Omkar2118/datasets huggingface dataset:
The Rambles

Collection of stream of consciousness.

Natural language processing dataset.
Individual thoughts are separated by a double new line.
DDXPlus
Full name: DDXPlus: A New Dataset For Automatic Medical Diagnosis

There has been a rapidly growing interest in Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the machine learning research literature, aiming to assist doctors in telemedicine services. These systems are designed to interact with patients, collect evidence about their symptoms and relevant antecedents, and possibly make predictions about the underlying diseases. Doctors would review the interactions, including the evidence and the predictions, collect if necessary additional information from patients, before deciding on next steps. Despite recent progress in this area, an important piece of doctors' interactions with patients is missing in the design of these systems, namely the differential diagnosis. Its absence is largely due to the lack of datasets that include such information for models to train on. In this work, we present a large-scale synthetic dataset of roughly 1.3 million patients that includes a differential diagnosis, along with the ground truth pathology, symptoms and antecedents for each patient. Unlike existing datasets which only contain binary symptoms and antecedents, this dataset also contains categorical and multi-choice symptoms and antecedents useful for efficient data collection. Moreover, some symptoms are organized in a hierarchy, making it possible to design systems able to interact with patients in a logical way. As a proof-of-concept, we extend two existing AD and ASD systems to incorporate the differential diagnosis, and provide empirical evidence that using differentials as training signals is essential for the efficiency of such systems or for helping doctors better understand the reasoning of those systems.

Introducing paper: DDXPlus: A New Dataset For Automatic Medical Diagnosis
Smarty4covid
Full name: The smarty4covid dataset and knowledge base: a framework enabling interpretable analysis of audio signals

Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset
and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during
COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep
breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self
reported information is also included (e.g. COVID-19 virus tests), thus providing a comprehensive dataset for the development
of COVID-19 risk detection models. The smarty4covid dataset is released in the form of a web-ontology language (OWL)
knowledge base enabling data consolidation from other relevant datasets, complex queries and reasoning. It has been utilized
towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing
records, and (ii) identify cough, breath and voice segments in crowd-sourced audio recordings. A new framework utilizing
the smarty4covid OWL knowledge base towards generating counterfactual explanations in opaque AI-based COVID-19 risk
detection models is proposed and validated.
Rosario Dataset
Full name: The Rosario dataset: Multisensor data for localization and mapping in agricultural environments

Agricultural dataset collected on-board out weed removing robot. The dataset is composed by six different sequences in a soybean field and it contains stereo images, IMU measurements, wheel odometry and GPS-RTK (positional ground-truth)
IC13

The IC13 dataset contains 561 images: 420 for training and 141 for testing. It inherits data from the IC03 dataset and extends it with new images. Similar to IC03 dataset, the IC13 dataset contains 1,015 cropped text instance images after removing the words with non-alphanumeric characters.