Raw data, clean data, and SQL query output tables as spreadsheets to support Tableau story and github repository available at https://github.com/brittabeta/Bellabeat-Case-Study-SQL-Excel-Tableau

Case Study 1- Bike Sharing Introduction: 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. There are two types of members are sharing bike differently! 1.) Annual members- who bought annual membership. 2.) Casual members- who bought or buying single-ride passes, full-day passes.

Phase_1- Ask- 1. Identify the business task- • 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? 2. Consider key stakeholders- Lily Moreno: The director of marketing and manager, Cyclistic marketing analytics team, Cyclistic executive team.

Phase_2- Prepare--
I downloaded and store it in my excel sheet, I am using only one month (April_2020) data, and using excel for solving task, I am also sorting and filtering my data according to requirement. I downloaded data from public source and it’s fully reliable, unbiased. Data is also, complete, consistent and accurate. Phase_3- Process— • I downloaded 202004-divvy-tripdata.cvs data and I unzip the file and converted into .xls file, here I am using only April data because this case study is my first case study and only for my learning, so I want to keep it simple. I am using excel this time because I am more comfortable with excel then other tools. I also want to perform good analysis and don’t want to lost in multiple sheets & large dataset, in initial stage.

• I Checked the data errors, and corrected some errors, I also did some calculation in my sheet, and try to clean data, so I can use sheet appropriately, Phase_4- analyze— I organize my data, performed sorting and filtering multiple time as I needed, did some calculation, add few pivots table and try to analyze data properly, also try to Identify trends and relationships.

Phase_5- Share— • After completing my analysis, I used some charts to present my findings. First, I found Total count of ride is 16383 and annual members took 11552 count of ride what is 71% of total ride, and casual riders took only 29% of ride which is 4831.

• I also found that casual riders using ride for some times but members are taking ride anytime no matter if they need bike for long time or short time, they are taking ride without any second thought, because after buying annual pass they no need to pay (any extra money or) every time.

• Clark St & Elm St is a most bike rented point, people took 180 bikes from this station, and 132 are the annual member from that. Also, I found other station where we need more bikes. Likewise, we also can find station name where most people end their ride, so they have plenty space for bikes. Phase_6- Act— Feeling happy to share my finding with you, feeling little confident after completing my first case study.

Hello, This is my first post to Kaggle. The .rmd file is related to the Google Data Analytics Certification case study. I have several years of experience using Excel in a business setting, but this was my first exposure to R programming. R programming has captured my imagination and I am excited about the prospect of continuing to learn R and to improve my R skills. To that end, I would be grateful for any feedback on the R code. I am sure there are more efficient ways to accomplish what I was trying to accomplish with this case study. If you have any recommendations on sites, readings, or videos that you have found to be useful in continuing to learn R I would appreciate your recommendations. Thanks in advance, J. Craig Myers

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

This article introduces four case studies to integrate healthcare analytics topics into classroom learning. Each case study employs distinct analytical methods, including optimization with Excel Solver, multiple linear regression, Monte Carlo simulation, and time series forecasting models, providing diverse practical applications in healthcare analytics. The cases offer students hands-on experience in practical healthcare challenges, enhancing their analytical and decision-making skills. For each case, we provide a detailed background, an in-depth data description, and comprehensive teaching notes. These elements are structured to facilitate understanding and teaching analytics concepts. The article also summarizes student feedback collected from various courses where these case studies were implemented. This feedback consistently indicates that the cases significantly contributed to the student’s perceived learning, particularly in understanding and applying healthcare analytics in concrete scenarios. These case studies bridge the gap between theoretical knowledge and practical application and serve as a valuable resource for instructors seeking to enrich their healthcare analytics curriculum. Supplementary materials for this article are available online.

This project is part of my eagerness to learn more about data analytics and Python; it aims to apply Python and Tableau in real-world scenarios.

Description of the task:

Value Inc is a retail store that sells household items worldwide in bulk. The Sales Manager has no sales reporting but has a brief idea of current sales. He also needs to know the monthly cost, profit and top-selling products. He wants a dashboard and says the data is stored in an Excel sheet.

The interviews and Read Me file are presented in PDF format, while the interview metadata is formatted in an Excel spreadsheet. Regarding the interviews, it is important to highlight the fact the files present a version translated into the English language, as the interviews were carried out in German. Therefore, there are some passages which are not grammatically correct. The working version of the data, that which was used for the activities in WP 2 is a translation of the transcript in German. The original transcripts were not used for any manner of analysis, as the working language of T-Factor is English, and the output they informed (the Advanced Case Study portfolio) was also written and analysed in English. Therefore, the dataset only includes the translated transcripts. The generation of these translations entailed the transcription of the original audio files into text, by hand. Subsequently, these files were processed using the DeepL translation software. Due to the bulkiness of the text, it was agreed upon by the researchers that the raw transcripts would not be corrected for grammar, but instead, interpreted while carrying out the qualitative analysis pertaining the activities of Work Package 2, making use of the language skills within the team.

Does Unit-Tested Code Crash? A Case Study of Eclipse: Replication Package

This is a replication package associated with the paper titled “Does Unit-Tested Code Crash? A Case Study of Eclipse”. Below is a description of the package’s contents.

Data

Data files associated with the paper are provided in the data directory.

Text file tested-crashed.txt

Data specifying whether methods were tested and whether they crashed (according to the criteria adopted in the study). Extracted from matches.xlsx. The data are used as input for Fisher’s test (RQ1).

Spreadsheet matches.xlsx

Test coverage data and calculations associated with failed methods, class coverage, and matching method coverage results are provided in an Excel spreadsheet. Below is the description of the spreadsheet’s contents.

Worksheet Test Coverage

Contains the data regarding the JaCoCo test code coverage analysis.

• Class: The name of the class in which a method appears in JVM internal form notation
• Method: The method’s name
• Parameters: The method’s arguments in JVM parameter descriptor format; required to handle Java’s {} polymporphism
• Class Has Unit Test: Whether the corresponding class has associated unit test code
• Class Unit-Test Line Density: The ratio of lines in class’s test code over those in the class’s implementation code
• Covered Instructions / Branches / Lines: As reported by JaCoCo
• Total Instructions / Branches / Lines: As reported by JaCoCo
• Covered Instructions / Branches / Lines ratio: The ratio between the two preceding values; 1 for methods without any branches
• Top-1 / Top-6 / Top-10 : In how many stack traces the method appears within; the top-10 / top-6 / the very first stack frame(s)
• Tested: TRUE if the method is considered tested by having a test code coverage above the median (0.966) and an associated test class
• Crashed: TRUE if the method has crashed as evidenced by its appearance on the topmost stack frame
• Stack trace file names: in which the method appeared

Worksheet Test Existence

Contains the data of the analysis regarding the existence of test code.

• Class: Class containing implementation code
• TestClassNames: Classes that contain tests for the above
• Number of relevant tests
• Lines in class test code
• Lines of class
• Class Unit-Test Line Density: The ratio between the two above

Worksheet Metrics

Contains the derivation of metrics reported in the paper. In the cases of tables these are formatted in LaTeX for direct incorporation into the text.

Spreadsheet jacoco.xlsx

Complete test coverage data obtained from JaCoCo are provided in an Excel spreadsheet. Below is the description of the spreadsheet’s contents.

Worksheet Data

Contains the following method code coverage fields as reported by JaCoCo, as well as the calculated percentages.

• Class
• Method
• Parameters
• Covered Instructions
• Total Instructions
• % Covered Instructions
• Covered Branches
• Total Branches
• % Covered Branches
• Covered Lines
• Total Lines
• % Covered Lines

Worksheet Metrics

Contains the derivation of numbers reported in the preliminary quantitative analysis and Figure 2.

Compressed tar archive eclipse-src.tar.gz

Contains the Eclipse source code used for running the Eclipse tests with JaCoCo code coverage analysis. It was obtained from the Eclipse source code repositories as follows.

• Clone the Eclipse aggreagator repository into a directory named z by running: git clone -b master --recursive git://git.eclipse.org/gitroot/platform/eclipse.platform.releng.aggregator.git z
• In the z directory, checking out the used release by running cd z && git submodule foreach git checkout M20160212-1500
• Checking out the release for the main repository by running: git checkout M20160212-1500
• Applying the patch eclipse-src.diff

Patch file eclipse-src.diff

See above.

Zip file incidents.zip

Contains the 126,026 incidents (crash report stack traces and meta-data) associated with EclipseProduct org.eclipse.epp.package.java.product and BuildID 4.5.2.M20160212-1500. This is a subset from the two million incidents available as the AERI stack traces data set.

The subset of incidents was extracted from the full AERI data set with the following command.

for f in *; do
 grep -q org.eclipse.epp.package.java.product $f && grep -q 4.5.2.M20160212-1500 $f && mv $f selected-files/
done

Compressed file jacoco.xml.gz

Contains the results of the JaCoCo code coverage analysis over the Eclipse testing.

Code

The following scripts are provided in the src directory

• extract.py: script for extracting crash (incidents) and coverage (JaCoCo) data
• unit-tested-classes.py: script for finding the classes with associated unit test code
• merge.py: script for matching crash (incidents) with coverage (JaCoCo) data
• fisher.r: R script for running Fisher’s test

This zip file contains data files for 3 activities described in the accompanying PPT slides 1. an excel spreadsheet for analysing gain scores in a 2 group, 2 times data array. this activity requires access to –https://campbellcollaboration.org/research-resources/effect-size-calculator.html to calculate effect size. 2. an AMOS path model and SPSS data set for an autoregressive, bivariate path model with cross-lagging. This activity is related to the following article: Brown, G. T. L., & Marshall, J. C. (2012). The impact of training students how to write introductions for academic essays: An exploratory, longitudinal study. Assessment & Evaluation in Higher Education, 37(6), 653-670. doi:10.1080/02602938.2011.563277 3. an AMOS latent curve model and SPSS data set for a 3-time latent factor model with an interaction mixed model that uses GPA as a predictor of the LCM start and slope or change factors. This activity makes use of data reported previously and a published data analysis case: Peterson, E. R., Brown, G. T. L., & Jun, M. C. (2015). Achievement emotions in higher education: A diary study exploring emotions across an assessment event. Contemporary Educational Psychology, 42, 82-96. doi:10.1016/j.cedpsych.2015.05.002 and Brown, G. T. L., & Peterson, E. R. (2018). Evaluating repeated diary study responses: Latent curve modeling. In SAGE Research Methods Cases Part 2. Retrieved from http://methods.sagepub.com/case/evaluating-repeated-diary-study-responses-latent-curve-modeling doi:10.4135/9781526431592

Objective: This study investigates the Euclidean geometry learning opportunities presented in Further Education and Training (FET) mathematics textbooks. Specifically, it examines the alignment of textbook content with the Curriculum and Assessment Policy Statement (CAPS) curriculum, levels of geometric thinking promoted, representational forms, contextual features, and expected responses.Methodology: The research analyzed three FET mathematics textbook series to identify strengths and weaknesses in Euclidean geometry content. This study adopted the interpretivist paradigm. The study used a qualitative research approach and a case study research design. Purposive sampling techniques were used to select the textbooks currently used for teaching. This study used textbook analysis as the data collection method. Deductive content analysis was used as a data analysis strategy. In this study, interrater reliability was used to preserve the quality of data coding and reporting among coders as a percentage of agreement between three coders (Belur et al., 2021).Data collectionThis study employed various textbook analysis instruments that were specifically designed within its framework, including the content coverage instrument, mathematical activity instrument, geometric thinking levels instrument, representation forms instrument, contextual features instrument, and answer forms instrument. 1.1.1 Content coverage instrumentThe study employed a content coverage instrument as a data collection tool, with a focus on textbook topics and subtopics. The content coverage instrument, in the form of a checklist, listed all the topics and subtopics of Euclidean geometry in the grade 10–12 curriculum and assessed whether each content was covered in the respective textbooks based on their corresponding grade levels. The aim was to provide a comprehensive assessment of the extensive range of content knowledge that students are required to acquire at each school level, specifically Grades 10-12, using a rubric. The rubric for assessment was designed to gather data and emphasised the extent of Euclidean geometry content coverage. The rubric focused on content coverage and provided a space to indicate if a subtopic was covered (by ticking) or not covered (-).A checklist form was used to gather data from the textbook tasks by indicating the topics and subtopics covered in each textbook series. The checklist was developed from the CAPS guideline document for Grades 10–12. This instrument was used to examine the selected textbook content coverage to determine the extent to which the textbooks align with the CAPS Mathematics guideline document. This instrument divided the Euclidean geometry content into three categories: Grade 10, Grade 11, and Grade 12, as stipulated in the CAPS Mathematics guideline document for FET-level mathematics. To bolster results objectivity, all CAPS checklist items were quantified using dichotomous (yes/no) responses, summarised by scoring rubrics to justify different responses. A mathematical activity form tool was developed to collect data regarding the nature of mathematical activities in both worked examples and exercise tasks within each textbook. The form was designed in the format of a rubric based on Gracin’s (2018) mathematical activity framework: representation and modelling, calculation and operation, interpretation, and argumentation and reasoning. The rubric consists of five major sections, with the first section focusing on the nature of the mathematical activities required to successfully engage with geometry questions. A rubric was provided for the nature of mathematical activities for each geometry task, which was broken down into four categories to explore the nature of tasks more clearly. The categories of mathematical activities focused on representation and modelling, calculation and operation, interpretation, and argumentation and reasoning.As this study intended to investigate the students’ OTL afforded by textbooks, an evaluation form was used to gather data. A form containing the four kinds of Euclidean geometry task types was included in the evaluation form used to examine the nature of each Euclidean geometry task. This form consisted of a list of the characteristics of each mathematical activity required to carry out the geometry tasks: Representation and modelling (R), Calculation and operation (C), Interpretation (I), and Argumentation and reasoning (A).” This form serves as a classification template, categorising tasks according to the competence the tasks demand of the students. Table 4.5 presents exemplary geometric tasks, categorised by skill, alongside corresponding evaluation indicators used to assess mathematical proficiency. A representation form instrument was utilised as a data collection instrument regarding the type type of representation used in presenting of the geometry ideas in each textbook sries (see section 3.3). A rubric was utilised to capture the type of representation, with a designated space for each task. This rubric provided a space for documenting the representation format for the tasks. To make the captured data clear, we divided the rubric into four distinct sections: pure mathematics, verbal, visual, and combined forms of problem presentation.Data analysisThis study used a qualitative deductive content analysis (QDCA) approach to analyse the collected data. In a DCA, research findings are allowed to emerge from the textbooks examined (Pertiwi & Wahidin, 2020). A deductive approach was appropriate because the codes and categories were drawn from theoretical considerations, not from the text itself (Islam & Asadullah, 2018).The researcher created nine Excel files, each with a four-column table, as shown in the figure below. Every column represents the type of mathematical activity category: Representation (R), Calculation (C), Interpretation (I), and Argumentation (A). Based on the Gracin (2018) framework, the researcher and two scorers read every worked example task and exercise task in each textbook examined in this study, extracted the mathematical activity required to complete the task successfully, and recorded it in the corresponding Excel file. If the tasks required more than one activity, the researcher considered the one that was dominantly required by the task author. The figure below shows the Excel sheet used to score the mathematical tasks for this study. To examine the geometric thinking embedded in textbook tasks, a comprehensive analysis framework was employed. This involved utilising a rubric to categorise tasks according to their corresponding geometric thinking levels, spanning from Level 0 to Level 4. For instance, tasks requiring students to define properties of a geometric figure were classified as informal deduction, whereas tasks demanding formal proofs were coded as formal deduction.The analysis process commenced with a meticulous review of worked examples and exercise tasks to identify the embedded level of geometric thinking. Subsequently, Excel tables were utilised to record the geometry levels present in Euclidean geometry tasks, and their frequencies were calculated. The results, which highlighted the predominant levels in the textbook series, were then subjected to in-depth analysis. This study classified each task based on the dimensions of Zhu and Fan's (2006) answer forms and subsequently coded the problem as depicted in Figure 4.13. In this study, the researcher conducted the process of classifying the tasks based on the answers to the question forms by reading the task questions and coding them as either open-ended or closed-ended problems.The researcher examined the types of tasks within the Euclidean geometry content in terms of their representation form and contextual features. This study used Zhu and Fan's (2006) framework to classify and code Euclidean geometry tasks found in textbooks. This study analysed the following classification of tasks: "Pure mathematical (R1), verbal (R2), visual (R3), and combined form (R4), based on Zhu and Fan's (2006) theoretical framework. In particular, each task was analysed against these representation-type categories in each textbook. An Excel table, as shown in the figure above, recorded the analysis of the representation forms.To investigate the contextual features of mathematical tasks, the researcher systematically collected tasks from each textbook and created an Excel sheet to score the type of context presented in each problem. Zhu and Fan's (2006) theoretical framework provided the foundation for categorising and coding tasks, enabling a comprehensive analysis. This study classified the tasks into two distinct categories: Zhu and Fan (2006) define application problems (C1) as tasks presented in real-life situations, illustrating practical applications of mathematical concepts. Non-application problems (C2) are tasks that lack context and solely concentrate on mathematical procedures and calculations. We coded tasks presented in situations mirroring real-life scenarios as application tasks and tasks lacking context as non-application tasks. The coded data was meticulously counted, and the frequencies were recorded in tables using Microsoft Excel, as depicted in Figure 4.13. This systematic analysis facilitated a nuanced understanding of the contextual features of mathematical tasks across the examined textbooks. This study used the CAPS Mathematics guidelines as the foundation for developing an OTL analytical tool to classify the mathematical content. The CAPS Mathematics analytical tool encompasses the content areas that students should master in all grades. Next, I outlined the OTL categories, offering comprehensive details on the interpretation and analysis of the data. To analyse the data, I used a rubric for each textbook series. The researchers conducted a thorough review of each textbook task, utilising the CAPS Mathematics document as a benchmark to

