sparcopen / doathon

Our discussion forum (see "issues") for the OpenCon Do-A-Thon, a day of trying, making, testing and doing to advance Open Research & Education. See our full website, with more information (including Github Help, and how to get involved).
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How might we make data sharing "a routine" in the scientific community? #57

Open shervinsafavi opened 6 years ago

shervinsafavi commented 6 years ago

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At a glance

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Description

[//]: # "======================= Insert a paragraph providing more context for your project or challenge focuses on. For project leads, this is a good place to give some broader context about your project—beyond the scope of the do-a-thon. If you're posting a challenge, this is a good chance to say how the problem arise or why it feels relevant to you. ============================" We all know, making research data open is extremely needed/helpful for reproducibility and advancement of science, medicine and technology, but how might we can make it "a routine"? We need to know what are the caveats in data sharing, what does encourage/demotivate scientists from sharing their research data? Ideally we would like establish the culture of data sharing in all scientific disciplines, therefore appropriate investigations and actions have to be made in field-specific manner. For instance, in the field of machine learning (~ a branch of artificial intelligence) the openness culture is ahead of what I/we observe in the neurosciences therefore what we need to make in the latter is different.

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How can others contribute?

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We hope with help of many bright minds we can come up with minimally doable plan in direction of achieving the goal of making data sharing a routine. Given the cultural variability across scientific disciplines we thought we start from a narrow filed. We would suggest non-clinical neuroscience as the start point, since I've already initiated a few informal investigations in this field. But if volunteer from other field are ready to take the lead why not going beyond!

Our raw idea is, the main caveats in data sharing are the following:

  1. Infrastructure: Scientist are not aware of appropriate infrastructure or is not available at completely (this includes data repositories, data standards, required metadata and many more).
  2. Credit: Lack of appropriate credit system for sharing data might demotivate scientist for sharing their data.

We need to have both simultaneity to make data sharing a routine.

To reach this minimal step we need to define multiple doable steps which we need to know your opinion on that. During the Do-A-Thon (as well as after!) We invite anyone to help tackle this challenge. You don’t need to be physically in Berlin (if your are, find us in the Goethe-saal right corner at the end) to share your thoughts and ideas! Either share your ideas in the the Do-A-Thon session at OpenCon 2017 or write them in the following Google document: https://goo.gl/rVwX6k

By the way, this a link to brainstorm on a related topic happened during an unconference duing OpenCon 2016: https://goo.gl/3gqZyL

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This post is part of the OpenCon 2017 Do-A-Thon. Not sure what's going on? Head here.

npscience commented 6 years ago

Hi @shervinsafavi - great idea! I've added to your doc:

"A useful resource: https://www.datacite.org/ Including a database of data repositories so you can find the right place to share your data: https://www.re3data.org/"

cartoonist commented 6 years ago

Approaches

To achieve the main objective there are couple of non-exclusive approaches:

Infrastructure

What is the infrastructure?

How?

Credit system

Why h-index is not sufficient

Define data index (d-Index) which can be used:

How?

Assuming that d-index is an effective metric,

Current status:

We are going to investigate

[*] Data can be raw or processed depends on difficulty in storing and/or processing raw data.


[1] Cameron, B. D. (2005). Trends in the usage of ISI bibliometric data: Uses, abuses, and implications. portal: Libraries and the Academy, 5(1), 105-125.

cartoonist commented 6 years ago

This might be useful: https://pdfs.semanticscholar.org/d53c/594e80ef35b9b3782d86abfcaf402fb0a2df.pdf