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4,694 results for “data analysis”
DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks
<p>Dataset and code used in V Wåhlstrand-Skärström, et al, "DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks", published in Journal of Microscopy. In this work, we develop a new approach for FRAP analysis based on deep neural networks. From a numerical FRAP model developed in previous work, we generate a very large set of realistic, simulated recovery curve data. The data is used for training deep neural network regression models for prediction of e.g. the diffusion coefficient. We compare the performance of the neural network estimation framework to conventional least squares estimation on simulated and <br> experimental data. Herein, the simulated FRAP data used for the training, validation, and test data sets, the experimental data, and the Matlab and Python/Tensorflow code are supplied.</p>
Flexible metagenome analysis using the MGX framework -- Benchmark data
<p>Synthetic benchmark metagenomes and annotations used to benchmark taxonomic classification approaches</p> <p>in https://doi.org/10.1186/s40168-018-0460-1</p>
Data in the analysis of Comparative Study of the Impact of Dummy Variables on Regression Coefficients and Canonical Correlation Indices: an Empirical perspective
<p>This data is used in the analysis related to the article Comparative Study of the Impact of Dummy Variables on Regression Coefficients and Canonical Correlation Indices: an Empirical perspective. The data contain Crude Oil Prices, Exchange Rates, and Dummy variables coded 1 for the era before the Covid-19 outbreak and 0 for the era of Covid-19.</p> <p> </p>
Ringlaser and seismic data at Fürstenfeldbruck and Wettzell for time-frequency analysis of microseisms
<p>Ringlaser rotation data and seismic data at Fürstenfeldbruck and Wettzell for the time-frequency analysis of seismic noise. Programs are also attached.</p>
Data from: Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study
<p><span><span>Type 2 diabetes (T2D) is a global public health challenge. Whilst the advent of genome-wide association studies has identified >400 genetic variants associated with T2D, our understanding of its biological mechanisms and translational insights is still limited. The EPIC-InterAct project, centred in 8 countries in the European Prospective Investigations into Cancer and Nutrition study, is one of the largest prospective studies of T2D. Established as a nested case-cohort study to investigate the interplay between genetic and lifestyle behavioural factors on the risk of T2D, a total of 12,403 individuals were identified as incident T2D cases and a representative sub-cohort of 16,154 individuals was selected from a larger cohort of 340,234 participants with a follow-up time of 3.99 million person-years. We describe the results from a genome-wide association analysis between more than 8.9 million SNPs and T2D risk among 22,326 individuals (9,978 cases and 12,348 non-cases) from the EPIC-InterAct study. The summary statistics to be shared provide a valuable resource to facilitate further investigations into the genetics of T2D. </span></span></p>
Data from: Individual differences in behaviour explain variation in survival: a meta-analysis
<p>Research focusing on among-individual differences in behaviour ("animal personality") has been blooming for over a decade. One of the central theories explaining the maintenance of such behavioural variation posits that individuals expressing greater "risky" behaviours should suffer higher mortality. Here, for the first time, we synthesize the existing empirical evidence for this key prediction. Our results did not support this prediction as there was no directional relationship between riskier behaviour and greater mortality; however there was a significant absolute relationship between behaviour and survival. In total, behaviour explained a significant, but small, portion (5.8%) of the variance in survival. We also found that risky (versus "shy") behavioural types live significantly longer in the wild, but not in the laboratory. This suggests that individuals expressing risky behaviours might be of overall higher quality but the lack of predation pressure and resource restrictions mask this effect in laboratory environments. Our work implies that individual differences in behaviour explain important differences in survival but not in the direction predicted by theory. Importantly, this suggests that the models predicting survival trade-offs may need revision and/or empiricists may need to reconsider their proxies of risky behaviours when testing such theory.</p>
Data from: Specimen-based analysis of morphology and the environment in ecologically dominant grasses: the power of the herbarium
Herbaria contain a cumulative sample of the world's flora, assembled by thousands of people over several hundred years. Recent advances in computation, DNA sequencing, and image manipulation have allowed us to capitalize on this resource. Using herbarium material, we conducted a species-level analysis of a major clade in the grass tribe Andropogoneae, which includes the dominant species of the world's grasslands, from the genera Andropogon, Schizachyrium, Hyparrhenia, and other groups. We imaged 188 of the 250 available species of the clade, georeferenced the specimens, and extracted climatic variables for each. Using semiand fully automated image analysis techniques, we extracted spikelet morphological characters and correlated these with environmental variables. We are currently generating chloroplast genome sequences to correct for phylogenetic covariance and here present an analysis of a subset of 81 species, representing the power of this approach. In addition to taxonomic/phylogenetic observations, we find all morphological and ecological characters are homoplasious but variable among clades. For example, sessile spikelet length is positively correlated with awn length when all accessions are considered, but when separated by clade, the relationship is positive for five sub-clades and negative for three others. Macrohair density and pedicel length were negatively correlated with precipitation.
