Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

United States - Breakdown of Revenue by Type of Customer: Subscriber Line Charges - Business Firms, Not-for-Profit Organizations, and Government (Federal, State, and Local) for Wired Telecommunications Carriers, All Establishments, Employer Firms was 1338.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Breakdown of Revenue by Type of Customer: Subscriber Line Charges - Business Firms, Not-for-Profit Organizations, and Government (Federal, State, and Local) for Wired Telecommunications Carriers, All Establishments, Employer Firms reached a record high of 1816.00000 in January of 2011 and a record low of 749.00000 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Breakdown of Revenue by Type of Customer: Subscriber Line Charges - Business Firms, Not-for-Profit Organizations, and Government (Federal, State, and Local) for Wired Telecommunications Carriers, All Establishments, Employer Firms - last updated from the United States Federal Reserve on October of 2025.

Blockchain data query: 🐳Line Chart

Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

This is a 1:2,000,000 coverage of streams for the conterminous United States.

Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for atomistic predictions are based on atomic distance information, they do not explicitly incorporate bond angles, which are critical for distinguishing many atomic structures. Furthermore, many material properties are known to be sensitive to slight changes in bond angles. We present an Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond angles. We demonstrate that angle information can be explicitly and efficiently included, leading to improved performance on multiple atomistic prediction tasks. We ALIGNN models for predicting 52 solid-state and molecular properties available in the JARVIS-DFT, Materials project, and QM9 databases. ALIGNN can outperform some previously reported GNN models on atomistic prediction tasks with better or comparable model training speed.

Digital line graph (DLG) data are digital representations of cartographic information. DLGs of map features are converted to digital form from maps and related sources. Large-scale DLG data are derived from USGS 1:20,000-, 1: 24,000-, and 1: 25,000-scale 7.5-minute topographic quadrangle maps and are available in nine categories: (1) hypsography, (2) hydrography, (3)vegetative surface cover, (4) non-vegetative features, (5) boundaries, (6)survey control and markers, (7) transportation, (8) manmade features, and (9)Public Land Survey System. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the Statement of Loans. The World Bank complies with all sanctions applicable to World Bank transactions.

Data for Figure SPM.4 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure SPM.4 panel a shows global emissions projections for CO2 and a set of key non-CO2 climate drivers, for the core set of five IPCC AR6 scenarios. Figure SPM.4 panel b shows attributed warming in 2081-2100 relative to 1850-1900 for total anthropogenic, CO2, other greenhouse gases, and other anthropogenic forcings for five Shared Socio-economic Pathway (SSP) scenarios.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.

Figure subpanels

The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.

List of data provided

This dataset contains:

• Projected emissions from 2015 to 2100 for the five scenarios of the AR6 WGI core scenario set (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5)
• Projected warming for all anthropogenic forcers, CO2 only, non-CO2 greenhouse gases (GHGs) only, and other anthropogenic components for 2081-2100 relative to 1850-1900, for SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.

The five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.

Data provided in relation to figure

Panel a:

The first column includes the years, while the next columns include the data per scenario and per climate forcer for the line graphs.

• Data file: Carbon_dioxide_Gt_CO2_yr.csv. relates to Carbon dioxide emissions panel
• Data file: Methane_Mt_CO2_yr.csv. relates to Methane emissions panel
• Data file: Nitrous_oxide_Mt N2O_yr.csv. relates to Nitrous oxide emissions panel

• Data file: Sulfur_dioxide_Mt SO2_yr.csv. relates to Sulfur dioxide emissions panel

  Panel b:

• Data file: ts_warming_ranges_1850-1900_base_panel_b.csv. [Rows 2 to 5 relate to the first bar chart (cyan). Rows 6 to 9 relate to the second bar chart (blue). Rows 10 to 13 relate to the third bar chart (orange). Rows 14 to 17 relate to the fourth bar chart (red). Rows 18 to 21 relate to the fifth bar chart (brown).].

Sources of additional information

The following weblink are provided in the Related Documents section of this catalogue record: - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapter 1, which contains details on the input data used in Table 1.SM.1..(Cross-Chapter Box 1.4, Figure 2). - Link to related publication for input data used in panel a.

