Changing Scientific
Landscape

as science becomes increasingly data driven and computational, as well as collaborative and interdisciplinary, there is increased demand for tools that are easy to extend, share, and customize:

˲˲ ˲ Star scientist → Research teams. traditionally, science was driven by key scientists. today, science is driven by collaborating co-author teams, often comprising experts from multiple disciplines and geospatial locations5, 17;

˲˲ Users → Contributors. Web 2.0 technologies empower users to contribute to Wikipedia and exchange images, videos, and code via Fickr, youtube, and SourceForge.net. Wikispecies, WikiProfessionals, and WikiProteins combine wiki and semantic technology to support real-time community annotation of scientific data sets14;

˲˲ Disciplinary → Cross-disciplinary. the best tools frequently borrow and synergistically combine methods and techniques from different disciplines of science, empowering interdisciplinary and/or international teams of researchers, practitioners, and educators to collectively fine-tune and interpret results;

˲˲ Single specimen → Data streams. Microscopes and telescopes were originally used to study one specimen at a time. today, many researchers must make sense of massive data streams of multiple data types and formats and of different dynamics and origin; and

˲˲ ˲ Static instrument → Evolving cyberinfrastructure. the importance of hardware instruments that are static and expensive tends to decrease relative to software tools and services that are highly flexible and evolving to meet the needs of different sciences. Some of the most successful tools and services are decentralized, increasing scalability and fault tolerance.

good software-development practices make it possible for “a million minds” to design flexible, scalable software that can be used by many:

˲˲ Modularity. Software modules with well-defined functionality accept contributions from multiple users reduce costs and increase flexibility in tool development, augmentation, and customization;

˲˲ Standardization. Standards accelerate development, as existing code is leveraged, helping pool resources, support interoperability, and ease migration from research code to production code and hence the transfer of research results into industry applications and products; and

˲˲ ˲ Open data and open code. the practice of making data sets and code freely available allows users to check, improve, and repurpose data and code, easing replication of scientific studies.

 

or months to set up and run can now be designed and optimized in a few hours. Users can also share, rerun, and improve automatically generated work logs. Workflows designed, validated, and published in peer-reviewed works can be used by science-policy analysts and policymakers alike. As of January 2011, the Sci2 tool was being used by the National Science Foundation, the National Institutes of Health, the U.S. Department of Energy, and private foundations adding novel plug-ins and workflows relevant for making decisions involving science policy.

The Sci2 tool supports many differ-
ent analyses and visualizations used
to communicate results to a range of
stakeholders. Common workflows and
references to peer-reviewed papers
are given in Börner et al. 3 and the Sci2
wiki ( http://sci2.wiki.cns.iu.edu). Four
sample studies are discussed here and
included in Figure 2, I–IV: