You’ll have read on this site, and perhaps others, about the push towards “open access” for journal articles. But what is open access, and how does it fit into the wider “open movement”?
The topic has been much talked about of late, not least in the context of Open Access Week – a crowd-based movement promoting the push toward open access for journal articles. After decades of paying staff to generate journal content and then having to pay again to access that content, universities appear to have finally woken up.
This is one of several different faces of a continuing trend towards an open exchange of information and innovation. So what are those faces, and how do these relate to “old school” or “closed” approaches of developing and exploiting intellectual property?
“Open source” is often seen as the first of the open movements.
Emerging out of the Free Software Movement in the 1980s, open source provides access to the source code of computer software so others can further develop that code.
The alternative, “closed” approach, still adopted by many software developers, involves hiding or encrypting a piece of software’s source code to prevent others “reverse engineering” it and creating rival or derivative products.
There are many different flavours of open source, with many tricks and traps for those who aren’t aware of the detail of the conditions of access.
Understanding what is or isn’t permitted when it comes to editing open-source software requires analysis of the licence terms. Consider the GNU Public Licences, which have different models for software, software used over networks, manuals and a library of exceptions for different purposes.
All contain different provisions around rights of use, restrictions on downstream works, warranties, indemnities and other matters.
Many people don’t bother trying to untangle the differences, and fail to understand what is cost-free, what is in the public domain, and whether they are required to give others access to the source code of works they create.
Many benefits have flowed from the use of open-source models of software and hardware development, including the Linux operating system, Arduino electronics, and even key software embodied in nearly every mobile phone (developed by an Australian company Open Kernel Labs – acquired last month by General Dynamics).
The open-source model has spilled over into biology and science. One example is molecular biologist Richard Jefferson’s sharing of his discovery of a key genetic tool – the beta-glucuronidase gene – a key indicator of gene activity used in plant genetic engineering.
Jefferson gave research labs free access to this tool but also charged for its commercial use. He then invested the proceeds in the foundation of the independent non-profit institute Cambia to further promote open-source initiatives.
“Open innovation” is a concept promoted by the author and academic Henry Chesbrough. Unsurprisingly, this approach boils down to the need to engage with other people and their perspectives to develop better products or services.
Of course, open innovation has been happening since time immemorial, embraced by companies such as:
- Siemens, with its attempt to tap the minds of consumers, researchers and external partners to supplement internal innovation
- Cadbury, with a similar program to embrace innovation partnerships with universities, small to medium enterprises and other multinationals
- Lilly, with its open innovation drug discovery program.
Open data: a patchwork quilt
Newer than any of the “open” movements is the push for so-called “open data” – essentially open access to data sets rather than just research publications.
There are many different groups working on this from different angles, including:
- the Open Data Commons, a sort-of copyright-based Creative Commons (all articles on The Conversation are Creative Commons)
- the Open Data Foundation, directed at enabling access to consistent metadata to inform public policy and decision-making)
- Open Data Protocol, for querying or updating data that can break down “silos” that prevent sharing between applications.
At the moment there are no universally-accepted standards for open data. Too often researchers struggle with clunky paths to access: navigating the shoals of differing access conditions and even having to divert funding into legal resources to map and better articulate the basis on which they share data between themselves (let alone with others).
The promise of understanding, innovation, efficiency and effectiveness held out by making use of “Big Data” in the public and private sector will not be fully realised until there is better progress on common standards for open data.
And beyond data and data structures there needs to be a change in culture and mindset – otherwise some predict the promise will turn into a minefield.
Crowdsourcing of ideas and funding is all the rage: from philanthropic purposes, to start-ups facilitated by the likes of Kickstarter, to multinational companies, which now routinely use crowdsourcing to gather innovation they can’t generate internally.
The rise of crowdsourcing raises a number of understandable concerns. There’s potential for scandal: where a crowdsourced project takes money, fails or doesn’t even use it for the project; or exploitation: where those who contribute ideas to a site seeking competitive generation of ideas for a project have their ideas appropriated with no reward.
Still, the signals are clear: this style of engagement is growing. Kickstarter is a good example – US$350 million has been pledged for more than 30,000 projects since April 2009, with a rapid escalation this year.
A plethora of other crowd-sourcing platforms have emerged with a variety of business models, some for-profit and others not – the educational not-for-profit the Khan Academy is a case in point.
“Open” models are often cast in opposition to closed or proprietary models, which are founded on protecting and exploiting intellectual property. But this apparent opposition is an illusion.
Many of the open models are actually built on intellectual property rights – it is the way in which those rights are used that differs from proprietary models.
Consider “copyleft”, which uses copyright to force others who make derivative works of underlying enabling software platforms to also make their modifications or additions freely available to the benefit of all (and to enable the creation of yet further derivative works).
There is nothing new about successful business models that stack proprietary approaches on top of open layers.
An open conclusion
Individuals can and do choose how open or closed they wish to be in their work. This does not have to be an ideologically fixed and static choice between opposites.
A more natural and sophisticated stance is to blend the two approaches to best fit the context. Being open – through social networks, in research or business – is the trend and has much to commend it, but a blend of different approaches gives us a healthy innovation ecosystem.