19,997 machine learning datasets
19,997 dataset results
The dataset consists of a set of tasks in email and the associated people. Within each email, a task is indicated by a special <mark> tag (one per HIT); a set of email recipients (one or more per email) is also provided. The subset of recipients (possibly empty) who are responsible for the marked task is also provided. Each recipient is identified by their email address. Sometimes recipients are email groups(“some_group”@enron.com); they are referred to implicitly by the sender of the email. There are 5998 unique emails.
With two listening files, four multimedia was made. Two multimedia from each listening file. The difference between the two versions prepared from the same audio file was in applying or violating of Mayer’s multimedia learning principles. Thirty-nine participants were randomly divided into two groups. One group watched the with principle of the first audio file and the without principle of the second audio file, and the other group watched the opposite way. Meanwhile, their electroencephalography(EEG) were recorded. Then they participated in a post-test and finally completed a self-reported cognitive load questionnaire.
A dataset including texts by humans (labeled 0) and then rephrased by ChatGPT (labeled 1), created to train models for machine-generated text detection.
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This dataset provides simulated flood inundation maps of Abu Dhabi's coast under 174 different shoreline protection scenarios. The maps were produced with a high-fidelity physics-based hydrodynamic simulator under a 0.5-meter sea level rise projection. The details of the hydrodynamic model are reported in [1].
RES-Q is a natural language instruction-based benchmark for evaluating $\textbf{R}$epository $\textbf{E}$diting $\textbf{S}$ystems, which consists of 100 handcrafted repository editing tasks derived from real GitHub commits. Given an edit instruction and a code repository, RES-Q evaluates an LLM system’s ability to interpret edit instructions, gather information, and construct appropriate edits to the repository.
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GelSight Young's Modulus Dataset by Michael Burgess
We present MoleculeCLA: a large-scale dataset consisting of approximately 140,000 small molecules derived from computational ligand-target binding analysis, providing nine properties that cover chemical, physical, and biological aspects.
The dataset contains around 180K rendered images with 100K classified as anomaly and 80K normal. Different types of abnormalities include: missing parts, broken parts, swapped components, mis-alignments. The query pose is unknown. Testing is performed on previously unseen instances.
AGB-DE is a legal NLP corpus for the automated detection of potentially void clauses in German standard form consumer contracts. It consists of 3,764 clauses that have been legally assessed by experts and annotated as potentially void (1) or valid (0). Additionally, each clause is annotated with a topic label.
CAsT-answerability dataset contains binary answerability labels on three levels: sentence, passage, and ranking. It contains around 1.8k answerable and 1.9k unanswerable question-passage pairs. Sentence- and passage-level answerability is divided into train (90%), and test (10%) portions; the splitting is done on the question level to avoid information leakage. Ranking-level answerability has only a test set.
A Collection of Multilingual Parallel Datasets for 5 Indonesian Local Languages
This is a dataset for predicting 5-Formylcytidine Modifications on mRNA.
Pulmonary hypertension (PH) is a syndrome complex that accompanies a number of diseases of different etiologies, associated with basic mechanisms of structural and functional changes of the pulmonary circulation vessels and revealed pressure increasing in the pulmonary artery. The structural changes in the pulmonary circulation vessels are the main limiting factor determining the prognosis of patients with PH. Thickening and irreversible deposition of collagen in the pulmonary artery branches walls leads to rapid disease progression and a therapy effectiveness decreasing. In this regard, histological examination of the pulmonary circulation vessels is critical both in preclinical studies and clinical practice. However, measurements of quantitative parameters such as the average vessel outer diameter, the vessel walls area, and the hypertrophy index claimed significant time investment and the requirement for specialist training to analyze micrographs. A dataset of pulmonary circulation
A new in-context visual question answering dataset encompassing interleaved image and EHR data derived from MIMIC-IV and MIMIC-CXR-JPG databases.
To collect the metrics data, we deploy three benchmark microservice systems: Online Boutique, Sock Shop, and Train Ticket, on a Kubernetes cluster consisting of one master node and five worker nodes. Then, we deploy a monitoring system to monitor and collect resource-level and service-level metrics. To generate traffic, we use the load generators supplied by these systems and tailor them to explore all services with a load of 40-50 requests per second. Initially, we operate the applications normally to gather metrics data under normal conditions. Then, we inject faults into the running services. We execute into the designated container using kubectl exec. For CPU hog and memory leak, we use stress-ng to stress the container resource. For network delay and packet loss, we use tc (traffic control) to manipulate the traffic of the container. Specifically, we inject faults into five targeted services of Sock Shop (carts, catalogue, orders, payment, and user), five targeted services of Onli
The Language Model Council (LMC) is a novel benchmarking framework proposed to address the challenge of ranking Large Language Models (LLMs) on highly subjective tasks¹. These tasks can include areas related to emotional intelligence, creative writing, or persuasiveness, which often lack majoritarian human agreement¹.
Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) and this corresponding dataset aim to provide tools for measuring user enjoyment from an external perspective to supplement self-reported user enjoyment responses in human-robot interaction research, with future potential application for autonomous detection of user enjoyment in real-time in robots and agents for adapting conversations contingently to provide enjoyable and long-lasting interactions.