Omkar2118/datasets huggingface dataset:
OLIVES Dataset
Full name: Ophthalmic Labels for Investigating Visual Eye Semantics

Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. While the clinical labels, fundus images and OCT scans are instrumental measurements, the vectorized biomarkers are interpreted attributes from the other measurements. Clinical practitioners use all these data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between these relevant data modalities. Existing datasets are limited in that: (i) they view the problem as disease prediction without assessing biomarkers, and (ii) they do not consider the explicit relationship among all four data modalities over the treatment period. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitations. This is the first OCT and fundus dataset that includes clinical labels, biomarker labels, and time-series patient treatment information from associated clinical trials. The dataset consists of 1268 fundus eye images each with 49 OCT scans, and 16 biomarkers, along with 3 clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. OLIVES dataset has advantages in other fields of machine learning research including self-supervised learning as it provides alternate augmentation schemes that are medically grounded.

Introducing paper: OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics
WiRe57

We manually performed the task of Open Information Extraction on 5 short documents, elaborating tentative guidelines for the task, and resulting in a ground truth reference of 347 tuples. [section 1]

A small corpus of 57 sentences taken from the beginning of 5 documents in English was used as the source text from which to extract tuples. Three documents are Wikipedia articles (Chilly Gonzales, the EM algorithm, and Tokyo) and two are newswire articles (taken from Reuters, hence the Wi-Re name). [section 3.1]

Introducing paper: WiRe57 : A Fine-Grained Benchmark for Open Information Extraction
DocOIE

We manually annotate 800 sentences from 80 documents in two domains (Healthcare and Transportation) to form a DocOIE dataset for evaluation.

Introducing paper: DocOIE: A Document-level Context-Aware Dataset for OpenIE
ATPChecker
Full name: Automated Third-party library Privacy compliance Checker

A novel dataset for identifying privacy policy compliance of Android third-party libraries.

Introducing paper: Demystifying Privacy Policy of Third-Party Libraries in Mobile Apps
OSASUD
Full name: OSASUD: A dataset of stroke unit recordings for the detection of Obstructive Sleep Apnea Syndrome

Polysomnography (PSG) is a fundamental diagnostical method for the detection of Obstructive Sleep Apnea Syndrome (OSAS). Historically, trained physicians have been manually identifying OSAS episodes in individuals based on PSG recordings. Such a task is highly important for stroke patients, since in such cases OSAS is linked to higher mortality and worse neurological deficits. Unfortunately, the number of strokes per day vastly outnumbers the availability of polysomnographs and dedicated healthcare professionals. The data in this work pertains to 30 patients that were admitted to the stroke unit of the Udine University Hospital, Italy. Unlike previous studies, exclusion criteria are minimal. As a result, data are strongly affected by noise, and individuals may suffer from several comorbidities. Each patient instance is composed of overnight vital signs data deriving from multi-channel ECG, photoplethysmography and polysomnography, and related domain expert’s OSAS annotations. The dataset aims to support the development of automated methods for the detection of OSAS events based on just routinely monitored vital signs, and capable of working in a real-world scenario.

Introducing paper: AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning