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Carpentries Instructor Training
Unrestricted Use
CC BY
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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 Wrangling and Processing for Genomics
Unrestricted Use
CC BY
Rating
0.0 stars

Data Carpentry lesson to learn how to use command-line tools to perform quality control, align reads to a reference genome, and identify and visualize between-sample variation. A lot of genomics analysis is done using command-line tools for three reasons: 1) you will often be working with a large number of files, and working through the command-line rather than through a graphical user interface (GUI) allows you to automate repetitive tasks, 2) you will often need more compute power than is available on your personal computer, and connecting to and interacting with remote computers requires a command-line interface, and 3) you will often need to customize your analyses, and command-line tools often enable more customization than the corresponding GUI tools (if in fact a GUI tool even exists). In a previous lesson, you learned how to use the bash shell to interact with your computer through a command line interface. In this lesson, you will be applying this new knowledge to carry out a common genomics workflow - identifying variants among sequencing samples taken from multiple individuals within a population. We will be starting with a set of sequenced reads (.fastq files), performing some quality control steps, aligning those reads to a reference genome, and ending by identifying and visualizing variations among these samples. As you progress through this lesson, keep in mind that, even if you aren’t going to be doing this same workflow in your research, you will be learning some very important lessons about using command-line bioinformatic tools. What you learn here will enable you to use a variety of bioinformatic tools with confidence and greatly enhance your research efficiency and productivity.

Subject:
Applied Science
Computer Science
Genetics
Information Science
Life Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Thomas
Ahmed R. Hasan
Aniello Infante
Anita Schürch
Dev Paudel
Erin Alison Becker
Fotis Psomopoulos
François Michonneau
Gaius Augustus
Gregg TeHennepe
Jason Williams
Jessica Elizabeth Mizzi
Karen Cranston
Kari L Jordan
Kate Crosby
Kevin Weitemier
Lex Nederbragt
Luis Avila
Peter R. Hoyt
Rayna Michelle Harris
Ryan Peek
Sheldon John McKay
Sheldon McKay
Taylor Reiter
Tessa Pierce
Toby Hodges
Tracy Teal
Vasilis Lenis
Winni Kretzschmar
dbmarchant
Date Added:
08/07/2020
Mr. Griggs' Work
Conditional Remix & Share Permitted
CC BY-NC-SA
Rating
0.0 stars

The students will learn about the importance of responsibility, dependability, punctuality, honesty, and effort in the workplace through the reading of the book, "Mr. Griggs' Work." The students will have the opportunity to explore these character traits in their own work setting.

Subject:
Business and Communication
Material Type:
Lesson Plan
Provider:
University of North Carolina at Chapel Hill School of Education
Provider Set:
LEARN NC Lesson Plans
Author:
William Hodge
Date Added:
02/15/2005
Project Organization and Management for Genomics
Unrestricted Use
CC BY
Rating
0.0 stars

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:
Business and Communication
Genetics
Life Science
Management
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