A survey held in the U.S. in April 2023 revealed that 46 percent of respondents aged 45 to 64 years never watched CNN, the highest when ranked by age group. Those aged 18 to 29 years were slightly more likely to watch CNN every day or a few times per week than their older peers.

A survey held in the U.S. in April 2023 revealed that ** percent of Black respondents watched CNN every day and ** percent did so a few times a week. By contrast, less than ** percent of white and Hispanic respondents viewed CNN daily, and white Americans in particular were the most likely to say they never tuned into the channel for news, at ** percent.

This statistic illustrates the share of CNN viewers in the United States as of 2018. The results were sorted by age. In 2018, ***** percent of respondents aged 18 to 29 years stated they watch CNN. The Statista Global Consumer Survey offers a global perspective on consumption and media usage, covering the offline und online world of the consumer.

Five tables and four figures of this paper. Table 1 shows statistics of the evaluation data set. Table 2 presents a sample of CSDN data set. Table 3 is a comparison on Task 1 with different aspects. Table 4 is a comparison of different aspects on Task 2. Table 5 shows performance of UIR-SIST system in SMP CUP 2017. Figure 1 shows a system architecture. Figure 2 is a framework of CNNs model based on weighted-blog-embeddings in Task 2. Figure 3 presents a framework of the stacking model in Task 3. Figure 4 shows an example of daily statistics of user behaviors. Note: “Add” refers to “add favoriates”, and “send” refers to “send private messages”. Five tables and four figures of this paper. Table 1 shows statistics of the evaluation data set. Table 2 presents a sample of CSDN data set. Table 3 is a comparison on Task 1 with different aspects. Table 4 is a comparison of different aspects on Task 2. Table 5 shows performance of UIR-SIST system in SMP CUP 2017. Figure 1 shows a system architecture. Figure 2 is a framework of CNNs model based on weighted-blog-embeddings in Task 2. Figure 3 presents a framework of the stacking model in Task 3. Figure 4 shows an example of daily statistics of user behaviors. Note: “Add” refers to “add favoriates”, and “send” refers to “send private messages”.

During a survey held in the U.S. in spring 2023, nine percent of respondents stated they watched CNN every day. Meanwhile, over ** percent said that they did not watch the channel at all. Fox News remains the most watched cable news network in the United States, with CNN generally behind Fox and MSNBC.

CNN/DailyMail non-anonymized summarization dataset.

There are two features: - article: text of news article, used as the document to be summarized - highlights: joined text of highlights with and around each highlight, which is the target summary

The machine learning codes (in Python) are uploaded. These codes can be used for: 1- first-order flow statistics prediction; 2- second-order flow statistics predictions; and 3- flow predictions by considering the "divergence-free" constraint. A series of sample training datasets are also included as " trainingdata.zip". Plus, some sample results (prediction results) are placed in "resultdata.zip." Below, we explain how to use each of these codes: The codes here works in the following steps: 1. run readdata.py to preprocess data from ASCII tecplot format to binary numpy format. this step can be skipped since all data are prepared in ./train, ./test and ./check 2. run main.py to train the CNN for u, v, w components separately. 3. run check.py to run the trained model and output the predictions. 4. run fluctuation.py to calculate u', v', w' for second-order statistics code. contents in subfolders: flc1 --u' v' w' data input --input data of CNN, instantaneous flow fields target --LES flow fields, target for training and testing output --CNN predictions Below are more details of the fil contents: 1. code 1.1 1st order statistics 1.1.1 main.py is the main code to train CNN 1.1.2 readdata.py is the preprocessing code to convert ASCII tecplot file to numpy file 1.1.3 reverse.py do the data augmentation by reversing the flow field 1.1.4 check.py produce the prediction using trained CNN 1.1.5 data.py, dataset.py and model.py are sub-functions called by main.py 1.2 2nd order statistics 1.2.1 main.py is the main code to train CNN 1.2.2 readdata.py is the preprocessing code to convert ASCII tecplot file to numpy file 1.2.3 check.py produce the prediction using trained CNN 1.2.4 data.py, dataset.py and model.py are sub-functions called by main.py 1.3 divergence-free 1.3.1 main.py is the main code to train CNN 1.3.2 readdata.py is the preprocessing code to convert ASCII tecplot file to numpy file 1.3.3 check.py produce the prediction using trained CNN 1.3.4 SelfDefLoss.py, dataset.py and model.py are sub-functions called by main.py 2. trainingdata 2.1 Result020000.plt to Result029000.plt are instantaneous LES results used to train the CNN. 2.2 Result150000-avg.plt is the time-averaged LES results used to train the CNN. 3. resultdata 3.1 River1 This folder contains the results of River 1 3.1.1 CNN_UV.plt is the CNN predicted time-averaged u and v velocity components 3.1.2 CNN_W.plt is the CNN predicted time-averaged w velocity component 3.1.3 CNN_2nd Order Statistics.plt is the CNN predicted 2nd order statistics components 3.1.4 LES Results.plt is the LES time-averaged results 3.1.5 RANS Results.plt is the RANS results 3.1.6 div_with dive-free.dat is the divergence result of the CNN with physical constraint 3.1.7 div_without dive-free.dat is the divergence result of the CNN without physical constraint

Demographics of RT data set.

