The Medicare Outpatient Hospitals by Provider and Service dataset provides information on services for Original Medicare Part B beneficiaries by OPPS hospitals. These datasets contain information on the number of services, payments, and submitted charges organized by provider CMS Certified Number (CCN) and comprehensive Ambulatory Payment Classification (APC).

Determining concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, a process key for aerosol-cloud interactions (ACI). However, due to sparse coverage of in-situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source, temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN is one of the goals for quantifying ACI processes and achieving uncertainty reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (RA: EAC4) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMS are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatio-temporal quantification of CCN. Therefore, the CCN retrieved from the CAMS aerosol reanalysis succeed the sole use of AOD as a proxy for global CCN. This CCN dataset features CCN concentrations of global coverage for various supersaturations and aerosol species covering the years from 2003 to 2021 with daily frequency and a spatial resolution of 0.75×0.75 degree and 60 vertical levels. Apart from the CAMSRA data, which is available every 3 hours, CCN are currently only computed once a day at 00:00 UTC. The data comprises 3-D fields of total CCN computed for six different supersaturations (s: 0.1, 0.2, 0.4, 0.6, 0.8 and 1 %) and 3-D CCN fields containing aerosol species CCN from sulfate (SO4), hydrophilic black carbon (BCh) and organic matter (OMh) and three size bins of sea salt aerosol (SS) computed for two supersaturations (s: 0.02 % and 0.8 %) comprising additional aerosol information in the lower and upper supersaturation range, respectively. The current choice of data frequency, resolution and variable dependencies such as supersaturation is made regarding general interest and suitability as well as file size, data storage and computational costs. This dataset offers the opportunity to be used for evaluation of general circulation and earth system models as well as in studies of aerosol-cloud interactions.

The file name of the data sets is composed as follows.

project: QUAERERE (Quantifying aerosol-cloud-climate effects by regime) experiment: CCNCAMS (Cloud condensation nuclei derived from the CAMS reanalysis) version: v1 dataset: Total_CCN (total cloud condensation nuclei) and Aerosol_species_CCN (aerosol species cloud condensation nuclei) year: 2003 to 2021 mon: 1 to 12

Acknowledgement: This dataset was generated using Copernicus Atmosphere Monitoring Service information [2003-2021]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. The source data is downloaded from the Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS) (https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=overview)

This data set contains CCN Counter data collected by the University of Alaska at Fairbanks for the VOCALS experiment. The data are contained in a single ASCII file.

The Cloud Condensation Nuclei (CCN) Counter measures nucleied particle number concentration at different supersaturation. Rather than the traditional version of CCN counter where the supersaturation ratio is controlled by adjusting the temperature difference between coolers and thermocouples, in this version the supersaturation ratio is adjusted by scanning the sheath and sampling flow. This dataset contains ASCII text files listing particle number concentration under different supersaturation ratio using CCN mostly over investigator-defined sampling periods. It covers most of the ICE-T campaign period except RF13.

As part of the 3rd Intensive Campaign of SAFARI 2000, the South African Weather Bureau Aerocommander, JRA, flew 19 missions, for a total of 28 separate flights conducted between August 15th and September 7th, 2000. JRA worked closely with the other Aerocommander, JRA, and was dedicated to the measurement of trace gas and aerosol properties. A suite of trace analyzers (for O3, SO2, CO and NO), laser aerosol probes and atmospheric probes were present for all flights. Other instruments and sampling units present for some of the flights included, a nephelometer (Elias), CO flasks (Novelli) for MOPITT validation purposes, and VOC canisters for the collection and characterization of volatile organic compounds present over various land surface types.

[IMPORTANT NOTE: Sample file posted on Datarade is not the complete dataset, as Datarade permits only a single CSV file. Visit https://www.careprecise.com/healthcare-provider-data-sample.htm for more complete samples.] Updated every month, CarePrecise developed the AHD to provide a comprehensive database of U.S. hospital information. Extracted from the CarePrecise master provider database with information all of the 6.3 million HIPAA-covered US healthcare providers and additional sources, the Authoritative Hospital Database (AHD) contains records for all HIPAA-covered hospitals. In this database of hospitals we include bed counts, patient satisfaction data, hospital system ownership, hospital charges and cases by Zip Code®, and more. Most records include a cabinet-level or director-level contact. A PlaceKey is provided where available.

The AHD includes bed counts for 95% of hospitals, full contact information on 85%, and fax numbers for 62%. We include detailed patient satisfaction data, employee counts, and medical procedure volumes.

The AHD integrates directly with our extended provider data product to bring you the physicians and practice groups affiliated with the hospitals. This combination of data is the only commercially available hospital dataset of this depth.

NEW: Hospital NPI to CCN Rollup A CarePrecise Exclusive. Using advanced record-linkage technology, the AHD now includes a new file that makes it possible to mine the vast hospital information available in the National Provider Identifier registry database. Hospitals may have dozens of NPI records, each with its own information about a unit, listing facility type and/or medical specialties practiced, as well as separate contact names. To wield the power of this new feature, you'll need the CarePrecise Master Bundle, which contains all of the publicly available NPI registry data. These data are available in other CarePrecise data products.

Counts are approximate due to ongoing updates. Please review the current AHD information here: https://www.careprecise.com/detail_authoritative_hospital_database.htm

The AHD is sold as-is and no warranty is offered regarding accuracy, timeliness, completeness, or fitness for any purpose.

This data set is comprised of CCN data collected on board the CIRPAS Twin Otter aircraft from 16 October 2008 through 13 November 2008.

The data comprises 3-D fields of total cloud condensation nuclei numbers computed for six different supersaturations (0.1, 0.2, 0.4, 0.6, 0.8 and 1%). Total ccn are the sum of four aerosol species ccn originating from sea salt (in three size bins), sulfate and hydrophilic black carbon and organic matter. Total ccn is computed using CAMS reanalysis EAC4 aerosol mass mixing ratios.

Variables contained in dataset Total_CCN: ps (surface air pressure), z (surface geopotential), gh (geopotential height), ccn_ss01 (total cloud condensation nuclei at 0.1% supersaturation), ccn_ss02 (total cloud condensation nuclei at 0.2% supersaturation), ccn_ss04 (total cloud condensation nuclei at 0.4% supersaturation), ccn_ss06 (total cloud condensation nuclei at 0.6% supersaturation), ccn_ss08 (total cloud condensation nuclei at 0.8% supersaturation), ccn_ss10 (total cloud condensation nuclei at 1.0% supersaturation)

Five CCN field campaigns were conducted during 2016 to 2019 in Eastern China. The stations are located in Nanjing (NJ, 32.21°N, 118.70°E, 22 m above sea level), Lushan (LS, 29.58°E, 115.98°E, 1165 m above sea level), Shenzhen (SZ, 22.48°N, 114.56°E, 230 m above sea level), and Zhanjiang (ZJ, 21.01°N, 110.53°E, 51 m above sea level), respectively.Please contact to wang@tropos.de, clu@nuist.edu.cn, or niusj@nuist.educn for requesting these CCN data.

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Unlock the full potential of CNN broadcast data with our comprehensive dataset featuring transcripts, program schedules, headlines, topics, and multimedia resources. This all-in-one dataset is designed to empower media analysts, researchers, journalists, and advocacy groups with actionable insights for media analysis, transparency studies, and editorial assessments.

Dataset Features

Transcripts: Access detailed broadcast transcripts, including headlines, content, author details, and publication dates. Perfect for analyzing media framing, topic frequency, and news narratives across various programs. Program Schedules: Explore program schedules with accurate timing, show names, and related metadata to track news coverage patterns and identify trends. Topics and Keywords: Analyze categorized topics and keywords to understand content diversity, editorial focus, and recurring themes in news broadcasts. Multimedia Content: Gain access to videos, images, and related articles linked to each broadcast for a holistic understanding of the news presentation. Metadata: Includes critical data points like publication dates, last updates, content URLs, and unique IDs for easier referencing and cross-analysis.

Customizable Subsets for Specific Needs Our CNN dataset is fully customizable to match your research or analytical goals. Focus on transcripts for in-depth media framing analysis, extract multimedia for content visualization studies, or dive into program schedules for broadcast trend analysis. Tailor the dataset to ensure it aligns with your objectives for maximum efficiency and relevance.

Popular Use Cases

Media Analysis: Evaluate news framing, content diversity, and topic coverage to assess editorial direction and media focus. Transparency Studies: Analyze journalistic standards, corrections, and retractions to assess media integrity and accountability. Audience Engagement: Identify recurring topics and trends in news content to understand audience preferences and behavior. Market Analysis: Track media coverage of key industries, companies, and topics to analyze public sentiment and industry relevance. Journalistic Integrity: Use transcripts and metadata to evaluate adherence to reporting practices, fairness, and transparency in news coverage. Research and Scholarly Studies: Leverage transcripts and multimedia to support academic studies in journalism, media criticism, and political discourse analysis.

Whether you are evaluating transparency, conducting media criticism, or tracking broadcast trends, our CNN dataset provides you with the tools and insights needed for in-depth research and strategic analysis. Customize your access to focus on the most relevant data points for your unique needs.

CalNex was a joint project of The California Air Resources Board (CARB), the National Oceanic and Atmospheric Administration (NOAA) and the California Energy Commission (CEC). This project was a joint field study of atmospheric processes over California and the eastern Pacific coastal region in 2010. The Pacific Marine Environmental Laboratory (PMEL) Atmospheric Chemistry Group made Aerosol chemical, physical, and optical measurements aboard the R/V Atlantis from May 14 through June 8, 2010. cdm_data_type=Trajectory cdm_trajectory_variables=trajectory_id comment=CCN Measurements: A Droplet Measurement Technologies CCN Counter (DMT CCNC) was used to determine CCN concentrations of sub-1 um particles at supersaturations ranging from 0.1 to 0.62%. A multijet cascade impactor with a 50% aerodynamic cut-off diameter of 1.1 um was upstream of the CCNC. The sampled air was dried prior to reaching the CCNC. Details concerning the characteristics of the DMT CCN counter can be found in Roberts and Nenes [2005] and Lance et al. [2006]. The CCN counter was calibrated before and during the experiment as outlined by Lance et al. [2006]. The uncertainty associated with the CCN number concentration is estimated to be less than +/- 10% [Roberts and Nenes, 2005]. Uncertainty in the instrumental supersaturation is less than +/- 10% for the operating conditions of this experiment [Roberts and Nenes, 2005].

The data are in 10 second time intervals and include CCN concentration (in n/cm^3), CCN/CN ratio, and Supersaturation (in %).

Lance, S., J. Medina, J.N. Smith, and A. Nenes, Mapping the operation of the DMT continuous flow CCN counter, Aer. Sci. Tech., 40, 242 - 254, 2006. Roberts, G.C. and A. Nenes, A continuous-flow streamwise thermal gradient CCN chamber for atmospheric measurements, Aer. Sci. Tech., 39, 206 - 221, 2005. contributor_name=Coffman, Derek/NOAA-PMEL/Address: 7600 Sand Pt. Wy. NE,Seattle,WA 98115 /email: derek.coffman@noaa.gov Conventions=COARDS, CF-1.6, ACDD-1.3, NCCSV-1.0 dimensions=time=36001 Easternmost_Easting=-117.143892 featureType=Trajectory geospatial_lat_max=38.56342 geospatial_lat_min=32.631448 geospatial_lat_units=degrees_north geospatial_lon_max=-117.143892 geospatial_lon_min=-122.981272 geospatial_lon_units=degrees_east geospatial_vertical_max=18.0 geospatial_vertical_min=18.0 geospatial_vertical_positive=up geospatial_vertical_units=m infoUrl=https://www.pmel.noaa.gov/acg/data/index.html institution=NOAA keywords_vocabulary=GCMD Science Keywords Northernmost_Northing=38.56342 platform=Atlantis project=CalNex sourceUrl=(local files) Southernmost_Northing=32.631448 standard_name_vocabulary=CF Standard Name Table v70 subsetVariables=trajectory_id, duration, altitude target_sample_rh=60% time_coverage_end=2010-06-08T12:00:00Z time_coverage_start=2010-05-14T12:00:00Z Westernmost_Easting=-122.981272

A Certificate of Convenience and Necessity (CCN) is issued by the Public Utility Commission of Texas (PUCT), and authorizes a utility to provide water and/or sewer service to a specific service area. The CCN obligates the water or sewer retail public utility to provide continuous and adequate service to every customer who requests service in that area. The maps and digital data provided in the Water and Sewer CCN Viewer delineate the official CCN service areas and CCN facility lines issued by the PUCT and its predecessor agencies. This dataset is a Texas statewide polygon layer of sewer CCN service areas. The CCNs were digitized from Texas Department of Transportation (TxDOT) county mylar maps. The mylar maps were the base maps on which the CCNs were originally drawn and maintained. CCNs are currently created and maintained using digitizing methods, coordinate geography or imported from digital files submitted by the applicant. TxDOT digital county urban road files are used as the base maps on which the CCNs are geo-referenced. It is best to view the sewer CCN service area data in conjunction with the sewer CCN facility line data, since these two layers together represent all of the retail public sewer utilities in Texas.*Important Notes: The CCN spatial dataset and metadata were last updated on: October 4, 2022The official state-wide CCN spatial dataset includes all types of CCN services areas: water and sewer CCN service areas; water and sewer CCN facility lines. This CCN spatial dataset is updated on a quarterly, or as needed basis using Geographic Information System (GIS) software called ArcGIS 10.8.2.The complete state-wide CCN spatial dataset is available for download from the following website: http://www.puc.texas.gov/industry/water/utilities/gis.aspxThe Water and Sewer CCN Viewer may be accessed from the following web site: http://www.puc.texas.gov/industry/water/utilities/map.htmlIf you have questions about this CCN spatial dataset or about CCN mapping requirements, please e-mail CCN Mapping Staff: water@puc.texas.govTYPE - Indicates whether a CCN is considered a water or a sewer system. If the CCN number begins with a '"1", the CCN is considered a water system (utility). If a CCN number begins with a "2", the CCN is considered a sewer system (utility).CCN_NO - A unique five-digit number assigned to each CCN when it is created and approved by the Commission. *CCN number starting with an ‘N’ indicates an exempt utility.UTILITY - The name of the utility which owns the CCN.COUNTY - The name(s) of the county(ies) in which the CCN exist.CCN_TYPE –One of three types:Bounded Service Area: A certificated service area with closed boundaries that often follow identifiable physical and cultural features such as roads, rivers, streams and political boundaries. Facilities +200 Feet: A certificated service area represented by lines. They include a buffer of a specified number of feet (usually 200 feet). The lines normally follow along roads and may or may not correspond to distribution lines or facilities in the ground.Facilities Only: A certificated service area represented by lines. They are granted for a "point of use" that covers only the customer connections at the time the CCN is granted. Facility only service lines normally follow along roads and may or may not correspond to distribution lines or facilities in the ground.STATUS – For pending dockets check the PUC Interchange Filing Search

Available data for paper "Long-term observations of cloud condensation nuclei over the Amazon rain forest - Part 2: Variability and characteristic differences under near-pristine, biomass burning, and long-range transport conditions", ACP, 2018, "https://doi.org/10.5194/acp-18-10289-2018". When using the data, please refer to the paper mentioned above. If further data needed, please contact the corresponding authors.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

