The number of users in the 'Online Dating' segment of the eservices market in the United States was forecast to continuously increase between 2024 and 2028 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the indicator is estimated to reach ***** million users and therefore a new peak in 2028. Notably, the number of users of the 'Online Dating' segment of the eservices market was continuously increasing over the past years.Find further information concerning revenue in the United States and revenue growth in Indonesia. The Statista Market Insights cover a broad range of additional markets.

This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

Columns:

• Order ID: Unique identifier for each sales order.
• Date:Date of the sales transaction.
• Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
• Product Name:Specific name or model of the product sold.
• Quantity:Number of units of the product sold in the transaction.
• Unit Price:Price of one unit of the product.
• Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
• Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
• Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

Insights:

• 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
• 2. Explore the popularity of different product categories across regions.
• 3. Investigate the impact of payment methods on sales volume or revenue.
• 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
• 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.

It’s Valentines Day - a day when people think about love and relationships. How people meet and form relationship works a lot quicker than in our parent’s or grandparent’s day. I’m sure many of you are told how it used to be - you met someone, dated them for a while, proposed, got married. People who grew up in small towns maybe had one shot at finding love, so they made sure they didn’t mess it up.

Today finding a date is not a challenge - finding a match is probably the issue. In the last 20 years we’ve gone from traditional dating to online dating to speed dating to online speed dating. Now you just swipe left or swipe right, if that’s your thing.

In 2002-2004, Columbia University ran a speed-dating experiment where they tracked data over 21 speed dating sessions for mostly young adults meeting people of the opposite sex.

1. The first five columns are demographic - we may want to use them to look at subgroups later.
2. The next seven columns are important. dec is the raters decision on whether this individual was a match and then follows scores out of ten on six characteristics: attractiveness, sincerity, intelligence, fun, ambitiousness and shared interests.
3. The like column is an overall rating. The prob column is a rating on whether the rater believed that interest would be reciprocated and the final column is a binary on whether the two had met prior to the speed date, with the lower value indicating that they had met before.

Original data

Acknowlegement

Foto von Helena Lopes auf Unsplash

What influences love at first sight? (Or, at least, love in the first four minutes?) This dataset was compiled by Columbia Business School professors Ray Fisman and Sheena Iyengar for their paper Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment.

Data was gathered from participants in experimental speed dating events from 2002-2004. During the events, the attendees would have a four minute "first date" with every other participant of the opposite sex. At the end of their four minutes, participants were asked if they would like to see their date again. They were also asked to rate their date on six attributes: Attractiveness, Sincerity, Intelligence, Fun, Ambition, and Shared Interests.

The dataset also includes questionnaire data gathered from participants at different points in the process. These fields include: demographics, dating habits, self-perception across key attributes, beliefs on what others find valuable in a mate, and lifestyle information. See the Speed Dating Data Key document below for details.

For more analysis from Iyengar and Fisman, read Racial Preferences in Dating.

Data Exploration Ideas

• What are the least desirable attributes in a male partner? Does this differ for female partners?
• How important do people think attractiveness is in potential mate selection vs. its real impact?
• Are shared interests more important than a shared racial background?
• Can people accurately predict their own perceived value in the dating market?
• In terms of getting a second date, is it better to be someone's first speed date of the night or their last?

BackgroundUniversity campus clinics provide crucial sexual health services to students, including STI/HIV screening, testing, contraception, and counseling. These clinics are essential for engaging young adults who may lack access to primary care or have difficulty reaching off-campus services. Dating apps are widely used by young adults, yet there is a lack of studies on how they affect sexual practices. This study aimed to evaluate the use of dating apps, engagement in condomless sexual activity, and the prevalence of STIs among young adult college students in Northern Texas.MethodsA cross-sectional survey was conducted from August to December 2022 among undergraduate and graduate students aged 18–35 at a large university in Northern Texas. A total of 122 eligible participants completed the survey, which assessed demographics, sexual behaviors, dating app use, and STI/HIV testing practices. Descriptive statistics, bivariate analyses, and multivariate Poisson regression analyses with robust variance were performed to identify factors associated with dating app use and condomless sexual activity.ResultsTwo-thirds of participants reported using dating apps. Significant differences were found between app users and non-users regarding demographic factors and unprotected sexual behaviors. Dating app users were more likely to report multiple sexual partners, inconsistent condom use, and a higher likelihood of engaging in unprotected sex. Poisson regression analysis indicated that app use was associated with residing in large urban areas, frequent use of campus STI/HIV screening services, and having multiple sexual partners (p 

For Best Experience: Use Light Theme 💡 in Kaggle as *some column names might not be visible in Dark Theme.🌙*

Dataset: Club Penguin All Parties Dataset Overview This dataset chronicles all known parties and special events held in the online game Club Penguin. Each row represents a unique event, capturing essential information like dates, themes, and other notable details. This dataset provides valuable insight into the evolution of events and the types of features and rewards associated with each one.

Column Descriptions

Party: The name of the event, often reflecting its theme, such as "Halloween Party" or "Puffle Discovery." This field captures the central idea or theme of each event and may also indicate its iteration if it recurred over multiple years. Start date: The launch date of each event, typically formatted as "Month Day, Time (Time Zone)" or "Month-Day." It provides the exact moment each party began, useful for analyzing seasonal trends or frequency of events throughout the year. End date: The end date of the event, similarly formatted. This information helps calculate the duration of each event and assess patterns in event length over time. Free items: A list of unique items introduced or given away for free during the event. Items may include costumes, accessories, or other collectibles, which added value to the player's experience and incentivized participation. Notes: Additional information or historical significance about the event. Notes may include details like "First ever Halloween party," indicating a debut, or other trivia that highlights the event's relevance in the game’s history. Characters: Lists any in-game characters who played a central role or made special appearances during the event. This can include Club Penguin mascots or themed characters that enhanced the event’s interactivity. Locations and the Special/Added Party Rooms: This field specifies in-game locations that were transformed, decorated, or newly introduced for the event. The inclusion of this column provides insight into the extent of changes made to the game’s environment, showing the level of immersion offered to players. Year: The year the event took place, allowing for quick chronological sorting and filtering. This column is helpful for identifying patterns in event scheduling, such as holiday events and recurring themes across different years. Potential Use Cases This dataset is ideal for researchers, analysts, and enthusiasts interested in:

Event Timeline Analysis: Studying how the frequency, duration, and timing of events evolved over time. Item Collection Trends: Understanding the diversity of items introduced and assessing their impact on player engagement. Thematic Trends: Exploring changes in event themes, especially seasonal or recurring themes. Gaming Culture Studies: Gaining insights into popular culture, in-game traditions, and player experience.

Dataset Use Cases This dataset is ideal for enthusiasts, researchers, and analysts interested in:

Timeline Analysis: Analyzing the frequency, duration, and timing of Club Penguin events. Thematic Trends: Examining how themes evolved over time, identifying recurring themes or seasonal patterns. Player Engagement: Assessing which types of events may have been more engaging based on the number of new items or transformed locations. Gaming Culture Studies: Gaining insights into popular culture and seasonal trends within online games through thematic analysis. Acknowledgments Special thanks to Club Penguin Wiki Fandom website for compiling this dataset, which captures a nostalgic part of online gaming history.

For a summary of the case study, please go to "Portfolio Project".

Context

This data analysis was meant to show that men have their own issues in society that are being ignored. The mental health has been declining especially for men. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. This data analysis was meant to show that men have their own issues in society that are being ignored. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. These variables may require a separate dataset going into more detail about them.

A space dedicated just for men and another just for women to speak about their problems with help and constructive criticism for growth and for social belonging maybe required to improve the mental health of society (among other variables). This does not mean that the struggles of women are nonexistent. There are already a multitude of datasets and articles dedicated to some of the possible struggles of women from MSNBC, CNN, NBC, BBC, Netflix movies, and even popular secular music like recent songs WAP from Megan Thee Stallion, God is a Women by Arianna Grande, etc. This dataset's objective was not made to continue to light a flame between the already hostile relationships that modern men and women have with each other. Awareness without bias is the goal.

For the results, please read the portfolio project and leave comments.

Content

Where the data were obtained:

1. The first excel file was obtained from https://data.world/vizzup/mental-health-depression-disorder-data/workspace/file?filename=Mental+health+Depression+disorder+Data.xlsx

2. The second excel file was obtained from https://ourworldindata.org/grapher/male-vs-female-suicide

3. The third excel file was obtained from https://ourworldindata.org/suicide

4. The fourth excel file was obtained from https://ourworldindata.org/drug-use

Inspiration

I want to be the best data analyst ever, so criticism (regardless of the harshness), it will be greatly appreciated. What would you have added/improved on? Was it easy to understand? What else do you want me to make a dataset on?

The dataset contains information about the contents of 100 Terms of Service (ToS) of online platforms. The documents were analyzed and evaluated from the point of view of the European Union consumer law. The main results have been presented in the table titled "Terms of Service Analysis and Evaluation_RESULTS." This table is accompanied by the instruction followed by the annotators, titled "Variables Definitions," allowing for the interpretation of the assigned values. In addition, we provide the raw data (analyzed ToS, in the folder "Clear ToS") and the annotated documents (in the folder "Annotated ToS," further subdivided).

SAMPLE: The sample contains 100 contracts of digital platforms operating in sixteen market sectors: Cloud storage, Communication, Dating, Finance, Food, Gaming, Health, Music, Shopping, Social, Sports, Transportation, Travel, Video, Work, and Various. The selected companies' main headquarters span four legal surroundings: the US, the EU, Poland specifically, and Other jurisdictions. The chosen platforms are both privately held and publicly listed and offer both fee-based and free services. Although the sample cannot be treated as representative of all online platforms, it nevertheless accounts for the most popular consumer services in the analyzed sectors and contains a diverse and heterogeneous set.

CONTENT: Each ToS has been assigned the following information: 1. Metadata: 1.1. the name of the service; 1.2. the URL; 1.3. the effective date; 1.4. the language of ToS; 1.5. the sector; 1.6. the number of words in ToS; 1.7–1.8. the jurisdiction of the main headquarters; 1.9. if the company is public or private; 1.10. if the service is paid or free. 2. Evaluative Variables: remedy clauses (2.1– 2.5); dispute resolution clauses (2.6–2.10); unilateral alteration clauses (2.11–2.15); rights to police the behavior of users (2.16–2.17); regulatory requirements (2.18–2.20); and various (2.21–2.25). 3. Count Variables: the number of clauses seen as unclear (3.1) and the number of other documents referred to by the ToS (3.2). 4. Pull-out Text Variables: rights and obligations of the parties (4.1) and descriptions of the service (4.2)

ACKNOWLEDGEMENT: The research leading to these results has received funding from the Norwegian Financial Mechanism 2014-2021, project no. 2020/37/K/HS5/02769, titled “Private Law of Data: Concepts, Practices, Principles & Politics.”

ZARA UK Fashion Dataset offers an extensive collection of fashion product data from ZARA's UK online store, providing a detailed overview of available items. This dataset is valuable for analyzing the European fashion retail market, particularly in the UK, and includes fields such as product titles, URLs, SKUs, MPNs, brands, prices, currency, images, breadcrumbs, country, availability, unique IDs, and timestamps for when the data was scraped.

Key Features:

• Product Details: Includes title, URL, SKU (Stock Keeping Unit), MPN (Manufacturer Part Number), and brand for each product, helping to uniquely identify and differentiate items.
• Pricing Information: Features the price of each product along with the currency used (GBP) to understand the pricing strategies of ZARA in the UK market.
• Visual Data: High-quality images of each product, essential for visual merchandising analysis and online consumer behavior studies.
• Categorical Information: Breadcrumbs data provide context on the product's placement within ZARA's website structure, helping to analyze navigation and product hierarchy.
• Geographical Focus: Specific to the UK market, making it relevant for studies on British fashion retail and consumer trends.
• Availability Status: Includes real-time availability data, which is crucial for understanding stock levels, popular products, and restocking practices.
• Unique Identifiers: Each product is tagged with a uniq_id, ensuring data integrity and making it easier to track and analyze over time.
• Data Collection Timestamp: The scraped_at field records the exact time and date when the data was collected, aiding in time-based analysis of inventory and pricing.

