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  • Bérénice Batut
Four simple recommendations to encourage best practices in research software
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Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.

Subject:
Computer Science
Information Science
Material Type:
Reading
Provider:
F1000Research
Author:
Alejandra Gonzalez-Beltran
Allegra Via
Andrew Treloar
Bérénice Batut
Bernard Pope
Björn GrüningJonas Hagberg
Brane Leskošek
Carole Goble
Daniel S. Katz
Daniel Vaughan
David Mellor
Federico López Gómez
Ferran Sanz
Harry-Anton Talvik
Horst Pichler
Ilian Todorov
Jon Ison
Josep Ll. Gelpí
Leyla Garcia
Luis J. Oliveira
Maarten van Gompel
Madison Flannery
Manuel Corpas
Maria V. Schneider
Martin Cook
Mateusz Kuzak
Michelle Barker
Mikael Borg
Monther Alhamdoosh
Montserrat González Ferreiro
Nathan S. Watson-Haigh
Neil Chue Hong
Nicola Mulder
Petr Holub
Philippa C. Griffin
Radka Svobodová Vařeková
Radosław Suchecki
Rafael C. Jiménez
Robert Pergl
Rob Hooft
Rowland Mosbergen
Salvador Capella-Gutierrez
Simon Gladman
Sonika Tyagi
Steve Crouchc
Victoria Stodden
Xiaochuan Wang
Yasset Perez-Riverol
Date Added:
08/07/2020
Introduction to Cloud Computing for Genomics
Unrestricted Use
CC BY
Rating

Data Carpentry lesson to learn how to work with Amazon AWS cloud computing and how to transfer data between your local computer and cloud resources. The cloud is a fancy name for the huge network of computers that host your favorite websites, stream movies, and shop online, but you can also harness all of that computing power for running analyses that would take days, weeks or even years on your local computer. In this lesson, you’ll learn about renting cloud services that fit your analytic needs, and how to interact with one of those services (AWS) via the command line.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Abigail Cabunoc Mayes
Adina Howe
Amanda Charbonneau
ammatsun
Bérénice Batut
Bob Freeman
Brittany N. Lasseigne, PhD
Caryn Johansen
Chris Fields
Darya Vanichkina
David Mawdsley
Erin Becker
François Michonneau
Greg Wilson
Jason Williams
Joseph Stachelek
Kari L. Jordan, PhD
Katrin Leinweber
Maxim Belkin
Michael R. Crusoe
Piotr Banaszkiewicz
Raniere Silva
Rémi Emonet
Renato Alves
Stephen Turner
Taylor Reiter
Thomas Morrell
Tracy Teal
vuw-ecs-kevin
William L. Close
Date Added:
03/28/2017
Introduction to the Command Line for Genomics
Unrestricted Use
CC BY
Rating

Data Carpentry lesson to learn to navigate your file system, create, copy, move, and remove files and directories, and automate repetitive tasks using scripts and wildcards with genomics data. Command line interface (OS shell) and graphic user interface (GUI) are different ways of interacting with a computer’s operating system. The shell is a program that presents a command line interface which allows you to control your computer using commands entered with a keyboard instead of controlling graphical user interfaces (GUIs) with a mouse/keyboard combination. There are quite a few reasons to start learning about the shell: For most bioinformatics tools, you have to use the shell. There is no graphical interface. If you want to work in metagenomics or genomics you’re going to need to use the shell. The shell gives you power. The command line gives you the power to do your work more efficiently and more quickly. When you need to do things tens to hundreds of times, knowing how to use the shell is transformative. To use remote computers or cloud computing, you need to use the shell.

Subject:
Computer Science
Information Science
Genetics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Amanda Charbonneau
Amy E. Hodge
Anita Schürch
Bastian Greshake Tzovaras
Bérénice Batut
Colin Davenport
Diya Das
Erin Alison Becker
François Michonneau
Giulio Valentino Dalla Riva
Jessica Elizabeth Mizzi
Karen Cranston
Kari L Jordan
Mattias de Hollander
Mike Lee
Niclas Jareborg
Omar Julio Sosa
Rayna Michelle Harris
Ross Cunning
Russell Neches
Sarah Stevens
Shannon EK Joslin
Sheldon John McKay
Siva Chudalayandi
Taylor Reiter
Tobi
Tracy Teal
Tristan De Buysscher
Date Added:
08/07/2020
Project Organization and Management for Genomics
Unrestricted Use
CC BY
Rating

Data Carpentry Genomics workshop lesson to learn how to structure your metadata, organize and document your genomics data and bioinformatics workflow, and access data on the NCBI sequence read archive (SRA) database. Good data organization is the foundation of any research project. It not only sets you up well for an analysis, but it also makes it easier to come back to the project later and share with collaborators, including your most important collaborator - future you. Organizing a project that includes sequencing involves many components. There’s the experimental setup and conditions metadata, measurements of experimental parameters, sequencing preparation and sample information, the sequences themselves and the files and workflow of any bioinformatics analysis. So much of the information of a sequencing project is digital, and we need to keep track of our digital records in the same way we have a lab notebook and sample freezer. In this lesson, we’ll go through the project organization and documentation that will make an efficient bioinformatics workflow possible. Not only will this make you a more effective bioinformatics researcher, it also prepares your data and project for publication, as grant agencies and publishers increasingly require this information. In this lesson, we’ll be using data from a study of experimental evolution using E. coli. More information about this dataset is available here. In this study there are several types of files: Spreadsheet data from the experiment that tracks the strains and their phenotype over time Spreadsheet data with information on the samples that were sequenced - the names of the samples, how they were prepared and the sequencing conditions The sequence data Throughout the analysis, we’ll also generate files from the steps in the bioinformatics pipeline and documentation on the tools and parameters that we used. In this lesson you will learn: How to structure your metadata, tabular data and information about the experiment. The metadata is the information about the experiment and the samples you’re sequencing. How to prepare for, understand, organize and store the sequencing data that comes back from the sequencing center How to access and download publicly available data that may need to be used in your bioinformatics analysis The concepts of organizing the files and documenting the workflow of your bioinformatics analysis

Subject:
Computer Science
Information Science
Genetics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Amanda Charbonneau
Bérénice Batut
Daniel O. S. Ouso
Deborah Paul
Erin Alison Becker
François Michonneau
Jason Williams
Juan A. Ugalde
Kevin Weitemier
Laura Williams
Paula Andrea Martinez
Peter R. Hoyt
Rayna Michelle Harris
Taylor Reiter
Toby Hodges
Tracy Teal
Date Added:
08/07/2020