We present a novel multiyear global dataset of height-resolved number concentrations of cloud condensation nuclei (NCCN) at a supersaturation of 0.20 %. The NCCN is estimated from the spaceborne CALIPSO (Cloud-Aerosol Lidar and Infra-Red Pathfinder Satellite Observation) lidar measurements using the OMCAM (Optical Modeling of CALIPSO Aerosol Microphysics) algorithm. The data also includes aerosol-type-specific NCCN for five aerosol types: dust, marine, polluted continental, elevated smoke, and clean continental. It has a monthly resolution, covering the time period from June 2006 to December 2021. We further provide a 3D climatology of aerosol-type-specific NCCN estimated from the complete time series. Data are provided as yearly netCDF files (netCDF version 4), given at a horizontal latitude-longitude grid of 2° × 5° and a vertical resolution of 60 m.

This dataset is from the paper: "Design of an Interpretable Convolutional Neural Network for Stress Concentration Prediction in Rough Surfaces" by Kantzos et. al. (2019)Paper Abstract: We present the application of a Convolutional Neural Network (CNN) to relate stress concentrations to surface roughness. Stress concentrations at the low points of rough surfaces are one of the primary causes of fatigue crack initiation but there is no generally accepted way to analyze rough surfaces to predict crack initiation. Synthetically generated rough surfaces, instantiated in a mechanical model allow for the simulation of stress concentrations, creating a database of surface images and corresponding mechanical data. In this work, the CNN is designed and trained to interpret a height map of a surface and, from that data, to predict the stress concentrations created by the surface. Using a simple architecture, the CNN achieved R2 = 0.75 in prediction for test images, i.e., those not used in training. This CNN can be adapted for experimental surfaces thus creating a new and straightforward tool for prediction of crack initiation. Considerable work was taken to minimize the complexity of the CNN architecture and to make it interpretable via viewports.Herein are 4 files that can be downloaded to analyze the data, reproduce figures from the paper, and train new CNNs. The files are compressed directories.

During TRACER, the Texas A&M Rapid Onsite Atmospheric Measurements Van (ROAM-V) was deployed to capture airmasses behind (maritime) and ahead (continental) of the passage of the sea-breeze front through Houston. On select sampling days, ROAM-V sampled in the morning/mid-day on the coast and then transited to a second inland site for the afternoon/evening. The suite of instruments deployed on ROAM-V included a Condensation Particle Counter (CPC; GRIMM Model 5.403 CPC), Scanning Mobility Particle Sizer (SMPS; TSI 3750 detector, TSI 3082 classifier, TSI 3088 neutralizer, TSI 3081A Differential Mobility Analyzer), Cloud Condensation Nuclei counter (Droplet Measurement Technologies CCN Counter), micro pulse lidar (Droplet Measurement Technologies Micro Pulse LiDAR (miniMPL)), and a Davis Rotating Uniform size-cut Monitor (DRUM; DRUMAir 4-DRUM). Before sampling at each location, the latitude and longitude were recorded using the GPS on the phone application “My Altitude”.Onboard the ROAM-V, aerosol samples are drawn through a shared isokinetic inlet at a flow rate ranging from 3.5 to 7.0 LPM. A portion of this flow is directed through a cyclone impactor (Brechtel, Inc. Model SCC 0.732) and 0.5 LPM is directed to the CCN. To calculate particle losses, we used a two-step method. First, the measured SMPS size distributions were used to calculate particle loss through the inlet during sampling. Second, the corrected SMPS data was used to calculate the average of the total losses per scan down the CCN line. Then, the correction was applied to the CCN data. This calculation was done separately for each deployment location due to changes in the measured size distributions between locations. Particle loss from diffusion (based on Kesten, 1991 and Gormley, 1949), inertial impaction in 90-degree bends (based on Aerosol Measurement, 2011 and Crane, 1977), and cyclone impactor efficiency (based on Dirgo, 1985) were included in the loss calculation. When the SMPS was not sampling at a location (in the case of an instrument malfunction or operator error), the reported CPC data was corrected with an average of the total losses for the entire campaign at the specified deployment location (e.g., if we needed to correct Galveston data, then the average of all calculated losses at Galveston was taken). These flatline corrections were used for all data on 22/07/13, 22/07/20, 22/07/22, and the data from Galveston on 22/08/09. The supersaturation uncertainty is estimated conservatively at +/- 0.03%, where variation in the inlet temperature, pressure, and calibration technique prevents a more accurate measurement. Confidence in the reported supersaturation measurements is based on a pre-campaign calibration (following the methods from our previous work and Deng, 2014 based on Rose, 2008) in addition to inter-comparisons with the DOE for two days (22/08/18 and 22/09/01) where TAMU was co-located with AMF1. The inter-comparisons show good agreement between our instrument and the DOEs instrument on both days at all supersaturations. After the last inter-comparison on 22/09/01, there was no indication of a malfunction by our instrument through the rest of the campaign. Unfortunately, the instrument was dropped during demobilization. A post-campaign calibration was conducted, which showed a substantial departure from the pre-campaign calibration. The drop may have damaged the instrument’s ability to produce the desired supersaturations. Therefore, we do consider the data after 22/09/01 to be correct, but it should be used with caution. The CCN counter sampled for 3 minutes at each supersaturation setpoint (0.2, 0.4, 0.6, 0.8, 1.0, and 1.2%). At the end of a cycle, the instrument was set to 0.01% supersaturation for 5 minutes. The data is comprised of the last 60 seconds of each supersaturation set point (0.2, 0.4, 0.6, 0.8, 1.0, and 1.2%) to ensure the instrument stabilized and was able to reach thermal equilibrium. We removed the data during the periods where there were operational difficulties, setup, or maintenance. This data was collected for ARM Field Campaign AFC07055 and supported by DOE ASR grant DE-SC0021047. For any further questions, please feel free to contact the instrument PI, Sarah D. Brooks, sbrooks@tamu.edu.Rose et. al. Calibration and Measurement Uncertainties of a Continuous-Flow Cloud Condensation Nuclei Counter (DMT-CCNC): CCN Activation of Ammonium Sulfate and Sodium Chloride Aerosol Particles in Theory and Experiment. Atmos. Chem. Phys., 8, 1153-1179, 2008.Deng et. al. Using Raman Microspectroscopy to Determine Chemical Composition and Mixing State of Airborne Marine Aerosols over the Pacific Ocean. Aerosol Science and Technology, Vol 48, Issue 2, 2014.Kesten et. al. Calibration of a TSI Model 3025 Ultrafine Condensation Particle Counter. Aerosol Science and Technology, 15:2, 107-111, 1991.Gormley et. al. Diffusion from a Stream Flowing through a Cylindrical Tube. Proceedings of the Royal Irish Academy, Vol 52, 163-169, 1948.Aerosol Measurement: Principles, Techniques, and Applications, Third Edition. John Wiley & Sons, Inc, 2011.Crane et. al. Inertial Deposition of Particles in a Bent Pipe. Journal of Aerosol Science, Vol 8, 161-170, 1977.Dirgo et. al. Cyclone Collection Efficiency: Comparison of Experimental Results with Theoretical Predictions. Aerosol Science and Technology, 4:4, 401-415, 1985.

