Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
79
datasets available to search
ShareScore release 0.9.0
Dataset results
79 results for “bots”
Bots in Software Development: A Systematic Literature Review [Data Set]
<p>This repository contains al the artifacts of the research: Bots in Software Development: A Systematic Literature Review </p>
BotHawk: GitHub Bot Account Detection Dataset
<div> <p>The <strong>"BotHawk: GitHub Bot Account Detection Dataset"</strong> is a specialized resource developed for the research and development community focused on identifying and understanding bot accounts within the GitHub ecosystem. This comprehensive dataset offers an in-depth look into the behaviors, contributions, and patterns of bot accounts, distinguishing them from human user activities on GitHub.</p> <p>Designed to enhance the accuracy and efficiency of bot detection algorithms, the BotHawk Dataset encompasses a variety of features, including commit history, issue participation, pull request activities, and other interaction metrics specific to GitHub. It provides an invaluable foundation for developing machine learning models capable of distinguishing between bot-generated and human-generated activities, contributing to more secure and trustworthy interactions within the GitHub platform.</p> <p>This dataset not only serves as a tool for academic research in cybersecurity, machine learning, and software engineering but also offers practical applications for developers and platform administrators seeking to improve the health of their projects by filtering out bot-based noise and manipulation. The BotHawk dataset is instrumental in advancing our comprehension of bot dynamics within the GitHub ecosystem, supporting the broader objective of fostering a secure, transparent, and collaborative open-source community.</p> </div>
Dataset for supervised bot detection on Twitter
<p>This is the dataset I used for my Bachelor Thesis "Fake-tweet: Desarrollo de un modelo para la detección de bots en Twitter", where I developed a supervised method for bot detection on Twitter.</p> <p>This dataset contains both genuine users (3474) and social spambots (4912) and has 69 features defined for each of these accounts. Such features can be divided in 3 categories: content, account information or account usage features.</p> <p> </p>
A Dataset of Bot and Human Activities in GitHub
<p><strong>A Dataset of Bot and Human Activities in GitHub</strong></p> <p>This repository provides an updated version of a dataset of GitHub contributor activities that is accompanied by a paper published at MSR 2023 in the <strong>Data and Tool Showcase Track</strong>. The paper is entitled <strong>A Dataset of Bot and Human Activities in GitHub</strong> and is co-authored by Natarajan Chidambaram, Alexandre Decan and Tom Mens (Software Engineering Lab, University of Mons, Belgium). DOI: <a href="https://www.doi.org/10.1109/MSR59073.2023.00070">https://www.doi.org/10.1109/MSR59073.2023.00070</a>. This work is done as a part of Natarajan Chdiambaram's PhD research in the context of <a href="https://www.digitalwallonia.be/ia/">DigitalWallonia4.AI research project ARIAC (grant number 2010235)</a> and <a href="https://trail.ac/en/">TRAIL</a>.</p> <p>The dataset contains 1,015,422 high-level activities made by 350 bots and 620 human contributors on GitHub between 25 November 2022 and 15 April 2023. The activities were generated from 1,221,907 low-level events obtained from the GitHub's Event API and cover 24 distinct activity types. This dataset facilitates the characterisation of bot and human behaviour in GitHub repositories, by enabling the analysis of activity sequences and activity patterns of bot and human contributors. This dataset could lead to better bot identification tools and empirical studies on how bots play a role in collaborative software development.