LINE number database is an extensive list of people who use the LINE app. Assume you have a list of everyone in your school, but you build a separate list of only the students who enjoy playing soccer. This is what a LINE Database User List looks like. It allows businesses to target a certain set of people who may be interested in their offer. LINE number database is useful as it may be updated regularly. Businesses, like teachers, may update their lists as new users join LINE or as their interests change. This keeps the list functional and allows firms to contact the relevant people. LINE number database may help businesses measure user engagement and improve their marketing campaigns over time. Businesses that keep the list updated and use it appropriately may develop greater ties with their audience and achieve better outcomes. Finally, the LINE Number Database is an effective tool for businesses to reach the appropriate individuals at the correct time. This valuable database is available on List To Data. LINE data is a valuable resource for businesses seeking to connect with potential customers. This dataset encompasses information about individuals who utilize the LINE messaging app. LINE is a popular messaging platform with over 90 million monthly active users worldwide. Users can seamlessly communicate through messages, voice and video calls, and share engaging stickers. This database has information about users, like their names, phone numbers, email addresses, and sometimes even what they like to do on the app. LINE data is a very useful tool. It helps to sell things or provide services. They use the LINE app user database to find people who might be interested in their offers. But businesses need to be careful with this information. People’s details, like their phone numbers and email addresses, are private. Businesses should always ask for permission before using this information. They also need to keep it safe so that no one else can see it. If businesses respect users’ privacy, people will trust them more and be happier to hear about what the business offers. This data is available on List To Data.

This statistic shows a timeline with the amount of monthly active LINE users worldwide as of the fourth quarter of 2016. As of that period, the mobile messaging app recorded 217 million monthly active users (MAU).The Japanese messenger app LINE was developed by engineers at NHN Japan, an arm of the South Korean Naver Corporation, in response to damaged telecommunications infrastructure nationwide in the aftermath of the devastating Tōhoku earthquake in March 2011. It was initially meant as an alternative communication channel for internal company use, but was released to the general public later that year. Although it was intended as a mobile app for devices such as smartphones and tablet computers, later versions were made available on personal computers. Its main functions are exchanges of text, photo, video and audio messages, as well as performing of free Voice over IP (VoIP) conversations and video conferences, playing games and interacting with both personal connections and public accounts. As of 2012, LINE developed into a social network, with features similar to Facebook, such as a timeline where users can post status updates and other content. The sticker store, where users can create and sell their own LINE chat stickers, is one of LINE’s most popular features among Japanese users.

As of **********, three Meta-owned mobile apps, Facebook Messenger, Facebook, and Instagram were reported to collect the largest amount of data from global iOS users. Each of the three mobile apps collected ** data points across ** segments, including *** data points regarding user content, and **** data points regarding users' contact info. Mobile apps Line, PayPal, Amazon Shopping, and LinkedIn followed, collecting ** data points from users according to the privacy details section in the Apple App Store.

As of March 2021, Facebook Messenger was the mobile messaging and video calls app found to collect the largest amount of data from global iOS users, with over 30 data points collected across 14 segments. Line ranked second with 26 data points, while WeChat collected a total number of 23 data points from iOS users. The most collected data segments for messaging and video call apps were users' contact information and user content.

The global encrypted messaging apps market, valued at $357 million in 2025, is projected to experience robust growth, driven by a compound annual growth rate (CAGR) of 11.4% from 2025 to 2033. This expansion is fueled by several key factors. Increasing concerns about data privacy and security in the digital age are leading consumers and businesses to adopt end-to-end encrypted communication platforms. The rise of remote work and collaboration tools further accelerates this demand, as organizations prioritize secure communication channels for sensitive information. The proliferation of smartphones and readily available high-speed internet access also contributes significantly to market growth, making encrypted messaging apps easily accessible globally. Furthermore, the continuous evolution of encryption technologies and the incorporation of new features, such as self-destructing messages and enhanced security protocols, are attracting a broader user base. Competition among established players like WhatsApp, Telegram, and Signal, alongside the emergence of niche players focusing on specific security needs, fuels innovation and further enhances market dynamism. Segment-wise, the enterprise application segment is expected to demonstrate faster growth compared to the individual segment, driven by the increasing adoption of secure communication solutions within corporate environments. While Android currently holds a larger market share than iOS due to its wider global reach, both platforms are experiencing parallel growth, reflecting the ubiquitous nature of smartphones. Geographically, North America and Europe currently represent significant market shares, owing to high internet penetration and strong awareness of data security issues. However, rapid growth is anticipated in regions like Asia-Pacific and the Middle East & Africa, fueled by increasing smartphone adoption and rising digital literacy. Regulatory changes promoting data privacy and stringent security standards in various regions further bolster the market's growth trajectory. Despite the challenges associated with maintaining the balance between user-friendliness and robust security, the market is poised for substantial expansion in the coming years.