Synoptic charts are maps of the entire Sun produced in Carrington coordinates. Synoptic maps are constructed by merging together solar observations taken over many days. Magnetic-field synoptic charts are produced using central meridian data from HMI full-disk magnetograms.Synoptic maps are constructed from HMI 720s line-of-sight Magnetograms collected over a 27-day solar rotation. Near-central-meridian data from 20 magnetograms contribute to each point in the final map.HMI 720s line-of-sight magnetograms are first converted to 'radial field magnetograms' by dividing by the cosine of the angle from disk center, i.e. for this purpose we assume that HMI measures the line-of-sight component of a purely radial magnetic field. Individual 'radial' magnetograms are then remapped and interpolated onto a very high-resolution Carrington coordinate grid. For each Carrington longitude the values from the 20 magnetograms obtained closest in time to the central meridian passage (CMP) of that longitude are averaged. By using a constant number of contributing magnetograms, the variation of the noise over the entire map is minimized. Generally all data are taken within about 2 degrees of CMP. The effective temporal width of the HMI synoptic map contribution is about three hours, i.e. 20 720-s magnetograms are obtained within about 90 minutes of central meridian passage. The final HMI synoptic maps have a size of 3600 x 1440, which means the resolution is lower than the disk-center resolution of a single HMI magnetogram. A two-dimensional Gaussian function is applied to high-resolution remapped data to reduce the spatial resolution before generating the 3600*1440 synoptic maps. The width of the Gaussian is 3 pixels. The upper limit of the noise is 2.3 Mx cm2.

A Snellen chart is an eye chart that can be used to measure visual acuity. The Snellen chart is printed with eleven lines of block letters. The first line consists of one very large letter, which may be one of several letters, for example E, H, or N. Subsequent rows have increasing numbers of letters that decrease in size. A person taking the test covers one eye from 6 metres/20 feet away, and reads aloud the letters of each row, beginning at the top. The smallest row that can be read accurately indicates the visual acuity in that specific eye.

This map is produced by applying a boxcar average to the high-resolution line-of-sight synoptic map, hmi.Synoptic_Ml_720s.Synoptic charts are maps of the entire Sun produced in Carrington coordinates. Synoptic maps are constructed by merging together solar observations taken over many days. Magnetic-field synoptic charts are produced using central meridian data from HMI full-disk magnetograms.Synoptic maps are constructed from HMI 720s line-of-sight Magnetograms collected over a 27-day solar rotation. Near-central-meridian data from 20 magnetograms contribute to each point in the final map.HMI 720s line-of-sight magnetograms are first converted to 'radial field magnetograms' by dividing by the cosine of the angle from disk center, i.e. for this purpose we assume that HMI measures the line-of-sight component of a purely radial magnetic field. Individual 'radial' magnetograms are then remapped and interpolated onto a very high-resolution Carrington coordinate grid. For each Carrington longitude the values from the 20 magnetograms obtained closest in time to the central meridian passage (CMP) of that longitude are averaged. By using a constant number of contributing magnetograms, the variation of the noise over the entire map is minimized. Generally all data are taken within about 2 degrees of CMP. The effective temporal width of the HMI synoptic map contribution is about three hours, i.e. 20 720-s magnetograms are obtained within about 90 minutes of central meridian passage. The final HMI synoptic maps have a size of 3600 x 1440, which means the resolution is lower than the disk-center resolution of a single HMI magnetogram. A two-dimensional Gaussian function is applied to high-resolution remapped data to reduce the spatial resolution before generating the 3600*1440 synoptic maps. The width of the Gaussian is 3 pixels. The upper limit of the noise is 2.3 Mx cm2.

Data presentation for scientific publications in small sample size studies has not changed substantially in decades. It relies on static figures and tables that may not provide sufficient information for critical evaluation, particularly of the results from small sample size studies. Interactive graphics have the potential to transform scientific publications from static reports of experiments into interactive datasets. We designed an interactive line graph that demonstrates how dynamic alternatives to static graphics for small sample size studies allow for additional exploration of empirical datasets. This simple, free, web-based tool (http://statistika.mfub.bg.ac.rs/interactive-graph/) demonstrates the overall concept and may promote widespread use of interactive graphics.

Rate and number of infant deaths per 1,000 live births by year. The total rate and number variables include all available races and are not limited to white and black races. Blank cells indicate that the data are not available.

Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

The source data includes original data on all the plotted figures involved in the main content and supplementary information.

Poverty headcount ratio at national poverty lines (% of population) in Tajikistan was reported at 19.8 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Tajikistan - Poverty headcount ratio at national poverty line (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Freebase is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong type system; its properties are purposefully represented in reverse pairs; and it uses mediator objects to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. We make available several variants of the Freebase dataset by inclusion and exclusion of these data modeling idiosyncrasies. This is the first-ever publicly available full-scale Freebase dataset that has gone through proper preparation.

Dataset Details

The dataset consists of the four variants of Freebase dataset as well as related mapping/support files. For each variant, we made three kinds of files available:

• Subject matter triples file
  • fb+/-CVT+/-REV One folder for each variant. In each folder there are 5 files: train.txt, valid.txt, test.txt, entity2id.txt, relation2id.txt Subject matter triples are the triples belong to subject matters domains—domains describing real-world facts.
    • Example of a row in train.txt, valid.txt, and test.txt:
      • 2, 192, 0
    • Example of a row in entity2id.txt:
      • /g/112yfy2xr, 2
    • Example of a row in relation2id.txt:
      • /music/album/release_type, 192
    • Explaination
      • "/g/112yfy2xr" and "/m/02lx2r" are the MID of the subject entity and object entity, respectively. "/music/album/release_type" is the realtionship between the two entities. 2, 192, and 0 are the IDs assigned by the authors to the objects.
• Type system file
  • freebase_endtypes: Each row maps an edge type to its required subject type and object type.
    • Example
      • 92, 47178872, 90
    • Explanation
      • "92" and "90" are the type id of the subject and object which has the relationship id "47178872".
• Metadata files
  • object_types: Each row maps the MID of a Freebase object to a type it belongs to.
    • Example
      • /g/11b41c22g, /type/object/type, /people/person
    • Explanation
      • The entity with MID "/g/11b41c22g" has a type "/people/person"
  • object_names: Each row maps the MID of a Freebase object to its textual label.
    • Example
      • /g/11b78qtr5m, /type/object/name, "Viroliano Tries Jazz"@en
    • Explanation
      • The entity with MID "/g/11b78qtr5m" has name "Viroliano Tries Jazz" in English.
  • object_ids: Each row maps the MID of a Freebase object to its user-friendly identifier.
    • Example
      • /m/05v3y9r, /type/object/id, "/music/live_album/concert"
    • Explanation
      • The entity with MID "/m/05v3y9r" can be interpreted by human as a music concert live album.
  • domains_id_label: Each row maps the MID of a Freebase domain to its label.
    • Example
      • /m/05v4pmy, geology, 77
    • Explanation
      • The object with MID "/m/05v4pmy" in Freebase is the domain "geology", and has id "77" in our dataset.
  • types_id_label: Each row maps the MID of a Freebase type to its label.
    • Example
      • /m/01xljxh, /government/political_party, 147
    • Explanation
      • The object with MID "/m/01xljxh" in Freebase is the type "/government/political_party", and has id "147" in our dataset.
  • entities_id_label: Each row maps the MID of a Freebase entity to its label.
    • Example
      • /g/11b78qtr5m, Viroliano Tries Jazz, 2234
    • Explanation
      • The entity with MID "/g/11b78qtr5m" in Freebase is "Viroliano Tries Jazz", and has id "2234" in our dataset.
    • properties_id_label: Each row maps the MID of a Freebase property to its label.
      • Example
        • /m/010h8tp2, /comedy/comedy_group/members, 47178867
      • Explanation
        • The object with MID "/m/010h8tp2" in Freebase is a property(relation/edge), it has label "/comedy/comedy_group/members" and has id "47178867" in our dataset.
    • uri_original2simplified and uri_simplified2original: The mapping between original URI and simplified URI and the mapping between simplified URI and original URI repectively.
      • Example
        • uri_original2simplified
          • "http://rdf.freebase.com/ns/type.property.unique": "/type/property/unique"
        • uri_simplified2original
          • "/type/property/unique": "http://rdf.freebase.com/ns/type.property.unique"
      • Explanation
        • The URI "http://rdf.freebase.com/ns/type.property.unique" in the original Freebase RDF dataset is simplified into "/type/property/unique" in our dataset.
        • The identifier "/type/property/unique" in our dataset has URI http://rdf.freebase.com/ns/type.property.unique in the original Freebase RDF dataset.

