31 datasets found
  1. A

    ‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 4, 2021
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-vehicle-miles-traveled-during-covid-19-lock-downs-636d/latest
    Explore at:
    Dataset updated
    Jan 4, 2021
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/vehicle-miles-travelede on 13 February 2022.

    --- Dataset description provided by original source is as follows ---

    About this dataset

    **This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

    Overview

    Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

    This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

    Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

    This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

    Findings

    • Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
    • Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
    • New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least.

    About This Data

    The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

    Included Data

    01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

    02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

    03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

    Additional Data Queries

    * Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

    * Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

    * Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

    Interactive

    The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

    This dataset was created by Angeliki Kastanis and contains around 0 samples along with Date At Low, Mean7 County Vmt At Low, technical information and other features such as: - County Name - County Fips - and more.

    How to use this dataset

    • Analyze State Name in relation to Baseline Jan Vmt
    • Study the influence of Date At Low on Mean7 County Vmt At Low
    • More datasets

    Acknowledgements

    If you use this dataset in your research, please credit Angeliki Kastanis

    Start A New Notebook!

    --- Original source retains full ownership of the source dataset ---

  2. Birthday Paradox Visitor Data

    • kaggle.com
    Updated Jan 22, 2023
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    The Devastator (2023). Birthday Paradox Visitor Data [Dataset]. https://www.kaggle.com/datasets/thedevastator/birthday-paradox-visitor-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 22, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    The Devastator
    Description

    Birthday Paradox Visitor Data

    Exploring Probability and Patterns of Day of the Week Birthdays

    By data.world's Admin [source]

    About this dataset

    This dataset contains daily visitor-submitted birthdays and associated data from an ongoing experimentation known as the Birthday Paradox. Be enlightened as you learn how many people have chosen the same day of their birthday as yours. Get a better perspective on how this phenomenon varies day-to-day, including recent submissions within the last 24 hours. This experiment is published under the MIT License, giving you access to detailed information behind this perplexing cognitive illusion. Find out now why the probability of two people in the same room having birthday matches is much higher than one might expect!

    More Datasets

    For more datasets, click here.

    Featured Notebooks

    • 🚨 Your notebook can be here! 🚨!

    How to use the dataset

    This dataset provides data on the Birthday Paradox Visitor Experiments. It contains information such as daily visitor-submitted birthdays, the total number of visitors who have submitted birthdays, the total number of visitors who guessed the same day as their birthday, and more. This dataset can be used to analyze patterns in visitor behavior related to the Birthday Paradox Experiment.

    In order to use this dataset effectively and efficiently, it is important to understand its fields and variables:
    - Updated: The date when this data was last updated
    - Count: The total number of visitors who have submitted birthdays
    - Recent: The number of visitors who have submitted birthdays in the last 24 hours
    - binnedDay: The day of the week for a given visitor's birthday submission
    - binnedGuess: The day of week that a given visitor guessed their birthday would fall on 6) Tally: Total number of visitors who guessed same day as their birthday 7) binnedTally: Total number of visitors grouped by guess day

    To begin using this dataset you should first filter your data based on desired criteria such as date range or binnedDay. For instance, if you are interested in analyzing Birthady Paradox Experiment results for Monday submissions only then you can filter your data by binnedDay = 'Monday'. Then further analyze your filtered query by examining other fields such as binnedGuess and comparing it with tally or binnedTally results accordingly. For example if we look at Monday entries above we should compare 'Monday' tallies with 'Tuesday' guesses (or any other weekday). ` Furthermore understanding updates from recent field can also provide interesting insights into user behavior related to Birthady Paradox Experiment -- trackingt recent entries may yield valuable trends over time.

    By exploring various combinations offields available in this dataset users will be ableto gain a better understandingof how user behaviordiffers across different daysofweek both within a singledayandover periodsoftimeaccordingtodifferent criteria providedbythisdataset

    Research Ideas

    • Analyzing the likelihood of whether a person will guess their own birthday correctly.
    • Estimating which day of the week is seeing the most number of visitors submitting their birthdays each day and analyzing how this varies over time.
    • Investigating how likely it is for two people from different regions to have the same birthday by comparing their respective submission rates on each day of the week

    Acknowledgements

    If you use this dataset in your research, please credit the original authors. Data Source

    License

    See the dataset description for more information.

    Columns

    File: data.csv | Column name | Description | |:----------------|:-----------------------------------------------------------------------------------| | updated | The date and time the data was last updated. (DateTime) | | count | The total number of visitor submissions. (Integer) | | recent | The number of visitor submissions in the last 24 hours. (Integer) | | binnedDay | The day of the week the visitor submitted their birthday. (String) | | binnedGuess | The day of the week the visitor guessed their birthday. (String) | | tally | The total number of visitor guesses that matched their actual birthdays. (Integer) | | binnedTally | The day of the week the visitor guessed their birthday correctly. (String) |

    Acknowledgement...

  3. Chicago Crime

    • kaggle.com
    zip
    Updated Apr 17, 2018
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    City of Chicago (2018). Chicago Crime [Dataset]. https://www.kaggle.com/chicago/chicago-crime
    Explore at:
    zip(0 bytes)Available download formats
    Dataset updated
    Apr 17, 2018
    Dataset authored and provided by
    City of Chicago
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    Chicago
    Description

    Context

    Approximately 10 people are shot on an average day in Chicago.

    http://www.chicagotribune.com/news/data/ct-shooting-victims-map-charts-htmlstory.html http://www.chicagotribune.com/news/local/breaking/ct-chicago-homicides-data-tracker-htmlstory.html http://www.chicagotribune.com/news/local/breaking/ct-homicide-victims-2017-htmlstory.html

    Content

    This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. This data includes unverified reports supplied to the Police Department. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time.

    Update Frequency: Daily

    Fork this kernel to get started.

    Acknowledgements

    https://bigquery.cloud.google.com/dataset/bigquery-public-data:chicago_crime

    https://cloud.google.com/bigquery/public-data/chicago-crime-data

    Dataset Source: City of Chicago

    This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source —https://data.cityofchicago.org — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

    Banner Photo by Ferdinand Stohr from Unplash.

    Inspiration

    What categories of crime exhibited the greatest year-over-year increase between 2015 and 2016?

    Which month generally has the greatest number of motor vehicle thefts?

    How does temperature affect the incident rate of violent crime (assault or battery)?

    https://cloud.google.com/bigquery/images/chicago-scatter.png" alt=""> https://cloud.google.com/bigquery/images/chicago-scatter.png

  4. o

    Golf Play Dataset Extended

    • opendatabay.com
    .undefined
    Updated Jun 23, 2025
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    Datasimple (2025). Golf Play Dataset Extended [Dataset]. https://www.opendatabay.com/data/ai-ml/23026657-8212-4f36-84a0-f6064a0b889b
    Explore at:
    .undefinedAvailable download formats
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Datasimple
    Area covered
    Education & Learning Analytics
    Description

    Overview This Extended Golf Play Dataset is a rich and detailed collection designed to extend the classic golf dataset. It includes a variety of features to cover many aspects of data science. This dataset is especially useful for teaching because it offers many small datasets within it, each one created for a different learning purpose.

