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67 results for “Empirical Analysis”
Data from: Empirical and theoretical study of Atelostomate (Echinoidea, Echinodermata) plate architecture: using graph analysis to reveal structural constraints
Describing patterns of connectivity among organs is essential for identifying anatomical homologies among taxa. It is also critical for revealing morphogenetic processes and the associated constraints that control the morphological diversification of clades. This is particularly relevant for studies of organisms with skeletons made of discrete elements such as arthropods, vertebrates, and echinoderms. Nonetheless, relatively few studies devoted to morphological disparity have considered connectivity patterns as a level of morphological organization or developed comparative frameworks with proper tools. Here, we analyze connectivity patterns among apical plates in Atelostomata, the most diversified clade among irregular echinoids. The clade comprises approximately 1600 fossil and Recent species (e.g., 25% of post-Paleozoic species of echinoids) and shows high levels of morphological disparity. Plate connectivity patterns were analyzed using tools and statistics of graph theory. To describe and explore the diversity of connectivity patterns among plates, we symbolized each pattern as a graph in which plates are coded as nodes that are connected pairwise by edges. We then generated a comparative framework as a morphospace of connections, in which the disparity of plate patterns observed in nature was mapped and analyzed. Main results show that apical plate patterns are both highly disparate between and within atelostomate groups and limited in number; overall, they also constitute small, compact, and simple structures compared to possible random patterns. Main traits of the evolution of apical plate patterns reveal the existence of strong morphogenetic constraints that are phylogenetically determined. In contrast, evolutionary radiations within atelostomates were accompanied by a clear increase in disparity, suggesting a release of some constraints at the origin of clades.
Data from: Species diversity can be overestimated by a fixed empirical threshold: insights from DNA barcoding of the genus Cletus (Hemiptera: Coreidae) and the meta-analysis of COI data from previous phylogeographical studies
The use of genetic distances to identify species within the framework of DNA barcoding has to some extent improved the development of biodiversity studies. However, using a fixed empirical threshold to delimit species may lead to overestimating species diversity. In this study, we use a new data set of COI sequences for 366 specimens within the genus of Cletus as well as conduct an analysis on the same genetic data for collected morphologically defined species from previous phylogeographical studies, to test whether high intraspecific genetic divergences are common with the premises of comprehensive sampling. The results indicate C. graminis Hsiao & Cheng 1964, is the same species with C. punctiger (Dallas, 1852) and should be synonymized and that the distributional record of C. pugnator (Fabricius, 1787) in China is correct. High intraspecific genetic differentiations (0%–4.35%) were found in C. punctiger. Furthermore, as to the mined data, the maximum intraspecific K2P distances of 186 species (48.44% of 384) exceed 3%, and 101 species (26.30%) can be divided into two or more clusters with a threshold of 3% in cluster analysis. If genetic distance is used to delimit species boundaries, the minimum interspecific K2P distance of the congeneric species should be considered rather than only using the fixed empirical value; otherwise, the species richness may be overestimated in some cases.
Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset
<p>This is a replication data package for a paper titled "<em>Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset</em>".</p> <p>Paper abstract: Even though Static Code Analysis (SCA) tools are integrated into many modern software building and testing pipelines, their practical impact is still seriously hindered by the excessive number of false positive warnings they usually produce. To cope with this problem, researchers have proposed several post-processing methods that aim to filter out false hits (or equivalently identify ``actionable'' warnings) after the SCA tool produced its results. However, we found that most of these approaches are targeted (i.e., deal with only a few SCA warning types) and evaluated on synthetic benchmarks or small-scale manually collected data sets (i.e., with typical sample sizes of several hundred).</p> <p>In this paper, we present a code embedding-based approach for filtering false positive warnings produced by 160 different SonarQube rule checks, one of the most widely adopted SCA tools today. We evaluate the method on a dataset containing 224,484 real-world warning samples fixed (true positive samples) or ignored (false positive samples) by the developers, which we collected from 9,958 different open-source Java projects from GitHub using a data mining approach. It is the most extensive real-world study and public dataset we know of in this area. Our method works with an accuracy of 91% (best F1-score of 81.3% and AUC of 95.3%) for the classification of SonarQube warnings.</p>
An empirical analysis of AfCFTA economic growth prospects in the SADC region Evidence using ARDL-PMG estimation techniques: Dataset
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An empirical analysis of challenges of implementing AfCFTA on South Africa's trade in the SADC region: Evidence using the PPML Estimation Technique
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Data from: Conventional analysis of trial-by-trial adaptation is biased: empirical and theoretical support using a Bayesian estimator
Research on human motor adaptation has often focused on how people adapt to self-generated or externally-influenced errors. Trial-by-trial adaptation is a person's response to self-generated errors. Externally-influenced errors applied as catch-trial perturbations are used to calculate a person's perturbation adaptation rate. Although these adaptation rates are sometimes compared to one another, we show through simulation and empirical data that the two metrics are distinct. We demonstrate that the trial-by-trial adaptation rate, often calculated as a coefficient in a linear regression, is biased under typical conditions. We tested 12 able-bodied subjects moving a cursor on a screen using a computer mouse. Statistically different adaptation rates arise when sub-sets of trials from different phases of learning are analyzed from within a sequence of movement results. We propose a new approach to identify when a person's learning has stabilized in order to identify steady-state movement trials from which to calculate a more reliable trial-by-trial adaptation rate. Using a Bayesian model of human movement, we show that this analysis approach is more consistent and provides a more confident estimate than alternative approaches. Constraining analyses to steady-state conditions will allow researchers to better decouple the multiple concurrent learning processes that occur while a person makes goal-directed movements. Streamlining this analysis may help broaden the impact of motor adaptation studies, perhaps even enhancing their clinical usefulness.
