Introduction: I have chosen to complete a data analysis project for the second course option, Bellabeats, Inc., using a locally hosted database program, Excel for both my data analysis and visualizations. This choice was made primarily because I live in a remote area and have limited bandwidth and inconsistent internet access. Therefore, completing a capstone project using web-based programs such as R Studio, SQL Workbench, or Google Sheets was not a feasible choice. I was further limited in which option to choose as the datasets for the ride-share project option were larger than my version of Excel would accept. In the scenario provided, I will be acting as a Junior Data Analyst in support of the Bellabeats, Inc. executive team and data analytics team. This combined team has decided to use an existing public dataset in hopes that the findings from that dataset might reveal insights which will assist in Bellabeat's marketing strategies for future growth. My task is to provide data driven insights to business tasks provided by the Bellabeats, Inc.'s executive and data analysis team. In order to accomplish this task, I will complete all parts of the Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act). In addition, I will break each part of the Data Analysis Process down into three sections to provide clarity and accountability. Those three sections are: Guiding Questions, Key Tasks, and Deliverables. For the sake of space and to avoid repetition, I will record the deliverables for each Key Task directly under the numbered Key Task using an asterisk (*) as an identifier.

Section 1 - Ask:

A. Guiding Questions:
1. Who are the key stakeholders and what are their goals for the data analysis project? 2. What is the business task that this data analysis project is attempting to solve?

B. Key Tasks: 1. Identify key stakeholders and their goals for the data analysis project *The key stakeholders for this project are as follows: -Urška Sršen and Sando Mur - co-founders of Bellabeats, Inc. -Bellabeats marketing analytics team. I am a member of this team.

1. Identify the business task. *The business task is: -As provided by co-founder Urška Sršen, the business task for this project is to gain insight into how consumers are using their non-BellaBeats smart devices in order to guide upcoming marketing strategies for the company which will help drive future growth. Specifically, the researcher was tasked with applying insights driven by the data analysis process to 1 BellaBeats product and presenting those insights to BellaBeats stakeholders.

Section 2 - Prepare:

A. Guiding Questions: 1. Where is the data stored and organized? 2. Are there any problems with the data? 3. How does the data help answer the business question?

B. Key Tasks:

1. Research and communicate the source of the data, and how it is stored/organized to stakeholders. *The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through user Mobius in an open-source format. Therefore, the data is public and available to be copied, modified, and distributed, all without asking the user for permission. These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk reportedly (see credibility section directly below) between 03/12/2016 thru 05/12/2016.
   *Reportedly (see credibility section directly below), thirty eligible Fitbit users consented to the submission of personal tracker data, including output related to steps taken, calories burned, time spent sleeping, heart rate, and distance traveled. This data was broken down into minute, hour, and day level totals. This data is stored in 18 CSV documents. I downloaded all 18 documents into my local laptop and decided to use 2 documents for the purposes of this project as they were files which had merged activity and sleep data from the other documents. All unused documents were permanently deleted from the laptop. The 2 files used were: -sleepDay_merged.csv -dailyActivity_merged.csv

2. Identify and communicate to stakeholders any problems found with the data related to credibility and bias. *As will be more specifically presented in the Process section, the data seems to have credibility issues related to the reported time frame of the data collected. The metadata seems to indicate that the data collected covered roughly 2 months of FitBit tracking. However, upon my initial data processing, I found that only 1 month of data was reported. *As will be more specifically presented in the Process section, the data has credibility issues related to the number of individuals who reported FitBit data. Specifically, the metadata communicates that 30 individual users agreed to report their tracking data. My initial data processing uncovered 33 individual ...

