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53 results for “principal component analysis”
Beak Shape in Birds and Squid: Principal Components Analysis of 2D Landmarks
<p>R code to analyze observations of beak traces from specimens of birds and squid.</p> <p>Notes are in the code. Watch for updates.</p> <p>Where the csv files include data published by different authors, the doi references to the original publications are included in the R code. I took care to correctly download/process/transcribe where applicable, but please do notify me if there are errors.</p>
Choice of the right supporting electrolyte in electrochemical reductions: a principal component analysis
<h2>Introduction</h2> <p>This dataset contains the raw data as well as an HTML-based visualization of our dataset using Python Bokeh. We have also added a feature to highlight commercially available supporting electrolytes. The data is taken from the PubChem database. For each of the 6650 cations, the known neutral compounds in the PubChem dataset were identified with their corresponding anions. For each of these compounds, the vendor information stored in PubChem was queried.</p> <h2>Directory structure</h2> <ul> <li>Raw Data <ul> <li>[<a href="../records/10813969/files/raw_data.tar.xz?download=1" target="_blank" rel="noopener">raw_data.tar.xz</a>] Compressed directory with the output from the automated feature calculation.</li> <li>[<a href="../records/10813969/files/raw_data.csv?download=1" target="_blank" rel="noopener">raw_data.csv</a>] CSV file with the values of the calculated properties of all cations.</li> </ul> </li> <li>Visualization <ul> <li>[<a href="../records/10813969/files/pca_qac_tool_QC.html?download=1" target="_blank" rel="noopener">pca_qac_tool_QC.html</a>] HTML page with Javascript to display PC1 and PC2 for the quantum chemical PCA model.</li> <li>[<a href="../records/10813969/files/pca_qac_tool_RDKit.html?download=1" target="_blank" rel="noopener">pca_qac_tool_RDKit.html</a>] HTML page with Javascript to display PC1 and PC2 for the PCA model based on non empirical RDKit descriptors.</li> </ul> </li> <li>Tools <ul> <li>[<a href="../records/10813969/files/pca_qac_tool.py?download=1" target="_blank" rel="noopener">pca_qac_tool.py</a>] Python script to generate the HTML output using Bokeh. Depends on the data_pca_qac.csv and the data_commercial.json file.</li> <li>[<a href="../records/10813969/files/PubChem_get_Vendor_information.py?download=1" target="_blank" rel="noopener">PubChem_get_Vendor_information.py</a>] Crawler that checks a list of PubChem CIDs for net-neutral compounds and whether they are commercially available.</li> <li>[<a href="../records/10813969/files/RDKit_Descriptor-2D.py?download=1" target="_blank" rel="noopener">RDKit_Descriptor-2D.py</a>] Python script to calculate all available 2D RDkit descriptors based on a list of SMILES strings.</li> <li>[<a href="../records/10813969/files/RDKit_Descriptor-3D.py?download=1" target="_blank" rel="noopener">RDKit_Descriptor-3D.py</a>] Python script to calculate the RDKit 3D descriptors based on the CREST and ORCA GeoOpt geometries.</li> </ul> </li> </ul>
R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on derived metrics
<p>This repository contains R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on spectral and LiDAR-derived metrics. The scripts cover LiDAR data processing, canopy height model (CHM) generation, calculation of forest canopy metrics, and PCA analysis.</p>
Data behind The ALCHEMI atlas: principal component analysis reveals starburst evolution in NGC 253
<p>This depository is for additional files of the PCA paper using the ALCHEMI survey.</p> <p>std_datalist.csv: This is a csv file that includes standardized intensities for all the transitions/continua.</p> <p>pca_alchemi_corrmatrix.py: This is a python file to plot a correlation matrix of standardized intensities. It displays a transition pair when you hover the cursor on the matrix element. It uses std_datalist.csv.</p>
Fig. 5. A in Morphometric Analysis And Interrelationship Of Seven Indonesian Hornbill Species (Aves, Bucerotidae) Utilizing Principal Component And Cluster Analysis
Fig. 5. A dendrogram illustrating the relationships among the seven Indonesian hornbill species based on 14 morphometric characters, constructed using the Average Linkage model. Legend: Aa = Anthracoceros albirostris, Am = Anthracoceros malayanus, Ru = Rhyticeros undulatus, Rp = Rhyticeros plicatus, Ac = Aceros cassidix, Br = Buceros rhinoceros, dan Bb = Buceros bicornis.
Fig. 1 in Morphometric Analysis And Interrelationship Of Seven Indonesian Hornbill Species (Aves, Bucerotidae) Utilizing Principal Component And Cluster Analysis
Fig. 1. Hornbill genus grouping based on a combination of body length characters (PC1) and beak characters (PC3): A — genus Rhyticeros; B — genus Buceros; C — genus Anthracoceros.
