A Virtual Crowdsourcing Community for Open Collaboration in Science Processes

Although science has become an increasingly collaborative endeavor over the last hundred years, only little attention has been devoted to supporting scientific on-line communities. Our work focuses on scientific collaborations that revolve around complex science questions that require significant coordination to synthesize multi-disciplinary findings, enticing contributors to remain engaged for extended periods of time, and continuous growth to accommodate new contributors as needed as the work evolves over time. This paper presents a virtual crowdsourcing community for open collaboration in science processes to address these challenges. Our solution is based on the Semantic MediaWiki and extends it with new features for scientific collaboration. We present preliminary results from the usage of the interface in a pilot research project

The First Provenance Challenge

The first Provenance Challenge was set up in order to provide a forum for the community to help understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a Functional Magnetic Resonance Imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarise the participants contributions

An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models

Understanding the interactions between natural processes and human activities poses major challenges as it requires the integration of models and data across disparate disciplines. It typically takes many months and even years to create valid end-to-end simulations as different models need to be configured in consistent ways and generate data that is usable by other models. MINT is a novel framework for model integration that captures extensive knowledge about models and data and aims to automatically compose them together. MINT guides a user to pose a well-formed modeling question, select and configure appropriate models, find and prepare appropriate datasets, compose data and models into end-to-end workflows, run the simulations, and visualize the results. MINT currently includes hydrology, agriculture, and socioeconomic models.Office of the VP for Researc

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

A comparison of (semantic) markup languages

Several languages have been proposed as candidates for semantic markup. We needed to adopt a language for our current research on developing user-oriented tools operating over the Semantic Web. This paper presents the results of our analysis of three candidates that we considered: XML, RDF, and DAML+OIL along with their associated schemas and ontology specifications. The analysis focuses on the expressiveness of each language, and is presented along several dimensions and summarized in a comparison table.

A comparison of (semantic) markup languages

Several languages have been proposed as candidates for semantic markup. We needed to adopt a language for our current research on developing user-oriented tools operating over the Semantic Web. This paper presents the results of our analysis of three candidates that we considered: XML, RDF, and DAML+OIL along with their associated schemas and ontology specifications. The analysis focuses on the expressiveness of each language, and is presented along several dimensions and summarized in a comparison table. A surprising result of our analysis is the decision to adopt XML(Schema) for practical reasons, since it is able to accommodate a relatively expressive set of constructs and is widely known and commercially supported. We also discuss how we plan to complement XML(S) with a small set of conventions, so that we will have an easier transition to other markup languages in the future