    Core Features: Outlook: Type of weather (sunny, cloudy, rainy, snowy). Temperature: How hot or cold it is, in Celsius. Humidity: How much moisture is in the air, as a percent. Windy: If it is windy or not (True or False). Play: If golf was played or not (Yes or No). Extra Features: ID: Each player's unique number. Date: The day the data was recorded. Weekday: What day of the week it is. Holiday: If the day is a special holiday (Yes or No). Season: Time of the year (spring, summer, autumn, winter). Crowded-ness: How crowded the golf course is. PlayTime-Hour: How long people played golf, in hours. Text Features: Review: What players said about their day at golf. EmailCampaign: Emails the golf place sent every day. MaintenanceTasks: Work done to take care of the golf course. Mini Datasets Collection This dataset includes a special set of mini datasets:

    Each mini dataset focuses on a specific teaching point, like how to clean data or how to combine datasets. They're perfect for beginners to practice with real examples. Along with these datasets, you'll find notebooks with step-by-step guides that show you how to use the data. Learning With This Dataset Students can use this dataset to learn many skills:

    Seeing Data: Learn how to make graphs and see patterns. Sorting Data: Find out which data helps to predict if golf will be played. Finding Odd Data: Spot data that doesn't look right. Understanding Data Over Time: Look at how things change day by day or month by month. Grouping Data: Learn how to put similar days together. Learning From Text: Use players' reviews to get more insights. Making Recommendations: Suggest the best time to play golf based on past data. Who Can Use This Dataset This dataset is for everyone:

    New Learners: It's easy to understand and has guides to help you learn. Teachers: Great for classes on how to see and understand data. Researchers: Good for testing new ways to analyze data.

    Original Data Source: ⛳️ Golf Play Dataset Extended

  5. Z

    MobMeter: a global human mobility data set based on smartphone trajectories

    • data.niaid.nih.gov
    • zenodo.org
    Updated Sep 4, 2024
    + more versions
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    Finazzi, Francesco (2024). MobMeter: a global human mobility data set based on smartphone trajectories [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6984637
    Explore at:
    Dataset updated
    Sep 4, 2024
    Dataset authored and provided by
    Finazzi, Francesco
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This data set is supplement to this Scientific Reports article.

    The data set provides estimates of country-level daily mobility metrics (uncertainty included) for 17 countries from March 11, 2020 to present. Estimates are based on more than 3.8 million smartphone trajectories.

    Metrics:

    Estimated daily average travelled distance by people.

    Estimated percentage of people who did not move during the 24 hours of the day.

    Countries: Argentina (ARG), Chile (CHL), Colombia (COL), Costa Rica (CRI), Ecuador (ECU), Greece (GRC), Guatemala (GTM), Italy (ITA), Mexico (MEX), Nicaragua (NIC), Panama (PAN), Peru (PER), Philippines (PHL), Slovenia (SVN), Turkey (TUR), United States (USA) and Venezuela (VEN).

    Covered period: from March 11, 2020 to present.

    Temporal resolution: daily.

    Temporal smoothing:

    No smoothing.

    7-day moving average.

    14-day moving average.

    21-day moving average.

    28-day moving average.

    Uncertainty: 95% bootstrap confidence interval.

    Data ownership

    Anonymized data on smartphone trajectories are collected, owned and managed by Futura Innovation SRL. Smartphone trajectories are stored and analyzed on servers owned by Futura Innovation SRL and not shared with third parties, including the author of this repository and his organization (University of Bergamo).

    Contribution

    Ilaria Cremonesi of Futura Innovation SRL is the data owner and data manager.

    Francesco Finazzi of University of Bergamo developed the statistical methodology for the data analysis and the algorithms implemented on Futura Innovation SRL servers.

    Repository update

    CSV files of this repository are regularly produced by Futura Innovation SRL and published by the repository's author after validation.

  6. P

    How to Login DuckDuckGo Account? | A Step-By-Step Guide Dataset

    • paperswithcode.com
    Updated Jun 17, 2025
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    (2025). How to Login DuckDuckGo Account? | A Step-By-Step Guide Dataset [Dataset]. https://paperswithcode.com/dataset/how-to-login-duckduckgo-account-a-step-by
    Explore at:
    Dataset updated
    Jun 17, 2025
    Description

    For Login DuckDuckGo Please Visit: 👉 DuckDuckGo Login Account

    In today’s digital age, privacy has become one of the most valued aspects of online activity. With increasing concerns over data tracking, surveillance, and targeted advertising, users are turning to privacy-first alternatives for everyday browsing. One of the most recognized names in private search is DuckDuckGo. Unlike mainstream search engines, DuckDuckGo emphasizes anonymity and transparency. However, many people wonder: Is there such a thing as a "https://duckduckgo-account.blogspot.com/ ">DuckDuckGo login account ?

    In this comprehensive guide, we’ll explore everything you need to know about the DuckDuckGo login account, what it offers (or doesn’t), and how to get the most out of DuckDuckGo’s privacy features.

    Does DuckDuckGo Offer a Login Account? To clarify up front: DuckDuckGo does not require or offer a traditional login account like Google or Yahoo. The concept of a DuckDuckGo login account is somewhat misleading if interpreted through the lens of typical internet services.

    DuckDuckGo's entire business model is built around privacy. The company does not track users, store personal information, or create user profiles. As a result, there’s no need—or intention—to implement a system that asks users to log in. This stands in stark contrast to other search engines that rely on login-based ecosystems to collect and use personal data for targeted ads.

    That said, some users still search for the term DuckDuckGo login account, usually because they’re trying to save settings, sync devices, or use features that may suggest a form of account system. Let’s break down what’s possible and what alternatives exist within DuckDuckGo’s platform.

    Saving Settings Without a DuckDuckGo Login Account Even without a traditional DuckDuckGo login account, users can still save their preferences. DuckDuckGo provides two primary ways to retain search settings:

    Local Storage (Cookies) When you customize your settings on the DuckDuckGo account homepage, such as theme, region, or safe search options, those preferences are stored in your browser’s local storage. As long as you don’t clear cookies or use incognito mode, these settings will persist.

    Cloud Save Feature To cater to users who want to retain settings across multiple devices without a DuckDuckGo login account, DuckDuckGo offers a feature called "Cloud Save." Instead of creating an account with a username or password, you generate a passphrase or unique key. This key can be used to retrieve your saved settings on another device or browser.

    While it’s not a conventional login system, it’s the closest DuckDuckGo comes to offering account-like functionality—without compromising privacy.

    Why DuckDuckGo Avoids Login Accounts Understanding why there is no DuckDuckGo login account comes down to the company’s core mission: to offer a private, non-tracking search experience. Introducing login accounts would:

    Require collecting some user data (e.g., email, password)

    Introduce potential tracking mechanisms

    Undermine their commitment to full anonymity

    By avoiding a login system, DuckDuckGo keeps user trust intact and continues to deliver on its promise of complete privacy. For users who value anonymity, the absence of a DuckDuckGo login account is actually a feature, not a flaw.

    DuckDuckGo and Device Syncing One of the most commonly searched reasons behind the term DuckDuckGo login account is the desire to sync settings or preferences across multiple devices. Although DuckDuckGo doesn’t use accounts, the Cloud Save feature mentioned earlier serves this purpose without compromising security or anonymity.

    You simply export your settings using a unique passphrase on one device, then import them using the same phrase on another. This offers similar benefits to a synced account—without the need for usernames, passwords, or emails.

    DuckDuckGo Privacy Tools Without a Login DuckDuckGo is more than just a search engine. It also offers a range of privacy tools—all without needing a DuckDuckGo login account:

    DuckDuckGo Privacy Browser (Mobile): Available for iOS and Android, this browser includes tracking protection, forced HTTPS, and built-in private search.

    DuckDuckGo Privacy Essentials (Desktop Extension): For Chrome, Firefox, and Edge, this extension blocks trackers, grades websites on privacy, and enhances encryption.

    Email Protection: DuckDuckGo recently launched a service that allows users to create "@duck.com" email addresses that forward to their real email—removing trackers in the process. Users sign up for this using a token or limited identifier, but it still doesn’t constitute a full DuckDuckGo login account.

    Is a DuckDuckGo Login Account Needed? For most users, the absence of a DuckDuckGo login account is not only acceptable—it’s ideal. You can:

    Use the search engine privately

    Customize and save settings

    Sync preferences across devices

    Block trackers and protect email

    —all without an account.

    While some people may find the lack of a traditional login unfamiliar at first, it quickly becomes a refreshing break from constant credential requests, data tracking, and login fatigue.

    The Future of DuckDuckGo Accounts As of now, DuckDuckGo maintains its position against traditional account systems. However, it’s clear the company is exploring privacy-preserving ways to offer more user features—like Email Protection and Cloud Save. These features may continue to evolve, but the core commitment remains: no tracking, no personal data storage, and no typical DuckDuckGo login account.

    Final Thoughts While the term DuckDuckGo login account is frequently searched, it represents a misunderstanding of how the platform operates . Unlike other tech companies that monetize personal data, DuckDuckGo has stayed true to its promise of privacy .

  7. P

    Dashlane Login | How to Login Dashlane Account? Dataset

    • paperswithcode.com
    Updated Jun 17, 2025
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    (2025). Dashlane Login | How to Login Dashlane Account? Dataset [Dataset]. https://paperswithcode.com/dataset/dashlane-login-how-to-login-dashlane-account
    Explore at:
    Dataset updated
    Jun 17, 2025
    Description

    (Toll Free) Number +1-341-900-3252

    It's hard to keep track of passwords (Toll Free) Number +1-341-900-3252 in our digital world. It might be hard to remember logins, keep your accounts safe (Toll Free) Number +1-341-900-3252 , and manage many accounts at once. This is where Dashlane comes in. It makes managing passwords easy because it has a simple UI and strong security. This tutorial is for you if you want to know how to log in to your Dashlane account and what makes (Toll Free) Number +1-341-900-3252 it different from other password managers.

    (Toll Free) Number +1-341-900-3252

    Why should you use Dashlane to manage your passwords?

    It's crucial to know why millions of people around the world choose Dashlane before we get into the login process. Here's how it helps people in their daily lives:

    (Toll Free) Number +1-341-900-3252

    Better security Dashlane uses strong encryption to keep your credentials safe. You don't have to worry about hackers getting to your data using AES-256 encryption, which is the best in the business.

    (Toll Free) Number +1-341-900-3252

    Easy to use on all devices Dashlane makes it easy to store passwords on various devices. You can easily get to your login information on any device, whether it's a smartphone, laptop, or tablet.

    Easier to log in After you set it up, Dashlane's autofill function lets you log in to apps and websites without having to type in your login and password. Not only does it go faster, but it also gets rid of mistakes.

    Keeping an eye on the dark web Dashlane does more than merely keep track of passwords. It also checks the dark web for leaks of personal information. You will be notified right away if your information has been leaked.

    Use a VPN to keep your privacy safe Dashlane has a virtual private network (VPN) in addition to passwords to keep your private browsing safe on public Wi-Fi.

    How to Access Your Dashlane Account

    Whether you're new to Dashlane or use it every day, it's easy to log in. To safely log into your account and start managing your passwords, do the following:

    (Toll Free) Number +1-341-900-3252

    Step 1: Get the Dashlane app Downloading Dashlane is the first thing you need to do if you're new. It works on all of the most popular platforms, including Windows, macOS, iOS, and Android. You can also use Dashlane as a browser extension on popular browsers including Chrome, Firefox, and Edge.

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    Step 3: Type in Your Email Address Type in the email address that is linked to your Dashlane login account. This will take you to the login page.

    Step 4: Give your master password You'll need to make a Master Password the first time you log in. This is a single, strong (Toll Free) Number +1-341-900-3252 password that opens your vault. To get back in, just type in your master password. Tip: Your master password should be hard to guess but easy to remember. Think about using numerals, letters, and special characters in both upper and lower case.

    Step 5: Verify (If Necessary) If you have two-factor authentication (2FA) set up on your account, you will also need to check this step. Dashlane can ask you to enter a code that was delivered to your email or made by an authentication app.

    (Toll Free) Number +1-341-900-3252

    Step 6: Open Your Vault Once you sign in, you'll see your password vault. This is where you can manage your stored logins, credit card information, and confidential notes.

    Important Security Features of Dashlane

    Dashlane puts your safety first with these cutting-edge (Toll Free) Number +1-341-900-3252 features:

    Encryption with AES-256 Your private information is stored with military-grade encryption, which keeps it safe from hackers.

    Architecture with No Knowledge Dashlane uses a zero-knowledge security model, which means that the corporation can't see or get to your passwords.

    Ways to log in with biometrics You may make things easier without giving up security by turning on biometric authentication, such as Face ID or fingerprint scanning, on devices that allow it.

    Information about the health of your password Dashlane doesn't just keep your passwords safe; it also looks at them. It indicates passwords that are weak or have been used before, which helps you make your accounts stronger.

    (Toll Free) Number +1-341-900-3252

    Access in an emergency You can let a trustworthy person in if there is an emergency. This makes it easier to keep (Toll Free) Number +1-341-900-3252 track of critical accounts.

    How to Get the Most Out of Dashlane

    To get the most out of your Dashlane login account, follow these tips:

    Turn on Autofill: Autofill can help you save time when you log in, especially to sites you visit often.

    Change your passwords often: Change your passwords every now and then (Toll Free) Number +1-341-900-3252 to make them more secure. You can make strong, unique passwords in seconds using Dashlane's Password Generator.

    Turn on two-factor authentication: Always use two-factor authentication (2FA) to add an extra layer of security to your account. This way, even if your password is stolen, your account will still be safe.

    Use the Password Health Tool: Check your password health score often and change any credentials that are marked.

    What Makes Dashlane Unique

    Dashlane is different from other password managers since it is easy to use and has sophisticated capabilities like monitoring the dark web and built-in VPN services. Dashlane keeps your information safe without slowing you down when you check in for work, shop online, or manage your personal accounts.

