Academic Torrents – a torrent sharing website for Academics & Researchers

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Academic Torrents

Sharing data is hard. Emails have size limits, and setting up servers is too much work. We’ve designed a distributed system for sharing enormous datasets – for researchers, by researchers. The result is a scalable, secure, and fault-tolerant repository for data, with blazing fast download speeds.

One aim of this site is to create the infrastructure to allow open access journals to operate at low cost. By facilitating file transfers, the journal can focus on its core mission of providing world class research. After peer review the paper can be indexed on this site and diseminated throughout our system.

Large dataset delivery can be supported by researchers in the field that have the dataset on their machine. A popular large dataset doesn’t need to be housed centrally. Researchers can have part of the dataset they are working on and they can help host it together.

Libraries can host this data to host papers from their own campus without becoming the only source of the data. So even if a library’s system is broken other universities can participate in getting that data into the hands of researchers.

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Best Practices for Scientific Computing

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A summary of a very interesting paper on “Best Practices for Scientific Computing” I read a year ago.

andrea cirillo's blog

I reproduce here below principles from the amazing paper Best Practices for Scientific Computing, published on 2012 by a group of US and UK professors. The main purpose of the paper is to “teach”  good programming habits shared from professional developers to people  that weren’t born developer, and became developers just for professional purposes.

Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently

Best Practices for Scientific Computing

  1. Write programs for people, not computers.

    1. a program should not require its readers to hold more than a handful of facts in memory at once
    2. names should be consistent, distinctive and meaningful
    3. code style and formatting should be consistent
    4. all aspects of software development should be broken down into tasks roughly an hour long
  2. Automate repetitive tasks.

    1. rely on the computer to repeat tasks
    2. save recent commands in…

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Assorted links – Data Science with R

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last updated: 2015-08-29

References & Most helpful commands

Tutorials & Handy packages

Hands-on dplyr tutorial for faster data manipulation in R Interactive Visualizations From R Using Rcharts rMaps – Interactive Maps from R (github repo) (requires “devtools” from cran)
Using R for Psychological Research – Personality Project, William Revelle
DataCamp courses
Try R by Code School (on codeschool)
Introduction to R, Leada

Visualization Packages

see Assorted links – Data Visualization (to be published later)

Papers

Tidy Data, Hadley Wickham [PDF]

Journals

Big Data & Society – Open-access journal

Hacks for better productivity

Sublime and R

Using Sublime Text 2 for R Using R in Sublime Text 3

Books

Video (training) courses

Introduction to Data Science with R, Garrett Grolemund, O’Reilly Media

Lists of Resources by others

Data Mining

Scraping Twitter and Web Data Using R – Pablo Barbera

Numerical Analysis
Interoperability
Data Sources

see Assorted links – Data sources (To be published later)

If you’d like to contribute to this list, please leave them in the comments below.

NASA’s JPL releases Two Math Libraries

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NASA’s Jet Propulsion Laboratory (JPL) has released two large math libraries under an open source (BSD) license. Via Degenerate Conic:

“MATH77 is a library of Fortran 77 subroutines implementing various numerical algorithms. It was developed over decades at JPL, and contains some very high-quality and time-tested code. The code is released under a BSD-type license. There is also a C version for people who love semicolons.”

This goldmine includes basic mathematical functions, random number generators, linear algebra routines, solvers for systems of nonlinear equations, curve fitting, interpolation, and quadrature routines, and much more. The libraries are available at Netlib and are accompanied by 619 pages of detailed documentation.

[via jblevins.org]

Fortran Wiki

Wolfram Language Overview

Link

Wolfram Language Overview