<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper proposes a <italic>holistic</italic> framework for the development of <italic>models</italic> for the assessment of research activities and their impacts. It distinguishes three dimensions, including in an original way, <italic>data</italic> as a main dimension, together with <italic>theory</italic> and <italic>methodology</italic>. Each dimension of the framework is further characterized by three main building blocks: education, research, and innovation (<italic>theory</italic>); efficiency, effectiveness, and impact (<italic>methodology</italic>); and availability, interoperability, and “unit-free” property (<italic>data</italic>). 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 <italic>ethics</italic> of research evaluation. It can act as a <italic>common denominator</italic> 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.</p></abstract>

A Framework for the Assessment of Research and Its Impacts

<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2017-0019_s_010_w2aab3b7b3b1b6b1aab1c17b1Aa"><h3>Purpose</h3><p>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.</p></sec><sec id="j_jdis-2017-0019_s_011_w2aab3b7b3b1b6b1aab1c17b2Aa"><h3>Design/methodology/approach</h3><p>Personal recollections and literature review.</p></sec><sec id="j_jdis-2017-0019_s_012_w2aab3b7b3b1b6b1aab1c17b3Aa"><h3>Findings</h3><p>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).</p></sec><sec id="j_jdis-2017-0019_s_013_w2aab3b7b3b1b6b1aab1c17b4Aa"><h3>Research limitations</h3><p>This paper reflects the opinions of the author and is not a comprehensive nor technically based review of literature-based discovery.</p></sec><sec id="j_jdis-2017-0019_s_014_w2aab3b7b3b1b6b1aab1c17b5Aa"><h3>Practical implications</h3><p>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 (<ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://arrowsmith.psych.uic.edu)">http://arrowsmith.psych.uic.edu)</ext-link>, as does BITOLA which is maintained by Dmitar Hristovski (<ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://http://ibmi.mf.uni-lj.si/bitola)">http://http://ibmi.mf.uni-lj.si/bitola)</ext-link>, and Epiphanet which is maintained by Trevor Cohen (<ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://epiphanet.uth.tmc.edu/)">http://epiphanet.uth.tmc.edu/)</ext-link>. 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.</p></sec><sec id="j_jdis-2017-0019_s_015_w2aab3b7b3b1b6b1aab1c17b6Aa"><h3>Originality/value</h3><p>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.</p></sec></abstract>

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

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2017-0021_s_006_w2aab3b7b5b1b6b1aab1c17b1Aa"><h3>Purpose</h3><p>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.</p></sec><sec id="j_jdis-2017-0021_s_007_w2aab3b7b5b1b6b1aab1c17b2Aa"><h3>Design/methodology/approach</h3><p>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.</p></sec><sec id="j_jdis-2017-0021_s_008_w2aab3b7b5b1b6b1aab1c17b3Aa"><h3>Findings</h3><p>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.</p></sec><sec id="j_jdis-2017-0021_s_009_w2aab3b7b5b1b6b1aab1c17b4Aa"><h3>Research limitations</h3><p>Some manual data cleaning could improve the study, such as removing incorrect facts found by open information extraction.</p></sec><sec id="j_jdis-2017-0021_s_010_w2aab3b7b5b1b6b1aab1c17b5Aa"><h3>Practical implications</h3><p>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.</p></sec><sec id="j_jdis-2017-0021_s_011_w2aab3b7b5b1b6b1aab1c17b6Aa"><h3>Originality/value</h3><p>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.</p></sec></abstract>

School of Informatics, Computing, and Engineering

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

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2017-0020_s_005_w2aab3b7b4b1b6b1aab1c17b1Aa"><h3>Purpose</h3><p>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.</p></sec><sec id="j_jdis-2017-0020_s_006_w2aab3b7b4b1b6b1aab1c17b2Aa"><h3>Design/methodology/approach</h3><p>To ensure both accuracy and impartiality, we design four criteria, the reviewers’ <italic>fitness degree</italic>, <italic>research intensity</italic>, <italic>academic association</italic>, and potential <italic>conflict of interest</italic>, 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.</p></sec><sec id="j_jdis-2017-0020_s_007_w2aab3b7b4b1b6b1aab1c17b3Aa"><h3>Findings</h3><p>Simulation results show that the proposed method is quite accurate and impartial during expert assignment.</p></sec><sec id="j_jdis-2017-0020_s_008_w2aab3b7b4b1b6b1aab1c17b4Aa"><h3>Research limitations</h3><p>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.</p></sec><sec id="j_jdis-2017-0020_s_009_w2aab3b7b4b1b6b1aab1c17b5Aa"><h3>Practical implications</h3><p>The proposed method can help project funding agencies (e.g. the National Natural Science Foundation of China) find better experts for project peer review.</p></sec><sec id="j_jdis-2017-0020_s_010_w2aab3b7b4b1b6b1aab1c17b6Aa"><h3>Originality/value</h3><p>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.</p></sec></abstract>

An Accurate and Impartial Expert Assignment Method for Scientific Project Review

Contents Index to Volume 2

AHEAD OF PRINT

Volume 9 (2024): Issue 1 (February 2024)

Volume 8 (2023): Issue 4 (November 2023)

Volume 8 (2023): Issue 3 (June 2023)

Volume 8 (2023): Issue 2 (April 2023)

Volume 8 (2023): Issue 1 (February 2023)

Volume 7 (2022): Issue 4 (November 2022)

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 of Data and Information Science

Journal of Data and Information Science (JDIS, formerly Chinese Journal of Library and Information Science), sponsored by the Chinese Academy of Sciences (CAS) and published quarterly by the National Science Library of CAS, is the first internationally published English-language academic journal in Library and Information Science and related fields from China.  The Journal of Data and Information Science (JDIS) focuses on data-based research oriented toward the exploration of scientific research and innovation. The main areas of interest are science of science, evidence-based policymaking, research evaluation, computational social science, and scientometrics/bibliometrics/altmetrics/ informetrics. Emphasis is given to research that focuses on data, analytics, and knowledge discovery, and supports decision making and science policy. This includes modeling, innovation, data security, media and communications, and social development. Topics may include studies of metadata or full content data, text or non-textural data, structured or non-structural data, domain-specific or cross-domain data, and dynamic or interactive data.  Specific topic areas may include (but are not limited to):    Knowledge organization  Knowledge discovery and data mining  Knowledge integration and fusion  Semantic Web  Science of science  Bibliometrics and scientometrics  Analytic and diagnostic informetrics  Competitive intelligence  Predictive analysis  Social network analysis and metrics  Semantic and interactively analytic retrieval  Evidence-based policy analysis  Intelligent knowledge production  Knowledge-driven workflow management and decision-making  Knowledge-driven collaboration and its management  Domain knowledge infrastructure with knowledge fusion and analytics  Training for data &amp; information scientists  Development of data and information services    JDIS publishes theoretical and empirical work. Systematic reviews are welcome and applied research in development of advanced methods, services, and best practices is also an important part. But simple application of established informetrics on a specific research field or country is out of the scope.Welcome to submit your papers to JDIS.  Why subscribe and read  JDIS is the first and only English journal from China in Library and Information Science and related fields. With an aim to disseminate the cutting-edge research in these fields, it is devoted to the study and application of the theories, methods, techniques, services, and infrastructural facilities using big data to support knowledge discovery for decision and policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. JDIS has gathered a big body of high profile experts across the world who contribute their research to the journal. The international authors account for around 62% in its first publication year (2016).  Why submit  JDIS is the first and only English journal from China in Library and Information Science and related fields. It owns a number of world front-line scholars as editorial board members or reviewers. The turnaround time on average for a manuscript from submission to final decision is less than two and a half months.  Archiving  Sciendo archives the contents of this journal in Portico- digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 

Journal of Data and Information Science

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

Perspective

A Framework for the Assessment of Research and Its Impacts

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

Expert Review

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

Research Paper

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

An Accurate and Impartial Expert Assignment Method for Scientific Project Review

Contents Index

Contents Index to Volume 2

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