As of January 2025, around 13.7 percent of paid iOS apps admitted collecting data from users engaging with their mobile products. In comparison, approximately 53 percent of free-to-download iOS apps reported they collect private data from users worldwide, while approximately 86 percent of paid apps have not declared whether they collect users' privacy data.

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

Data standardization is an important part of effective management. However, sometimes people have data that doesn't match. This dataset includes different ways that counties might get written by different people. It can be used as a lookup table when you need County to be your unique identifier. For example, it allows you to match St. Mary's, St Marys, and Saint Mary's so that you can use it with disparate data from other data sets.

As of February 2022, 71 percent of healthcare leaders surveyed globally said they have confidence in the actionable insights their hospital/healthcare facility is able to extract from available data. Overall, healthcare leaders had high confidence in the data utilization process of their organization and the value that data can bring to their work.

Bangladesh number dataset provides contact information from trusted sources. We only collect phone numbers from reliable sources and define this information. To ensure transparency, we also provide the source URL to show where the information was collected from. In addition, we offer 24/7 support. If you have a question or need help, we’re always here. However, we care about accuracy, so we carefully collect the Bangladesh number dataset from trusted sources. You may rely on this data for business or personal use. With customer support, you’ll never have to wait when you need help or more information. We use opt-in data to respect privacy. This way, we contact only people who want to hear from you. Bangladesh phone data gives you access to contacts in Bangladesh. Here you can filter information by gender, age, and relationship status. This makes it easy to find exactly the people you want to connect with. We define this data by ensuring it follows all GDPR rules to keep it safe and legal. Our system works hard to remove any invalid data so you get only accurate and valid numbers. List to Data is a helpful website for finding important phone numbers quickly. Also, our Bangladesh phone data is suitable for doing business targeting specific groups. You can easily filter your list to focus on specific types of customers. Since we remove invalid data regularly, you don’t have to deal with old or useless numbers. We assure you that all data follows strict GDPR rules, so you can use it without any problems. Bangladesh phone number list is a collection of phone numbers from people in Bangladesh. We define this list by providing 100% correct and valid phone numbers that are ready to use. Also, we offer a replacement guarantee if you ever receive an invalid number. This means you will always have accurate data. We collect phone numbers that we provide based on customer’s permission. Moreover, we work hard to provide the best Bangladesh phone number list for businesses and personal use. We gather data correctly, so you won’t have to worry about getting outdated or incorrect information. Our replacement guarantee means you’ll always have valid numbers, so you can relax and feel confident.

Success.ai provides a robust, enterprise-grade solution with access to over 150 million verified employee profiles, encompassing comprehensive B2B and B2C contact data. This extensive database is crafted to assist organizations in targeting key decision-makers, enhancing recruitment processes, and powering dynamic B2B marketing initiatives. Our offerings are designed to meet diverse industry needs, from small businesses to large enterprises, ensuring global coverage and up-to-date information.

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China Internet Service: Place to Get Online: Net bar data was reported at 19.000 % in Dec 2018. This records a decrease from the previous number of 21.200 % for Jun 2018. China Internet Service: Place to Get Online: Net bar data is updated semiannually, averaging 20.890 % from Jun 1999 (Median) to Dec 2018, with 39 observations. The data reached an all-time high of 42.400 % in Dec 2008 and a record low of 4.000 % in Jun 1999. China Internet Service: Place to Get Online: Net bar data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Device and Place for Internet Access.

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

China Internet Service: Place to Get Online: Work Unit data was reported at 40.600 % in Dec 2018. This records a decrease from the previous number of 41.400 % for Jun 2018. China Internet Service: Place to Get Online: Work Unit data is updated semiannually, averaging 35.700 % from Dec 1998 (Median) to Dec 2018, with 40 observations. The data reached an all-time high of 50.000 % in Dec 1998 and a record low of 20.700 % in Dec 2008. China Internet Service: Place to Get Online: Work Unit data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Device and Place for Internet Access.

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1: The Interstellar Boundary Explorer (IBEX) has operated in space since 2008 updating our knowledge of the outer heliosphere and its interaction with the local interstellar medium. Start-time: 2008-12-25. There are currently 16 releases of IBEX-HI and/or IBEX-LO data covering 2009-2019. 2: This data set is from the Release 16 (6 months-cadence) IBEX-Hi map data for the years 2009-2019 in the form of omnidirectional ENA (hydrogen) fluxes with Compton-Getting correction (cg) of flux spectra for spacecraft motion and correction for ENA survival probability (sp) between 1 and 100 AU. 3. The data consist of all-sky maps in Solar Ecliptic Longitude (east and west) and Latitude angles for ENA (hydrogen) fluxes from IBEX-Hi energy bands 2-6 in numerical data form. Energy channels 2-6 have FWHM ranges of 0.52-0.95, 0.84-1.55, 1.36-2.50, 1.99-3.75, 3.13-6.00 keV, respectively. The corresponding center-point energies are 0.71, 1.11, 1.74, 2.73, and 4.29 keV. Details of the data and enabled science from Release 10 are given in the following journal publication: 4: McComas, D. J., et al. (2017), Seven Years of Imaging the Global Heliosphere with IBEX, Astrophys. J. Supp. Ser., 229(2), 41 (32 pp.), 5: http://doi.org/10.3847/1538-4365/aa66d8 6. The following codes are used to define dataset types:- cg = Compton-Getting corrections have been applied to the data to account for the speed of the spacecraft relative to the direction of arrival of the ENAs.- nocg = no Compton-Getting corrections- sp = survival probability corrections have been applied to the data to account for the loss of ENAs due to radiation pressure, photoionization and ionization via charge exchange with solar wind protons as they stream through the heliosphere. This correction scales the data out from IBEX at 1 AU to ~100 AU. In the original data this mode is denoted as Tabular.- noSP - no survival probability corrections have been applied to the data.- omni = data from all directions.- ram = data was collected when the spacecraft was ramming into the incoming ENAs.- antiram = data was collected when the spacecraft was moving away from the incoming ENAs. 7. The following list associates Release 16 map numbers (1-22) with mission year (1-9), orbits (11-471b), and dates (12/25/2008-12/26/2019):- Map 1: Map2009A, year 1, orbits 11-34, dates 12/25/2008-06/25/2009- Map 2: Map2009B, year 1, orbits 35-58, dates 06/25/2009-12/25/2009- Map 3: Map2010A, year 2, orbits 59-82, dates 12/25/2009-06/26/2010- Map 4: Map2010B, year 2, orbits 83-106, dates 06/26/2010-12/26/2010- Map 5: Map2011A, year 3, orbits 107-130a, dates 12/26/2010-06/25/2011- Map 6: Map2011B, year 3, orbits 130b-150a, dates 06/25/2011-12/24/2011- Map 7: Map2012A, year 4, orbits 150b-170a, dates 12/24/2011-06/22/2012- Map 8: Map2012B, year 4, orbits 170b-190b, dates 06/22/2012-12/26/2012- Map 9: Map2013A, year 5, orbits 191a-210b, dates 12/26/2012-06/26/2013- Map 10: Map2013B, year 5, orbits 211a-230b, dates 06/26/2013-12/26/2013- Map 11: Map2014A, year 6, orbits 231a-250b, dates 12/26/2013-06/26/2014- Map 12: Map2014B, year 6, orbits 251a-270b, dates 06/26/2014-12/24/2014- Map 13: Map2015A, year 7, orbits 271a-290b, dates 12/24/2014-06/24/2015- Map 14: Map2015B, year 7, orbits 291a-310b, dates 06/24/2015-12/23/2015- Map 15: Map2016A, year 8, orbits 311a-330b, dates 12/24/2015-06/23/2016- Map 16: Map2016B, year 8, orbits 331a-351a, dates 06/24/2016-12/26/2016- Map 17: Map2017A, year 9, orbits 351b-371a, dates 12/26/2016-06/24/2017- Map 18: Map2017B, year 9, orbits 371b-391a, dates 06/25/2017-12/25/2017- Map 19: Map2018A, year 10, orbits 391b-411b, dates 12/25/2017-06/28/2018- Map 20: Map2018B, year 10, orbits 412a-431b, dates 06/29/2018-12/26/2018- Map 21: Map2019A, year 11, orbits 432a-451b, dates 12/27/2018-06/27/2019- Map 22: Map2019B, year 11, orbits 452a-471b, dates 06/28/2019-12/26/2019* 8: This particular data set, denoted in the original ascii files as hvset_cg_tabular_NX for N=2009-2019, which indicates a year data collected, and X = A or B, showing first or second half of the year, includes pixel map data from all directions (omnidirectional), CG, SP, 6 month cadence.

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Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

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Oman phone number database is a database of phone numbers that are 100% correct and valid. This data is the most essential tool for growing a telemarketing business. However, if you have a database that is 95% accurate, it will help you in running a business with pleasure. Similarly, you will get those people’s information in detail, which will also help you a lot. Finally, buy this contact number list from our website List to Data at a low rate. As a result, using it will make your business more beneficial. Oman mobile number data will help you build a good image for your telemarketing business. Furthermore, it will help start marketing through SMS and calls with people worldwide. So, buying this database will be the best option for your business. On the other hand, this mobile phone has accurate information about local citizens. The number data of Oman follows an opt-in process. People are permitted to share their phone numbers, so you can safely use their contact information without issues. Yet, buying this contact number list will be faithful for your business because you can get it cheaply. So, if you want to collect this data, then you can visit our site, List to Data.

Kokoro Speech Dataset

Kokoro Speech Dataset is a public domain Japanese speech dataset. It contains 34,958 short audio clips of a single speaker reading 9 novel books. The format of the metadata is similar to that of LJ Speech so that the dataset is compatible with modern speech synthesis systems.

The texts are from Aozora Bunko, which is in the public domain. The audio clips are from LibriVox project, which is also in the public domain. Readings are estimated by MeCab and UniDic Lite from kanji-kana mixture text. Readings are romanized which are similar to the format used by Julius.

