Towards Reusability of Computational Experiments - Capturing and Sharing Research Objects from Knowledge Discovery Processes

Abstract

Abstract:Calls for more reproducible research by sharing code and data are released in a large number of fields from biomedical science to signal processing. At the same time, the urge to solve data analysis bottlenecks in the biomedical field generates the need for more interactive data analytics solutions. These interactive solutions are oriented towards wet lab users whereas bioinformaticians favor custom analysis tools. In this position paper we elaborate on why Reproducible Research, by presenting code and data sharing as a gold standard for reproducibility misses important challenges in data analytics. We suggest new ways to design interactive tools embedding constraints of reusability with data exploration. Finally, we seek to integrate our solution with Research Objects as they are expected to bring promising advances in reusability and partial reproducibility of computational work.

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

“…We argue that this design science research dimension effectively extends the reusability and generalisability of the industry-standard CRISP-DM knowledge discovery process (e.g. Lefebvre et al, 2015), thus contributing to the scientific body of knowledge by providing proven recipes for properly addressing the three key data science dilemmas given a problem-specific challenge. Spruit and Jagesar (2016) note that MAM as a discipline is inspired by Method Engineering, "the engineering discipline to design, construct and adapt methods, techniques and tools for the development of information systems" (Brinkkemper, 1996).…”

Automated Business Goal Extraction from E-mail Repositories to Bootstrap Business Understanding

“…As a result, there is much room for improvement when it comes to structuring the initial stages of the data mining process. This also explains the many task-specific extensions to the generic knowledge discovery process, which have been proposed over the years, including those in the domains of interactive data mining [8], big data processing [9], reproducible research [10] and personalised recommendation systems [11]. In this paper, we propose the NAMBU method for automatic business goal extraction from an organization's textual resources.…”

Research data management for open science

“…Section III "FAIR technology" consists of Chapters 6 and 7. Chapter 6 illustrates the need for designing reproducible and reusable research software with reproducible, researchoriented knowledge discovery in databases process (RRO-KDD) (Lefebvre, Omta and Spruit, 2015). Chapter 7 presents design principles for open science readiness in laboratories.…”

“…Moreover, we suggest a way to make forensic results accessible to a broader audience at the management level of laboratories and data stewards to identify weak spots in reproducibility and open scholarship in laboratories through the instantiation of a dashboard. Lefebvre, A., Spruit, M., & Omta, W. (2015). Towards reusability of computational experiments Capturing and sharing Research Objects from knowledge discovery processes.…”

“…However, while substantial efforts have been made to deploy Open Science at a European level to make science more efficient and reliable (as explained further in Section 2.2), the essential act of publishing research data still encounters significant resistance from academics (Borgman, 2012;Tsai et al, 2016). The problem of "opening" data, which is seen as beneficial for verification and reuse of existing scientific material (Lefebvre et al, 2015;Mannheimer et al, 2016;Peng, 2011), is a striking example of one of the challenges universities or research institutes are facing when offering data management support to researchers. On the one hand, universities implement such programs utilizing data management plans (DMPs) to secure public funding in the coming years (Simms et al, 2016).…”