescent species tree inference in a challenging radiation of Australian lizards
Accurate gene tree inference is an important aspect of species tree estimation in a summary-coalescent framework. Yet, in empirical studies, inferred gene trees differ in accuracy due to stochastic variation in phylogenetic signal between targeted loci. Empiricists should, therefore, examine the consistency of species tree inference, while accounting for the observed heterogeneity in gene tree resolution of phylogenomic data sets. Here, we assess the impact of gene tree estimation error on summary-coalescent species tree inference by screening ${\sim}2000$ exonic loci based on gene tree resolution prior to phylogenetic inference. We focus on a phylogenetically challenging radiation of Australian lizards (genus Cryptoblepharus, Scincidae) and explore effects on topology and support. We identify a well-supported topology based on all loci and find that a relatively small number of high-resolution gene trees can be sufficient to converge on the same topology. Adding gene trees with decreasing resolution produced a generally consistent topology, and increased confidence for specific bipartitions that were poorly supported when using a small number of informative loci. This corroborates coalescent-based simulation studies that have highlighted the need for a large number of loci to confidently resolve challenging relationships and refutes the notion that low-resolution gene trees introduce phylogenetic noise. Further, our study also highlights the value of quantifying changes in nodal support across locus subsets of increasing size (but decreasing gene tree resolution). Such detailed analyses can reveal anomalous fluctuations in support at some nodes, suggesting the possibility of model violation. By characterizing the heterogeneity in phylogenetic signal among loci, we can account for uncertainty in gene tree estimation and assess its effect on the consistency of the species tree estimate. We suggest that the evaluation of gene tree resolution should be incorporated in the analysis of empirical phylogenomic data sets. This will ultimately increase our confidence in species tree estimation using summary-coalescent methods and enable us to exploit genomic data for phylogenetic inference.
Data from: Estimating correlated rates of trait evolution with uncertainty
Correlated evolution among traits, which can happen due to genetic constraints, ontogeny, and selection, can have an important impact on the trajectory of phenotypic evolution. For example, shifts in the pattern of evolutionary integration may allow the exploration of novel regions of the morphospace by lineages. Here we use phylogenetic trees to study the pace of evolution of several traits and their pattern of evolutionary correlation across clades and over time. We use regimes mapped to the branches of the phylogeny to test for shifts in evolutionary integration while incorporating the uncertainty related to trait evolution and ancestral regimes with joint estimation of all parameters of the model using Bayesian Markov chain Monte Carlo. We implemented the use of summary statistics to test for regime shifts based on a series of attributes of the model that can be directly relevant to biological hypotheses. In addition, we extend Felsenstein's pruning algorithm to the case of multivariate Brownian motion models with multiple rate regimes. We performed extensive simulations to explore the performance of the method under a series of scenarios. Finally, we provide two test cases; the evolution of a novel buccal morphology in fishes of the family Centrarchidae and a shift in the trajectory of evolution of traits during the radiation of anole lizards to and from the Caribbean islands.
Data from: Correlation between genetic diversity and environmental suitability: taking uncertainty from ecological niche models into account
The hindcast of shifts in the geographical ranges of species as estimated by ecological niche modelling (ENM) has been coupled with phylogeographical patterns, allowing the inference of past processes that drove population differentiation and genetic variability. However, more recently, some studies have suggested that maps of environmental suitability estimated by ENM may be correlated to species' abundance, raising the possibility of using environmental suitability to infer processes related to population demographic dynamics and genetic variability. In both cases, one of the main problems is that there is a wide variation in ENM development methods and climatic models. In this study, we analyse the relationship between heterozygosity (He) and environmental suitability from multiple ENMs for 25 population estimates for Dipteryx alata, a widely distributed, endemic tree species of the Cerrado region of central Brazil. We propose a new approach for generating a statistical distribution of correlations under randomly generated ENM. The confidence intervals from these distributions indicate how model selection with different properties affects the ability to detect a correlation of interest (e.g. the correlation between He and suitability). Additionally, our approach allows us to explore which particular ensemble of ENMs produces the better result for finding an association between environmental suitability and He. Caution is necessary when choosing a method or a climatic data set for modelling geographical distributions, but the new approach proposed here provides a conservative way to evaluate the ability of ensembles to detect patterns of interest.
Data from: Bayesian and likelihood phylogenetic reconstructions of morphological traits are not discordant when taking uncertainty into consideration: a comment on Puttick et al
Puttick et al. (2017 Proc. R. Soc. B 284, 20162290 (doi:10.1098/rspb.2016.2290)) performed a simulation study to compare accuracy among methods of inferring phylogeny from discrete morphological characters. They report that a Bayesian implementation of the Mk model (Lewis 2001 Syst. Biol. 50, 913–925 (doi:10.1080/106351501753462876)) was most accurate (but with low resolution), while a maximum-likelihood (ML) implementation of the same model was least accurate. They conclude by strongly advocating that Bayesian implementations of the Mk model should be the default method of analysis for such data. While we appreciate the authors' attempt to investigate the accuracy of alternative methods of analysis, their conclusion is based on an inappropriate comparison of the ML point estimate, which does not consider confidence, with the Bayesian consensus, which incorporates estimation credibility into the summary tree. Using simulation, we demonstrate that ML and Bayesian estimates are concordant when confidence and credibility are comparably reflected in summary trees, a result expected from statistical theory. We therefore disagree with the conclusions of Puttick et al. and consider their prescription of any default method to be poorly founded. Instead, we recommend caution and thoughtful consideration of the model or method being applied to a morphological dataset.
