Open Science, somethimes referred to as Open Research or Open Scholarship, is the idea that the products of research, especially publicly-funded research, should be made widely available without charge, both to make the reults of reserch, especially in medicine widely available, and to promote new research based on it.
See the National Academies Press (2018) document, Open Science by Design: Realizing a Vision for 21st Century Research https://nap.nationalacademies.org/catalog/25116/open-science-by-design-realizing-a-vision-for-21st-century
The three main generally recognized pillars of open science are:
Other facets sometimes included in open science are:
For more detailed discussion of each of these, see the box below.
The open access publishing movement grew out of an intersection of the tremendous growth in subscription prices of scholarly journals in the 1980s and 1990s, and the desire to make scientific discoveries, especially in biomedicind, freely available to doctors, researchers and the general public. The development of the World Wide Web as a platform for scholarly publishing facilitated the movement.
The are two terms which are frequently used synonymously, but are really distinct.
In the world of print, scholarly journals were typically supported by subscriptions, either from individual or institution / libraries, with some also receving significan revenue from advertisers, notably Nature and Science Electronic intituional subscriptions led to a decline in individual subscrtions, which in turn decreased the attractiveness of scholarly journals to advertisers. If subscription-free, open access pubblishing was to work, new economic models would have to be devised. Among those are:
While many researchers have embraced the principles of open access publishing, many have not. However, they may be required to publish open access, usually in one of two ways.
Following on the OA publishing movement came a push for Open Data. Access to the original data on which research conclusions are based is essential for other reearchers to verify research results and to build on them. Support for open data grew most quickly in the discovery sciences, that is, those which uncover what is already existing in nature. This was especially true for areaas of research depending n pooling large collections of data. The "big data" fields includie genome research, astrophysics and some areas of environmental science. Adoption of open data has been more slow in areas of invention science, that is, where researchers are creating new substances or devices, such s pharmaceutical chemistry or most ares of engineering. Not coincidentally, there are areas which often lead to patentable inventions.
In 2016, a group defined the FAIR Data Principles. The acroym stands for:
FAIR data principles are implemented in different ways by different research communities. Data may be stored in institutional repositoires, cush as those mantained by both the UC and UCSB, or in discipline specific epositories. Examples of the latter include the Protein Data Bank for prtoein sequences, and the Cambridge Crystallographi database for organic and organometallic substnce crystal structues. In genral, all crystallography journals now require authors to deposit their data in the CCDC databse.
As mentioned in the discussion of copyright in Lecture 6, computer software is an expression of an idea (algorithm) in a fixed form, and is therefore protected by copyright (though some software is also patented). However, the software used to collect and interpret scientific data is an essential part of being able to verify and reproduce that data and any conclusions drawn. So, FAIR data implies the availableility of the associted software in open form.
There are vaious repositories in which software develoers may deposity their code, making it available for reuse and for the creating of derivative works, usually under an "attribution" and "share alike" licence. Theese include: