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Volume 7 (2022): Issue 3 (August 2022)

Volume 7 (2022): Issue 2 (April 2022)

Volume 7 (2022): Issue 1 (February 2022)

Volume 6 (2021): Issue 4 (November 2021)

Volume 6 (2021): Issue 3 (June 2021)

Volume 6 (2021): Issue 2 (April 2021)

Volume 6 (2021): Issue 1 (February 2021)

Volume 5 (2020): Issue 4 (November 2020)

Volume 5 (2020): Issue 3 (August 2020)

Volume 5 (2020): Issue 2 (April 2020)

Volume 5 (2020): Issue 1 (February 2020)

Volume 4 (2019): Issue 4 (December 2019)

Volume 4 (2019): Issue 3 (August 2019)

Volume 4 (2019): Issue 2 (May 2019)

Volume 4 (2019): Issue 1 (February 2019)

Volume 3 (2018): Issue 4 (November 2018)

Volume 3 (2018): Issue 3 (August 2018)

Volume 3 (2018): Issue 2 (May 2018)

Volume 3 (2018): Issue 1 (February 2018)

Volume 2 (2017): Issue 4 (December 2017)

Volume 2 (2017): Issue 3 (August 2017)

Volume 2 (2017): Issue 2 (May 2017)

Volume 2 (2017): Issue 1 (February 2017)

Volume 1 (2016): Issue 4 (November 2016)

Volume 1 (2016): Issue 3 (August 2016)

Volume 1 (2016): Issue 2 (May 2016)

Volume 1 (2016): Issue 1 (February 2016)

Journal Details
Format
Journal
eISSN
2543-683X
First Published
30 Mar 2017
Publication timeframe
4 times per year
Languages
English

Search

Volume 2 (2017): Issue 4 (December 2017)

Journal Details
Format
Journal
eISSN
2543-683X
First Published
30 Mar 2017
Publication timeframe
4 times per year
Languages
English

Search

6 Articles

Perspective

Open Access

A Framework for the Assessment of Research and Its Impacts

Published Online: 29 Dec 2017
Page range: 7 - 42

Abstract

Abstract

This paper proposes a holistic framework for the development of models for the assessment of research activities and their impacts. It distinguishes three dimensions, including in an original way, data as a main dimension, together with theory and methodology. Each dimension of the framework is further characterized by three main building blocks: education, research, and innovation (theory); efficiency, effectiveness, and impact (methodology); and availability, interoperability, and “unit-free” property (data). The different dimensions and their nine constituent building blocks are attributes of an overarching concept, denoted as “quality.” Three additional quality attributes are identified as implementation factors (tailorability, transparency, and openness) and three “enabling” conditions (convergence, mixed methods, and knowledge infrastructures) complete the framework. A framework is required to develop models of metrics. Models of metrics are necessary to assess the meaning, validity, and robustness of metrics. The proposed framework can be a useful reference for the development of the ethics of research evaluation. It can act as a common denominator for different analytical levels and relevant aspects and is able to embrace many different and heterogeneous streams of literature. Directions for future research are provided.

Keywords

  • Evaluation of research
  • Efficiency
  • Effectiveness
  • Impacts
  • Modeling
  • Responsible metrics
  • Ethics of research evaluation
Open Access

Understanding Big Data for Industrial Innovation and Design: The Missing Information Systems Perspective

Published Online: 29 Dec 2017
Page range: 1 - 6

Abstract

Abstract

This paper identifies a need to complement the current rich technical and mathematical research agenda on big data with a more information systems and information science strand, which focuses on the business value of big data. An agenda of research for information systems would explore motives for using big data in real organizational contexts, and consider proposed benefits, such as increased effectiveness and efficiency, production of high-quality products/services, creation of added business value, and stimulation of innovation and design. Impacts of such research on the academic community, the industrial and business world, and policy-makers are discussed.

Keywords

  • Big data
  • Business value
  • Information systems
  • Information science

Expert Review

Open Access

Rediscovering Don Swanson:The Past, Present and Future of Literature-based Discovery

Published Online: 29 Dec 2017
Page range: 43 - 64

Abstract

Abstract

The late Don R. Swanson was well appreciated during his lifetime as Dean of the Graduate Library School at University of Chicago, as winner of the American Society for Information Science Award of Merit for 2000, and as author of many seminal articles. In this informal essay, I will give my personal perspective on Don’s contributions to science, and outline some current and future directions in literature-based discovery that are rooted in concepts that he developed.

Personal recollections and literature review.

The Swanson A-B-C model of literature-based discovery has been successfully used by laboratory investigators analyzing their findings and hypotheses. It continues to be a fertile area of research in a wide range of application areas including text mining, drug repurposing, studies of scientific innovation, knowledge discovery in databases, and bioinformatics. Recently, additional modes of discovery that do not follow the A-B-C model have also been proposed and explored (e.g. so-called storytelling, gaps, analogies, link prediction, negative consensus, outliers, and revival of neglected or discarded research questions).

This paper reflects the opinions of the author and is not a comprehensive nor technically based review of literature-based discovery.

The general scientific public is still not aware of the availability of tools for literature-based discovery. Our Arrowsmith project site maintains a suite of discovery tools that are free and open to the public (http://arrowsmith.psych.uic.edu), as does BITOLA which is maintained by Dmitar Hristovski (http://http://ibmi.mf.uni-lj.si/bitola), and Epiphanet which is maintained by Trevor Cohen (http://epiphanet.uth.tmc.edu/). Bringing user-friendly tools to the public should be a high priority, since even more than advancing basic research in informatics, it is vital that we ensure that scientists actually use discovery tools and that these are actually able to help them make experimental discoveries in the lab and in the clinic.

This paper discusses problems and issues which were inherent in Don’s thoughts during his life, including those which have not yet been fully taken up and studied systematically.

Keywords

  • Literature-based discovery
  • Biography
  • Text mining
  • Knowledge discovery in databases
  • Implicit information
  • Information science

Research Paper

Open Access

Using Machine Reading to Understand Alzheimer’s and Related Diseases from the Literature

Published Online: 29 Dec 2017
Page range: 81 - 94

Abstract

Abstract

This paper aims to better understand a large number of papers in the medical domain of Alzheimer’s disease (AD) and related diseases using the machine reading approach.

The study uses the topic modeling method to obtain an overview of the field, and employs open information extraction to further comprehend the field at a specific fact level.

Several topics within the AD research field are identified, such as the Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS), which can help answer the question of how AIDS/HIV and AD are very different yet related diseases.

Some manual data cleaning could improve the study, such as removing incorrect facts found by open information extraction.

This study uses the literature to answer specific questions on a scientific domain, which can help domain experts find interesting and meaningful relations among entities in a similar manner, such as to discover relations between AD and AIDS/HIV.

Both the overview and specific information from the literature are obtained using two distinct methods in a complementary manner. This combination is novel because previous work has only focused on one of them, and thus provides a better way to understand an important scientific field using data-driven methods.

Keywords

  • Machine reading
  • Alzheimer’s disease
  • Knowledge discovery
  • Data mining
Open Access

An Accurate and Impartial Expert Assignment Method for Scientific Project Review

Published Online: 29 Dec 2017
Page range: 65 - 80

Abstract

Abstract

This paper proposes an expert assignment method for scientific project review that considers both accuracy and impartiality. As impartial and accurate peer review is extremely important to ensure the quality and feasibility of scientific projects, enhanced methods for managing the process are needed.

To ensure both accuracy and impartiality, we design four criteria, the reviewers’ fitness degree, research intensity, academic association, and potential conflict of interest, to express the characteristics of an appropriate peer review expert. We first formalize the expert assignment problem as an optimization problem based on the designed criteria, and then propose a randomized algorithm to solve the expert assignment problem of identifying reviewer adequacy.

Simulation results show that the proposed method is quite accurate and impartial during expert assignment.

Although the criteria used in this paper can properly show the characteristics of a good and appropriate peer review expert, more criteria/conditions can be included in the proposed scheme to further enhance accuracy and impartiality of the expert assignment.

The proposed method can help project funding agencies (e.g. the National Natural Science Foundation of China) find better experts for project peer review.

To the authors’ knowledge, this is the first publication that proposes an algorithm that applies an impartial approach to the project review expert assignment process. The simulation results show the effectiveness of the proposed method.

Keywords

  • Expert assignment
  • Accuracy
  • Impartiality
  • Randomized algorithm

Contents Index

Open Access

Contents Index to Volume 2

Published Online: 29 Dec 2017
Page range: 95 - 96

Abstract

6 Articles

Perspective

Open Access

A Framework for the Assessment of Research and Its Impacts

Published Online: 29 Dec 2017
Page range: 7 - 42

Abstract

Abstract

This paper proposes a holistic framework for the development of models for the assessment of research activities and their impacts. It distinguishes three dimensions, including in an original way, data as a main dimension, together with theory and methodology. Each dimension of the framework is further characterized by three main building blocks: education, research, and innovation (theory); efficiency, effectiveness, and impact (methodology); and availability, interoperability, and “unit-free” property (data). The different dimensions and their nine constituent building blocks are attributes of an overarching concept, denoted as “quality.” Three additional quality attributes are identified as implementation factors (tailorability, transparency, and openness) and three “enabling” conditions (convergence, mixed methods, and knowledge infrastructures) complete the framework. A framework is required to develop models of metrics. Models of metrics are necessary to assess the meaning, validity, and robustness of metrics. The proposed framework can be a useful reference for the development of the ethics of research evaluation. It can act as a common denominator for different analytical levels and relevant aspects and is able to embrace many different and heterogeneous streams of literature. Directions for future research are provided.

Keywords

  • Evaluation of research
  • Efficiency
  • Effectiveness
  • Impacts
  • Modeling
  • Responsible metrics
  • Ethics of research evaluation
Open Access

Understanding Big Data for Industrial Innovation and Design: The Missing Information Systems Perspective

Published Online: 29 Dec 2017
Page range: 1 - 6

Abstract

Abstract

This paper identifies a need to complement the current rich technical and mathematical research agenda on big data with a more information systems and information science strand, which focuses on the business value of big data. An agenda of research for information systems would explore motives for using big data in real organizational contexts, and consider proposed benefits, such as increased effectiveness and efficiency, production of high-quality products/services, creation of added business value, and stimulation of innovation and design. Impacts of such research on the academic community, the industrial and business world, and policy-makers are discussed.

Keywords

  • Big data
  • Business value
  • Information systems
  • Information science

Expert Review

Open Access

Rediscovering Don Swanson:The Past, Present and Future of Literature-based Discovery

Published Online: 29 Dec 2017
Page range: 43 - 64

Abstract

Abstract

The late Don R. Swanson was well appreciated during his lifetime as Dean of the Graduate Library School at University of Chicago, as winner of the American Society for Information Science Award of Merit for 2000, and as author of many seminal articles. In this informal essay, I will give my personal perspective on Don’s contributions to science, and outline some current and future directions in literature-based discovery that are rooted in concepts that he developed.

Personal recollections and literature review.

The Swanson A-B-C model of literature-based discovery has been successfully used by laboratory investigators analyzing their findings and hypotheses. It continues to be a fertile area of research in a wide range of application areas including text mining, drug repurposing, studies of scientific innovation, knowledge discovery in databases, and bioinformatics. Recently, additional modes of discovery that do not follow the A-B-C model have also been proposed and explored (e.g. so-called storytelling, gaps, analogies, link prediction, negative consensus, outliers, and revival of neglected or discarded research questions).

This paper reflects the opinions of the author and is not a comprehensive nor technically based review of literature-based discovery.

The general scientific public is still not aware of the availability of tools for literature-based discovery. Our Arrowsmith project site maintains a suite of discovery tools that are free and open to the public (http://arrowsmith.psych.uic.edu), as does BITOLA which is maintained by Dmitar Hristovski (http://http://ibmi.mf.uni-lj.si/bitola), and Epiphanet which is maintained by Trevor Cohen (http://epiphanet.uth.tmc.edu/). Bringing user-friendly tools to the public should be a high priority, since even more than advancing basic research in informatics, it is vital that we ensure that scientists actually use discovery tools and that these are actually able to help them make experimental discoveries in the lab and in the clinic.

This paper discusses problems and issues which were inherent in Don’s thoughts during his life, including those which have not yet been fully taken up and studied systematically.

Keywords

  • Literature-based discovery
  • Biography
  • Text mining
  • Knowledge discovery in databases
  • Implicit information
  • Information science

Research Paper

Open Access

Using Machine Reading to Understand Alzheimer’s and Related Diseases from the Literature

Published Online: 29 Dec 2017
Page range: 81 - 94

Abstract

Abstract

This paper aims to better understand a large number of papers in the medical domain of Alzheimer’s disease (AD) and related diseases using the machine reading approach.

The study uses the topic modeling method to obtain an overview of the field, and employs open information extraction to further comprehend the field at a specific fact level.

Several topics within the AD research field are identified, such as the Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS), which can help answer the question of how AIDS/HIV and AD are very different yet related diseases.

Some manual data cleaning could improve the study, such as removing incorrect facts found by open information extraction.

This study uses the literature to answer specific questions on a scientific domain, which can help domain experts find interesting and meaningful relations among entities in a similar manner, such as to discover relations between AD and AIDS/HIV.

Both the overview and specific information from the literature are obtained using two distinct methods in a complementary manner. This combination is novel because previous work has only focused on one of them, and thus provides a better way to understand an important scientific field using data-driven methods.

Keywords

  • Machine reading
  • Alzheimer’s disease
  • Knowledge discovery
  • Data mining
Open Access

An Accurate and Impartial Expert Assignment Method for Scientific Project Review

Published Online: 29 Dec 2017
Page range: 65 - 80

Abstract

Abstract

This paper proposes an expert assignment method for scientific project review that considers both accuracy and impartiality. As impartial and accurate peer review is extremely important to ensure the quality and feasibility of scientific projects, enhanced methods for managing the process are needed.

To ensure both accuracy and impartiality, we design four criteria, the reviewers’ fitness degree, research intensity, academic association, and potential conflict of interest, to express the characteristics of an appropriate peer review expert. We first formalize the expert assignment problem as an optimization problem based on the designed criteria, and then propose a randomized algorithm to solve the expert assignment problem of identifying reviewer adequacy.

Simulation results show that the proposed method is quite accurate and impartial during expert assignment.

Although the criteria used in this paper can properly show the characteristics of a good and appropriate peer review expert, more criteria/conditions can be included in the proposed scheme to further enhance accuracy and impartiality of the expert assignment.

The proposed method can help project funding agencies (e.g. the National Natural Science Foundation of China) find better experts for project peer review.

To the authors’ knowledge, this is the first publication that proposes an algorithm that applies an impartial approach to the project review expert assignment process. The simulation results show the effectiveness of the proposed method.

Keywords

  • Expert assignment
  • Accuracy
  • Impartiality
  • Randomized algorithm

Contents Index

Open Access

Contents Index to Volume 2

Published Online: 29 Dec 2017
Page range: 95 - 96

Abstract

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