Potential Use Cases:

• Market Research: Analyze UK and European fashion trends, consumer preferences, and competitive positioning within the fast fashion sector.
• E-commerce Analysis: Study ZARA's product placement, pricing, and availability to optimize online retail strategies.
• Stock Management: Use SKU and availability data to predict inventory needs and enhance supply chain efficiency.
• Brand Analysis: Examine the impact of brand identity on consumer choices and product performance in the UK market.
• Academic Research: Ideal for research projects focused on fashion retail, marketing strategies, and consumer behavior in Europe.

Data Sources:

The data is meticulously collected from ZARA's official UK website and other reliable retail databases, reflecting the latest product offerings and market dynamics specific to the UK and European fashion markets.

• ZARA US Retail Products Dataset: Explore over 10,000 product records from ZARA's USA online store, including titles, prices, images, and availability.
  
  
• Fashion Products Dataset from GAP.com: Access detailed product information from GAP's online store, featuring over 4,500 fashion items with attributes like price, brand, color, reviews, and images.
  
  
• Myntra Fashion Products Dataset: A comprehensive dataset from Myntra.com, offering over 12,000 fashion products with detailed attributes for in-depth analysis.

Context

This dataset was built using the Philadelphia Federal Reserve's State Coincident Indices and the Bry-Boschan Method for business cycle dating. In the tradition of Owyang, Piger, et al. business cycles are calculated on the state level which provides interesting analysis opportunities for looking at recession timing for different regions or sectors present in different states. The MSA level data utilizes the Economic Coincident Indices available on the St. Louis FRED website and uses a variant of the non-parametric algorithm described in Metro Business Cycles (Arias et al. 2016) to date MSA level recessions.

Content

This data is from 1982 through 2018 and includes whether the economy is in a recession or not, with forward looking and backward looking data available for observations as well. Additionally, various FRED St. Louis series were joined, like the University of Michigan Consumer Sentiment Index and the Global Price of Brent Crude. The 2012 value added as a percent for different NAICS groups is included as well for sectoral analysis, although better data over time for this would prove beneficial. The industries file attempts to correct this, but has fewer years available.

Acknowledgements

Special thanks to the researchers at the Federal Reserve Banks of Philadelphia and St. Louis for collecting and making available much of the data that went into this dataset.

Inspiration

I was inspired by researchers that have attempted to take business cycle dating to the state and MSA level. Local business cycle dating methodologies allow for a more robust understanding of what goes into a recession and how sectoral composition can affect a state or MSA's "resilience" to recessions. This could have applications for weighting business cycle risk for companies based on geographic dispersion of customers, as well as local policymakers if local forecasting could be done successfully.

This dataset contains data from the experiment and python code for the project titled “Love or politics? Political views regarding the war in Ukraine in an online dating experiment”.

Paper abstract: How polarized is Russian society regarding the war in Ukraine? Political views have an impact on various behaviors, including relationship formation. In this paper I study the extent of polarization in the Russian society regrading the war in Ukraine by conducting a field experiment on a large Russian dating site and collecting data on more than 3,000 profile evaluations. The findings reveal sizable penalties for those who express pro-war or anti-war positions on their dating profiles, suggesting considerable levels of polarization in the Russian society regarding the war. Age of the online dating site users is the most divisive factor, as younger individuals are less likely to approach pro-war profiles but not anti-war profiles, while older individuals are less likely to respond positively to profiles indicating anti-war views but not pro-war views.

The experiment was conducted in October - November, 2022, on a large online dating site in Russia in three Russian regions: Moscow, Saint Petersburg, and Sverdlovskaya oblast. There are three separate data files, one for each region. Each file contains information on male dating site users that have been liked by and/or have viewed the experimental female profiles. The description is available at https://mpra.ub.uni-muenchen.de/118862/ . The folder also contains python code for data analysis.