Data from: Incorporating uncertainty into forest management planning: timber harvest, wildfire and climate change in the boreal forest
In an effort to ensure the sustainability of their forests, boreal forest managers often use forest planning models to make future projections of timber supply and other key services, such as habitat for wildlife. Projecting the fate of these services has proven to be challenging, however, as major uncertainties exist regarding the principal drivers of boreal ecosystem dynamics, including the future spatial and temporal distribution of wildfire and timber harvesting. Existing forest planning models are not well suited to dealing with this uncertainty because they produce deterministic projections based on central tendencies of these drivers. Here we present a new approach for incorporating uncertainty into forest management planning, which we demonstrate using two landscapes in the Canadian boreal forest. Our approach takes the assumptions contained within the latest forest management plans for each of these landscapes, including parameterizations of their deterministic forest planning models, and converts these assumptions into equivalent parameterizations of a stochastic, spatially-explicit state-and-transition simulation model (STSM). We then use Monte Carlo simulations with the STSM to "stress-test" the forest management plan with respect to a range of possible future uncertainties, including uncertainties in future levels and patterns of wildfire and timber harvest, along with the possible changes in wildfire that might result from future climate change. Our analysis demonstrates the importance of incorporating stochastic variability into projections of future ecosystem condition. The STSM projections that acknowledged variability in wildfire and timber harvest differed from the deterministic forest planning model projections that were based solely on mean values. Our analysis also suggests that there is an increased risk of shortfalls in timber harvest, for both boreal landscapes, associated with future projections for changes in wildfire due to climate change, and that management strategies aimed at reducing the future level of timber harvest offer an opportunity to mitigate these risks. We believe our approach provides a new risk-based framework for incorporating uncertainty into forest management, including the effects of climate change.
Data from: Joint estimation of survival and breeding probability in female dolphins and calves with uncertainty in state assignment
While the population growth rate in long-lived species is highly sensitive to adult survival, reproduction can also significantly drive population dynamics. Reproductive parameters can be challenging to estimate as breeders and non-breeders may vary in resighting probability and reproductive status may be difficult to assess. We extended capture–recapture (CR) models previously fitted for data on other long-lived marine mammals to estimate demographic parameters while accounting for detection heterogeneity between individuals and state uncertainty regarding reproductive status. We applied this model to data on 106 adult female bottlenose dolphins observed over 13 years. The detection probability differed depending on breeding status. Concerning state uncertainty, offspring were not always sighted with their mother, and older calves were easier to detect than young-of-the-year (YOY), respectively 0.79 (95% CI 0.59–0.90) and 0.58 (95% CI 0.46–0.68). This possibly led to inaccurate reproductive status assignment of females. Adult female survival probability was high (0.97 CI 95% 0.96–0.98) and did not differ according to breeding status. Young-of-the-year and 1-year-old calves had a significantly higher survival rate than 2-year-old (respectively 0.66 CI 95% 0.50-0.78 and 0.45 CI 95% 0.29–0.61). This reduced survival is probably related to weaning, a period during which young are exposed to more risks since they lose protection and feeding from the mother. The probability of having a new YOY was high for breeding females that had raised a calf to the age of 3 or lost a 2-year-old calf (0.71, CI 95% 0.45– 0.88). Yet this probability was much lower for non-breeding females and breeding females that had lost a YOY or a 1-year-old calf (0.33, 95% CI 0.26–0.42). The multievent CR framework we used is highly flexible and could be easily modified for other study questions or taxa (marine or terrestrial) aimed at modelling reproductive parameters.
FIGURE 9 in Male secondary sexual characters resolve taxonomic uncertainty: five new species and a review of the formerly monotypic rove beetle genus Mimosticus Sharp (Coleoptera: Staphylinidae: Staphylininae)
FIGURE 9. Abparameral view of median lobe of the aedeagus, showing internal sac in situ: Mimosticus viridipennis, Sharp (A), M. tenuiformis Brunke and Solodovnikov (B), M. aeneipennis Brunke and Solodovnikov (C), M. sharpi Brunke and Solodovnikov (D). Copulatory sclerite of internal sac of M. sharpi (E), M. pseudosharpi Brunke and Solodovnikov (F) and M. latens Brunke and Solodovnikov (G). Scale bars = 0.2 mm. a—ventral apex of paramere showing peg setae-like structures, b—copulatory sclerite, arrow—apical margin of copulatory sclerite.
FIGURE 5 in Male secondary sexual characters resolve taxonomic uncertainty: five new species and a review of the formerly monotypic rove beetle genus Mimosticus Sharp (Coleoptera: Staphylinidae: Staphylininae)
FIGURE 5. Male tergite VIII of M. latens Brunke and Solodovnikov (A). Female tergite X of M. sharpi Brunke and Solodovnikov (B). Scale bars = 1 mm.
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.