</p> <p><strong>Files description</strong></p> <p>The following files are provided as part of the archive:</p> <ul> <li>bot_activities.json - A JSON file containing 754,165 activities made by 350 bot contributors;</li> <li>human_activities.json - A JSON file containing 261,258 activities made by 620 human contributors (anonymized);</li> <li>JsonSchema.json - A JSON schema that validates the above datasets;</li> <li>bots.txt - A TEXT file containing login names of all the 350 bots</li> </ul> <p><strong>Example</strong></p> <p>Below is an example of a <em>Closing pull request</em> activity:</p> <pre><code>{ "date": "2022-11-25T18:49:09+00:00", "activity": "Closing pull request", "contributor": "typescript-bot", "repository": "DefinitelyTyped/DefinitelyTyped", "comment": { "length": 249, "GH_node": "IC_kwDOAFz6BM5PJG7l" }, "pull_request": { "id": 62328, "title": "[qunit] Add `test.each()`", "created_at": "2022-09-19T17:34:28+00:00", "status": "closed", "closed_at": "2022-11-25T18:49:08+00:00", "merged": false, "GH_node": "PR_kwDOAFz6BM4_N5ib" }, "conversation": { "comments": 19 }, "payload": { "pr_commits": 1, "pr_changed_files": 5 } }</code></pre> <p><strong>List of activity types</strong></p> <p>In total, we have identified 24 different high-level activity types from 15 different low-level event types. They are <em>Creating repository</em>, <em>Creating branch</em>, <em>Creating tag</em>, <em>Deleting tag</em>, <em>Deleting repository</em>, <em>Publishing a release</em>, <em>Making repository public</em>, <em>Adding collaborator to repository</em>, <em>Forking repository</em>, <em>Starring repository</em>, <em>Editing wiki page</em>, <em>Opening issue</em>, <em>Closing issue</em>, <em>Reopening issue</em>, <em>Transferring issue</em>, <em>Commenting issue</em>, <em>Opening pull request</em>, <em>Closing pull request</em>, <em>Reopening pull request</em>, <em>Commenting pull request</em>, <em>Commenting pull request changes</em>, <em>Reviewing code</em>, <em>Commenting commits</em>, <em>Pushing commits</em>.</p> <p><strong>List of fields</strong></p> <p>Not only does the dataset contain a list of activities made by bot and human contributors, but it also contains some details about these activities. For example, <em>commenting issue</em> activities provide details about the author of the comment, the repository and issue in which the comment was created, and so on.</p> <p>For all activity types, we provide the <strong>date</strong> of the activity, the <strong>contributor</strong> that made the activity, and the <strong>repository</strong> in which the activity took place. Depending on the activity type, additional fields are provided. In this section, we describe for each activity type the different fields that are provided in the JSON file. It is worth to mention that we also provide the corresponding JSON schema alongside with the datasets.</p> <p><em><strong>Properties</strong></em></p> <ul> <li><strong>date</strong> <ul> <li>Date on which the activity is performed</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-25T09:55:19+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li><strong>activity</strong> <ul> <li>The activity performed by the contributor</li> <li>Type: <code>string</code></li> <li>e.g., "Commenting pull request"</li> </ul> </li> <li><strong>contributor</strong> <ul> <li>The login name of the contributor who performed this activity</li> <li>Type: <code>string</code></li> <li>e.g., "analysis-bot", "anonymised" in the case of a human contributor</li> </ul> </li> <li><strong>repository</strong> <ul> <li>The repository in which the activity is performed</li> <li>Type: <code>string</code></li> <li>e.g., "apache/spark", "anonymised" in the case of a human contributor</li> </ul> </li> <li><strong>issue</strong> <ul> <li>Issue information - provided for <em>Opening issue</em>, <em>Closing issue, Reopening issue</em>, <em>Transferring issue</em> and <em>Commenting issue</em></li> <li>Type: <code>object</code></li> <li><em><strong>Properties</strong></em> <ul> <li><strong>id</strong> <ul> <li>Issue number</li> <li>Type: <code>integer</code></li> <li>e.g., 35471</li> </ul> </li> <li><strong>title</strong> <ul> <li>Issue title</li> <li>Type: <code>string</code></li> <li>e.g., "error building handtracking gpu example with bazel", "anonymised" in the case of a human contributor</li> </ul> </li> <li><strong>created_at</strong> <ul> <li>The date on which this issue is created</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-10T13:07:23+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li><strong>status</strong> <ul> <li>Current state of the issue</li> <li>Type: <code>string</code></li> <li>"open" or "closed"</li> </ul> </li> <li><strong>closed_at</strong> <ul> <li>The date on which this issue is closed. "null" will be provided if the issue is open</li> <li>Types: <code>string</code>, <code>null</code></li> <li>e.g., "2022-11-25T10:42:39+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li><strong>resolved</strong> <ul> <li>The