Phylogenies provide a useful way to understand the evolutionary history of genetic samples, and data sets with more than a thousand taxa are becoming increasingly common, notably with viruses (e.g., human immunodeficiency virus (HIV)). Dating ancestral events is one of the first, essential goals with such data. However, current sophisticated probabilistic approaches struggle to handle data sets of this size. Here, we present very fast dating algorithms, based on a Gaussian model closely related to the Langley–Fitch molecular-clock model. We show that this model is robust to uncorrelated violations of the molecular clock. Our algorithms apply to serial data, where the tips of the tree have been sampled through times. They estimate the substitution rate and the dates of all ancestral nodes. When the input tree is unrooted, they can provide an estimate for the root position, thus representing a new, practical alternative to the standard rooting methods (e.g., midpoint). Our algorithms exploit the tree (recursive) structure of the problem at hand, and the close relationships between least-squares and linear algebra. We distinguish between an unconstrained setting and the case where the temporal precedence constraint (i.e., an ancestral node must be older that its daughter nodes) is accounted for. With rooted trees, the former is solved using linear algebra in linear computing time (i.e., proportional to the number of taxa), while the resolution of the latter, constrained setting, is based on an active-set method that runs in nearly linear time. With unrooted trees the computing time becomes (nearly) quadratic (i.e., proportional to the square of the number of taxa). In all cases, very large input trees (>10,000 taxa) can easily be processed and transformed into time-scaled trees. We compare these algorithms to standard methods (root-to-tip, r8s version of Langley–Fitch method, and BEAST). Using simulated data, we show that their estimation accuracy is similar to that of the most sophisticated methods, while their computing time is much faster. We apply these algorithms on a large data set comprising 1194 strains of Influenza virus from the pdm09 H1N1 Human pandemic. Again the results show that these algorithms provide a very fast alternative with results similar to those of other computer programs. These algorithms are implemented in the LSD software (least-squares dating), which can be downloaded from http://www.atgc-montpellier.fr/LSD/, along with all our data sets and detailed results. An Online Appendix, providing additional algorithm descriptions, tables, and figures can be found in the Supplementary Material available on Dryad at http://dx.doi.org/10.5061/dryad.968t3.

Abstract

How Couples Meet and Stay Together (HCMST) is a study of how Americans meet their spouses and romantic partners.

• The study is a nationally representative study of American adults.
• 4,002 adults responded to the survey, 3,009 of those had a spouse or main
  romantic partner.
• The study oversamples self-identified gay, lesbian, and bisexual adults
• Follow-up surveys were implemented one and two years after the main survey, to study couple dissolution rates. Version 3.0 of the dataset includes two follow- up surveys, waves 2 and 3.
• Waves 4 and 5 are provided as separate data files that can be linked back to the main file via variable caseid_new.

The study will provide answers to the following research questions:

1. Do traditional couples and nontraditional couples meet in the same way? What kinds of couples are more likely to have met online?
2. Have the most recent marriage cohorts (especially the traditional heterosexual same-race married couples) met in the same way their parents and grandparents did?
3. Does meeting online lead to greater or less couple stability?
4. How do the couple dissolution rates of nontraditional couples compare to the couple dissolution rates of more traditional same-race heterosexual couples?
5. How does the availability of civil union, domestic partnership or same-sex marriage rights affect couple stability for same-sex couples? This study will provide the first nationally representative data on the couple dissolution rates of same-sex couples.

Methodology

Universe:

The universe for the HCMST survey is English literate adults in the U.S.

**Unit of Analysis: **

Individual

**Type of data collection: **

Survey Data

**Time of data collection: **

Wave I, the main survey, was fielded between February 21 and April 2, 2009. Wave 2 was fielded March 12, 2010 to June 8, 2010. Wave 3 was fielded March 22, 2011 to August 29, 2011. Wave 4 was fielded between March and November of 2013. Wave 5 was fielded between November, 2014 and March, 2015. Dates for the background demographic surveys are described in the User's Guide, under documentation below.