The audio clips were split and transcripts were aligned automatically by Voice100.

Sample data

Listen from your browser or download randomly sampled 100 clips.

File Format

Metadata is provided in metadata.csv. This file consists of one record per line, delimited by the pipe character (0x7c). The fields are:

• ID: this is the name of the corresponding .wav file
• Transcription: Kanji-kana mixture text spoken by the reader (UTF-8)
• Reading: Romanized text spoken by the reader (UTF-8)

Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz.

Statistics

The dataset is provided in different sizes, large, small, tiny. small and tiny don't share same clips. large contains all available clips, including small and tiny.

Large:
Total clips: 34958
Min duration: 3.007 secs
Max duration: 14.745 secs
Mean duration: 4.978 secs
Total duration: 48:20:24

Small:
Total clips: 8812
Min duration: 3.007 secs
Max duration: 14.431 secs
Mean duration: 4.951 secs
Total duration: 12:07:12

Tiny:
Total clips: 285
Min duration: 3.019 secs
Max duration: 9.462 secs
Mean duration: 4.871 secs
Total duration: 00:23:08

How to get the data

Because of its large data size of the dataset, audio files are not included in this repository, but the metadata is included.

To make .wav files of the dataset, run

$ bash download.sh

to download the metadata from the project page. Then run

$ pip3 install torchaudio
$ python3 extract.py --size tiny

This prints a shell script example to download MP3 audio files from archive.org and extract them if you haven't done it already.

After doing so, run the command again

$ python3 extract.py --size tiny

to get files for tiny under ./output directory.

You can give another size name to the --size option to get dataset of the size.

Pretrained Tacotron model

• Audio Samples
• Pretrained model

Pretrained Tacotron model trained with Kokoro Speech Dataset and audio samples are available. The model was trained for 21K steps with small. According to the above repo, "Speech started to become intelligible around 20K steps" with LJ Speech Dataset. Audio samples read the first few sentences from Gon Gitsune which is not included in small.

Books

The dataset contains recordings from these books read by ekzemplaro

• 明暗 (Meian) 16:39:29 Online text
• こころ (Kokoro) 08:46:41 Online text
• 田舎教師 (Inaka Kyoshi) 08:13:26 Online text
• 野分 (Nowaki) 4:40:49 Online text
• 草枕 (Kusamakura) 04:27:35 Online text
• 坊っちゃん (Botchan) 04:26:27 Online text
• 雁 (Gan) 03:41:31 Online text
• ごん狐 (Gon gitsune) 0:15:42 Online text
• [コーカサスの禿鷹 (Caucasus no Hagetaka)](https://l...

This layer file consists of three related datasets:
- Statutory boundary polygons of State Forests
- Lands managed by the Division of Forestry within the statutory boundaries, known as Management Units
- Lands managed by the Division of Forestry outside of the statutory boundaries, known as Other Forestry Lands

State Forests - Statutory Boundaries:
This theme shows the boundaries of those areas of Minnesota that have been legislatively designated as State Forests ( http://www.dnr.state.mn.us/state_forests/index.html )

Minnesota's 58 state forests were established to produce timber and other forest crops, provide outdoor recreation, protect watersheds, and perpetuate rare and distinctive species of native flora and fauna. The mapped boundaries are based on legislative/statutory language and are described in broad terms based on legal descriptions. Private or other ownerships included inside a State Forest boundary are typically NOT identified in legislative language and subsequently are NOT mapped in this layer. It is important to note that these data do not represent public ownership. State Forest boundaries often include private land and should not be used to determine ownership. Ownership information can be found in State Surface Interests Administered by MNDNR or by Counties ( https://gisdata.mn.gov/dataset/plan-stateland-dnrcounty ) and the GAP Stewardship 2008 layer ( http://gisdata.mn.gov/dataset/plan-gap-stewardship-2008 ).

Data has been updated during 2009 by the MNDNR Forest Resource Assessment office.

State Forests - Management Units
This theme shows the land owned and managed by the Division of Forestry within the Statutory Boundaries. The shapes were derived mostly from county parcel data, where available, and from plat maps and other ownership resources. This data presents an approximate location of the land ownership and is intended for cartographic purposes only. It is not survey quality and should never be used to resolve land ownership disputes.

State Forests - Other Forest Lands
This theme shows State Forest lands outside of the State Forest Statutory Boundaries. It was derived from MNDNR's Land Records System PLS40 data layer. Sub-40 shapes are not represented. Partial PLS40 ownership is represented as a whole PLS40. This data is not survey quality and should never be used to resolve land ownership disputes.

The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely.

MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 1.1.

The Near Infrared Mapping Spectrometer (NIMS) on the Galileo spacecraft took unique data of Comet Shoemaker-Levy/9's impact with Jupiter. A preliminary analysis of this data is presented in this submission to the Planetary Data System (PDS). It consists of nine small tables with detached labels and documentation.