Hans Dillaerts – Page 265 – InfoDoc MicroVeille

Artificial intelligence in peer review: How can evolutionary computation support journal editors?

Posté le 6 décembre 2017 par Hans Dillaerts

Authors : Maciej J. Mrowinski, Piotr Fronczak, Agata Fronczak, Marcel Ausloos, Olgica Nedic

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors workloads, are treated as trade secrets by publishing houses and are not shared publicly.

To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies.

The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy).

Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.

URL : https://arxiv.org/abs/1712.01682

Exploration of an Interdisciplinary Scientific Landscape

Posté le 6 décembre 2017 par Hans Dillaerts

Author : Juste Raimbault

Patterns of interdisciplinarity in science can be quantified through diverse complementary dimensions. This paper studies as a case study the scientific environment of a generalist journal in Geography, Cybergeo, in order to introduce a novel methodology combining citation network analysis and semantic analysis.

We collect a large corpus of around 200,000 articles with their abstracts and the corresponding citation network that provides a first citation classification. Relevant keywords are extracted for each article through text-mining, allowing us to construct a semantic classification.

We study the qualitative patterns of relations between endogenous disciplines within each classification, and finally show the complementarity of classifications and of their associated interdisciplinarity measures. The tools we develop accordingly are open and reusable for similar large scale studies of scientific environments.

URL : https://arxiv.org/abs/1712.00805

Striving Toward Openness: But What Do We Really Mean?

Posté le 2 décembre 2017 par Hans Dillaerts

Author : Vivien Rolfe

The global open education movement is striving toward openness as a feature of academic policy and practice, but evidence shows that these ambitions are far from mainstream, and levels of awareness in institutions is often disappointingly low.

Those advocating for open education are seeking to widen engagement, but how targeted and persuasive are their messages? The aim of this research is to explore the voices often unheard, those of the teachers and professional service staff with whom we are engaging.

This research presents a series of interviews with those involved in open education at De Montfort University in the UK, with the aim of gaining a better perspective of what openness means to them. T

he interviews were analysed through an interpretive lens allowing each individual to create their own story and reflect their own personal view of openness. The results of this study are that in this university, openness is represented by five elements – staff pedagogy and practice, benefits to learners, accessibility and access to content, institutional structures, and values and culture.

This work shows the importance of adopting critical approaches to gain a deeper understanding of the philosophical and pedagogic stances within institutions. By giving a voice to all those involved we will be able to develop appropriate and more persuasive arguments to widen our sphere of influence as a community of open educators.

URL : Striving Toward Openness: But What Do We Really Mean?

Alternative loation : http://www.irrodl.org/index.php/irrodl/article/view/3207

Big Data and Data Science: Opportunities and Challenges of iSchools

Posté le 2 décembre 2017 par Hans Dillaerts

Authors : Il-Yeol Song, Yongjun Zhu

Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers.

At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools’ opportunities and suggestions in data science education.

We argue that iSchools should empower their students with “information computing” disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains.

As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application-based. These three foci will serve to differentiate the data science education of iSchools from that of computer science or business schools.

We present a layered Data Science Education Framework (DSEF) with building blocks that include the three pillars of data science (people, technology, and data), computational thinking, data-driven paradigms, and data science lifecycles.

Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches.

This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.

URL : Big Data and Data Science: Opportunities and Challenges of iSchools

DOI : https://doi.org/10.1515/jdis-2017-0011

Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

Posté le 2 décembre 2017 par Hans Dillaerts

Author : Jane Greenberg

Purpose

The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research. This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science.

Design/methodology/approach

This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts big metadata, smart metadata, and metadata capital as part of a metadata lingua franca connecting to data science.

Findings

The “utilitarian nature” and “historical and traditional views” of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part of a metadata lingua franca to help frame research in the data science research space.

Research limitations

There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore.

Practical implications

The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem.

Originality/value

Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.

URL : Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

DOI : https://doi.org/10.1515/jdis-2017-0012

Patent Citations Analysis and Its Value in Research Evaluation: A Review and a New Approach to Map Technology-relevant Research

Posté le 2 décembre 2017 par Hans Dillaerts

Authors : Anthony F.J. van Raan

Purpose

First, to review the state-of-the-art in patent citation analysis, particularly characteristics of patent citations to scientific literature (scientific non-patent references, SNPRs). Second, to present a novel mapping approach to identify technology-relevant research based on the papers cited by and referring to the SNPRs.

Design/methodology/approach

In the review part we discuss the context of SNPRs such as the time lags between scientific achievements and inventions. Also patent-to-patent citation is addressed particularly because this type of patent citation analysis is a major element in the assessment of the economic value of patents.

We also review the research on the role of universities and researchers in technological development, with important issues such as universities as sources of technological knowledge and inventor-author relations.

We conclude the review part of this paper with an overview of recent research on mapping and network analysis of the science and technology interface and of technological progress in interaction with science.

In the second part we apply new techniques for the direct visualization of the cited and citing relations of SNPRs, the mapping of the landscape around SNPRs by bibliographic coupling and co-citation analysis, and the mapping of the conceptual environment of SNPRs by keyword co-occurrence analysis.

Findings

We discuss several properties of SNPRs. Only a small minority of publications covered by the Web of Science or Scopus are cited by patents, about 3%–4%. However, for publications based on university-industry collaboration the number of SNPRs is considerably higher, around 15%.

The proposed mapping methodology based on a “second order SNPR approach” enables a better assessment of the technological relevance of research.

Research limitations

The main limitation is that a more advanced merging of patent and publication data, in particular unification of author and inventor names, in still a necessity.

Practical implications

The proposed mapping methodology enables the creation of a database of technology-relevant papers (TRPs). In a bibliometric assessment the publications of research groups, research programs or institutes can be matched with the TRPs and thus the extent to which the work of groups, programs or institutes are relevant for technological development can be measured.

Originality/value

The review part examines a wide range of findings in the research of patent citation analysis. The mapping approach to identify a broad range of technology-relevant papers is novel and offers new opportunities in research evaluation practices.

URL : Patent Citations Analysis and Its Value in Research Evaluation: A Review and a New Approach to Map Technology-relevant Research

DOI : https://doi.org/10.1515/jdis-2017-0002