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79 results for “bot”
DATASET WITH CLASSIFICATION OF ACCOUNTS THAT DISCLOSE SCIENTIFIC ARTICLES ON TWITTER BETWEEN BOT, HUMAN AND CYBORG THROUGH BOTOMETER
<p>This dataset is the result of research that analyzed accounts that tweeted scientific articles in order to classify them as bots, humans or cyborgs through Botometer. Three articles were selected among those with the highest scores on the Altmetric platform and a sample of 18,316 Twitter user accounts were classified.</p>
Prehistorisch bot
Bot met menselijke bewerking uit de prehistorie. Bone with prehistoric marks made by men Source: Objaverse 1.0 / Sketchfab
Uninvited Guests: Analyzing the Identity and Behavior of Certificate Transparency Bots - Dataset Artifact
<p>Curated dataset of CT bots as described in our paper, "Uninvited Guests: Analyzing the Identity and Behavior of Certificate Transparency Bots".</p>
The Good First Issue Recommendation Dataset from "GFI-Bot: Automated Good First Issue Recommendation on GitHub"
<p>This is a good first issue (GFI) recommendation dataset created from the GFI-Bot project (<a href="https://github.com/osslab-pku/gfi-bot">https://github.com/osslab-pku/gfi-bot</a>). For more information about the GFI recommendation problem and GFI-Bot, please check our publications:</p> <ul> <li>Wenxin Xiao, Hao He, Weiwei Xu, Xin Tan, Jinhao Dong, and Minghui Zhou. 2022. Recommending Good First Issues in GitHub OSS Projects. In Proceedings of the 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 21–29, 2022. ACM. <a href="https://hehao98.github.io/files/2022-recgfi.pdf">https://hehao98.github.io/files/2022-recgfi.pdf</a></li> <li>Hao He, Haonan Su, Wenxin Xiao, Runzhi He, and Minghui Zhou. 2022. GFI-Bot: Automated Good First Issue Recommendation on GitHub. In Proceedings of the 2022 ACM 30th Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2022, Singapore, November 14-16, 2022. ACM. <a href="https://hehao98.github.io/files/2022-gfibot.pdf">https://hehao98.github.io/files/2022-gfibot.pdf</a></li> </ul> <p>The dataset is a MongoDB dump and needs to be restored to a MongoDB instance before use. This can be done via the official <a href="https://www.mongodb.com/docs/database-tools/mongorestore/"><code>mongorestore</code></a> tool by running a command like this in the <code>dataset/</code> folder:</p> <pre><code class="language-bash">mongorestore --uri={{ your mongodb url }} --gzip </code></pre> <p>In the <code>gfibot.dataset</code> collection, each document describes the state of an issue at a certain time (either at the time of issue creation or at the time of issue resolution). The <code>resolver_commit_num</code> is the ground truth label (i.e., # of commits the issue resolver has made in the repository before issue resolution, excluding commits for resolving the issue itself; <code>resolver_commit_num = 0</code> means the resolver is someone completely new to the repository). The remaining fields can be used as features or further analyzed to derive new features.</p> <p>The <code>gfibot.resolved_issue</code> collection additionally provides information about which GitHub user resolved this issue and in what commit or pull request. This information can be used to study problems like, e.g., personalized good first issue recommendation or newcomer retention mechanisms.</p> <p>This dataset can be used to evaluate new GFI recommendation approaches. We hope it will be helpful in advancing GFI recommendation research and other future studies on open-source software onboarding.</p>
Incentivizing news consumption on social media platforms using large language models and realistic bot accounts
<p>This project examines how to enhance users' exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a large-scale two-week long field experiment on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing two hardcoded elements: a URL to the topic-relevant section of quality news organization and an encouragement to follow its Twitter account. Treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our intervention enhances the following of news media organization, the sharing/liking of news content and the tweeting/liking of political content. We find that the treated users followed more news accounts and the users in the female bot treatment were more likely to like news content than the control.</p>
A Dataset of bot and human contributors names in GitHub
<h4>A Dataset of Bot and Human Contributors' names in GitHub</h4> <p>This repository provides a dataset of 2,150 contributors (1,035 bots and 1,115 humans) that were active enough (made at least 5 events in GitHub) as of 3 May 2024. This dataset accompanies the paper titled <strong>A Bot Identification Model and Tool Based on GitHub Activity Sequences</strong> published at the <strong>Journal of Systems and Software (JSS), </strong>see<strong> <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jss.2024.112287" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.jss.2024.112287</span></span></a></strong>. This research paper is co-authored by Natarajan Chidambaram, Alexandre Decan and Tom Mens (Software Engineering Lab, University of Mons, Belgium). This work is supported by Service Public de Wallonie Recherche under grant number 2010235 - ARIAC by DigitalWallonia4.AI, by the Fonds de la Recherche Scientifique – FNRS under grant numbers J.0147.24, T.0149.22, and F.4515.23.</p> <h4>Files description</h4> <p>bots.txt - contains the login name of bots, one per line</p> <p>humans.txt - contains the login name of humans, one per line.</p>
The Power of Bots: Understanding Bots in OSS Projects (Dataset)
<p>This package includes the main<strong> source code</strong> and <strong>data</strong> to replicate the method available in the paper "<em>The Power of Bots: Understanding Bots in OSS Projects</em>".</p> <p>If you have any questions, get in touch with mairieli at ime dot usp dot br</p>
Dependency Management Bots in Open-Source Systems - Prevalence and Adoption
<p>This is a re-analysis package for the paper: Dependency Management Bots in Open-Source Systems - Prevalence and Adoption. The package includes scripts and processed data collected from GitHub about five investigated dependency management bots: Dependabot, Depfu, Greenkeeper, Pyup and Renovate.</p>
Datasets used in the paper "The Face of Deception: The Impact of AI-Generated Photos on Malicious Social Bots"
<p>Datasets used in the paper "The Face of Deception: The Impact of AI-Generated Photos on Malicious Social Bots"</p> <p>We changed the datasets' titles and omitted authors' names for the blind review process. After the review, we will upload it to GitHub in an unanonymised form.</p> <p>Check README.md for details.</p>
Understanding the Helpfulness of Stale Bot for Pull-based Development
<p>Dataset for the "Understanding the Helpfulness of Stale Bot for Pull-based Development: An Empirical Study of 20 Large Open-Source Projects" paper published in the ACM Transactions on Software Engineering and Methodology (TOSEM) journal.</p>
Fox8-23 AI-powered social bot dataset
<p>We release the fox8-23 dataset, which contains the recent tweets from 1140 AI-powered social bots and 1140 human accounts collected from Twitter.</p> <p>See the GitHub repo https://github.com/osome-iu/AIBot_fox8 for details.</p>
Incentivizing news consumption on social media platforms using large language models and realistic bot accounts
Open the record for dataset details and reuse information.
