Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members. In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path.

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.

Ask

How do annual members and casual riders use Cyclistic bikes differently?

Guiding Question:

What is the problem you are trying to solve?
  How do annual members and casual riders use Cyclistic bikes differently?
How can your insights drive business decisions?
  The insight will help the marketing team to make a strategy for casual riders

Prepare

Guiding Question:

Where is your data located?
  Data located in Cyclistic organization data.

How is data organized?
  Dataset are in csv format for each month wise from Financial year 22.

Are there issues with bias or credibility in this data? Does your data ROCCC? 
  It is good it is ROCCC because data collected in from Cyclistic organization.

How are you addressing licensing, privacy, security, and accessibility?
  The company has their own license over the dataset. Dataset does not have any personal information about the riders.

How did you verify the data’s integrity?
  All the files have consistent columns and each column has the correct type of data.

How does it help you answer your questions?
  Insights always hidden in the data. We have the interpret with data to find the insights.

Are there any problems with the data?
  Yes, starting station names, ending station names have null values.

Process

Guiding Question:

What tools are you choosing and why?
  I used R studio for the cleaning and transforming the data for analysis phase because of large dataset and to gather experience in the language.

Have you ensured the data’s integrity?
 Yes, the data is consistent throughout the columns.

What steps have you taken to ensure that your data is clean?
  First duplicates, null values are removed then added new columns for analysis.

How can you verify that your data is clean and ready to analyze? 
 Make sure the column names are consistent thorough out all data sets by using the “bind row” function.

Make sure column data types are consistent throughout all the dataset by using the “compare_df_col” from the “janitor” package.
Combine the all dataset into single data frame to make consistent throught the analysis.
Removed the column start_lat, start_lng, end_lat, end_lng from the dataframe because those columns not required for analysis.
Create new columns day, date, month, year, from the started_at column this will provide additional opportunities to aggregate the data
Create the “ride_length” column from the started_at and ended_at column to find the average duration of the ride by the riders.
Removed the null rows from the dataset by using the “na.omit function”
Have you documented your cleaning process so you can review and share those results? 
  Yes, the cleaning process is documented clearly.

Analyze Phase:

Guiding Questions:

How should you organize your data to perform analysis on it? The data has been organized in one single dataframe by using the read csv function in R Has your data been properly formatted? Yes, all the columns have their correct data type.

What surprises did you discover in the data?
  Casual member ride duration is higher than the annual members
  Causal member widely uses docked bike than the annual members
What trends or relationships did you find in the data?
  Annual members are used mainly for commute purpose
  Casual member are preferred the docked bikes
  Annual members are preferred the electric or classic bikes
How will these insights help answer your business questions?
  This insights helps to build a profile for members

Share

Guiding Quesions:

Were you able to answer the question of how ...

• Objective: To predict the prices of houses in the City of Melbourne
• Approach: Using Decision Tree and Random Forest https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Ffc6fb7d0bd8e854daf7a6f033937a397%2FPicture1.png?generation=1693489996707941&alt=media" alt="">
• Data Cleaning:
• Date column is shown as a character vector which is converted into a date vector using the library ‘lubridate’
• We create a new column called age to understand the age of the house as it can be a factor in the pricing of the house. We extract the year from column ‘Date’ and subtract it from the column ‘Year Built’
• We remove 11566 records which have missing values
• We drop columns which are not significant such as ‘X’, ‘suburb’, ‘address’, (we have kept zipcode as it serves the purpose in place of suburb and address), ‘type’, ‘method’, ‘SellerG’, ‘date’, ‘Car’, ‘year built’, ‘Council Area’, ‘Region Name’
• We split the data into ‘train’ and ‘test’ in 80/20 ratio using the sample function
• Run libraries ‘rpart’, ‘rpart.plot’, ‘rattle’, ‘RcolorBrewer’
• Run decision tree using the rpart function. ‘Price’ is the dependent variable https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F6065322d19b1376c4a341a4f22933a51%2FPicture2.png?generation=1693490067579017&alt=media" alt="">
• Average price for 5464 houses is $1084349
• Where building area is less than 200.5, the average price for 4582 houses is $931445. Where building area is less than 200.5 & age of the building is less than 67.5 years, the avg price for 3385 houses is $799299.6.
• $4801538 is the Highest average prices of 13 houses where distance is lower than 5.35 & building are is >280.5
  https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F136542b7afb6f03c1890bae9b07dc464%2FDecision%20Tree%20Plot.jpeg?generation=1693490124083168&alt=media" alt="">
• We use the caret package for tuning the parameter and the optimal complexity parameter found is 0.01 with RMSE 445197.9 https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Feb1633df9dd61ba3a51574873b055fd0%2FPicture3.png?generation=1693490163033658&alt=media" alt="">
• We use library (Metrics) to find out the RMSE ($392107), MAPE (0.297) which means an accuracy of 99.70% and MAE ($272015.4)
• Variables ‘postcode’, longitude and building are the most important variables
• Test$Price indicates the actual price and test$predicted indicates the predicted price for particular 6 houses. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F620b1aad968c9aee169d0e7371bf3818%2FPicture4.png?generation=1693490211728176&alt=media" alt="">
• We use the default parameters of random forest on the train data https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fe9a3c3f8776ee055e4a1bb92d782e19c%2FPicture5.png?generation=1693490244695668&alt=media" alt="">
• The below image indicates that ‘Building Area’, ‘Age of the house’ and ‘Distance’ are the most important variables that affect the price of the house. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fc14d6266184db8f30290c528d72b9f6b%2FRandom%20Forest%20Variables%20Importance.jpeg?generation=1693490284920037&alt=media" alt="">
• Based on the default parameters, RMSE is $250426.2, MAPE is 0.147 (accuracy is 99.853%) and MAE is $151657.7
• Error starts to remain constant between 100 to 200 trees and thereafter there is almost minimal reduction. We can choose N tree=200. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F365f9e8587d3a65805330889d22f9e60%2FNtree%20Plot.jpeg?generation=1693490308734539&alt=media" alt="">
• We tune the model and find mtry = 3 has the lowest out of bag error
• We use the caret package and use 5 fold cross validation technique
• RMSE is $252216.10 , MAPE is 0.146 (accuracy is 99.854%) , MAE is $151669.4
• We can conclude that Random Forest give us more accurate results as compared to Decision Tree
• In Random Forest , the default parameters (N tree = 500) give us lower RMSE and MAPE as compared to N tree = 200. So we can proceed with those parameters.

Version 5 release notes:
Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.
Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.
Version 4 release notes:
Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics.
Version 3 release notes:
Add data for 2016.Order rows by year (descending) and ORI.Version 2 release notes:
Fix bug where Philadelphia Police Department had incorrect FIPS county code.
The Arrests by Age, Sex, and Race data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1980-2015 into a single file. These files are quite large and may take some time to load.
All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

I did not make any changes to the data other than the following. When an arrest column has a value of "None/not reported", I change that value to zero. This makes the (possible incorrect) assumption that these values represent zero crimes reported. The original data does not have a value when the agency reports zero arrests other than "None/not reported." In other words, this data does not differentiate between real zeros and missing values. Some agencies also incorrectly report the following numbers of arrests which I change to NA: 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99999, 99998.

To reduce file size and make the data more manageable, all of the data is aggregated yearly. All of the data is in agency-year units such that every row indicates an agency in a given year. Columns are crime-arrest category units. For example, If you choose the data set that includes murder, you would have rows for each agency-year and columns with the number of people arrests for murder. The ASR data breaks down arrests by age and gender (e.g. Male aged 15, Male aged 18). They also provide the number of adults or juveniles arrested by race. Because most agencies and years do not report the arrestee's ethnicity (Hispanic or not Hispanic) or juvenile outcomes (e.g. referred to adult court, referred to welfare agency), I do not include these columns.

To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. Please note that some of the FIPS codes have leading zeros and if you open it in Excel it will automatically delete those leading zeros.

I created 9 arrest categories myself. The categories are:
Total Male JuvenileTotal Female JuvenileTotal Male AdultTotal Female AdultTotal MaleTotal FemaleTotal JuvenileTotal AdultTotal ArrestsAll of these categories are based on the sums of the sex-age categories (e.g. Male under 10, Female aged 22) rather than using the provided age-race categories (e.g. adult Black, juvenile Asian). As not all agencies report the race data, my method is more accurate. These categories also make up the data in the "simple" version of the data. The "simple" file only includes the above 9 columns as the arrest data (all other columns in the data are just agency identifier columns). Because this "simple" data set need fewer columns, I include all offenses.

As the arrest data is very granular, and each category of arrest is its own column, there are dozens of columns per crime. To keep the data somewhat manageable, there are nine different files, eight which contain different crimes and the "simple" file. Each file contains the data for all years. The eight categories each have crimes belonging to a major crime category and do not overlap in crimes other than with the index offenses. Please note that the crime names provided below are not the same as the column names in the data. Due to Stata limiting column names to 32 characters maximum, I have abbreviated the crime names in the data. The files and their included crimes are:

Index Crimes
MurderRapeRobberyAggravated AssaultBurglaryTheftMotor Vehicle TheftArsonAlcohol CrimesDUIDrunkenness
LiquorDrug CrimesTotal DrugTotal Drug SalesTotal Drug PossessionCannabis PossessionCannabis SalesHeroin or Cocaine PossessionHeroin or Cocaine SalesOther Drug PossessionOther Drug SalesSynthetic Narcotic PossessionSynthetic Narcotic SalesGrey Collar and Property CrimesForgeryFraudStolen PropertyFinancial CrimesEmbezzlementTotal GamblingOther GamblingBookmakingNumbers LotterySex or Family CrimesOffenses Against the Family and Children
Other Sex Offenses
ProstitutionRapeViolent CrimesAggravated AssaultMurderNegligent ManslaughterRobberyWeapon Offenses
Other CrimesCurfewDisorderly ConductOther Non-trafficSuspicion
VandalismVagrancy
Simple
This data set has every crime and only the arrest categories that I created (see above).
If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

This dataset contains all the data and code needed to reproduce the analyses in the manuscript: Penn, H. J., & Read, Q. D. (2023). Stem borer herbivory dependent on interactions of sugarcane variety, associated traits, and presence of prior borer damage. Pest Management Science. https://doi.org/10.1002/ps.7843 Included are two .Rmd notebooks containing all code required to reproduce the analyses in the manuscript, two .html file of rendered notebook output, three .csv data files that are loaded and analyzed, and a .zip file of intermediate R objects that are generated during the model fitting and variable selection process. Notebook files 01_boring_analysis.Rmd: This RMarkdown notebook contains R code to read and process the raw data, create exploratory data visualizations and tables, fit a Bayesian generalized linear mixed model, extract output from the statistical model, and create graphs and tables summarizing the model output including marginal means for different varieties and contrasts between crop years. 02_trait_covariate_analysis.Rmd: This RMarkdown notebook contains R code to read raw variety-level trait data, perform feature selection based on correlations between traits, fit another generalized linear mixed model using traits as predictors, and create graphs and tables from that model output including marginal means by categorical trait and marginal trends by continuous trait. HTML files These HTML files contain the rendered output of the two RMarkdown notebooks. They were generated by Quentin Read on 2023-08-30 and 2023-08-15. 01_boring_analysis.html 02_trait_covariate_analysis.html CSV data files These files contain the raw data. To recreate the notebook output the CSV files should be at the file path project/data/ relative to where the notebook is run. Columns are described below. BoredInternodes_26April2022_no format.csv: primary data file with sugarcane borer (SCB) damage Columns A-C are the year, date, and location. All location values are the same. Column D identifies which experiment the data point was collected from. Column E, Stubble, indicates the crop year (plant cane or first stubble) Column F indicates the variety Column G indicates the plot (integer ID) Column H indicates the stalk within each plot (integer ID) Column I, # Internodes, indicates how many internodes were on the stalk Columns J-AM are numbered 1-30 and indicate whether SCB damage was observed on that internode (0 if no, 1 if yes, blank cell if that internode was not present on the stalk) Column AN indicates the experimental treatment for those rows that are part of a manipulative experiment Column AO contains notes variety_lookup.csv: summary information for the 16 varieties analyzed in this study Column A is the variety name Column B is the total number of stalks assessed for SCB damage for that variety across all years Column C is the number of years that variety is present in the data Column D, Stubble, indicates which crop years were sampled for that variety ("PC" if only plant cane, "PC, 1S" if there are data for both plant cane and first stubble crop years) Column E, SCB resistance, is a categorical designation with four values: susceptible, moderately susceptible, moderately resistant, resistant Column F is the literature reference for the SCB resistance value Select_variety_traits_12Dec2022.csv: variety-level traits for the 16 varieties analyzed in this study Column A is the variety name Column B is the SCB resistance designation as an integer Column C is the categorical SCB resistance designation (see above) Columns D-I are continuous traits from year 1 (plant cane), including sugar (Mg/ha), biomass or aboveground cane production (Mg/ha), TRS or theoretically recoverable sugar (g/kg), stalk weight of individual stalks (kg), stalk population density (stalks/ha), and fiber content of stalk (percent). Columns J-O are the same continuous traits from year 2 (first stubble) Columns P-V are categorical traits (in some cases continuous traits binned into categories): maturity timing, amount of stalk wax, amount of leaf sheath wax, amount of leaf sheath hair, tightness of leaf sheath, whether leaf sheath becomes necrotic with age, and amount of collar hair. ZIP file of intermediate R objects To recreate the notebook output without having to run computationally intensive steps, unzip the archive. The fitted model objects should be at the file path project/ relative to where the notebook is run. intermediate_R_objects.zip: This file contains intermediate R objects that are generated during the model fitting and variable selection process. You may use the R objects in the .zip file if you would like to reproduce final output including figures and tables without having to refit the computationally intensive statistical models. binom_fit_intxns_updated_only5yrs.rds: fitted brms model object for the main statistical model binom_fit_reduced.rds: fitted brms model object for the trait covariate analysis marginal_trends.RData: calculated values of the estimated marginal trends with respect to year and previous damage marginal_trend_trs.rds: calculated values of the estimated marginal trend with respect to TRS marginal_trend_fib.rds: calculated values of the estimated marginal trend with respect to fiber content Resources in this dataset:Resource Title: Sugarcane borer damage data by internode, 1993-2021. File Name: BoredInternodes_26April2022_no format.csvResource Title: Summary information for the 16 sugarcane varieties analyzed. File Name: variety_lookup.csvResource Title: Variety-level traits for the 16 sugarcane varieties analyzed. File Name: Select_variety_traits_12Dec2022.csvResource Title: RMarkdown notebook 2: trait covariate analysis. File Name: 02_trait_covariate_analysis.RmdResource Title: Rendered HTML output of notebook 2. File Name: 02_trait_covariate_analysis.htmlResource Title: RMarkdown notebook 1: main analysis. File Name: 01_boring_analysis.RmdResource Title: Rendered HTML output of notebook 1. File Name: 01_boring_analysis.htmlResource Title: Intermediate R objects. File Name: intermediate_R_objects.zip

