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awesome-public-datasets

A topic-centric list of HQ open datasets.

bert

TensorFlow code and pre-trained models for BERT

biobert

Bioinformatics'2020: BioBERT: a pre-trained biomedical language representation model for biomedical text mining

biopython.github.io

Source of biopython.org website, migrated from MediaWiki

chem-bert

pre-training BERT with molecular data

code

Compilation of R and Python programming codes on the Data Professor YouTube channel.

data_preprocessing

deepjazz

Deep learning driven jazz generation using Keras & Theano!

jazz-standards

Jazz standards transcription in Finale 2012 format. Inclues lyrics

kafka-docker

literaturebaseddiscovery

machine-learning

머신러닝 입문자 혹은 스터디를 준비하시는 분들에게 도움이 되고자 만든 repository입니다. (This repository is intented for helping whom are interested in machine learning study)

medical

medline-pubmed-search-engine

mapped with CPT and ICD codes

music-genre-classification

Music genres is the taste, style and relax giving flow of a music. The genre of music refers to multiple types and categorization of music. The different types of famous music genre that we widely known are rock, jazz, reggae, classical, folk, blues, R & B, metal, dubstep, techno, country music, electro and pop. The key success of music in music industry is the genres of classified music that becomes a significant part of communicating music that provides bonding with relatively to human and masses of people. In contrast, the genre that falls under top-level style of rock are punk, indie, shoegaze, AOR and metal. They are basically subgenre of a music classification and it is important describing music to other people. In practical life, music is often used for multiple purposes due to physiological and social effects. Companies like Spotify, Soundcloud, Apple Music, Wynk & products like Shazam use music classification to provide their customers different flavour of music by recommending music they prefer to listen. we use python libraries such as Librosa and PyAudio library for audio processing in Python. We apply and use GTZAN dataset that is composed of 1000 audio tracks each 30-second-long representing 10 genres with 22050Hz mono audio file of 16bit in .au format for dataset. The functionality and working of music genre classification determine the help of Machine Learning algorithms. The algorithm such as KNN and artificial neural network (ANN) analyses and find out the similar similarity of genre features of music and classify it.

music21

music21 is a Toolkit for Computational Musicology

music21_guide

Python Music21 User_Guide KR

natural-product-function

scripts for predicting natural product activity from biosynthetic gene cluster sequences

phytonutraceutical

plant

prostate

py-earth

A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines

scamp

a Suite in Python for Computer-Assisted Music [MIRROR of https://git.sr.ht/~marcevanstein/scamp]

systematic-review-datasets

A collection of fully labeled systematic review datasets (title-abstract screening)