This program establishes a deep learning model of CNN+LSTM, which is used for continuous monitoring of exercise heart rate with PPG signals containing motion artifacts, and has achieved good results in the PPG-DaLiA database. The description is as follows: 1. The file main_program_file is the main file, including model construction, data processing, data training, model data verification, and other processing programs for PPG signals that are not used in this article. model: build exercise heart rate monitoring model file; activity_time.xls: Collect each activity time node of each volunteer signal obtained from the PPG-DaLiA database; original_data_read.py: signal data preprocessing program (signal from the PPG-DaLiA database); ppg_filed_hr_cornet_estimate.py: training and prediction program for all volunteers’ PPG signals; ppg_filed_hr_cornet_estimate_single.py: a program to predict the PPG signal of a single volunteer; _1d_cnn, _2d_cnn, ppg_excerise_cnn_type.py, ppg_filed_hr_cnn_estimate.py: programs that use the CNN method for prediction; spc_hr_cornet_estimate.py, spc_hr_cnn_estimate.py: programs for predicting and verifying using other database PPG signals. save_model_estimate_hr.py, save_model_estimate_hr_spc.py: save the heart rate prediction model and the model program for the heart rate prediction model to be used in the SPC database. out_fig: model prediction picture output folder; 2. Data source The data comes from the PPG-DaLiA database (PPG Data For Daily Life Activity, https://archive.ics.uci.edu/ml/datasets/PPG-DaLiA): The database comes from Robert Bosch GmbH and Bosch Sensortec GmbH. The signals in this database come from 15 volunteers of different ages and different physical conditions. PPG and heart rate data are continuously collected during different exercises. The preprocessing of the downloaded data is in the program original_data_read.py. 3.other _0_basic_fun, ch3_preprocess, my_pyhht_lib: some external references of the main program, mainly the functions called by the data preprocessing part, and the main program can view their functions.This program establishes a deep learning model of CNN+LSTM, which is used for continuous monitoring of exercise heart rate with PPG signals containing motion artifacts, and has achieved good results in the PPG-DaLiA database. The description is as follows: 1. The file main_program_file is the main file, including model construction, data processing, data training, model data verification, and other processing programs for PPG signals that are not used in this article. model: build exercise heart rate monitoring model file; activity_time.xls: Collect each activity time node of each volunteer signal obtained from the PPG-DaLiA database; original_data_read.py: signal data preprocessing program (signal from the PPG-DaLiA database); ppg_filed_hr_cornet_estimate.py: training and prediction program for all volunteers’ PPG signals; ppg_filed_hr_cornet_estimate_single.py: a program to predict the PPG signal of a single volunteer; _1d_cnn, _2d_cnn, ppg_excerise_cnn_type.py, ppg_filed_hr_cnn_estimate.py: programs that use the CNN method for prediction; spc_hr_cornet_estimate.py, spc_hr_cnn_estimate.py: programs for predicting and verifying using other database PPG signals. save_model_estimate_hr.py, save_model_estimate_hr_spc.py: save the heart rate prediction model and the model program for the heart rate prediction model to be used in the SPC database. out_fig: model prediction picture output folder; 2. Data source The data comes from the PPG-DaLiA database (PPG Data For Daily Life Activity, https://archive.ics.uci.edu/ml/datasets/PPG-DaLiA): The database comes from Robert Bosch GmbH and Bosch Sensortec GmbH. The signals in this database come from 15 volunteers of different ages and different physical conditions. PPG and heart rate data are continuously collected during different exercises. The preprocessing of the downloaded data is in the program original_data_read.py. 3.other _0_basic_fun, ch3_preprocess, my_pyhht_lib: some external references of the main program, mainly the functions called by the data preprocessing part, and the main program can view their functions.

This dataset contains over 27,000 news articles sourced from CNN.com, including full content, metadata, and media fields. Each article is enriched with publish dates, author information, descriptions, and full raw + cleaned content—perfect for media research, sentiment analysis, topic modeling, and natural language processing (NLP) projects.

Last crawled in July 2021, this collection offers a historical snapshot of CNN’s reporting and editorial content.

Use Cases:

• News content analysis

• Fake news detection & bias tracking

• Topic classification and clustering

• Training AI/NLP models

• Historical news trend research

• Media monitoring tools

Update Frequency:

Archived — no current updates, great for snapshot-based analysis