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Applying Predictive Analytics on Research Information to Enhance Funding Discovery and Strengthen Collaboration in Project Proposals
Abstract
In academic and industrial research, writing a project proposal is one of the essential but time-consuming activities. Nevertheless, most proposals end in rejection. Moreover, research funding is getting more competitive these days. Funding agencies are increasingly looking for more extensive and more interdisciplinary research proposals. To increase the funding success rate, this PhD project focuses on three open challenges: poor data quality, inefficient funding discovery, and ineffective collaborative team building. We envision a Predictive Analytics-based approach that involves analyzing research information and using statistical and machine learning models that can assure data quality, increase funding discovery efficiency and the effectiveness of collaboration building. Accordingly, the goal of this PhD project is to support decision-making process to maximize the funding success rates of universities.
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Although many studies have been conducted on the drivers of and barriers to research collaborations, current literature provides limited insights into the ways in which individual researchers choose to engage in different collaborative projects. Using a choice experiment, we studied the factors that drive this choice using a representative sample of 3145 researchers from Western Europe and North America who publish in English. We find that for most researchers, the expected publication of research in scientific journals deriving from a project is the most decisive factor driving their collaboration choices. Moreover, most respondents prefer to collaborate with other partners than industry. However, different factors’ influence varies across groups of researchers. These groups are characterised as going for the ‘puzzle’ (60% of the sample), the ‘ribbon’ (33%) or the ‘gold’ (8%), i.e., primarily oriented toward intellectual goals, recognition or money, respectively. This heterogeneity shows that a combination of interventions will be required for governments aiming to promote university–industry collaborations.
To provide scientific institutions with comprehensive and well-maintained documentation of their research information in a current research information system (CRIS), they have the best prerequisites for the implementation of text and data mining (TDM) methods. Using TDM helps to better identify and eliminate errors, improve the process, develop the business, and make informed decisions. In addition, TDM increases understanding of the data and its context. This not only improves the quality of the data itself, but also the institution’s handling of the data and consequently the analyses. This present paper deploys TDM in CRIS to analyze, quantify, and correct the unstructured data and its quality issues. Bad data leads to increased costs or wrong decisions. Ensuring high data quality is an essential requirement when creating a CRIS project. User acceptance in a CRIS depends, among other things, on data quality. Not only is the objective data quality the decisive criterion, but also the subjective quality that the individual user assigns to the data.
The exponential growth of data in digital environments has highlighted the emergence of developing analytics processes for data visualization and evaluation. The same happens at the university research level. Therefore, universities need to have specific analytical processes for research evaluation.
The aim of our study is to find the way to apply general data analytical methods and technologies, specifically for university research environments, to help improve the results of their research.
We have found that universities that maintain a Current Research Information System, CRIS (CERIF Compliant), containing high quality data, are able to implement these analytical processes and improve their results in the assessment and evaluation of their research data.
This paper explains the process to implement such methodologies and techniques. Some results are also explained.
High-quality data are the precondition for analyzing and using big data and for guaranteeing the value of the data. Currently, comprehensive analysis and research of quality standards and quality assessment methods for big data are lacking. First, this paper summarizes reviews of data quality research. Second, this paper analyzes the data characteristics of the big data environment, presents quality challenges faced by big data, and formulates a hierarchical data quality framework from the perspective of data users. This framework consists of big data quality dimensions, quality characteristics, and quality indexes. Finally, on the basis of this framework, this paper constructs a dynamic assessment process for data quality. This process has good expansibility and adaptability and can meet the needs of big data quality assessment. The research results enrich the theoretical scope of big data and lay a solid foundation for the future by establishing an assessment model and studying evaluation algorithms.
In the context of Open Science, researchers are encouraged to publish their research datasets in digital data repositories so that others can find and reuse it.
Experienced scientists reveal how to avoid application pitfalls to submit successful proposals. Experienced scientists reveal how to avoid application pitfalls to submit successful proposals. Aerin Jacob and Tim Burkhart doing field work in the Hart ranges of northern BC
Research information management systems (RIMS) are the emerging new service in academic and research libraries. RIMS support universities and libraries in managing their institute, faculty, and researcher information through a single interface. They also allow the researcher to deposit and share their research with the public and enable the reuse of that research. An implementation of RIMS in universities or libraries ensures the proper management of research information for future use. RIMS disseminates research information and publications and supports data, academic, and administrative work by faculty and researchers. Traditionally, an institutional repository, digital library, and research data management software were used to manage research information as part of an institutional repository, but these applications have failed to manage more specialist researcher information and more detailed faculty profiles, etc. Consequently, various specialist software companies have brought RIMS onto the market with applications and products that meet the requirements of individual researchers, libraries, and universities in the management of research information. This chapter provides a comparative evaluation of RIMS (i.e., PURE-Elsevier, Converis-Thomson Routers, and Symplectic Elements). This study contributes towards an understanding of RIMS and assists with the selection of the appropriate software application for implementation of a RIMS system in universities and libraries.
Scientists can use search skills and solid connections to find grants from foreign governments, foundations and crowdfunding. Scientists can use search skills and solid connections to find grants from foreign governments, foundations and crowdfunding. Credit: Ikon Images/Getty Scientists celebrating investment on light bulb trampoline
The success or failure of a RIS in a scientific institution is largely related to the quality of the data available as a basis for the RIS applications. The most beautiful Business Intelligence (BI) tools (reporting, etc.) are worthless when displaying incorrect, incomplete, or inconsistent data. An integral part of every RIS is thus the integration of data from the operative systems. Before starting the integration process (ETL) of a source system, a rich analysis of source data is required. With the support of a data quality check, causes of quality problems can usually be detected. Corresponding analyzes are performed with data profiling to provide a good picture of the state of the data. In this paper, methods of data profiling are presented in order to gain an overview of the quality of the data in the source systems before their integration into the RIS. With the help of data profiling, the scientific institutions can not only evaluate their research information and provide information about their quality, but also examine the dependencies and redundancies between data fields and better correct them within their RIS.
This book provides guidelines for practicing design science in the fields of information systems and software engineering research. A design process usually iterates over two activities: first designing an artifact that improves something for stakeholders and subsequently empirically investigating the performance of that artifact in its context. This validation in context is a key feature of the book - since an artifact is designed for a context, it should also be validated in this context.
Predictive analytics is the process of forecasting future courses of action by analyzing historical and current facts. It's now a priority in many organizations because it can suggest the most favorable future planning by letting decision makers combine data about the four W's - that is, what, who, where, and when - to analyze why and how. Apart from business, it plays a vital role in higher education planning. Higher education plays an important role in a nation's socioeconomic development. As an educational management tool, predictive analytics can help improve education quality by letting us analyze critical issues in education such as enrollment management and curriculum development. This article presents an analytical study of the prospects of predictive analytics in education planning, focusing on technical education. As a case study, the authors discuss an All India Council for Technical Education-sponsored project at Delhi Technical University, India.
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