Cashback Apps Market Outlook 2032

The global cashback apps market size was USD 3.5 Billion in 2023 and is likely to reach USD 6.4 Billion by 2032, expanding at a CAGR of 6.8% during 2024–2032. The market is propelled by the growth of ecommerce and hospitality industry.



Increasing consumer demand for value-added services is expected to drive the market during the projection period. Cashback apps offer cash rewards for purchases and are becoming an integral part of the consumer shopping experience. The integration of advanced technologies, such as AI and machine learning, is enhancing the personalization and efficiency of cashback apps, making them appealing to consumers. Furthermore, the development of cashback apps that support multiple payment methods is a significant trend in the market.

Growing partnerships between cashback apps and retailers is another key development. These partnerships are mutually beneficial, with cashback apps driving customer traffic and sales for retailers, and retailers providing attractive offers for app users. Moreover, the use of cashback apps in customer loyalty programs is increasing, with businesses leveraging these apps to reward and retain their customers.



Rising importance of data analytics in the cashback apps market is a significant development. The use of data analytics tools in analyzing user behavior and preferences is enabling the development of targeted and personalized offers. This not only enhances the user experience but also increases the effectiveness of marketing campaigns. The increasing focus on data privacy and security, driven by regulatory requirements, is expected to shape the future development of cashback apps.

Impact of Artificial Intelligence (AI) in Cashback Apps Market

The use of "https://dataintelo.com/report/artificial-intelligence-market" style="color:#0563c1; " target="_blank"><span styl

Electronic supporting information (ESI) occupies a fundamental position in the way scientists report their work. It is a key element in lightening the writing of the core manuscript and makes concise communication easier for the authors. Computational chemistry, as all fields related to structural studies of molecules, tends to generate huge amounts of data that should be inserted in the ESI. ESI reports originating from computational chemistry works generally reach tens of sheets long and include 3D depictions, coordinates, energies, and other characteristics of the structures involved in the molecular process understudy. While most experienced users end up building scripts that dig throughout the output files searching for the relevant data, this is not the case for users without programming experience or time. Here we present an automated ESI generator supported by both web-based and command line interfaces. Focused on quantum mechanics calculations outputs so far, we trust that the community would find this tool useful. Source code is freely available at https://github.com/insilichem/esigen. A web app public demo can be found at http://esi.insilichem.com.

This map feeds into a web app that allows a user to examine the known status of structures damaged by the wildfire. If a structure point does not appear on the map it may still have been impacted by the fire. Specific addresses can be searched for in the search bar. Use the imagery and topographic basemaps and photos to positively identify a structure. Photos may only be available for damaged and destroyed structuresFor more information about the wildfire response efforts, visit the CAL FIRE incident page.