A dataset of Bot Commits
<p>This dataset contains information about 13,762,430 commits by 461 bots, each of whom have created more than 1000 commits, that have committed code in Git.</p> <p><br> The data is stored in a gzipped csv file (";" as the separator) with the following format in each line:</p> <p>"author_id"; "commit-sha"; "time-of-the-commit"; "timezone"; "files-modified-by-the-commit"; "projects-the-commit-is-associated-with"; "commit-message". In the case of having multiple projects and/or files for a given commit, they are separated by ','.</p> <p><br> These bots were detected using the BIMAN bot detection approach using the World of Code(http://worldofcode.org/) dataset.</p> <p>For details of the approach, see the corresponding paper in MSR 2020. </p> <p><br> If you're using this data for your research, please don't forget to cite it!!!</p>
Bot-NonBot-Commit-Msg
<p>This dataset contains the commit messages of the 13,150 bots and 13,150 human developers, as part of the data used in https://dl.acm.org/doi/10.1145/3379597.3387478. For privacy concerns, the developer identities have been replaced with their corresponding SHA1 values. The format of the data is:</p> <p><commit SHA>; <SHA1 value for developer identity>;commit message; <whether the developer is a bot or non-bot>; all git repositories the commit is a part of (separated by ;)</p> <p>git repo format: userName_repoName</p>
Bot Ollie (Un-Textured)
Simple Robot Design. Feel free to download Source: Objaverse 1.0 / Sketchfab
Dataset from the Am J Bot 2016 103(9) paper: "Disentangling plasticity of serotiny, a key adaptive trait in a Mediterranean conifer"
<p>Raw data from the American Journal of Botany paper: "Disentangling plasticity of serotiny, a key adaptive trait in a Mediterranean conifer" Am J Bot. 2016 Sep;103(9):1582-91. doi: 10.3732/ajb.1600199</p>
BSO-GBD:Behavior Sequence-Oriented GitHub Bot Detection model
<p>Our dataset is primarily divided into three parts. Firstly, there are human accounts (Human) and bot accounts (Bot). Both of these types appear similar to regular developer accounts, lacking any distinctive markings. The second category comprises self-bot accounts, typically formed by GitHub apps. These accounts generally bear an explicit "bot" label, and we exclude them in our classification.</p> <p>The dataset is structured as follows: a total of 4786 ordinary accounts, with 4325 being Human accounts and 461 being Bot accounts. Each account internally consists of a CSV table with the following fields: user_name; repo_name; event_type_L1; event_type_L2; event_type_L3; timestamp. These data constitute our raw dataset.</p>
Fig. 7. Myosotis antarctica subsp. antarctica. Illustration reproduced from Bot. Antarct. Voy. I in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Fig. 7. Myosotis antarctica subsp. antarctica. Illustration reproduced from Bot. Antarct. Voy. I. (Fl. Antarct.) Part I, plate 38 (Hooker 1844). Illustration by W. H. Fitch. This image is in the public domain, downloaded from the Biodiversity Heritage Library (https:// www.biodiversitylibrary.org/page/13448452#page/81/ mode/1up, accessed 8 June 2021). Draft pencil drawings for this figure are attached to the type specimen of M. antarctica (K0007878799; visible online at http:// apps.kew.org/herbcat/getImage.do?imageBarcode= K000787899, accessed 8 June 2021), which was collected by J. D. Hooker from Campbell Island.
FIGURE 5. Potential corbiculate bee models for Cuterebra yanayacui mimicry. A–B in A new bot fly species of Cuterebra (Diptera: Oestridae) from Ecuador
FIGURE 5. Potential corbiculate bee models for Cuterebra yanayacui mimicry. A–B. Eufrisea magrettii (ECUADOR; BMEC); C–D. Eulaema mocsaryi (BRAZIL; BMEC); E–F. Bombus ecuadorius (ECUADOR; BMEC); A, C, E. Dorsal view, B, D, F. Lateral view. Scale bars: 1 cm.
FIGURE 4. Male terminalia. A–B in A new bot fly species of Cuterebra (Diptera: Oestridae) from Ecuador
FIGURE 4. Male terminalia. A–B. Cuterebra yanayacui, sp. nov., paratype (ECUADOR; INHS): A. Ventral view, B. Lateral view; C–D. Cuterebra ornata (PANAMA; MCZ); C. Ventral view, D. Lateral view; Scale bars: 0.5 mm; Abbreviations: cerccercus; distph- distiphallus; epand- epandrium; hypd- hypandrium; ph- phallus; phapod- phallapodeme; sur- surstylus.
ScienceDex guides
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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)
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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.