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

Abstract (en): For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 7 release notes:Add data from 2018Version 6 release notes:Adds data in the following formats: SPSS and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 5 release notes: Adds data for 1960-1974 and 2017. Note: many columns (including number of female officers) will always have a value of 0 for years prior to 1971.Removes support for .csv and .sav files.Adds a number_of_months_reported variable for each agency-year. A month is considered reported if the month_indicator column for that month has a value of "normal update" or "reported, not data."The formatting of the monthly data has changed from wide to long. This means that each agency-month has a single row. The old data had each agency being a single row with each month-category (e.g. jan_officers_killed_by_felony) being a column. Now there will just be a single column for each category (e.g. officers_killed_by_felony) and the month can be identified in the month column. This also results in most column names changing. As such, be careful when aggregating the monthly data since some variables are the same every month (e.g. number of officers employed is measured annually) so aggregating will be 12 times as high as the real value for those variables. Adds a date column. This date column is always set to the first of the month. It is NOT the date that a crime occurred or was reported. It is only there to make it easier to create time-series graphs that require a date input.All the data in this version was acquired from the FBI as text/DAT files and read into R using the package asciiSetupReader. The FBI also provided a PDF file explaining how to create the setup file to read the data. Both the FBI's PDF and the setup file I made are included in the zip files. Data is the same as from NACJD but using all FBI files makes cleaning easier as all column names are already identical. Version 4 release notes: Add data for 2016.Order rows by year (descending) and ORI.Version 3 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The LEOKA data sets contain highly detailed data about the number of officers/civilians employed by an agency and how many officers were killed or assaulted. All the data was acquired from the FBI as text/DAT files and read into R using the package asciiSetupReader. The FBI also provided a PDF file explaining how to create the setup file to read the data. Both the FBI's PDF and the setup file I made are included in the zip files. About 7% of all agencies in the data report more officers or civilians than population. As such, I removed the officers/civilians per 1,000 population variables. You should exercise caution if deciding to generate and use these variables yourself. Several agency had impossible large (>15) officer deaths in a single month. For those months I changed the value to NA. See the R code for a complete list. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data.The UCR Handbook (https://ucr.fbi.gov/additional-ucr-publications/ucr_handbook.pdf/view) describes the LEOKA data as follows:"The UCR Program collects data from all contributing agencies ... on officer line-of-duty deaths and assaults. Reporting agencies must submit data on ... their own duly sworn officers feloniously or accidentally killed or assaulted in the line of duty. The purpose of this data collection is to identify situations in which officers are killed or assaulted, describe the incidents statistically, and publish the data to aid agencies in developing policies to improve officer safety."... agencies must record assaults on sworn officers. Reporting agencies must count all assaults that resulted in serious injury or assaults in which a weapon was used that could have caused serious injury or death. They must include other assaults not causing injury if the assault involved more than mere verbal abuse or minor resistance to an arrest. In other words, agencies must include in this section all assaults on officers, whether or not the officers sustained injuries." Law enforcement agencies in the United States.Smallest Geographic Unit: Police agency jurisdiction

User Agreement, Public Domain Dedication, and Disclaimer of Liability. By accessing or downloading the data or work provided here, you, the User, agree that you have read this agreement in full and agree to its terms. The person who owns, created, or contributed a work to the data or work provided here dedicated the work to the public domain and has waived his or her rights to the work worldwide under copyright law. You can copy, modify, distribute, and perform the work, for any lawful purpose, without asking permission. In no way are the patent or trademark rights of any person affected by this agreement, nor are the rights that any other person may have in the work or in how the work is used, such as publicity or privacy rights. Pacific Science & Engineering Group, Inc., its agents and assigns, make no warranties about the work and disclaim all liability for all uses of the work, to the fullest extent permitted by law. When you use or cite the work, you shall not imply endorsement by Pacific Science & Engineering Group, Inc., its agents or assigns, or by another author or affirmer of the work. This Agreement may be amended, and the use of the data or work shall be governed by the terms of the Agreement at the time that you access or download the data or work from this Website. Description This dataverse contains the data referenced in Rieth et al. (2017). Issues and Advances in Anomaly Detection Evaluation for Joint Human-Automated Systems. To be presented at Applied Human Factors and Ergonomics 2017. Each .RData file is an external representation of an R dataframe that can be read into an R environment with the 'load' function. The variables loaded are named ‘fault_free_training’, ‘fault_free_testing’, ‘faulty_testing’, and ‘faulty_training’, corresponding to the RData files. Each dataframe contains 55 columns: Column 1 ('faultNumber') ranges from 1 to 20 in the “Faulty” datasets and represents the fault type in the TEP. The “FaultFree” datasets only contain fault 0 (i.e. normal operating conditions). Column 2 ('simulationRun') ranges from 1 to 500 and represents a different random number generator state from which a full TEP dataset was generated (Note: the actual seeds used to generate training and testing datasets were non-overlapping). Column 3 ('sample') ranges either from 1 to 500 (“Training” datasets) or 1 to 960 (“Testing” datasets). The TEP variables (columns 4 to 55) were sampled every 3 minutes for a total duration of 25 hours and 48 hours respectively. Note that the faults were introduced 1 and 8 hours into the Faulty Training and Faulty Testing datasets, respectively. Columns 4 to 55 contain the process variables; the column names retain the original variable names. Acknowledgments. This work was sponsored by the Office of Naval Research, Human & Bioengineered Systems (ONR 341), program officer Dr. Jeffrey G. Morrison under contract N00014-15-C-5003. The views expressed are those of the authors and do not reflect the official policy or position of the Office of Naval Research, Department of Defense, or US Government.

This data package is associated with the publication “Water Column Respiration in the Yakima River Basin is Explained by Temperature, Nutrients and Suspended Solids” submitted to EGU Biogeochemistry (Laan et al. 2025). In this research, water column respiration (ERwc) data, surface water chemistry data, organic matter (OM) chemistry data, and publicly available geospatial data were used in analysis to evaluate the variability in ERwc at 47 sites across the Yakima River basin in Washington, USA.In addition to this readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions.The data package includes the data inputs, and outputs, and R scripts to reproduce all the analyses performed in the manuscript and create manuscript figures. The data package is comprised of three main folders (Code, Data, and Figures). The Code folder is comprised of four scripts and three analysis-specific subfolders that contain the R scripts to perform the analyses described in the publication and create publication figures. The Data folder is comprised of two “.csv” files and four subfolders that contain data input and output files. The Published_Data folder contains a readme that directs the user to download the appropriate files and add to this folder when using scripts. The Figures folder includes figures from the manuscript in “.pdf” and “.png” formats and a folder with intermediate figure files. This data package is associated with a GitHub repository which can be found at https://github.com/river-corridors-sfa/rcsfa-RC2-SPS-ERwc.

GOLD daytime disk scan (DAY) measurements are used to derive the ratio of the column abundance of thermospheric O relative to N2, conventionally referred to as O/N2 or ΣO/N2, but abbreviated to ON2 for the GOLD data product. ON2 is derived from dayside Level 1C data after binning pixels 2x2 for approximately 68 disk scan measurements performed per day by GOLD in nominal operation.

Algorithm heritage

The disk ON2 retrieval algorithm was originally developed by Computational Physics, Inc. (CPI) for use with GUVI and SSUSI radiance images (Strickland et al., 1995). The GOLD implementation of this algorithm takes advantage of GOLD's ability to transmit the full spectrum to maximize the signal-to-noise ratio and eliminate atomic emission lines that contaminate the N2 LBH bands (e.g., N I 149.3 nm). This algorithm has been extensively documented and applied over the past several decades (e.g., Evans et al. [1995]; Christensen et al. [2003]; Strickland et al. [2004]) and Correira et al. [2021] describe the implementation used for the GOLD data.

Algorithm theoretical basis

The geophysical parameter retrieved, O/N2, is the ratio of the vertical column density of O relative to N2, defined at a standard reference N2 depth of 1017 cm-2, which is chosen to minimize uncertainty in the derived O/N2. It is retrieved directly from the ratio of the O I 135.6 nm and N2 LBH band intensities measured by GOLD on the dayside disk (DAY measurement mode). The AURIC atmospheric radiance model (Strickland et al. [1999]) is used to derive this relationship as a function of solar zenith angle and to create the look-up table (LUT) used by the algorithm.

References

Christensen, A. B., et al. (2003), Initial observations with the Global Ultraviolet Imager (GUVI) in the NASA TIMED satellite mission, J. Geophys. Res., vol. 108, NO. A12, 1451, doi:10.1029/2003JA009918.

Correira, J., Evans, J. S., Lumpe, J. D., Krywonos, A., Daniell, R., Veibell, V., et al. (2021). Thermospheric composition and solar EUV flux from the Globalscale Observations of the Limb and Disk (GOLD) mission. Journal of Geophysical Research: Space Physics, 126, e2021JA029517. https://doi.org/10.1029/2021JA029517

Evans, J. S., D. J. Strickland and R. E. Huffman (1995), Satellite remote sensing of thermospheric O/N2 and solar EUV: 2. Data analysis, J. Geophys. Res., vol. 100, NO. A7, pages 12,227-12,233.

Strickland, D. J., R. R. Meier, R. L. Walterscheid, J. D. Craven, A. B. Christensen, L. J. Paxton, D. Morrison, and G. Crowley (2004), Quiet-time seasonal behavior of the thermosphere seen in the far ultraviolet dayglow, J. Geophys. Res., vol. 109, A01302, doi:10.1029/2003JA010220.

Strickland, D.J., J. Bishop, J.S. Evans, T. Majeed, P.M. Shen, R.J. Cox, R. Link, and R.E. Huffman (1999), Atmospheric Ultraviolet Radiance Integrated Code (AURIC): theory, software architecture, inputs and selected results, JQSRT, 62, 689-742.

Strickland, D. J., J. S. Evans, and L. J. Paxton (1995), Satellite remote sensing of thermospheric O/N2 and solar EUV: 1. Theory, J. Geophys. Res., 110, A7, pages 12,217-12,226.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 13 release notes:Adds 2022 dataVersion 12 release notes:Adds 2021 data.Version 11 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will (probably, I haven't seen confirmation either way) be the last LEOKA data they release. Changes .rda file to .rds.Version 10 release notes:Changes release notes description, does not change data.Version 9 release notes:Adds data for 2019.Version 8 release notes:Fix bug for years 1960-1971 where the number of months reported variable was incorrectly down by 1 month. I recommend caution when using these years as they only report either 0 or 12 months of the year, which differs from every other year in the data. Added the variable officers_killed_total which is the sum of officers_killed_by_felony and officers_killed_by_accident.Version 7 release notes:Adds data from 2018Version 6 release notes:Adds data in the following formats: SPSS and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 5 release notes: Adds data for 1960-1974 and 2017. Note: many columns (including number of female officers) will always have a value of 0 for years prior to 1971. This is because those variables weren't collected prior to 1971. These should be NA, not 0 but I'm keeping it as 0 to be consistent with the raw data. Removes support for .csv and .sav files.Adds a number_of_months_reported variable for each agency-year. A month is considered reported if the month_indicator column for that month has a value of "normal update" or "reported, not data."The formatting of the monthly data has changed from wide to long. This means that each agency-month has a single row. The old data had each agency being a single row with each month-category (e.g. jan_officers_killed_by_felony) being a column. Now there will just be a single column for each category (e.g. officers_killed_by_felony) and the month can be identified in the month column. This also results in most column names changing. As such, be careful when aggregating the monthly data since some variables are the same every month (e.g. number of officers employed is measured annually) so aggregating will be 12 times as high as the real value for those variables. Adds a date column. This date column is always set to the first of the month. It is NOT the date that a crime occurred or was reported. It is only there to make it easier to create time-series graphs that require a date input.All the data in this version was acquired from the FBI as text/DAT files and read into R using the package asciiSetupReader. The FBI also provided a PDF file explaining how to create the setup file to read the data. Both the FBI's PDF and the setup file I made are included in the zip files. Data is the same as from NACJD but using all FBI files makes cleaning easier as all column names are already identical. Version 4 release notes: Add data for 2016.Order rows by year (descending) and ORI.Version 3 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The LEOKA data sets contain highly detailed data about the number of officers/civilians employed by an agency and how many officers were killed or assaulted. All the data was acquired from the FBI as text/DAT files and read into R using the package asciiSetupReader. The FBI also provided a PDF file explaining how to create the setup file to read the data. Both the FBI's PDF and the setup file I made are included in the zip files. About 7% of all agencies in the data report more officers or civilians than population. As such, I removed the officers/civilians per 1,000 population variables. You should exercise caution if deciding to generate and use these variables yourself. Several agency had impossible large (>15) officer deaths in a single month. For those months I changed the value to NA. The UCR Handbook (https://ucr.fbi.gov/additional-ucr-publications/ucr_handbook.pdf/view) describes the LEOKA data as follows:"The UCR Program collects data from all contributing agencies ... on officer line-of-duty deaths and assaults. Reporting agencies must submit data on ... their own duly sworn officers feloniously or accidentally killed or assaulted in the line of duty. The purpose of this data collect

compiled by Jonathan Groß
ORCID 0000-0002-2564-9530
jgross85 [AT] gmail [DOT] com

1. Introduction

1.1. General Disclaimer

This index file was created as a private research project with the goal to make the wealth of information in Wilhelm Heinrich Roscher's "Detailed Lexicon of Greek and Roman Mythology" (Ausführliches Lexikon der griechischen und römischen Mythologie) more accessible to everybody.

