Jian Qin, Ph.D. - US grants

The objective of the proposed research is to specify the mechanisms underlying the empirical link between dissociative tendencies and false memories. Three specific aims are designed to achieve this objective: 1) Establish an empirical link between high dissociative tendencies and proneness to reality monitoring errors; 2) Specify mechanisms by which high dissociative tendencies may contribute to reality monitoring failure; and 3) Examine conditions that exacerbate or mitigate the susceptibility of individuals with high dissociative tendencies to reality monitoring errors. The results from the six proposed studies will provide empirical data that contribute to understanding of the mechanisms by which high dissociative individuals are particularly' susceptible to false memories. The results also have considerable value to psychotherapists in evaluating which procedures are most appropriate to reduce the possibility of false memories.

DESCRIPTION (provided by applicant): The molecular mechanisms controlling the early development of the central nervous system in mammals remain poorly understood. Mouse genetics serves as a powerful tool to investigate the process. Here I will study a new recessive lethal mouse mutant, sister of open brain (sopb), exhibiting severe neural tube patterning defects. sopb was obtained from a phenotype-based genetic screen using N-ethyl-nitrosourea (ENU). Homozygous mutant embryos fail to close the cephalic and spinal neural tube, and exhibit other defects including supernumerary digits and poorly developed eyes. The neural tube of sopb mutants is ventralized with the ectopic expression of Shh target genes, and sopb appears to function downstream of Shh. The hypothesis of this project is that sopb acts as an antagonist of the Shh signaling pathway. This study will focus on four aims: 1) to investigate the primary defects in sopb mutants with respect to neural tube patterning based on the expression of various markers of cell type and of genes involved in cell-cell signaling, 2) to test whether the wild-type sopb gene product acts cell autonomously or non-cell autonomously for neural cell type specification using chimera analysis, 3) to place the sopb gene in the Shh signal transduction pathway using epistasis analysis, and 4) to positionally clone the sopb gene to understand its molecular function. The Sonic hedgehog (Shh) pathway plays important roles in embryonic patterning. The abnormal activity of the pathway has been shown to cause birth defects and various cancers. Therefore, identification and characterization of novel components of the pathway, such as sopb, will have important clinical implications.

Computer Science (31)

The "Enhancing Scientific Data Literacy in Undergraduate Science and Technology Students" project is creating a course where students learn the fundamental concepts of managing and manipulating scientific data in their discipline.

Intellectual Merit: The project is developing an interdisciplinary course that is promoting collaboration between disciplines in scientific research. The course is fostering a culture of interdisciplinary research among students and scientists.

Broader Impact: The project is targeting underrepresented groups and encouraging these future scientists to collaborate by providing them with a shared experience. It is involving both students and faculty from a broad range of disciplines. The PIs are disseminating their results through presentations at both regional and national conferences.

Understanding how individual scientists interact with one another and how such interaction impacts research productivity and knowledge diffusion is important for understanding the dynamics of scientific research collaboration. At the same time, information about patterns of collaboration and their consequences have implications for science policy. In quantitative research on collaboration networks, publication co-authorships and citation-linkages have been the primary source of data. As large data repositories, one of the signposts for cyberinfrastructure-enabled, data-driven science, become increasingly prevalent, however, they offer an alternative source of information about networks of scientific collaboration. This project investigates research collaboration networks emerging around one such international data repository, GenBank, and develops data products to support data-driven science policymaking and research. By utilizing this novel data source the project provides an unprecedented opportunity to validate and expand the theory of complex networks while generating rich data outputs and products to support science policy research and policymaking. This study fills a number of theoretical and methodological gaps identified by the 2008 roadmap for Science of Science Policy (SoSP), with a specific focus on how scientific collaboration networks form and evolve. The outcomes of this study address the lack of models and tools for network analysis, visual analytics, and science mapping outlined in the 2008 roadmap for SoSP. To accomplish the data collection and processing required for this project new computational programs will be developed to parse, extract, store, transform, split, merge, and filter the data; these will be applicable to the analysis of other similar data sources for science policy and innovation research.

Broader impacts. By making available dataset product prototypes the project will allow researchers, policy makers, and students to explore research networks in GenBank from longitudinal, thematic, geographical, institutional, and author dimensions. The multi-dimensional, interactive presentations of such datasets enable data-intensive science policy research and support science policymaking through filtering, sorting, associating, and visualization capabilities. The datasets and data products will be made available through an open access mechanism, so educators and undergraduate and graduate students have ample opportunities to use these resources for teaching and research. Students enrolled in Syracuse University's newly established Certificate for Advanced Study in Data Science (CAS DS) program will be able to participate in the project and gain skills in programming for data collection and processing, data quality verification, analysis, and visualization. In addition, the collaboration network analysis provides interested doctoral students an opportunity to do independent study or dissertation research. Findings from studying cyberinfrastructure-supported data sharing and knowledge diffusion is expected to advance policymakers' ability to properly assess the outcomes of federally funded research.

Analysis and management of large data sets are vital for progress in the data-intensive realm of scientific research and education. Scientists are producing, analyzing, storing and retrieving massive amounts of data. The anticipated growth in the analysis of scientific data raises complex issues of stewardship, curation and long-term access. Scientific data is tracked and described by metadata. This award will fund the design, development, and deployment of metadata-aware workflows to enable the management of large data sets produced by scientific analysis. Scientific workflows for data analysis are used by a broad community of scientists including astronomy, biology, ecology, and physics. Making workflows metadata-aware is an important step towards making scientific results easier to share, to reuse, and to support reproducibility. This project will pilot new workflow tools using data from the Laser Interferometer Gravitational-wave Observatory (LIGO), a data-intensive project at the frontiers of astrophysics. The goal of LIGO is to use gravitational waves---ripples in the fabric of spacetime---to explore the physics of black holes and understand the nature of gravity.

Efficient methods for accessing and mining the large data sets generated by LIGO's diverse gravitational-wave searches are critical to the overall success of gravitational-wave physics and astronomy. Providing these capabilities will maximize existing NSF investments in LIGO, support new modes of collaboration within the LIGO Scientific Collaboration, and better enable scientists to explain their results to a wider community, including the critical issue of data and analysis provenance for LIGO's first detections. The interdisciplinary collaboration involved in this project brings together computational and informatics theories and methods to solve data and workflow management problems in gravitational-wave physics. The research generated from this project will make a significant contribution to the theory and methods in identification of science requirements, metadata modeling, eScience workflow management, data provenance, reproducibility, data discovery and analysis. The LIGO scientists participating in this project will ensure that the needs of the community are met. The cyberinfrastructure and data-management scientists will ensure that the software products are well-designed and that the work funded by this award is useful to a broader community.

Cyberinfrastructure enables collaborative research and significantly impacts scientific capacity and knowledge diffusion. In response to the growing need for quantitatively evaluating outcomes and impact of federal investment on research, this project deploys new data, tools, metrics, and methods for assessing the impact of cyberinfrastructures and the data services built on them. This research helps researchers and policy makers understand how cyberinfrastructure affects collaboration dynamics and network structures of researchers. Datasets organized by longitudinal, thematic, topical, geographical, institutional, and author dimensions provided, which researchers, policy makers, and students can access and use to explore data-intensive science of science and innovation policy related research.

Metadata from GenBank, patent data from U.S. Patent and Trademark Office and funding data from NIH ExPORT are analyzed with descriptive statistics and models from Complex Network Analysis. The project not only examines the topological properties of the data submission and publication networks, but also the temporal ordering of collaborative relationships and the overlap of the sequence submission and publication networks. Through slicing, plotting, and visualizing data, appropriate sampling strategies and algorithms are developed to more deeply explore collaboration networks, both structurally and temporally. Algorithms used in community detection, machine learning, and visualization serve as primary computational methods in this research. Data products to be shared with research communities include 1) discovery lifecycle datasets containing sequence submissions, publications, and patents as well as the links between them and 2) funding factor datasets containing links between U.S. federal funding data and the discovery lifecycle datasets.

The goal of this proposed project is to develop a collaboration capacity framework and evaluate the collaboration capacity of science teams at macro-, meso-, and micro-levels through using GenBank metadata and other related data sources. The framework defines the Scientifc &Technical (S&T) human capital, cyberinfrastructure, and science policy as the enablers of collaboration capacity, the impact of which on collaboration capacity can be measured by data production and data-to-knowledge metrics such as team size and ratio of data to publications. GenBank metadata as the primary data source for this project offers a longitudinal coverage (1984-2018) and full research lifecycle traces from data production to publication to patent application, creating an unprecedented opportunity to study the biomedical research enterprise. This project will design and create datasets from GenBank metadata to generate analysis-ready data, which will be combined with statistics from NSF and NIH. The datasets will be used to develop computational models and test hypotheses that examine the correlation between collaboration capacity, team size, and connectedness of nodes, as well as the properties of disruptive nodes and their impact on productivity and innovation. In addition to statistics from NSF and NIH, the project will also combine events in science policy (e.g., mandates on data sharing), public health (e.g., outbreaks and prevalent chronic diseases), and funding to triangulate with the datasets and analyze collaboration capacity and policy implications. The data source and theoretical approach compensate for the limitations of publication-centric data sources used in past research on collaboration networks. The fact that the primary data source comes from basic biomedical research situates this study at the cutting-edge and allows us to gain more holistic insights into the impact of federal investment and policy on collaboration capacity. Our future research will use this longitudinal, rich data collection to continue deeper mining of collaboration in data production and data-to-knowledge lifecycle, particularly in relation to specific genes, diseases, and treatments that are key aspects in basic and clinical biomedical research.