272 Resources

Open Dataset

Showing 153-160 of 272 results
2012-2016 Facebook Posts

2012-2016 Facebook Posts

Stefan
Open Dataset
Creators: Martinchek, Patrick
Publication Date: 2016
Creators: Martinchek, Patrick

This dataset comprises Facebook posts from the 15 mainstream media sources during the years 2012 to 2016. It includes posts from the top mainstream media outlets, offering insights into their social media strategies and audience engagement during a significant period in digital media evolution.The dataset is structured to include various fields such as post content, timestamps, and engagement metrics like likes, shares, and comments. Each record represents a single Facebook post, allowing for detailed analysis of individual entries. It has a size of 861,17 MB.

Huge Collection of Reddit Votes

Huge Collection of Reddit Votes

Stefan
Open Dataset
Creators: Leake, Joseph
Publication Date: 2020
Creators: Leake, Joseph

The dataset covers data of over 44 million upvotes and downvotes cast by Reddit users between 2007 and 2020. This is a tab-delimited list of votes cast by reddit users who have opted-in to make their voting history public. Each row contains the submission id for the thread being voted on, the subreddit the submission was located in, the epoch timestamp associated with the vote, the voter’s username, and whether it was an upvote or a downvote. The votes included are from users who have chosen to make their voting history public, ensuring compliance with privacy preferences. There’s a separate file containing information about the submissions that were voted on. The dataset contains over 44 million voting records and has a size of 21,9 kB. Structurally, the dataset is organized into two main components:​

  1. Votes Data: A tab-delimited file where each row represents a vote with the following fields:​

    • submission_id: Identifier of the Reddit submission that received the vote.​

    • subreddit: Name of the subreddit where the submission was posted.​

    • created_time: Epoch timestamp indicating when the vote was cast.​

    • username: Reddit username of the voter.​

    • vote: Type of vote, either ‘upvote’ or ‘downvote’.​

  2. Submissions Data: A separate file containing information about the submissions that received votes, including details such as submission titles, authors, and timestamps.

Tracking Mastodon user numbers over time

Tracking Mastodon user numbers over time

Stefan
Open Dataset
Creators: Willison, Simon
Publication Date: 2022
Creators: Willison, Simon

Mastodon is definitely having a moment. User growth is skyrocketing as more and more people migrate over from Twitter. I’ve set up a new git scraper to track the number of registered user accounts on known Mastodon instances over time. The dataset collects data from numerous Mastodon instances, providing a holistic view of user distribution across the network. This approach captures the decentralized nature of Mastodon, offering insights into individual server growth and overall network expansion. By recording user numbers at regular intervals, the dataset enables the analysis of growth patterns over time, identifying trends and significant adoption milestones. The dataset includes user counts from approximately 1,830 Mastodon instances, with data points collected approximately every 20 minutes. This frequency allows for detailed temporal analysis of user growth. Data collection began on November 20, 2022, and has continued since then, capturing the rapid growth of Mastodon following significant events such as changes in other social media platforms.

The dataset is structured with each record representing a snapshot of user numbers across various Mastodon instances at a specific timestamp. Key fields include:​

  • Instance Name: The domain name of the Mastodon instance.​

  • User Count: The number of registered users on the instance at the time of data collection.​

  • Timestamp: The date and time when the data was collected.​

Characterizing Online Discussion Using Coarse Discourse Sequences

Characterizing Online Discussion Using Coarse Discourse Sequences

Stefan
Open Dataset
Creators: Zhang, Amy; Culbertson, Brian; Paritosh, Praveen
Publication Date: 2017
Creators: Zhang, Amy; Culbertson, Brian; Paritosh, Praveen

In this work, we present a novel method for classifying comments in online discussions into a set of coarse discourse acts towards the goal of better understanding discussions at scale. To facilitate this study, we devise a categorization of coarse discourse acts designed to encompass general online discussion and allow for easy annotation by crowd workers. We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit. Using our corpus, we demonstrate how the analysis of discourse acts can characterize different types of discussions, including discourse sequences such as Q&A pairs and chains of disagreement, as well as different communities. Finally, we conduct experiments to predict discourse acts using our corpus, finding that structured prediction models such as conditional random fields can achieve an F1 score of 75%. We also demonstrate how the broadening of discourse acts from simply question and answer to a richer set of categories can improve the recall performance of Q&A extraction.

Central African Republic: Displacement Data - Baseline Assessment

Central African Republic: Displacement Data - Baseline Assessment

Stefan
Open Dataset
Creators: International Organization for Migration (IOM)
Publication Date: 2024
Creators: International Organization for Migration (IOM)

This Data is about IDP, returnees from CAR (previous IDP) and returnees from other countries repartition by origin and period of displacement and between 2013 and the date of assessment. It offers data on the distribution of IDPs and returnees, including their origins, periods of displacement, and reasons for displacement. This information is crucial for understanding the dynamics of displacement within CAR and for planning humanitarian interventions. As a sub-component of mobility tracking, the baseline assessment collects data on the presence of displaced populations in defined geographic areas. This foundational data supports the identification of needs and the coordination of assistance efforts. Evaluation has been run in 6 prefectures (admin1), 16 sub-prefectures (admin2) and 367 localities. The number of observations varies across different rounds of data collection. For instance, Round 6 of the assessment identified a total displaced population of 1,074,983 individuals, comprising 580,692 IDPs and 375,684 returnees. The dataset is 16,7 kB and organized into several key components:​

  • IDP Data: Information on internally displaced persons, including their locations, demographics, and displacement periods.​

  • Returnee Data: Details about returnees, both former IDPs and those returning from other countries, including their areas of origin and return timelines.​

  • Reasons for Displacement: Categorization of displacement causes, such as armed conflicts, inter-community tensions, or preventive measures. Notably, 67% of internal displacements are linked to armed conflicts, 26% to inter-community tensions, and 6% are preventive. ​

  • Geographical Distribution: Data on the distribution of displaced populations across various regions and localities within CAR.

Crowdsourced air traffic data from The OpenSky Network 2020

Crowdsourced air traffic data from The OpenSky Network 2020

Stefan
Open Dataset
Creators: Olive, Xavier; Strohmeier, Martin; Lübbe, Jannis
Publication Date: 2022
Creators: Olive, Xavier; Strohmeier, Martin; Lübbe, Jannis

The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network’s more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. Leveraging a network of over 2,500 members, the dataset aggregates ADS-B signals received by volunteers worldwide, ensuring a rich and diverse data source. The dataset includes records of 41,900,660 flights, capturing data from 160,737 unique aircrafts. Flight operations involving 13,934 airports across 127 countries are documented. In total, the dataset has a size of 7,0 GB. Each month is represented by a separate CSV file, containing flight data for that specific period. ​ Each file includes the following columns:​

  • callsign: Identifier used for air traffic control communications.​
  • number: Commercial flight number, if available.​
  • icao24: Unique 24-bit address assigned to the aircraft’s transponder.​
  • registration: Aircraft’s registration number.
  • typecode: Aircraft model code.
  • origin: ICAO code of the departure airport.​
  • destination: ICAO code of the arrival airport.​
  • firstseen: Timestamp of the first detection during the flight.​
  • lastseen: Timestamp of the last detection during the flight.​
  • day: Date of the flight.
ConceptNet

ConceptNet

Stefan
Open Dataset
Creators: ConceptNet
Publication Date: 2021
Creators: ConceptNet

ConceptNet aims to give computers access to common-sense knowledge, the kind of information that ordinary people know but usually leave unstated.ConceptNet is a semantic network that represents things that computers should know about the world, especially for the purpose of understanding text written by people. Its “concepts” are represented using words and phrases of many different natural language — unlike similar projects, it’s not limited to a single language such as English. It expresses over 13 million links between these concepts, and makes the whole data set available under a Creative Commons license. ConceptNet is structured as a graph, where nodes represent concepts (words or phrases), and edges represent the relationships between these concepts. Each edge is labeled with a relation type, such as “IsA,” “PartOf,” or “RelatedTo,” indicating the nature of the relationship. The dataset is organized into sub-datasets based on language and relation types, allowing users to work with specific subsets relevant to their applications.

O Say Can You See: Early Washington, D.C., Law and Family Project

O Say Can You See: Early Washington, D.C., Law and Family Project

Stefan
Open Dataset
Creators: O Say Can You See Project
Publication Date: 2024
Creators: O Say Can You See Project

This site documents the challenge to slavery and the quest for freedom in early Washington, D.C., by collecting, digitizing, making accessible, and analyzing freedom suits filed between 1800 and 1862, as well as tracing the multigenerational family networks they reveal. The project encompasses hundreds of freedom suits from the Circuit Court for the District of Columbia, Maryland state courts, and the U.S. Supreme Court, providing invaluable insights into the legal battles fought by enslaved individuals seeking freedom. By exploring the web of litigants, jurists, attorneys, and community members present in case files, the project shows deep relationship mapping of early Washington, D.C., illustrating how each person is connected to others in the city and beyond. The dataset has a size of 3,0 kB and is organized into several interconnected components:​

  • People: A database of individuals involved in the cases, including litigants, jurists, attorneys, and community members, with detailed profiles and social connections.​

  • Families: Kinship and family networks of multigenerational Black, white, and mixed families, created using information derived from court records and genealogical research.​

  • Cases: A collection of hundreds of freedom cases from various courts, providing detailed accounts of each legal battle.​

  • Stories: Interactive analyses of the court cases, families, attorneys, and judges, focusing on historical or legal questions raised by these cases.

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