Question Paper Solutions of chapter Subsetting of Data Analysis with R, 2nd Semester , Bachelor of Computer Application 2023-2024

Source Code for the manuscript "Characterizing Variability and Uncertainty for Parameter Subset Selection in PBPK Models" -- This R code generates the results presented in this manuscript; the zip folder contains PBPK model files (for chloroform and DCM) and corresponding scripts to compile the models, generate human equivalent doses, and run sensitivity analysis.

The Sloan Digital Sky Survey (SDSS) is a comprehensive survey of the northern sky. This dataset contains a subset of this survey, of 60247 objects classified as galaxies, it includes a CSV file with a collection of information and a set of files for each object, namely JPG image files, FITS and spectra data. This dataset is used to train and explore the astromlp-models collection of deep learning models for galaxies characterisation.

The dataset includes a CSV data file where each row is an object from the SDSS database, and with the following columns (note that some data may not be available for all objects):

• objid: unique SDSS object identifier
• mjd: MJD of observation
• plate: plate identifier
• tile: tile identifier
• fiberid: fiber identifier
• run: run number
• rerun: rerun number
• camcol: camera column
• field: field number
• ra: right ascension
• dec: declination
• class: spectroscopic class (only objetcs with GALAXY are included)
• subclass: spectroscopic subclass
• modelMag_u: better of DeV/Exp magnitude fit for band u
• modelMag_g: better of DeV/Exp magnitude fit for band g
• modelMag_r: better of DeV/Exp magnitude fit for band r
• modelMag_i: better of DeV/Exp magnitude fit for band i
• modelMag_z: better of DeV/Exp magnitude fit for band z
• redshift: final redshift from SDSS data z
• stellarmass: stellar mass extracted from the eBOSS Firefly catalog
• w1mag: WISE W1 "standard" aperture magnitude
• w2mag: WISE W2 "standard" aperture magnitude
• w3mag: WISE W3 "standard" aperture magnitude
• w4mag: WISE W4 "standard" aperture magnitude
• gz2c_f: Galaxy Zoo 2 classification from Willett et al 2013
• gz2c_s: simplified version of Galaxy Zoo 2 classification (labels set)

Besides the CSV file a set of directories are included in the dataset, in each directory you'll find a list of files named after the objid column from the CSV file, with the corresponding data, the following directories tree is available:

sdss-gs/
├── data.csv
├── fits
├── img
├── spectra
└── ssel

Where, each directory contains:

• img: RGB images from the object in JPEG format, 150x150 pixels, generated using the SkyServer DR16 API
• fits: FITS data subsets around the object across the u, g, r, i, z bands; cut is done using the ImageCutter library
• spectra: full best fit spectra data from SDSS between 4000 and 9000 wavelengths
• ssel: best fit spectra data from SDSS for specific selected intervals of wavelengths discussed by Sánchez Almeida 2010

Changelog

• v0.0.3 - Increase number of objects to ~80k.
• v0.0.2 - Increase number of objects to ~60k.
• v0.0.1 - Initial import.

Source Code for the manuscript "Characterizing Variability and Uncertainty for Parameter Subset Selection in PBPK Models" -- This R code generates the results presented in this manuscript; the zip folder contains PBPK model files (for chloroform and DCM) and corresponding scripts to compile the models, generate human equivalent doses, and run sensitivity analysis.

About

This dataset contains threads from 23 political subreddits from July 2024 - November 2024 (about a week after the US election). Use this dataset as a baseline for subsets pertaining to Reddit's opinion on the 2024 election. We recommend using each thread's metadata as guidance. E.g.,

r/politics subset controversial comments subset highly upvoted posts subset leftist/liberal threads subset

etc.

  Subreddits

These are the subreddits scraped. Each conversation's… See the full description on the dataset page: https://huggingface.co/datasets/BinghamtonUniversity/2024-election-subreddit-threads-173k.

This is a clean subset of the data that was created by the OpenML R Bot that executed benchmark experiments on binary classification task of the OpenML100 benchmarking suite with six R algorithms: glmnet, rpart, kknn, svm, ranger and xgboost. The hyperparameters of these algorithms were drawn randomly. In total it contains more than 2.6 million benchmark experiments and can be used by other researchers. The subset was created by taking 500000 results of each learner (except of kknn for which only 1140 results are available). The csv-file for each learner is a table that for each benchmark experiment has a row that contains: OpenML-Data ID, hyperparameter values, performance measures (AUC, accuracy, brier score), runtime, scimark (runtime reference of the machine), and some meta features of the dataset.OpenMLRandomBotResults.RData (format for R) contains all data in seperate tables for the results, the hyperparameters, the meta features, the runtime, the scimark results and reference results.

