Cross‐campus Collaboration: A Scientometric and Network Case Study of Publication Activity Across Two Campuses of a Single Institution

Team science and collaboration have become crucial to addressing key research questions confronting society. Institutions that are spread across multiple geographic locations face additional challenges. To better understand the nature of cross‐campus collaboration within a single institution and the effects of institutional efforts to spark collaboration, we conducted a case study of collaboration at Cornell University using scientometric and network analyses. Results suggest that cross‐campus collaboration is increasingly common, but is accounted for primarily by a relatively small number of departments and individual researchers. Specific researchers involved in many collaborative projects are identified, and their unique characteristics are described. Institutional efforts, such as seed grants and topical retreats, have some effect for researchers who are central in the collaboration network, but were less clearly effective for others

Social network analysis of ERDF‐projects in Finland 2007‐2013

The aim of the European Regional Development Fund (ERDF) is to strengthen economic and social cohesion in the European Union by correcting imbalances between its regions. Therefore, ERDF-projects can be considered as an important tool for implementing National and Regional Innovation strategies across the Europe. By utilizing Social Network Analysis (SNA) methods and popularity based scientometrics approach, this study evaluates what kind of collaboration relationships are existing between Finnish ERDF-project actors and who are the leading ERDF-actors in Finland. The dataset covering the latest fully implemented EDRF program period (2007-2013) included 10.913 projects and 5.991 different organizations. Results revealed that great majority (67.6%) of all organizations had participated only in one project and only small portion (5.9%) of all projects included multiple beneficiaries. The list of most active organizations was heavily dominated by universities, which typically operated in multiple regions

Evaluation of the Impact of Engineering Education Research Grants Using Software Tools: A Foundation

The goal of our project was to provide the NSF with a software suite which evaluates the impact of engineering education research grants. We assisted the NSF by identifying interactions that influence the impact of grants and any measureable data within these interactions. We presented three deliverables and examined software tools to collect, organize, analyze, and visualize the quantifiable data within the interactions. Our endeavors serve as a framework for future investigation into grant impact evaluation

The effect of collaborative networks on healthcare research performance

We can all use assessment and appraisal to help us improve our performance in any area of life. Healthcare researchers are no exception. For healthcare researchers a system is required to measure research performance according to an accepted global benchmark. While there are existing systems that have been created to measure research performance in general, and healthcare research performance has been appraised with several bibliometric indicators, there is a lack of evidence to prove their validity and a deficiency of indicators that embrace social behaviours such as collaboration. In this thesis we endeavoured to enhance knowledge on healthcare research performance assessment, which has the potential to be integrated into systems that specifically appraise healthcare research performance. Ultimately, these systems may promote a performance-based culture that better reflects the quality and impact of healthcare research.Open Acces

Performance of public-private collaborations in advanced technology research networks : network analyses of Genome Canada projects

Globalisation and the quest for competitiveness in a global market represents a new era of connectedness within public-private networks of experts in an effort to pursue research objectives in advanced technology industries. Balancing the competing interests of public good and private gain, reducing the barriers in terms of access to knowledge and intellectual property and ensuring that efforts result in socially valuable outcomes in the form of new innovations can be difficult, to say the least. Although widely advocated and implemented, collaborations have not, as yet, been fully examined nor have appropriate performance evaluation models been developed to evaluate them. This dissertation hypothesizes that a history of social relationships or collaborative activity amongst network actors is positively correlated with high performance in networks. Incorporating descriptive statistics with the social network analysis tool, this dissertation proposes and tests a novel framework and compares two distinct Genome Canada funded research networks. Other factors explored are the roles of proximity, institution and research focus in characterizing network structure and in affecting performance

“As-You-Go” instead of “After-the-Fact”:A network approach to scholarly communication and evaluation

Scholarly research faces threats to its sustainability on multiple domains (access, incentives, reproducibility, inclusivity). We argue that “after-the-fact” research papers do not help and actually cause some of these threats because the chronology of the research cycle is lost in a research paper. We propose to give up the academic paper and propose a digitally native “as-you-go” alternative. In this design, modules of research outputs are communicated along the way and are directly linked to each other to form a network of outputs that can facilitate research evaluation. This embeds chronology in the design of scholarly communication and facilitates the recognition of more diverse outputs that go beyond the paper (e.g., code, materials). Moreover, using network analysis to investigate the relations between linked outputs could help align evaluation tools with evaluation questions. We illustrate how such a modular “as-you-go” design of scholarly communication could be structured and how network indicators could be computed to assist in the evaluation process, with specific use cases for funders, universities, and individual researchers

Structured and Unstructured Data Sciences and Business Intelligence for Analyzing Requirements Post Mortem

NPS NRP Technical ReportThe objective is to review requirements created within the DoD Requirements process and identify those that create excessive cost growth, and rank programs with significant cost growth. The research questions are: ' What are common elements of requirements that create excessive cost growth in Navy systems? ' Assuming the elements are identified, determine the risk (likelihood and magnitude) of cost growth from common elements for both procurement and sustainment costs. We propose structured and unstructured data sciences and business intelligence to address the research questions: ' Apply text analyses to the DoD programs requirements data from the operational requirements documents and previous processes. Locate the cost growth risks (likelihood and magnitude) in terms of characteristics including capability requirements (unstructured), key performance parameters (structured data), key systems attributes (structured data), keywords, themes, and entities. Tools include lexical link analysis, spaCy (https://spacy.io/), Orange, and https://prodi.gy/ (for classification). ' Apply Network/graph tools: visualize the risks and capabilities in terms of relations. Prioritize capability, program, system, or product using centrality analysis and correlate with the cost growth risk. ' Apply the integrated deep analytics of leveraging AI for learning, optimize, and wargame (LAILOW) framework, derived from the ONR funded projects. Patterns are learned from big data (if any) and used for the optimization of what if analysis. New operation and capability requirements anticipate uncertainty, unknowns, and unexpected situations when there is no or rare data. This motivates using wargame simulations to coevolve risks and capabilities using coevolutionary algorithms of selection, mutation, and crossover. The tasks include scoping the data and demonstrating the proposed methods. The deliverables include reports, a demonstration, and a paper approved by the sponsor.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Scientometric Analysis of Optimisation and Machine Learning Publications

Introduction: Optimisation is an important aspect of machine learning because it helps improve accuracy and reduce errors in the model's predictions. Purpose: The purpose of this research is to identify the global structure of optimization and machine learning. The work specifically looks at the collaborative network of countries in these fields, the top 20 authors in terms of production from 2015–2021, and the co-citation network of articles. Methodology: In this study, co-word analysis and social network analysis were used to conduct a descriptive study based on the scientometric approach and the content analysis method. In this research, around 17,500 articles on optimization and machine learning published between 2015 and 2021 were extracted. An ANOVA was performed to evaluate whether there was a significant difference between betweenness, closeness, and pagerank. The Dimensions database was utilised for the investigation without language constraints. Moreover, Bibliometrix was used for calculation and visualization. Findings: The results revealed a substantial difference between betweenness, proximity, and pagerank, indicating that this research has the potential to bring vital insights into future optimization and machine learning research