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10 results for “variable ranking”
Data for Bradter, Altringham, Kunin, Thom, O'Connell & Benton: Variable ranking and selection with random forest for unbalanced data. Environmental Data Science
<p>The data are used in 'Bradter, Altringham, Kunin, Thom, O'Connell & Benton: Variable ranking and selection with random forest for unbalanced data. Environmental Data Science' and are described in the ReadMe file and in the manuscript and Supporting information.</p>
Ranking Variable Importance for US Commercial Buildings via Sensitivity Analysis of Building Energy Models
<p>This zip file contails all code and simulation results which was used for the analysis. </p>
Figure 4 from: Donegan TM (2018) What is a species? A new universal method to measure differentiation and assess the taxonomic rank of allopatric populations, using continuous variables. ZooKeys 757: 1-67. https://doi.org/10.3897/zookeys.757.10965
Figure 4 Scatter-graphs of controlled unpooled effect size (x-axis) versus statistical significance (p
Figure 7 from: Donegan TM (2018) What is a species? A new universal method to measure differentiation and assess the taxonomic rank of allopatric populations, using continuous variables. ZooKeys 757: 1-67. https://doi.org/10.3897/zookeys.757.10965
Figure 7 Graph illustrating the "hard cut-off" approaches of Isler et al. (1998) and Tobias et al. (2010). The y-axis shows the score attributed under the relevant system, weighted for 1 = diagnosability. The x-axis shows effect sizes. In addition to division by three, the Tobias et al. (2010) scores are treated more conservatively by assuming that bare pooled effect sizes are equivalent to controlled unpooled effect sizes. The scores for effect sizes at the lower end of the graph are somewhat artificial with a starting score of 0.34. This is based on the lowest recorded controlled unpooled effect size which passed a statistical significance test for biometrics (see Table 20). In reality, some lower differentiation with larger samples will be scored and some higher variation with lower samples will not be scored at all: see Tables 20–21.
Figure 1 from: Donegan TM (2018) What is a species? A new universal method to measure differentiation and assess the taxonomic rank of allopatric populations, using continuous variables. ZooKeys 757: 1-67. https://doi.org/10.3897/zookeys.757.10965
Figure 1 Graphical depiction of datasets which satisfy the Level 1–5 statistical tests addressed in this study.
Figure 6 from: Donegan TM (2018) What is a species? A new universal method to measure differentiation and assess the taxonomic rank of allopatric populations, using continuous variables. ZooKeys 757: 1-67. https://doi.org/10.3897/zookeys.757.10965
Figure 6 Graph showing relationship between sample size (x-axis) and numbers of effective SD differences between means or effect sizes (y-axis) required in order to pass a test of diagnosability shown in the legend. Dashed lines represent the four boundaries for affording scores under Tobias et al. (2010). Solid lines represent the Levels 2, 4 and 5 (Isler et al. 1998) tests of diagnosis. The dotted line is based on diagnosability using actual values, for a pairwise comparison of two populations which marginally meet the Level 5 test, where results falling outside a 95% distribution are averaged out in their linear occurrence in the data set. In reality, a data point outside of the 95% distribution could occur randomly at any point along this line, including as the first data point or as data point numbers 96–100. Differences arising from usage of unpooled versus pooled standard deviations are ignored for purposes of simplicity.
Figure 3 from: Donegan TM (2018) What is a species? A new universal method to measure differentiation and assess the taxonomic rank of allopatric populations, using continuous variables. ZooKeys 757: 1-67. https://doi.org/10.3897/zookeys.757.10965
Figure 3 Scatter-graphs showing the effects of applying different corrections of effect size on the entire biometric data set. Scatter-graphs showing the effects of applying different corrections of effect size on the entire vocal data set. Each axis shows effect size, measured in a different way. A Controlling for sample size using unpooled data – x-axis: bare unpooled effect size; y-axis: controlled unpooled effect size B Controlling for sample size using pooled data – x-axis: bare pooled effect size; y-axis: controlled pooled effect size C Using pooled versus unpooled effect sizes without controlling for sample size – x-axis: bare unpooled effect size; y-axis: bare pooled effect size D Using pooled versus unpooled effect sizes and controlling for sample size – x-axis: controlled unpooled effect size; y-axis: controlled pooled effect size.
Figure 2 from: Donegan TM (2018) What is a species? A new universal method to measure differentiation and assess the taxonomic rank of allopatric populations, using continuous variables. ZooKeys 757: 1-67. https://doi.org/10.3897/zookeys.757.10965
Figure 2 Scatter-graphs showing the effects of applying different corrections of effect size on the entire vocal data set. Each axis shows effect size, measured in a different way. A Controlling for sample size using unpooled data – x-axis: bare unpooled effect size; y-axis: controlled unpooled effect size B Controlling for sample size using pooled data – x-axis: bare pooled effect size; y-axis: controlled pooled effect size C Using pooled versus unpooled effect sizes without controlling for sample size – x-axis: bare unpooled effect size; y-axis: bare pooled effect size D Using pooled versus unpooled effect sizes and controlling for sample size – x-axis: controlled unpooled effect size; y-axis: controlled pooled effect size. A single data point of greater than 25 effect sizes was excluded to improve presentation of the results.
Figure 5 from: Donegan TM (2018) What is a species? A new universal method to measure differentiation and assess the taxonomic rank of allopatric populations, using continuous variables. ZooKeys 757: 1-67. https://doi.org/10.3897/zookeys.757.10965
Figure 5 Logarithmic plot of the same data as in Figure 4, showing controlled unpooled effect size (x-axis) versus a logarithm of statistical significance (p
Figure 8 from: Donegan TM (2018) What is a species? A new universal method to measure differentiation and assess the taxonomic rank of allopatric populations, using continuous variables. ZooKeys 757: 1-67. https://doi.org/10.3897/zookeys.757.10965
Figure 8 Justification for Euclidian summation, using a univariate/bivariate example.
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.