This dataset tracks the updates made on the dataset "NLM LitArch Open Access Subset" as a repository for previous versions of the data and metadata.

This dataset provides geospatial location data and scripts used to analyze the relationship between MODIS-derived NDVI and solar and sensor angles in a pinyon-juniper ecosystem in Grand Canyon National Park. The data are provided in support of the following publication: "Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States". The data and scripts allow users to replicate, test, or further explore results. The file GrcaScpnModisCellCenters.csv contains locations (latitude-longitude) of all the 250-m MODIS (MOD09GQ) cell centers associated with the Grand Canyon pinyon-juniper ecosystem that the Southern Colorado Plateau Network (SCPN) is monitoring through its land surface phenology and integrated upland monitoring programs. The file SolarSensorAngles.csv contains MODIS angle measurements for the pixel at the phenocam location plus a random 100 point subset of pixels within the GRCA-PJ ecosystem. The script files (folder: 'Code') consist of 1) a Google Earth Engine (GEE) script used to download MODIS data through the GEE javascript interface, and 2) a script used to calculate derived variables and to test relationships between solar and sensor angles and NDVI using the statistical software package 'R'. The file Fig_8_NdviSolarSensor.JPG shows NDVI dependence on solar and sensor geometry demonstrated for both a single pixel/year and for multiple pixels over time. (Left) MODIS NDVI versus solar-to-sensor angle for the Grand Canyon phenocam location in 2018, the year for which there is corresponding phenocam data. (Right) Modeled r-squared values by year for 100 randomly selected MODIS pixels in the SCPN-monitored Grand Canyon pinyon-juniper ecosystem. The model for forward-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle. The model for back-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle + sensor zenith angle. Boxplots show interquartile ranges; whiskers extend to 10th and 90th percentiles. The horizontal line marking the average median value for forward-scatter r-squared (0.835) is nearly indistinguishable from the back-scatter line (0.833). The dataset folder also includes supplemental R-project and packrat files that allow the user to apply the workflow by opening a project that will use the same package versions used in this study (eg, .folders Rproj.user, and packrat, and files .RData, and PhenocamPR.Rproj). The empty folder GEE_DataAngles is included so that the user can save the data files from the Google Earth Engine scripts to this location, where they can then be incorporated into the r-processing scripts without needing to change folder names. To successfully use the packrat information to replicate the exact processing steps that were used, the user should refer to packrat documentation available at https://cran.r-project.org/web/packages/packrat/index.html and at https://www.rdocumentation.org/packages/packrat/versions/0.5.0. Alternatively, the user may also use the descriptive documentation phenopix package documentation, and description/references provided in the associated journal article to process the data to achieve the same results using newer packages or other software programs.

Specific output from ERA-I reanalysis (wave model component) containing interated parameters, see https://doi.org/10.5194/os-13-1-2017

Remarks on kernels and bandwidth selection for semiparametric density product estimator method. (DOC)

Proposing relevant catalyst descriptors that can relate the information on a catalyst’s composition to its actual performance is an ongoing area in catalyst informatics, as it is a necessary step to improve our understanding on the target reactions. Herein, a small descriptor-engineered data set containing 3289 descriptor variables and the performance of 200 catalysts for the oxidative coupling of methane (OCM) is analyzed, and a descriptor search algorithm based on the workflow of the Basin-hopping optimization methodology is proposed to select the descriptors that better fit a predictive model. The algorithm, which can be considered wrapper in nature, consists of the successive generation of random-based modifications to the descriptor subset used in a regression model and adopting them depending on their effect on the model’s score. The results are presented after being tested on linear and Support Vector Regression models with average cross-validation r2 scores of 0.8268 and 0.6875, respectively.

The merra2ools dataset has been assembled through the following steps:

The MERRA-2 collections tavg1_2d_flx_Nx (Surface Flux Diagnostics), tavg1_2d_rad_Nx (Radiation Diagnostics), and tavg1_2d_slv_Nx (Single-level atmospheric state variables) downloaded from NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) (https://disc.gsfc.nasa.gov/datasets?project=MERRA-2) using GNU Wget network utility (https://disc.gsfc.nasa.gov/data-access). Every of the three collections consist of daily netCDF-4 files with 3-dimensional variables (lon x lat x hour). 
The following variables obtained from the netCDF-4 files and merged into long-term time-series:



Northward (V) and Eastward (U) wind at 10 and 50 meters (V10M, V50M, U10M, U50M, respectively), and 10-meter air temperature (T10M) from the tavg1_2d_slv_Nx collection;
Incident shortwave land (SWGDN) and Surface albedo (ALBEDO) fro...

Subset of MedleyDB: Mix audio files and Melody Annotations for the 108 files in the MedleyDB multitrack dataset containing melody.