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Recording Chart Paper Market Size 2024-2028

The recording chart paper market size is forecast to increase by USD 753.71 million at a CAGR of 6.01% between 2023 and 2028.

The market is witnessing significant growth due to several key trends. The increasing adoption of automated systems across various industries is driving market growth. Automation in the healthcare, finance, and education sectors is leading to the replacement of traditional paper-based recording methods with digital alternatives. Another major trend is the increasing adoption of digital healthcare services, which is expected to boost the demand for electronic health records and digital chart papers. The internet has also facilitated remote patient monitoring and telemedicine, further expanding the market's scope. However, there are challenges that could hinder market growth. Resistance to change and reliance on traditional paper-based recording methods remain prevalent, particularly in certain industries and regions. Ensuring data security and privacy in digital recording systems is also a significant challenge that needs to be addressed to gain widespread acceptance.

What will be the Size of the Recording Chart Paper Market During the Forecast Period?

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The market encompasses a range of products utilized in various healthcare facilities, including hospitals, diagnostic centers, clinics, labs, and diagnostic centres, for documenting and analyzing vital signs and physiological characteristics of patients. These charts are integral to cardiovascular disease management, with an estimated 17.9 million CVD deaths worldwide each year, necessitating constant monitoring for heart attacks and strokes. The market is driven by the growing geriatric population, who are more susceptible to cardiovascular conditions. Digital monitoring devices, such as ultrasound transducers and digital ECG sheets, are increasingly replacing traditional printed documents due to their convenience and accuracy.
Medical instruments and gadgets, including chart sheets for electrocardiography (ECG) and electroencephalography (EEG) papers, remain essential tools for healthcare professionals in managing and documenting patients' medical states.

How is this Recording Chart Paper Industry segmented and which is the largest segment?

The recording chart paper industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Hospitals
  Diagnostic centers
  Clinics


Type

  Cardiology monitoring recording
  Ambulatory and EMS recording
  Ultrasound and OB-GYN recording
  Fetal monitoring recording


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  Asia

    China


  Rest of World (ROW)

By Application Insights

The hospitals segment is estimated to witness significant growth during the forecast period.

In the healthcare industry, chart paper plays a significant role in various medical departments and devices. In cardiology, it is used in electrocardiography (ECG) machines to document heart activity, contributing to the diagnosis and monitoring of cardiovascular diseases, including heart attacks and strokes. Neurology departments employ chart paper in electroencephalography (EEG) devices to measure brain electrical activity, essential for diagnosing conditions like epilepsy and other neurological disorders. Respiratory care units utilize chart paper in spirometry equipment for pulmonary function tests, aiding In the diagnosis of respiratory diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD). Surgical suites incorporate chart paper in anesthesia monitors to record vital signs during procedures, ensuring patient safety.

Get a glance at the Recording Chart Paper Industry report of share of various segments Request Free Sample

The hospitals segment was valued at USD 798.07 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 42% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American market holds a substantial share In the market due to advanced healthcare infrastructure, technological innovations, an aging population, and rising fertility rates. Cardiovascular diseases, including heart attacks and strokes, are major health concerns, driving the demand for recording chart papers in this region. Favorable government policies and research collaborations are expected to boost market growth, given the developed healthcare

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.