    Last Thoughts

    (Toll Free) Number +1-341-900-3252

    It's easier than ever to keep your internet safety in check. (Toll Free) Number +1-341-900-3252 Dashlane makes it easy to get to your accounts, keeps your private data safe, and protects you from breaches before they happen. Now that you know how to log in to Dashlane, why not give it a shot and take charge of your passwords? Dashlane is the greatest way to keep your online life safe because (Toll Free) Number +1-341-900-3252 your safety deserves the best.

  8. o

    DailyDialog: Multi-Turn Dialog+Intention+Emotion

    • opendatabay.com
    .undefined
    Updated Jun 23, 2025
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    Datasimple (2025). DailyDialog: Multi-Turn Dialog+Intention+Emotion [Dataset]. https://www.opendatabay.com/data/ai-ml/c2640303-2aa1-4d38-a323-4e674bf07b5b
    Explore at:
    .undefinedAvailable download formats
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Datasimple
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Data Science and Analytics
    Description

    The DailyDialog dataset is a meticulously curated collection of multi-turn dialogues that aims to accurately represent the way we communicate in our daily lives. It covers a wide range of topics that are relevant to our everyday experiences. What sets this dataset apart is that it includes human-written conversations, which means the language used is more natural and realistic, resulting in less noise and higher quality data.

    Each dialogue in the dataset consists of two or more participants engaging in a conversation. The conversations are provided in textual form, allowing for easy analysis and processing. Alongside the dialogues, there are also corresponding labels for communication intention and emotion attached to each utterance.

    The communication intention labels categorize each utterance based on its intended purpose or goal within the conversation. These categories provide valuable insights into how different participants express their intentions through speech.

    In addition to the communication intention labels, there are also emotion labels assigned to each utterance in the dialogues. These emotion labels capture the emotional state or sentiment expressed by participants during various points in the conversation.

    To facilitate model evaluation and testing, DailyDialog provides three separate files: validation.csv, train.csv, and test.csv. The validation set (validation.csv) contains dialogues with their respective communication intention and emotion labels for assessing model performance during development stages. The train set (train.csv) includes dialogues paired with corresponding communication intention and emotion labels for training purposes. Lastly, test.csv serves as an independent test set that enables evaluating models' proficiency by providing unseen dialogues along with their associated communication intention and emotion labels.

    Overall, DailyDialog stands out as a high-quality dataset due to its accurate representation of daily life conversations paired with comprehensive labeling of both communication intentions and emotions expressed throughout these dialogues. This makes it an invaluable resource for developing robust dialogue systems capable of understanding human interactions on a deeper level while being able to identify diverse intentions behind speech acts alongside various emotional states encountered during daily life exchanges

    How to use the dataset Welcome to the DailyDialog dataset! This high-quality multi-turn dialog dataset has been curated to reflect our daily communication style and covers a wide range of topics related to our everyday lives. The dataset consists of human-written conversations, making it less noisy and more realistic. Each conversation in the dataset has been manually labeled with communication intention and emotion information, providing valuable insights into the dialogues.

    To make the most of this dataset, here is a step-by-step guide on how you can use it effectively:

    Understanding the columns:

    dialog: This column contains the actual conversation between two or more participants. It is in text format. act: The act column represents the communication intention labels for each utterance in the dialogue. These labels categorize each utterance based on its intention. emotion: The emotion column contains emotion labels for each utterance in the dialogue. These labels represent the emotions expressed during that particular utterance. Familiarize yourself with validation.csv:

    The validation.csv file serves as a validation set for evaluating your model's performance. It contains pre-labeled conversations along with their corresponding communication intentions and emotion labels. Explore train.csv for training purposes:

    The train.csv file is meant for training purposes and provides conversations along with their communication intentions and emotion labels. Test your model using test.csv:

    Test.csv file has conversation along ithentensions or emotional label which can be addressed once program is recreated. Finally, remember that this DailyDialog dataset offers an excellent opportunity to develop models capable of understanding multi-turn dialogues in a wide range of everyday scenarios. By utilizing both communication intention and emotion information provided, you can gain valuable insights into analyzing human conversations.

    So dive into this rich resource, experiment with different techniques such as natural language processing and machine learning, and discover new ways of understanding and modeling human dialogues!

    Research Ideas Natural Language Processing: This dataset can be used for training NLP models to understand and generate more realistic and human-like dialogues. The communication intention labels can help in identifying the purpose or goal of each utterance, while the emotion labels can add emotional context to the conversations. Sentiment Analysis: With the emotion labels, this dataset can be used for sentiment analysis tasks t

  9. Z

    ComplexVAD Video Anomaly Detection Dataset

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jun 12, 2024
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    Yilmaz, Yasin (2024). ComplexVAD Video Anomaly Detection Dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_11475280
    Explore at:
    Dataset updated
    Jun 12, 2024
    Dataset provided by
    Cherian, Anoop
    Yilmaz, Yasin
    Mumcu, Furkan
    Jones, Mike
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    Introduction

    The ComplexVAD dataset consists of 104 training and 113 testing video sequences taken from a static camera looking at a scene of a two-lane street with sidewalks on either side of the street and another sidewalk going across the street at a crosswalk. The videos were collected over a period of a few months on the campus of the University of South Florida using a camcorder with 1920 x 1080 pixel resolution. Videos were collected at various times during the day and on each day of the week. Videos vary in duration with most being about 12 minutes long. The total duration of all training and testing videos is a little over 34 hours. The scene includes cars, buses and golf carts driving in two directions on the street, pedestrians walking and jogging on the sidewalks and crossing the street, people on scooters, skateboards and bicycles on the street and sidewalks, and cars moving in the parking lot in the background. Branches of a tree also move at the top of many frames.

    The 113 testing videos have a total of 118 anomalous events consisting of 40 different anomaly types.

    Ground truth annotations are provided for each testing video in the form of bounding boxes around each anomalous event in each frame. Each bounding box is also labeled with a track number, meaning each anomalous event is labeled as a track of bounding boxes. A single frame can have more than one anomaly labeled.

    At a Glance

    The size of the unzipped dataset is ~39GB

    The dataset consists of Train sequences (containing only videos with normal activity), Test sequences (containing some anomalous activity), a ground truth annotation file for each Test sequence, and a README.md file describing the data organization and ground truth annotation format.

    The zip files contain a Train directory, a Test directory, an annotations directory, and a README.md file.

    License

    The ComplexVAD dataset is released under CC-BY-SA-4.0 license.

    All data:

    Created by Mitsubishi Electric Research Laboratories (MERL), 2024

    SPDX-License-Identifier: CC-BY-SA-4.0

  10. M

    Movement Range Maps

    • catalog.midasnetwork.us
    txt, zip
    Updated Jul 7, 2023
    + more versions
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    MIDAS Coordination Center (2023). Movement Range Maps [Dataset]. https://catalog.midasnetwork.us/collection/304
    Explore at:
    zip, txtAvailable download formats
    Dataset updated
    Jul 7, 2023
    Dataset authored and provided by
    MIDAS Coordination Center
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Time period covered
    Mar 1, 2020 - May 22, 2022
    Variables measured
    disease, COVID-19, behavior, pathogen, Homo sapiens, host organism, infectious disease, human daily movement data set, Severe acute respiratory syndrome coronavirus 2
    Dataset funded by
    National Institute of General Medical Sciences
    Description

    The dataset includes information about how populations are responding to physical distancing measures. The metrics used in this dataset include Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day.