Thorough knowledge of the structure of analyzed data allows to form detailed scientific hypotheses and research questions. The structure of data can be revealed with methods for exploratory data analysis. Due to multitude of available methods, selecting those which will work together well and facilitate data interpretation is not an easy task. In this work we present a well fitted set of tools for a complete exploratory analysis of a clinical dataset and perform a case study analysis on a set of 515 patients. The proposed procedure comprises several steps: 1) robust data normalization, 2) outlier detection with Mahalanobis (MD) and robust Mahalanobis distances (rMD), 3) hierarchical clustering with Ward’s algorithm, 4) Principal Component Analysis with biplot vectors. The analyzed set comprised elderly patients that participated in the PolSenior project. Each patient was characterized by over 40 biochemical and socio-geographical attributes. Introductory analysis showed that the case-study dataset comprises two clusters separated along the axis of sex hormone attributes. Further analysis was carried out separately for male and female patients. The most optimal partitioning in the male set resulted in five subgroups. Two of them were related to diseased patients: 1) diabetes and 2) hypogonadism patients. Analysis of the female set suggested that it was more homogeneous than the male dataset. No evidence of pathological patient subgroups was found. In the study we showed that outlier detection with MD and rMD allows not only to identify outliers, but can also assess the heterogeneity of a dataset. The case study proved that our procedure is well suited for identification and visualization of biologically meaningful patient subgroups.

Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

In the way of my journey to earn the google data analytics certificate I will practice real world example by following the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Picking the Bellabeat example.

Case Study: How Does a Bike-Share Navigate Speedy Success?¶

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. By the end of this lesson, you will have a portfolio-ready case study. Download the packet and reference the details of this case study anytime. Then, when you begin your job hunt, your case study will be a tangible way to demonstrate your knowledge and skills to potential employers.

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. Characters and teams ● Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. ● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. ● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them. ● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

About the company

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. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs. Moreno has set a clear goal: 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 and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends

Three questions will guide the future marketing program:

How do annual members and casual riders use Cyclistic bikes differently? Why would casual riders buy Cyclistic annual memberships? How can Cyclistic use digital media to influence casual riders to become members? Moreno has assigned you the first question to answer: How do annual members and casual rid...

Hello! Welcome to the Capstone project I have completed to earn my Data Analytics certificate through Google. I chose to complete this case study through RStudio desktop. The reason I did this is that R is the primary new concept I learned throughout this course. I wanted to embrace my curiosity and learn more about R through this project. In the beginning of this report I will provide the scenario of the case study I was given. After this I will walk you through my Data Analysis process based on the steps I learned in this course:

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
6. Act

The data I used for this analysis comes from this FitBit data set: https://www.kaggle.com/datasets/arashnic/fitbit

" This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. "

Untargeted mass spectrometry is a robust tool for biology, but it usually requires a large amount of time on data analysis, especially for system biology. A framework called Multiple-Chemical nebula (MCnebula) was developed herein to facilitate the LC–MS data analysis process by focusing on critical chemical classes and visualization in multiple dimensions. This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify “features” (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. Notably, MCnebula can be used to explore the classification and structural characteristic of unknown compounds beyond the limit of the spectral library. Moreover, it is intuitive and convenient for pathway analysis and biomarker discovery because of its function of ABC selection and visualization. MCnebula was implemented in the R language. A series of tools in R packages were provided to facilitate downstream analysis in an MCnebula-featured way, including feature selection, homology tracing of top features, pathway enrichment analysis, heat map clustering analysis, spectral visualization analysis, chemical information query, and output analysis reports. The broad utility of MCnebula was illustrated by a human-derived serum data set for metabolomics analysis. The results indicated that “Acyl carnitines” were screened out by tracing structural classes of biomarkers, which was consistent with the reference. A plant-derived data set was investigated to achieve a rapid annotation and discovery of compounds in E. ulmoides.

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software’s capabilities to achieve the most accurate and credible results.

Introduction After completing my Google Data Analytics Professional Certificate on Coursera, I accomplished a Capstone Project, recommended by Google, to improve and highlight the technical skills of data analysis knowledge, such as R programming, SQL, and Tableau. In the Cyclistic Case Study, I performed many real-world tasks of a junior data analyst. To answer the critical business questions, I followed the steps of the data analysis process: ask, prepare, process, analyze, share, and act. **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. Characters and teams Cyclistic: A bike-share program that has grown to a fleet of 5,824 bicycles that are tracked 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 at any time. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. Stakeholders Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. Cyclistic marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals and how you, as a junior data analyst, can help Cyclistic achieve them. *Cyclistic executive team: *The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

This dataset contains several files related to our research paper titled "Attention Allocation to Projection Level Alleviates Overconfidence in Situation Awareness". These files are intended to provide a comprehensive overview of the data analysis process and the presentation of results. Below is a list of the files included and a brief description of each:

R Scripts: These are scripts written in the R programming language for data processing and analysis. The scripts detail the steps for data cleaning, transformation, statistical analysis, and the visualization of results. To replicate the study findings or to conduct further analyses on the dataset, users should run these scripts.

R Markdown File: Offers a dynamic document that combines R code with rich text elements such as paragraphs, headings, and lists. This file is designed to explain the logic and steps of the analysis in detail, embedding R code chunks and the outcomes of code execution. It serves as a comprehensive guide to understanding the analytical process behind the study.

HTML File: Generated from the R Markdown file, this file provides an interactive report of the results that can be viewed in any standard web browser. For those interested in browsing the study's findings without delving into the specifics of the analysis, this HTML file is the most convenient option. It presents the final analysis outcomes in an intuitive and easily understandable manner. For optimal viewing, we recommend opening the HTML file with the latest version of Google Chrome or any other modern web browser. This approach ensures that all interactive functionalities are fully operational.

Together, these files form a complete framework for the research analysis, aimed at enhancing the transparency and reproducibility of the study.

We demonstrate a two-step data analysis method to increase the accuracy of single-molecule Förster Resonance Energy Transfer (smFRET) experiments. Most current smFRET studies are at a time resolution on the millisecond level. When the system also contains molecular dynamics on the millisecond level, simulations show that large errors are present (e.g., > 40%) because false state assignment becomes significant during data analysis. We introduce and confirm an additional step after normal smFRET data analysis that is able to reduce the error (e.g., < 10%). The idea is to use Monte Carlo simulation to search ideal smFRET trajectories and compare them to the experimental data. Using a mathematical model, we are able to find the matches between these two sets, and back guess the hidden rate constants in the experimental results.

This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

This resource contains a Jupyter Notebook that is used to introduce hydrologic data analysis and conservation laws. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) calculate the residence time of water in land and rivers for the global hydrologic cycle; (2) quantify the relative and absolute uncertainties in components of the water balance; (3) navigate public websites and databases, extract key watershed attributes, and perform basic hydrologic data analysis for a watershed of interest; (4) assess, compare, and interpret hydrologic trends in the context of a specific watershed.

Please note that in problems 3-8, the user is asked to use an R package (i.e., dataRetrieval) and select a U.S. Geological Survey (USGS) streamflow gage to retrieve streamflow data and then apply the hydrological data analysis to the watershed of interest. We acknowledge that the material relies on USGS data that are only available within the U.S. If running for other watersheds of interest outside the U.S. or wishing to work with other datasets, the user must take some further steps and develop codes to prepare the streamflow dataset. Once a streamflow time series dataset is obtained for an international catchment of interest, the user would need to read that file into the workspace before working through subsequent analyses.

Mass spectrometry-based proteomics is increasingly employed in biology and medicine. To generate reliable information from large data sets and ensure comparability of results it is crucial to implement and standardize the quality control of the raw data, the data processing steps and the statistical analyses. The MSPypeline provides a platform for the import of MaxQuant output tables, the generation of quality control reports, the preprocessing of data including normalization and exploratory analyses by statistical inference plots. These standardized steps assess data quality, provide customizable figures and enable the identification of differentially expressed proteins to reach biologically relevant conclusions.

How To Cite?

Alinaghi, N., Giannopoulos, I., Kattenbeck, M., & Raubal, M. (2025). Decoding wayfinding: analyzing wayfinding processes in the outdoor environment. International Journal of Geographical Information Science, 1–31. https://doi.org/10.1080/13658816.2025.2473599

Link to the paper: https://www.tandfonline.com/doi/full/10.1080/13658816.2025.2473599

Folder Structure

The folder named “submission” contains the following:

1. “pythonProject”: This folder contains all the Python files and subfolders needed for analysis.
2. ijgis.yml: This file lists all the Python libraries and dependencies required to run the code.