2023 Updates to the National Incident Feature Service and Event Geodatabase For 2023, there are no schema updates and no major changes to GeoOps or the GISS Workflow! This is a conscious choice and is intended to provide a needed break for both users and administrators. Over the last 5 years, nearly every aspect of the GISS position has seen a major overhaul and while the advancements have been overwhelmingly positive, many of us are experiencing change fatigue. This is not to say there is no room for improvement. Many great suggestions were received throughout the season and in the GISS Survey, and they will be considered for inclusion in 2024. That there are no critical updates necessary also indicates that we have reached a level of maturity with the current state, and that is good news for everyone. Please continue to submit your ideas; they are appreciated and valuable insight, even if the change is not implemented. For information on 2023 AGOL updates please see the NWCG page. There are three smaller changes worth noting this year: Standard Symbology is now the default on the NIFS For most workflows, the update will be seamless. All the Event Standard symbols are now supported in Field Maps and Map Viewer. Most users will now see the same symbols in all print and digital products. However, in AGOL some web apps do not support the complex line symbols. The simplified lines will still be present in the official Editing Apps (Operations, SITL, and GISS), and any custom apps built with the Web App Builder (WAB) interface. Experience Builder can be used for any new app creation. If you must use WAB or another app that cannot display the complex line symbology in the NIFS, please contact wildfireresponse@firenet.gov for guidance. Event Line now has Preconfigured Labels Labels on Event Line have historically been uncommon, but to speed their implementation when necessary, color-coded labels classes have been added to the NIFS and the lyrx files provided in the GIS Folder Structure. They can be disabled or modified as needed, should they interfere with any of your workflows. “Restricted” Folder added to GeoOps Folder Structure At the base level within the 2023_Template, a ‘restricted’ folder is now included. This folder should be used for all data and products that contain sensitive, restricted, or controlled-unclassified information. This will aid the DOCL and any future FOIA liaisons in protecting this information. When using OneDrive, this folder can optionally be password protected. Reminder: Sensitive Data is not allowed to be hosted within the NIFC Org.

This Zenodo repository contains raw data tables, a Shiny app (via dockerfile), and a sqlite database that makes up the p53motifDB (p53 motif database).

The p53motifDB is a compendium of genomic locations in the human hg38 reference genome that contain recognizable DNA sequences that match the binding preferences for the transcription factor p53. Multiple types of genomic, epigenomic, and genome variation data were integrated with these locations in order to let researchers quickly generate hypotheses about novel activities of p53 or validate known behaviors.

Raw data tables

The raw data tables (raw_tables.tar.gz) are divided into the "primary" table, containing p53 motif locations and other biographical information relating to those genomic locations. The "accesory" tables contain additional descriptive or quantitative information that can be queried based on the information in the "primary" table. A description of table schema for the primary table and all accessory tables can be found in Schema_p53motifDB.xlsx.

Table_1_DataSources.xlsx contains information about all raw and processed data sources that were used in the construction of the p53motifDB.

Shiny App

The Shiny App is designed to allow rapid filtering, querying, and downloading of the primary and accessory tables. Users can access a web-based version at https://p53motifDB.its.albany.edu. Users can also deploy the Shiny app locally by downloading and extracting p53motifDB_shiny.zip and doing one of of the following:

Option 1: From the extracted folder, run the included Dockerfile to create a Docker image which will deploy to localhost port 3838.

Option 2: From the shiny_p53motifDB subfolder, run app.R from R or RStudio. This requires a number of dependencies, which may not be compatible with your current version of R. We highly recommend accessing the Shiny app via the web or through the Dockerfile.

sqlite Database

Users can perform more complex database queries (beyond those available in the Shiny app) by first downloading sqlite_db.tar.gz. Unpacking this file will reveal the database file p53motifDB.db. This is a sqlite database file containing the same "primary" and "accessory" data from raw_tables.tar.gz and can be used/queried using standard structured query language. The schema of this database, inlcuding relationships between tables, can be seen in p53motifDB_VISUAL_schema.pdf or additional information about each table and the column contents can be examined in the file Schema_p53motifDB.xlsx.

The gzipped TAR file sqlite_db.tar.gz also contains all of the files and information neccessary to reconstruct the p53motifDB.db via R. Users can source the included R script (database_sqlite_commit.R) or can open, examine, and run via RStudio. We strongly advise unpacking the TAR file which will produce a folder called sqlite_db and then running the included R script from within that folder using either source or running line-by-line in RStudio. The result of this script will be p53motifDB.db and an RData object (sqlite_construction.RData) written to the sqlite_db folder.

If opening and running database_sqlite_commit.R via RStudio, please uncomment line 10 and comment out lines 13 and 14.

Please also be aware of the minimal package dependencies in R. The included version of p53motifDB.db was created using R (v. 3.4.0) and the following packages (and versions) available via CRAN:

RSQLite (v. 2.3.7), DBI (v. 1.2.3), tidyverse (2.0.0), and utils (v. 4.3.0) packages

Credit

The p53motifDB was created by Morgan Sammons, Gaby Baniulyte, and Sawyer Hicks.

Please let us know if you have any questions, comments, or would like additional datasets included in the next version of the p53motifDB by contacting masammons(at)albany.edu

Ebit Time Series for Apple Inc. Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts, as well as advertising services include third-party licensing arrangements and its own advertising platforms. In addition, the company offers various subscription-based services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.

Change-Receivables Time Series for Apple Inc. Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts, as well as advertising services include third-party licensing arrangements and its own advertising platforms. In addition, the company offers various subscription-based services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.

Parenting Apps Market Outlook 2032

The global Parenting Apps Market size was USD 542.5 Million in 2023 and is likely to reach USD 1049 Million by 2032, expanding at a CAGR of 7.6% during 2024–2032. The market growth is attributed to the increasing penetration of smartphones and digital literacy among parents, along with the growing use of technology for child development and parenting support needs.



Increasing penetration of smartphones and the internet is a major trend propelling the market. The widespread availability of high-speed internet and affordable smartphones has made parenting apps more accessible than ever, contributing significantly to the growth of the market.

Surging preference for personalized and multilingual parenting apps is a notable trend in the global market. This surge is driven by the need to cater to a diverse user base, enhancing accessibility and user-friendliness for non-English speaking parents. By offering multilingual support, app developers are able to reach a wider audience and increase their share in the market.

• 
  

  In April 2021, Weldon, a parenting app platform, announced the acquisition of Family Five Pte Ltd, expediting the launch of its first personalized, expert-guided platform for parental support. The platform offers parents access to coaching through live workshops and group sessions, community interaction, and age-specific content for every stage of their parenting journey.

  
  

Rising demand for robust data security measures in parenting apps is another key trend shaping the market. With more parents turning to these apps for child-rearing advice and support, there is a rising need for stringent data protection measures. In response, app developers are focusing on incorporating advanced encryption methods and implementing strict privacy policies to protect user data and maintain trust.

Impact of Artificial Intelligence (AI) on Parenting Apps Market</span

The data set represents processed data from individual web browsing histories collected during the EU Referendum campaign as part of ICM Unlimited Reflected Life's panel. Each line of data represents the number of times an individual user visited a news & information domain during the data collection period.The advent of Web 2.0 - the second generation of the World Wide Web, that allows users to interact, collaborate, create and share information online, in virtual communities - has radically changed the media environment, the types of content the public is exposed to as well as the exposure process itself. Individuals are faced with a wider range of options (from social and traditional media), new patterns of exposure (socially mediated and selective), and alternate modes of content production (e.g. user-generated content). In order to understand change (and stability) in opinions and behaviour, it is necessary to measure to what information a person has been exposed. The measures social scientists have traditionally used to capture information exposure usually rely on self-reports of newspaper reading and television news broadcast viewing. These measures do not take into account that individuals browse and share diverse information from social and traditional media on a wide range of platforms. According to the OECD's Global Science Forum 2013 report, social scientists' inability to anticipate the Arab Spring was partly due to a failure to understand 'the new ways in which humans communicate' via social media and the ways they are exposed to information. And social media's mixed record for predicting the results of recent UK elections suggests better tools and a unified methodology are needed to analyze and extract political meaning from this new type of data. We argue that a new set of tools, which models exposure as a network and incorporates both social and traditional media sources, is needed in the social sciences to understand media exposure and its effects in the age of digital information. Whether one is consuming the news online or producing/consuming information on social media, the fundamental dynamic of consuming public affairs news involves formation of ties between users and media content by a variety of means (e.g. browsing, social sharing, search). Online media exposure is then a process of network formation that links sources and consumers of content via their interactions, requiring a network perspective for its proper understanding. We propose a set of scalable network-oriented tools to 1) extract, analyse, and measure media content in the age of "big media data", 2) model the linkages between consumers and producers of media content in complex information networks, and 3) understand co-development of network structures with consumer attitudes/behaviours. In order to develop and validate these tools, we bring together an interdisciplinary and international team of researchers at the interface of social science and computer science. Expertise in network analysis, text mining, statistical methods and media analysis will be combined to test innovative methodologies in three case studies including information dynamics in the 2015 British election and opinion formation on climate change. Developing a set of sophisticated network and text analysis tools is not enough, however. We also seek to build national capacity in computational methods for the analysis of online 'big' data. We contracted with ICM Unlimited to capture web browsing history data from their Reflected Life panel. Reflected Life is a digital toolkit ICM use to track the digital profile of online panel members. Users download the Reflected Life App onto their phones, tablets and desktops. The app is easily downloaded onto each users digital device from which it tracks and shares each and every URL the user visits and their search history. Over the course of the study, ICM provided every URL our panel has visited. These web browsing histories were collected for 3,310 panel members during the UK's EU referendum campaign, we captured of the digital footprint of respondents over 12 weeks prior to the referendum.

