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.
168
datasets available to search
ShareScore release 0.7.1
Dataset results
168 results for “Genetics: evolutionary”
Genetic assignments for Spring Evolutionary Significant Unit reanalysis, Central Valley Chinook Salmon populations, CA, 2011-2024
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is difficult to visually distinguish individuals from the different Evolutionarily Significant Units (ESU). As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to genetic lineage; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program compliance monitoring programs. The genetic lineage was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central V
Data from: Evolutionary potential and constraints in an aposematic species: Genetic correlations between warning coloration and fitness components in wood tiger moths
<p>Phenotypic data and pedigrees of two laboratory populations of wood tiger moths (<em>Arctia plantaginis</em>) of Finnish (=FIN) and Estonian (=EST) ancestry.</p> <p><strong>Pedigree: </strong><br>ID: individual identifier<br>sire = Father<br>dam=mother</p> <p><strong>Pheno.data: </strong><br>ID: individual identifier<br>Sex: 1=male; 2=female<br>hatchingdate: date when larva hatched<br>pupadate: date of pupation<br>adultdate: date of exclusion<br>Pupa.Weight: weight of pupa [mg]<br>Female.Colour = hindwing colour of females. In this species hindwing colour in females varies continuously from yellow to red. It was quantified by visual matching of hinwdings against a colour scale ranging from 1 = yellow to 6 = red. <br>Signal.Size = larva signal size. Larvae show an orange patch of variable size on the back of their black body. The size is given as number of segments<br>Egg.N = egg number produced by the individual<br>Off.N = offspring number. Larvae were counted 2-3 weeks after egg laying</p> <p> </p>
GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
<p>Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided and prepared by:</p> <p>Thüringer Fernwasserversorgung,</p> <p>IMProvT research project (S. Moritz)</p> <p><br> </p> <p>Industrial Challenge: Monitoring of drinking-water quality</p> <p> </p> <p>Description:</p> <p>Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.</p> <p>Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.</p> <p> </p> <p>Submission deadline:</p> <p>June 30, 2017</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/">http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/</a></p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Data from: How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue
<p>Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation, and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity, and the fixation of maladaptive alleles, hindered adaptation, and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.</p>
A lack of genetic diversity and minimal adaptive evolutionary divergence in introduced Mysis shrimp after 50 years
<p>The successes of introduced populations in novel habitats often provide powerful examples of evolution and adaptation. In the 1950's, opossum shrimp (<em>Mysis diluviana</em>) individuals from Clearwater Lake in Minnesota, USA were transported and introduced to Twin Lakes in Colorado, USA by fisheries managers to supplement food sources for trout. Shrimp were subsequently introduced from Twin Lakes into numerous lakes throughout Colorado. Because managers kept detailed records of the timing of the introductions, we had the opportunity to test for evolutionary divergence within a known time interval. Here, we used reduced representation genomic data to investigate patterns of genetic diversity and test for genetic divergence between populations and for evidence of adaptive evolution within the introduced populations in Colorado. We found overall very low levels of genetic diversity across all populations, with evidence for some genetic divergence between the Minnesota source population and the introduced populations in Colorado. There was also little differentiation among the Colorado populations, consistent with the known provenance of a single founding population, with the exception of the population from Gross Reservoir, Colorado. Demographic modeling suggests that the population in Gross Reservoir is of hybrid origin, with an earlier founding population from an unknown source being later supplemented from another population. Despite the overall low genetic diversity we observed, F<sub>ST</sub> outlier and environmental association analyses identified multiple loci exhibiting signatures of selection and adaptive variation related to elevation and lake depth. The success of introduced species is thought to be limited by genetic variation, but our results imply that populations with limited genetic variation can become established in a wide range of novel environments.</p>
Fig. 4 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 4. Distribution of pairwise values of genetic distances amongst species with allopatric areas: 1 — for the Western Palearctic genus Sylvaemus; 2 — for the Eastern Palearctic genera Apodemus and Alsomys; 3 — for the Palearctic Muridae as a whole, including species of genera Micromys and Mus.
Fig. 3 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 3. Distribution of pairwise intraspecies genetic distances within: 1 — the Western Palearctic genus Sylvaemus; 2 — the Eastern Palearctic genera Apodemus and Alsomys; 3 — in general for the Palearctic Muridae, including Micromys and Mus.
Fig. 2 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 2. Phenogram of genetic distances (Tamura, Nei, 1993) calculated from cytb sequences amongst representatives of the genera/subgenera Alsomys, Apodemus and genera Micromys, Mus, Rattus, constructed using the UPGMA algorithm. Representatives of the Arvicolidae and Cricetidae as well as S. s. dichrurus, S. flavicollis, S. (K.) mystacinus and S. (K.) epimelas were taken as outgroups.
Fig. 5 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 5. Distribution of pairwise genetic distances amongst taxa: 1 — Western Palearctic genus Sylvaemus, 2 — Eastern Palearctic genera Apodemus, Alsomys, 3 — Western Palearctic genus Sylvaemus and contrarily Eastern Palearctic genera Apodemus, Alsomys.
Fig. 1 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 1. Phenogram of genetic distances calculated from cytb sequences amongst representatives of the genera Sylvaemus, Rattus, constructed using the UPGMA algorithm, as mentioned above. Microtus arvalis (Arvicolidae) and Cricetus cricetus (Cricetidae) are used as outgroups.
Data from: Testing the evolutionary potential of an alpine plant: Phenotypic plasticity in response to growth temperature outweighs parental environmental effects and other genetic causes of variation
Open the record for dataset details and reuse information.
A lack of genetic diversity and minimal adaptive evolutionary divergence in introduced Mysis shrimp after 50 years
Open the record for dataset details and reuse information.
A decision-making framework to maximize the evolutionary potential of populations: Genetic and genomic insights from the common midwife toad (Alytes obstetricans) at its range limits
Open the record for dataset details and reuse information.
Data from: How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue
Open the record for dataset details and reuse information.
Data from: Resistance to gapeworm parasite has both additive and dominant genetic components in house sparrows, with evolutionary consequences for ability to respond to parasite challenge
<p>Host parasite relationships are likely to change over the coming decades in response to climate change and increased anthropogenic stressors. Understanding the genetic architecture of parasite resistance will aid prediction of species' responses to intensified parasite challenge. The gapeworm "Syngamus trachea" is prevalent in natural bird populations and causes symptomatic infections ranging from mild to severe. The parasite may affect ecological processes by curtailing bird populations and is important due to its propensity to spread to commercially farmed birds. Our large scale dataset on an insular house sparrow metapopulation in northern Norway includes information on gapeworm prevalence and infection intensity, allowing assessment of the genetics of parasite resistance in a natural system. To determine whether parasite resistance has a heritable genetic component, we performed variance component analyses using animal models. Resistance to gapeworm had substantial additive genetic and dominance variance, and genome wide association studies to identify SNPs associated with gapeworm resistance yielded multiple loci linked to immune function. Together with genome partitioning results, this indicates that resistance to gapeworm is under polygenic control in the house sparrow, and likely in other bird species. Hence, our results provide the foundation needed to study any eco-evolutionary processes related to gapeworm infection, and show that it is necessary to use methods suitable for polygenic and non-additive genetic effects on the phenotype.</p>
Data from: Can dominance genetic variance be ignored in evolutionary quantitative genetic analyses of wild populations?