Setting Up the Environment

1. Use the ijgis.yml file to create a Python project and environment. Ensure you activate the environment before running the code.
2. The pythonProject folder contains several .py files and subfolders, each with specific functionality as described below.

Subfolders

1. Data_4_IJGIS

• This folder contains the data used for the results reported in the paper.
• Note: The data analysis that we explain in this paper already begins with the synchronization and cleaning of the recorded raw data. The published data is already synchronized and cleaned. Both the cleaned files and the merged files with features extracted for them are given in this directory. If you want to perform the segmentation and feature extraction yourself, you should run the respective Python files yourself. If not, you can use the “merged_…csv” files as input for the training.

2. results_[DateTime] (e.g., results_20240906_15_00_13)

• This folder will be generated when you run the code and will store the output of each step.
• The current folder contains results created during code debugging for the submission.
• When you run the code, a new folder with fresh results will be generated.

Python Files

1. helper_functions.py

• Contains reusable functions used throughout the analysis.
• Each function includes a description of its purpose and the input parameters required.

2. create_sanity_plots.py

• Generates scatter plots like those in Figure 3 of the paper.
• Although the code has been run for all 309 trials, it can be used to check the sample data provided.
• Output: A .png file for each column of the raw gaze and IMU recordings, color-coded with logged events.
• Usage: Run this file to create visualizations similar to Figure 3.

3. overlapping_sliding_window_loop.py

• Implements overlapping sliding window segmentation and generates plots like those in Figure 4.
• Output:
  • Two new subfolders, “Gaze” and “IMU”, will be added to the Data_4_IJGIS folder.
  • Segmented files (default: 2–10 seconds with a 1-second step size) will be saved as .csv files.
  • A visualization of the segments, similar to Figure 4, will be automatically generated.

4. gaze_features.py & imu_features.py (Note: there has been an update to the IDT function implementation in the gaze_features.py on 19.03.2025.)

• These files compute features as explained in Tables 1 and 2 of the paper, respectively.
• They process the segmented recordings generated by the overlapping_sliding_window_loop.py.
• Usage: Just to know how the features are calculated, you can run this code after the segmentation with the sliding window and run these files to calculate the features from the segmented data.

5. training_prediction.py

• This file contains the main machine learning analysis of the paper. This file contains all the code for the training of the model, its evaluation, and its use for the inference of the “monitoring part”. It covers the following steps:

a. Data Preparation (corresponding to Section 5.1.1 of the paper)

• Prepares the data according to the research question (RQ) described in the paper. Since this data was collected with several RQs in mind, we remove parts of the data that are not related to the RQ of this paper.
• A function named plot_labels_comparison(df, save_path, x_label_freq=10, figsize=(15, 5)) in line 116 visualizes the data preparation results. As this visualization is not used in the paper, the line is commented out, but if you want to see visually what has been changed compared to the original data, you can comment out this line.

b. Training/Validation/Test Split

• Splits the data for machine learning experiments (an explanation can be found in Section 5.1.1. Preparation of data for training and inference of the paper).
• Make sure that you follow the instructions in the comments to the code exactly.
• Output: The split data is saved as .csv files in the results folder.

c. Machine and Deep Learning Experiments

This part contains three main code blocks:

iii. One for the XGboost code with correct hyperparameter tuning:
Please read the instructions for each block carefully to ensure that the code works smoothly. Regardless of which block you use, you will get the classification results (in the form of scores) for unseen data. The way we empirically test the confidence threshold of

• MLP Network (Commented Out): This code was used for classification with the MLP network, and the results shown in Table 3 are from this code. If you wish to use this model, please comment out the following blocks accordingly.
• XGBoost without Hyperparameter Tuning: If you want to run the code but do not want to spend time on the full training with hyperparameter tuning (as was done for the paper), just uncomment this part. This will give you a simple, untuned model with which you can achieve at least some results.
• XGBoost with Hyperparameter Tuning: If you want to train the model the way we trained it for the analysis reported in the paper, use this block (the plots in Figure 7 are from this block). We ran this block with different feature sets and different segmentation files and created a simple bar chart from the saved results, shown in Figure 6.

Note: Please read the instructions for each block carefully to ensure that the code works smoothly. Regardless of which block you use, you will get the classification results (in the form of scores) for unseen data. The way we empirically calculated the confidence threshold of the model (explained in the paper in Section 5.2. Part II: Decoding surveillance by sequence analysis) is given in this block in lines 361 to 380.

d. Inference (Monitoring Part)

• Final inference is performed using the monitoring data. This step produces a .csv file containing inferred labels.
• Figure 8 in the paper is generated using this part of the code.

6. sequence_analysis.py

• Performs analysis on the inferred data, producing Figures 9 and 10 from the paper.
• This file reads the inferred data from the previous step and performs sequence analysis as described in Sections 5.2.1 and 5.2.2.

Licenses

The data is licensed under CC-BY, the code is licensed under MIT.

Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed repeatedly over time, pose additional challenges, as they have special features that should be taken into account in the IDA steps before addressing the research question. We propose a systematic approach in longitudinal studies to examine data properties prior to conducting planned statistical analyses. In this paper we focus on the data screening element of IDA, assuming that the research aims are accompanied by an analysis plan, meta-data are well documented, and data cleaning has already been performed. IDA data screening comprises five types of explorations, covering the analysis of participation profiles over time, evaluation of missing data, presentation of univariate and multivariate descriptions, and the depiction of longitudinal aspects. Executing the IDA plan will result in an IDA report to inform data analysts about data properties and possible implications for the analysis plan—another element of the IDA framework. Our framework is illustrated focusing on hand grip strength outcome data from a data collection across several waves in a complex survey. We provide reproducible R code on a public repository, presenting a detailed data screening plan for the investigation of the average rate of age-associated decline of grip strength. With our checklist and reproducible R code we provide data analysts a framework to work with longitudinal data in an informed way, enhancing the reproducibility and validity of their work.

These R scripts were used to preprocess and analyze the data of the VR acceptance study. The script "RunAnalyses" executes each data-analysis step. The script "SummarizeData" will preprocess and create the datasets used for the analyses. Some parts of this script may not run accurately on the raw dataset that is publicly shared, since the raw data of the demographics were adjusted to protect participant's privacy.

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

Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using data other individuals provide about themselves, possibly even anonymously.

• What makes our data unique? Our datasets cover the entirety of individuals' data: be it data from social media, e-commerce, health, fitness tracker, browser, on-chain wallet, cell phone gprs or meta. A unique allocation is already made when the data is collected by the respective owner of the data.

• How is our data sourced? The data is obtained directly from the respective platform or the respective original data collector, either via a manual download/upload process or by granting permission for real-time data retrieval via an api interface.

• What are primary use-cases? There are no limits to the possibilities for analysis through our in-house analysis platform. The process takes place in three basic and a potential fourth additional step.

1. Filter for the necessary data sets for your own analysis, based on the desired criteria (e.g. purchases of product X on Amazon, age, region & gender in combination with fitness metrics at level Y).

2. Display how many data sets with the desired criteria are available for an analysis.

3. Create an individual analysis to e.g. get a market segmentation or target persona for this specific customer group

4. All users of the WeDataNation app automatically receive a wallet to manage the revenue share and other tokens held on it. Direct airdrops can be made for coupons or other marketing campaigns, thus enabling direct interaction with the target group that has filtered and analyzed itself. This enables 100% traceability and reduces costs through lower scattering losses in executed marketing campaigns.

• Process of the analysis The entire analysis is carried out in compute-to-data model, whereby the original data is only available in anonymized form and the privacy of the owners of the data, who participate in the sales via opt-in models, is thereby guaranteed. This generates a high level of willingness and consent among potential data suppliers to provide data.

• How does this Data Product fit into our broader data offering? The market entry is focused on europe. However, data is collected worldwide and an analysis is theoretically also possible for sub-regions of the eu or other regions worldwide. Our in-house analysis platform can also be used for this purpose.

• Status of product development? The development of the analysis platform is currently being carried out in close consultation with potential interested users.

• Are you interested in our service? Contact us via Partner@WeDataNation.io and become an early partner too, so that we can take your needs into account when developing the functionalities of our platform.