The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services

Out of cite, out of mind: the current state of practice, policy, and technology for the citation of data

PREFACE The growth in the capacity of the research community to collect and distribute data presents huge opportunities. It is already transforming old methods of scientific research and permitting the creation of new ones. However, the exploitation of these opportunities depends upon more than computing power, storage, and network connectivity. Among the promises of our growing universe of online digital data are the ability to integrate data into new forms of scholarly publishing to allow peer-examination and review of conclusions or analysis of experimental and observational data and the ability for subsequent researchers to make new analyses of the same data, including their combination with other data sets and uses that may have been unanticipated by the original producer or collector. The use of published digital data, like the use of digitally published literature, depends upon the ability to identify, authenticate, locate, access, and interpret them. Data citations provide necessary support for these functions, as well as other functions such as attribution of credit and establishment of provenance. References to data, however, present challenges not encountered in references to literature. For example, how can one specify a particular subset of data in the absence of familiar conventions such as page numbers or chapters? The traditions and good practices for maintaining the scholarly record by proper references to a work are well established and understood in regard to journal articles and other literature, but attributing credit by bibliographic references to data are not yet so broadly implemented

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside

Background: Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery. Results: We developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action. Conclusions: This work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine. Availability: TMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql

Institutional and Individual Influences on Scientists\u27 Data Sharing Behaviors

In modern research activities, scientific data sharing is essential, especially in terms of data-intensive science and scholarly communication. Scientific communities are making ongoing endeavors to promote scientific data sharing. Currently, however, data sharing is not always well-deployed throughout diverse science and engineering disciplines. Disciplinary traditions, organizational barriers, lack of technological infrastructure, and individual perceptions often contribute to limit scientists from sharing their data. Since scientists\u27 data sharing practices are embedded in their respective disciplinary contexts, it is necessary to examine institutional influences as well as individual motivations on scientists\u27 data sharing behaviors. The objective of this research is to investigate the institutional and individual factors which influence scientists\u27 data sharing behaviors in diverse scientific communities. Two theoretical perspectives, institutional theory and theory of planned behavior, are employed in developing a conceptual model, which shows the complementary nature of the institutional and individual factors influencing scientists\u27 data sharing behaviors. Institutional theory can explain the context in which individual scientists are acting; whereas the theory of planned behavior can explain the underlying motivations behind scientists\u27 data sharing behaviors in an institutional context. This research uses a mixed-method approach by combining qualitative and quantitative methods: (1) interviews with the scientists in diverse scientific disciplines to understand the extent to which they share their data with other researchers and explore institutional and individual factors affecting their data sharing behaviors; and (2) survey research to examine to what extent those institutional and individual factors influence scientists\u27 data sharing behaviors in diverse scientific disciplines. The interview study with 25 scientists shows three groups of data sharing factors, including institutional influences (i.e. regulative pressures by funding agencies and journals and normative pressure); individual motivations (i.e. perceived benefit, risk, effort and scholarly altruism); and institutional resources (i.e. metadata and data repositories). The national survey (with 1,317 scientists in 43 disciplines) shows that regulative pressure by journals; normative pressure at a discipline level; and perceived career benefit and scholarly altruism at an individual level have significant positive relationships with data sharing behaviors; and that perceived effort has a significant negative relationship. Regulative pressure by funding agencies and the availability of data repositories at a discipline level and perceived career risk at an individual level were not found to have any significant relationships with data sharing behavior

Reports required by government auditing standards and the uniform guidance for the year ended June 30, 2018

This is an audit of Clemson University’s compliance with the types of compliance requirements described in the OMB Compliance Supplement that could have a direct and material effect on each of the University’s major federal programs. The University’s major federal programs are identified in the summary of auditor’s results section of the accompanying schedule of findings and questioned costs

XSEDE: eXtreme Science and Engineering Discovery Environment Third Quarter 2012 Report

The Extreme Science and Engineering Discovery Environment (XSEDE) is the most advanced, powerful, and robust collection of integrated digital resources and services in the world. It is an integrated cyberinfrastructure ecosystem with singular interfaces for allocations, support, and other key services that researchers can use to interactively share computing resources, data, and expertise.This a report of project activities and highlights from the third quarter of 2012.National Science Foundation, OCI-105357