Excel data from ICP-MS measurements during the optimisation of the trypsin digestion protocol for digesting proteins. Lineage: Please read paper entitled 'Trypsin Digestion to Prevent Acid Induced Precipitation of Metalloproteins for Accurate ICP-MS Analysis: Nitrogenase Case Study', published in the journal Analytical Biochemistry

The dataset (Excel) corresponds to the data used to generate the gap analysis of the published paper "Critically examining the knowledge base required to mechanistically project climate impacts: A case study of Europe's fish and shellfish" with DOI: 10.1111/faf.12359

It contains 245 cases and 14 variables. The explanation of the variables is contained in the paper.

Funding Information: the project CERES - Climate change and European Aquatic RESources leading to this results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 678193 in H2020‐EU.3.2. ‐ SOCIETAL CHALLENGES ‐ Food security, sustainable agriculture and forestry, marine, maritime and inland water research, and the bioeconomy.

This dataset provides the output of sonic boom simulations under various flight conditions, including predictions using Carlson’s method and higher-fidelity nonlinear propagation simulations (using propaBoom). The data is categorized by flight phase (climb, cruise, descent) and atmosphere models (ISA, SBPW3). Each flight phase includes ray tracing results and sonic boom signatures for multiple azimuth angles. The dataset includes: Carlson Method Predictions: Peak overpressure and signal duration for various off-track angles. Nonlinear Propagation Simulations: Ray tracing data (ground intersections and angles) and shock wave signatures at the beginning of the non-linear shock wave propagation and at ground level. Files are provided in CSV format for easy analysis using standard tools (e.g., Excel, pandas). The dataset contains simulation results for different azimuth angles, e.g., acoustic pressure, and ray intersections for thorough investigation of sonic boom propagation effects. Folder Structure: carlson_predictions.zip: Carlson method results for climb, cruise, and descent operating conditions. nonlinear_propagation_predictions.zip: High-fidelity simulation outputs including ray tracing and propagated ground signatures. readme.md: Readme file with information on the datasets. Usage: Researchers can use these datasets to compare sonic boom prediction methods under the effects of flight conditions and atmospheric variations on sonic boom ground signatures. Contact: For any questions or inquiries regarding this dataset, please contact Jacob Jäschke (jacob.jaeschke[at]tuhh.de) (https://orcid.org/0000-0002-5155-4877).

The SMARTDEST DATASET WP3 v1.0 includes data at sub-city level for 7 cities: Amsterdam, Barcelona, Edinburgh, Lisbon, Ljubljana, Turin, and Venice. It is made up of information extracted from public sources at the local level (mostly, city council open data portals) or volunteered geographic information, that is, geospatial content generated by non-professionals using mapping systems available on the Internet (e.g., Geofabrik). Details on data sources and variables are included in a ‘metadata’ spreadsheet in the excel file. The same excel file contains 5 additional spreadsheets. The first one, labelled #1, was used to perform the analysis on the determinants of the geographical spread of tourism supply in SMARTDEST case study’s cities (in the main document D3.3, section 4.1), The second one (labelled #2) offers information that would allow to replicate the analysis on tourism-led population decline reported in section 4.3. As for spreadsheets named #3-AMS, #4-BCN, and #5-EDI, they refer to data sources and variables used to run follow-up analyses discussed in section 5.1, with the objective of digging into the causes of depopulation in Amsterdam, Barcelona, and Edinburgh, respectively. The column ‘row’ can be used to merge the excel file with the shapefile ‘db_task3.3_SmartDest’. Data are available at the buurt level in Amsterdam (an administrative unit roughly corresponding to a neighbourhood), census tract level in Barcelona and Ljubljana, for data zones in Edinburgh, statistical zones in Turin, and località in Venice.

This excel spreadsheet data is from online and self-administration survey with 55 experts, engaged in the design and implementation of conservation measures in Mozambique. More specifically, it is about the Niassa National Reserve (NNR), the largest Protected Area (PA) in the country and third in Africa. The survey included four sections of both compulsory and non-compulsory questions, mostly in closed ended Likert-scale. In the first section, experts were asked about the main practices that threaten conservation in the NNR and actors who are directly or indirectly responsible for each practice, reasons for local people’s involvement with those practices. The second section was about the effectiveness and limitations of the current compensation measures implemented to engage local people in conservation. In the third section, respondents were asked to select new measures to enhance the current conservation status and engage local people more effectively in conservation. The last section was about the socio-economic profile of respondents. The survey was conducted from June to September 2017. In the first sheet are the raw data, while in the second are questions and the respective codes Data are provided for public use and can serve as a benchmark for further collaboration in order to conduct more comprehensive research, comparative analysis as well as panels data can be derived. This data can also have application in other fields such as statistics, mathematics and computation.

Case notes from HCS staff-community member interactions in Wave 1 counties imported from inconsistently-formatted Excel files Data Types: Meeting Records Temporal Features: Continuous Metadata / Data: Data Primary Unit of Analysis: Community Members, Community Organizations Counties: Wave 1 County Data Only Study Component: Core Data Collected Only in Ohio Primary Data Purpose: Community Engagement and Interaction Logs Topics: Community and Media Engagement

This case study is based on a data-centered health and wellness femtech company called Bellabeat which is based in San Francisco, California. I will be performing the tasks of a Junior Data Analyst in order to answer the key business questions of this case study through the steps of the data analysis process thought in the entirety of the Coursera Google Data Analytics program: Ask, Prepare, Process, Analyze, Share, and Act.

About Bellabeat:

It is a wellness femtech company that manufactures health-focused smart products founded by Urška Sršen and Sando Mur. They aim to develop beautifully designed technology that informs and inspires women around the world. Collecting various data on activities and reproductive health to empower women with knowledge about their own health and habits. Sršen believes that an analysis of Bellabeat's available consumer data would reveal more opportunities for growth.

The task:

As a member of the Bellabeat marketing analytics team, my job is to identify and analyze the trends in smart device usage and how could these trends apply to Bellabeat customers and help influence Bellabeat's marketing strategy.

The dataset to be used in this case study has been made available through Mobius. We will be using the Fitbit Fitness Tracker Data which contains personal fitness tracker from 30 fitbit users.

Google BigQuery SQL and Microsoft Excel will be used to prepare, process, analyze and visualize the data.

A single-centre retrospective case control study was conducted. The protocol defined cases as all neonatal deaths or NICU admissions occurring within an eight-year period from 2012 to 2020, although no neonatal deaths occurred during this period following a vaginal breech birth. Controls were identified as the two vaginal breech births directly prior to the case where no neonatal death nor NICU admission occurred. Two previous births were used to prevent bias on the understanding that an adverse outcome can affect clinical decision-making for subsequent births.12 Any NICU admission was included because this indicates a neonate which requires additional observation, tests and/or intervention. Neonates who are not admitted are deemed as generally well.13 Additionally, separation from the baby was considered an important outcome by our Patient and Public Involvement Group,14 who also requested more information on the timing of cord clamping.The study was conducted within the maternity unit at a London District General Hospital which serves a large population of 176,313 people. Two thirds are of white British ethnicity and one third from Black, Asian and Minority Ethnic (BAME) backgrounds. The community the hospital serves is thought of as affluent, with good employment rates, particularly employment in high-end jobs. The hospital itself serves a wider community than the borough it is situated within and has 5000 births per year. It has a level two NICU situated within the maternity unit. The Algorithm was not in use at the site, and none of the authors were employed by the Trust, during the time period covered by the study. Fifteen cases and thirty controls were identified from routine electronic health records. The Medical Record Numbers were sent to the Health Records Department for the complete files to be retrieved. Data were extracted by the lead researcher from the intrapartum care records and recorded anonymously in a Microsoft Excel spreadsheet.A structured data collection tool was developed based on Reitter et al.13 The data collection tool consisted of information usually recorded in the notes during a breech birth and included: lead professional, type of breech, position, epidural, fetal monitoring, meconium, what emerged first, time each part of the breech born, documented manoeuvres used, time performed and information related to the condition of the neonate at birth.To calculate our sample size, based on the work of Reitter et al,11 we hypothesised that the rate of exposure to a pelvis-to-head interval >3 minutes would be 25% among controls and 75% among cases. Using a case:control ratio of 1:2, we determined that 15 independent cases and 30 controls were required to infer an association between a pelvis-to-head interval >3 minutes and the composite neonatal outcome with a confidence interval of 95% and a power of 80%. First, we calculated the time to event interval for variables of interest. We then reported descriptive statistics for all variables, including means, medians and range for continuous variables. Exposures and confounders were converted into binary variables, reflecting the cut-offs used in the Algorithm. These were then tested against the primary outcome using the non-parametric chi-square, or Fisher’s Exact tests where cell frequencies were too small for the chi-square test. Logistic regression analysis was used to test the predictive values of meeting or exceeding the recommended time limits in the Physiological Breech Birth Algorithm. Further logistic regression analyses were conducted with all variables that showed an association with the composite neonatal outcome to determine their predictive value, and additional variables to explore their potential as confounding factors for investigation in future studies. Finally, a Receiver Operating Characteristics (ROC) curve analysis was conducted to compare the sensitivity and specificity of the 7-5-3 minute time limits. All statistical analyses were performed using IBM SPSS version 26.

