This dataset provides daily, aggregated mobile app usage statistics, including launch counts, session lengths, and retention rates, segmented by platform, country, and device type. It enables detailed analysis of user engagement, retention, and growth trends across different mobile applications and markets, supporting strategic decisions for app development and marketing.

This dataset supports a retrospective cohort study evaluating the 1- through 5-year retention rates of Advanced Practice Providers (APPs) in hospital medicine. The study compares outcomes between providers who completed a 6-month post-graduate fellowship and those who completed general onboarding at a multi-hospital health system in Massachusetts.The data includes de-identified employment dates, training pathway designation, and year-based retention outcomes. A secondary file includes the SPSS cross-tabulation and chi-square output used to evaluate statistical differences across retention time points. These data were used to support findings reported in the manuscript submitted to The Journal of Hospital Medicine.Files included:APP_Hospital_Medicine_5_Year_RawData.xlsxAPP_Hospital_Medicine_SPSS_Output.xlsxREADME_APP_Fellowship_Retention.txt

Apprenticeship national achievement rate tables.Academic year: 2021/22 and 2022/23Indicators: Leavers, Completers, Achievers, Pass rate, Retention rate, Achievement rateFilters: Sector Subject Area T1, Level, Standard and Framework

The Measurable AI Amazon Consumer Transaction Dataset is a leading source of email receipts and consumer transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan) - EMEA (Spain, United Arab Emirates)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

Apprenticeship national achievement rate tables.Academic year: 2020/21 to 2022/23Indicators: Leavers, Completers, Achievers, Pass rate, Retention rate, Achievement rateFilters: Level, Age, Sex, LLDD, Ethnicity

The Measurable AI FoodPanda Food & Grocery Transaction dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Hong Kong, Taiwan, Singapore, Thailand, Malaysia, Philippines, Pakistan)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the FoodPanda food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

Apprenticeship national achievement rate tables.Academic year: 2020/21 to 2022/23Indicators: Leavers, Completers, Achievers, Pass rate, Retention rate, Achievement rateFilters: Standard and Framework flag, STEM, Sector Subject Area T1, Level, Detailed Level, Age, IMD deprivation quintile

Predict students' dropout and academic success

Investigating the Impact of Social and Economic Factors

By [source]

About this dataset

This dataset provides a comprehensive view of students enrolled in various undergraduate degrees offered at a higher education institution. It includes demographic data, social-economic factors and academic performance information that can be used to analyze the possible predictors of student dropout and academic success. This dataset contains multiple disjoint databases consisting of relevant information available at the time of enrollment, such as application mode, marital status, course chosen and more. Additionally, this data can be used to estimate overall student performance at the end of each semester by assessing curricular units credited/enrolled/evaluated/approved as well as their respective grades. Finally, we have unemployment rate, inflation rate and GDP from the region which can help us further understand how economic factors play into student dropout rates or academic success outcomes. This powerful analysis tool will provide valuable insight into what motivates students to stay in school or abandon their studies for a wide range of disciplines such as agronomy, design, education nursing journalism management social service or technologies

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How to use the dataset

This dataset can be used to understand and predict student dropouts and academic outcomes. The data includes a variety of demographic, social-economic and academic performance factors related to the students enrolled in higher education institutions. The dataset provides valuable insights into the factors that affect student success and could be used to guide interventions and policies related to student retention.

Using this dataset, researchers can investigate two key questions: - which specific predictive factors are linked with student dropout or completion? - how do different features interact with each other? For example, researchers could explore if there any demographic characteristics (e.g., gender, age at enrollment etc.) or immersion conditions (e.g., unemployment rate in region) are associated with higher student success rates, as well as understand what implications poverty has for educational outcomes. By answering these questions, research insight is generated which can provide critical information for administrators on formulating strategies that promote successful degree completion among students from diverse backgrounds in their institutions.

In order to use this dataset effectively it is important that scientists familiarize themselves with all variables provided in the dataset including categorical (qualitative) variables such as gender or application mode; numerical variables such as number of curricular units at the beginning of semesters or age at enrollment; ordinal data measurement type variables such as marital status; studied trends over time such as inflation rate or GDP; frequency measurements variables like percentage of scholarship holders; etc.. Additionally scientists should make sure they aware off all potential bias included in the data prior running analysis–for example understanding if one population is underrepresented compared another -as this phenomenon could lead unexpected results if not taken into consideration while conducting research undertaken using this data set.. Finally it would be important for practitioners realize that this current Kaggle Dataset contains only one semester-worth information on each admission intake whereas additional studies conducted for a longer time period might be able provide more accurate results related selected topic area due further deterioration retention achievement coefficients obtained from those gradually accurate experiments unfolding different year-long admissions seasons

Research Ideas

• Prediction of Student Retention: This dataset can be used to develop predictive models that can identify student risk factors for dropout and take early interventions to improve student retention rate.
• Improved Academic Performance: By using this data, higher education institutions could better understand their students' academic progress and identify areas of improvement from both an individual and institutional perspective. This will enable them to develop targeted courses, activities, or initiatives that enhance academic performance more effectively and efficiently.
• Accessibility Assistance: Using the demographic information included in the dataset, institutions could develop s...

This randomized controlled trial research study will be evaluating an app, reSET-O, owned by Pear Therapeutics, Inc., to evaluate treatment retention rates in individuals with opioid use disorder after initiating medication assisted treatment.

BackgroundSalt reduction is a cost-effective, and rather challenging public health strategy for controlling chronic diseases. The AppSalt program is a school-based multi-component mobile health (mhealth) salt reduction program designed to tackle the high salt intake in China. This mixed-methods process evaluation was conducted to investigate the implementation of this program across sites, identify factors associated with the implementation, and collect evidence to optimize the intervention design for future scale-up.MethodsMixed methods were used sequentially to collect data regarding five process evaluation dimensions: fidelity, dose delivered, dose received, reach, and context. Quantitative data were collected during the intervention process. Participation rate of intervention activities was calculated and compared across cities. The quantitative data was used for the selection of representative intervention participants for the qualitative interviews. Qualitative data were collected in face-to-face semi-structured interviews with purposively selected students (n = 33), adult family members (n = 33), teachers (n = 9), heads of schools (n = 9), key informants from local health, and education departments (n = 8). Thematic analysis technique was applied to analyze the interview transcripts using NVivo. The qualitative data were triangulated with the quantitative data during the interpretation phase.ResultsThe total number of families recruited for the intervention was 1,124. The overall retention rate of the AppSalt program was 97%. The intervention was implemented to a high level of fidelity against the protocol. About 80% of intervention participants completed all the app-based salt reduction courses, with a significant difference across the three cities (Shijiazhuang: 95%; Luzhou: 73%; Yueyang: 64%). The smartphone app in this program was perceived as a feasible and engaging health education tool by most intervention participants and key stakeholders. Through the interviews with participants and key stakeholders, we identified some barriers to implementing this program at primary schools, including the left-behind children who usually live with their grandparents and have limited access of smartphones; perceived adverse effects of smartphones on children (e.g., eyesight damage); and overlooked health education curriculum at Chinese primary schools.ConclusionThis process evaluation demonstrated the feasibility and acceptability of using smartphone applications delivered through the education system to engage families in China to reduce excessive salt intake.Clinical Trial RegistrationThe AppSalt study was registered at www.chictr.org.cn, identifier: ChiCTR1800017553. The date of registration is August 3, 2018.

Abstract Objective The aim of this randomized, controlled, prospective clinical trial was to evaluate the performances of two different universal adhesives and one etch-rinse adhesive for restoration of non-carious cervical lesions (NCCLs). Material and Methods Twenty patients with at least seven NCCLs were enrolled. Lesions were divided into seven groups according to adhesive systems and application modes: GSE: GLUMA Universal-self-etch, GSL: GLUMA Universal-selective etching, GER: GLUMA Universal-etch-and-rinse, ASE: All-Bond Universal-self-etch, ASL: All-Bond Universal-selective etching, AER: All-Bond Universal-etch-and-rinse, SBE (Control): Single Bond2-etch-and-rinse. A total of 155 NCCLs were restored with a nano hybrid composite (Tetric N-Ceram). Restorations were scored with regard to retention, marginal discoloration, marginal adaptation, recurrent caries and post-operative sensitivity using modified United States Public Health Service (USPHS) criteria after one week, 6, 12 and 24 months. Statistical evaluations were performed using Chi-square tests (p=0.05). Results The recall rate was 81.9% after the 24-month follow-up. The cumulative retention rates for self-etch groups (GSE: 72.2%, ASE:75%) were significantly lower than other experimental groups (GSL: 93.7%, GER: 100%, ASL: 94.1%, AER: 100%, SBE: 100%) at the 24-month follow-up (p<0.05). Regarding marginal adaptation and marginal discoloration, GSE and ASE groups demonstrated more bravo scores after 6 and 12-month observations but differences were not significant (p>0.05). Only one restoration from ASL group demonstrated post-operative sensitivity at 6 and 12-month observations. No secondary caries was observed on the restorations at any recall. At the end of 24-month observations, no significant differences were detected among groups regarding any of the criteria assessed, except retention. Conclusion GLUMA Universal and All-Bond Universal showed better results in etch-and-rinse and selective etching mode compared to the self-etch mode regarding retention. Etch-and-rinse and selective etching application modes of the current universal adhesives tended to provide better clinical outcomes considering the criteria evaluated at the end of 24-month evaluation.

Mobile App Statistics (Apple iOS app store)

The ever-changing mobile landscape is a challenging space to navigate. . The percentage of mobile over desktop is only increasing. Android holds about 53.2% of the smartphone market, while iOS is 43%. To get more people to download your app, you need to make sure they can easily find your app. Mobile app analytics is a great way to understand the existing strategy to drive growth and retention of future user.

With million of apps around nowadays, the following data set has become very key to getting top trending apps in iOS app store. This data set contains more than 7000 Apple iOS mobile application details. The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Interactive full Shiny app can be seen here( https://multiscal.shinyapps.io/appStore/)

Data collection date (from API); July 2017

Dimension of the data set; 7197 rows and 16 columns

Content:

appleStore.csv

1. "id" : App ID

2. "track_name": App Name

3. "size_bytes": Size (in Bytes)

4. "currency": Currency Type

5. "price": Price amount

6. "rating_count_tot": User Rating counts (for all version)

7. "rating_count_ver": User Rating counts (for current version)

8. "user_rating" : Average User Rating value (for all version)

9. "user_rating_ver": Average User Rating value (for current version)

10. "ver" : Latest version code

11. "cont_rating": Content Rating

12. "prime_genre": Primary Genre

13. "sup_devices.num": Number of supporting devices

14. "ipadSc_urls.num": Number of screenshots showed for display

15. "lang.num": Number of supported languages

16. "vpp_lic": Vpp Device Based Licensing Enabled

appleStore_description.csv

1. id : App ID
2. track_name: Application name
3. size_bytes: Memory size (in Bytes)
4. app_desc: Application description

Acknowledgements

The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Inspiration

1. How does the App details contribute the user ratings?
2. Try to compare app statistics for different groups?

Reference: R package From github, with devtools::install_github("ramamet/applestoreR")

Licence

Copyright (c) 2018 Ramanathan Perumal

It's the raw data of iOS game app. It's included postback events which in-app game environment.

I'd like to share the small dataset that you could practice how to optimize the mobile game using in-app data. I believe that it may help you to practice if you're just a beginner or never seen such a dataset before. You could simulate how to optimize the in-app game data. Which channel is good or bad.

Let's think about it if you are working at a game company and you are a game performance marketer which channel should be optimized ASAP? You could practice following the below KPI. Good Luck!

[Information of App] Publisher: - Category: Game OS: iOS Language: Korean Age: 12+ Price: Free

[Information of Event Postback] open: app open af_complete_registration: registration join_the_guild: join the guild purchase: purchase af_level_5_achieved: achieved level 5 af_level_8_achieved: achieved level 8 af_level_10_achieved: achieved level 10 af_level_15_achieved: achieved level 15 af_level_20_achieved: achieved level 20 auto_play : after level5, it’s allowed autoplay

=====Collect data(.csv file)===== - Raw data (1)channel_event.csv (2)d1.csv

• Pivoting data (using raw data) (1)gagong.csv

[Information of channel_event.csv] event: postbacked event name channel: channel name country: country language: language os: mobile phone operating system device: mobile device

[Information of d1.csv] channel: channel name install: counted installs day1: day 1 retention

[Information of gagong.csv] channel: channel name install: counted installs af_complete_registration: once a user completed the registration it's pushed(counted) af_level5_achieved: achieved level5 af_level8_achieved: achieved level8 af_purchase day1: purchase nru: new registered user rate Lv5: achieved level 5 rate purchase_rate: purchase rate day1_retention: day 1 retention rate

This dataset captures user perceptions related to trust, perceived security, ease of payment, customer retention, and switching costs in the context of neobanking services. The data was collected through a structured survey focused on evaluating how individuals assess their financial security and loyalty when using digital financial platforms, particularly neobanks.

The survey includes Likert-scale items across five main constructs—Trust, Perceived Security, Ease of Payment, Customer Retention, and Cost of Switching to Traditional Banks—as well as demographic variables such as gender, age, and educational level. I took the initiative to reorganize the data for better clarity, the resulting dataset provides a solid foundation for statistical analysis, modeling of user satisfaction, or deeper behavioral insights. This resource can support academic research, product development, or strategic planning aimed at enhancing user experience and trust in digital financial services.

All individuals who responded to the survey hold an account with at least one of the neobanks listed in the attached Excel file. The specific bank to which each respondent belongs is not included in the dataset, in order to avoid bias, promotional implications, or any inference regarding the quality or preference of a particular institution for the purposes of this study.

If you found this dataset valuable for your analysis or projects, please consider giving it an upvote. Your support not only acknowledges the effort invested in preparing and sharing this resource, but it also helps elevate its visibility within the Kaggle community, enabling more researchers and practitioners to benefit from it. Thank you for your contribution to open data!

Features

Age * Open answer, the answer is a number

Gender * Female=1 * Male=2 * Other=3

Educational level * 1= Primary school * 2= High school (Secundaria) * 3= High school (Preparatoria) * 4= Bachelor degree * 5= Postgraduate

EP= Easy of payment (1 Strongly disagree 2 Disagree 3 Neither agree nor disagree 4 Agree 5 Strongly agree) * EP1. The payment procedures of this application are efficient * EP2. Financial transactions (e.g., sending money, payments, purchases, etc.) in this application do not take a lot of time * EP3. Financial operations (e.g., sending money, payments, purchases, etc.) in this application are user-friendly * EP4. Financial operations (e.g., sending money, payments, purchases, etc.) through this app are easy to use * EP5. Financial transactions (e.g., sending money, payments, purchases, etc.) through the application do not involve a lot of data entry

SCTB= Switching cost of moving to a traditional bank (1 Strongly disagree 2 Disagree 3 Neither agree nor disagree 4 Agree 5 Strongly agree) * SCTB1. It would take me too long to switch from this neobank to a traditional bank (eg, Banamex, BBVA, HSBC, Santander, Banorte) * SCTB2. It would take me too long to switch to a traditional bank (e.g., Banamex, BBVA, HSBC, Santander, Banorte) * SCTB3. It would take me too long to become familiar with the policies of a traditional bank (e.g., Banamex, BBVA, HSBC, Santander, Banorte) * SCTB4. It would take me too long to complete the forms to switch to a traditional bank (e.g., Banamex, BBVA, HSBC, Santander, Banorte) * SCTB5. I am not sure if I would receive any benefits if I switched to a traditional bank (e.g., Banamex, BBVA, HSBC, Santander, Banorte)

CR= Customer retention (1 Strongly disagree 2 Disagree 3 Neither agree nor disagree 4 Agree 5 Strongly agree) * CR1. I frequently recommend that other people use the services of this neobank * CR2. In the future, I will continue to make financial transactions through this company * CR3. I would recommend this neobank to friends, family and acquaintances

T= Trust (1 Strongly disagree 2 Disagree 3 Neither agree nor disagree 4 Agree 5 Strongly agree) * T1. I trust this company * T2. I trust this company's advice * T3. I consider this company to be honest * T4. This company's application is safe to use

PS= Perceived security (1 Strongly disagree 2 Disagree 3 Neither agree nor disagree 4 Agree 5 Strongly agree) * PS1. It seems that this neobank is very secure * PS2. I feel confident to perform financial transactions (e.g., sending money, payments, purchases, etc.) through this app * PS3. The security systems of this application seem to be rigorous * PS4. When performing financial transactions on this application I am reassured by its security procedures * PS5. In general, this application seems to be concerned about security

Bonini, P., Kind, T., Tsugawa, H., Barupal, D.K. and Fiehn, O., 2020. Retip: retention time prediction for compound annotation in untargeted metabolomics. Analytical Chemistry.

Paper abstract : Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching and retention time. We here show how retention times can be predicted from molecular structures. Two large, publicly available data sets were used for model training in machine learning: the Fiehn hydrophilic interaction liquid chromatography data set (HILIC) of 981 primary metabolites and biogenic amines,and the RIKEN plant specialized metabolome annotation (PlaSMA) database of 852 secondary metabolites that uses reversed-phase liquid chromatography (RPLC). Five different machine learning algorithms have been integrated into the Retip R package: the random forest, Bayesian-regularized neural network, XGBoost, light gradient-boosting machine (LightGBM), and Keras algorithms for building the retention time prediction models. A complete workflow for retention time prediction was developed in R. It can be freely downloaded from the GitHub repository (https://www.retip.app). Keras outperformed other machine learning algorithms in the test set with minimum overfitting, verified by small error differences between training, test, and validation sets. Keras yielded a mean absolute error of 0.78 min for HILIC and 0.57 min for RPLC. Retip is integrated into the mass spectrometry software tools MS-DIAL and MS-FINDER, allowing a complete compound annotation workflow. In a test application on mouse blood plasma samples, we found a 68% reduction in the number of candidate structures when searching all isomers in MS-FINDER compound identification software. Retention time prediction increases the identification rate in liquid chromatography and subsequently leads to an improved biological interpretation of metabolomics data.

https://pubs.acs.org/doi/10.1021/acs.analchem.9b05765