Omkar2118/datasets huggingface dataset:
MHSMA
Full name: The Modified Human Sperm Morphology Analysis

The MHSMA dataset is a collection of human sperm images from 235 patients with male factor infertility. Each image is labeled by experts for normal or abnormal sperm acrosome, head, vacuole, and tail.

The training, validation, and test sets contain 1000, 240, and 300 images, respectively.

Images are available in two different crop sizes: 128x128- and 64x64-pixel.

Paper: [A novel deep learning method for automatic assessment of human sperm images](https://doi.org/10.1016/j.compbiomed.2019.04.030)
Metric-Type of Numerical Tables

**Metric-Type of Numerical Tables** is a dataset extracted from scientific papers (ACL anthology website) consisting of header tables, captions, and metric-types.

Image source: [Suadaa et al.](https://arxiv.org/pdf/2102.00819v1.pdf)

Introducing paper: Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers
Deeply vocal characterizer

Deeply vocal characterizer is a human nonverbal vocalization dataset. This sample dataset consists of about 0.6 hours(56.7 hours in the full set) of audio(16 kHz, 16-bit, mono) across 16 human nonverbal vocalization classes, including throat-clearing, coughing, laughing, panting, and etc. The audio contents are crowdsourced by the general public of South Korea.

The dataset is a subset(approximately 1%) of a much bigger dataset which were recorded under the same circumstances as these open-source datasets. Please contact us(contact@deeplyinc.com) for the full set with the research/commercial license.
Deeply Korean read speech

Deeply Korean read speech corpus contains pairs of Korean speakers reading a script with *__3 distinct text sentiments (negative, neutral, positive)__*, with *__3 distinct voice sentiments (negative, neutral, positive)__*, are recorded. The recordings took place in *__3 different types of places__*, which are *an anechoic chamber, studio apartment, and dance studio*, of which the level of reverberation differs. And in order to examine the effect of the distance of mic from the source and device, every experiment is recorded at *__3 distinct distances__* with *__2 types of smartphone__*, *iPhone X, and Galaxy S7*.

This sample dataset consists of about 3 hours(290 hours in the full set) of audio(16 kHz, 16-bit, mono), and one pair of speakers. The dataset is a subset(approximately 1%) of a much bigger dataset which were recorded under the same circumstances as these open-source datasets. Please contact us(contact@deeplyinc.com) for the full set with the research/commercial license.
Deeply Parent-Child vocal interaction

Deeply Parent-Child Vocal Interaction contains the interaction of 24 pairs of parent and child(total 48 speakers), such as *__reading fairy tales, singing children’s songs, conversing, and others__*, is recorded. The recordings took place in *__3 different types of places__*, which are *an anechoic chamber, studio apartment, and dance studio*, of which the level of reverberation differs. And in order to examine the effect of the distance of mic from the source and device, every experiment is recorded at *__3 distinct distances)__* with *__2 types of smartphone__*, *iPhone X, and Galaxy S7*.

This sample dataset consists of about 3 hours(282 hours in the full set) of audio(16 kHz, 16-bit, mono), and one pair of speakers. The dataset is a subset(approximately 1%) of a much bigger dataset which were recorded under the same circumstances as these open-source datasets. Please contact us(contact@deeplyinc.com) for the full set with the research/commercial license.
Lesion Boundary Segmentation Dataset

Lesion Boundary Segmentation Dataset is a dataset for lesion segmentation from the ISIC2018 challenge. The dataset contains skin lesions and their corresponding annotations.

Image source :[]()

Introducing paper: Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
HOC
Full name: Hallmarks of Cancer

The **Hallmarks of Cancer** (**HOC*) corpus consists of 1852 PubMed publication abstracts manually annotated by experts according to the Hallmarks of Cancer taxonomy. The taxonomy consists of 37 classes in a hierarchy. Zero or more class labels are assigned to each sentence in the corpus. 

Source: [Hallmarks of Cancer Corpus](https://s-baker.net/resource/hoc/)

Image source: [Hallmarks of Cancer Corpus](https://s-baker.net/resource/hoc/)

Introducing paper: Automatic semantic classification of scientific literature according to the hallmarks of cancer
MIT-BIH AFDB
Full name: MIT-BIH Atrial Fibrilation Database

This database includes 25 long-term ECG recordings of human subjects with atrial fibrillation (mostly paroxysmal).

Of these, 23 records include the two ECG signals (in the .dat files); records 00735 and 03665 are represented only by the rhythm (.atr) and unaudited beat (.qrs annotation files.

