Integrating Fragmented Scientific Knowledge Prashant Gupta, Mark Gahegan and Gillian Dobbie Centre for eResearch, Department of Computer Science, University of Auckland {p.gupta, m.gahegan, g.dobbie}@auckland.ac.nz Making sense of disconected data, methods, models and workflows Key efforts to aggregate scientific knowledge, as shown below, focus on the reproducibility of a single experiment, whereas we focus on integrating science as an ongoing and evolving process. All of these systems integrate data and methods but ignore conceptual structures and thus provide a mechanistic view of science, rather than as a dynamic and cyclic process of evolution. Research Executable Workflow Linked Data/ Object [1] Paper [2] Systems [3] Science [4] Research Question: How do we restore the connections among scientific artifacts, that are often evident in the mind of a researcher, in our digital representations to present an integated view of scientific knowledge? Scientific knowledge which is usually well connected in a knowledge producer’s mind is often fragmented by various computational tools into individual artifacts - data, methods, code, schema and workflows - with no explicit connections. Reusing Carole’s experiment. Bill tried to use Carole’s work to compare land cover categories in NZ and Australia. He had a hard time making sense of her work because of the lack of details of the process and the decisions made. Knowledge consumer having a hard time understanding another researcher’s work. Carole’s experiment. Carole synthesized land cover categories (e.g. forest and water) and built a land cover map for NZ and published the map using an online mapping tool without any details of the process. Science digitization misplaces knowledge The state of knowledge integration in science 1. S. Bechhofer, D. De Roure, M. Gamble, C. Goble, and I. Buchan, “Research objects: Towards exchange and reuse of digital knowledge,” presented at The Future of the Web for Collaborative Science, NC, USA, 2010. 2. J. P. Mesirov, “Accessible reproducible research,” Science, vol. 327, pp. 415–416, Jan. 2010. 3. T. Kauppinen and G. M. Espindola, “Linked open science communicating, sharing and evaluating data, methods and results for executable papers,” presented at the Int. Conf. Computational Science, 2011, pp. 726–731. 4. E. Deelman, D. Gannon, M. Shields, and I. Taylor, “Workflows and e-Science: An overview of workflow system features and capabilities,” Future Generation Computer Systems, vol. 25, pp. 528–540, May 2009. In contrast, this work focuses both on data and conceptual structures, such as categories and models, and allows them, and the connections among them to change and evolve over time, making them dynamic and live. Integrated and live approach to science We propose a conceptual model (cloud in the figure below) to ensure that data, methods, categories and schemas stay connected and co-evolve during the conduct of research. We created a computational tool – AdvoCate – that implements this model and facilitates the modeling of scientific categories, integrates various artifacts (using semantic technologies - RDF), remembers their details and versions, and analyzes and mediates change information among them. Live connections that allow change information to mediate from one scientific artifact to another using semantic technologies (RDF) The model remembers the research that Carole performs by connecting various scientific artifacts - data, categories and map - and recording their details while the experiment is done. Bill can now explore these connections to make sense of Carole’s work, and use them to compare NZ and Australia land cover maps. Conclusion: This work supports an integrating and live approach towards scientific knowledge, connecting scientific artifacts of an experiment and synchronizing them with the practice of science, which supports a deeper and clearer understanding and communication of scientific research. References