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Sample Gantt Chart

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.

The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

🇺🇸 미국

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Comparison of out-of-sample results for stock chart images and stock time series data using the feature fusion LSTM-CNN model.

Connected TV (CTV) is an abbreviation for "connected television," encompassing televisions that have the capability to connect to the internet. This enables users to access a diverse range of sources to stream shows, movies, and various video content on their CTVs.

VentiveIQ offers comprehensive viewership data for OTT/CTV, supplemented with IMDB metadata, Device Graph, and IP Addresses associated with households. This data is accessible for both the United States and select international countries. It is conveniently categorized to facilitate audience building and can be seamlessly integrated with additional data sets such as demographics, online behavior/intent data, and personally identifiable information (PII) for enhanced insights and analysis.

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).

Stock Charts

This dataset is a collection of a sample of images from tweets that I scraped using my Discord bot that keeps track of financial influencers on Twitter. The data consists of images that were part of tweets that mentioned a stock. This dataset can be used for a wide variety of tasks, such as image classification or feature extraction.

  FinTwit Charts Collection

This dataset is part of a larger collection of datasets, scraped from Twitter and labeled by a… See the full description on the dataset page: https://huggingface.co/datasets/StephanAkkerman/stock-charts.

This dataset contains a CSV file and an RDF Turtle file. Both files contain information on a few people mentioned in the Irish Exchequer Payments 1270-1326, a book written by Connolly, P and published by the Irish Manuscripts Commission in 1998. A historian transcribed those people in a CSV file, subsequently transformed into RDF using an R2RML mapping. This dataset contains the records and the output of a handful of people transcribed in this way. This dataset illustrates how the Beyond 2022 project avails of CIDOC-CRM to populate its knowledge graph.

Beyond 2022 is funded by the Government of Ireland, through the Department of Culture, Heritage and the Gaeltacht, under the Project Ireland 2040 framework. The project is also partially supported by the ADAPT Centre for Digital Content Technology under the SFI Research Centres Programme (Grant 13/RC/2106).

Dataset Overview

This dataset contains historical stock price data for Tesla, Inc. (TSLA) spanning from June 2010 to the present. The data includes daily records of Tesla's stock prices, covering various financial metrics for each trading day.

File Information

• File Name: TSLA.csv
• Number of Entries: 3499
• Number of Columns: 7
• File Size: 191.5 KB

Columns

1. Date (object): The date of the trading day in YYYY-MM-DD format.
2. Open (float64): The opening price of the stock on the given day.
3. High (float64): The highest price of the stock during the trading day.
4. Low (float64): The lowest price of the stock during the trading day.
5. Close (float64): The closing price of the stock on the given day.
6. Adj Close (float64): The adjusted closing price, accounting for corporate actions like stock splits and dividends.
7. Volume (int64): The total number of shares traded during the day.

Sample Data

Here are the first few rows of the dataset to give you a glimpse of the data structure:

Example Chart

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12685278%2F9479dbe596f198163a6641a5606bea6b%2FTS.png?generation=1716412477393105&alt=media" alt="">

(If you want to get the code for the chart, please access the [Tesla Stock Historical Data - Chart Examples] https://www.kaggle.com/code/girumwondemagegn/tesla-stock-historical-data-updated-may-2024) in my notebooks.)

Acknowledgements

The data has been sourced from publicly available financial records and is intended for educational and research purposes.

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

Billboard Hot weekly charts

Billboard Hot 100 Weekly Charts with Spotify Audio Features

By Sean Miller [source]

About this dataset

The Billboard Hot 100 Weekly Charts with Audio dataset is a comprehensive collection that combines the historical data of the Billboard Hot 100 weekly singles charts with detailed audio features extracted from Spotify. The dataset provides valuable insights into the popularity and musical attributes of songs that have appeared on the Billboard charts.

The primary dataset, Hot Stuff.csv, includes information about each song's position on the weekly charts. It contains columns such as the Billboard Chart URL, WeekID, Song name, Performer name, unique SongID (concatenation of song and performer), Current week on chart, Instance (indicating breaks in chart appearances), Previous week position, Peak Position (highest chart position reached), and Weeks on Chart.

The second dataset, Hot 100 Audio Features.csv, provides in-depth audio features of each song sourced from Spotify's Web API. This includes various metrics such as danceability (suitability for dancing based on musical elements), energy level (intensity and activity), key (musical key signature), loudness (overall volume level in decibels dB), mode (major or minor key), speechiness rating (presence of spoken words in songs), acousticness rating (acoustic quality measure), instrumentalness rating (likelihood of a song being instrumental), liveness rating (presence of a live audience during recording/performance) valence rating(musical positiveness conveyed by a song). Additionally it provides tempo in BPM and time signature(e.g., 4/4 -the rhythm pattern).