Roscher's Lexicon, originally published by B. G. Teubner in Leipzig from 1884 to 1937, is the most complete resource on Greek and Roman mythological names to date and also encompasses mythological (and religious) subjects from Sumeran, Akkadian, Babylonian, Hittite, Egyptian, Celtic, Germanic and other neighbouring cultures.

The Lexicon was reprinted three times in the latter half of the 20th century (by Georg Olms in Hildesheim), even after its pictorial content had been superseded by the Lexicon Iconographicum Mythologiae Classicae (1981–1999, 2009). Unfortunately, since the last reprint of 1992/1993, Roscher's Lexicon has been out of stock at both publishers (Olms and De Gruyter Brill).

Since the late 2000s, Roscher's Lexicon (the 6 main volumes and 4 supplements) was digitised by initiatives such as Google Books and the Internet Archive, and its contents can now be viewed there (with OCR text). One prominent use case of these scans is the German Wikipedia, where more than 2,500 pages use Roscher's Lexicon as a reference with a link to a scanned page in the Internet Archive.

1.2. Licensing

This dataset is released under the CC0 1.0 Universal License (https://creativecommons.org/publicdomain/zero/1.0/deed.en). I chose this license in order to maximise the usefulness of the data to everybody.

Use and reuse of this data is strongly encouraged, and one use case has already been initiated by the author:
-https://mythogram.wikibase.cloud/wiki/Project:Roscher%27s_Lexicon_of_Mythology (presentation of the information from the index file as Linked Open Data, finished on 28 May 2024 with emendations until 5 August 2024)

Although not technically required by the licensing agreement, the author would appreciate being informed about other uses of the data.

The contents of the Lexicon themselves are mostly in the Public Domain as of 2024. Additionally, many of the smaller entries do not reach the threshold of originality. This includes most of the cover addenda.

2. Description of Data

The index file is formatted as tabular data. This file was created with LibreOffice Calc (originally in 7.6.6.3, in LibreOffice Calc 24.2 as of version 1.1 of this file) and is stored in its native .ods format. For convenience, an .xlsx version is also provided. Both files are practically identical, but the .ods file is to be regarded as the 'original'.

Data is stored in several tabs:
(A) 'main alphabet' with the headwords of the main work (excluding addenda and corrigenda from the covers; for these see below).
(B) 'cover addenda' with the additional entries
(C) 'authors' with information on the authors
(D) 'fascicles' with information on the individual issues of the Lexicon

The Tabs are available separately as .csv files (with tab separation, so strictly speaking it should be .tsv).

Tabs A and B are almost identical in structure, with the columns:
A id = unique entry ID (not authoritative, just a means to identify individual entries)
B headword = lemma of the entry as stated by the Lexicon
C subject_type = classification scheme for the subject matter of the article (again, not authoritative and in places even contentious)
D vol = volume number
E fascicle = issue number (not found in most exemplars, assigned according to my own research)
F date = publication date of the entry (inferred from the issue date of the fascicle)
G–H col1,2 = start and end column
I colspan = span of columns
J–M author1,2,3,4 = author of the entry (please refer to Tab C, column A)
N entry_type = classification of entry (article, cross-reference, addendum, correction)
O scan = URL to a scan of the start column in the Internet Archive
P Wikidata = ID of the Wikidata item representing the subject (incomplete as of Version 1.1)
Q FactGrid = ID of the FactGrid item representing the subject (mostly missing as of Version 1.1)
R Mythogram = ID of the (bibliographic) Mythogram item representing the Lexicon entry
S redirect_target = target headword as stated, if the entry is a cross-reference
T remarks = remarks on the entry or subject (such as 'non-entity', 'duplicate', 'double lemma')
U PD = if the entry is in the Public Domain (either 'yes' or year where it enters the PD)

Tab B has two additional columns, which are mostly empty as of version 1.1
V referring_to = target entry (in the main alphabet) of the correction or addenda
W excerpt = textual excerpt from the entry

Tab C has information on the authors:
A short_name = for sorting reasons
B full_name = full name
C Wikidata = Wikidata item
D FactGrid = FactGrid item
E Mythogram = Mythogram item
F yob = year of birth
G yod = year of death
H vols = volumes contributed to
I article_count = number of articles written (not counting corrections and addenda from Tab B)
J–L namestring1,2,3 = name as written in the Lexicon
M remarks = remarks on completeness and certainty of data

Tab D informs about the individual fascicles of the Lexicon as they appeared from 1884 to 1937:
A no. = fascicle number
B vol = volume(s) the fascicle belongs to
C colspan = column span of the fascicle
D headwords = headwords contained in the fascicle as advertised on the cover page
E issue_date = date of publication of the fascicle as stated on the cover page
F quires = quire numbers of the fascicle
G quire_count = quire count of the fascicle (calculated from column numbers: in some cases, at the end of a volume, quires were shortened, returning rational numbers here)
H remarks = remarks (in German)

3. Version History and Change Log

--------------------------------------------------
Version 1.1 (August 11th, 2024)
-Tab A, column P (Wikidata Q-ids): added 2,380 out of 15,489 = 15.4%)
-Tab A, column R (Mythogram Q-ids): completed
-Tab C: added data for author Wilhelm Windisch (translated Cumont's article on 'Mithras')
-minor corrections to some entries (typos)
-volume number changed from 3.2 to 3.1 for 125 entries (Pasikrateia–Peirithoos)
-fascicle number changed from 104/105 to 106/107 for the last 12 entries (Tameobrigus–Kerberos [Nachtrag])
-addition of 11 missed entries, values in column A renumbered accordingly

--------------------------------------------------
Version 1.0 (May 4th, 2024)
-Tabs A–B with complete and checked data for columns A–N and S
-Tab A with complete data for column O
-Tabs C and D with complete data

--------------------------------------------------
Prior to publication:

-collection and checking of data (roughly 376 hours of work, started in July 2023 and finished on Star Wars Day 2024)

Abstract–Definitive screening designs permit the study of many quantitative factors in a few runs more than twice the number of factors. In practical applications, researchers often require a design for m quantitative factors, construct a definitive screening design for more than m factors and drop the superfluous columns. This is done when the number of runs in the standard m-factor definitive screening design is considered too limited or when no standard definitive screening design (sDSD) exists for m factors. In these cases, it is common practice to arbitrarily drop the last columns of the larger design. In this article, we show that certain statistical properties of the resulting experimental design depend on the exact columns dropped and that other properties are insensitive to these columns. We perform a complete search for the best sets of 1–8 columns to drop from sDSDs with up to 24 factors. We observed the largest differences in statistical properties when dropping four columns from 8- and 10-factor definitive screening designs. In other cases, the differences are small, or even nonexistent.

Reddit: /r/news

Exploring Topics, Scores, and Engagement

By Reddit [source]

About this dataset

This dataset provides an in-depth look into learning what communities find important and engaging in the news. With this data, researchers can discover trends related to user engagement and popular topics within subreddits. By examining the “score” and “comms_num” columns, our researchers will be able to pinpoint which topics are most liked, discussed or shared within the various subreddits. Researchers may also gain insights into not only how popular a topic is but how it is growing over time. Additionally, by exploring the body column of our dataset, researchers can understand more about which types of news stories drive conversation within particular subreddits—providing an opportunity for deeper analysis of that subreddit’s diverse community dynamics

The dataset includes eight columns: title, score, id, url, comms_num created**body and timestamp** which can help us identify key insights into user engagement among popular subreddits. With this data we may also determine relationships between topics of discussion and their impact on user engagement allowing us to create a better understanding surrounding issue-based conversations online as well as uncover emerging trends in online news consumption habits

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For more datasets, click here.

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How to use the dataset

This dataset is useful for those who are looking to gain insight into the popularity and user engagement of specific subreddits. The data includes 8 different columns including title, score, id, url, comms_num, created, body and timestamp. This can provide valuable information about how users view and interact with particular topics across various subreddits.

In this guide we’ll look at how you can use this dataset to uncover trends in user engagement on topics within specific subreddits as well as measure the overall popularity of these topics within a subreddit.

1) Analyzing Score: By analyzing the “score” column you can determine which news stories are popular in a particular subreddit and which ones aren't by looking at how many upvotes each story has received. With this data you will be able to determine trends in what types of stories users preferred within a particular subreddit over time.

2) Analyzing Comms_Num: Similarly to analyzing the score column you can analyze the “comms_num” column to see which news stories had more engagement from users by tracking number of comments received on each post. Knowing these points can provide insight into what types of stories tend to draw more comment activity from users in certain subreddits from one day or an extended period of time such tracking post activity for multiple weeks or months at once 3) Analyzing Body: Additionally by looking at the “body” column for each post researchers can gain a better understanding which kinds of topics/news draw attention among specific Reddit communities.. With that complete picture researchers have access not only to data measuring Reddit buzz but also access topic discussion/comments helping generate further insights into why certain posts might be popular or receive more comments than others

Overallthis dataset provides valuable insights about user engagedment related specifically topics trending accross subsbreddits allowing anyone interested reseraching such things easier way access those insights all one place

Research Ideas

• Grouping news topics within particular subreddits and assessing the overall popularity of those topics in terms of scores/user engagement.
• Correlating user engagement with certain news topics to understand how they influence discussion or reactions on a subreddit.
• Examining the potential correlation between score and the actual body content of a given post to assess what types of content are most successful in gaining interest from users and creating positive engagement for posts

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: news.csv | Column name | Description ...

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 3 release notes:Adds data in the following formats: Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 2 release notes:Adds data for 2017.Adds a "number_of_months_reported" variable which says how many months of the year the agency reported data.Property Stolen and Recovered is a Uniform Crime Reporting (UCR) Program data set with information on the number of offenses (crimes included are murder, rape, robbery, burglary, theft/larceny, and motor vehicle theft), the value of the offense, and subcategories of the offense (e.g. for robbery it is broken down into subcategories including highway robbery, bank robbery, gas station robbery). The majority of the data relates to theft. Theft is divided into subcategories of theft such as shoplifting, theft of bicycle, theft from building, and purse snatching. For a number of items stolen (e.g. money, jewelry and previous metals, guns), the value of property stolen and and the value for property recovered is provided. This data set is also referred to as the Supplement to Return A (Offenses Known and Reported). All the data was received directly from the FBI as text or .DTA files. I created a setup file based on the documentation provided by the FBI and read the data into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here: https://github.com/jacobkap/crime_data. The Word document file available for download is the guidebook the FBI provided with the raw data which I used to create the setup file to read in data.There may be inaccuracies in the data, particularly in the group of columns starting with "auto." To reduce (but certainly not eliminate) data errors, I replaced the following values with NA for the group of columns beginning with "offenses" or "auto" as they are common data entry error values (e.g. are larger than the agency's population, are much larger than other crimes or months in same agency): 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99942. This cleaning was NOT done on the columns starting with "value."For every numeric column I replaced negative indicator values (e.g. "j" for -1) with the negative number they are supposed to be. These negative number indicators are not included in the FBI's codebook for this data but are present in the data. I used the values in the FBI's codebook for the Offenses Known and Clearances by Arrest data.To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. If an agency has used a different FIPS code in the past, check to make sure the FIPS code is the same as in this data.

The Audio Cartography project investigated the influence of temporal arrangement on the interpretation of information from a simple spatial data set. I designed and implemented three auditory map types (audio types), and evaluated differences in the responses to those audio types.

The three audio types represented simplified raster data (eight rows x eight columns). First, a "sequential" representation read values one at a time from each cell of the raster, following an English reading order, and encoded the data value as loudness of a single fixed-duration and fixed-frequency note. Second, an augmented-sequential ("augmented") representation used the same reading order, but encoded the data value as volume, the row as frequency, and the column as the rate of the notes play (constant total cell duration). Third, a "concurrent" representation used the same encoding as the augmented type, but allowed the notes to overlap in time.

Participants completed a training session in a computer-lab setting, where they were introduced to the audio types and practiced making a comparison between data values at two locations within the display based on what they heard. The training sessions, including associated paperwork, lasted up to one hour. In a second study session, participants listened to the auditory maps and made decisions about the data they represented while the fMRI scanner recorded digital brain images.

The task consisted of listening to an auditory representation of geospatial data ("map"), and then making a decision about the relative values of data at two specified locations. After listening to the map ("listen"), a graphic depicted two locations within a square (white background). Each location was marked with a small square (size: 2x2 grid cells); one square had a black solid outline and transparent black fill, the other had a red dashed outline and transparent red fill. The decision ("response") was made under one of two conditions. Under the active listening condition ("active") the map was played a second time while participants made their decision; in the memory condition ("memory"), a decision was made in relative quiet (general scanner noises and intermittent acquisition noise persisted). During the initial map listening, participants were aware of neither the locations of the response options within the map extent, nor the response conditions under which they would make their decision. Participants could respond any time after the graphic was displayed; once a response was entered, the playback stopped (active response condition only) and the presentation continued to the next trial.

Data was collected in accordance with a protocol approved by the Institutional Review Board at the University of Oregon.

• Additional details about the specific maps used in this are available through University of Oregon's ScholarsBank (DOI 10.7264/3b49-tr85).

• Details of the design process and evaluation are provided in the associated dissertation, which is available from ProQuest and University of Oregon's ScholarsBank.

• Scripts that created the experimental stimuli and automated processing are available through University of Oregon's ScholarsBank (DOI 10.7264/3b49-tr85).

Preparation of fMRI Data

Conversion of the DICOM files produced by the scanner to NiFTi format was performed by MRIConvert (LCNI). Orientation to standard axes was performed and recorded in the NiFTi header (FMRIB, fslreorient2std). The excess slices in the anatomical images that represented tissue in the next were trimmed (FMRIB, robustfov). Participant identity was protected through automated defacing of the anatomical data (FreeSurfer, mri_deface), with additional post-processing to ensure that no brain voxels were erroneously removed from the image (FMRIB, BET; brain mask dilated with three iterations "fslmaths -dilM").

Preparation of Metadata

The dcm2niix tool (Rorden) was used to create draft JSON sidecar files with metadata extracted from the DICOM headers. The draft sidecar file were revised to augment the JSON elements with additional tags (e.g., "Orientation" and "TaskDescription") and to make a more human-friendly version of tag contents (e.g., "InstitutionAddress" and "DepartmentName"). The device serial number was constant throughout the data collection (i.e., all data collection was conducted on the same scanner), and the respective metadata values were replaced with an anonymous identifier: "Scanner1".

Preparation of Behavioral Data

The stimuli consisted of eighteen auditory maps. Spatial data were generated with the rgeos, sp, and spatstat libraries in R; auditory maps were rendered with the Pyo (Belanger) library for Python and prepared for presentation in Audacity. Stimuli were presented using PsychoPy (Peirce, 2007), which produced log files from which event details were extracted. The log files included timestamped entries for stimulus timing and trigger pulses from the scanner.

• Log files are available in "sourcedata/behavioral".
• Extracted event details accompany BOLD images in "sub-NN/func/*events.tsv".
• Three column explanatory variable files are in "derivatives/ev/sub-NN".

References

Audacity® software is copyright © 1999-2018 Audacity Team. Web site: https://audacityteam.org/. The name Audacity® is a registered trademark of Dominic Mazzoni.

FMRIB (Functional Magnetic Resonance Imaging of the Brain). FMRIB Software Library (FSL; fslreorient2std, robustfov, BET). Oxford, v5.0.9, Available: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/

FreeSurfer (mri_deface). Harvard, v1.22, Available: https://surfer.nmr.mgh.harvard.edu/fswiki/AutomatedDefacingTools)

LCNI (Lewis Center for Neuroimaging). MRIConvert (mcverter), v2.1.0 build 440, Available: https://lcni.uoregon.edu/downloads/mriconvert/mriconvert-and-mcverter

Peirce, JW. PsychoPy–psychophysics software in Python. Journal of Neuroscience Methods, 162(1–2):8 – 13, 2007. Software Available: http://www.psychopy.org/

Python software is copyright © 2001-2015 Python Software Foundation. Web site: https://www.python.org

Pyo software is copyright © 2009-2015 Olivier Belanger. Web site: http://ajaxsoundstudio.com/software/pyo/.

R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available: https://www.R-project.org/.

rgeos software is copyright © 2016 Bivand and Rundel. Web site: https://CRAN.R-project.org/package=rgeos

Rorden, C. dcm2niix, v1.0.20171215, Available: https://github.com/rordenlab/dcm2niix

spatstat software is copyright © 2016 Baddeley, Rubak, and Turner. Web site: https://CRAN.R-project.org/package=spatstat

sp software is copyright © 2016 Pebesma and Bivand. Web site: https://CRAN.R-project.org/package=sp

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 4 release notes:Adds data for 2018Version 3 release notes:Adds data in the following formats: Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 2 release notes:Adds data for 2017.Adds a "number_of_months_reported" variable which says how many months of the year the agency reported data.Property Stolen and Recovered is a Uniform Crime Reporting (UCR) Program data set with information on the number of offenses (crimes included are murder, rape, robbery, burglary, theft/larceny, and motor vehicle theft), the value of the offense, and subcategories of the offense (e.g. for robbery it is broken down into subcategories including highway robbery, bank robbery, gas station robbery). The majority of the data relates to theft. Theft is divided into subcategories of theft such as shoplifting, theft of bicycle, theft from building, and purse snatching. For a number of items stolen (e.g. money, jewelry and previous metals, guns), the value of property stolen and and the value for property recovered is provided. This data set is also referred to as the Supplement to Return A (Offenses Known and Reported). All the data was received directly from the FBI as text or .DTA files. I created a setup file based on the documentation provided by the FBI and read the data into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here: https://github.com/jacobkap/crime_data. The Word document file available for download is the guidebook the FBI provided with the raw data which I used to create the setup file to read in data.There may be inaccuracies in the data, particularly in the group of columns starting with "auto." To reduce (but certainly not eliminate) data errors, I replaced the following values with NA for the group of columns beginning with "offenses" or "auto" as they are common data entry error values (e.g. are larger than the agency's population, are much larger than other crimes or months in same agency): 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99942. This cleaning was NOT done on the columns starting with "value."For every numeric column I replaced negative indicator values (e.g. "j" for -1) with the negative number they are supposed to be. These negative number indicators are not included in the FBI's codebook for this data but are present in the data. I used the values in the FBI's codebook for the Offenses Known and Clearances by Arrest data.To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. If an agency has used a different FIPS code in the past, check to make sure the FIPS code is the same as in this data.

k inputs are considered here, resulting in k + 1 points in parameter space. The base point is given by (a1, a2, …, ak). In OT the ai are elements from a discrete set and bi = ai ± |δi|, whereas in the radial design (as in [30]) ai and bi can take any value in [0, 1]. Table adapted from [30].

Reddit: /r/Art

Examining Content by Title, Score, ID, URL, Comments, Create Date, and Timestamp

By Reddit [source]

About this dataset

This dataset offers an in-depth exploration of the artistic world of Reddit, with a focus on the posts available on the website. By examining the titles, scores, ID's, URLs, comments, creation dates and timestamps associated with each post about art on Reddit, researchers can gain invaluable insight into how art enthusiasts share their work and build networks within this platform. Through analyzing this data we can understand what sorts of topics attract more attention from viewers and how members interact with one another in online discussions. Moreover, this dataset has potential to explore some of the larger underlying issues that shape art communities today - from examining production trends to better understanding consumption patterns. Overall, this comprehensive dataset is an essential resource for those aiming to analyze and comprehend digital spaces where art is circulated and discussed - giving unique insight into how ideas are created and promoted throughout creative networks

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How to use the dataset

This dataset is an excellent source of information related to online art trends, providing comprehensive analysis of Reddit posts related to art. In this guide, we’ll discuss how you can use this dataset to gather valuable insights about the way in which art is produced and shared on the web.
First and foremost, you should start by familiarizing yourself with the columns included in the dataset. Each post contains a title, score (number of upvotes), URL, comments (number of comments), created date and timestamp. When interpreting each column individually or comparing different posts/threads, these values will provide invaluable insight into topics such as most discussed or favored content within the Reddit community.
After exploring the general features within each post/thread in your analysis it’s time to move onto more specific components such as body content (including images) and creative dates - when users began responding and interacting with content posted about a specific topic or action related item). Utilizing these variables will help researchers uncover meaningful patterns regarding how communities interact with certain types of content over longer periods of time & also give context from what type of topics are trending at any given moment when analyzing at shorter intervals.
Finally one last creative output that can stem from using this data set revolves around examining titles for common words & phrases that appear often among posts discussing similar types of artwork or other forms media production - identifying potential keywords & symbols associated across several different groups can paint a holistic picture regards what kind engagement each group desires while they engage amongst other like-minded individuals further aided by parameters presented through number scores what helps measure overall reception per submissions or individual thoughts presented in comment thread discussions among others known similar outlets available on site itself! Here's hoping utilizing these techniques may bring attention to some possible conclusions derived already exists previously undiscovered apart our eyes – good luck everyone!

Research Ideas

• Analyzing topics and themes within art posts to determine what content is most popular.
• Examining the score of art posts to determine how the responding audience engages with each piece.
• Comparing across different subreddits to explore the ‘meta-discourse’ of topics that appear in multiple forums or platforms

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Art.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | title | The title of the post. (String) | | score | The number of upvotes the post has received. (Integer) | | url | The URL of the post. (String) | | comms_num | ...

Colors of Top 50 IMDb Movies

Hex Codes, CSS Colors and Decimal Codes

By Priyanka Dobhal [source]

About this dataset

This dataset contains the Hex codes of the Top 50 movies as listed in IMDb, including their color name and decimal code (R, G, B). Each movie was analyzed and the hex codes were identified to determine how they contribute to the overall color of each movie poster. This dataset provides a helpful resource for anyone looking to identify potential colors that could work well within a certain design, whether it be for posters or websites - challenging creativity by allowing a user to explore both vibrant and subtle hues. The Color Hex code provides an easy way to access thousands of shades with just six characters demonstrating how understanding combinations of simple mathematics can lead to amazing creations

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How to use the dataset

This dataset provides a comprehensive collection of the top 50 IMDb movie posters and their associated color codes. It allows users to quickly identify the predominant colors in each poster for creative purposes.

The data is divided into 7 columns, including the name of the movie, color name, hex code, HTML/CSS Color Name, Hex Code #RRGGBB and Decimal Code (R,G and B).

The ‘Movie’ column contains the title of each film so that it can be easily identified. The ‘Color’ column contains a descriptive name for each color found in each poster as well as its corresponding hex code (listed in its own column). The HTML/CSS Color Name column records create further details on this color by providing a more specific coding language to follow if desired. Finally, the Decimal Code (R, G & B) provides numbers which will aid those looking to replicate exact shades found across many pieces of software and coding languages.

This dataset has numerous potential uses: art educators searching for culturally relevant material; illustrators seeking inspiration; designers looking to develop aesthetic ideas; developers streamlining web building content or video editors creating stunning visuals are but a few examples within just this sector who would benefit from assimilating such data into their projects

Research Ideas

• Marketers can use this dataset to match their products' colors with the posters of the most popular movies to get more visibility and reach out to wider markets.
• Designers can use this data to identify which specific color hex codes are being used in the movie posters and create colour palettes for themselves for more creative works.
• Audience feedback surveys could be conducted using this data by asking them what their favorite movie poster colors are and thus improve marketing efforts even further by targeting a particular color palette that would please audiences more efficiently

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: IMDB_Movies_Poster_Color.csv | Column name | Description | |:--------------|:-------------------------------------------------------------------------------| | Movie | The title of the movie. (String) | | Hex Code | The hex code of the color used in the movie poster design. (String) | | Count | The number of times the hex code appears in the movie poster design. (Integer) |

File: color hex code.csv | Column name | Description | |:--------------------------|:---------------------------------------------------------------------------------| | Color | The name of the color used in the movie poster design. (String) | | HTML / CSS Color Name | The HTML/CSS color name associated with the hex code. (String) | | Hex Code #RRGGBB | The hex code associated with the color used in the movie poster design. (String) | | Decimal Code (R,G,B) | The RGB values associated with the hex code. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Priyanka Dobhal.

Reddit: /r/technology (Submissions & Comments)

Title, Score, ID, URL, Comment Number, and Timestamp

By Reddit [source]

About this dataset

This dataset, labeled as Reddit Technology Data, provides thorough insights into the conversations and interactions around technology-related topics shared on Reddit – a well-known Internet discussion forum. This dataset contains titles of discussions, scores as contributed by users on Reddit, the unique IDs attributed to different discussions, URLs of those hidden discussions (if any), comment counts in each discussion thread and timestamps of when those conversations were initiated. As such, this data is supremely valuable for tech-savvy people wanting to stay up to date with the new developments in their field or professionals looking to keep abreast with industry trends. In short, it is a repository which helps people make sense and draw meaning out of what’s happening in the technology world at large - inspiring action on their part or simply educating them about forthcoming changes

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How to use the dataset

The dataset includes six columns containing title, score, url address link to the discussion page on Reddit itself ,comment count ,created time stamp meaning when was it posted/uploaded/communicated and body containing actual text written regarding that particular post/discussion. By separately analyzing each column it can be made out what type information user require in regard with various aspects related to technology based discussions. One can develop hypothesis about correlations between different factors associated with rating or comment count by separate analysis within those columns themselves like discuss what does people comment or react mostly upon viewing which type of post inside reddit ? Does high rating always come along with extremely long comments.? And many more .By researching this way one can discover real facts hidden behind social networking websites such as reddit which contains large amount of rich information regarding user’s interest in different topics related to tech gadgets or otherwise .We can analyze different trends using voice search technology etc in order visualize users overall reaction towards any kind of information shared through public forums like stack overflow sites ,facebook posts etc .These small instances will allow us gain heavy insights for research purpose thereby providing another layer for potential business opportunities one may benefit from over a given period if not periodcally monitored .

Research Ideas

• Companies can use this dataset to create targeted online marketing campaigns directed towards Reddit users interested in specific areas of technology.
• Academic researchers can use the data to track and analyze trends in conversations related to technology on Reddit over time.
• Technology professionals can utilize the comments and discussions on this dataset as a way of gauging public opinion and consumer sentiment towards certain technological advancements or products

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: technology.csv | Column name | Description | |:--------------|:--------------------------------------------------------------------------| | title | The title of the discussion. (String) | | score | The score of the discussion as measured by Reddit contributors. (Integer) | | url | The website URL associated with the discussion. (String) | | comms_num | The number of comments associated with the discussion. (Integer) | | created | The date and time the discussion was created. (DateTime) | | body | The body content of the discussion. (String) | | timestamp | The timestamp of the discussion. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.