Quantifying the impact of weak, strong, and super ties in scientific careers

Scientists are frequently faced with the important decision to start or terminate a creative partnership. This process can be influenced by strategic motivations, as early career researchers are pursuers, whereas senior researchers are typically attractors, of new collaborative opportunities. Focusing on the longitudinal aspects of scientific collaboration, we analyzed 473 collaboration profiles using an ego-centric perspective which accounts for researcher-specific characteristics and provides insight into a range of topics, from career achievement and sustainability to team dynamics and efficiency. From more than 166,000 collaboration records, we quantify the frequency distributions of collaboration duration and tie-strength, showing that collaboration networks are dominated by weak ties characterized by high turnover rates. We use analytic extreme-value thresholds to identify a new class of indispensable `super ties', the strongest of which commonly exhibit >50% publication overlap with the central scientist. The prevalence of super ties suggests that they arise from career strategies based upon cost, risk, and reward sharing and complementary skill matching. We then use a combination of descriptive and panel regression methods to compare the subset of publications coauthored with a super tie to the subset without one, controlling for pertinent features such as career age, prestige, team size, and prior group experience. We find that super ties contribute to above-average productivity and a 17% citation increase per publication, thus identifying these partnerships - the analog of life partners - as a major factor in science career development.Comment: 13 pages, 5 figures, 1 Tabl

Towards distributed architecture for collaborative cloud services in community networks

Internet and communication technologies have lowered the costs for communities to collaborate, leading to new services like user-generated content and social computing, and through collaboration, collectively built infrastructures like community networks have also emerged. Community networks get formed when individuals and local organisations from a geographic area team up to create and run a community-owned IP network to satisfy the community’s demand for ICT, such as facilitating Internet access and providing services of local interest. The consolidation of today’s cloud technologies offers now the possibility of collectively built community clouds, building upon user-generated content and user-provided networks towards an ecosystem of cloud services. To address the limitation and enhance utility of community networks, we propose a collaborative distributed architecture for building a community cloud system that employs resources contributed by the members of the community network for provisioning infrastructure and software services. Such architecture needs to be tailored to the specific social, economic and technical characteristics of the community networks for community clouds to be successful and sustainable. By real deployments of clouds in community networks and evaluation of application performance, we show that community clouds are feasible. Our result may encourage collaborative innovative cloud-based services made possible with the resources of a community.Peer ReviewedPostprint (author’s final draft

How to Create an Innovation Accelerator

Too many policy failures are fundamentally failures of knowledge. This has become particularly apparent during the recent financial and economic crisis, which is questioning the validity of mainstream scholarly paradigms. We propose to pursue a multi-disciplinary approach and to establish new institutional settings which remove or reduce obstacles impeding efficient knowledge creation. We provided suggestions on (i) how to modernize and improve the academic publication system, and (ii) how to support scientific coordination, communication, and co-creation in large-scale multi-disciplinary projects. Both constitute important elements of what we envision to be a novel ICT infrastructure called "Innovation Accelerator" or "Knowledge Accelerator".Comment: 32 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Collaborative platforms for streamlining workflows in Open Science

Despite the internet’s dynamic and collaborative nature, scientists continue to produce grant proposals, lab notebooks, data files, conclusions etc. that stay in static formats or are not published online and therefore not always easily accessible to the interested public. Because of limited adoption of tools that seamlessly integrate all aspects of a research project (conception, data generation, data evaluation, peer-reviewing and publishing of conclusions), much effort is later spent on reproducing or reformatting individual entities before they can be repurposed independently or as parts of articles.

We propose that workflows - performed both individually and collaboratively - could potentially become more efficient if all steps of the research cycle were coherently represented online and the underlying data were formatted, annotated and licensed for reuse. Such a system would accelerate the process of taking projects from conception to publication stages and allow for continuous updating of the data sets and their interpretation as well as their integration into other independent projects.

A major advantage of such workflows is the increased transparency, both with respect to the scientific process as to the contribution of each participant. The latter point is important from a perspective of motivation, as it enables the allocation of reputation, which creates incentives for scientists to contribute to projects. Such workflow platforms offering possibilities to fine-tune the accessibility of their content could gradually pave the path from the current static mode of research presentation into
a more coherent practice of open science

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports