# Open Access Scientific Resources



### Preprint papers

* [[https://unpaywall.org/]]

* [ arXiv](/https://arxiv.org/ ): Papers on many fields such as physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics

* [ medRxiv](/https://www.medrxiv.org/ ): Papers on Health Sciences

* [ Papers with Code](/https://paperswithcode.com/ ): Link to the arXiv paper + implementations available

* [ bioRxiv](/https://www.biorxiv.org/ ): Papers on life sciences

* [ Europe PMC](/https://europepmc.org/ ): Papers on life sciences

* [ OSF](/https://osf.io/preprints/ ): Papers on many fields



### Open Access Journals

* [ MDPI Journals](/https://www.mdpi.com/ ): More than 100 open access journals

* [ IEEE Access](/https://ieeeaccess.ieee.org/ ): open access journal from the Institute of Electrical and Electronics Engineers (IEEE)

* [ Archive.org Journals](/https://archive.org/details/journals ): collection of open access journals from many publishers



### Open Books

* [ Reproducible Medical Research with R](/https://bookdown.org/pdr_higgins/rmrwr/ )

* [ Mathematics for Machine Learning](/https://mml-book.github.io/ )

* [ Information Theory, Inference, and Learning Algorithms](/http://www.inference.org.uk/mackay/itila/book.html )

* [ NIST/SEMATECH e-Handbook of Statistical Methods](/https://www.itl.nist.gov/div898/handbook/ )

* [ Bayes Rules# An Introduction to Applied Bayesian Modeling](/https://www.bayesrulesbook.com/ )

* [ Modern Data Science with R](/https://mdsr-book.github.io/mdsr2e/index.html )

* [ Circular Visualization in R](/https://jokergoo.github.io/circlize_book/book/ )

* [[https://github.com/mattansb/Practical-Applications-in-R-for-Psychologists]]

* [ Trustworthy Machine Learning](/http://www.trustworthymachinelearning.com/ )

* [ Introduction to Probability for Data Science](/https://probability4datascience.com/ )

* [ Problem Solving with Algorithms and Data Structures using Python](/https://runestone.academy/ns/books/published/pythonds/index.html )

* [ Data Science at the Command Line](/https://datascienceatthecommandline.com/ )

* [ Hands-On Machine Learning with R](/https://bradleyboehmke.github.io/HOML/ )

* [ R for Clinical Study Reports and Submission](/https://r4csr.org/ )

* [ Introduction to Information Retrieval](/https://nlp.stanford.edu/IR-book/ )

* [ Feature Engineering and Selection](/http://www.feat.engineering/ )