The individual recordings are each 10 hours in duration, and contain two ECG signals each sampled at 250 samples per second with 12-bit resolution over a range of ±10 millivolts. The original analog recordings were made at Boston's Beth Israel Hospital (now the Beth Israel Deaconess Medical Center) using ambulatory ECG recorders with a typical recording bandwidth of approximately 0.1 Hz to 40 Hz. The rhythm annotation files (with the suffix .atr) were prepared manually; these contain rhythm annotations of types (AFIB (atrial fibrillation), (AFL (atrial flutter), (J (AV junctional rhythm), and (N (used to indicate all other rhythms). (The original rhythm annotation files, still available in the old directory, used AF, AFL, J, and N to mark these rhythms; the atr annotations in this directory have been revised for consistency with those used for the MIT-BIH Arrhythmia Database.) Beat annotation files (with the suffix .qrs) were prepared using an automated detector and have not been corrected manually. For some records, manually corrected beat annotation files (with the suffix .qrsc) are available. (The .qrs annotations may be useful for studies of methods for automated AF detection, where such methods must be robust with respect to typical QRS detection errors. The .qrsc annotations may be preferred for basic studies of AF itself, where QRS detection errors would be confounding.) Note that in both .qrs and .qrsc files, no distinction is made among beat types (all beats are labelled as if normal).

Source: [MIT-BIH Atrial Fibrillation Database](https://physionet.org/content/afdb/1.0.0/)
ARC-DA
Full name: ARC Direct Answer Questions

**ARC Direct Answer Questions** (**ARC-DA**) dataset consists of 2,985 grade-school level, direct-answer ("open response", "free form") science questions derived from the ARC multiple-choice question set released as part of the AI2 Reasoning Challenge in 2018.

### How the dataset was built
These questions were derived from the ARC multiple-choice question set released as part of the AI2 Reasoning Challenge in 2018. The ARC Easy and ARC Challenge set questions in the original dataset were combined and then filtered/modified by the following process:

- Turking: Each of the multiple-choice questions was presented as a direct answer question to five crowdsourced workers to gather additional answers.

- Heuristic filtering: The questions were filtered based on the following heuristic filters:
    - Questions having a threshold number of turker answers, as a proxy for concreteness of the question.
    - Questions having at least two turker-provided answers with word overlap, as a measure of confidence in the correctness of the answers, and also straightforwardness of the question.
    - Other heuristics to identify questions that only make sense as multiple-choice questions, such as, questions starting with the phrase “Which of the following”.

- Further manual vetting: We had volunteers in house do another pass of vetting where they:
     - Marked highly open-ended questions with too many answer choices, such as “Name an insect”, or otherwise invalid questions, for removal. These are filtered out.
     - Removed some of the bad answers gathered from turking.
     - Reworded questions to make them more suited to direct answer question format, for e.g., a question such as “What element is contained in table salt?” which would make sense as a multiple-choice question, needs be reworded to something like “Name an element present in table salt”.
     - Added any additional answers to the questions they could think of that were not present in the turker provided answers.

Image source: [ARC-DA dataset](https://allenai.org/data/arc-da)

Introducing paper: Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge
Switchboard-1 Corpus

The Switchboard-1 Telephone Speech Corpus (LDC97S62) consists of approximately 260 hours of speech and was originally collected by Texas Instruments in 1990-1, under DARPA sponsorship. The first release of the corpus was published by NIST and distributed by the LDC in 1992-3.

Switchboard is a collection of about 2,400 two-sided telephone conversations among 543 speakers (302 male, 241 female) from all areas of the United States. A computer-driven robot operator system handled the calls, giving the caller appropriate recorded prompts, selecting and dialing another person (the callee) to take part in a conversation, introducing a topic for discussion and recording the speech from the two subjects into separate channels until the conversation was finished. About 70 topics were provided, of which about 50 were used frequently. Selection of topics and callees was constrained so that: (1) no two speakers would converse together more than once and (2) no one spoke more than once on a given topic.

Source: [https://catalog.ldc.upenn.edu/LDC97S62](https://catalog.ldc.upenn.edu/LDC97S62)
MRDA
Full name: ICSI Meeting Recorder Dialog Act Corpus

The **MRDA** corpus consists of about 75 hours of speech from 75 naturally-occurring meetings among 53 speakers. The tagset used for labeling is a modified version of the SWBD-DAMSL tagset. It is annotated with three types of information: marking of the dialogue act segment boundaries, marking of the dialogue acts and marking of correspondences between dialogue acts.

Description from [NLP Progress](http://nlpprogress.com/english/dialogue.html)