Science 2.0 study

Updates on progress and discussions on results of Science 2.0: implications for European policies on research and innovation study

Archive for the month “June, 2012”

Shape with us the future research priorities of the EU – comment on the draft policy recommendations

Science 2.0 provides new opportunities and challenges to the European Research Area. It’s not a matter of embracing or rejecting the shift, but rather to understand and design appropriate policy measures that will grasp the opportunities and overcome the challenges.

Approaching to the final phase of our study we have drafted an initial version of policy recommendations for the European Commission that we would like to discuss with  researchers, science2.0 evangelists, publishers, representatives of funding bodies, librarians and other interested parties. Therefore we have published the recommendations as a commentable document open for your suggestions, comments and add-ons.

Our recommendations are clustered around four challenges:

  • RESEARCHERS REPUTATION AND EVALUATION  – the supremacy of impact factor

Scientists are still following the old ‘publish or perish’ rule, frequently passing over the opportunities to be engaged in activities that do not ultimately result in a peer-review article. The career process is not inducive to sharing data and code, and to collaborate at early stage of the scientific process.

  • EU RESEARCH FUNDING – rigid funding instruments

Current research funding is mainly roadmap-based, and not conducive to open and serendipitous research activities which are confined to limited areas such as ERC and Fet-Open. Evaluation system narrowly focusses on articles and patents as research outcomes. 

  • SKILLS – lack of data and scientific literacy

There’s a need more and better data-literate scientists across all disciplines, as well as greater awareness and scientific literacy of citizens.

  • STANDARDS AND INFRASTRUCTURES – immature infrastructure and lack of standards

Without common standards for data management, the open access and open data policies cannot be scaled up. There’s a need for a stronger physical and institutional infrastructure for the growing amount of scientific data and publications to create a favourable environment to the development of science 2.0


Note: These policy recommendations are based on desk research (see the references in our Mendeley group), interviews with stakeholders and case studies (the results of our research will be published together with the final version of the policy recommendations). They are also build upon existing recommendations such as:  the LiquidPub  project final recommendations and Surfboard for Riding the Wave report by Knowledge Exchange (Graaf & Waaijers, 2011).

This study takes a broader view to the full research cycle, beyond open access to scientific publications, which is a well analysed theme with clear policy recommendations already existing and embraced by policy-makers. These recommendations are therefore to be considered as additional to existing Open Access debate.


The pervasive metaphor of genome

“Genome” used to be a technical word used by scientists. It has become a metaphor, a paradigm of today.

The Human Genome Project was an unprecedented effort to carefully and patiently map the information of the Human Genome.

It refers to an inductive approach that rather than modeling and developing theories about the Genome, it adopted an inductive approach, collecting all available information and processing it. It’s THE classical big data project.

What is interesting is that Genome has now become a metaphor, extending the approach to research efforts in very different fields.

For instance, the Music Genome Project described all the possible features of the published music, and led to the creation of the Pandora music services. As Wikipedia describes,

A given song is represented by a vector (a list of attributes) containing approximately 400 “genes” (analogous to trait-determining genes for organisms in the field of genetics). Each gene corresponds to a characteristic of the music, for example, gender of lead vocalist, level of distortion on the electric guitar, type of background vocals, etc. Rock and pop songs have 150 genes, rap songs have 350, and jazz songs have approximately 400.

I now came across the Startup Genome Project , which analyzes a large amount of variables for understanding “the secret of success” of startups.

What these project share is an inductive method. Little theory, but focus on getting lots of data, and see what patterns emerge.

This is the new scientific method that Chris Anderson referred to in Wired. It’s extending from genetics to many other fields.

The emerging institutional setting of Science 2.0

There are lots of interesting stories about Science 2.0. But in the context of this study we are uncovering a far richer and more substantial infrastructure, that we consider the emergent self-organising institutional setting of Science 2.0.

First there is a self-regulation effort for open access. While many funding agencies are paying more attention to open access, the great surge in open access behaviour by research institutions is mainly due to self-regulation.

Secondly, there is an emerging meso-level infrastructure for coordinating this bottom-up effort. The market for “crowdsourcing” and “open innovation” solution is exploding: companies such as Innocentive, ChallengePost, and many others offer solutions for reaching out to a mass of potential innovators. Not only they offer the technological platform: they offer most importantly the process design, and the database of people. Recently, open source efforts have become available like Pybossa. Other nonprofit project include SciFundChallenge, which help citizens finding interesting challenges to participate in.

Interoperability standards are becoming available for example in the field of annotation, in order to facilitate data sharing and collaboration beyond the interoperability of bibliographies (which can now be considered a fait accompli ).

Culture is also changing, with increasing reward for scientists who share. Alternative metrics are being developed to measure reputation, such as AltMetrics and PeerProduction, as described in a previous post.

So probably what we need is not just old-style top-down policies and regulation on Science 2.0, but also a softer mix of tools, methodologies and people.

Post Navigation