Data from: Genomic analysis and prediction within a US public collaborative winter wheat regional testing nursery
The development of inexpensive, whole-genome profiling enables a transition to allele-based breeding using genomic prediction models. These models consider alleles shared between lines to predict phenotypes and select new lines based on estimated breeding values. This approach can leverage highly-unbalanced datasets common to breeding programs. The Southern Regional Performance Nursery (SRPN) is a public nursery established by the USDA-ARS in 1931 to characterize performance and quality of near-release wheat varieties from breeding programs in the US Central Plains. New entries are submitted annually and can be reentered only once. The trial is grown at more than 30 locations each year and lines are evaluated for grain yield, disease resistance, and agronomic traits. Overall genetic gain is measured across years by including common check cultivars for comparison. We have generated whole-genome profiles via genotyping-by-sequencing for 939 SPRN entries dating back to 1992. We measured the diversity within the nursery and have explored its potential use as a GS training population. GS prediction models across years (average r= 0.33) outperformed year-to-year phenotypic correlation for yield (r=0.27) for a majority of the years evaluated, suggesting that genomic selection has the potential to outperform low heritability selection on yield in these highly variable environments. We also examined the predictability of programs using both program-specific and whole-set training populations. Generally, the predictability of a program was similar with both approaches. These results suggest that wheat breeding programs can collaboratively leverage the immense datasets that are generated from regional testing networks.
Data from: Analysis of local-scale background concentrations of methane and other gas-phase species in the Marcellus Shale
The Marcellus Shale is a rapidly developing unconventional natural gas resource found in part of the Appalachian region. Air quality and climate concerns have been raised regarding development of unconventional natural gas resources. Two ground-based mobile measurement campaigns were conducted to assess the impact of Marcellus Shale natural gas development on local scale atmospheric background concentrations of air pollution and climate relevant pollutants in Pennsylvania. The first campaign took place in Northeastern and Southwestern PA in the summer of 2012. Compounds monitored included methane (CH4), ethane, carbon monoxide (CO), nitrogen dioxide, and Proton Transfer Reaction Mass Spectrometer (PTR-MS) measured volatile organic compounds (VOC) including oxygenated and aromatic VOC. The second campaign took place in Northeastern PA in the summer of 2015. The mobile monitoring data were analyzed using interval percentile smoothing to remove bias from local unmixed emissions to isolate local-scale background concentrations. Comparisons were made to other ambient monitoring in the Marcellus region including a NOAA SENEX flight in 2013. Local background CH4 mole fractions were 140 ppbv greater in Southwestern PA compared to Northeastern PA in 2012 and background CH4 increased 100 ppbv from 2012 to 2015. CH4 local background mole fractions were not found to have a detectable relationship between well density or production rates in either region. In Northeastern PA, CO was observed to decrease 75 ppbv over the three year period. Toluene to benzene ratios in both study regions were found to be most similar to aged rural air masses indicating that the emission of aromatic VOC from Marcellus Shale activity may not be significantly impacting local background concentrations. In addition to understanding local background concentrations the ground-based mobile measurements were useful for investigating the composition of natural gas emissions in the region.
Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales
1. Multiscale codependence analysis (MCA) quantifies the joint spatial distribution of a pair of variables in order to provide a spatially-explicit assessment of their relationships to one another. For the sake of simplicity, the original definition of MCA only considered a single response variable (e.g. a single species). However, that definition would limit the application of MCA when many response variables are studied jointly, for example when one wants to study the effect of the environment on the spatial organisation of a multi-species community in an explicit manner. 2. In the present paper, we generalize MCA to multiple response variables. We conducted a simulation study to assess the statistical properties (i.e. type I error rate and statistical power) of multivariate MCA (mMCA) and found that it had honest type I error rate and sufficient statistical power for practical purposes, even with modest sample sizes. We also exemplified mMCA by applying it to two ecological data sets. 3. The simulation study confirmed the adequacy of mMCA from a statistical standpoint: it has honest type I error rates and sufficient power to be useful in practice. Using mMCA, we were able to detect variation in fish community structure along the Doubs River (in France), which was associated with large spatial structures in the variation of physical and chemical variables related to water quality. Also, mMCA usefully described the spatial variation of an Oribatid mite community structure associated with a gradient of water content superimposed on various smaller-scale spatial features associated with vegetation cover in the peat blanket surrounding Lac Geai (in Québec, Canada). 4. In addition to demonstrating the soundness of mMCA in theory and practice, we further discuss the strengths and assumptions of mMCA and describe other potential scenarios where it would be helpful to biologists interested in assessing influence of environmental conditions on community structure in a spatially-explicit way.
