The open science stack

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The open science stack

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open_sci_industry

Open science stack in industry talk.

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The open science stack

Creating open science workflows

What is open science??

Complete transparency in the scientific process

Open science workflows

(open science workflows Hampton et al 2014)

Why Open Science?

Crisis in public confidence

Why Open Science?

Combat high profile retractions

Why Open Science?

Combat high profile retractions

"The debunkers could do their debunking only because of a bit of luck: Data they needed happened to be available not from its original source, but through another researcher who had posted it to meet a journal’s open-data policies. (fivethirtyeight.com)"

Why Open Science?

Journals care.

Why Open Science?

Journals care.

"the major hurdle to overcome when trying to convince others that we should strive for Open Science: it is a major pain in the ass and is really expensive, in terms of both the money and amount of time required.

We need to stop telling people 'You should' and get better at telling people 'Here’s how' " - Emilio Bruna, UF, editor Biotropica

What is the open science stack?

A stack is a complete group of components that work together to produce a goal.

What is the open science stack?

  • Open lab notebooks / sharing
  • Open Data
  • Open Source / code sharing
  • Reproducible writing
  • Open Access / pre-prints

Open science stack is all the tools you need to produce open science

What is the open science stack?

  • Open lab notebooks / sharing
  • Open Data
  • Open Source / code sharing
  • Reproducible writing
  • Open Access / pre-prints

Open science stack is all the tools you need to produce open science

Open data

“Open data and content can be freely used, modified, and shared by anyone for any purpose” - Open Knowledge Foundation

Advantages of open data

Your data can be used long after you're gone

(Figure 1D - Vines et al 2014)

Advantages of open data

Increased citation (9%)

(Figure 2 - Piowar and Vision 2013)

Have a plan for your data

(dataone.org)

http://dmptool.org

TL;DR rules for sharing open data

Use an open format Use a metadata standards Use an open license Use an open repository

Open data formats

What makes a format open?

  • ASCII based
  • Binary but maintained by an open consortium
  • Machine independent
  • Machine readable (should be)

Data format examples

Open

  • FASTA / EMBL / Genbank
  • NeXML / NEXUS
  • GeoJSON / KML
  • CSV
  • NetCDF/HDF5

Closed

  • Excel
  • Any proprietary DB
    • Oracle
    • Access
  • ESRI shape file

  • Know your discipline specific standard
  • Know your funding agency policy
  • Know your journal's policy
  • Know your repository's policy

Some metadata standards

  • EML - Ecology
  • Darwin Core - Biodiversity data
  • CF - Climate data
  • ISO 19115 - GIS data
  • MIMS / MIMARK - Genomic / Metagenomic data

License please!

"To anyone who wants to photocopy, bind, and give a copy of the book to their loved one — more power to them. He/She will likely be disappointed that you’re so cheap, though." - Randall Munroe (xkcd)

License please!

Your most open choice, public domain!

Choose a Creative Commons license that fits your comfort level

No license does not mean your data is open!

http://creativecommons.org/choose/

Data repositories

Ideally:

  • Persistent with fail safes
  • Require metadata
  • Allow versioning
  • Issue a DOI for citability
  • Be open (with an API)!

Data repositories

Some suggestions

  • General purpose - Figshare / Zenodo
  • Biodiversity - GBIF / KNB
  • Nucleic acid sequences - Genbank / EMBL

For more suggestions:

http://www.nature.com/sdata/data-policies/repositories

http://journals.plos.org/plosone/s/data-availability

Open source / code sharing

Advantages of open source

  • Facilitates reproducibility
  • Enables collaboration
  • Incentivises writing clean code (future you thanks you)
  • More people will use what you build

Sharing code

  • Use version control! (git / svn)
  • Write human readable comments
  • Use a license (MIT / GPL / BSD)
  • Share on a public repository (GitHub / Bitbucket)
  • Use an open source platform (e.g. NOT matlab, mathematica)
  • Distribute it (CRAN / pipy)
  • Archive releases and assign DOI's http://guides.github.com/activities/citable-code/

Sharing code and data

Wolkovich et al. 2012

Open Science, Reproducibility, and Industry

Open standards facilitate government and industry sharing

Open Science, Reproducibility, and Industry

Open standards facilitate government and industry sharing

Open Science, Reproducibility, and Industry

Open standards facilitate government and industry sharing

Relies on Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) format

Open Science, Reproducibility, and Industry

Sharing happens between companies

Sharing between AstraZeneca and Sanofi

Open Science, Reproducibility, and Industry

Sharing happens between companies

Sharing between 23AndMe and Pfizer and 23AndMe and Genentech

Open Science, Reproducibility, and Industry

"Although the issue of irreproducible data has been discussed between scientists for decades, it has recently received greater attention as the costs of drug development have increased along with the number of late-stage clinical-trial failures and the demand for more effective therapies." (doi:10.1038/483531a)

Open Science, Reproducibility, and Industry

Data science project workflow

"It is possible to achieve some measure of traditional success while being open. Grants; publications; tenure. 'nuff said." - C. Titus Brown, UC Davis http://bit.ly/ossohsu @emhrt_