There is not one correct way to practice Open Science, but it can be helpful to think of Open Science practices and tools as options within your overall research workflow.
You may have seen a graphic like this before, but if not this is an example of a basic, simplified model of a workflow, a cycle moving from initial discovery and searching of information, to data collection, then analysis, writing up results, publication, and lastly a consideration of outreach and impact of your work.
Real workflows are never this simple. This is an example of more complex, iterative model (https://doi.org/10.6084/m9.figshare.3468641.v1).
You can see how multiple cycles nest within the larger cycle as each aspect of the workflow requires revisions, edits, clarifying experiments, etc. Here is an even more realistic view of what your day to day workflow might look like:
Thomas Van Hoey (2020), then a graduate student in linguistics, took an honest look at his own workflow, which is probably a better representation of how most of us get through the day (https://www.thomasvanhoey.com/post/handling-research).
Workflows have become an interesting research topic in their own right. This next representation is part of a global survey by Kramer and Bosman (2016) charting the changing landscape of scholarly research. They surveyed 20,000 researchers worldwide to get a sense of which products they use to comprise their workflows (10.6084/m9.figshare.3468641.v1). A workflow does not exist on its own, but is made of choices - which software, applications and products to use, how do they work together effectively, and what makes sense for a given task, subject domain or project.
Increasingly researchers are being asked to open their work, not just their publications but the various components of their methods and processes. This allows others to benefit from your work, to reproduce it, but it’s also helpful to you as you begin your research to be able to go back and understand the decisions you made at various junctures.
(From: Marwick et al, 2017, Open science in archaeology; Danchev 2021, Reproducible Data Science with Python on the Cloud)
At Berkeley, there are various resources to start you on your path towards open, transparent and reproducible research. We rely on many proprietary products in our day-to-day work, but there is no one-size-fits-all way of practicing open science. Be flexible, adaptable and curious in your approach!
Services:
UC Berkeley’s Research Data Management Program can help you with Data Management Plans, data sharing, backup and security, preservation and archiving.
Products:
Compare Dryad, Zenodo and other Repositories: https://zenodo.org/record/3946720
Writing: Make use of collaborative writing tools. Many of us use Microsoft Word or Google Docs as writing software.
More information: https://guides.lib.berkeley.edu/write-cite
Citing: Streamline your research and writing workflows by adopting reference management software (also known as citation management software).
More information: https://eps-libraries-berkeley.github.io/volt/Organizing/Reference_Management_and_Citations.html
Preregistration -
Some fields have adopted the practice of preregistration, or registration, to publicly file hypotheses and research plans. Example:
Preprints -
There are many preprint sites available depending on your discipline and the focus of your article. In Earth Science, two examples are: EarthArXiv (Earth Science focused preprint site hosted by California Digital Library) and ESSOAR (Earth and Space Science Open Archive from the American Geophysical Union). Find a directory or preprint servers at ASAPbio.
Support for open access -
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