  11. P

    {Oficial~Telefóno}¿El servicio de atención al cliente de United está...

    • paperswithcode.com
    Updated Jun 23, 2025
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    (2025). {Oficial~Telefóno}¿El servicio de atención al cliente de United está disponible las 24 horas? Dataset [Dataset]. https://paperswithcode.com/dataset/oficial-telefono-el-servicio-de-atencion-al
    Explore at:
    Dataset updated
    Jun 23, 2025
    Description

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  12. Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset...

    • zenodo.org
    zip
    Updated Apr 8, 2025
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    Piotr Wozniak; Piotr Wozniak; Tomasz Krzeszowski; Tomasz Krzeszowski; Bogdan Kwolek; Bogdan Kwolek (2025). Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots [Dataset]. http://doi.org/10.5281/zenodo.11504582
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 8, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Piotr Wozniak; Piotr Wozniak; Tomasz Krzeszowski; Tomasz Krzeszowski; Bogdan Kwolek; Bogdan Kwolek
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The dataset is password-protected until the article is accepted by the Scientific Data Journal. After acceptance, the dataset will be available to everyone. For any questions, comments or other issues please contact Piotr Woźniak, email: p.wozniak@prz.edu.pl.

    License

    The MDDRobots dataset is made available under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/.

    Summary

    The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions.

    It is obligatory to cite the following paper in every work that uses the dataset:

    Wozniak, P.; Krzeszowski T.; Kwolek B.: Multi-Domain Dataset for Indoor Place Recognition and Anomaly Detection by Mobile Robots, Scientific Data, ISSN: 2052-4463, 2024.

    Data description

    The data is divided into five sets, each containing data for different cameras, which have further subsets. Each subset (Training, Test 1, Test 2, and Test 3) consists of nine sequences. There are a total of 87,750 three-channel RGB color images in PNG format organized into 19 zip folders. Each image in the sequence is labeled to represent a room. The number of images for each subset differs due to the division into training and testing data, as well as different methods of recording the sequences. To ensure a balanced dataset, each room in the sequence has the same number of images. Different environmental changes are introduced in each subset, mainly due to changes in the route, robot, and recording equipment. The rooms are well-lit but not overexposed. Test 1 data are closest to those from the training set. Test 3 sequences present changed conditions, such as a different time of day, a changed lighting system, and intensive equipment changes. Test 2 sequences pose the most significant challenge as they contain various recorded activities performed by people moving around rooms. This sequence data does not appear in the Xtion subset.

    Dataset structure

    • RobotPiCamera_DataSet
      • DataSet_RobotPiCamera_RGB_train
      • DataSet_RobotPiCamera_RGB_test1
      • DataSet_RobotPiCamera_RGB_test2
      • DataSet_RobotPiCamera_RGB_test3
    • Xtion_DataSet
      • DataSet_XTION_RGB_train
      • DataSet_XTION_RGB_test1
      • DataSet_XTION_RGB_test3
    • GOPRO_DataSet
      • DataSet_GOPRO_RGB_train
      • DataSet_GOPRO_RGB_test1
      • DataSet_GOPRO_RGB_test2
      • DataSet_GOPRO_RGB_test3
    • iPhone_DataSet
      • DataSet_IPHONE_RGB_train
      • DataSet_IPHONE_RGB_test1
      • DataSet_IPHONE_RGB_test2
      • DataSet_IPHONE_RGB_test3
    • P40PRO_DataSet
      • DataSet_P40PRO_RGB_train
      • DataSet_P40PRO_RGB_test1
      • DataSet_P40PRO_RGB_test2
      • DataSet_P40PRO_RGB_test3

    Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.

    Total Images (Images per Place)

    SubsetMountedTrainingTest 1Test 2Test 3
    Pi CameraRobot7200 (800)5400 (600)5400 (600)5400 (600)
    XtionRobot7200 (800) 1800 (200) -1800 (200)
    GoProHand5400 (600)4500 (500)4500 (500)4500 (500)
    iPhoneHand5400 (600) 4500 (500)4500 (500)4500 (500)
    P40ProHand5400 (600)4050 (450)3150 (350) 3150 (350)


    Further information

    For any questions, comments or other issues please contact Piotr Woźniak

  13. d

    Vehicle Miles Traveled

    • data.world
    csv, zip
    Updated Aug 30, 2023
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    The Associated Press (2023). Vehicle Miles Traveled [Dataset]. https://data.world/associatedpress/vehicle-miles-traveled
    Explore at:
    csv, zipAvailable download formats
    Dataset updated
    Aug 30, 2023
    Authors
    The Associated Press
    Time period covered
    Mar 1, 2020 - Dec 31, 2020
    Description

    **This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

    Overview

    Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

    This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

    Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

    This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

    Findings

    • Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
    • Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
    • New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least. ## About This Data The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

    Included Data

    01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

    02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

    03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

    Additional Data Queries

    * Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

    * Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

    * Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

    Interactive

    The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

    @(https://interactives.ap.org/vmt-map/)

    Interactive Embed Code

    Using the Data

    This data can help put your county's mobility in context with your state and over time. The data set contains different measures of change - daily comparisons and seven day rolling averages. The rolling average allows for a smoother trend line for comparison across counties and states. To get the full picture, there are also two available baselines - vehicle miles traveled in January 2020 (pre-pandemic) and vehicle miles traveled at each geography's low point during the pandemic.

    Caveats

    • The data from StreetLight Data, Inc does not include data for some low-population counties with low VMT (<5,000 miles/day in their baseline month of January 2020). In our analyses, we only include the 2,779 counties that have daily data for the entire period (March 1, 2020 to current).
    • In some cases, a lack of decline in mobility from March to April can indicate that movement in the county is essential to keeping the larger economy going or that residents need to drive further to reach essentials businesses like grocery stores compared to other counties.
    • The VMT includes both passenger and commercial miles, so truck traffic is included. However, the proxy is based on the "total number of trip starts and ends for all devices whose most frequent location is in this county". It does not count the VMT of trucks cutting through a county.
    • For those instances where travel begins in one county and ends in another, the county where the miles are recorded is always the vehicle’s home county. ###### Contact reporter Angeliki Kastanis at akastanis@ap.org.
  14. Data from: LifeSnaps: a 4-month multi-modal dataset capturing unobtrusive...