Existing studies investigating 30-day in-hospital stroke case fatality rates in sub-Saharan Africa have produced varying results, underscoring the significance of obtaining precise and reliable estimations for this indicator. Consequently, this study aimed to conduct a systematic review and update of the current scientific evidence regarding 30-day in-hospital stroke case fatality and associated risk factors in sub-Saharan Africa. Medline/PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), APA PsycNet (encompassing PsycINFO and PsychArticle), Google Scholar, and Africa Journal Online (AJOL) were systematically searched to identify potentially relevant articles. Two independent assessors extracted the data from the eligible studies using a pre-tested and standardized excel spreadsheet. Outcomes were 30-day in-hospital stroke case fatality and associated risk factors. Data was pooled using random effects model. Ninety-three (93) studies involving 42,057 participants were included. The overall stroke case fatality rate was 27% [25%-29%]. Subgroup analysis revealed 24% [21%-28%], 25% [21%-28%], 29% [25%-32%] and 31% [20%-43%] stroke case fatality rates in East Africa, Southern Africa, West Africa, and Central Africa respectively. Stroke severity, stroke type, untyped stroke, and post-stroke complications were identified as risk factors. The most prevalent risk factors were low (

This repository contains the raw and synthesized data used in the paper “Sustainability and Performance Trustworthiness of IoT Monitoring Software Architectures in the Edge”, published in the Journal of Systems and Software.

The article analyzes sustainability as a key attribute for assessing the trustworthiness of IoT monitoring software architectures deployed at the Edge. In the context of Industry 4.0 and Green software development, the study explores the relationship between sustainability and performance in Edge-based IoT monitoring systems, where energy consumption and response time are critical concerns. An exploratory experimental study is conducted using an indoor environmental monitoring IoT system as a case study. Four different IoT monitoring software architecture configurations are evaluated. Based on thirty experimental executions, the study examines how the distribution of monitoring and processing activities between Edge nodes and servers affects energy consumption and response time. The results demonstrate that balancing responsibilities between the Edge and the server enables the construction of trustworthy IoT monitoring software architectures that reduce both energy consumption and response time.

This repository includes two compressed files containing the experimental datasets used in the study:

1. EnergyConsumption_IoTMonitoringEdgeArchitectures.zip
   This file contains the datasets related to the energy consumption evaluation. It is composed of four folders:

   • AdditionalMetrics: Raw data obtained from 24 experiments that were not directly used to calculate energy consumption but were generated by the measurement tools.

   • BasalEnergyConsumption: Raw data from experiments conducted to measure the basal energy consumption of the Smart Gateway, along with an Excel file containing the basal energy consumption calculations.

   • DataSynthesis: Analysis and synthesis of the energy consumption results.

   • EnergyMeasurementExperiments: Raw energy consumption data obtained from the 24 experiments.

2. ResponseTime_IoTMonitoringEdgeArchitectures.zip
   This file contains the datasets related to response time measurements. It includes five Excel files:

   • Architecture1.xlsx, Architecture2.xlsx, Architecture3.xlsx, and Architecture4.xlsx: Each file contains the raw response time measurements collected for the corresponding software architecture configuration, including measurements obtained at both the server and the Edge.

   • TimesArchitecturesDataSynthesis.xlsx: An Excel file that provides the synthesis and analysis of the response time data across the four evaluated architectures.