MAPE and PB statistics for IBFI compared with other imputation methods (mean, median, mode, PMM, and Hotdeck) for 20% missingness of type MAR and all parameters tested (RN, TH, TC, RH, and PR).

ABOUT DATASET

This is the R markdown notebook. It contains step by step guide for working on Data Analysis with R. It helps you with installing the relevant packages and how to load them. it also provides a detailed summary of the "dplyr" commands that you can use to manipulate your data in the R environment.

Anyone new to R and wish to carry out some data analysis on R can check it out!

Big R (Rural King Supply) operates a retail business model focused on serving the needs of rural communities with a broad range of products for farm, home, and outdoor living. The Big R (Rural King Supply) strategy centers around offering a diverse product assortment, including farm supplies, animal feed, pet food, hardware, tools, lawn and garden supplies, clothing, footwear, sporting goods, and even some food items. You can download the complete list of key information about Big R (Rural King Supply) locations, contact details, services offered, and geographical coordinates, beneficial for various applications like store locators, business analysis, and targeted marketing. The Big R (Rural King Supply) data you can download includes:

Identification & Location:

Store_name store_number, address, city, state, zip_code, latitude, longitude, geo_accuracy, country_code, county,

Contact Information:

Phone_number,

Operational Detail & Services

Store_hours,

Statistical details of the SRGC time series dataset.

This dataset contains estimated total organic carbon (TOC) calculated using the delta log R (dlogR) method, developed by Passey and others (1990), within BasinMod modeling software for the Tuscaloosa marine shale (TMS) in southwestern Mississippi, U.S.A. BasinMod is a modeling software developed by Platte River Associates, Inc. Using version 2021-03-03 of the software, sonic and resistivity logs and a background TOC of 0.8 weight percent were used to calculate dlogR TOC in the TMS high resistivity zone (HRZ) after baselining these two log curves in a non-source rock interval within the TMS. Due to variable mineralogy between the upper and lower sections of the TMS, two baselines were selected to determine whether the position of the baseline affects dlogR TOC results: 1) an upper baseline interval that extends from the top of the TMS and is characterized by relatively low resistivity and limited resistivity variability; and 2) a lower baseline interval that begins at the base of the upper baseline interval and extends to the top of the HRZ and exhibits increasing resistivity, relative to the upper interval, that transitions to the higher values in the HRZ. The dlogR calculated TOC values from these two baselining methods show that using the lower baseline interval results in an improved comparison of measured TOC to calculated TOC.

The dataset includes a PDF file containing the results and an Excel file with the following tables:

Table S1 Results of comparing the performance of MetaFetcheR to MetaboAnalystR using Diamanti et al. Table S2 Results of comparing the performance of MetaFetcheR to MetaboAnalystR for Priolo et al. Table S3 Results of comparing the performance of MetaFetcheR to MetaboAnalyst 5.0 webtool using Diamanti et al. Table S4 Results of comparing the performance of MetaFetcheR to MetaboAnalyst 5.0 webtool for Priolo et al. Table S5 Data quality test results for running 100 iterations on HMDB database. Table S6 Data quality test results for running 100 iterations on KEGG database. Table S7 Data quality test results for running 100 iterations on ChEBI database. Table S8 Data quality test results for running 100 iterations on PubChem database. Table S9 Data quality test results for running 100 iterations on LIPID MAPS database. Table S10 The list of metabolites that were not mapped by MetaboAnalystR for Diamanti et al. Table S11 An example of an input matrix for MetaFetcheR. Table S12 Results of comparing the performance of MetaFetcheR to MS_targeted using Diamanti et al. Table S13 Data set from Diamanti et al. Table S14 Data set from Priolo et al. Table S15 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Diamanti et al. Table S16 Results of comparing the performance of MetaFetcheR to CTS using LIPID MAPS identifiers available in Diamanti et al. Table S17 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Priolo et al. Table S18 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Priolo et al. (See the "index" tab in the Excel file for more information)

Small-compound databases contain a large amount of information for metabolites and metabolic pathways. However, the plethora of such databases and the redundancy of their information lead to major issues with analysis and standardization. Lack of preventive establishment of means of data access at the infant stages of a project might lead to mislabelled compounds, reduced statistical power and large delays in delivery of results.

We developed MetaFetcheR, an open-source R package that links metabolite data from several small-compound databases, resolves inconsistencies and covers a variety of use-cases of data fetching. We showed that the performance of MetaFetcheR was superior to existing approaches and databases by benchmarking the performance of the algorithm in three independent case studies based on two published datasets.

The dataset was originally published in DiVA and moved to SND in 2024.

This is a complete list of R/C Theatres locations along with their geographical coordinates. R/C Theatres is a chain of movie theatres located in New England. They also offer private rentals for meetings and parties.

China R & D: Full-time Equivalent: High Technology Industry (Statistics Cover Large and Medium-sized Enterprises) data was reported at 139.800 Person-Years tt in 2023. This records an increase from the previous number of 125.400 Person-Years tt for 2022. China R & D: Full-time Equivalent: High Technology Industry (Statistics Cover Large and Medium-sized Enterprises) data is updated yearly, averaging 52.600 Person-Years tt from Dec 1995 (Median) to 2023, with 23 observations. The data reached an all-time high of 139.800 Person-Years tt in 2023 and a record low of 5.780 Person-Years tt in 1995. China R & D: Full-time Equivalent: High Technology Industry (Statistics Cover Large and Medium-sized Enterprises) data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OS: Research and Development: Full-time Equivalent: High Technology Industry.

The MERGE dataset is a collection of audio, lyrics, and bimodal datasets for conducting research on Music Emotion Recognition. A complete version is provided for each modality. The audio datasets provide 30-second excerpts for each sample, while full lyrics are provided in the relevant datasets. The amount of available samples in each dataset is the following:

• MERGE Audio Complete: 3554
• MERGE Audio Balanced: 3232
• MERGE Lyrics Complete: 2568
• MERGE Lyrics Balanced: 2400
• MERGE Bimodal Complete: 2216
• MERGE Bimodal Balanced: 2000

Additional Contents

Each dataset contains the following additional files:

• av_values: File containing the arousal and valence values for each sample sorted by their identifier;
• tvt_dataframes: Train, validate, and test splits for each dataset. Both a 70-15-15 and a 40-30-30 split are provided.

Metadata

A metadata spreadsheet is provided for each dataset with the following information for each sample, if available:

• Song (Audio and Lyrics datasets) - Song identifiers. Identifiers starting with MT were extracted from the AllMusic platform, while those starting with A or L were collected from private collections;
• Quadrant - Label corresponding to one of the four quadrants from Russell's Circumplex Model;
• AllMusic Id - For samples starting with A or L, the matching AllMusic identifier is also provided. This was used to complement the available information for the samples originally obtained from the platform;
• Artist - First performing artist or band;
• Title - Song title;
• Relevance - AllMusic metric representing the relevance of the song in relation to the query used;
• Duration - Song length in seconds;
• Moods - User-generated mood tags extracted from the AllMusic platform and available in Warriner's affective dictionary;
• MoodsAll - User-generated mood tags extracted from the AllMusic platform;
• Genres - User-generated genre tags extracted from the AllMusic platform;
• Themes - User-generated theme tags extracted from the AllMusic platform;
• Styles - User-generated style tags extracted from the AllMusic platform;
• AppearancesTrackIDs - All AllMusic identifiers related with a sample;
• Sample - Availability of the sample in the AllMusic platform;
• SampleURL - URL to the 30-second excerpt in AllMusic;
• ActualYear - Year of song release.

Citation

If you use some part of the MERGE dataset in your research, please cite the following article:

Louro, P. L. and Redinho, H. and Santos, R. and Malheiro, R. and Panda, R. and Paiva, R. P. (2024). MERGE - A Bimodal Dataset For Static Music Emotion Recognition. arxiv. URL: https://arxiv.org/abs/2407.06060.

BibTeX:

@misc{louro2024mergebimodaldataset,
title={MERGE -- A Bimodal Dataset for Static Music Emotion Recognition},
author={Pedro Lima Louro and Hugo Redinho and Ricardo Santos and Ricardo Malheiro and Renato Panda and Rui Pedro Paiva},
year={2024},
eprint={2407.06060},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2407.06060},
}

Acknowledgements

This work is funded by FCT - Foundation for Science and Technology, I.P., within the scope of the projects: MERGE - DOI: 10.54499/PTDC/CCI-COM/3171/2021 financed with national funds (PIDDAC) via the Portuguese State Budget; and project CISUC - UID/CEC/00326/2020 with funds from the European Social Fund, through the Regional Operational Program Centro 2020.

Renato Panda was supported by Ci2 - FCT UIDP/05567/2020.

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This Tutorial consists of four tutorials that deal with integrating the statistical programming language R with ArcGIS for Desktop. Several concepts are covered which include configuring ArcGIS with R, writing basic R scripts, writing R scripts that work with ArcGIS data, and constructing R Tools for use within ArcGIS Pro. It is recommended that the tutorials are completed in sequential order. Each of the four tutorials (as well as a version of this document), can viewed directly from your Web browser by following the links below. However, you must obtain a complete copy of the tutorial files by downloading the latest release (or by cloning the tutorial repository on GitHub) if you wish to follow the tutorials interactively using ArcGIS and R software, along with pre-configured sample data.To download the tutorial documents and datasets, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/r-arcgis-tutorials.gitSoftware & Solutions Used: ArcGIS Pro 3.4 Internet browser (e.g., Mozilla Firefox, Google Chrome, Safari) R Statistical Computing Language – version 4.3.3 R-ArcGIS Bindings – version 1.0.1.311RStudio Desktop – version 2024.09.0+375Time to Complete: 2.5 h (excludes installation time)File Size: 115 MBDate Created: November 2017Last Updated: December 2024

Ryder System total long term liabilities for the quarter ending March 31, 2025 were $10.221B, a 2.64% decline year-over-year. Ryder System total long term liabilities for 2024 were $10.284B, a 13.51% increase from 2023. Ryder System total long term liabilities for 2023 were $9.06B, a 11.27% increase from 2022. Ryder System total long term liabilities for 2022 were $8.142B, a 3.91% increase from 2021.

Complete datasets and R scripts related to article published in Applied Vegetation Science: "Soil phosphorous availability determines the contribution of small, individual grassland remnants to the conservation of landscape-scale biodiversity" 1. plot_metadata.csv - for all 162 vegetation plots the dataset provides a metadata on the geographical and environmental characteristics of the plot. 2. soil.csv - for all 162 vegetation plots the dataset provides the raw data on each of the four measured soil characteristics: Phosphorous, pH, Carbon and Nitrogen. 3. vegdat.csv - for all 162 vegetation plots, this dataset provides the recorded abundance (ACFOR scale) of all 174 recorded species. 4. planttraits.csv - for all 174 species, this dataset lists the level of habitat specialisation. 5. PlueBaeten_analyses.nb.html R scripts on the full statistical analyses of the data in html format 6. PlueBaeten_analyses.Rmd R scripts on the full statistical analyses of the data in R Markdown format

The GOES-R Advanced Baseline Imager (ABI) Total Precipitable Water product contains an image with pixel values identifying the integrated column water vapor amount from the surface to a height corresponding to an atmospheric pressure of 300 hPa. The units of measure for the total precipitable water value is millimeters. The product includes three types of data quality information. One describes the overall quality of the data pixels, providing an assessment of the derived stability indices data values for on-earth pixels. The second provides information about the quality of the physical retrieval for on-earth pixels, identifying failure conditions. The third provides information about the quality of the first guess skin temperature for on-earth pixels, identifying temperature threshold failure conditions for on-earth pixels. The Derived Stability Indices product images are produced on the ABI fixed grid at 10 km resolution for Full Disk, CONUS, and Mesoscale coverage regions from GOES East and West. Product data is produced under the following conditions: Clear sky; Geolocated source data to local zenith angles of 80 degrees for both daytime and nighttime conditions.

China R & D Projects: Full-time Equivalent: Higher Education data was reported at 83.500 Person-Years tt in 2023. This records an increase from the previous number of 72.600 Person-Years tt for 2022. China R & D Projects: Full-time Equivalent: Higher Education data is updated yearly, averaging 33.500 Person-Years tt from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 83.500 Person-Years tt in 2023 and a record low of 22.480 Person-Years tt in 2005. China R & D Projects: Full-time Equivalent: Higher Education data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OS: Research and Development: Full-time Equivalent.