Data from: Spider webs, stable isotopes and molecular gut content analysis: multiple lines of evidence support trophic niche differentiation in a community of Hawaiian spiders
1. Adaptive radiations are typically characterized by niche partitioning among their constituent species. Trophic niche partitioning is particularly important in predatory animals, which rely on limited food resources for survival. 2. We test for trophic niche partitioning in an adaptive radiation of Hawaiian Tetragnatha spiders, which have diversified in situ on the Hawaiian Islands. We focus on a community of nine species belonging to two different clades, one web building and the other actively hunting, which co-occur in wet forest on East Maui. We hypothesize that trophic niches differ significantly both 1) among species within a clade, indicating food resource partitioning, and 2) between the two clades, corresponding with their differences in foraging strategy. 3. To assess niches of the spider species, we measure a) web architecture, the structure of the hunting tool, and b) site choice, the physical placement of the web in the habitat. We then test whether differences in these parameters translate into meaningful differences in trophic niche by measuring c) stable isotope signatures of carbon and nitrogen in the spiders' tissues, and d) gut content of spiders based on metabarcoding data. 4. We find significant interspecific differences in web architecture and site choice. Importantly, these differences are reflected in stable isotope signatures among the five web-building species, as well as significant isotopic differences between web-builders and active hunters. Gut content data also show interspecific and inter-clade differences. Pairwise overlaps of web architecture between species are positively correlated with overlaps of isotopic signature. 5. Our results reveal trophic niche partitioning among species within each clade, as well as between the web-building and actively hunting clades. Based on the correlation between web architecture and stable isotopes, it appears that the isotopic signatures of spiders' tissues are influenced by architectural differences among their webs. Our findings indicate an important link between web structure, microhabitat preference and diet in the Hawaiian Tetragnatha.
Data from: Population genomic analysis of Brook Trout Salvelinus fontinalis in Pennsylvania's Appalachian region
Informed conservation of stream fishes requires detailed understanding of the effects of both natural processes and anthropogenic activities on genetic diversity. Brook Trout Salvelinus fontinalis, a salmonid native to eastern North America, typically resides in cold, high-quality stream ecosystems. The species has not only faced historical anthropogenic pressures, but also confronts current and future pressures. In a genetic analysis we used a reduced representation sequencing method (ddRADseq) to characterize 63 individuals from 23 streams where Brook Trout are native in the Appalachian region of Pennsylvania. A total of 2,590 loci passed filtering criteria, and 53% displayed significant association with a major stream drainage basin (Susquehanna or Allegheny; mean FST = 0.085). Mapping of the sequencing reads to the Atlantic Salmon Salmo salar genome revealed no clustering of high interdrainage FST values to specific genome regions. Evidence for genetic heterogeneity within each drainage basin was also detected. Stepwise regression of observed heterozygosity against geographic and environmental features revealed that drainage basin and effective area of watersheds were significant predictors of observed heterozygosity of Brook Trout within streams. Natural features such as waterfalls and major drainage basin, as well as the effects of dams and acid-mine drainage have fragmented habitat and shaped genetic diversity within Brook Trout populations in the Appalachian region of Pennsylvania, overall indicating the vulnerability of this species to increased industrialization.
Data from: A new digital method of data collection for spatial point pattern analysis in grassland communities
<p>A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of <i>Stipa</i><i> grandis</i> in grazed and ungrazed <i>S. grandis</i> communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of <i>S. grandis</i> by using the pair correlation function <i>g</i>(<i>r</i>) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of <i>S. grandis</i> were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.</p>
Data from: Novel sources of (co)variation in nestling begging behavior and hunger at different biological levels of analysis
<p>Biological hypotheses predicting patterns of offspring begging typically concern the covariance with hunger and/or development at specific hierarchical levels. For example, hunger drives within-individual patterns of begging, but begging also drives food intake among individuals within broods, and begging and food intake can covary positively or negatively among genotypes or broods. Testing biological phenomena that occur at multiple levels therefore requires the partitioning of covariance between traits of interest to ensure that each level-specific relationship is appropriately assessed. We performed a partial cross-fostering study on a wild population of great tits (Parus major), then used multivariate mixed-models to partition variation and covariation in nestling begging effort and two metrics of nestling hunger within versus among individual nestlings and broods. At the within-individual level, we found that nestlings begged more intensely when hungrier (positive correlation between begging and hunger). However, among individuals, nestlings that were fed more frequently also begged more intensely on average (negative correlation between begging and hunger). Variation in nestling mass did not give rise to the negative correlation between begging and hunger among nestlings, but we did find that lighter nestlings begged more intensely than their heavier biological siblings, suggesting that this effect may be driven by a genetic component linked to offspring size. Our study illustrates how patterns of covariance can differ across biological levels of analysis and addresses biological mechanisms that could produce these previously obscured patterns.</p>
Data from: Long-term population dynamics of dreissenid mussels (Dreissena polymorpha and D. rostriformis): a cross-system analysis
Dreissenid mussels (including the zebra mussel Dreissena polymorpha and the quagga mussel D. rostriformis) are among the world's most notorious invasive species, with large and widespread ecological and economic effects. However, their long‐term population dynamics are poorly known, even though these dynamics are critical to determining impacts and effective management. We gathered and analyzed 67 long‐term (>10 yr) data sets on dreissenid populations from lakes and rivers across Europe and North America. We addressed five questions: (1) How do Dreissena populations change through time? (2) Specifically, do Dreissena populations decline substantially after an initial outbreak phase? (3) Do different measures of population performance (biomass or density of settled animals, veliger density, recruitment of young) follow the same patterns through time? (4) How do the numbers or biomass of zebra mussels or of both species combined change after the quagga mussel arrives? (5) How does body size change over time? We also considered whether current data on long‐term dynamics of Dreissena populations are adequate for science and management. Individual Dreissena populations showed a wide range of temporal dynamics, but we could detect only two general patterns that applied across many populations: (1) Populations of both species increased rapidly in the first 1–2 yr after appearance, and (2) quagga mussels appeared later than zebra mussels and usually quickly caused large declines in zebra mussel populations. We found little evidence that combined Dreissena populations declined over the long term. Different measures of population performance were not congruent; the temporal dynamics of one life stage or population attribute cannot generally be accurately inferred from the dynamics of another. We found no consistent patterns in the long‐term dynamics of body size. The long‐term dynamics of Dreissena populations probably are driven by the ecological characteristics (e.g., predation, nutrient inputs, water temperature) and their temporal changes at individual sites rather than following a generalized time course that applies across many sites. Existing long‐term data sets on dreissenid populations, although clearly valuable, are inadequate to meet research and management needs. Data sets could be improved by standardizing sampling designs and methods, routinely collecting more variables, and increasing support.
Data from: Analysis of evolutionary relationships provides new clues to the origins of weedy rice
<p>Weedy rice (Oryza sativa f. spontanea) is considered to be a pest in modern rice production systems because it competes for resources, has poor yield characteristics, and subsequently has a negative effect on rice grain yield. The evolutionary relationships among weedy rice, landrace rice, improved rice cultivars, and wild rice are largely unknown. In this study, we conducted a population genetic analysis based on neutral markers and gene haplotypes in 524 rice accessions and a comparative transcriptomic analysis using 15 representative samples. The results showed that weedy rice populations have the highest level of genetic diversity (He=0.8386), and can be divided into two groups (japonica-type and indica-type). The japonica-type weedy rice accessions from HLJ, JL, LN, and NX provinces clustered with the landraces grown in these same provinces. The indica-types from JS province also clustered with the indica-type landraces from JS province. Comparative transcriptome analysis of weedy rice populations, improved rice populations. and landrace rice from HLJ, JL and LN provinces showed that the weedy rice still clustered with the landrace rice, and that the improved rice lines comprise a single population. Thirty-two differentially expressed genes were shared by the improved rice and landrace rice groups as well as between the improved rice and weedy rice groups. Using GO analysis, we identified 19 shared GO terms in the improved rice and landrace rice groups as well as between the improved rice and weedy rice groups. Our results suggest that weedy rice populations in China have diverse origins, and comparative transcriptome analysis of different types of rice from HLJ, JL, and LN suggests that improved rice populations have become a medium or end point in the evolution of weedy rice, which provides a new perspective for the study of weedy rice origins and lays a solid foundation for rice breeding.</p>