    • zenodo.org
    • explore.openaire.eu
    zip
    Updated Oct 20, 2022
    + more versions
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    Sofia Yfantidou; Sofia Yfantidou; Christina Karagianni; Stefanos Efstathiou; Stefanos Efstathiou; Athena Vakali; Athena Vakali; Joao Palotti; Joao Palotti; Dimitrios Panteleimon Giakatos; Dimitrios Panteleimon Giakatos; Thomas Marchioro; Thomas Marchioro; Andrei Kazlouski; Elena Ferrari; Šarūnas Girdzijauskas; Šarūnas Girdzijauskas; Christina Karagianni; Andrei Kazlouski; Elena Ferrari (2022). LifeSnaps: a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild [Dataset]. http://doi.org/10.5281/zenodo.6832242
    Explore at:
    zipAvailable download formats
    Dataset updated
    Oct 20, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Sofia Yfantidou; Sofia Yfantidou; Christina Karagianni; Stefanos Efstathiou; Stefanos Efstathiou; Athena Vakali; Athena Vakali; Joao Palotti; Joao Palotti; Dimitrios Panteleimon Giakatos; Dimitrios Panteleimon Giakatos; Thomas Marchioro; Thomas Marchioro; Andrei Kazlouski; Elena Ferrari; Šarūnas Girdzijauskas; Šarūnas Girdzijauskas; Christina Karagianni; Andrei Kazlouski; Elena Ferrari
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    LifeSnaps Dataset Documentation

    Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

    The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

    Data Import: Reading CSV

    For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

    Data Import: Setting up a MongoDB (Recommended)

    To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

    To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

    For the Fitbit data, run the following:

    mongorestore --host localhost:27017 -d rais_anonymized -c fitbit 

    For the SEMA data, run the following:

    mongorestore --host localhost:27017 -d rais_anonymized -c sema 

    For surveys data, run the following:

    mongorestore --host localhost:27017 -d rais_anonymized -c surveys 

    If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

    Data Availability

    The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

    {
      _id: 
  15. NOAA GSOD

    • kaggle.com
    zip
    Updated Aug 30, 2019
    + more versions
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    NOAA (2019). NOAA GSOD [Dataset]. https://www.kaggle.com/datasets/noaa/gsod
    Explore at:
    zip(0 bytes)Available download formats
    Dataset updated
    Aug 30, 2019
    Dataset provided by
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    Authors
    NOAA
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Overview

    Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries.

    Content

    Over 9000 stations' data are typically available.

    The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches)

    Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel

    Querying BigQuery tables

    You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

    Acknowledgements

    This public dataset was created by the National Oceanic and Atmospheric Administration (NOAA) and includes global data obtained from the USAF Climatology Center. This dataset covers GSOD data between 1929 and present, collected from over 9000 stations. Dataset Source: NOAA

    Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

    Photo by Allan Nygren on Unsplash

  16. Climate Change: Earth Surface Temperature Data

    • kaggle.com
    • redivis.com
    zip
    Updated May 1, 2017
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    Berkeley Earth (2017). Climate Change: Earth Surface Temperature Data [Dataset]. https://www.kaggle.com/datasets/berkeleyearth/climate-change-earth-surface-temperature-data
    Explore at:
    zip(88843537 bytes)Available download formats
    Dataset updated
    May 1, 2017
    Dataset authored and provided by
    Berkeley Earthhttp://berkeleyearth.org/
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Area covered
    Earth
    Description

    Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

    us-climate-change

    Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

    Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

    We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

    In this dataset, we have include several files:

    Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

    • Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
    • LandAverageTemperature: global average land temperature in celsius
    • LandAverageTemperatureUncertainty: the 95% confidence interval around the average
    • LandMaxTemperature: global average maximum land temperature in celsius
    • LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
    • LandMinTemperature: global average minimum land temperature in celsius
    • LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
    • LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
    • LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

    Other files include:

    • Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
    • Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
    • Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
    • Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

    The raw data comes from the Berkeley Earth data page.

  17. P

    How Do I Contact "Bitdefender Customer Service"? A Simple Guide Dataset

    • paperswithcode.com
    Updated Jun 18, 2025
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    (2025). How Do I Contact "Bitdefender Customer Service"? A Simple Guide Dataset [Dataset]. https://paperswithcode.com/dataset/how-do-i-contact-bitdefender-customer-service
    Explore at:
    Dataset updated
    Jun 18, 2025
    Description

    Click Here : Bitdefender Customer Service

    =================================================================================

    In today's fast-paced digital world, one of the most critical things for people and organizations to do is keep their cyber security up to date. Bitdefender is a well-known firm that provides strong antivirus and internet security software. Like with any other service or product, users may have problems or questions about their subscriptions, features, billing, or installation. At this point, Bitdefender's customer service is a very significant aspect of the support system. This complete guide is called "How Do I Get in Touch with 'Bitdefender Customer Service'?" This easy-to-follow article will show you how to get in touch with Bitdefender's support team in a few different ways so you can obtain fast, useful, and skilled help.

    Understanding How Important Bitdefender Customer Service Is When it comes to cybersecurity services, customer service is highly vital for keeping people pleased. Both new and long-time customers may have problems that pop up out of the blue. These can be issues with installation, activation keys, system compatibility, payment, or security. Bitdefender has a number of help options that are tailored to these situations. If you know how to reach them customer care, you may get your problems fixed quickly and with as little hassle as possible.

    Here are some things you should know before you call Bitdefender Support. You may speed up the process by doing a few things before you call Bitdefender's customer service. Be ready with the following information:

    Peacock Tv Login Peacock Tv Sign in Bitdefender Login Account Bitdefender Sign in Account Norton Login Norton Sign in

    The email address for your Bitdefender account

    Your Bitdefender Central login details

    The key or code that allows you utilize your product

    The device or operating system that is having the difficulty

    A full explanation of the problem or error message you are getting

    Being ready implies that the support crew can help you right away without having to call you back several times.

    First, you need go to Bitdefender Central. When you think, "How do I reach 'Bitdefender Customer Service'?" First, you need to go to Bitdefender Central. This online dashboard lets you keep track of your account, installations, devices, and subscriptions. You can also use customer assistance options like live chat, sending tickets, and articles that help you fix difficulties.

    You may get to Bitdefender Central by signing into your account on the Bitdefender website. To get to the "Support" area, which is normally near the bottom of the dashboard, click on it. Here you may discover a number of useful articles, video lectures, and ways to get in touch with us.

    Chat Support: Talk to a Bitdefender employee right away for help One of the fastest and easiest ways to reach Bitdefender customer service is through live chat. You can get this tool from Bitdefender Central and talk to a live person in real time. The chat service is there to assist you fix problems right away, whether they have to do with your account or technology.

    To start a chat session, click the "Contact Support" or "Chat with an Expert" button. Once you get in touch, explain your situation in detail and follow the support person's instructions. This is the simplest way to deal with issues that need to be repaired fast but aren't too hard.

    Email Support: For Help That Is Thorough and Well-Documented Email support is another useful option if you need to send in papers or give detailed explanations. On Bitdefender's Central platform, people can make a support ticket. This choice is appropriate for hard situations like disputed charges, license transfers, or technical problems that keep coming up and need more support.

    To put in a support ticket, go to the Bitdefender Central customer service page, fill out the form, explain your problem, and attach any files that are important. If your problem is simple, a representative will usually come back to you within a few hours to a day.