There are 3 types of melody annotations released:

melody1: "The f0 curve of the predominant melodic line drawn from a single source"

melody2: "The f0 curve of the predominant melodic line drawn from multiple sources"

melody3: "The f0 curves of all melodic lines drawn from multiple sources"

For further details, refer to the MedleyDB website.

Further Annotation and Metadata files are version controlled and are available in the MedleyDB github repository: Metadata can be found here, Annotations can be found here.

For detailed information about the dataset, please visit MedleyDB's website.

If you make use of MedleyDB for academic purposes, please cite the following publication:

R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, "MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014.

The zip-file contains the data and code accompanying the paper 'Effects of nutrient enrichment on freshwater macrophyte and invertebrate abundance: A meta-analysis'. Together, these files should allow for the replication of the results.

The 'raw_data' folder contains the 'MA_database.csv' file, which contains the extracted data from all primary studies that are used in the analysis. Furthermore, this folder contains the file 'MA_database_description.txt', which gives a description of each data column in the database.

The 'derived_data' folder contains the files that are produced by the R-scripts in this study and used for data analysis. The 'MA_database_processed.csv' and 'MA_database_processed.RData' files contain the converted raw database that is suitable for analysis. The 'DB_IA_subsets.RData' file contains the 'Individual Abundance' (IA) data subsets based on taxonomic group (invertebrates/macrophytes) and inclusion criteria. The 'DB_IA_VCV_matrices.RData' contains for all IA data subsets the variance-covariance (VCV) matrices. The 'DB_AM_subsets.RData' file contains the 'Total Abundance' (TA) and 'Mean Abundance' (MA) data subsets based on taxonomic group (invertebrates/macrophytes) and inclusion criteria.

The 'output_data' folder contains maps with the output data for each data subset (i.e. for each metric, taxonomic group and set of inclusion criteria). For each data subset, the map contains random effects selection results ('Results1_REsel_

The 'scripts' folder contains all R-scripts that we used for this study. The 'PrepareData.R' script takes the database as input and adjusts the file so that it can be used for data analysis. The 'PrepareDataIA.R' and 'PrepareDataAM.R' scripts make subsets of the data and prepare the data for the meta-regression analysis and mixed-effects regression analysis, respectively. The regression analyses are performed in the 'SelectModelsIA.R' and 'SelectModelsAM.R' scripts to calculate the regression model results for the IA metric and MA/TA metrics, respectively. These scripts require the 'RandomAndFixedEffects.R' script, containing the random and fixed effects parameter combinations, as well as the 'Functions.R' script. The 'CreateMap.R' script creates a global map with the location of all studies included in the analysis (figure 1 in the paper). The 'CreateForestPlots.R' script creates plots showing the IA data distribution for both taxonomic groups (figure 2 in the paper). The 'CreateHeatMaps.R' script creates heat maps for all metrics and taxonomic groups (figure 3 in the paper, figures S11.1 and S11.2 in the appendix). The 'CalculateStatistics.R' script calculates the descriptive statistics that are reported throughout the paper, and creates the figures that describe the dataset characteristics (figures S3.1 to S3.5 in the appendix). The 'CreateFunnelPlots.R' script creates the funnel plots for both taxonomic groups (figures S6.1 and S6.2 in the appendix) and performs Egger's tests. The 'CreateControlGraphs.R' script creates graphs showing the dependency of the nutrient response to control concentrations for all metrics and taxonomic groups (figures S10.1 and S10.2 in the appendix).

The 'figures' folder contains all figures that are included in this study.

This data product, sourced from the NEON data portal for the purposes of the ACCE DTP tutorial, contains processed individual level data from measurements of woody individuals and shrub groups.

The Dutch CELEX data is derived from R.H. Baayen, R. Piepenbrock & L. Gulikers, The CELEX Lexical Database (CD-ROM), Release 2, Dutch Version 3.1, Linguistic Data Consortium, University of Pennsylvania, Philadelphia, PA, 1995.Apart from orthographic features, the CELEX database comprises representations of the phonological, morphological, syntactic and frequency properties of lemmata. For the Dutch data, frequencies have been disambiguated on the basis of the 42.4m Dutch Instituut voor Nederlandse Lexicologie text corpora.To make for greater compatibility with other operating systems, the databases have not been tailored to fit any particular database management program. Instead, the information is presented in a series of plain ASCII files, which can be queried with tools such as AWK and ICON. Unique identity numbers allow the linking of information from different files.This database can be divided into different subsets:· orthography: with or without diacritics, with or without word division positions, alternative spellings, number of letters/syllables;· phonology: phonetic transcriptions with syllable boundaries or primary and secondary stress markers, consonant-vowel patterns, number of phonemes/syllables, alternative pronunciations, frequency per phonetic syllable within words;· morphology: division into stems and affixes, flat or hierarchical representations, stems and their inflections;· syntax: word class, subcategorisations per word class;· frequency of the entries: disambiguated for homographic lemmata.

FDR of the WGA, the PSA, and the WEI (

     r

     = 2, 5, 10) when the prioritized region sizes were 2 Mb and 20 Mb (with adjustment to the PSA), respectively; power comparison between the WGA, the PSA, and the WEI (

     r

     = 2, 5, 10) when 14 2-Mb, 14 20-Mb, 22 2-Mb, and 22 20-Mb regions were prioritized (with adjustment to the PSA), respectively.
    (DOC)

This index was compiled by Miss Mollie Bentley from various records she has used relating to the police. These include: Almanac listings, Colonial Secretary's Office Records, Police Gazettes, various police department occurrence books and letter books, police journals, government gazettes, estimates, York police records etc.\r \r Entry is by name of policeman. Information given varies but is usually about appointments, promotions, retirements, transfers etc.\r \r The Western Australian Biographical Index (WABI) is a highly used resource at the State Library of Western Australia. A recent generous contribution by the Friends of Battye Library (FOBS) has enabled SLWA to have the original handwritten index cards scanned and later transcribed.\r \r The dataset contains: several csv files with data describing card number, card text and url link to image of the original handwritten card.\r \r The transcription was crowd-sourced and we are aware that there are some data quality issues including:\r \r * Some cards are missing\r * Transcripts are crowdsourced so may contain spelling errors and possibly missing information\r * Some cards are crossed out. Some of these are included in the collection and some are not\r * Some of the cards contain relevant information on the back (usually children of the person mentioned). This info should be on the next consecutive card\r * As the information is an index, collected in the 1970s from print material, it is incomplete. It is also unreferenced.\r It is still a very valuable dataset as it contains a wealth of information about early settlers in Western Australia. It is of particular interest to genealogists and historians.

The Sloan Digital Sky Survey (SDSS) is a comprehensive survey of the northern sky. This dataset contains a subset of this survey, of 100077 objects classified as galaxies, it includes a CSV file with a collection of information and a set of files for each object, namely JPG image files, FITS and spectra data. This dataset is used to train and explore the astromlp-models collection of deep learning models for galaxies characterisation.

The dataset includes a CSV data file where each row is an object from the SDSS database, and with the following columns (note that some data may not be available for all objects):

objid: unique SDSS object identifier

mjd: MJD of observation

plate: plate identifier

tile: tile identifier

fiberid: fiber identifier

run: run number

rerun: rerun number

camcol: camera column

field: field number

ra: right ascension

dec: declination

class: spectroscopic class (only objetcs with GALAXY are included)

subclass: spectroscopic subclass

modelMag_u: better of DeV/Exp magnitude fit for band u

modelMag_g: better of DeV/Exp magnitude fit for band g

modelMag_r: better of DeV/Exp magnitude fit for band r

modelMag_i: better of DeV/Exp magnitude fit for band i

modelMag_z: better of DeV/Exp magnitude fit for band z

redshift: final redshift from SDSS data z

stellarmass: stellar mass extracted from the eBOSS Firefly catalog

w1mag: WISE W1 "standard" aperture magnitude

w2mag: WISE W2 "standard" aperture magnitude

w3mag: WISE W3 "standard" aperture magnitude

w4mag: WISE W4 "standard" aperture magnitude

gz2c_f: Galaxy Zoo 2 classification from Willett et al 2013

gz2c_s: simplified version of Galaxy Zoo 2 classification (labels set)

Besides the CSV file a set of directories are included in the dataset, in each directory you'll find a list of files named after the objid column from the CSV file, with the corresponding data, the following directories tree is available:

sdss-gs/ ├── data.csv ├── fits ├── img ├── spectra └── ssel

Where, each directory contains:

img: RGB images from the object in JPEG format, 150x150 pixels, generated using the SkyServer DR16 API

fits: FITS data subsets around the object across the u, g, r, i, z bands; cut is done using the ImageCutter library

spectra: full best fit spectra data from SDSS between 4000 and 9000 wavelengths

ssel: best fit spectra data from SDSS for specific selected intervals of wavelengths discussed by Sánchez Almeida 2010

Changelog

v0.0.4 - Increase number of objects to ~100k.

v0.0.3 - Increase number of objects to ~80k.

v0.0.2 - Increase number of objects to ~60k.

v0.0.1 - Initial import.

Data from the IFLS, merged across waves, most outcomes taken from wave 5. Includes birth order, family structure, Big 5 Personality, intelligence tests, and risk lotteries

Table of variables

This table contains variable names, labels, and number of missing values. See the complete codebook for more.

[truncated]

Note

This dataset was automatically described using the codebook R package (version 0.8.2).