China R & D: Full-time Equivalent: High Technology Industry: Electronic Components data was reported at 8.374 Person-Years tt in 2017. This records a decrease from the previous number of 8.501 Person-Years tt for 2016. China R & D: Full-time Equivalent: High Technology Industry: Electronic Components data is updated yearly, averaging 8.096 Person-Years tt from Dec 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 8.501 Person-Years tt in 2016 and a record low of 6.517 Person-Years tt in 2011. China R & D: Full-time Equivalent: High Technology Industry: Electronic Components data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OS: Research and Development: Full-time Equivalent: High Technology Industry.

Input data for R script.

(ecological model: AICc = 203.3, r = 0.557; complete model: AICc = 166.8, r = 0.767).

File List 1. MCMC_simple.r (MD5: e4e851bf6ceed824025ae6f5f7d414cc) 2. run_sims_double.r (MD5: ca75d138f8ef241ca1427d6e6ff70399) 3. run_sims_double_PO.R (MD5: 49dce2eaee28bfd2640405a9bbea0a1c) 4. run_sims_double_Mis.R (MD5: 25f00cb61b9e943cc6414ffaca9f01ef) 5. run_sims_single.R (MD5: dd43419d1e359e2b9348ba3cc6b51715) 6. run_double_only_hab.R (MD5: 3fdd916184d710f198f081d41f986e95) 7. run_double_only_nohab.R (MD5: 2c264f955d2e2911508a0cf58eb07cc1) 8. run_double_only_pooled.R (MD5: be47889c03d27aab86ace13dc279a5ac) 9. PolarData_Bins234.RData (MD5: 85faa1757702073f34f3525b70816bf3) 10. Statistics_OnEffort_ByEvent.csv (MD5: cb2fa96fc0627d412b2b85327fe63262) 11. ObsInfo.csv (MD5: 27027e601f80d14895d51d98fc90a206) 12. Scale.csv (MD5: a4e223f8fcf3063ba2c8236b697ac52b) Description The simulation study uses six R scripts to simulate data and perform MCMC. As suggested by their names, the scripts run_sims_single.R, run_sims_double.r, run_sims_double_PO.R, and run_sims_double_Mis.R are essentially drivers to run simulations for single observer surveys, double observer surveys (with both misclassification and "unknown" species), double observer surveys with partial species observation, and double observer surveys with misclassification (but no unknown species category), respectively. These scripts call functions to simulate and analyze data, all of which are included in MCMC_simple.r. The seal analysis relies heavily on the R package, hierarchicalDS (v2.0), which is available as a supplementary .zip file, as well on the comprehensive R archive network (CRAN; cran.r-project.org). The package contains all additional requisite R code together with help files. The scripts, run_double_only_hab.R, run_double_only_nohab.R, and run_double_only_pooled.R format data and call hierarchicalDS to run models λ(cov, j), λ(j) and λ(pooled), respectively. Each of these scripts references several data files. In particular, PolarData_Bins234.Rdata includes double observer data, where each row represents a different observation, and columns consist of "Transect" (transect identifier; transects 1–19 included double observers), "Match" (which delineates individual groups of seals), "Observer" (a factor variable giving observer ID), "Obs" (= TRUE if the observer saw the group of seals and FALSE otherwise), "Species" (an integer giving the species seen; 0 = missed, 1 = bearded, 2 = ribbon, 3 = ringed, 4 = spotted, 5 = unknown), Alt.500 (an indicator for whether altitude own was 500 feet (Alt.500 = 1), or 400 feet (Alt.500 = 0), "Pos" (an indicator for whether the observation was made in the back seat (Pos = 1) or front seat (Pos = 0), "Distance" (distance bin 2, 3, or 4), and "Group" (integer giving the number of individuals associated with the current observation). We use data from the field "SumOfSumOfElapDist" in file Statistics_OnEffort_ByEvent.csv to give distance (in km) for each transect; data from the fields "Event", "Side," and "Observer" in the file ObsInfo.csv provide information on the transect, seat in the aircraft (L = left [not used in the analysis], R = front seat, S = back seat), and observer ID associated with each transect; data from the fields Bin, Altitude, and BinWidth_m from the file Scale.csv provide information on the relative bin width (in m) for different bins and altitudes.

R-Code for the analyses of the tent trial data

This dataset tracks annual total revenue from 1990 to 2021 for Blue Springs R-IV School District