issue is resolved or not_planned/still open</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li><strong>GH_node</strong> <ul> <li>The GitHub node of this issue</li> <li>Type: <code>string</code></li> <li>e.g., "IC_kwDOC27xRM5PHTBU", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>pull_request</strong> <ul> <li>Pull request information - provided for <em>Opening pull request</em>, <em>Closing pull request</em>, <em>Reopening pull request</em>, <em>Commenting pull request changes</em> and <em>Reviewing code</em></li> <li>Type: <code>object</code></li> <li><em><strong>Properties</strong></em> <ul> <li><strong>id</strong> <ul> <li>Pull request number</li> <li>Type: <code>integer</code></li> <li>e.g., 35471</li> </ul> </li> <li><strong>title</strong> <ul> <li>Pull request title</li> <li>Type: <code>string</code></li> <li>e.g., "error building handtracking gpu example with bazel", "anonymised" in the case of a human contributor</li> </ul> </li> <li><strong>created_at</strong> <ul> <li>The date on which this pull request is created</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-10T13:07:23+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li><strong>status</strong> <ul> <li>Current state of the pull request</li> <li>Type: <code>string</code></li> <li>"open" or "closed"</li> </ul> </li> <li><strong>closed_at</strong> <ul> <li>The date on which this pull request is closed. "null" will be provided if the pull request is open</li> <li>Types: <code>string</code>, <code>null</code></li> <li>e.g., "2022-11-25T10:42:39+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li><strong>merged</strong> <ul> <li>The PR is merged or rejected/still open</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li><strong>GH_node</strong> <ul> <li>The GitHub node of this pull request</li> <li>Type: <code>string</code></li> <li>e.g., "PR_kwDOC7Q2kM5Dsu3-", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>review</strong> <ul> <li>Pull request review information - provided for <em>Reviewing code</em></li> <li>Type: <code>object</code></li> <li><em><strong>Properties</strong></em> <ul> <li><strong>status</strong> <ul> <li>Status of the review</li> <li>Type: <code>string</code></li> <li>"changes_requested" or "approved" or "dismissed"</li> </ul> </li> <li><strong>GH_node</strong> <ul> <li>The GitHub node of this review</li> <li>Type: <code>string</code></li> <li>e.g., "PRR_kwDOEBHXU85HLfIn", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>conversation</strong> <ul> <li>Comments information in issue or pull request - Provided for <em>Opening issue</em>, <em>Closing issue</em>, <em>Reopening issue</em>, <em>Transferring issue</em>, <em>Commenting issue</em>, <em>Opening pull request</em>, <em>Closing pull request</em>, <em>Reopening pull request</em> and <em>Commenting pull request</em></li> <li>Type: <code>object</code></li> <li><em><strong>Properties</strong></em> <ul> <li><strong>comments</strong> <ul> <li>Number of comments present in the corresponding issue or pull request</li> <li>Type: <code>integer</code></li> <li>e.g., 5</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>comment</strong> <ul> <li>Comment information - Provided for all the activities for which the field issue or pull_request is reported and additionally for commit comment</li> <li>Type: <code>object</code></li> <li><em><strong>Properties</strong></em> <ul> <li><strong>length</strong> <ul> <li>Length of the comment text (or description text if comment is not provided)</li> <li>Type: <code>integer</code></li> <li>e.g., 25</li> </ul> </li> <li><strong>GH_node</strong> <ul> <li>The GitHub node of this comment or description. "null" will be provided if there is no comment expected</li> <li>Types: <code>string</code>, <code>null</code></li> <li>e.g., "IC_kwDOEj6V8c5PHT78", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>gitref</strong> <ul> <li>Tag information - provided for <em>Creating branch</em>, <em>Creating tag</em>, <em>Deleting branch</em>, <em>Deleting tag</em>, <em>Editing wiki page</em> and <em>Publishing a release</em></li> <li>Type: <code>object</code></li> <li><em><strong>Properties</strong></em> <ul> <li><strong>type</strong> <ul> <li>Type of the gitref</li> <li>Type: <code>string</code></li> <li>"tag" or "branch" or "commit"</li> </ul> </li> <li><strong>name</strong> <ul> <li>Name of the gitref</li> <li>Type: <code>string</code></li> <li>e.g., "cherry-pick-11-to-release-4.10"</li> </ul> </li> <li><strong>description_length</strong> <ul> <li>Length of the description text provided while creating the gitref. "null" be provided if the type is "branch" or "commit" as they do not have any description</li> <li>Type: <code>integer</code>, <code>null</code></li> <li>e.g., 23</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>release</strong> <ul> <li>Release information - provided for <em>Publishing a release</em></li> <li>Type: <code>object</code></li> <li><em><strong>Properties</strong></em> <ul> <li><strong>name</strong> <ul> <li>The name of the release that is created. "null" will be provided if the name is not provided</li> <li>Type: <code>string</code>, <code>null</code></li> <li>e.g., "v0.65.9"</li> </ul> </li> <li><strong>description_length</strong> <ul> <li>Length of the description of the release that is created</li> <li>Type: <code>integer</code></li> <li>e.g., 888</li> </ul> </li> <li><strong>created_at</strong> <ul> <li>The date at which the release is created (activity date is the release published date)</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-25T11:34:48+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li><strong>prerelease</strong> <ul> <li>If the release that is created is a prerelease or not</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li><strong>new_tag</strong> <ul> <li>If a new tag is created for this release or another tag is re-used</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li><strong>GH_node</strong> <ul> <li>The corresponding release node ID</li> <li>Type: <code>string</code></li> <li>e.g., "RE_kwDOCm6M2s4FBGxT", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>page</strong> <ul> <li>Page information - provided for <em>Editing wiki page</em></li> <li>Type: <code>object</code></li> <li><em><strong>Properties</strong></em> <ul> <li><strong>name</strong> <ul> <li>Name of the page</li> <li>Type: <code>string</code></li> <li>e.g., "Workflow-status"</li> </ul> </li> <li><strong>title</strong> <ul> <li>Title of the page</li> <li>Type: <code>string</code></li> <li>e.g., "Workflow status"</li> </ul> </li> <li><strong>new</strong> <ul> <li>If the page is created new or existing page is edited</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>payload</strong> <ul> <li>Other additional details - Provided for <em>Opening pull request</em>, <em>Closing pull request</em>, <em>Reopening pull request</em> and <em>pushing commits</em></li> <li>Type: <code>object</code></li> <li><em><strong>Properties</strong></em> <ul> <li><strong>pr_commits</strong> <ul> <li>The number of commits in this pull request</li> <li>Type: <code>integer</code></li> <li>e.g., 3</li> </ul> </li> <li><strong>pr_changed_files</strong> <ul> <li>The number of files that are changed in this pull request</li> <li>Type: <code>integer</code></li> <li>e.g., 2</li> </ul> </li> <li><strong>pushed_commits</strong> <ul> <li>The number of commits present in this push</li> <li>Type: <code>integer</code></li> <li>e.g., 4</li> </ul> </li> <li><strong>distinct_pushed_commits</strong> <ul> <li>The distinct number of commits present in this push</li> <li>Type: <code>integer</code></li> <li>e.g., 1</li> </ul> </li> <li><strong>github_push_id</strong> <ul> <li>The corresponding GitHub push ID</li> <li>Type: <code>integer</code></li> <li>e.g., 11790446870, "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p><strong>Mapping between activities and events</strong></p> <p>For many activity types, the corresponding activity can be observed by the occurrence of a single event type. For example, the activity types <em>Forking repository</em> and <em>Starring repository</em> would require the occurrence of a single event type for each as given below.</p> <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td><em>Forking repository</em></td> <td><code>ForkEvent</code></td> <td>-</td> </tr> <tr> <td><em>Starring repository</em></td> <td><code>WatchEvent</code></td> <td>action = "started"</td> </tr> </tbody> </table> <p>However, in some cases, the same event type yields different activity types depending on the value present in the payload. For example, three different activity types can be generated from the same low-level event type CreateEvent, depending on the value of its ref_type (either "repository", "branch", or "tag") present in the payload.