19,997 machine learning datasets
19,997 dataset results
Nailfold capillaroscopy stands as a traditional and classical method for health condition assessment. However, manually employing this method poses challenges as the process of human measurement is not only time-consuming but also hindered by subjective criteria. In this research, we are pioneering the construction of a large dataset comprising 321 capillaroscopy images, 219 videos, and 68 clinic reports. This dataset includes annotations such as segmentations and keypoints from experts, forming a crucial resource for training deep-learning models. Leveraging our dataset, we introduce an end-to-end nailfold capillary analysis pipeline capable of automatically detecting and measuring a comprehensive set of morphological and dynamic features. The experiments demonstrate that our automated analysis algorithms achieve remarkable accuracy, which holds promise for quantitative medical research and pervasive computing in human health. We plan to open-source our datasets and code soon to facil
This study concentrates on evaluating the efficacy of Large Language Models (LLMs) in healthcare, with a specific focus on their application in personal anomalous health monitoring. Our research primarily investigates the capabilities of LLMs in interpreting and analyzing physiological data obtained from FDA-approved devices. We conducted an extensive analysis using anomalous physiological data gathered in a simulated low-air-pressure plateau environment. This allowed us to assess the precision and reliability of LLMs in understanding and evaluating users' health status with notable specificity. Our findings reveal that LLMs exhibit exceptional performance in determining medical indicators, including a Mean Absolute Error (MAE) of less than 1 beat per minute for heart rate and less than 1% for oxygen saturation (SpO2). Furthermore, the Mean Absolute Percentage Error (MAPE) for these evaluations remained below 1%, with the overall accuracy of health assessments surpassing 85%. In image
This dataset contains raw data of baseline experimental results. Link: https://drive.google.com/drive/folders/1KBRcPLbM2a8Li3HQ92lGTEfizOGtZ7OM
This dataset is a collection of memes from various existing datasets, online forums, and freshly scrapped contents. It contains both global-context memes and Singapore-context memes, in different splits. It has textual description and a label stating if it is offensive under Singapore society's standards. It can be used to train content moderation models in a culturally complex society.
Retrospectively collected medical data has the opportunity to improve patient care through knowledge discovery and algorithm development. Broad reuse of medical data is desirable for the greatest public good, but data sharing must be done in a manner which protects patient privacy. The Medical Information Mart for Intensive Care (MIMIC)-III database provided critical care data for over 40,000 patients admitted to intensive care units at the Beth Israel Deaconess Medical Center (BIDMC). Importantly, MIMIC-III was deidentified, and patient identifiers were removed according to the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. MIMIC-III has been integral in driving large amounts of research in clinical informatics, epidemiology, and machine learning. Here we present MIMIC-IV, an update to MIMIC-III, which incorporates contemporary data and improves on numerous aspects of MIMIC-III. MIMIC-IV adopts a modular approach to data organization, highlighting d
The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier to the field's progress. Here we describe MIMIC-IV-Note: a collection of deidentified free-text clinical notes for patients included in the MIMIC-IV clinical database. MIMIC-IV-Note contains 331,794 deidentified discharge summaries from 145,915 patients admitted to the hospital and emergency department at the Beth Israel Deaconess Medical Center in Boston, MA, USA. The database also contains 2,321,355 deidentified radiology reports for 237,427 patients. All notes have had protected health information removed in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. All notes are linkable to MIMIC-IV providing important context to the clinical data therein. The database is intended to stimulate
The HRI Dataset comprises a total of 3,200 image pairs. Each image pair comprises a clean background image, a depth image, a rain layer mask image, and a rainy image. It contains three scenes: lane, citystreet and japanesestreet, with image resolutions of $2048\times1024$. The lane scene contains 1,600 image pairs, consisting of images from 4 camera viewpoints, each viewpoint containing 100 images of different moments, and each moment containing 4 different intensities of rainy scenes. The citystreet scene contains 600 image pairs, consisting of images from 6 camera viewpoints, each viewpoint containing 25 images of different moments, and each moment containing 4 different intensities of rainy scenes. The japanesestreet scene contains 1,000 image pairs, consisting of images from 10 camera viewpoints, each viewpoint containing 25 images of different moments, and each moment containing 4 different intensities of rainy scenes.
We developed ANCHOLIK-NER, a Bangla Regional Named Entity Recognition dataset focusing on the Sylhet, Chittagong, and Barishal dialects. It comprises 10,443 sentences, evenly distributed across the three regions, with entities categorized into 10 types. The dataset, sourced from both formal (57.54%) and informal (42.46%) texts, enhances NER models by capturing regional linguistic nuances.
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This dataset is designed to help train simple machine learning models that serve educational and research purposes in the speech recognition domain, mainly for keyword spotting tasks.
Description:
First PHP Webshell Opcode Incremental Dataset Motivation To improve the robustness of PHP webshell detection by analyzing low-level opcode patterns, circumventing common code obfuscation and evasion techniques.
To construct such a dataset, a straightforward approach was scraping images from the web. The main source for our dataset is ShipSpotting, which serves as a repository for user uploaded images, hosting a vast collection of ship images, amounting to approximately 3 million. Furthermore, for each image, valuable supplementary information is available, such as the type of the ship, and present and past names. Next, we made sure that as many images as possible were collected in our dataset, since in deep learning, the quantity of training data directly influences the quality of results.
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TRADES-LOB comprises simulated TRADES market data for Tesla and Intel, for 29/01 and 30/01. Specifically, the dataset is structured into four CSV files, each containing 50 columns. The initial six columns delineate the order features, followed by 40 columns that represent a snapshot of the LOB across the top 10 levels. The concluding four columns provide key financial metrics: mid-price, spread, order volume imbalance, and Volume-Weighted Average Price (VWAP), which can be useful for downstream financial tasks, such as stock price prediction. In total, the dataset is composed of 265,986 rows and 13,299,300 cells, which is similar in size to the benchmark FI-2010 dataset.
Olympic 2024 is a human-annotated dataset that contains 220 high-quality instance. Each instance consists of an input tuple (user instruction, response 1, response confidence of response 1, response 2, response confidence of response 2) and an output tuple (evaluation explanation, evaluation result). The evaluation result would be either ‘1’ or ‘2’, indicating that response 1 or response 2 is better. To ensure the quality of human annotations, we involve three experts to concurrently annotate the same data point during the annotation process.
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Mathematical dataset containing formulas based on the AMPS Khan dataset and the ARQMath dataset V1.3. Based on the retrieved LaTeX formulas, more equivalent versions have been generated by applying randomized LaTeX printing with this SymPy fork. The formulas are intended to be well applicable for MLM. For instance, a masking for a formula like (a+b)^2 = a^2 + 2ab + b^2 makes sense (e.g., (a+[MASK])^2 = a^2 + [MASK]ab + b[MASK]2 -> masked tokens are deducable by the context), in contrast, formulas such as f(x) = 3x+1 are not (e.g., [MASK](x) = 3x[MASK]1 -> [MASK] tokens are ambigious).
Mathematical dataset containing mathematical texts, i.e., texts containing LaTeX formulas, based on the AMPS Khan dataset and the ARQMath dataset V1.3. Based on the retrieved LaTeX texts, more mathematically equivalent versions have been generated by applying randomized LaTeX printing with this SymPy fork. A positive id corresponds to the ARQMath post id of the generated text version, a negative id indicates an AMPS text.