According to a January 2022 analysis of the leading dating apps downloaded from the Apple App Store, the Badoo mobile app was indexed as collecting the largest number of data types from its users' activity. Bumble and HER ranked second, with an index value of close to 68, respectively. Grindr had a reported index value of 62, while market leader Tinder was indexed with a value of 38.4.

Description of the dataset

The online dating romance scam is a relatively new and under-reported international crime with serious financial and emotional consequences. Little is known about psychological characteristics that may put people at risk of victimization. This study was interested in the typology of victims of this crime. This website includes information on the scams and scammers, and a discussion forum used by members to exchange information and offer support. The majority of site users are victims, but others who have an interest but have not themselves been defrauded also visit it. With the moderators’ permission, we were able to post a recruitment message on the forum. In the period from 17th May to 8th September 2011, 603 individuals accessed the survey. Of these, 405 completed it fully and indicated that their data could be used for analysis in their answer to the second informed consent item at the end of the questionnaire.

Variables include the following: Country (country of residence); DOB (date of birth); Sex; sexuality, status (relationship status), length (longest amount of time spent in a relationship), education (level), job, job status, income, how_ recruited, interview (if want to be followed up), sample (where recruited), ISE (total internet self efficacy score), UCLA (loneliness scale score), BSSS (sensation seeking score), love finds (love finds a way score), One and only (one and only score), idealisation (idealisation score), tot_romanticism, extraversion, openness, neuroticism, conscientiousness, agreeableness, tricked by, age, type of victim, lost cash or not.

This project investigated the types of people conned by the online romance scam and how such deception psychologically affects a person, as well as the types of strategies that scammers use to con their victims. Specifically, the objectives of the research are to: (1) Devise a typology of the personality traits as well as other characteristics for the types of individuals who are more likely to be conned by the online romance scam. (2) Examine the persuasive techniques employed to con individuals. (3) Examine the psychological consequences of being taken in by such a scam. (4) Inform interested parties of the results of this project to help prevent these scams from happening in the first place.

CAL FIRE's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, the National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data.

This app contains three pages of maps and documentation of the historical fire perimeter metadata:

Historical Fire Perimeters: The landing page highlights the recent large fires (≥5,000 acres) on a backdrop of all of the dataset's documented fire perimeters dating back to 1878. This map includes perimeters symbolized by decade, county boundaries, California Vegetation, and NAIP imagery back to 2005. This page provides users the ability to add their own data or filter the fire perimeter data. It cleanly lists fire perimeters shown on the map with their name, year, and GIS calculated acreage. The user can navigate to the CAL FIRE current incident webpage or provide comments to the dataset's steward.

Times Burned: The second page provides a map showing an analysis performed annually on the fire perimeter dataset to show case burn frequency from 1950 to present for fires greater than one acre.

Fire Across Time: This third page provides a time enabled layer of the fire perimeter dataset, featuring a time slider to allow users to view the perimeter dataset across time.

The final page provides the user with the dataset's metadata, including its most current data dictionary.

For any questions, please contact the data steward:

Kim Wallin, GIS Specialist

CAL FIRE, Fire & Resource Assessment Program (FRAP)

kimberly.wallin@fire.ca.gov

This dataset is about books and is filtered where the book is Creating dynamic UIs with Android fragments : create engaging apps with fragments to provide a rich user interface that dynamically adapts to the individual characteristics of your customers' tablets and smartphones. It has 7 columns such as author, BNB id, book, book publisher, and ISBN. The data is ordered by publication date (descending).

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 with a composite date. Meaning, there are separate year, month, and day columns.

1 Description

SEN2VENµS is an open dataset for the super-resolution of Sentinel-2 images by leveraging simultaneous acquisitions with the VENµS satellite. The dataset is composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meters resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.

2 Files organization

The dataset is composed of separate sub-datasets, one for each site, as described in table 1.

Table 1: Number of patches and pairs for each site, along with VENµS viewing zenith angle


    Site
    Number of patches
    Number of pairs
    VENµS Zenith Angle




    FR-LQ1
    4888
    18
    1.795402


    NARYN
    3814
    25
    5.010906


    FGMANAUS
    129
    4
    7.232127


    MAD-AMBO
    1443
    19
    14.788115


    ARM
    15859
    39
    15.160683


    BAMBENW2
    9018
    34
    17.766533


    ES-IC3XG
    8823
    35
    18.807686


    ANJI
    2314
    16
    19.310494


    ATTO
    2258
    9
    22.048651


    ESGISB-3
    6057
    19
    23.683871


    ESGISB-1
    2892
    13
    24.561609


    FR-BIL
    7105
    30
    24.802892


    K34-AMAZ
    1385
    21
    24.982675


    ESGISB-2
    3067
    13
    26.209776


    ALSACE
    2654
    17
    26.877071


    LERIDA-1
    2281
    6
    28.524780


    ESTUAMAR
    912
    13
    28.871947


    SUDOUE-5
    2176
    20
    29.170244


    KUDALIAR
    7269
    20
    29.180855


    SUDOUE-6
    2435
    14
    29.192055


    SUDOUE-4
    935
    7
    29.516127


    SUDOUE-3
    5363
    14
    29.998115


    SO1
    12018
    36
    30.255978


    SUDOUE-2
    9700
    27
    31.295256


    ES-LTERA
    1701
    19
    31.971764


    FR-LAM
    7299
    22
    32.054056


    SO2
    738
    22
    32.218481


    BENGA
    5858
    29
    32.587334


    JAM2018
    2564
    18
    33.718953

For each site, the sub-dataset folder contains a set of files for each date, following this naming convention as the pair id: {site_name}_{mgrs_tile}_{acquisition_date}. For each pair, 5 files are available, as shown in table 2. Patches are encoded as ready-to-use tensors as serialized by the well known Pytorch library1. As such they can be loaded by a simple call to the torch.load() function. Note that bands are separated into two groups (10m and 20m Sentinel2 bands), which leads to four separate tensor files (2 groups of bands (\times) source and target resolution). Tensor shape is [n,c,w,h] where (n) is the number of patches, (c=4) is the number of bands, (w) is the patch width and (h) is the patch height. In order to save storage space, they are encoded as 16 bits signed integers and should be converted back to floating point surface reflectance by dividing each and every value by 10 000 upon reading.

Table 2: Naming convention for files associated to each pair. {id} is {site_name}_{mgrs_tile}_{acquisition_date}.


    File
    Content




    {id}_05m_b2b3b4b8.pt
    5m patches (\(256\times256\) pix.) for S2 B2, B3, B4 and B8 (from VENµS)


    {id}_10m_b2b3b4b8.pt
    10m patches (\(128\times128\) pix.) for S2 B2, B3, B4 and B8 (from Sentinel-2)


    {id}_05m_b5b6b7b8a.pt
    5m patches (\(256\times256\) pix.) for S2 B5, B6, B7 and B8A (from VENµS)


    {id}_20m_b5b6b7b8a.pt
    20m patches (\(64\times64\) pix.) for S2 B5, B6, B7 and B8A (from Sentinel-2)


    {id}_patches.gpkg
    GIS file with footprint of each patch

Each file comes with a master index.csv CSV (Comma Separated Values) file, with one row for each pair sampled in the given site, and columns as described in table 3, separated with tabs.

Table 3: Columns of the index.csv file indexing pairs for each site. For file naming conventions, refer to table 2.


    Column
    Description




    venus_product_id
    ID of the sampled VENµS L2A product


    sentinel2_product_id
    ID of the sampled Sentinel-2 L2A product


    tensor_05m_b2b3b4b8
    Name of the 5m tensor file for S2 B2, B3, B4 and B8 (from VENµS)


    tensor_10m_b2b3b4b8
    Name of the 10m tensor file for S2 B2, B3, B4 and B8 (from Sentinel-2)


    tensor_05m_b5b6b7b8a
    Name of the 5m tensor file for S2 B5, B6, B7 and B8A (from VENµS)


    tensor_20m_b5b6b7b8a
    Name of the 20m tensor file for S2 B5, B6, B7 and B8A (from Sentinel-2)


    s2_tile
    Sentinel-2 MGRS tile


    vns_site
    Name of VENµS site


    date
    Acquisition date as YYYY-MM-DD


    venus_zenith_angle
    VENµS zenith viewing angle in degrees


    patches_gpkg
    Name of the GIS file with footprint for each patch


    nb_patches
    Number of patches for this pair

Each site folder is compressed to a different 7z file.

3 Licencing

3.1 Sentinel-2 patches

3.1.1 Copyright

Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The processing uses algorithms developed by Theia's Scientific Expertise Centres. Note: Copernicus Sentinel-2 Level 1C data is subject to this license: https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf

3.1.2 Licence

Files {id}_05m_b2b3b4b8.pt and {id}_05m_b5b6b7b8a.pt are distributed under the the original licence of the Sentinel-2 Theia L2A products, which is the Etalab Open Licence Version 2.0 2.

3.2 VENµS patches

3.2.1 Copyright

Value-added data processed by CNES for the Theia data centre www.theia-land.fr using VENµS satellite imagery from CNES and Israeli Space Agency. The processing uses algorithms developed by Theia's Scientific Expertise Centres.

3.2.2 Licence

Files {id}_05m_b2b3b4b8.pt and {id}_05m_b5b6b7b8a.pt are distributed under the original licence of the VENµS products, which is Creative Commons BY-NC 4.0 3.

3.3 Remaining files

All remaining files are distributed under the Creative Commons BY 4.0 4 licence.

4 Note to users

Note that even if the VenµS2 dataset is sorted by sites and by pairs, we strongly encourage users to apply the full set of machine learning best practices when using it : random keeping separate pairs (or even sites) for testing purpose, and randomization of patches accross sites and pairs in the training and validation sets.

5 Citing

Please cite the following data paper (preprint, submitted to MDPI Data) and zenodo link when publishing work derived from this dataset:

Michel, J.; Vinasco-Salinas, J.; Inglada, J.; Hagolle, O. SEN2VENµS, a Dataset for the Training of Sentinel-2 Super-Resolution Algorithms. Data 2022, 7, 96. https://doi.org/10.3390/data7070096

https://zenodo.org/deposit/6514159

Footnotes:

1

https://pytorch.org/

2

https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf

3

https://creativecommons.org/licenses/by-nc/4.0/

4

https://creativecommons.org/licenses/by/4.0/

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) - Continental Europe - USA

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.

We seek to mitigate the challenges with web-scraped and off-the-shelf POI data, and provide tailored, complete, and manually verified datasets with Geolancer. Our goal is to help represent the physical world accurately for applications and services dependent on precise POI data, and offer a reliable basis for geospatial analysis and intelligence.

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Quadrant’s POI data are manually collected and verified by Geolancers. Our network of freelancers, maps cities and neighborhoods adding and updating POIs on our proprietary app Geolancer on their smartphone. Compared to other methods, this process guarantees accuracy and promises a healthy stream of POI data. This method of data collection also steers clear of infringement on users’ privacy and sale of their location data. These purpose-built apps do not store, collect, or share any data other than the physical location (without tying context back to an actual human being and their mobile device).

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The main goal of POI data is to identify a place of interest, establish its accurate location, and help businesses understand the happenings around that place to make better, well-informed decisions. POI can be essential in assessing competition, improving operational efficiency, planning the expansion of your business, and more.

It can be used by businesses to power their apps and platforms for last-mile delivery, navigation, mapping, logistics, and more. Combined with mobility data, POI data can be employed by retail outlets to monitor traffic to one of their sites or of their competitors. Logistics businesses can save costs and improve customer experience with accurate address data. Real estate companies use POI data for site selection and project planning based on market potential. Governments can use POI data to enforce regulations, monitor public health and well-being, plan public infrastructure and services, and more. A few common and widespread use cases of POI data are:

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• Advertising and marketing deployment and ROI assessment.
• Real-estate mapping for online sales and renting platforms.About Geolancer

ABOUT GEOLANCER

Quadrant's POI-as-a-Service is powered by Geolancer, our industry-leading manual verification project. Geolancers, equipped with a smartphone running our proprietary app, manually add and verify POI data points, ensuring accuracy and authenticity. Geolancer helps data buyers acquire data with the update frequency suited for their specific use case.

As of September 2021, around 51 percent of Tinder users in India were aged between 25 and 34 years and around 25 percent of users were aged between 35 and 44 years. Tinder is the most popular dating app in India.

In 2023, Tinder's direct revenue amounted to 1.9 billion U.S. dollars, an increase of around seven percent from the previous year. Tinder is an online dating application that allows users to anonymously swipe to like or dislike other profiles based on photos. It is owned by the internet company Match Group, Inc.