Recommend a topic for a future episode

[aprilcs]

This issue is to collect recommendations for future topics.

Recommendations should include what the topic would be, how it relates to issues or innovations in science, and any references or background that support the recommendation.

If you see a recommendation you like, please add a +1!

[dasaderi]

Preprints. It would be awesome to get Jessica Polka from ASAPbio.

[Abdelazeem-Elhabyan]

I would like to recommend
Open sources for learning online in field like programming or bioinformatics and genomics
These fields are open for anyone to learn online on his own
We can ask learners how they discovered their path and how they used online open resources for learning

[tsloan1377]

I think an episode about open-source scientific hardware would be very interesting.
It relates to the accessibility of science, as it allows certain technologies to be more broadly available, and can lower the barrier of entry.
Dr Joshua Pearce (Michigan Tech) is a leader here, would be a great interviewee. He has many publications on open hardware such as 3d printers or microscopes:
https://www.ncbi.nlm.nih.gov/pubmed/?term=Pearce%20JM%5BAuthor%5D&cauthor=true&cauthor_uid=28488563
and he wrote the manual on how to do it yourself:
http://www.appropedia.org/Open-source_Lab

[tsloan1377]

The transition towards open science.
This would be a discussion about a recent trend for organizations to move away from a model of filing for patents for innovations, and towards making all research open and available.
Availability of scientific data and publications are a possible subtopic. and another could deal with more recent trends like open lab books (Lab Scribbles), and the open institution (Montreal Neurological Institute).

A few interesting people on this topic are: Dr Aled Edwards (Structural Genomics Consortium)
http://www.thesgc.org/about/what_is_the_sgc
and Dr Guy Rouleau (Montreal Neurological Institute - open science initiative)
http://www.mcgill.ca/neuro/open-science-0

[chartgerink]

Why selective publication (i.e., publication bias) is harmful to science, even if we have meta-analyses, negative results are published a bit (but not equally) and why selective publication occurs (author misinterprets nonsignificant finding as something having failed in the methods, author doesn't submit, editor more likely to reject).

Happy to contribute in discussing this topic as well :-)

[GrantRVD]

In the same vein as @chartgerink's suggestion, it could be very meaningful to have a podcast on reproducibility studies (e.g. those by Ioannidis et al.). Most reporting I see on the topic either paints it as not-an-issue because the measurements don't mean what the lay explanations suggest they do or they hail it as the end of science of X because we have no idea which results are true. Maybe an open-source discussion on the topic will be able to find the line more clearly.

[DamienIrving]

How to improve the state of tertiary science education.

[DamienIrving]

The reproducibility crisis in modern research.

As this survey shows this is a massive issue and the causes for it differ depending on which research discipline you're in, so you might need a series of episodes on different aspects of the issue. e.g:

• Code and software documentation
• Data archiving and documentation
• Experimental design
• Statistics
• etc, etc