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Links and existing "notebooks for journals" #10
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@freeman-lab or @andrewosh as biologists, do you know anything about galaxy? |
scienceopen.com are trying to build the github-of-science in terms of ⭐ and post-pub review. might be interested in using "this project" to add interactivity to papers. |
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The Exec&Share platform has "companion sites" to traditional papers that provide on-line execution of the underlying code. Limitations: (1) no reusability except same code with different input data (2) each companion site requires human input from the Exec&Share team. |
IPOL Journal · Image Processing On Line - http://www.ipol.im Each article contains a text on an algorithm and its source code, with an online demonstration facility and an archive of experiments. |
There is also the (proprietary) Computable Document Format (CDF) by Wolfram: I've no idea if this "format" is popular. |
The Geoscience paper of the future initiative via @ctb in #22 |
Collaborative cloud-enabled tools allow rapid, reproducible biological insights because, there is nothing new under the sun. Could serve as a good example of both the power of this kind of approach but also the fact that it takes several jupyter core devs to get it working nicely (not sure how misleading a statement that is). |
@betatim I was at that hackathon - it also led to my diginorm paper, which beat the ISME paper out to preprint ;). The Jupyter folk are working hard to build out collaborative editing - Google is in on it, too, for example. See: http://blog.jupyter.org/2015/07/07/project-jupyter-computational-narratives-as-the-engine-of-collaborative-data-science/ for the stunning vision the Jupyter folk have of the future... |
Once the new version of colaboratory materialises we are all out of business. Luckily for us it turns out that simultaneously editing a word doc is much easier than editing&executing code 😎 |
COPDESS: Coalition for Publishing Data in the Earth and Space Sciences. http://www.copdess.org/ |
I don't expect usable collaborative-editing-and-computing tools to be around any time soon. That's a research project, not just software development. It's of course possible that at some point general enthusiasm about a prototype creates a hype bubble that declares the problem solved, but then that won't last for long anyway. Those who disagree should look at the history of parallel computing, data synchronization, and the persisting difficulties with merging changes in datasets with complex structure. Any task requiring the reconciliation of independent changes is hard. |
Nature announcement and demo
Jupyter gallery of reproducible papers
Gigascience uses Galaxy, which is a nice example. Ties researchers to galaxy though (how much is it used in biomed?) and hard to adopt for outsiders (like particle phys). Clicking a random article I could find the supporting data, but no "run it right here, right now"
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