Fig. 3 in Morphometric Analysis And Interrelationship Of Seven Indonesian Hornbill Species (Aves, Bucerotidae) Utilizing Principal Component And Cluster Analysis
Fig. 3. The combination of tail length and head length of two hornbill species within the genus Anthracoceros.
Fig. 4 in Morphometric Analysis And Interrelationship Of Seven Indonesian Hornbill Species (Aves, Bucerotidae) Utilizing Principal Component And Cluster Analysis
Fig. 4. The combination of head length and tail length of three hornbill species within the genus Rhyticeros.
Figure 3 in Interpopulation differences in shell forms of the pearl oyster, Pinctada imbricata radiata (Bivalvia: Pterioida), in the northern Persian Gulf inferred from principal component analysis and elliptic Fourier analysis
Figure 3. The first and second principal components scores of P. imbricata radiata from Hendourabi () and Lavan (▲) islands generated by PCA.
Figure 5 in Interpopulation differences in shell forms of the pearl oyster, Pinctada imbricata radiata (Bivalvia: Pterioida), in the northern Persian Gulf inferred from principal component analysis and elliptic Fourier analysis
Figure 5. Shape variation of EFA analysis in P. imbricata radiata from Lavan and Hendourabi islands.
Figure 4 in Interpopulation differences in shell forms of the pearl oyster, Pinctada imbricata radiata (Bivalvia: Pterioida), in the northern Persian Gulf inferred from principal component analysis and elliptic Fourier analysis
Figure 4. The first and second principal components scores of P. imbricata radiata from Hendourabi () and Lavan (▲) islands calculated by PCA performed on the normalized EFDs.
Fig. 7. Geometric morphometric analyses. A. Principal Component Analysis. B in Early steps in the radiation of notoungulate mammals in southern South America: A new henricosborniid from the Eocene of Patagonia
Fig. 7. Geometric morphometric analyses. A. Principal Component Analysis. B. Canonical Variate Analysis.
Fig. 2. Principal Component Analysis plot showing the 42 in Evidence of genetic connectivity between fragmented pig populations in a tropical urban city-state
Fig. 2. Principal Component Analysis plot showing the 42 individuals from the Central Catchment Nature Reserve (CCNR) and the Northeast differentiated by sex and age class. Individuals exhibiting genetic admixture are labelled. Percentage variation accounted for by each principal component is indicated in brackets.
Fig. S1. Principal Component Analysis plot showing 28 in Evidence of genetic connectivity between fragmented pig populations in a tropical urban city-state
Fig. S1. Principal Component Analysis plot showing 28 out of 42 individuals from the Central Catchment Nature Reserve (CCNR) and the Northeast with kinship values <0.2. Individuals are differentiated by sex and age class. Individuals exhibiting genetic admixture are labelled. Percentage variation accounted for by each principal component is indicated in brackets.
Surface Strain Data and Principal Component Analysis from Crystal Plasticity Simulations
<p>This is a dataset of surface strain data along y-z surfaces during tensile loading along the x direction of polycrystalline Al samples. Surface strain data are recorded during periodic intervals. Principal component analysis is implemented on the loading sequences.</p>
Dataset for: Guidelines for standardising the application of discriminant analysis of principal components to genotype data
<p><span>Data and scripts required to replicate the analyses in Thia (2022) "<span class="fontstyle0">Guidelines for standardising the application of discriminant analysis of principal components to genotype data" in <em>Molecular Ecology</em>.</span></span></p> <p><span>This study aimed to address methodological misunderstandings and misuse of the DAPC method in population genetics. The analyses are used to illustrate that for genotype data comprising <em>k</em> effective populations, there are only <em>k</em><span>−</span>1 PC axes that describe populations structure, and that are biologically informative. These PC axes are the only suitable axes for modelling the among-population differences with a DA. Use of many more than <em>k</em><span>−1 PC axes leads to decreasing biological relevancy of the final DA solution, with implications for misinterpretations of population structure.</span></span></p>
Figure 2 in Interpopulation differences in shell forms of the pearl oyster, Pinctada imbricata radiata (Bivalvia: Pterioida), in the northern Persian Gulf inferred from principal component analysis and elliptic Fourier analysis
Figure 2. Morphological measurements of the shell in P. imbricata radiata that were used in PCA.
Enhancing stock price data analysis through variants of principal component analysis
<p>The dataset used in the research titled "Enhancing stock price data analysis through variants of principal component analysis". It includes the daily closing prices of top 100 stocks in S&P500 from 29th March 2020 to 28th March 2023.</p>
Dataset for: Guidelines for standardising the application of discriminant analysis of principal components to genotype data
Open the record for dataset details and reuse information.
FIGURE 3. Principal Component Analysis for male individuals using 31 in Phenotypic divergence in large sized cricket frog species that crossed the geographical barriers within peninsular India
FIGURE 3. Principal Component Analysis for male individuals using 31 morphometric characters (Table 1) transformed to their ratio to SVL for F. kalinga from the Eastern Ghats and the Western Ghats.
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International Brain Laboratory public data
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OpenNeuro
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