Geographic coverage:

United States of America

Smallest geographic unit:

US region

**Sample description: **

The survey was carried out by survey firm Knowledge Networks (now called GfK). The survey respondents were recruited from an ongoing panel. Panelists are recruited via random digit dial phone survey. Survey questions were mostly answered online; some follow-up surveys were conducted by phone. Panelists who did not have internet access at home were given an internet access device (WebTV). For further information about how the Knowledge Networks hybrid phone-internet survey compares to other survey methodology, see attached documentation.

The dataset contains variables that are derived from several sources. There are variables from the Main Survey Instrument, there are variables generated from the investigators which were created after the Main Survey, and there are demographic background variables from Knowledge Networks which pre-date the Main Survey. Dates for main survey and for the prior background surveys are included in the dataset for each respondent. The source for each variable is identified in the codebook, and in notes appended within the dataset itself (notes may only be available for the Stata version of the dataset).

Respondents who had no spouse or main romantic partner were dropped from the Main Survey. Unpartnered respondents remain in the dataset, and demographic background variables are available for them.

**Sample response rate: **

Response to the main survey in 2009 from subjects, all of whom were already in the Knowledge Networks panel, was 71%. If we include the the prior initial Random Digit Dialing phone contact and agreement to join the Knowledge Networks panel (participation rate 32.6%), and the respondents’ completion of the initial demographic survey (56.8% completion), the composite overall response rate is a much lower .326*.568*.71= 13%. For further information on the calculation of response rates, and relevant citations, see the Note on Response Rates in the documentation. Response rates for the subsequent waves of the HCMST survey are simpler, using the denominator of people who completed wave 1 and who were eligible for follow-up. Response to wave 2 was 84.5%. Response rate to wave 3 was 72.9%. Response rate to wave 4 was 60.0%. Response rate to wave 5 was 46%. Response to wave 6 was 91.3%. Wave 6 was Internet only, so people who had left the GfK KnowledgePanel were not contacted.

**Weights: **

See "Notes on the Weights" in the Documentation section.

Usage

When you use the data, you agree to the following conditions:

1. I will not use the data to identify individuals.
2. I will not charge a fee for the data if I distribute it to others.
3. I will inform the contact person abo

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices: As a service that is subject to Viral loads (very high usage), avoid adding Filters that use a Date/Time type field. These queries are not cacheable and WILL be subject to Rate Limiting by ArcGIS Online. To accommodate filtering events by Date/Time, we encourage using the included "Age" fields that maintain the number of Days or Hours since a record was created or last modified compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be supplied to many users without adding load on the service.When ingesting this service in your applications, avoid using POST requests, these requests are not cacheable and will also be subject to Rate Limiting measures.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Tweet Sentiment's Impact on Stock Returns

862,231 Labeled Instances

By [source]

About this dataset

This dataset contains 862,231 labeled tweets and associated stock returns, providing a comprehensive look into the impact of social media on company-level stock market performance. For each tweet, researchers have extracted data such as the date of the tweet and its associated stock symbol, along with metrics such as last price and various returns (1-day return, 2-day return, 3-day return, 7-day return). Also recorded are volatility scores for both 10 day intervals and 30 day intervals. Finally, sentiment scores from both Long Short - Term Memory (LSTM) and TextBlob models have been included to quantify the overall tone in which these messages were delivered. With this dataset you will be able to explore how tweets can affect a company's share prices both short term and long term by leveraging all of these data points for analysis!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

In order to use this dataset, users can utilize descriptive statistics such as histograms or regression techniques to establish relationships between tweet content & sentiment with corresponding stock return data points such as 1-day & 7-day returns measurements.

The primary fields used for analysis include Tweet Text (TWEET), Stock symbol (STOCK), Date (DATE), Closing Price at the time of Tweet (LAST_PRICE) a range of Volatility measures 10 day Volatility(VOLATILITY_10D)and 30 day Volatility(VOLATILITY_30D ) for each Stock which capture changes in market fluctuation during different periods around when Twitter reactions occur. Additionally Sentiment Polarity analysis undertaken via two Machine learning algorithms LSTM Polarity(LSTM_POLARITY)and Textblob polarity provide insight into whether people are expressing positive or negative sentiments about each company at given times which again could influence thereby potentially influence Stock Prices over shorter term periods like 1-Day Returns(1_DAY_RETURN),2-Day Returns(2_DAY_RETURN)or longer term horizon like 7 Day Returns*7DAY RETURNS*.Finally MENTION field indicates if names/acronyms associated with Companies were specifically mentioned in each Tweet or not which gives extra insight into whether company specific contexts were present within individual Tweets aka “Company Relevancy”

Research Ideas

• Analyzing the degree to which tweets can influence stock prices. By analyzing relationships between variables such as tweet sentiment and stock returns, correlations can be identified that could be used to inform investment decisions.
• Exploring natural language processing (NLP) models for predicting future market trends based on textual data such as tweets. Through testing and evaluating different text-based models using this dataset, better predictive models may emerge that can give investors advance warning of upcoming market shifts due to news or other events.
• Investigating the impact of different types of tweets (positive/negative, factual/opinionated) on stock prices over specific time frames. By studying correlations between the sentiment or nature of a tweet and its effect on stocks, insights may be gained into what sort of news or events have a greater impact on markets in general

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: reduced_dataset-release.csv | Column name | Description | |:----------------------|:-------------------------------------------------------------------------------------------------------| | TWEET | Text of the tweet. (String) | | STOCK | Company's stock mentioned in the tweet. (String) | | DATE | Date the tweet was posted. (Date) | | LAST_PRICE | Company's last price at the time of tweeting. (Float) ...

Title: The Burger Spot Sales Data

Description:

Dive into the savory world of 'The Burger Spot' with our comprehensive dataset, offering a delectable blend of sales information that captures the essence of this popular eatery's operations. This dataset is a rich resource for anyone interested in the dynamics of the fast-food industry, customer behavior, and sales trends.

Dataset Overview: 'The Burger Spot' dataset is meticulously compiled to provide a detailed look at the sales performance across various metrics. It includes data on areas served, items sold, dates of transactions, number of orders, and the types of orders (online, takeaway, dining). The dataset spans an entire year, providing ample scope for analysis of seasonal trends, item popularity, and ordering patterns.

Key Features: - Area: Geographic locations where 'The Burger Spot' operates, featuring both real and imaginative trendy names. - Item: A list of mouth-watering menu items including a variety of burgers, fries, and sauces, each with a unique twist. - Date: Transaction dates, allowing for time-series analysis and identification of peak sales periods. - Orders: The number of orders placed for each item, which can be used to gauge popularity and customer preference. - Type of Order: Categorization of each order by its purchase method - online, takeaway, or dining in.

Potential Use-Cases: This dataset is perfect for those looking to: - Conduct explanatory data visualization to derive actionable business insights. - Analyze customer preferences and seasonal trends. - Develop predictive models for sales forecasting. - Understand the impact of different ordering methods on sales volume.

Ideal for: - Data analysts and scientists looking to practice exploratory data analysis (EDA) and visualization. - Students and educators in need of real-world data for projects and assignments. - Business strategists and consultants focusing on the food service industry.

Access & Use Information: 'The Burger Spot' dataset is available for public use and can be utilized for academic, research, and educational purposes. We encourage Kagglers to share their insights, notebooks, and visualizations with the community to foster collaborative learning and innovation.

Embark on a data-driven journey to uncover the flavors that drive 'The Burger Spot's' success and contribute to the collective understanding of the fast-food industry's data landscape.

Context

Airline data holds immense importance as it offers insights into the functioning and efficiency of the aviation industry. It provides valuable information about flight routes, schedules, passenger demographics, and preferences, which airlines can leverage to optimize their operations and enhance customer experiences. By analyzing data on delays, cancellations, and on-time performance, airlines can identify trends and implement strategies to improve punctuality and mitigate disruptions. Moreover, regulatory bodies and policymakers rely on this data to ensure safety standards, enforce regulations, and make informed decisions regarding aviation policies. Researchers and analysts use airline data to study market trends, assess environmental impacts, and develop strategies for sustainable growth within the industry. In essence, airline data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the aviation sector.

Content

This dataset comprises diverse parameters relating to airline operations on a global scale. The dataset prominently incorporates fields such as Passenger ID, First Name, Last Name, Gender, Age, Nationality, Airport Name, Airport Country Code, Country Name, Airport Continent, Continents, Departure Date, Arrival Airport, Pilot Name, and Flight Status. These columns collectively provide comprehensive insights into passenger demographics, travel details, flight routes, crew information, and flight statuses. Researchers and industry experts can leverage this dataset to analyze trends in passenger behavior, optimize travel experiences, evaluate pilot performance, and enhance overall flight operations.

Dataset Glossary (Column-wise)

• Passenger ID - Unique identifier for each passenger
• First Name - First name of the passenger
• Last Name - Last name of the passenger
• Gender - Gender of the passenger
• Age - Age of the passenger
• Nationality - Nationality of the passenger
• Airport Name - Name of the airport where the passenger boarded
• Airport Country Code - Country code of the airport's location
• Country Name - Name of the country the airport is located in
• Airport Continent - Continent where the airport is situated
• Continents - Continents involved in the flight route
• Departure Date - Date when the flight departed
• Arrival Airport - Destination airport of the flight
• Pilot Name - Name of the pilot operating the flight
• Flight Status - Current status of the flight (e.g., on-time, delayed, canceled)

Structure of the Dataset

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Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable Synthetic datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Kevin Woblick on Unsplash

Thumbnail by: Airplane icons created by Freepik - Flaticon

This dataset provides a detailed look into transactional behavior and financial activity patterns, ideal for exploring fraud detection and anomaly identification. It contains 2,512 samples of transaction data, covering various transaction attributes, customer demographics, and usage patterns. Each entry offers comprehensive insights into transaction behavior, enabling analysis for financial security and fraud detection applications.

Key Features:

• TransactionID: Unique alphanumeric identifier for each transaction.
• AccountID: Unique identifier for each account, with multiple transactions per account.
• TransactionAmount: Monetary value of each transaction, ranging from small everyday expenses to larger purchases.
• TransactionDate: Timestamp of each transaction, capturing date and time.
• TransactionType: Categorical field indicating 'Credit' or 'Debit' transactions.
• Location: Geographic location of the transaction, represented by U.S. city names.
• DeviceID: Alphanumeric identifier for devices used to perform the transaction.
• IP Address: IPv4 address associated with the transaction, with occasional changes for some accounts.
• MerchantID: Unique identifier for merchants, showing preferred and outlier merchants for each account.
• AccountBalance: Balance in the account post-transaction, with logical correlations based on transaction type and amount.
• PreviousTransactionDate: Timestamp of the last transaction for the account, aiding in calculating transaction frequency.
• Channel: Channel through which the transaction was performed (e.g., Online, ATM, Branch).
• CustomerAge: Age of the account holder, with logical groupings based on occupation.
• CustomerOccupation: Occupation of the account holder (e.g., Doctor, Engineer, Student, Retired), reflecting income patterns.
• TransactionDuration: Duration of the transaction in seconds, varying by transaction type.
• LoginAttempts: Number of login attempts before the transaction, with higher values indicating potential anomalies.

This dataset is ideal for data scientists, financial analysts, and researchers looking to analyze transactional patterns, detect fraud, and build predictive models for financial security applications. The dataset was designed for machine learning and pattern analysis tasks and is not intended as a primary data source for academic publications.

This dataset is about book subjects. It has 4 rows and is filtered where the books is How to work a room : the ultimate guide to making lasting connections, in person and online. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about book subjects. It has 3 rows and is filtered where the books is The digital turn : how the internet transforms our existence. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.