272 Resources

Open Dataset

Showing 209-216 of 272 results
Pinterest Fashion Compatibility

Pinterest Fashion Compatibility

Stefan
Open Dataset
Creators: Kang, Wang-Cheng; Kim, Eric; Leskovec, Jure; Rosenberg, Charles; McAuley, Julian
Publication Date: 2019
Creators: Kang, Wang-Cheng; Kim, Eric; Leskovec, Jure; Rosenberg, Charles; McAuley, Julian

This dataset is a structured collection of images and metadata designed to study the compatibility of fashion products within real-world scenes. It enables detailed analysis of how fashion items appear in different settings and supports applications in machine learning, recommendation systems, and virtual styling tools. One of its key features is the scene-product pairing, where fashion items in real-world images are annotated with bounding boxes and linked to corresponding product images. In total, the dataset includes 47,739 scene images, 38,111 product images, and 93,274 scene-product pairs, making it a comprehensive resource for fashion compatibility research.

The dataset is about 29 MB large and includes:

  • Scenes: 47,739
  • Products: 38,111
  • Scene-Product Pairs: 93,274
EndoMondo Fitness Tracking Data

EndoMondo Fitness Tracking Data

Stefan
Open Dataset
Creators: Ni, Jianmo; Muhlstein, Larry; McAuley, Julian
Publication Date: 2019
Creators: Ni, Jianmo; Muhlstein, Larry; McAuley, Julian

This is a collection of workout logs from users of EndoMondo. It contains sequential sensor data such as GPS coordinates (latitude, longitude, altitude), heart rate measurements, speed, and distance, making it valuable for studying workout patterns, performance tracking, and personalized fitness recommendations. Additionally, it includes user metadata such as anonymized user IDs, gender, and sport type, along with contextual factors like weather conditions. The dataset has a size of approximately 2.9 GB and consists of 1,104 users with 253,020 recorded workouts.

The dataset covers multiple components:​

  • User Information: Anonymized user identifiers and gender.​

  • Workout Details: Each workout log includes sport type, sequential data for GPS coordinates (latitude, longitude, altitude) with timestamps, heart rate measurements, and derived metrics such as speed and distance.

CrowdTangle Platform and API

CrowdTangle Platform and API

Stefan
Open Dataset
Creators: Garmur, Matt; King, Gary; Mukerjee, Zagreb; Persily, Nate; Silverman, Brandon
Publication Date: 2019
Creators: Garmur, Matt; King, Gary; Mukerjee, Zagreb; Persily, Nate; Silverman, Brandon

This document describes the CrowdTangle API and user interface being provided to researchers
by Social Science One under its collaboration framework with Facebook. CrowdTangle is a
content discovery and analytics platform designed to give content creators the data and insights
they need to succeed. This dataset enables users to monitor public content interactions, track trends, and identify influential accounts. The CrowdTangle API surfaces stories, and data to measure their social performance and identify influencers. This codebook describes the data’s scope, structure, and fields.

CrowdTangle’s dataset offers insights into public posts made by pages, groups, or verified profiles that have either surpassed 100,000 likes since 2014 or have been tracked by any active API user. The dataset includes all public posts from pages, groups, or verified profiles meeting the aforementioned criteria since 2014.

Key features include:

  • Content Discovery: Access to real-time data on trending posts, facilitating the identification of viral content and emerging topics.​

  • Performance Analytics: Metrics such as likes, shares, comments, and interaction rates, allowing for the assessment of content engagement.​

  • Influencer Identification: Tools to pinpoint accounts with significant influence within specific niches or broader audiences.

Facebook URL Shares

Facebook URL Shares

Stefan
Open Dataset
Creators: Solomon Messing; Bogdan State; Chaya Nayak; Gary King; Nate Persily
Publication Date: 2018
Creators: Solomon Messing; Bogdan State; Chaya Nayak; Gary King; Nate Persily

The data describes web page addresses (URLs) that have been shared on Facebook starting January 1, 2017 and ending about a month before the present day. URLs are included if shared by at least 20 unique accounts, and shared publicly at least once. We estimate the full data set will contain on the order of 2 million unique urls shared in 300 million posts, per week. By doing so, this dataset provides insights into the dissemination of web content on Facebook, capturing the dynamics of how information spreads across the platform. Researchers can use this data to explore patterns in user engagement, the virality of content, and the reach of various web pages within the Facebook ecosystem. The dataset’s focus on URLs shared by a minimum number of unique accounts ensures that the data represents content with a certain level of engagement, filtering out less significant shares.

The dataset is structured to include the following key components:​

  • URL Information: Each entry includes the web page address (URL) that was shared on Facebook.

  • Share Metrics: Data on the number of times each URL was shared, including the count of unique accounts that shared it and the total number of posts containing the URL.​

  • Engagement Metrics: Information on user interactions with the shared URLs, such as likes, comments, and shares.

Facebook Ad Library

Facebook Ad Library

Stefan
Open Dataset
Creators: Franklin Fowler, Erika; Franz, Mike; King, Gary; Martin, Greg; Mukerjee, Zagreb; Persily, Nate
Publication Date: 2019
Creators: Franklin Fowler, Erika; Franz, Mike; King, Gary; Martin, Greg; Mukerjee, Zagreb; Persily, Nate

The Ad Library API provides programmatic access to the Facebook Ad Library, a collection of all political advertisements run on Facebook and Instagram since May 2018 in the US, and for other dates in different countries. The codebook describes the scope, structure, and fields of these data. The Ad Library offers detailed information about each advertisement, including:​

  • Ad Creative: Visual and textual content of the ad.​

  • Impressions: Number of times the ad was displayed.​

  • Spend: Estimated amount spent on the ad.​

  • Demographics: Age, gender, and location breakdown of the audience reached.

Given that the Ad Library archives all ads related to political content, social issues, and elections since May 2018, the number of observations runs into the millions. The Ad Library’s data is structured to include various attributes for each advertisement:​

  • Ad ID: Unique identifier for each ad.​

  • Page ID and Name: Information about the page running the ad.​

  • Ad Creative: Content and format of the ad.​

  • Impressions and Spend: Metrics indicating the ad’s reach and budget.​

  • Demographic Distribution: Breakdown of the audience by age, gender, and location.

​

Multi-aspect Reviews

Multi-aspect Reviews

Stefan
Open Dataset
Creators: Julian McAuley; Jure Leskovec; Dan Jurafsky
Publication Date: 2013
Creators: Julian McAuley; Jure Leskovec; Dan Jurafsky
These datasets include reviews with multiple rated dimensions.It is particularly valuable for research in sentiment analysis, recommender systems, and user modeling, as it allows for a nuanced understanding of user opinions beyond overall ratings.​The most comprehensive of these are beer review datasets from Ratebeer and Beeradvocate, which include sensory aspects such as taste, look, feel, and smell. The data set is about 1 GB large.
Ratebeer:

  • Number of users: 40,213
  • Number of items: 110,419
  • Number of ratings/reviews: 2,855,232
  • Timespan: April, 2000 – November, 2011

BeerAdvocate:

  • Number of users: 33,387
  • Number of items: 66,051
  • Number of ratings/reviews: 1,586,259
  • Timespan: January, 1998 – November, 2011

The datasets are structured in a JSON format, with each entry representing a single review that includes:​

  • Product Information: Details about the beer being reviewed.​

  • User Information: Anonymized identifiers of the reviewers.​

  • Review Content: Textual feedback provided by the user.​

  • Ratings: Numerical scores for overall satisfaction and specific aspects (appearance, aroma, palate, taste).

Goodreads-books

Goodreads-books

Stefan
Open Dataset
Creators: Zając, Zygmunt
Publication Date: 2019
Creators: Zając, Zygmunt

The primary reason for creating this dataset is the requirement of a good clean dataset of books. It contains important features such as book titles, authors, average ratings, ISBN identifiers, language codes, number of pages, ratings count, text reviews count, publication dates, and publishers. A distinctive aspect of this dataset is its ability to support a wide range of book-related analyses, such as trends in book popularity, author influence, and reader preferences. The data set is 1.56 MB large and was scraped via the Goodreads API. It encompasses over 10,000 observations, each representing a unique book entry with multiple attributes. The structure of the dataset is straightforward, consisting of a single CSV file with the following key columns:

  • bookID: A unique identification number for each book.
  • title: The official title of the book.
  • authors: Names of the authors, with multiple authors separated by a delimiter.
  • average_rating: The average user rating for the book.
  • isbn & isbn13: The 10-digit and 13-digit International Standard Book Numbers, respectively.
  • language_code: The primary language in which the book is published (e.g., ‘eng’ for English).
  • num_pages: The total number of pages in the book.
  • ratings_count: The total number of ratings the book has received from users.
  • text_reviews_count: The total number of text reviews written by users.
  • publication_date: The original publication date of the book.
  • publisher: The name of the publishing house.
COVID-19 Twitter Chatter Dataset

COVID-19 Twitter Chatter Dataset

Stefan
Open Dataset
Creators: Banda, Juan M.; Tekumalla, Ramya; Wang, Guanyu; Yu, Jingyuan; Liu, Tuo; Ding, Yuning; Artemova, Katya; Tutubalina, Elena; Chowell, Gerardo
Publication Date: 2024
Creators: Banda, Juan M.; Tekumalla, Ramya; Wang, Guanyu; Yu, Jingyuan; Liu, Tuo; Ding, Yuning; Artemova, Katya; Tutubalina, Elena; Chowell, Gerardo

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets. The dataset is 14.2 GB large.

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