The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical datasets
<p class="CxSpFirst">The software program STRUCTURE is one of the most cited tools for determining population structure. To infer the optimal number of clusters from STRUCTURE output, the Δ<i>K</i> method is often applied. However, a recent study relying on simulated microsatellite data suggested that this method has a downward bias in its estimation of <i>K</i> and is sensitive to uneven sampling. If this finding holds for empirical datasets, conclusions about the scale of gene flow may have to be revised for a large number of studies. To determine the impact of method choice, we applied recently described estimators of <i>K</i> to re-estimate genetic structure in 41 empirical microsatellite datasets; 15 from a broad range of taxa and 26 focused on a diverse phylogenetic group, coral. We compared alternative estimates of <i>K</i> (Puechmaille statistics) with traditional (Δ<i>K</i> and posterior probability) estimates and found widespread disagreement of estimators across datasets. Thus, one estimator alone is insufficient for determining the optimal number of clusters regardless of study organism or evenness of sampling scheme. Subsequent analysis of molecular variance (AMOVA) between clustering solutions did not necessarily clarify which solution was best. To better infer population structure, we suggest a combination of visual inspection of STRUCTURE plots and calculation of the alternative estimators at various thresholds in addition to Δ<i>K</i>. Differences between estimators could reveal patterns with important biological implications, such as the potential for more population structure than previously estimated, as was the case for many studies reanalyzed here.</p>
Data from: Empirical and theoretical study of Atelostomate (Echinoidea, Echinodermata) plate architecture: using graph analysis to reveal structural constraints
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Data from: Assessment of identity disequilibrium and its relation to empirical heterozygosity fitness correlations: a meta-analysis
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Data associated with: Going round the twist – An empirical analysis of shell coiling in helicospiral gastropods
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Data from: Species diversity can be overestimated by a fixed empirical threshold: insights from DNA barcoding of the genus Cletus (Hemiptera: Coreidae) and the meta-analysis of COI data from previous phylogeographical studies
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Data from: Conventional analysis of trial-by-trial adaptation is biased: empirical and theoretical support using a Bayesian estimator
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The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical datasets
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How Magic Is Zero? An Empirical Analysis of Initial Development Releases in Three Software Package Distributions
<p>This is the replication package for "How Magic Is Zero? An Empirical Analysis of Initial Development Releases in<br> Three Software Package Distributions" accepted for publication in SoHeal 2020.</p> <p>The notebooks in "notebooks/" require the dependencies specified in "requirements.txt" to be installed.<br> They rely on data files in "data/". These data can be obtained by running the "convert.py" scripts in that folder.<br> The script requires data files from "data-raw/". These files can be generated by running "extract.py" in that folder.<br> This script requires the libraries.io data dump, as explained in the README file contained in this folder.</p> <p>If you don't have these data, or if you don't want to download them, you can ask for the required data/*.csv.gz files by email.</p> <p> </p>
Replication Package for "On Conclusion Validity of Empirical SE-studies with Sentiment Analysis Tools: Is Platform-Specific Retraining Enough?"
<p>This is the replication package for the paper "On Conclusion Validity of Empirical SE-studies with Sentiment Analysis Tools: Is Platform-Specific Retraining Enough?"</p>
Dataset for An Empirical Study of Static Analysis Tools for Secure Code Review
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Investigating Economic Growth Catalysts: An Empirical Analysis of Determinants of Domestic Private Investment in Ethiopia
<p>This is the original data that supports the findings of the research entitled "<strong>Investigating Economic Growth Catalysts: An Empirical Analysis of Determinants of Domestic Private Investment in Ethiopia"</strong></p>
The dataset of the paper titled "Human Value Requirements in AI Systems: Empirical Analysis of Amazon Alexa"
<p>This package includes the dataset (user reviews) of Amazon Alexa and the corresponding labels of human values used in this paper which was submitted to EmpiRE 2023. It also includes the details of the results with some visualizations.</p>
Trial Comparing a Strategy Based on Molecular Analysis to the Empiric Strategy in Patients With CUP
ClinicalTrials.gov study NCT01540058. IPD Sharing: Not stated. Countries: 3. Publications: 0.
Data from: When do species-tree and concatenated estimates disagree? An empirical analysis with higher-level scincid lizard phylogeny
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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.