</p> <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td><em>Creating repository</em></td> <td><code>CreateEvent</code></td> <td>ref_type = "repository"</td> </tr> <tr> <td><em>Creating branch</em></td> <td><code>CreateEvent</code></td> <td>ref_type = "branch"</td> </tr> <tr> <td><em>Creating tag</em></td> <td><code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> </tbody> </table> <p>In some cases, there is no one-to-one mapping between events and activities. This is because some actions on GitHub may generate more than a single event and lead to a sequence of one mandatory event and a second optional event (marked with <em>?</em>). For example, for the activity type <em>Publishing a release</em>, event type ReleaseEvent is mandatory with payload's action value = "published", while event type CreateEvent is optional as it is required only when a new tag is created along with the published release.</p> <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td><em>Publishing a release</em></td> <td><code>ReleaseEvent</code></td> <td>action = "published"</td> </tr> <tr> <td> </td> <td><em>?</em> <code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> </tbody> </table> <p>All the identified activities along with their events type(s) and payload information is given in the following table.</p> <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td><em>Creating repository</em></td> <td><code>CreateEvent</code></td> <td>ref_type = "repository"</td> </tr> <tr> <td><em>Creating branch</em></td> <td><code>CreateEvent</code></td> <td>ref_type = "branch"</td> </tr> <tr> <td><em>Creating tag</em></td> <td><code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> <tr> <td><em>Deleting tag</em></td> <td><code>DeleteEvent</code></td> <td>ref_type = "tag"</td> </tr> <tr> <td><em>Deleting repository</em></td> <td><code>DeleteEvent</code></td> <td>ref_type = "branch"</td> </tr> <tr> <td><em>Publishing a release</em></td> <td><code>ReleaseEvent</code></td> <td>action = "published"</td> </tr> <tr> <td> </td> <td><em>?</em> <code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> <tr> <td><em>Making repository public</em></td> <td><code>PublicEvent</code></td> <td>-</td> </tr> <tr> <td><em>Adding collaborator to repository</em></td> <td><code>MemberEvent</code></td> <td>action = "added"</td> </tr> <tr> <td><em>Forking repository</em></td> <td><code>ForkEvent</code></td> <td>-</td> </tr> <tr> <td><em>Starring repository</em></td> <td><code>WatchEvent</code></td> <td>action = "started"</td> </tr> <tr> <td><em>Editing wiki page</em></td> <td><code>GollumEvent</code></td> <td>pages-->action = "created" or "edited"</td> </tr> <tr> <td><em>Opening issue</em></td> <td><code>IssuesEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td><em>Closing issue</em></td> <td><code>IssuesEvent</code></td> <td>action = "closed"</td> </tr> <tr> <td> </td> <td><em>?</em> <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td><em>Reopening issue</em></td> <td><code>IssuesEvent</code></td> <td>action = "reopened"</td> </tr> <tr> <td> </td> <td><em>?</em> <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td><em>Transferring issue</em></td> <td><code>IssuesEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td><em>Commenting issue</em></td> <td><code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td><em>Opening pull request</em></td> <td><code>PullRequestEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td><em>Closing pull request</em></td> <td><code>PullRequestEvent</code></td> <td>action = "closed"</td> </tr> <tr> <td> </td> <td><em>?</em> <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td><em>Reopening pull request</em></td> <td><code>PullRequestEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td> </td> <td><em>?</em> <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td><em>Commenting pull request</em></td> <td><code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td><em>Commenting pull request changes</em></td> <td><code>PullrequestReviewCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td> </td> <td><em>?</em> <code>PullRequestReviewEvent</code></td> <td>action = "created"</td> </tr> <tr> <td><em>Reviewing code</em></td> <td><code>PullRequestReviewEvent</code></td> <td>action = "created"</td> </tr> <tr> <td><em>Commenting commits</em></td> <td><code>CommitCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td><em>Pushing commits</em></td> <td><code>PushEvent</code></td> <td>-</td> </tr> </tbody> </table> <p> </p>
A ground-truth dataset to identify bots in GitHub
<p>This dataset is a ground truth dataset we used to identify bots. Each account in this dataset is rated by at least 3 raters with high interrater agreement.</p> <p>===</p> <p>This dataset is outdated (it was created in 2020) and therefore no longer recommended for use. Many of the classified GitHub bot accounts are no longer active or even available today, and some may even have changed their status from bot to human (or conversely) since. If you want to use a ground-truth dataset of bot accounts for academic (or other) purposes, we therefore recommend to use a more recent and more complete dataset of GitHub bot accounts. Such a dataset can be found here:</p> <p><a href="https://doi.org/10.5281/zenodo.7740520">https://doi.org/10.5281/zenodo.7740520</a></p> <p>===</p>
League of Legends Illicit Bot Prevalence Data
<p>Data set for investigating and measuring illicit bot prevalence in North American and Western Europe League of Legends PvP matches associated with</p> <p>C. S. Lee and I. Ramler, "Rise of the bots: Bot prevalence and its impact on match outcomes in league of Legends," <em>2015 International Workshop on Network and Systems Support for Games (NetGames)</em>, Zagreb, 2015, pp. 1-6.<br> doi: 10.1109/NetGames.2015.7382992</p> <p>Description of Variables:</p> <ul> <li>level: level of summoner</li> <li>matchId: de-identified identification number for match</li> <li>winner: flag indicating whether or not the team won</li> <li>kill: number of kills</li> <li>death: number of deaths</li> <li>assist: number of kills</li> <li>timeCreated: match creation time (UTC-05 for N. Amer, UTC-00 for EUW)</li> <li>duration: match duration (seconds)</li> <li>matchType: match type</li> <li>numOfRunes: number of runes</li> <li>numOfMasteries: number of masteries</li> <li>isBot: Flag indicating whether or not the player is a bot</li> </ul>
BotHawk: GitHub Bot Account Detection Dataset
<p>The <strong>"BotHawk: GitHub Bot Account Detection Dataset"</strong> is a specialized resource developed for the research and development community focused on identifying and understanding bot accounts within the GitHub ecosystem. This comprehensive dataset offers an in-depth look into the behaviors, contributions, and patterns of bot accounts, distinguishing them from human user activities on GitHub.</p> <p>Designed to enhance the accuracy and efficiency of bot detection algorithms, the BotHawk Dataset encompasses a variety of features, including commit history, issue participation, pull request activities, and other interaction metrics specific to GitHub. It provides an invaluable foundation for developing machine learning models capable of distinguishing between bot-generated and human-generated activities, contributing to more secure and trustworthy interactions within the GitHub platform.</p> <p>This dataset not only serves as a tool for academic research in cybersecurity, machine learning, and software engineering but also offers practical applications for developers and platform administrators seeking to improve the health of their projects by filtering out bot-based noise and manipulation. The BotHawk dataset is instrumental in advancing our comprehension of bot dynamics within the GitHub ecosystem, supporting the broader objective of fostering a secure, transparent, and collaborative open-source community.</p>
List of Twitter bots that mention scientific publications
<p>This collection of datasets comes from the paper titled <em>"The Botization of Science? Large-scale study of the presence and impact of Twitter bots in science dissemination"</em> and includes two files:</p> <ol> <li><strong>Twitter_bots_list.txt</strong> - Contains a list of 11,073 Twitter bots that mention scientific publications, as identified in the study.</li> <li> <p><strong>Twitter_botscores.tsv</strong> - Includes the botscores of 4,872,369 Twitter accounts analyzed in the study, providing a measure of the likelihood that an account is a bot.</p> </li> </ol>
GLO-Bot Data
<p><strong>GLO-Bot data for analysis</strong></p> <p>For both the six accessions (HL-##) and the diversity panel (GWA#) we provide:</p> <ul> <li>The full inverted images input to GLO-RIAv2 <ul> <li>6accessions_images.zip and GWA#.zip</li> </ul> </li> <li>Files for detailed information about the accessions and ID numbers <ul> <li>6accessions-rhizotrons.csv and diversiry-rhizotrons.csv</li> </ul> </li> <li>The raw root data from GLO-RIAv2 <ul> <li>####-all-root-data-[local/global/roi].csv for the full root system analysis</li> <li>####-tip-root-data-[local/global/roi].csv for the per day tip tracking analysis</li> </ul> </li> <li>The calculated traits <ul> <li>6accessions-all_traits.csv and diversity-calculated_traits.csv</li> </ul> </li> <li>The fitted traits (diversity only) <ul> <li>diversity-bv_fitted_phenotypes.csv</li> </ul> </li> </ul>
TwiBot22: Towards Graph-Based Twitter Bot Detection
<p>Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph structure of the Twitter network, and they exhibit promising performance when confronting novel Twitter bots that traditional methods fail to detect.</p> <p>However, very few of the existing Twitter bot detection datasets are graph-based, and even these few graph-based datasets suffer from <strong>limited dataset scale</strong>, <strong>incomplete graph structure</strong>, as well as <strong>low annotation quality</strong>. In fact, the lack of a large-scale graph-based Twitter bot detection benchmark that addresses these issues has seriously hindered the development and evaluation of novel graph-based bot detection approaches.</p> <p>In this paper, we propose <strong>TwiBot-22</strong>, a comprehensive graph-based Twitter bot detection benchmark that presents the largest dataset to date, provides diversified entities and relations on the Twitter network, and has considerably better annotation quality than existing datasets.</p> <p>In addition, we re-implement 35 representative Twitter bot detection baselines and evaluate them on 9 datasets, including TwiBot-22, to promote a fair comparison of model performance and a holistic understanding of research progress.</p> <p>To facilitate further research, we consolidate all implemented codes and datasets into <strong>the TwiBot-22 evaluation framework</strong>, where researchers could consistently evaluate new models and datasets.</p> <p>Due to the limitation at Zenodo, you can access the whole dataset and more information on this <a href="https://twibot22.github.io/">website</a>.</p>
Autonomy Is An Acquired Taste: Exploring Developer Preferences for GitHub Bots
<p>Software bots are an important part of software development, and the rise of AI-based code tools will make them even more important. Software bots can do many tasks, and can exhibit a range of interaction behaviors in a project. But in order for the bot to be effective, it must be accepted by the developers and project community with which the bot interacts. The main purpose of this study is to enumerate some of these factors, and to explain what leads to perceived bad behavior in the context of a bot's autonomous actions and persona. We find developers prefer bots which are personable but show little autonomy. This dataset contains the replication package of the study. For Phase I, we shared the full and detailed interview guide, anonymized interview transcripts, and the result of the data analysis, i.e., codebook. For Phase II, the survey design and data. <br> </p>
Dataset for paper: The spread of low-credibility content by social bots
<p>Data for the paper <em>The spread of low-credibility content by social bots </em>by Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, and Filippo Menczer</p> <p>Code is available at https://github.com/IUNetSci/HoaxyBots</p>
Fig. 3 in Distribution, prevalence and intensity of moose nose bot fly (Cephenemyia ulrichii) larvae in moose (Alces alces) from Norway
Fig. 3. The predicted parasite intensity of moose nose bot fly larvae for harvested calves (red), yearlings (blue) and adult (green) moose in central and southern Norway. Predictions from the highest ranked intensity model with study area and age group. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Distribution, prevalence and intensity of moose nose bot fly (Cephenemyia ulrichii) larvae in moose (Alces alces) from Norway
Fig. 2. The predicted infection prevalence of moose nose bot fly larvae with increasing moose (host) density. The shaded area shows the 95% confidence interval. Predictions from the highest ranked model with moose density. In the plot we used the function "jitter" in the R package ggeffects (Lüdecke, 2018), which adds small random variation to the data points to better reflect the amount of data for moose densities. Hence, the points do not reflect exact values as they are binomial.
Fig. 1 in Distribution, prevalence and intensity of moose nose bot fly (Cephenemyia ulrichii) larvae in moose (Alces alces) from Norway
Fig. 1. Study areas in southern (Oslo, AurskogHøland and Kongsvinger) and central Norway (Selbu, Tydal, Malvik, Stjørdal and Meråker) with location and moose density (moose density, see Materials and methods) in sampling municipalities. Red filled circle indicate where the moose nose bot fly (Cephenemyia ulrichii) was first found in Norway, and open circles show where moose heads were examined without detection of the moose nose bot fly in 1987 (Nilssen and Haugerud, 1994). Blue circles indicate where the moose nose bot fly were found in Sweden in the late 1970s and 1980s (Steen et al., 1988). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Typical intracranial myiasis in Nigerian red river hogs (Potamochoerus porcus) caused by an unknown bot fly (Diptera: Oestridae)
Fig. 2. Image of the third instar larva of the oestrid bot fly pictured in Fig. 1 from the intracranial supra-meningeal space of the Nigerian red river hog. A) dorsal; B) ventral. Scale bars = 1 mm. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Typical intracranial myiasis in Nigerian red river hogs (Potamochoerus porcus) caused by an unknown bot fly (Diptera: Oestridae)
Fig. 3. Images of a third instar larva of the oestrid bot fly pictured in Figs. 1–2 from the intracranial supra-meningeal space of the Nigerian red river hog. A) left lateral habitus; B) left caudo-lateral view; C) left caudal spiracular plate. Dorsal spinule bands (dsb), latero-ventral spinule bands (lsb), ventral spinule bands (vsb), caudal spiracular plate (spl), peritreme (ptr), spiracular papillae (sp), ecdysial scar (es). Scale bars = 1 mm (A, B); 0.1 mm (C). (For interpretation of the references to colour in this figure
Fig. 1. A in Typical intracranial myiasis in Nigerian red river hogs (Potamochoerus porcus) caused by an unknown bot fly (Diptera: Oestridae)
Fig. 1. A) hunter in Cross River National Park splitting the head of a recently killed red river hog with a machete. B) bot fly larvae (arrows) visible in the frontal sinus (s) and the intracranial supra-meningeal space (i) next to the brain (B) of the red river hog. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Studies on the embryonic development and larval infection potential of the stomach bot flies, Gasterophilus pecorum
Fig. 3. Life history of G. pecorum and prediction of egg state larvae population. Note: The survival period of egg state larvae of G. pecorum at different temperatures (a); The monthly average temperatures in Kalamaili nature reserve (KNR) in recent ten years (b); Changes in parasite population in vitro of the host (c), +: mild; ++: moderate; +++: considerable; ++++: severe; ⸭: potential.
Fig. 1 in Studies on the embryonic development and larval infection potential of the stomach bot flies, Gasterophilus pecorum
Fig. 1. The relationship between the embryonic development period and the temperature of G. pecorum.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.