Data from: Enzyme polymorphism, oxygen and injury: a lipidomic analysis of flight-induced oxidative damage in a SDH-polymorphic insect
When active tissues receive insufficient oxygen to meet metabolic demand, succinate accumulates and has two fundamental effects: it causes ischemia-reperfusion injury while also activating the hypoxia-inducible factor pathway (HIF). The Glanville fritillary butterfly (Melitaea cinxia) possesses a balanced polymorphism in Sdhd, shown previously to affect HIF pathway activation and tracheal morphology and used here to experimentally test the hypothesis that variation in succinate dehydrogenase affects oxidative injury. We stimulated butterflies to fly continuously in a respirometer (3 min duration), which typically caused episodes of exhaustion and recovery, suggesting a potential for cellular injury from hypoxia and reoxygenation in flight muscles. Indeed, flight muscle from butterflies flown on consecutive days had lipidomic profiles similar to rested paraquat-injected butterflies, but distinct from rested untreated butterflies. Many butterflies showed a decline in flight metabolic rate (FMR) on Day 2, and there was a strong inverse relationship between the ratio of Day 2 to Day 1 FMR and the abundance of sodiated adducts of phosphatidylcholines and coenzyme Q (CoQ). This result is consistent with elevation of sodiated lipids caused by disrupted intracellular ion homeostasis in mammalian tissues after hypoxia-reperfusion. Butterflies carrying the Sdhd M allele had higher abundance of lipid markers of cellular damage, but the association was reversed in field-collected butterflies, where focal individuals typically flew for seconds at a time rather than continuously. These results indicate that Glanville fritillary flight muscles can be injured by episodes of high exertion, but injury severity appears to be determined by an interaction between SDH genotype and behavior (prolonged vs. intermittent flight).
Data from: Reticulate evolution within a spruce (Picea) species complex revealed by population genomic analysis
The role of reticulation in the rapid diversification of organisms is attracting greater attention in evolutionary biology. Here, we report a population genomics approach to test the role of hybridization and introgression in the evolution of the Picea likiangensis species complex. Based on 84,793 SNPs detected in transcriptomes of 82 trees collected from 35 localities, we identified 18 hybrids (including backcrosses) distributed within the range boundaries of the four taxa. Coalescent simulations, for each pair of taxa and for all taxa taken together, rejected several tree-like divergence models and supported instead a reticulate evolution model with secondary contacts occurring during Pleistocene glacial cycles after initial divergence in the late Pliocene. Significant gene flow occurred among some taxa after secondary contact according to an analysis based on modified ABBA-BABA statistics that accommodated a rapid diversification scenario. A novel finding was that introgression between certain taxa can contribute to increasing divergence (and possibly reproductive isolation) between those taxa and other taxa within a complex at some loci. These results illuminate the reticulate nature of evolution within the P. likiangensis complex and highlight the value of population genomic data in detecting the effects of introgression in the rapid diversification of related taxa.
Informing antenna design for Global 21-cm experiments using a simulated Bayesian data analysis pipeline (supplementary data)
<p>These are the posterior files, foreground simulation data sets and chromaticity factor values used to produce the results for <a href="https://arxiv.org/abs/2106.10193">arXiv:2106.10193</a>.</p> <p>Plots of the fitted signal and residuals for each case are included, as is a plotting function to reproduce key figures.</p> <p>Naming conventions:</p> <ul> <li>f0: Centre frequency of the 21cm signal present in the simulated data</li> <li>A: Amplitude of the simulated 21cm signal present in the simulated data</li> <li>M_sig: Model being fit to the data includes a 21cm signal</li> <li>M_nosig: Model being fit to the data is a foreground only</li> </ul> <p>Software used:</p> <ul> <li><a href="https://github.com/PolyChord/PolyChordLite/tree/839292290a7747dbee82933bb9f7f955ac45c3ca">PolyChord</a></li> </ul> <p> </p>
Data from: Genomic analysis reveals hidden biodiversity within colugos, the sister group to primates
Colugos are one of the most poorly studied mammals despite their centrality to resolving supraordinal primate relationships. Two described species of these gliding mammals are the sole living members of the order Dermoptera, distributed throughout Southeast Asia. We generated a draft genome sequence for a Sunda colugo and a Philippine colugo reference alignment, and used these to identify colugo-specific genetic changes that were enriched in sensory and musculo-skeletal related genes that likely underlie their nocturnal and gliding adaptations. Phylogenomic analysis and catalogs of rare genomic changes overwhelmingly support the contested hypothesis that colugos are the sister group to primates (Primatomorpha), to the exclusion of treeshrews. We captured ~140-kb of orthologous sequence data from colugo museum specimens sampled across their range, and identified large genetic differences between many geographically isolated populations that may result in a 300% increase in the number of recognized colugo species. Our results identify conservation units to mitigate future losses of this enigmatic mammalian order.
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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.