The document discusses open access and open science. It argues that while open access to content and data is important, solely focusing on this is not enough, and risks allowing proprietary control over scholarly infrastructure. It notes that originally, open access was envisioned as thriving institutional repositories playing a central role for universities, but is now largely achieved through article processing charges paid to commercial publishers. The document advocates for positioning repositories as the foundation for a distributed, globally networked infrastructure that is research-centric, open to innovation, and collectively managed by scholars.

1. In pursuit of open science, open
access is not enough
October 6, 2020
Amy Brand, PhD
The 11th Portuguese-Brazilian Open Science Conference

4. Image: SciELO

8. “Everything we have gained by opening content
and data will be under threat if we allow the
enclosure of scholarly infrastructures.”
Bilder G, Lin J, Neylon C (2015) Principles for Open Scholarly
Infrastructure

14. Self-archiving Pay to publish

16. Self-archiving Pay to publish

19. ”…a contract is a pure publish
agreement if the agreed payment
enables an institution’s authors to
publish in fully open access
journals.”

20. The original OA
vision: thriving,
connected institutional
repositories and a central
role for universities
How OA is being
achieved now: largely
through APCs paid to
commercial and larger
society publishers

22. To position repositories as the foundation for a distributed, globally networked infrastructure for scholarly communication, on top
of which layers of value added services will be deployed, thereby transforming the system, making it more research-centric, open to
and supportive of innovation, while also collectively managed by the scholarly community.

25. Technology companies develop proprietary
knowledge networks as key business
technologies today. However, because these
networks are proprietary and expensive to
construct, government, academia, small
businesses, and nonprofits do not have access
to them. In contrast, an open knowledge
network (OKN) would be available to all
stakeholders, including the researchers
who will help push this technology further.
An OKN requires a nonproprietary, public–
private development effort that spans the
entire data science community and will result in
an open, shared infrastructure.

36. Arcadia project

39. BIODIVERSITY/MONOCULTURE

40. Q&A