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Carpentries Instructor Training
Unrestricted Use
CC BY
Rating
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A two-day introduction to modern evidence-based teaching practices, built and maintained by the Carpentry community.

Subject:
Applied Science
Computer Science
Education
Higher Education
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Aleksandra Nenadic
Alexander Konovalov
Alistair John Walsh
Allison Weber
Amy E. Hodge
Andrew B. Collier
Anita Schürch
AnnaWilliford
Ariel Rokem
Brian Ballsun-Stanton
Callin Switzer
Christian Brueffer
Christina Koch
Christopher Erdmann
Colin Morris
Dan Allan
DanielBrett
Danielle Quinn
Darya Vanichkina
David Jennings
Eric Jankowski
Erin Alison Becker
Evan Peter Williamson
François Michonneau
Gerard Capes
Greg Wilson
Ian Lee
Jason M Gates
Jason Williams
Jeffrey Oliver
Joe Atzberger
John Bradley
John Pellman
Jonah Duckles
Jonathan Bradley
Karen Cranston
Karen Word
Kari L Jordan
Katherine Koziar
Katrin Leinweber
Kees den Heijer
Laurence
Lex Nederbragt
Maneesha Sane
Marie-Helene Burle
Mik Black
Mike Henry
Murray Cadzow
Neal Davis
Neil Kindlon
Nicholas Tierney
Nicolás Palopoli
Noah Spies
Paula Andrea Martinez
Petraea
Rayna Michelle Harris
Rémi Emonet
Rémi Rampin
Sarah Brown
Sarah M Brown
Sarah Stevens
Sean
Serah Anne Njambi Kiburu
Stefan Helfrich
Steve Moss
Stéphane Guillou
Ted Laderas
Tiago M. D. Pereira
Toby Hodges
Tracy Teal
Yo Yehudi
amoskane
davidbenncsiro
naught101
satya-vinay
Date Added:
08/07/2020
Data Management with SQL for Ecologists
Unrestricted Use
CC BY
Rating
0.0 stars

Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Christina Koch
Donal Heidenblad
Katy Felkner
Rémi Rampin
Timothée Poisot
Date Added:
03/20/2017
Databases and SQL
Unrestricted Use
CC BY
Rating
0.0 stars

Software Carpentry lesson that teaches how to use databases and SQL In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and Valentina Roerich led expeditions to the Pole of Inaccessibility in the South Pacific, and then onward to Antarctica. Two years ago, their expeditions were found in a storage locker at Miskatonic University. We have scanned and OCR the data they contain, and we now want to store that information in a way that will make search and analysis easy. Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets. These lessons will show how to use a database to explore the expeditions’ data.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Amy Brown
Andrew Boughton
Andrew Kubiak
Avishek Kumar
Ben Waugh
Bill Mills
Brian Ballsun-Stanton
Chris Tomlinson
Colleen Fallaw
Dan Michael Heggø
Daniel Suess
Dave Welch
David W Wright
Deborah Gertrude Digges
Donny Winston
Doug Latornell
Erin Alison Becker
Ethan Nelson
Ethan P White
François Michonneau
George Graham
Gerard Capes
Gideon Juve
Greg Wilson
Ioan Vancea
Jake Lever
James Mickley
John Blischak
JohnRMoreau@gmail.com
Jonah Duckles
Jonathan Guyer
Joshua Nahum
Kate Hertweck
Kevin Dyke
Louis Vernon
Luc Small
Luke William Johnston
Maneesha Sane
Mark Stacy
Matthew Collins
Matty Jones
Mike Jackson
Morgan Taschuk
Patrick McCann
Paula Andrea Martinez
Pauline Barmby
Piotr Banaszkiewicz
Raniere Silva
Ray Bell
Rayna Michelle Harris
Rémi Emonet
Rémi Rampin
Seda Arat
Sheldon John McKay
Sheldon McKay
Stephen Davison
Thomas Guignard
Trevor Bekolay
lorra
slimlime
Date Added:
03/20/2017
Reproducibility, Preservation, and Access to Research with ReproZip and ReproServer
Conditional Remix & Share Permitted
CC BY-SA
Rating
0.0 stars

The adoption of reproducibility remains low, despite incentives becoming increasingly common in different domains, conferences, and journals. The truth is, reproducibility is technically difficult to achieve due to the complexities of computational environments.To address these technical challenges, we created ReproZip, an open-source tool that packs research along with all the necessary information to reproduce it, including data files, software, OS version, and environment variables. Everything is then bundled into an .rpz file, which users can use to reproduce the work with ReproUnzip and an unpacker (Docker, Vagrant, and Singularity). The .rpz file is general and contains rich metadata: more unpackers can be added as needed, better guaranteeing long-term preservation.However, installing the unpackers can still be burdensome for secondary users of ReproZip bundles. In this paper, we will discuss how ReproZip and our new tool ReproServer can be used together to facilitate access to well-preserved, reproducible work. ReproServer is a cloud application that allows users to upload or provide a link to a ReproZip bundle, and then interact with/reproduce the contents from the comfort of their browser. Users are then provided a stable link to the unpacked work on ReproServer they can share with reviewers or colleagues.

Subject:
Applied Science
Life Science
Physical Science
Social Science
Material Type:
Activity/Lab
Provider:
New York University
Author:
Fernando Chirigati
Rémi Rampin
Vicky Steeves
Date Added:
05/31/2019
Writing reproducible geoscience papers using R Markdown, Docker, and GitLab
Unrestricted Use
CC BY
Rating
0.0 stars

Reproducibility is unquestionably at the heart of science. Scientists face numerous challenges in this context, not least the lack of concepts, tools, and workflows for reproducible research in today's curricula.This short course introduces established and powerful tools that enable reproducibility of computational geoscientific research, statistical analyses, and visualisation of results using R (http://www.r-project.org/) in two lessons:1. Reproducible Research with R MarkdownOpen Data, Open Source, Open Reviews and Open Science are important aspects of science today. In the first lesson, basic motivations and concepts for reproducible research touching on these topics are briefly introduced. During a hands-on session the course participants write R Markdown (http://rmarkdown.rstudio.com/) documents, which include text and code and can be compiled to static documents (e.g. HTML, PDF).R Markdown is equally well suited for day-to-day digital notebooks as it is for scientific publications when using publisher templates.2. GitLab and DockerIn the second lesson, the R Markdown files are published and enriched on an online collaboration platform. Participants learn how to save and version documents using GitLab (http://gitlab.com/) and compile them using Docker containers (https://docker.com/). These containers capture the full computational environment and can be transported, executed, examined, shared and archived. Furthermore, GitLab's collaboration features are explored as an environment for Open Science.Prerequisites: Participants should install required software (R, RStudio, a current browser) and register on GitLab (https://gitlab.com) before the course.This short course is especially relevant for early career scientists (ECS).Participants are welcome to bring their own data and R scripts to work with during the course.All material by the conveners will be shared publicly via OSF (https://osf.io/qd9nf/).

Subject:
Physical Science
Material Type:
Activity/Lab
Provider:
New York University
Author:
Daniel Nüst
Edzer Pebesma
Markus Konkol
Rémi Rampin
Vicky Steeves
Date Added:
05/11/2018