Furthermore , this comprehensive dataset encompasses Spotify-related features: track preview URL for audio samples before full streaming or purchase decisions; total duration measured in milliseconds; explicit content indication; album details for songs; genre details provided by Spotify.

With this combined data set, researchers can analyze trends and patterns over time regarding how different audio features relate to a song's popularity and performance on the Billboard Hot 100. It offers endless possibilities for studying the influence of specific music attributes on commercial success and understanding the preferences of popular music audiences.

Whether you are interested in exploring genre-based trends, discovering correlations between chart positions and audio features, or investigating how certain attributes contribute to a song's longevity on the charts, this dataset serves as a valuable resource for deep analysis and insights into Billboard Hot 100 songs

How to use the dataset

• Understanding the Datasets:

  • The dataset consists of two files: Hot Stuff.csv and Hot 100 Audio Features.csv.
  • The Hot Stuff.csv file contains the weekly Hot 100 singles chart data, including song names, performer names, chart positions, and other relevant information.
  • The Hot 100 Audio Features.csv file contains detailed audio features for each song extracted from Spotify, such as danceability, energy, instrumentalness, etc.
  • Both files can be merged using common attributes like Performer and Song to get a combined view of both datasets.

• Exploring the Hot Stuff.csv File:

  • This file provides information about each song's position on that week's Hot 100 singles chart.
  • Important columns in this file are:
    • WeekID: The week identifier.
    • Song name: The name of the song.
    • Performer name: The name of the performer or artist.
    • Current week on chart: Represents how many weeks the song has been on the chart at that particular point in time.
    • Instance: Indicates whether it is a separate entry for an already listed song (for example, an instance value of 6 means it appeared for the sixth time).
    • Previous week position: The position of the song on the previous week's chart.
    • Peak Position: The highest position reached by a particular song on any given week's chart.
    • Weeks on Chart: Represents how many weeks a specific entry has spent on the chart so far.

• Exploring the Hot 100 Audio Features.csv File:

  • This file provides detailed audio features for each song extracted from Spotify using the Spotify Web API.
  • It contains attributes like danceability, energy, instrumentalness, tempo, etc., which help capture different aspects of the song's musical characteristics.
  • Important columns in this file are:
    • Performer: The name of the performer or artist of the song.
    • Song: The name of the song.
    • spotify_genre: The genre(s) of the song according to Spotify....

This part of the data release includes graphical representation (figures) of data from sediment cores collected in 2009 offshore of Palos Verdes, California. This file graphically presents combined data for each core (one core per page). Data on each figure are continuous core photograph, CT scan (where available), graphic diagram core description (graphic legend included at right; visual grain size scale of clay, silt, very fine sand [vf], fine sand [f], medium sand [med], coarse sand [c], and very coarse sand [vc]), multi-sensor core logger (MSCL) p-wave velocity (meters per second) and gamma-ray density (grams per cc), radiocarbon age (calibrated years before present) with analytical error (years), and pie charts that present grain-size data as percent sand (white), silt (light gray), and clay (dark gray). This is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Figures.pdf. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

Context

This is a sample finance data for data visualization purpose. The sample datasets can be used as a complement to the data Furniture Superstore 2017 - 2018 that can be found in https://www.kaggle.com/zhoumeixing/furniture-superstore-2017-2018 In this datasets, you'll find balance sheet, budget, chart of accounts, income statement, and data of certain company. Those data can be combined as an integrated table in database by using certain key of each table.

DataSet for training the PyTorch Graph Network Simulator. https://github.com/geoelements/gns. The repository contains the data sets for water drop sample

United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Softcover, Pamphlet, Sample Book, and Other Binding of Books and Materials Printed Elsewhere was 156.74500 Index Dec 2011=100 in June of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Softcover, Pamphlet, Sample Book, and Other Binding of Books and Materials Printed Elsewhere reached a record high of 156.74500 in June of 2025 and a record low of 100.00000 in December of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Softcover, Pamphlet, Sample Book, and Other Binding of Books and Materials Printed Elsewhere - last updated from the United States Federal Reserve on November of 2025.

Results from runs analyses of 2000 simulated run charts with 24 data points and a shift of 2 SD introduced in the last 12 samples of half the simulations.