🇺🇸 미국 English This map feeds into a web app that allows a user to examine the known status of structures damaged by the wildfire. If a structure point does not appear on the map it may still have been impacted by the fire. Specific addresses can be searched for in the search bar. Use the imagery and topographic basemaps and photos to positively identify a structure. Photos may only be available for damaged and destroyed structuresFor more information about the wildfire response efforts, visit the CAL FIRE incident page.

A structure line is a linear representation of a structure used to support or protect a track.

This layer is part of the Ontario Railway Network (ORWN) suite of 7 Data Classes that have been adapted from Natural Resources Canada GEOBASE National Railway Network (NRWN) standards. Note: Features in this layer should not be duplicated in the ORWN Structure Point layer.

Although mainly used as a base data feature appearing on cartographic products and views, users will benefit from having additional railway-associated attributes available to them.

Additional Documentation

ORWN - User Guide (Word) ORWN Structure Line - Data Description (PDF) ORWN Structure Line - Documentation (Word)

Status Required: data needs to be generated or updated

Maintenance and Update Frequency
Not planned: there are no plans to update the data

Contact
Ontario Ministry of Natural Resources and Forestry - Provincial Mapping Unit, pmu@ontario.ca

DescriptionThe features in this layer have been created from information extracted from SAP. When an SAP user is mapping a project from the CJ20N transaction, these GIS representations are created.Used by SAP GIS Locator web app to read/write projects GIS data from SAP PRD environment. From 9/19/2016 onward.Last UpdateContinuouslyUpdate FrequencyContinuouslyData OwnerDivision of Transportation DevelopmentData ContactGIS Support UnitCollection MethodProjectionNAD83 / UTM zone 13NCoverage AreaStatewideTemporalDisclaimer/LimitationsThere are no restrictions and legal prerequisites for using the data set. The State of Colorado assumes no liability relating to the completeness, correctness, or fitness for use of this data.

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

Description

Road development in the Congo Basin forest is continuously monitored from 2019 onwards in high spatial and temporal detail. A deep learning method is applied to 10 m scale Sentinel-1 and Sentinel-2 imagery for automated road detections on a monthly basis. This version presents 5 years of road development (46,311 km) from 2019-2023.

The data is composed of line features distributed in .shp and .geojson formats. The following attributes are stored for the line features:

• NetworkID: A unique ID for each connected road network.
• SegLenM: The length of the road segment (in meters).
• NetLenM: The length of the connected road network (in meters).
• Month: The road segment opening month.
• Year: The road segment opening year.
• MonthNum: The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations.

Additional information

• More information about the forest road mapping project can be found at: https://wur.eu/forest-roads
• Continuously updated road maps can be interactively viewed at: https://nrtwur.users.earthengine.app/view/forest-roads
• The dataset can be accessed in Google Earth Engine at: ee.FeatureCollection('projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2023-12')
• The scientific paper (Slagter et al., 2024) describing the methods to produce this dataset can be found at: https://doi.org/10.1016/j.rse.2024.114380" target="_blank" rel="noopener">https://doi.org/10.1016/j.rse.2024.114380

Citation

Please cite the following when referring to this dataset: