Omkar2118/datasets huggingface dataset:
Helsinki Prosody Corpus

The Helsinki Prosody Corpus is a dataset for predicting prosodic prominence from written text. The prosodic annotations are automatically generated, high quality prosodic for the 'clean' subsets of LibriTTS corpus (Zen et al., 2019), comprising of 262.5 hours of read speech from 1230 speakers. The transcribed sentences were aligned and then prosodically annotated with word-level acoustic prominence labels.

Source: [Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations](https://arxiv.org/abs/1908.02262)

Introducing paper: Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
WMCA
Full name: Wide Multi Channel Presentation Attack

The Wide Multi Channel Presentation Attack (WMCA) database consists of 1941 short video recordings of both bonafide and presentation attacks from 72 different identities. The data is recorded from several channels including color, depth, infra-red, and thermal.

Additionally, the pulse reading data for bonafide recordings is also provided.

Preprocessed images for some of the channels are also provided for part of the data used in the reference publication.

The WMCA database is produced at Idiap within the framework of “IARPA BATL” and “H2020 TESLA” projects and it is intended for investigation of presentation attack detection (PAD) methods for face recognition systems.

Introducing paper: Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network
AQUAINT

The **AQUAINT** Corpus consists of newswire text data in English, drawn from three sources: the Xinhua News Service (People's Republic of China), the New York Times News Service, and the Associated Press Worldstream News Service. It was prepared by the LDC for the AQUAINT Project, and will be used in official benchmark evaluations conducted by National Institute of Standards and Technology (NIST).

Source: [Linguistic Data Consortium](https://catalog.ldc.upenn.edu/LDC2002T31)
Image Source: [https://catalog.ldc.upenn.edu/LDC2002T31](https://catalog.ldc.upenn.edu/LDC2002T31)
Species-800
Full name: Species-800

**Species-800** is a corpus for species entities, which is based on manually annotated abstracts. It comprises 800 PubMed abstracts that contain identified organism mentions. To increase the corpus taxonomic mention diversity the 800 abstracts were collected by selecting 100 abstracts from the following 8 categories: bacteriology, botany, entomology, medicine, mycology, protistology, virology and zoology. 800 has been annotated with a focus at the species level; however, higher taxa mentions (such as genera, families and orders) have also been considered.
LINNAEUS

LINNAEUS is a general-purpose dictionary matching software, capable of processing multiple types of document formats in the biomedical domain (MEDLINE, PMC, BMC, OTMI, text, etc.). It can produce multiple types of output (XML, HTML, tab-separated-value file, or save to a database). It also contains methods for acting as a server (including load balancing across several servers), allowing clients to request matching over a network. A package with files for recognizing and identifying species names is available for LINNAEUS, showing 94% recall and 97% precision compared to LINNAEUS-species-corpus.

Source: [LINNAEUS](http://linnaeus.sourceforge.net/)
Adverse Drug Events (ADE) Corpus

Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports.

A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F₁ score of 0.70 indicating a potential useful application of the corpus.