<p>Accurately estimating genetic variance components is important for studying evolution in the wild. Empirical work on domesticated and wild outbred populations suggests that dominance genetic variance represents a substantial part of genetic variance, and theoretical work predicts that ignoring dominance can inflate estimates of additive genetic variance. Whether this issue is pervasive in natural systems is unknown, because we lack estimates of dominance variance in wild populations obtained <i>in situ</i>. Here, we estimate dominance and additive genetic variance, maternal variance, and other sources of non-genetic variance in 8 traits measured in over 9000 wild nestlings linked through a genetically resolved pedigree. We find that dominance variance, when estimable, does not statistically differ from zero and represents a modest amount (2-36%) of genetic variance. Simulations show that 1) inferences of all variance components for an average trait are unbiased; 2) the power to detect dominance variance is low; 3) ignoring dominance can mildly inflate additive genetic variance and heritability estimates but such inflation becomes substantial when maternal effects are also ignored. These findings hence suggest that dominance is a small source of phenotypic variance in the wild and highlight the importance of proper model construction for accurately estimating evolutionary potential.</p>
Data from: Behavioral syndromes shape evolutionary trajectories via conserved genetic architecture
Behaviors are often correlated within broader syndromes, creating the potential for evolution in one behavior to drive evolutionary changes in other behaviors. Despite demonstrations that behavioral syndromes are common, this potential for evolutionary effects has not been demonstrated. Here we show that populations of field crickets (Gryllus integer) exhibit a genetically conserved behavioral syndrome structure, despite differences in average behaviors. We found that the distribution of genetic variation and genetic covariance among behavioral traits was consistent with genes and cellular mechanisms underpinning behavioral syndromes rather than correlated selection. Moreover, divergence among populations' average behaviors was constrained by the genetically conserved behavioral syndrome. Our results demonstrate that a conserved genetic architecture linking behaviors has shaped the evolutionary trajectories of populations in disparate environments—illustrating an important way for behavioral syndromes to result in shared evolutionary fates.
Summary data for plots in: Eco-evolutionary extinction and recolonization dynamics reduce genetic load and increase time to extinction in highly inbred populations
<p>Understanding how genetic and ecological effects can interact to shape genetic loads within and across local populations is key to understanding ongoing persistence of systems that should otherwise be susceptible to extinction through mutational meltdown. Classic theory predicts short persistence times for metapopulations comprising small local populations with low connectivity, due to accumulation of deleterious mutations. Yet, some such systems have persisted over evolutionary time, implying the existence of mechanisms that allow metapopulations to avoid mutational meltdown. We first hypothesize a mechanism by which the combination of stochasticity in the numbers and types of mutations arising locally (genetic stochasticity), resulting in local extinction and recolonization through evolving dispersal, facilitates metapopulation persistence. We then test this mechanism using a spatially and genetically explicit individual-based model. We show that genetic stochasticity in highly structured metapopulations can result in local extinctions, which can favour increased dispersal, thus allowing recolonization of empty habitat patches. This causes fluctuations in metapopulation size and transient gene flow, which reduces genetic load and increases metapopulation persistence over evolutionary time. Our suggested mechanism and simulation results provide an explanation for the conundrum presented by the continued persistence of highly structured populations with inbreeding mating systems that occur in diverse taxa.</p>
Data and supplementary materials from: Large genetic divergence underpins cryptic local adaptation across ecological and evolutionary gradients
<p><span>Environmentally covarying local adaptation is a form of cryptic local adaptation in which the covariance of the genetic and environmental effects on a phenotype obscures the divergence between locally adapted genotypes. Here, we systematically document the magnitude and drivers of the genetic effect (V<sub>G</sub>) for two forms of environmentally covarying local adaptation: counter- and cogradient variation. Using a hierarchical Bayesian meta-analysis, we calculated the overall effect size of V<sub>G</sub> as 1.05 and 2.13 for populations exhibiting countergradient or cogradient variation, respectively. These results indicate that the genetic contribution to phenotypic variation represents a 1.05 to 2.13 standard deviation change in trait value between the most disparate populations depending on if populations are expressing counter- or cogradient variation. We also found that while there was substantial variance among abiotic and biotic covariates, the covariates with the largest mean effects were temperature (2.41) and gamete size (2.81). Our results demonstrate the pervasiveness and large genetic effects underlying environmentally covarying local adaptation in wild populations and highlights the importance of accounting for these effects in future studies.</span></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.