Community-Driven Data Analysis Training for Biology

Galaxy Training Network

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable.

Original languageEnglish (US)
Pages (from-to)752-758.e1
JournalCell Systems
Volume6
Issue number6
DOIs
StatePublished - Jun 27 2018

Fingerprint

Biological Science Disciplines
Web Browser
Research Personnel
Explosions
Internet
Curriculum
Teaching
Software
Maintenance
Research
Datasets

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

Cite this

Galaxy Training Network. / Community-Driven Data Analysis Training for Biology. In: Cell Systems. 2018 ; Vol. 6, No. 6. pp. 752-758.e1.
@article{342a06736ee9418a8612499ccd234338,
title = "Community-Driven Data Analysis Training for Biology",
abstract = "The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable.",
author = "{Galaxy Training Network} and B{\'e}r{\'e}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'e}guen and Martin Cech and Chilton, {John M.} and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Freeberg, {Mallory Ann} and Simon Gladman and Youri Hoogstrate and Hotz, {Hans Rudolf} and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`e}re and {Le Corguill{\'e}}, Gildas and Thomas Manke and Fabien Mareuil and Fidel Ram{\'i}rez and Devon Ryan and Sigloch, {Florian Christoph} and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Bj{\"o}rn Gr{\"u}ning",
year = "2018",
month = "6",
day = "27",
doi = "10.1016/j.cels.2018.05.012",
language = "English (US)",
volume = "6",
pages = "752--758.e1",
journal = "Cell Systems",
issn = "2405-4712",
publisher = "Cell Press",
number = "6",

}

Community-Driven Data Analysis Training for Biology. / Galaxy Training Network.

In: Cell Systems, Vol. 6, No. 6, 27.06.2018, p. 752-758.e1.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Community-Driven Data Analysis Training for Biology

AU - Galaxy Training Network

AU - Batut, Bérénice

AU - Hiltemann, Saskia

AU - Bagnacani, Andrea

AU - Baker, Dannon

AU - Bhardwaj, Vivek

AU - Blank, Clemens

AU - Bretaudeau, Anthony

AU - Brillet-Guéguen, Loraine

AU - Cech, Martin

AU - Chilton, John M.

AU - Clements, Dave

AU - Doppelt-Azeroual, Olivia

AU - Erxleben, Anika

AU - Freeberg, Mallory Ann

AU - Gladman, Simon

AU - Hoogstrate, Youri

AU - Hotz, Hans Rudolf

AU - Houwaart, Torsten

AU - Jagtap, Pratik

AU - Larivière, Delphine

AU - Le Corguillé, Gildas

AU - Manke, Thomas

AU - Mareuil, Fabien

AU - Ramírez, Fidel

AU - Ryan, Devon

AU - Sigloch, Florian Christoph

AU - Soranzo, Nicola

AU - Wolff, Joachim

AU - Videm, Pavankumar

AU - Wolfien, Markus

AU - Wubuli, Aisanjiang

AU - Yusuf, Dilmurat

AU - Taylor, James

AU - Backofen, Rolf

AU - Nekrutenko, Anton

AU - Grüning, Björn

PY - 2018/6/27

Y1 - 2018/6/27

N2 - The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable.

AB - The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable.

UR - http://www.scopus.com/inward/record.url?scp=85048348317&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048348317&partnerID=8YFLogxK

U2 - 10.1016/j.cels.2018.05.012

DO - 10.1016/j.cels.2018.05.012

M3 - Article

C2 - 29953864

AN - SCOPUS:85048348317

VL - 6

SP - 752-758.e1

JO - Cell Systems

JF - Cell Systems

SN - 2405-4712

IS - 6

ER -