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152 results for “Chatbot”
Como and Phalen lake ChatBot water quality and recreation text survey, 2022 and 2023
We collected data from visitors to two urban lakes in Saint Paul, Minnesota, using a conversational chatbot to assess visitor perception of current lake water quality, trends in water quality over time, and other questions relevant to park managers. Data were collected at Como Lake in 2022 and 2023, and at Lake Phalen in 2023. Signs were installed at three locations around each lake with high pedestrian traffic. Each sign had a hook question (“How many watercraft are on the lake right now? Text the number to XXX-XXX-XXXX”). Visitors who responded to this question initiated a series of optional follow-up questions, using a conversational chatbot run by software that automates the sending and receiving of text messages. Survey questions included asking respondents about their primary purpose for visiting the lake today, how often they have visited the lake in the past 12 months, and whether they perceive that water quality in the lake is improving, getting worse, or remaining about the same. Respondents were asked to provide their ZIP code, used to estimate distance traveled to the lake. Respondents also had the opportunity to opt-in to future data collection via phone or text. An AI language model was then used to process the information and parse and synthesize responses. Unique anonymous identifiers were used to key survey response data.
Data from: CoAct Citizen Science chatbot explores social support networks in mental health based on lived experiences
<p>A data set on lived experiences in the context of social support in mental health, created within a Citizen Social Science project. </p> <p><br> Societies around the world increasingly encounter wicked and complex problems, such as those related to mental health, environmental justice, and youth employment. <strong>CoAct as a EU-funded global effort</strong> addresses these problems by deploying Citizen Social Science. </p> <p> </p> <p><strong>Citizen Social Science</strong> is understood here as participatory research co-designed and directly driven by citizen groups sharing a social concern. This methodology wants to give citizen groups an equal ‘seat at the table’ through <strong>active participation in research</strong>, from the design to the interpretation of the results and their transformation into concrete actions. Citizens thus act as <strong>co-researchers</strong> and are recognised as in-the-field competent experts. </p> <p> </p> <p>In Barcelona, a group of <strong>32 co-researchers</strong> work together with the OpenSystems group, Universitat de Barcelona, the Catalan Federation of Mental Health (Federació Salut Mental Catalunya), and with the help of many others on a better understanding of informal <strong>social support networks in mental health</strong> in the project <em>CoActuem per la Salut Mental</em> (lit. “We act together for mental health”). The co-researchers, who are either persons with a personal history of mental health problems or are family members of the latter, contributed their <strong>personal experiences related to social support</strong> in the form of <strong>222 micro-stories</strong>, each shorter than 400 characters, and most accompanied by an illustration by Pau Badia.</p> <p> </p> <p>Those micro-stories form the heart of the first co-created Citizen Science chatbot, the code of which is open on <a href="https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git">https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git</a> . The <strong>Telegram chatbot</strong> sends them to participants <strong>on a daily basis over the course of a year</strong> and asks them either, whether they and/ or their close surrounding lived this experience, too (stories of type C), or, how they would or would have reacted in the presented situation (stories of type T). The answers of each participant can be contrasted with the individual participants’ answer to a 32-questions <strong>socio-demographic survey</strong>. Further, the timing of the messages is included to allow for a broader analysis. </p> <p> </p> <p>The chatbot is still running, hence this data set will still be updated. For further information on the project <strong>CoAct</strong>, see <a href="https://coactproject.eu/">https://coactproject.eu/</a>. For further details on the co-creation process and purpose of the chatbot <strong>CoActuem per la Salut Mental</strong>, take a look on <a href="https://coactuem.ub.edu/">https://coactuem.ub.edu/</a>. Please direct your questions regarding the data set to <strong>coactuem[at]ub.edu</strong>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The CoAct project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 873048. We especially thank the co-researchers for the passion and time invested.</p>
Dataset Worldwide Survey on the Impact of AI Chatbots and Large Language Models in Dental Education: Insights from Dental Educators
<p><strong>This dataset contains responses from participants regarding their awareness, knowledge, and perceptions of AI-powered tools in dental education. The data was collected during May-June 2023 to investigate the potential enhancement that AI can bring to dental education. The dataset includes variables related to participants' demographics, experiences, perceptions, and opinions.</strong></p> <p><strong>Details in the published protocol by Uribe, S. E., & Maldupa, I. (2023, June 2). Chatbots In Dental Education - Research Protocol. https://doi.org/10.17605/OSF.IO/3BSG2</strong></p>
Chatbot Integration in few patterns
<p>Chatbots are software agents that are able to interact with humans in natural language. Their intuitive interaction paradigm is expected to significantly reshape the software landscape of tomorrow, while already today chatbots are invading a multitude of scenarios and contexts. This article takes a developer’s perspective, identifies a set of architectural patterns that capture different chatbot integration scenarios, and reviews state-of-the-art development aids.</p> <p>This dataset accompanies the research article of the same name, and provides supplementary material. It contains the list of papers reporting on "chatbot systems" with the list of application areas and chatbot integration patterns.</p>
OggyBug: A Test Automation Tool in Chatbots
<p>Backup video for the presentation of the paper titled "OggyBug: A Test Automation Tool in Chatbots", published in SAST - CBSoft 2020</p>
DATA: Chatbots in Airport Customer Service – Exploring Use Cases and Technology Acceptance
<p>This dataset (n=191) investigates use cases and technology acceptance of chatbots in airport customer service.</p>
Documentation artifacts for conversational SRS in chatbots: a systematic review and a new meta-model proposal and validation
<p>Context: Chatbots are complex applications due to their capacity to engage and maintain a conversation with humans. However, the conversational-related requirements of chatbots are hard to elicit, document, and test. Another challenge is the documentation, since there are not so many directions on how to register and test subjective requirements. </p> <p>Methods: We followed systematic literature review (SLR) guidelines and identified 42 studies that address the artifacts used by practitioners to document conversational-related requirements in literature. We also investigated what conversational requirements are addressed in requirements documentation.</p> <p>Results: The main results indicate that UML diagrams, prototypes, tables of requirements, conversational flows, and scenarios are present in most chatbot documentation. Except for UML diagrams, those artifacts are used to document standard requirements or conversational requirements. In those artifacts, context-dependent behavior, assertivity, error handling, and human-like attitude are the most approached conversational requirements in the studies. In sequence, based on our findings, we proposed the conversational integrated map and validated it by conducting a 2-step questionnaire among software practitioners experience in requirements engineering and chatbot requirement's specification.</p> <p>Conclusion: Future studies should investigate if existing artifacts are enough to address all complex aspects of chatbots' specific conversational requirements or require further adaptation. Future studies should investigate specific SRS needs for different types of softwares.</p>
Supplementary Material for Documentation artifacts for conversation-related requirements specification in chatbots: a systematic review and a meta-model proposal
<p>This is a supplementary data of the tertiary systematic literature review conducted in the paper "Conversation-related requirements specification in chatbots: a systematic review and a meta-model proposal".</p> <p>Context: Chatbots are complex applications due to their capacity to engage and maintain a conversation with humans. However, the conversational-related requirements of chatbots are hard to elicit, document, and test. Another challenge is the documentation since there are not so many directions on how to register and test subjective requirements.</p> <p>Methods: We followed systematic literature review (SLR) guidelines and identified 42 relevant papers that address the artifacts used by practitioners to document conversational-related requirements in literature. We also investigated what conversational requirements are addressed in requirements documentation.</p> <p>Results: The main results indicate that UML diagrams, prototypes, tables of requirements, conversational flows, and scenarios are present in most chatbot documentation. Except for UML diagrams, those artifacts are used to document standard requirements or conversational requirements. In those artifacts, context-dependent behavior, assertivity, error handling, and human-like attitude are the most approached conversational requirements in the studies. In sequence, based on our findings, we propose the conversational integrated map, a meta-model solution as documentation of conversational requirements.</p>
Chatbots: (S)elected Moderation. Measuring the Moderation of Election-Related Content Across Chatbots, Languages and Electoral Contexts
<p>AI Forensics had <a href="https://aiforensics.org/work/bing-chat-elections">previously exposed</a> that Microsoft Copilot's answers to simple election-related questions contained factual errors 30% of the time. In collaboration with Nieuwsuur, we uncovered how chatbots can recommend and support the dissemination of disinformation as a campaign strategy. Following those investigations as well as a request for information from the European Commission, Microsoft and Google introduced “moderation layers" to their chatbots so that they refuse to answer election-related prompts.</p> <p><strong>This dataset was produced during our investigation aimed at evaluating and comparing the effectiveness of these safeguards in different scenarios.</strong> In particular, we investigated the consistency with which electoral moderation was triggered, depending the language of the prompt and the electoral context.</p>
LLMs Languages Least Moderated: Testing Cross-National Moderation in the context of the EU and the US Elections on Chatbots
<p>AI Forensics had <a href="https://aiforensics.org/work/bing-chat-elections">previously exposed</a> that Microsoft Copilot's answers to simple election-related questions contained factual errors 30% of the time. In collaboration with Nieuwsuur, we uncovered how chatbots can recommend and support the dissemination of disinformation as a campaign strategy. Following those investigations as well as a request for information from the European Commission, Microsoft and Google introduced “moderation layers" to their chatbots so that they refuse to answer election-related prompts.</p> <p><strong>This dataset was produced as part<span> of project "LLMs: Languages Least Moderated" at the 2024 Digital Methods Summer School and Data Sprint, which AI Forensics facilitated</span> to allow participants to evaluate and compare the effectiveness of these safeguards in different scenarios.</strong> In particular, we investigated the consistency with which electoral moderation was triggered, depending the language of the prompt and the electoral context.</p>
French trainset for chatbots dealing with usual requests on bank cards
<p><strong>[EN] French training dataset for chatbots dealing with usual requests on bank cards.</strong></p> <ul> <li><strong>Description</strong>: This dataset represents examples of common customer requests relating to bank cards management. It can be used as a training set for a small chatbot intended to process these usual requests.</li> <li><strong>Content</strong>: The questions are asked in French. The dataset is divided into 10 intents of 100 questions each, for a total of 1 000 questions.</li> <li><strong>Intents scope</strong>: Intents are constructed in such a way that all questions arising from the same intention have the same response or action. The scope covered concerns: loss or theft of cards; the swallowed card; the card order; consultation of the bank balance; insurance provided by a card; card unlocking; virtual card management; management of bank overdraft; management of payment limits; management of contactless mode.</li> <li><strong>Origin</strong>: Intents scope is inspired by a chatbot currently in production, and the wording of the questions are inspired by the usual customers requests.</li> </ul> <p><br> <strong>[FR] Jeu d'entraînement en français d'assistants conversationnels traitant des demandes courantes sur les cartes bancaires.</strong></p> <ul> <li><strong>Description </strong>: Cet ensemble de données représente des exemples de demandes usuelles des clients concernant la gestion des cartes bancaires. Il peut être utilisé comme jeu d'entraînement pour un assistant conversationnel destiné à traiter ces demandes courantes.</li> <li><strong>Contenu </strong>: Les questions sont formulées en français. L'ensemble de données est divisé en 10 intentions de 100 questions chacune, pour un total de 1 000 questions.</li> <li><strong>Périmètre des intentions</strong> : Les intentions sont construites de telle manière que toutes les questions issues d'une même intention ont la même réponse ou action. Le périmètre couvert concerne : la perte ou le vol de cartes ; la carte avalée ; la commande des cartes ; la consultation du solde bancaire ; l'assurance fournie par une carte ; le déverrouillage de la carte ; la gestion de cartes virtuelles ; la gestion du découvert bancaire ; la gestion des plafonds de paiement ; la gestion du mode sans contact.</li> <li><strong>Origine </strong>: Le périmètre des intentions est inspiré par un chatbot actuellement en production, et la formulation des questions est inspirée de demandes courantes de clients.</li> </ul>
Dataset of the study: "Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard"
<p>This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot. This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.</p>
Evaluation of large language model chatbot responses to psychotic prompts: numerical ratings of prompt-response pairs
Open the record for dataset details and reuse information.
Survey on social characteristics of human-chatbot interaction
<p>These datasets contain the citations downloaded from Google Scholar, using the following search string:</p> <p><em>(chatbots + chatterbots + conversational agents + conversational interfaces + conversational systems + conversation systems + dialogue systems + digital assistants + intelligent assistants + conversational user interfaces + conversational UI) -</em><em>ECA -multimodal -robots -eye-gaze -gesture -</em><em>speech-based -speech-recognition -voice-based</em></p> <p>Extra options selections are: title only, exclude patents</p> <p>The search was performed between February and September 2018. The file "All.csv" contains all the 1046 papers selected. The other files contains the studies excluded per round. The file "Included.csv" is the primary studies analyzed in the survey, authors version available at: https://arxiv.org/abs/1904.02743</p>
Chacterizing Toolkits for Platform Independent Chatbot Development
<p>Context: With the increase in the use of conversational agents, especially those based on written language (chatbots), users can interact with machines through natural language. Problem: The growing demand for chatbots has raised problems related to building and deploying these conversational agents to different platforms, implying adaptation costs. Solution: We performed a systematic grey literature review to identify a set of DSL-supported tools for platform-independent chatbot development. IS Theory: In this research, we considered the Theory of Behavioral Decision scoping decision processing, the structure of choice, decision processes, and the Theory of Information Processing in what concerns data learning. Method: This research sought to list tools and DSLs for developing platform-independent chatbots, carried out through a review of the grey literature, addressing a qualitative analysis of primary studies. Summary of Results: After conducting the studies, we discovered 14 tools and 10 DSLs supporting the construction of platform-independent chatbots. Contributions and Impact in the IS area: A characterization of tools and DSLs in state of art supporting the construction of platform-independent chatbots.</p> <p>Protocolo de pesquisa, contém:</p> <ul> <li>String de Busca e Sinônimos</li> <li>Pequisas retornadas a partir da String de busca</li> <li>Execução dos critérios de inclusão e exclusão</li> <li>Avaliação da qualidade</li> <li>Extração de Dados</li> </ul>
Evaluating Risk Progression in Mental Health Chatbots with Escalating Prompts Dataset
<p>Excel dataset for "Evaluating Risk Progression in Mental Health Chatbots with Escalating Prompts" manuscript. </p>
Treatment Uptake Chatbot for Eating Disorders
ClinicalTrials.gov study NCT04806165. IPD Sharing: NO. Countries: 1. Publications: 0.
Standard Patient Training Versus Vik Chatbot Guided Training: a Randomized Controlled Trial for Asthma Patients
ClinicalTrials.gov study NCT05248126. IPD Sharing: YES. Countries: 1. Publications: 1.
Effect of AI Chatbot-Assisted Versus Traditional Case-Based Learning on Clinical Reasoning in Occupational Therapy Students: A Study on Parkinson's Disease
ClinicalTrials.gov study NCT07045077. IPD Sharing: NO. Countries: 1. Publications: 1.
Reflects the Intervention (AI Chatbot)
ClinicalTrials.gov study NCT06943911. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
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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)
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.