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Dataset results
65 results for “question answering”
TIQA: Technical Interview Question Answering dataset
<p>This is a Question Answering dataset comprising a list of technical interview-related questions and answers derived from two textbooks, one for Machine Learning and one for Deep Learning subjects. Each question-answer (QA) pair is accompanied by the subject (Machine Learning or Deep Learning) of the textbook and the specific context (section content from the textbook) from which the QA pair was curated. It also contain the Type information of each question.<br>The dataset is available in JSON lines format, with each line representing a JSON object. Each JSON object includes the following keys: Document, Question, Question Type, Subject, and Answer. The Document key contains the context paragraph(s) from the textbook in the form of a list of sentences. The Question key contains the specific question, Question Type contains the type of the Question, and the Subject key indicates the subject of the textbook, i.e., Machine Learning or Deep Learning. The Answer key includes the indexes of the context sentences that form the answer to the question. Note that the first sentence of the context has an index of 0.</p>
TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya
<div> <h3>What is TIGQA?</h3> <a href="https://github.com/hailaykidu/TigQA-Dataset#what-is-tigqa"></a></div> <p>TigQA is an expert-annotated dataset in Tigrinya, a low-resource language spoken by approximately 10 million speakers in Eritrea and the Tigray region of Ethiopia. Our proposed SQuAD-like dataset contains 2,685 question-answer pairs covering 122 diverse topics such as climate, water, and traffic. These pairs are from 537 context paragraphs in publicly accessible Tigrinya and Biology books, with the answers being provided by teachers from the region.</p> <div> </div> <div> </div>
Corpus for hybrid question answering
<p>Question answering corpus, from a compilation of several existing datasets (CLEF and TREC). The questions and answers were annotated by Spotlight. Then a manuel annotation was performed to validate the answers and the paths: 3 = correct, 2 = partial, 1 = related</p> <p>## Format</p> <p>Json array of objects, each object representing a question with its answers and other data :</p> <p>* answerType<br> * question<br> * answers : array of answers<br> * corpusName<br> * answersUris<br> * questionUris<br> * paths : array of path of length 1<br> * paths2 : array of path of length 2</p> <p> </p>
Training/evaluation data sets and databases for the operation of bio-answerfinder biomedical question answering system
<p>A zip file containing training/evaluation data sets for the bio-answerfinder biomedical question answering system training and evaluation. The zip file also contains SQLite databases for named entity lookups, morphology, nominalizations, acronyms, PubMED trained GLoVe word/phrase embeddings and vocabulary with document frequencies and SciCrunch ontology data for named entities such as proteins, anatomical structures,</p>
Complex Sequential Question Answering dataset
<p>While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While Question Answering (QA) and dialog systems have been studied independently, there is a need to study them closely to evaluate such real-world scenarios faced by bots involving both these tasks. Towards this end, we introduce the task of Complex Sequential QA which combines the two tasks of (i) answering factual questions through complex inferencing over a realistic-sized KG of millions of entities, and (ii) learning to converse through a series of coherently linked QA pairs. Through a labor intensive semi-automatic process, involving in-house and crowdsourced workers, we created a dataset containing around 200K dialogs with a total of 1.6M turns. Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in our dialogs require a larger subgraph of the KG. Specifically, our dataset has questions which require logical, quantitative, and comparative reasoning as well as their combinations. This calls for models which can: (i) parse complex natural language questions, (ii) use conversation context to resolve coreferences and ellipsis in utterances, (iii) ask for clarifications for ambiguous queries, and finally (iv) retrieve relevant subgraphs of the KG to answer such questions. However, our experiments with a combination of state of the art dialog and QA models show that they clearly do not achieve the above objectives and are inadequate for dealing with such complex real world settings. We believe that this new dataset coupled with the limitations of existing models as reported in this paper should encourage further research in Complex Sequential QA.</p> <p>Please visit <a href="https://amritasaha1812.github.io/CSQA/">https://amritasaha1812.github.io/CSQA/</a> for more details.</p>
MusiQAl: Music Question-Answering through Audio-Video Fusion
<p></p> <p>What are the most relevant questions to ask a machine about a music performance? MusiQAl is a dataset created and annotated by a research team at RITMO for the task of Music Question Answering (MQA).</p> <p><strong>Purpose</strong> The purpose of creating MusiQal is to address significant limitations in existing MQA datasets, which often focus only on instrument performance and overlook important qualitative characteristics such as interaction, performance energy, and audience engagement. MusiQAl bridges existing gaps by catering to three performance types: instrument, singing, and dancing. It introduces a new set of questions that capture nuanced aspects of music performance, inspired by music cognition theories and advancements in machine perception of music.</p> <p><strong>Utility</strong> Audio-video and question-answer pairs are all available in this repository. Extracted features are also provided, as well as the code for constructing and evaluating the dataset in SOTA models.</p> <p> </p> <p> </p>
Automatically Answering Developer Questions on Discord with Similar Conversations from the Past
<p>Replication Package for "Automatically Answering Developer Questions on Discord with Similar Conversations from the Past"</p>
SQA3D: Situated Question Answering in 3D Scenes
<p>We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (<strong>SQA3D</strong>). Given a scene context(e.g., 3D scan), SQA3D requires the tested agent to first understand its situation(position, orientation, etc.) in the 3D scene as described by text, then reason about its surrounding environment and answer a question under that situation. Based upon 650 scenes from ScanNet, we provide a dataset centered around 6.8k unique situations, along with 20.4k descriptions and 33.4k diverse reasoning questions for these situations. These questions examine a wide spectrum of reasoning capabilities for an intelligent agent, ranging from spatial relation comprehension to commonsense understanding, navigation, and multi-hop reasoning. SQA3D imposes a significant challenge to current multi-modal especially 3D reasoning models. We evaluate various state-of-the-art approaches and find that the best one only achieves an overall score of <strong>47.20%</strong>, while amateur human participants can reach <strong>90.06%</strong>. We believe SQA3D could facilitate future embodied AI research with stronger situation understanding and reasoning capability.</p>
Question Answering auf SNIK
<p>Mit der beständig fortschreitenden Digitalisierung wird es immer wichtiger, auch das Wissen über das Informationsmanagement, also die Verarbeitung von Informationen und die dazu nötigen Schritte digital und strukturiert erreichbar zu machen. Die Ontologie SNIK enthält Wissen aus dem Bereich des Informationsmanagements im Krankenhaus und soll künftig auch bei dem Studium der Medizininformatik helfen. Um das Wissen mittels geschriebener natürlicher Sprache verwendbar zu machen, wird Question Answering vorgeschlagen. Hierfür gibt es verschiedene Systeme, viele sind allerdings auf bestimmte Wissensbasen spezialisiert. Mittels dem hier ausgewählten System QAnswer ist es möglich, die Ontologie mit ausreichender Genauigkeit zu durchsuchen. Die Antworten werden anhand eines vorher definierten Fragenkataloges auf ihre Genauigkeit hin überprüft und bewertet. Das System ist über die SNIK-Website frei erreichbar.</p> <p>Dieses Archiv enthält die Daten, auf die Question Answering betrieben wurde, die verwendeten Trainingsfragen und anhand des Fragentyps die Auswertung mittels bestimmter Indikatoren.</p>
Replication Package for "The Double-edged Sword of Banning Generative AI on Online Question-and-Answer Communities: Evidence from Stack Exchange"
<p>This is a replication package for "The Double-edged Sword of Banning Generative AI on Online Question-and-Answer Communities: Evidence from Stack Exchange".</p>
ChatGPT impacts in programming education: A short list of input questions and output answers from ChatGTP
<p><strong>Five questions (in red) were addressed to ChatGTP ver3.5 between the period July 24<sup>th</sup> – July 28<sup>th</sup> 2023, in order to identify the opinion of ChatGTP in the field of computer science programming and programmers training. It is chosen to pose questions related to the main applications, advantages, disadvantages, future implications, and ethical considerations.</strong></p>
Answering key bumble bee conservation questions by studying discovered wild nests: A Bombus affinis case study
Open the record for dataset details and reuse information.
Conversation Question Answering Dataset for DBpedia
<p>A dataset consisting of 115 questions, resp. 50 conversations based on the DBpedia.</p>
Analysis of the design alternatives for Deaf patient-doctor discussion on self-management and question and answer (Q&A) session
<p>This analysis contributed the findings regarding the design alternatives for Deaf patient-doctor discussion on self-management and question and answer (Q&A) session to Co-evaluation Activity 3. These findings were used to improve Deaf-friendly mobile interfaces.</p>
Data from: Too much of a good thing? Finding the most informative genetic dataset to answer conservation questions
Molecular markers are a useful tool allowing conservation and population managers to shed light on genetic processes affecting threatened populations. However, as technological advancements in molecular techniques continue to evolve, conservationists are frequently faced with new genetic markers, each with nuanced variation in their characteristics as well as advantages and disadvantages for informing various questions. We used a well-studied population of Tasmanian devils from Maria Island to illustrate the issues associated with combining multiple genetic datasets and to help answer a question posed by many population managers: which dataset will provide the most precise and accurate estimates of the population processes we are trying to measure? We analysed individual heterozygosity (as internal relatedness, IR) of 96 individuals, calculated using four genetic marker types (putatively neutral microsatellites, MHC-linked microsatellites, reduced representation sequencing, and candidate region resequencing). We found no correlation in IR values across marker types, suggesting that various genetic markers reflect different aspects of genomic diversity. In addition, some marker types were more informative than others for conservation decision-making. Reduced representation sequencing provided the highest precision (lowest error) for estimating population-level genetic diversity, and most closely reflected genome-wide heterozygosity both theoretically and empirically. Within the conservation context, our results highlight important considerations when choosing a molecular technique for wildlife genetics.
Survey Questions and answers on Smart and sustainable Digital economy
<p>Survey Responses</p>
Evidence from the Literature to Answer Specific Research Questions
<p>All the relevant information that was extracted from the 21 selected articles to answer the four specific research questions (SRQ1, SRQ2, SRQ3, and SRQ4) are presented in a form of a complete table.</p>
Evidence from the Literature to Answer Specific Research Questions
<p>All the relevant information that was extracted from the 21 selected articles to answer the four specific research questions (SRQ1, SRQ2, SRQ3, and SRQ4) are presented in a form of a complete table.</p>
Evidence from the Literature to Answer Specific Research Questions
<p>All the relevant information that was extracted from the 21 selected articles to answer the four specific research questions (SRQ1, SRQ2, SRQ3, and SRQ4) are presented in a form of a complete table.</p>
Clinical questions in primary care: where to find the answers - a cross-sectional study
<p>Dataset for the study "Clinical questions in primary care: where to find the answers - a cross-sectional study". </p>
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)
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.