    Phone Support: Get in touch with a Bitdefender Agent Sometimes, the best and most reassuring thing to do is to call customer service right away. In some places, Bitdefender offers free phone support, which enables users clearly explain their concerns and get speedy solutions.

    You can find the relevant phone number for your country on the Bitdefender Contact page. The wait periods may be greater or shorter depending on how busy it is, but the agents are ready to answer any question, from minor problems to more complicated security issues.

    Websites and forums for the community If you want to fix problems on your own or learn more before talking to a professional, the Bitdefender Community Forum is a fantastic place to go. This platform lets users and official moderators speak about items and give advice, fixes, and information on software.

    The Knowledge Base section is another wonderful way to get in-depth information, answers to common questions, and step-by-step guides. A lot of people get answers here without having to call customer service.

    Help with Bitdefender for Business Users You might need more specific advice if your firm uses Bitdefender GravityZone or other corporate solutions. Business users can access dedicated enterprise help through the GravityZone portal. Enterprise users can report issues, start conversations, and seek for more help that is tailored to their security and infrastructure needs.

    Most business accounts come with account managers or technical support teams who can aid with deployment, integration, and ways to deal with threats in real time.

    How to Fix Common Problems Before Calling Support How to contact "Bitdefender Customer Service" "A Simple Guide" also tells you when you might not need to get in touch with them at all. You can fix a number of common problems on your own with Bitdefender. For example:

    Installation problems: Downloading the full offline installer generally cures the problem.

    Activation errors happen when the license numbers are inaccurate or the subscription has run out.

    Problems with performance can usually be fixed by changing the scan schedule or updating the program.

    The "My Subscriptions" option in Bitdefender Central makes it easy to deal with billing problems.

    Using these tools can save you time and cut down on the number of times you have to call customer service.

    What Remote Help Does for Tech Issues Bitdefender can also aid you with problems that are tougher to fix from a distance. You will need to install a remote access tool so that the technician can take control of your system and fix the problem themselves after you set up a time to chat to a support agent. This is especially useful for those who aren't very good with technology or for firms that have multiple levels of protection.

    Remote help makes sure that problems are handled in a competent way and gives you peace of mind that your digital security is still safe.

    How to Keep Bitdefender Safe and Up to Date Doing regular maintenance is one of the easiest ways to cut down on the need for customer service. You need to update your Bitdefender program on a regular basis to acquire the latest security updates, malware definitions, and functionality upgrades. To avoid compatibility issues, make sure that your operating system and any third-party software you use are also up to date.

    Regular scans, avoiding suspicious websites, and checking the Bitdefender dashboard for alerts will help keep your system safe and minimize the chances that you'll require support right away.

    What Bitdefender Mobile App Support can do You can also get support from the Bitdefender app on your Android or iOS device. The mobile interface lets you manage your devices, renew your membership, and even talk to customer care directly from your phone. This can be quite helpful for folks who need support while they're on the go or who are experiencing trouble with their phone, such setting up a VPN or parental controls.

    Keeping consumer data and conversation private Bitdefender keeps its clients' privacy very high when they talk to them. There are strict laws about privacy and data protection for all kinds of contact, such as phone calls, emails, chats, and remote help. When you need to get in touch with customer service, always utilize real means. Don't give out personal information unless the help process requires you to.

    Final Thoughts on How to Contact Bitdefender Customer Service Bitdefender's customer service is designed to help you with any issue, whether it's a technical problem, a query about a payment, or just a desire for guidance, swiftly, clearly, and professionally. Being able to contact someone, have the proper information ready, and choosing the best route to obtain help can make a great difference in how you feel about the whole thing.

  18. A

    ‘Pakistan Corona Virus Dataset’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Sep 30, 2021
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘Pakistan Corona Virus Dataset’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-pakistan-corona-virus-dataset-7f50/f59c6dcf/?iid=027-428&v=presentation
    Explore at:
    Dataset updated
    Sep 30, 2021
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Pakistan
    Description

    Analysis of ‘Pakistan Corona Virus Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/zusmani/pakistan-corona-virus-citywise-data on 30 September 2021.

    --- Dataset description provided by original source is as follows ---

    Context

    Pakistan witnessed its first Corona virus patient on February 26th 2020. It's a bumpy ride since then. The cases are increasing gradually and we haven't seen the worst yet. While, there are few government resources for cumulative updates, there is no place where you can find the city level patients data. It's also not possible to find the running chronological tally of patients as they test positive. We have decided to create our own dataset for all the researchers out there with such details so we can model the infection spread and forecast the situation in coming days. We hope, by doing so, we will be able to inform policy makers on various intervention models, and healthcare professionals to be ready for the influx of new patients. We certainly hope, that this little contribution will go a long way for saving lives in Pakistan

    Content

    The dataset contains seven columns for date, number of cases, number of deaths, number of people recovered, travel history of those cases, and location of the cases (province and city).

    The first version has the data from first case of February 26 2020 to April 19, 2020. We intend to publish weekly updates

    Acknowledgements

    Users are allowed to use, copy, distribute and cite the dataset as follows: “Zeeshan-ul-hassan Usmani, Sana Rasheed, Pakistan Corona Virus Data, Kaggle Dataset Repository, April 19, 2020.”

    Inspiration

    Some ideas worth exploring:

    Can we find the spread factor for the Corona virus in Pakistan?

    How long it takes for a positive case to infect another in Pakistan?

    How we can use this data to simulate lock down scenarios and find its impact on country's economy? Here is a good
    read to get started - http://zeeshanusmani.com/urdu/corona-economic-impact/

    How does Pakistan Corona virus spread compare against its neighbors and other developed counties?

    What would be the impact of this infection spread on country's economy and people living under poverty? Here are two briefs to get you started

    http://zeeshanusmani.com/urdu/corona/ http://zeeshanusmani.com/urdu/corona-what-to-learn/

    How do we visualize this dataset to inform policy makers? Here is one example https://zeeshanusmani.com/corona/

    Can we predict the number of cases in next 10 days and a month?

    --- Original source retains full ownership of the source dataset ---

  19. Energy Consumption of United States Over Time

    • kaggle.com
    Updated Dec 14, 2022
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    The Devastator (2022). Energy Consumption of United States Over Time [Dataset]. https://www.kaggle.com/datasets/thedevastator/unlocking-the-energy-consumption-of-united-state
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 14, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    The Devastator
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    Energy Consumption of United States Over Time

    Building Energy Data Book

    By Department of Energy [source]

    About this dataset

    The Building Energy Data Book (2011) is an invaluable resource for gaining insight into the current state of energy consumption in the buildings sector. This dataset provides comprehensive data on residential, commercial and industrial building energy consumption, construction techniques, building technologies and characteristics. With this resource, you can get an in-depth understanding of how energy is used in various types of buildings - from single family homes to large office complexes - as well as its impact on the environment. The BTO within the U.S Department of Energy's Office of Energy Efficiency and Renewable Energy developed this dataset to provide a wealth of knowledge for researchers, policy makers, engineers and even everyday observers who are interested in learning more about our built environment and its energy usage patterns

    More Datasets

    For more datasets, click here.

    Featured Notebooks

    • 🚨 Your notebook can be here! 🚨!

