We provide FAIR custom software solutions to our clients. We create, integrate and maintain custom FAIR applications.
Fair Bear Services provides management applications for FAIR Tooling that can be used easily by different parties.
We are hackers! We are working continuously on open source technologies, which we share with the world.
The FAIR web services that are created by Fair Bear Services are scalable applications that can be used by many different parties in order to GO Fair! Many more to come...
In 2019 we will be releasing open source applications, which can be checked out and even worked on by the community. We are super excited to share our open source stack with the world!
We attend FAIR events all over the world, in order to demo our FAIR tooling, increase our FAIR network & enhance our knowledge of the industry.
We have asked our clients to tell you about their experiences with us.
FBS has helped us with complex ICT projects for GFF. They think with us, have a good sense of the business processes that play with us and come up with creative, professional, scalable and easily maintainable solutions.
FBS is a great party to collaborate with. In advance the expectations are discussed extensively. The communication during the assignment - even if you are not technical at all - was very clear. Their way of working ensured that I received the exact product I was looking for. This is the power of The Bears!
Below a couple of answers to some freguently asked questions.
The FAIR principles were designed with data-driven and machine-assisted open science in mind. The final aim of following FAIR principles is that machines as well as people can Find, Access, Interoperate and thus Reuse each other’s research objects. In the original FAIR paper by Wilkinson et al. (2016) the principles were formulated but (consciously) no details on implementation choices were included. Soon, it appeared that deviations from the intentional meaning of the authors were circulating. Although this was a predictable development, a second paper will be published in which the FAIR principles are revisited, including some of the apparent circulating misperceptions.
Many, if not all, of the original designers of the FAIR principles are now involved in GO FAIR and therefore it can be safely assumed that the interpretation of the FAIR guiding principles as accepted in GO FAIR is as close to the original intention as possible.
There are also clear examples of institutes or networks that were already promoting or implementing FAIR principles “avant la lettre” (CERN, RDA, AGU to name a few examples). This should mitigate the misperception that FAIR was claimed as some new revolutionary concept. It is much rather a practical ‘attractor’ for FAIR-minded people and institutions to eventually converge on the needed steps to reach a fully functional and globally operational Internet of FAIR Data and Services.
FAIR is not a standard, although the acronym is frequently used in that context. The GO FAIR view is that standards are needed for the Internet of FAIR Data and Services and that ideally, standards, API’s and protocols are developed ‘following FAIR guiding principles’.
FAIR is not equivalent to open (and open is not equivalent to ‘free’): There are many reasons why data may be non-open and only available under certain conditions to certain users, including machines. As long as the accessibility conditions are properly described, non-open data can be entirely FAIR. Reciprocally, fully open and unrestricted data may score very low in FAIR metrics as they may for instance be non-actionable for machines.
FAIR principles do not, in themselves, cover the crucial aspects of intrinsic data quality or ethics. However, FAIR guiding principles request that optimal care is taken to enable users to determine the ‘usefulness’ (for their purpose) of the data and other research objects they find, which includes rich, machine readable provenance. Obviously, user defined metadata and comments on existing research objects will be increasingly useful to judge the reusability of the research objects.
FAIR data and open data are different, although there are similarities.
The key difference is that open data should be available to everyone to access, use, and share, without licences, copyright, or patents. It is expected that open data at most should be subject to attribution/share-alike licenses.
FAIR data, however, uses the term “Accessible” to mean accessible by appropriate people, at an appropriate time, in an appropriate way. This means that data can be FAIR when it is private, when it is accessible by a defined group of people, or when it is accessible by everyone (open data). It depends completely on the purpose of the data, where the data currently is in its lifecycle, and the end-usage of the data. For example, new experimental data may only be accessible by the generator and their group to start, then with consortia partners as the findings become refined, and finally with the public upon publication. Personally sensitive data may never be publicly accessible and usable. Commercially sensitive data may be held privately for stretches of time after collection and interpretation. Users are also free to use more restrictive licenses to govern how the data may be reused.
FAIR also explicitly includes other characteristics:
Findable: where data should be able to be found by appropriate people at appropriate times. This can include shared folders, drives, private databases, public databases or more. It really depends on what part of the data life cycle the data is currently in. The data will likely transition through a few of these different options during its lifecycle.
Interoperable/Re-usable: these characteristics refer more to how the data is formatted (e.g. standard formatting), whether the software for interpreting/interrogating/using the data is available (e.g. freely, with a license etc.
Source: Ask Open Science