The statistic shows the share of consumers who watch CNN in the United States as of **********, sorted by political affiliation. During the survey, ** percent of Democrat respondents stated that they watched the cable news channel.

Overview

This dataset contains 20000 pieces of text collected from Wikipedia, Gutenberg, and CNN/DailyMail. The text is cleaned by replacing symbols such as (.*?/) with a white space using automatic scripts and regex.

Data Source Distribution

1. 10,000 Wikipedia Articles: From the 20220301 dump.
2. 3,000 Gutenberg Books: Via the GutenDex API.
3. 7,000 CNN/DailyMail News Articles: From the CNN/DailyMail 3.0.0 dataset.

Why These Sources

The data was collected from these source to ensure the highest level of integrity against AI generated text. * Wikipedia: The 20220301 dataset was chosen to minimize the chance of including articles generated or heavily edited by AI. * Gutenberg: Books from this source are guaranteed to be written by real humans and span various genres and time periods. * CNN/DailyMail: These news articles were written by professional journalists and cover a variety of topics, ensuring diversity in writing style and subject matter.

Dataset Structure

The dataset consists of 5 CSV files. 1. CNN_DailyMail.csv: Contains all processed news articles. 2. Gutenberg.csv: Contains all processed books. 3. Wikipedia.csv: Contains all processed Wikipedia articles. 4. Human.csv: Combines all three datasets in order. 5. Shuffled_Human.csv: This is the randomly shuffled version of Human.csv.

Each file has 2 columns: - Title: The title of the item. - Text: The content of the item.

Uses

This dataset is suitable for a wide range of NLP tasks, including: - Training models to distinguish between human-written and AI-generated text (Human/AI classifiers). - Training LSTMs or Transformers for chatbots, summarization, or topic modeling. - Sentiment analysis, genre classification, or linguistic research.

Disclaimer

While the data was collected from such sources, the data may not be 100% pure from AI generated text. Wikipedia articles may reflect systemic biases in contributor demographics. CNN/DailyMail articles may focus on specific news topics or regions.

For details on how the dataset was created, click here to view the Kaggle notebook used.

Licensing

This dataset is published under the MIT License, allowing free use for both personal and commercial purposes. Attribution is encouraged but not required.

This data includes the training data used to create the models used in the study, as well as the digitized features converted to shapefiles which were used to create the training data. Lastly, this data contains the resulting accuracy metrics from the model run on three villages of interest. Metadata can be found within the data as well as in a zipped metadata folder. See the readme text file for more information surrounding each file.

Demographics of BD data set.

Patient demographics for the heart failure data set.

This survey focuses on President Clinton. Issues addressed include approval of President Clinton, the senate impeachment trial against him, allegations against him (Monica Lewinsky, perjury, obstruction of justice), his grand jury testimony, Whitewater, and fundraising practices. The bombings of US embassies in Kenya and Tanzania, and financial situation. Demographic data include marital status, religion, employment status, age, sex, education, race, party affiliation, political ideology, and income.

QUANTIFYING EFFECTS OF PARTIAL GENETIC BACKGROUNDS TO DECODE GENETIC DRIVERS OF CLINICAL PHENOTYPES

This survey focuses on President Clinton and the November Congressional Election. Issues addressed include approval of President Clinton, allegations against him, impeachment vote, Congressional elections, ethical standards of professionals, federal budget, and crime. Demographic data include marital status, religion, employment status, age, sex, education, race, party affiliation, political ideology, and income.

