The Measurable AI Dating App Consumer Transaction Dataset is a leading source of in-app purchases , offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our in-app and 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 - User overlap between competitors - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia - EMEA (Spain, United Arab Emirates) - USA - Europe

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Features/subscription plans purchased - No. of orders per user - 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 michelle@measurable.ai for a data dictionary and to find out our volume in each country.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13364933%2F23694fae55e2e76299358693ba6f32b9%2Flv-share.jpg?generation=1684843825246772&alt=media" alt=""> ➡️ There are total 3 datasets containing valuable information. ➡️ Understand people's fame and behavior's on a dating app platform. | Column Name | Description | |---------------------|------------------------------| | Age | The age of the user. | | Number of Users | The total number of users. | | Percent Want Chats | Percentage of users who want chats. | | Percent Want Friends| Percentage of users who want friendships. | | Percent Want Dates | Percentage of users who want romantic dates. | | Mean Kisses Received| Average number of kisses received by users. | | Mean Visits Received| Average number of profile visits received by users. | | Mean Followers | Average number of followers for each user. | | Mean Languages Known| Average number of languages known by users. | | Total Want Chats | Total count of users interested in chats. | | Total Want Friends | Total count of users looking for friendships. | | Total Want Dates | Total count of users seeking romantic dates. | | Total Kisses Received| Overall count of kisses received by users. | | Total Visits Received| Overall count of profile visits received by users. | | Total Followers | Overall count of followers for all users. | | Total Languages Spoken| Total count of languages spoken by all users. |

SUMMARY

When Dating apps like Tinder were becoming viral, people wanted to have the best profile in order to get more matches and more potential encounters. Unlike other previous dating platforms, those new ones emphasized on the mutuality of attraction before allowing any two people to get in touch and chat. This made it all the more important to create the best profile in order to get the best first impression.

Parallel to that, we Humans have always been in awe before charismatic and inspiring people. The more charismatic people tend to be followed and listened to by more people. Through their metrics such as the number of friends/followers, social networks give some ways of "measuring" the potential charisma of some people.

In regard to all that, one can then think:

what makes a great user profile ? how to make the best first impression in order to get more matches (and ultimately find love, or new friendships) ? what makes a person charismatic ? how do charismatic people present themselves ? In order to try and understand those different social questions, I decided to create a dataset of user profile informations using the social network Lovoo when it came out. By using different methodologies, I was able to gather user profile data, as well as some usually unavailable metrics (such as the number of profile visits).

Content

The dataset contains user profile infos of users of the website Lovoo.

The dataset was gathered during spring 2015 (april, may). At that time, Lovoo was expanding in european countries (among others), while Tinder was trending both in America and in Europe. At that time the iOS version of the Lovoo app was in version 3.

Accessory image data The dataset references pictures (field pictureId) of user profiles. These pictures are also available for a fraction of users but have not been uploaded and should be asked separately.

The idea when gathering the profile pictures was to determine whether some correlations could be identified between a profile picture and the reputation or success of a given profile. Since first impression matters, a sound hypothesis to make is that the profile picture might have a great influence on the number of profile visits, matches and so on. Do not forget that only a fraction of a user's profile is seen when browsing through a list of users.

https://s1.dmcdn.net/v/BnWkG1M7WuJDq2PKP/x480

Details about collection methodology In order to gather the data, I developed a set of tools that would save the data while browsing through profiles and doing searches. Because of this approach (and the constraints that forced me to develop this approach) I could only gather user profiles that were recommended by Lovoo's algorithm for 2 profiles I created for this purpose occasion (male, open to friends & chats & dates). That is why there are only female users in the dataset. Another work could be done to fetch similar data for both genders or other age ranges.

Regarding the number of user profiles It turned out that the recommendation algorithm always seemed to output the same set of user profiles. This meant Lovoo's algorithm was probably heavily relying on settings like location (to recommend more people nearby than people in different places or countries) and maybe cookies. This diminished the number of different user profiles that would be pr...

This dataset provides a collection of user reviews and ratings for dating applications, primarily sourced from the Google Play Store for the Indian region between 2017 and 2022. It offers valuable insights into user sentiment, evolving trends, and common feedback regarding dating apps. The data is particularly useful for practising Natural Language Processing (NLP) tasks such as sentiment analysis, topic modelling, and identifying user concerns.

Columns

• Index: A unique identifier for each review entry.
• Name: The name of the user who left the review.
• Username: The username of the reviewer.
• Review: The textual content of the review left by the user.
• Rating: The numerical rating given by the user to the app, indicating their satisfaction level.
• #ThumbsUp: A measure of how useful the review was perceived to be by other users.
• Date&Time: The specific date and time when the review was posted.
• App: The name of the dating application being reviewed.
• Label Count: A numerical label, the specific purpose of which is not detailed in the provided information, but it appears to relate to ranges of index or other numerical values within the dataset.

Distribution

The dataset is typically provided in a CSV file format. It contains a substantial number of records, estimated to be around 527,000 individual reviews. This makes it suitable for large-scale data analysis and machine learning projects. The dataset structure is tabular, with clearly defined columns for review content, metadata, and user feedback. Specific row/record counts are not exact but are indicated by the extensive range of index labels.

Usage

This dataset is ideally suited for a variety of analytical and machine learning applications: * Analysing trends in dating app usage and perception over the years. * Determining which dating applications receive more favourable responses and if this consistency has changed over time. * Identifying common issues reported by users who give low ratings (below 3/5). * Investigating the correlation between user enthusiasm and their app ratings. * Performing sentiment analysis on review texts to gauge overall user sentiment. * Developing Natural Language Processing (NLP) models for text classification, entity recognition, or summarisation. * Examining the perceived usefulness of top-rated reviews. * Understanding user behaviour and preferences across different dating apps.

Coverage

The dataset primarily covers user reviews from the Google Play Store, specifically for the Indian country region ('in'), despite being titled as "all regions" in some contexts. The data spans a time range from 2017 to 2022, offering a multi-year perspective on dating app trends and user feedback. There are no specific demographic details for the reviewers themselves beyond their reviews and ratings.

License

CCO

Who Can Use It

This dataset is suitable for: * Data Scientists and Analysts: For conducting deep dives into user sentiment, trend analysis, and predictive modelling. * NLP Practitioners and Researchers: As a practical dataset for training and evaluating natural language processing models, especially for text classification and sentiment analysis tasks. * App Developers and Product Managers: To understand user feedback, identify areas for improvement in their own or competing dating applications, and inform product development strategies. * Market Researchers: To gain insights into the consumer behaviour and preferences within the online dating market. * Students and Beginners: It is tagged as 'Beginner' friendly, making it a good resource for those new to data analysis or NLP projects.

Dataset Name Suggestions

• Google Play Dating App Reviews (India, 2017-2022)
• Indian Dating App User Reviews
• Mobile Dating App Reviews & Ratings
• Dating App Sentiment Analysis Dataset
• Google Play Dating App Feedback

Attributes

Original Data Source: Dating Apps Reviews 2017-2022 (all regions)

This dataset contains user reviews and comments from the Bumble dating application on the Google Play Store. Bumble is an online dating app where, in heterosexual matches, female users typically initiate the first contact. Beyond romantic connections, Bumble also facilitates finding friends through "BFF mode" and business networking via "Bumble Bizz". This dataset is valuable for understanding user experiences and sentiment towards the app.

Columns

• reviewId: A unique identifier for each user's review.
• userName: The name of the user who posted the review.
• userImage: A URL to the user's profile image.
• content: The textual comment or feedback provided by the user.
• score: The rating given by the user, ranging from 1 to 5.
• thumbsUpCount: The number of 'thumbs up' or likes a specific comment received.
• reviewCreatedVersion: The version number of the app on which the review was created.
• at: The date and time when the review was created.
• replyContent: The content of any reply made by the Bumble company to the user's comment.
• repliedAt: The date and time when the company's reply was posted.

Distribution

The dataset is typically provided as a data file, often in CSV format. It appears to contain a substantial number of records, with reviewId having 168,651 unique values. The data quality is rated as 5 out of 5, and the version of this dataset is 1.0.

Usage

This dataset is ideal for: * Natural Language Processing (NLP) tasks, such as sentiment analysis of user comments. * Market research to gain insights into user satisfaction and preferences regarding dating apps. * Analysing app performance based on user ratings and feedback. * Studying trends in social networks and popular culture related to online dating. * Identifying common user issues or popular features within the Bumble app.

Coverage

The dataset is global in its geographic scope. The reviews span a time period from 29 November 2015 to 28 June 2025. It primarily covers the experiences of Google Play Store users of the Bumble app. As of June 2016, 46.2% of Bumble's users were female.

License

CC-BY

Who Can Use It

• Data scientists and machine learning engineers interested in text analysis and sentiment modelling.
• App developers seeking direct user feedback to improve application features and user experience.
• Researchers focusing on online dating dynamics, social media behaviour, and popular culture.
• Businesses aiming to understand consumer sentiment and competitive landscapes in the social networking and dating industries.

Dataset Name Suggestions

• Bumble Google Play Reviews
• Bumble App User Feedback
• Bumble Play Store Ratings
• Bumble Dating App Reviews Dataset

Attributes

Original Data Source: Bumble Dating App - Google Play Store Review

This is a public dataset called OKCupid, collected by Kirkegaard and Bjerrekaer. The dataset is composed of 68,371 records and 2,626 variables. It is shared for educational purposes. Formatted in Arrow Parquet.Description from the authors:"A very large dataset (N=68,371, 2,620 variables) from the dating site OKCupid is presented and made publicly available for use by others. As an example of the analyses one can do with the dataset, a cognitive ability test is constructed from 14 suitable items. To validate the dataset and the test, the relationship of cognitive ability to religious beliefs and political interest/participation is examined. Cognitive ability is found to be negatively related to all measures of religious belief (latent correlations -.26 to -.35), and found to be positively related to all measures of political interest and participation (latent correlations .19 to .32). To further validate the dataset, we examined the relationship between Zodiac sign and every other variable. We found very scant evidence of any influence (the distribution of p-values from chi square tests was flat). Limitations of the dataset are discussed."

Open-access dataset regarding the survey "The association between dating apps and alcohol consumption in an Italian sample of active users, former users and non-users", by L.Flesia, V.Fietta, C.Foresta and M.Monaro

This dataset contains user reviews and ratings for the Hinge dating application, sourced from the Google Play Store. Hinge positions itself as a dating app focused on fostering long-term connections rather than superficial interactions, aiming to attract a younger demographic than Match.com and eHarmony. This data provides valuable insights into user sentiment, feedback, and experiences with the app, making it useful for understanding user satisfaction and identifying trends related to dating app usage.

Columns

• userName: The name of the user who submitted the review.
• userImage: A URL to the profile image that a user has.
• content: The textual comments or feedback made by a user in their review.
• score: The rating provided by the user, ranging from 1 to 5.
• thumbsUpCount: The number of 'thumbs up' received by a person's review.
• reviewCreatedVersion: The version number of the Hinge app on which the review was created.
• at: The date and time when the review was created.
• replyContent: The content of the reply provided to the comment by the company (Hinge/Match Group).
• repliedAt: The date and time when the company's reply was posted.
• reviewId: A unique identifier for each review.

Distribution

The dataset is usually provided in a CSV file format. While specific numbers for total rows or records are not explicitly available, there are 79,065 unique reviewId values and 71,017 unique userName values. The score column shows distributions such as 32,484 reviews with scores between 1.00-1.20 and 22,046 reviews with scores between 4.80-5.00. thumbsUpCount ranges from 0 to 1547. The reviewCreatedVersion is varied, with 79% falling into an "Other" category. The reviews span a considerable time range, with at (creation date) entries from 07 November 2017 to 29 June 2025.

Usage

This dataset is ideal for a variety of applications and use cases, including: * Natural Language Processing (NLP): Performing sentiment analysis on user comments to gauge overall app perception and user satisfaction. * App Performance Analysis: Evaluating user feedback to identify areas for improvement, new feature ideas, or bug detection. * Market Research: Understanding user preferences, pain points, and trends within the dating app market. * Social Science Research: Studying user behaviour, social interactions, and the impact of dating applications on relationships.

Coverage

The dataset's geographic scope is global. The time range for the reviews extends from 07 November 2017 to 29 June 2025. The demographic scope primarily covers users of the Hinge dating app, which targets a younger audience interested in long-term connections. No specific notes on data availability for particular groups or years beyond the stated date range are available in the provided materials.

License

CC-BY-SA

Who Can Use It

• Data Scientists & Analysts: For performing text analysis, sentiment analysis, and trend identification.
• NLP Researchers: To develop and test models for opinion mining and user feedback processing.
• App Developers & Product Managers: To gain insights into user experience and inform product development strategies.
• Social Science Researchers: For academic studies on digital dating, user behaviour, and social dynamics.
• Marketing Professionals: To understand brand perception and consumer sentiment related to the Hinge app.

Dataset Name Suggestions

• Hinge App Google Play Store Reviews
• Google Play Hinge User Feedback
• Hinge Dating App Review Data

Attributes

Original Data Source: Hinge Dating App - Google Play Store Review

The number of users in the 'Online Dating' segment of the eservices market in the United States was forecast to continuously increase between 2024 and 2028 by in total 5.3 million users (+8.76 percent). After the ninth consecutive increasing year, the indicator is estimated to reach 65.86 million users and therefore a new peak in 2028. Notably, the number of users of the 'Online Dating' segment of the eservices market was continuously increasing over the past years.Find further information concerning revenue in the United States and revenue growth in Indonesia. The Statista Market Insights cover a broad range of additional markets.

According to a survey conducted in March 2025, 65 percent of Tinder users in the United States identified as men, and 35 percent identified as women. As of April 2025, Tinder is the most used dating app worldwide.

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

Open-access dataset, related to the paper "What are you looking for? Investigating the association between dating app use and sexual risk behaviors"

SDCC Traffic Congestion Saturation Flow Data for January to June 2023. Traffic volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions. It is recommended that this dataset is read in conjunction with the ‘Traffic Data Site Names SDCC’ dataset.A detailed description of each column heading can be referenced below;scn: Site Serial numberregion: A group of Nodes that are operated under SCOOT control at the same common cycle time. Normally these will be nodes between which co-ordination is desirable. Some of the nodes may be double cycling at half of the region cycle time.system: SCOOT STC UTC (UTC-MX)locn: Locationssite: Site numbersday: Days of the week Monday to Sunday. Abbreviations; MO,TU,WE,TH,FR,SA,SU.date: Reflects correct actual Date of when data was collected.start_time: NOTE - Please ignore the date displayed in this column. The actual data collection date is correctly displayed in the 'date' column. The date displayed here is the date of when report was run and extracted from the system, but correctly reflects start time of 15 minute intervals. end_time: End time of 15 minute intervals.flow: A representation of demand (flow) for each link built up over several minutes by the SCOOT model. SCOOT has two profiles:(1) Short – Raw data representing the actual values over the previous few minutes(2) Long – A smoothed average of values over a longer periodSCOOT will choose to use the appropriate profile depending on a number of factors.flow_pc: Same as above ref PC SCOOTcong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

This dataset provides a detailed collection of Apple App Store reviews for a selection of popular mobile applications, including Uber, Waze, Facebook, Spotify, Netflix, Pinterest, X (formerly Twitter), TikTok, Tinder, and Instagram. It offers insights into public perception and user feedback over several years. The data can be used to extract sentiments and trends, identify app versions that received the most positive or negative feedback, and apply topic modelling to pinpoint common user pain points within these applications.

Columns

• index: A unique identifier for each review record.
• date: The specific date when the review was submitted.
• pseudoUserId: A pseudonymous identifier for the user who submitted the review.
• userName: The username of the reviewer.
• title: The title provided by the user for their review.
• review: The full text content of the user's review.
• rating: The numerical rating given by the user, typically on a scale of 1 to 5.
• isEdited: A boolean indicator showing whether the review has been edited (true) or not (false).
• country: The country from which the review originated.
• applicationName: The name of the application being reviewed.

Distribution

The dataset is typically provided in a CSV format. It contains approximately 174,508 records (rows) covering reviews for various applications. The distribution of ratings is as follows: 47,192 reviews are rated between 1.00 and 1.08, 15,217 between 1.96 and 2.04, 17,774 between 3.00 and 3.08, 18,183 between 3.96 and 4.04, and 76,143 between 4.92 and 5.00. The majority of reviews (173,886) are unedited, while 623 reviews have been marked as edited. The number of unique user IDs is 121,028, and there are 173,654 unique usernames.

Usage

This dataset is ideal for: * Sentiment analysis to gauge public opinion on specific app features or overall user satisfaction. * Trend analysis to observe changes in user feedback over time for different app versions. * Identifying pain points through topic modelling of review content, helping app developers prioritise improvements. * Market research to understand user needs and competitive landscapes within the mobile app ecosystem.

Coverage

The dataset primarily covers Apple App Store reviews from the US region. The reviews span a time period from September 2017 to November 2023.

License

CC0

Who Can Use It

• Data Scientists and Analysts: For building predictive models, performing natural language processing (NLP) tasks, and extracting actionable insights from unstructured text data.
• App Developers and Product Managers: To understand user feedback, identify bugs, and inform future product development and updates.
• Market Researchers: For competitive analysis, understanding user sentiment towards specific apps, and identifying market opportunities.
• Academics and Students: For research projects related to user experience, mobile app trends, and sentiment analysis.

Dataset Name Suggestions

• Apple App Store User Reviews (US)
• Mobile Application Feedback Dataset
• App Store Review Data (2017-2023)
• US App Store Public Perception Data

Attributes

Original Data Source: 🏆Uber, FB, Waze, etc US Apple App Store Reviews

This dataset provides information about the number of properties, residents, and average property values for Tinder Box Court cross streets in Boiling Springs, SC.

SDCC Traffic Congestion Saturation Flow Data for January to June 2022. Traffic volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions. It is recommended that this dataset is read in conjunction with the ‘Traffic Data Site Names SDCC’ dataset.A detailed description of each column heading can be referenced below;scn: Site Serial numberregion: A group of Nodes that are operated under SCOOT control at the same common cycle time. Normally these will be nodes between which co-ordination is desirable. Some of the nodes may be double cycling at half of the region cycle time.system: SCOOT STC UTC (UTC-MX)locn: Locationssite: Site numbersday: Days of the week Monday to Sunday. Abbreviations; MO,TU,WE,TH,FR,SA,SU.date: Reflects correct actual Date of when data was collected.start_time: NOTE - Please ignore the date displayed in this column. The actual data collection date is correctly displayed in the 'date' column. The date displayed here is the date of when report was run and extracted from the system, but correctly reflects start time of 15 minute intervals. end_time: End time of 15 minute intervals.flow: A representation of demand (flow) for each link built up over several minutes by the SCOOT model. SCOOT has two profiles:(1) Short – Raw data representing the actual values over the previous few minutes(2) Long – A smoothed average of values over a longer periodSCOOT will choose to use the appropriate profile depending on a number of factors.flow_pc: Same as above ref PC SCOOTcong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

Test data for TableToolkit unit tests. TableToolkit is a web application that displays spatio-temporal coverage of a dataset on a web form that allows users to subset those data. It was developed for Santa Barbara Coastal LTER but could be extended to any project. It takes advantage of the DataManager library distributed with Metacat and the Ruby on Rails web application framework. This test dataset is one of many. This particular one contains a dataset representing a single survey site.

Most people seek to establish romantic or intimate relationships in life, including people with mental health problems. However, this has been a neglected topic in mental health practice and research. This study aimed to investigate views of mental health and social care staff about the appropriateness of helping service users with romantic relationships, barriers to doing this, and suggestions for useful ways to support this. An online survey comprising both closed, multiple response and free-text questions was circulated to mental health organisations across the U.K. via social media, professional networks and use of snowballing sampling. A total of 63 responses were received. Quantitative data were analysed using descriptive statistics, and are reported as frequencies and percentages. Qualitative data were interpreted using thematic analysis, using an inductive approach. Although most participants reported that ‘finding a relationship’ conversations were appropriate in their job role, many barriers to supporting service users were identified, including: a lack of training; concerns about professional boundaries; concerns about service user capacity and vulnerability; and concerns about being intrusive. Participant suggestions for future support included educating service users on safe dating behaviours, and practical interventions such as assisting service users to use dating sites and engage with social activities to develop social skills and meet others. Staff were willing to help service users seek an intimate relationship but may need specific training or guidance to facilitate this confidently and safely. This study elucidates the need for further research in this area, particularly in understanding service user perspectives, and in developing resources to support staff in this work.

This dataset is about: Radiocarbon dating for Site MOT15-2. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.893286 for more information.

The Measurable AI UberEats E-Receipt 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 (Taiwan, Japan, Australia) - Americas (United States, Mexico, Chile) - EMEA (United Kingdom, France, Italy, United Arab Emirates, AE, South Africa)

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 UberEats 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.

This database consists of radiocarbon dates from archaeological contexts on the coastal plain of Georgia (samples from strictly geological contexts were not included). A comprehensive search was performed to find the original sources of dates reported for all archaeological sites in this area. As such, dates reported from any time (i.e., 1960s to the present) were incorporated, which includes dates with problems. Data were compiled by John Turck and numerous undergraduate work study and volunteer students over a large period of time (from January 2012 to July 2012, and from January 2013 to April 2013). John Turck added to and refined the dataset between May 2013 and February 2014. In general, the database was structured so the information could be easily input into CALIB's online calibration program. It includes information such as: sample IDs, raw age and standard deviation, delta 13 correction factor, adjusted age and standard deviation, site number and name, material, association, and references. Calibrated dates were not entered into this databse. These data can be used to aid in archaeological studies, refining our understanding of the timing of human occupations throughout the coastal plain, and especially in the coastal zone. These data can also be used to aid geological, and geomorphological studies. The nature of the data is such that it will need to be continually added to as new samples are processesd, and further refined as more information about dates entered previosuly are obtained. Note: The original radiocarbon date database contains sensitive information (i.e., the specific location of archaeological sites) that is for professional archaeologists only. If a professional archaeologist needs site location information, they can contact the Georgia Archaeological Site File.