    How to use the dataset

    This dataset provides comprehensive information regarding energy consumption in the buildings sector of the United States. It contains a number of key variables which can be used to analyze and explore the relations between energy consumption and building characteristics, technologies, and construction. The data is provided in both CSV format as well as tabular format which can make it helpful for those who prefer to use programs like Excel or other statistical modeling software.

    In order to get started with this dataset we've developed a guide outlining how to effectively use it for your research or project needs.

    • Understand what's included: Before you start analyzing the data, you should read through the provided documentation so that you fully understand what is included in the datasets. You'll want to be aware of any potential limitations or requirements associated with each type of data point so that your results are valid and reliable when drawing conclusions from them.

    • Clean up any outliers: You may need to take some time upfront investigating suspicious outliers within your dataset before using it in any further analyses — otherwise, they can skew results down the road if not dealt with first-hand! Furthermore, they could also make complex statistical modeling more difficult as well since they artificially inflate values depending on their magnitude within each example data point (i.e., one outlier could affect an entire model’s prior distributions). Missing values should also be accounted for too since these may not always appear obvious at first glance when reviewing a table or graphical representation - but accurate statistics must still be obtained either way no matter how messy things seem!

    • Exploratory data analysis: After cleaning up your dataset you'll want to do some basic exploring by visualizing different types of summaries like boxplots, histograms and scatter plots etc.. This will give you an initial case into what trends might exist within certain demographic/geographic/etc.. regions & variables which can then help inform future predictive models when needed! Additionally this step will highlight any clear discontinuous changes over time due over-generalization (if applicable), making sure predictors themselves don’t become part noise instead contributing meaningful signals towards overall effect predictions accuracy etc…

    • Analyze key metrics & observations: Once exploratory analyses have been carried out on rawsamples post-processing steps are next such as analyzing metrics such ascorrelations amongst explanatory functions; performing significance testing regression models; imputing missing/outlier values and much more depending upon specific project needs at hand… Additionally – interpretation efforts based

    Research Ideas

    • Creating an energy efficiency rating system for buildings - Using the dataset, an organization can develop a metric to rate the energy efficiency of commercial and residential buildings in a standardized way.
    • Developing targeted campaigns to raise awareness about energy conservation - Analyzing data from this dataset can help organizations identify areas of high energy consumption and create targeted campaigns and incentives to encourage people to conserve energy in those areas.
    • Estimating costs associated with upgrading building technologies - By evaluating various trends in building technologies and their associated costs, decision-makers can determine the most cost-effective option when it comes time to upgrade their structures' energy efficiency...
  20. SVG Code Generation Sample Training Data

    • kaggle.com
    Updated May 3, 2025
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    Vinothkumar Sekar (2025). SVG Code Generation Sample Training Data [Dataset]. https://www.kaggle.com/datasets/vinothkumarsekar89/svg-generation-sample-training-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 3, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Vinothkumar Sekar
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    This training data was generated using GPT-4o as part of the 'Drawing with LLM' competition (https://www.kaggle.com/competitions/drawing-with-llms). It can be used to fine-tune small language models for the competition or serve as an augmentation dataset alongside other data sources.

    The dataset is generated in two steps using the GPT-4o model. - In the first step, topic descriptions relevant to the competition are generated using a specific prompt. By running this prompt multiple times, over 3,000 descriptions were collected.

     
    prompt=f""" I am participating in an SVG code generation competition.
      
       The competition involves generating SVG images based on short textual descriptions of everyday objects and scenes, spanning a wide range of categories. The key guidelines are as follows:
      
       - Descriptions are generic and do not contain brand names, trademarks, or personal names.
       - No descriptions include people, even in generic terms.
       - Descriptions are concise—each is no more than 200 characters, with an average length of about 50 characters.
       - Categories cover various domains, with some overlap between public and private test sets.
      
       To train a small LLM model, I am preparing a synthetic dataset. Could you generate 100 unique topics aligned with the competition style?
      
       Requirements:
       - Each topic should range between **20 and 200 characters**, with an **average around 60 characters**.
       - Ensure **diversity and creativity** across topics.
       - **50% of the topics** should come from the categories of **landscapes**, **abstract art**, and **fashion**.
       - Avoid duplication or overly similar phrasing.
      
       Example topics:
                     a purple forest at dusk, gray wool coat with a faux fur collar, a lighthouse overlooking the ocean, burgundy corduroy, pants with patch pockets and silver buttons, orange corduroy overalls, a purple silk scarf with tassel trim, a green lagoon under a cloudy sky, crimson rectangles forming a chaotic grid,  purple pyramids spiraling around a bronze cone, magenta trapezoids layered on a translucent silver sheet,  a snowy plain, black and white checkered pants,  a starlit night over snow-covered peaks, khaki triangles and azure crescents,  a maroon dodecahedron interwoven with teal threads.
      
       Please return the 100 topics in csv format.
       """
     
    • In the second step, SVG code is generated by prompting the GPT-4o model. The following prompt is used to query the model to generate svg.
     
      prompt = f"""
          Generate SVG code to visually represent the following text description, while respecting the given constraints.
          
          Allowed Elements: `svg`, `path`, `circle`, `rect`, `ellipse`, `line`, `polyline`, `polygon`, `g`, `linearGradient`, `radialGradient`, `stop`, `defs`
          Allowed Attributes: `viewBox`, `width`, `height`, `fill`, `stroke`, `stroke-width`, `d`, `cx`, `cy`, `r`, `x`, `y`, `rx`, `ry`, `x1`, `y1`, `x2`, `y2`, `points`, `transform`, `opacity`
          
    
          Please ensure that the generated SVG code is well-formed, valid, and strictly adheres to these constraints. 
          Focus on a clear and concise representation of the input description within the given limitations. 
          Always give the complete SVG code with nothing omitted. Never use an ellipsis.
    
          The code is scored based on similarity to the description, Visual question anwering and aesthetic components.
          Please generate a detailed svg code accordingly.
    
          input description: {text}
          """
     

    The raw SVG output is then cleaned and sanitized using a competition-specific sanitization class. After that, the cleaned SVG is scored using the SigLIP model to evaluate text-to-SVG similarity. Only SVGs with a score above 0.5 are included in the dataset. On average, out of three SVG generations, only one meets the quality threshold after the cleaning, sanitization, and scoring process.

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Cite
Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-vehicle-miles-traveled-during-covid-19-lock-downs-636d/latest

‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ analyzed by Analyst-2

Explore at:
Dataset updated
Jan 4, 2021
Dataset authored and provided by
Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Analysis of ‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/vehicle-miles-travelede on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

  • Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
  • Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
  • New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least.

About This Data

The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

This dataset was created by Angeliki Kastanis and contains around 0 samples along with Date At Low, Mean7 County Vmt At Low, technical information and other features such as: - County Name - County Fips - and more.

How to use this dataset

  • Analyze State Name in relation to Baseline Jan Vmt
  • Study the influence of Date At Low on Mean7 County Vmt At Low
  • More datasets

Acknowledgements

If you use this dataset in your research, please credit Angeliki Kastanis

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

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