The global daily newsletter market, valued at $12.9 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 6.1% from 2025 to 2033. This expansion is driven by several key factors. The increasing demand for concise, curated news in a fast-paced world fuels the popularity of daily newsletters. The diverse range of subscription models, from monthly to annual plans, caters to varying consumer preferences and budgets. Furthermore, the market’s segmentation across age demographics allows publishers to tailor content and advertising strategies for specific audience segments. The rise of digital media consumption, coupled with the enhanced personalization capabilities offered by newsletters, contribute significantly to market growth. Competition amongst established media houses like CNN and BBC, alongside newer entrants such as The Daily Skimm and Axios, ensures a dynamic and evolving landscape. The strong presence of established players across North America and Europe, complemented by the emerging potential of markets in Asia Pacific, particularly China and India, signifies a geographically diverse market with significant future potential. The market's growth, however, is not without its challenges. Maintaining audience engagement in a crowded digital space is crucial. Competition for user attention, coupled with the need to continuously adapt to evolving content consumption patterns, requires innovative content strategies and robust subscriber retention techniques. Successfully navigating data privacy regulations and maintaining journalistic integrity are also key considerations for the sustained success of daily newsletters. The industry must address potential challenges posed by evolving reader preferences and the emergence of new information consumption channels to ensure continuous growth. Despite these challenges, the long-term outlook for the daily newsletter market remains positive, reflecting the enduring appeal of curated, convenient, and personalized news delivery.

CNN Test Imagery and LabelsThis zip file includes the test images used for reported metrics along with the via_region_data.json label file that has the mask shapes in JSON format as needed for the neural network. Species specific .json files are also included.test.zipCNN Training Imagery and Labels Subset 1This zip file includes the first subset of training images used for training this CNN. This data is only subset for ease of upload and download but should be combined by the user on their local machine. The full label file for all training data is included with both subsets and is identical. That file "via_region_data.json" has the mask shapes in and species IDs in a JSON format as needed for the neural network.train_subset_1.zipCNN Training Imagery and Labels Subset 2This zip file includes the second subset of training images used for training this CNN. This data is only subset for ease of upload and download but should be combined by the user on their local machine. The full label file for all training data is included with both subsets and is identical. That file "via_region_data.json" has the mask shapes in and species IDs in a JSON format as needed for the neural network.train_subset_2.zipCNN Validation Imagery and LabelsThis zip file includes the validation images used for training this CNN. The label file for all validation data is included. That file "via_region_data.json" has the mask shapes in and species IDs in a JSON format as needed for the neural network.val.zip The flourishing application of drones within marine science provides more opportunity to conduct photogrammetric studies on large and varied populations of many different species. While these new platforms are increasing the size and availability of imagery datasets, established photogrammetry methods require considerable manual input, allowing individual bias in techniques to influence measurements, increasing error and magnifying the time required to apply these techniques. Here, we introduce the next generation of photogrammetry methods utilizing a convolutional neural network to demonstrate the potential of a deep learning‐based photogrammetry system for automatic species identification and measurement. We then present the same data analysed using conventional techniques to validate our automatic methods. Our results compare favorably across both techniques, correctly predicting whale species with 98% accuracy (57/58) for humpback whales, minke whales, and blue whales. Ninety percent of automated length measurements were within 5% of manual measurements, providing sufficient resolution to inform morphometric studies and establish size classes of whales automatically. The results of this study indicate that deep learning techniques applied to survey programs that collect large archives of imagery may help researchers and managers move quickly past analytical bottlenecks and provide more time for abundance estimation, distributional research, and ecological assessments.

The global daily newsletter market, valued at $19,560 million in 2025, is poised for significant growth. While the exact Compound Annual Growth Rate (CAGR) isn't provided, considering the rising demand for curated news and personalized content, a conservative estimate of 8% CAGR is reasonable for the forecast period (2025-2033). This growth is driven by several factors: increasing digital consumption, a preference for concise and efficient news delivery, the rise of niche newsletters catering to specific interests (finance, technology, etc.), and the effectiveness of newsletter marketing for businesses. The market is segmented by subscription type (monthly and annual) and demographics, with a broad age range showing engagement. The competitive landscape is diverse, featuring established media giants like CNN and BBC alongside newer players like The Daily Skimm and Morning Brew that have successfully carved a niche. Geographic distribution reveals a strong presence across North America and Europe, with Asia-Pacific showing significant growth potential given the increasing internet penetration and smartphone usage. The market faces certain restraints, primarily the competition from other news sources and the challenge of maintaining consistent engagement and relevance. However, the ability to personalize content, leverage data analytics for improved targeting, and integrate seamlessly with various platforms strengthens the sector. The diversity in subscription models (monthly vs. annual) suggests a flexible approach catering to diverse consumer needs and budgetary preferences. The substantial presence of major news outlets signifies the long-term viability of the daily newsletter model; these outlets understand the evolving news consumption habits and effectively exploit their strengths to retain and attract new audiences in this format. Further expansion is expected in emerging markets, driven by increasing internet penetration and mobile device usage.

Summary of image characteristics and available demographic information.