The R-scripts and data in this study can be used to reproduce figures in the associated paper, which is based on a simulation of a scientific community. Model description: We construct a model in which there is a cost associated with sharing datasets whereas reusing such sets implies a benefit. In our calculations conflicting interests appear for researchers. Individual researchers are al ways better off not sharing and omitting the sharing cost, at the same time both sharing and not sharing researchers are better off if (almost) all researchers share. Namely, the more researchers share, the more benefit can be gained by the reuse of those datasets. We simulated several policy measures to increase benefits for researchers sharing or reusing datasets. Results point out that, although policies should be able to increase the rate of sharing researchers, and increased discoverability and dataset quality could partly compensate for costs, a better measure would be to directly lower the cost for sharing, or even turn it into a (citation-) benefit.

What is this ? In this case study, I use a bike-share company data to evaluate the biking performance between members and casuals, determine if there are any trends or patterns, and theorize what are causing them. I am then able to develop a recommendation based on those findings.

Content: Hi. This is my first data analysis project and also my first time to use R in my work. They are the capstone project for Google Data Analysis Certificate Course offered in Coursera. (https://www.coursera.org/professional-certificates/google-data-analytics) It is about operation data analysis of a frictional bike-share company in Chicago. For detailed background story, please check the pdf file (Case 01.pdf) for reference.

In this case study, I use a bike-share company data to evaluate the biking performance between members and casuals, determine if there are any trends or patterns, and theorize what are causing them by descriptive analysis. I am then able to develop a recommendation based on those findings.

First I will make a background introduction, my business tasks and objectives, and how I obtain the data sources for analysis. Also, they are the R code I worked in RStudio for data processing, cleaning and generating graphs for next part analysis. Next, there are my analysis of bike data, with graphs and charts generated by R ggplot2. At the end, I also provide some recommendations to business tasks, based on the data finding.

I understand that I am just new to data analysis and the skills or code is very beginner level. But I am working hard to learn more in both R and data science field. If you have any idea or feedback. Please feel free to comment.

Stanley Cheng 2021-09-30

Comprehensive dataset containing 136 verified Telephone exchange businesses in RS with complete contact information, ratings, reviews, and location data.

ABSTRACT Context: this document is designed to be along with those that are in the first edition of the new section of the Journal of Contemporary Administration (RAC): the tutorial-articles section. Objective: the purpose is to present the new section and discuss relevant topics of tutorial-articles. Method: I divide the document into three main parts. First, I provide a summary of the state of the art in open data and open code at the current date that, jointly, create the context for tutorial-articles. Second, I provide some guidance to the future of the section on tutorial-articles, providing a structure and some insights that can be developed in the future. Third, I offer a short R script to show examples of open data that, I believe, can be used in the future in tutorial-articles, but also in innovative empirical studies. Conclusion: finally, I provide a short description of the first tutorial-articles accepted for publication in this current RAC’s edition.

Serbia RS: Income Share Held by Lowest 20% data was reported at 9.000 % in 2015. This records an increase from the previous number of 8.700 % for 2013. Serbia RS: Income Share Held by Lowest 20% data is updated yearly, averaging 8.600 % from Dec 2002 (Median) to 2015, with 11 observations. The data reached an all-time high of 9.300 % in 2008 and a record low of 6.900 % in 2005. Serbia RS: Income Share Held by Lowest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Serbia – Table RS.World Bank: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Case study: How does a bike-share navigate speedy success?

Scenario:

As a data analyst on Cyclistic's marketing team, our focus is on enhancing annual memberships to drive the company's success. We aim to analyze the differing usage patterns between casual riders and annual members to craft a marketing strategy aimed at converting casual riders. Our recommendations, supported by data insights and professional visualizations, await Cyclistic executives' approval to proceed.

About the company

In 2016, Cyclistic launched a bike-share program in Chicago, growing to 5,824 bikes and 692 stations. Initially, their marketing aimed at broad segments with flexible pricing plans attracting both casual riders (single-ride or full-day passes) and annual members. However, recognizing that annual members are more profitable, Cyclistic is shifting focus to convert casual riders into annual members. To achieve this, they plan to analyze historical bike trip data to understand the differences and preferences between the two user groups, aiming to tailor marketing strategies that encourage casual riders to purchase annual memberships.

Project Overview:

This capstone project is a culmination of the skills and knowledge acquired through the Google Professional Data Analytics Certification. It focuses on Track 1, which is centered around Cyclistic, a fictional bike-share company modeled to reflect real-world data analytics scenarios in the transportation and service industry.

Dataset Acknowledgment:

We are grateful to Motivate Inc. for providing the dataset that serves as the foundation of this capstone project. Their contribution has enabled us to apply practical data analytics techniques to a real-world dataset, mirroring the challenges and opportunities present in the bike-sharing sector.

Objective:

The primary goal of this project is to analyze the Cyclistic dataset to uncover actionable insights that could help the company optimize its operations, improve customer satisfaction, and increase its market share. Through comprehensive data exploration, cleaning, analysis, and visualization, we aim to identify patterns and trends that inform strategic business decisions.

Methodology:

Data Collection: Utilizing the dataset provided by Motivate Inc., which includes detailed information on bike usage, customer behavior, and operational metrics. Data Cleaning and Preparation: Ensuring the dataset is accurate, complete, and ready for analysis by addressing any inconsistencies, missing values, or anomalies. Data Analysis: Applying statistical methods and data analytics techniques to extract meaningful insights from the dataset.

Visualization and Reporting:

Creating intuitive and compelling visualizations to present the findings clearly and effectively, facilitating data-driven decision-making. Findings and Recommendations:

Conclusion:

The Cyclistic Capstone Project not only demonstrates the practical application of data analytics skills in a real-world scenario but also provides valuable insights that can drive strategic improvements for Cyclistic. Through this project, showcasing the power of data analytics in transforming data into actionable knowledge, underscoring the importance of data-driven decision-making in today's competitive business landscape.

Acknowledgments:

Special thanks to Motivate Inc. for their support and for providing the dataset that made this project possible. Their contribution is immensely appreciated and has significantly enhanced the learning experience.

STRATEGIES USED

Case Study Roadmap - ASK

●What is the problem you are trying to solve? ●How can your insights drive business decisions?

Key Tasks ● Identify the business task ● Consider key stakeholders

Deliverable ● A clear statement of the business task

Case Study Roadmap - PREPARE

● Where is your data located? ● Are there any problems with the data?

Key tasks ● Download data and store it appropriately. ● Identify how it’s organized.

Deliverable ● A description of all data sources used

Case Study Roadmap - PROCESS

● What tools are you choosing and why? ● What steps have you taken to ensure that your data is clean?

Key tasks ● Choose your tools. ● Document the cleaning process.

Deliverable ● Documentation of any cleaning or manipulation of data

Case Study Roadmap - ANALYZE

● Has your data been properly formaed? ● How will these insights help answer your business questions?

Key tasks ● Perform calculations ● Formatting

Deliverable ● A summary of analysis

Case Study Roadmap - SHARE

● Were you able to answer all questions of stakeholders? ● Can Data visualization help you share findings?

Key tasks ● Present your findings ● Create effective data viz.

Deliverable ● Supporting viz and key findings

**Case Study Roadmap - A...

RS Group reported GBP0.2 in EPS Earnings Per Share for its fiscal semester ending in December of 2025. Data for RS Group | RS1 - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last December in 2025.

R markdown files for:

• Downloading and cleaning data from the State of Open Data survey 2021
• Basic visualisations of responses to questions in the State of Open Data survey 2021
• HTML file of those visualisations.

Free text fields are included in the markdown but have been turned off for knitting and in the HTML file.

Serbia RS: Income Share Held by Third 20% data was reported at 17.300 % in 2015. This records a decrease from the previous number of 17.400 % for 2013. Serbia RS: Income Share Held by Third 20% data is updated yearly, averaging 17.300 % from Dec 2002 (Median) to 2015, with 11 observations. The data reached an all-time high of 17.600 % in 2009 and a record low of 15.900 % in 2005. Serbia RS: Income Share Held by Third 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Serbia – Table RS.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

**Introduction ** This case study will be based on Cyclistic, a bike sharing company in Chicago. I will perform tasks of a junior data analyst to answer business questions. I will do this by following a process that includes the following phases: ask, prepare, process, analyze, share and act.

Background Cyclistic is a bike sharing company that operates 5828 bikes within 692 docking stations. The company has been around since 2016 and separates itself from the competition due to the fact that they offer a variety of bike services including assistive options. Lily Moreno is the director of the marketing team and will be the person to receive these insights from this analysis.

Case Study and business task Lily Morenos perspective on how to generate more income by marketing Cyclistics services correctly includes converting casual riders (one day passes and/or pay per ride customers) into annual riders with a membership. Annual riders are more profitable than casual riders according to the finance analysts. She would rather see a campaign targeting casual riders into annual riders, instead of launching campaigns targeting new costumers. So her strategy as the manager of the marketing team is simply to maximize the amount of annual riders by converting casual riders.

In order to make a data driven decision, Moreno needs the following insights: - A better understanding of how casual riders and annual riders differ - Why would a casual rider become an annual one - How digital media can affect the marketing tactics

Moreno has directed me to the first question - how do casual riders and annual riders differ?

Stakeholders Lily Moreno, manager of the marketing team Cyclistic Marketing team Executive team

Data sources and organization Data used in this report is made available and is licensed by Motivate International Inc. Personal data is hidden to protect personal information. Data used is from the past 12 months (01/04/2021 – 31/03/2022) of bike share dataset.

By merging all 12 monthly bike share data provided, an extensive amount of data with 5,400,000 rows were returned and included in this analysis.

Data security and limitations: Personal information is secured and hidden to prevent unlawful use. Original files are backed up in folders and subfolders.

Tools and documentation of cleaning process The tools used for data verification and data cleaning are Microsoft Excel and R programming. The original files made accessible by Motivate International Inc. are backed up in their original format and in separate files.

Microsoft Excel is used to generally look through the dataset and get a overview of the content. I performed simple checks of the data by filtering, sorting, formatting and standardizing the data to make it easily mergeable.. In Excel, I also changed data type to have the right format, removed unnecessary data if its incomplete or incorrect, created new columns to subtract and reformat existing columns and deleting empty cells. These tasks are easily done in spreadsheets and provides an initial cleaning process of the data.

R will be used to perform queries of bigger datasets such as this one. R will also be used to create visualizations to answer the question at hand.

Limitations Microsoft Excel has a limitation of 1,048,576 rows while the data of the 12 months combined are over 5,500,000 rows. When combining the 12 months of data into one table/sheet, Excel is no longer efficient and I switched over to R programming.

Wuhu Fo Usa R Sports Share Co Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

1200+ ''resting state'' functional MRI (R-fMRI) datasets independently collected at 33 sites and donated by the principal investigators for the purpose of providing the broader imaging community complete access to a large-scale functional imaging dataset. Age, sex and imaging center information are provided for each of the datasets. In accordance with HIPAA guidelines, all datasets are anonymous, with no protected health information included. We anticipate this data-sharing effort will equip researchers with a means of exploring and refining R-fMRI approaches, and facilitate the growing ethos of sharing and collaboration. Disclaimer: The ''1000 Functional Connectomes Project'' datasets are provided freely without assurance of quality or appropriateness for usage.

RS Group reported GBP1.4B in Sales Revenues for its fiscal semester ending in December of 2025. Data for RS Group | RS1 - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last December in 2025.

O acervo está sendo organizado em armários compactados, em ordem alfabética de famílias Consta do Index Herbariorum com a sigla R, e estima-se que a coleção possua cerca de 550.000 exemplares, sendo 95% destes de plantas vasculares. Além dos números da coleção, o Herbário apresenta grande importância histórica por ter como depositários Glaziou, Freire Allemão, Brade, Hoehne, Lutz, Riedel, Schwacke, Sellow, e o próprio Imperador Dom Pedro II. No universo de espécimes coletados, merecem destaque às coleções procedentes da Amazônia Legal, Expedições do Marechal Rondon, Alberto Sampaio, Polo Noroeste, Alto Xingu, Fernando de Noronha, Parque Nacional da Restinga de Jurubatiba, dentre outras. A coleção de tipos nomenclaturais possui um total de 5.600 exemplares e faz parte do Projeto "Latin American Plants Iniciative" (LAPI) da Fundação Mellon.

Bike sharing exercise Google Analytics Capstone Project

Cyclistic bike Share Overview

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.

Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments.

One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships.

• Customers who purchase single-ride or full-day passes are referred to as casual riders.

• Customers who purchase annual memberships are Cyclistic members.

Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders.

Objective:

Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics.

Moreno (Marketing Director) and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.

Collection of resting state fMRI (R-fMRI) datasets from sites around world. It demonstrates open sharing of R-fMRI data and aims to emphasize aggregation and sharing of well-phenotyped datasets.

Comprehensive dataset containing 19 verified Stock exchange building businesses in RS with complete contact information, ratings, reviews, and location data.

Overview

This dataset contains the raw data for the manuscript:
Perrier L, Blondal E, Ayala AP, Dearborn D, Kenny T, Lightfoot D, Reka R, Thuna M, Trimble L, MacDonald H. Research data management in academic institutions: A scoping review. PLOS One. 2017 May 23;12(5):e0178261. doi: 10.1371/journal.pone.0178261.

Full-text available at: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178261

Data and Documentation Files

Five files make up the dataset:

1. Data Dictionary: RDMScopingReview_DataDictionary.pdf
2. Data Abstraction Sheet: RDMScopingReview_StudyCharacteristics.csv
3. Data Abstraction Sheet: RDMScopingReview_Setting.csv
4. Data Abstraction Sheet: RDMScopingReview_DataCollectionTools.csv
5. Data Abstraction Sheet: RDMScopingReview_Outcomes.csv

Contact: Laure Perrier: orcid.org/0000-0001-9941-7129

A cloud-based collaborative platform which co-locates data, code, and computing resources for analyzing genome-scale data and seamlessly integrates these services allowing scientists to share and analyze data together. Synapse consists of a web portal integrated with the R/Bioconductor statistical package and will be integrated with additional tools. The web portal is organized around the concept of a Project which is an environment where you can interact, share data, and analysis methods with a specific group of users or broadly across open collaborations. Projects provide an organizational structure to interact with data, code and analyses, and to track data provenance. A project can be created by anyone with a Synapse account and can be shared among all Synapse users or restricted to a specific team. Public data projects include the Synapse Commons Repository (SCR) (syn150935) and the metaGenomics project (syn275039). The SCR provides access to raw data and phenotypic information for publicly available genomic data sets, such as GEO and TCGA. The metaGenomics project provides standardized preprocessed data and precomputed analysis of the public SCR data.

This artifact accompanies the SEET@ICSE article "Assessing the impact of hints in learning formal specification", which reports on a user study to investigate the impact of different types of automated hints while learning a formal specification language, both in terms of immediate performance and learning retention, but also in the emotional response of the students. This research artifact provides all the material required to replicate this study (except for the proprietary questionnaires passed to assess the emotional response and user experience), as well as the collected data and data analysis scripts used for the discussion in the paper.

Dataset

The artifact contains the resources described below.

Experiment resources

The resources needed for replicating the experiment, namely in directory experiment:

alloy_sheet_pt.pdf: the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment. The sheet was passed in Portuguese due to the population of the experiment.

alloy_sheet_en.pdf: a version the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment translated into English.

docker-compose.yml: a Docker Compose configuration file to launch Alloy4Fun populated with the tasks in directory data/experiment for the 2 sessions of the experiment.

api and meteor: directories with source files for building and launching the Alloy4Fun platform for the study.

Experiment data

The task database used in our application of the experiment, namely in directory data/experiment:

Model.json, Instance.json, and Link.json: JSON files with to populate Alloy4Fun with the tasks for the 2 sessions of the experiment.

identifiers.txt: the list of all (104) available participant identifiers that can participate in the experiment.

Collected data

Data collected in the application of the experiment as a simple one-factor randomised experiment in 2 sessions involving 85 undergraduate students majoring in CSE. The experiment was validated by the Ethics Committee for Research in Social and Human Sciences of the Ethics Council of the University of Minho, where the experiment took place. Data is shared the shape of JSON and CSV files with a header row, namely in directory data/results:

data_sessions.json: data collected from task-solving in the 2 sessions of the experiment, used to calculate variables productivity (PROD1 and PROD2, between 0 and 12 solved tasks) and efficiency (EFF1 and EFF2, between 0 and 1).

data_socio.csv: data collected from socio-demographic questionnaire in the 1st session of the experiment, namely:

participant identification: participant's unique identifier (ID);

socio-demographic information: participant's age (AGE), sex (SEX, 1 through 4 for female, male, prefer not to disclosure, and other, respectively), and average academic grade (GRADE, from 0 to 20, NA denotes preference to not disclosure).

data_emo.csv: detailed data collected from the emotional questionnaire in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D);

detailed emotional response data: the differential in the 5-point Likert scale for each of the 14 measured emotions in the 2 sessions, ranging from -5 to -1 if decreased, 0 if maintained, from 1 to 5 if increased, or NA denoting failure to submit the questionnaire. Half of the emotions are positive (Admiration1 and Admiration2, Desire1 and Desire2, Hope1 and Hope2, Fascination1 and Fascination2, Joy1 and Joy2, Satisfaction1 and Satisfaction2, and Pride1 and Pride2), and half are negative (Anger1 and Anger2, Boredom1 and Boredom2, Contempt1 and Contempt2, Disgust1 and Disgust2, Fear1 and Fear2, Sadness1 and Sadness2, and Shame1 and Shame2). This detailed data was used to compute the aggregate data in data_emo_aggregate.csv and in the detailed discussion in Section 6 of the paper.

data_umux.csv: data collected from the user experience questionnaires in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID);

user experience data: summarised user experience data from the UMUX surveys (UMUX1 and UMUX2, as a usability metric ranging from 0 to 100).

participants.txt: the list of participant identifiers that have registered for the experiment.

Analysis scripts

The analysis scripts required to replicate the analysis of the results of the experiment as reported in the paper, namely in directory analysis:

analysis.r: An R script to analyse the data in the provided CSV files; each performed analysis is documented within the file itself.

requirements.r: An R script to install the required libraries for the analysis script.

normalize_task.r: A Python script to normalize the task JSON data from file data_sessions.json into the CSV format required by the analysis script.

normalize_emo.r: A Python script to compute the aggregate emotional response in the CSV format required by the analysis script from the detailed emotional response data in the CSV format of data_emo.csv.

Dockerfile: Docker script to automate the analysis script from the collected data.

Setup

To replicate the experiment and the analysis of the results, only Docker is required.

If you wish to manually replicate the experiment and collect your own data, you'll need to install:

A modified version of the Alloy4Fun platform, which is built in the Meteor web framework. This version of Alloy4Fun is publicly available in branch study of its repository at https://github.com/haslab/Alloy4Fun/tree/study.

If you wish to manually replicate the analysis of the data collected in our experiment, you'll need to install:

Python to manipulate the JSON data collected in the experiment. Python is freely available for download at https://www.python.org/downloads/, with distributions for most platforms.

R software for the analysis scripts. R is freely available for download at https://cran.r-project.org/mirrors.html, with binary distributions available for Windows, Linux and Mac.

Usage

Experiment replication

This section describes how to replicate our user study experiment, and collect data about how different hints impact the performance of participants.

To launch the Alloy4Fun platform populated with tasks for each session, just run the following commands from the root directory of the artifact. The Meteor server may take a few minutes to launch, wait for the "Started your app" message to show.

cd experimentdocker-compose up

This will launch Alloy4Fun at http://localhost:3000. The tasks are accessed through permalinks assigned to each participant. The experiment allows for up to 104 participants, and the list of available identifiers is given in file identifiers.txt. The group of each participant is determined by the last character of the identifier, either N, L, E or D. The task database can be consulted in directory data/experiment, in Alloy4Fun JSON files.

In the 1st session, each participant was given one permalink that gives access to 12 sequential tasks. The permalink is simply the participant's identifier, so participant 0CAN would just access http://localhost:3000/0CAN. The next task is available after a correct submission to the current task or when a time-out occurs (5mins). Each participant was assigned to a different treatment group, so depending on the permalink different kinds of hints are provided. Below are 4 permalinks, each for each hint group:

Group N (no hints): http://localhost:3000/0CAN

Group L (error locations): http://localhost:3000/CA0L

Group E (counter-example): http://localhost:3000/350E

Group D (error description): http://localhost:3000/27AD

In the 2nd session, likewise the 1st session, each permalink gave access to 12 sequential tasks, and the next task is available after a correct submission or a time-out (5mins). The permalink is constructed by prepending the participant's identifier with P-. So participant 0CAN would just access http://localhost:3000/P-0CAN. In the 2nd sessions all participants were expected to solve the tasks without any hints provided, so the permalinks from different groups are undifferentiated.

Before the 1st session the participants should answer the socio-demographic questionnaire, that should ask the following information: unique identifier, age, sex, familiarity with the Alloy language, and average academic grade.

Before and after both sessions the participants should answer the standard PrEmo 2 questionnaire. PrEmo 2 is published under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons licence (CC BY-NC-ND 4.0). This means that you are free to use the tool for non-commercial purposes as long as you give appropriate credit, provide a link to the license, and do not modify the original material. The original material, namely the depictions of the diferent emotions, can be downloaded from https://diopd.org/premo/. The questionnaire should ask for the unique user identifier, and for the attachment with each of the depicted 14 emotions, expressed in a 5-point Likert scale.

After both sessions the participants should also answer the standard UMUX questionnaire. This questionnaire can be used freely, and should ask for the user unique identifier and answers for the standard 4 questions in a 7-point Likert scale. For information about the questions, how to implement the questionnaire, and how to compute the usability metric ranging from 0 to 100 score from the answers, please see the original paper:

Kraig Finstad. 2010. The usability metric for user experience. Interacting with computers 22, 5 (2010), 323–327.

Analysis of other applications of the experiment

This section describes how to replicate the analysis of the data collected in an application of the experiment described in Experiment replication.

The analysis script expects data in 4 CSV files,