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The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.

A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.

From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.

The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.

To the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

The purpose of this paper is to derive design principles for improving the open data publishing process of public organizations. Although governments create large amounts of data, the publication of open data is often cumbersome and there are no standard procedures and processes for opening data, blocking the easy publication of government data.

Action design research (ADR) was used to derive design principles. The literature was used as a foundation, and discussion sessions with civil servants were used to evaluate the usefulness of the principles.

Barriers preventing easy and low-cost publication of open data were identified and connected to design principles, which can be used to guide the design of an open data publishing process. Five new principles are: start thinking about the opening of data at the beginning of the process; develop guidelines, especially about privacy and policy sensitivity of data; provide decision support by integrating insight in the activities of other actors involved in the publishing process; make data publication an integral, well-defined and standardized part of daily procedures and routines; and monitor how the published data are reused.

The principles are derived using ADR in a single case. A next step can be to investigate multiple comparative case studies and detail the principles further. We recommend using these principles to develop a reference architecture.

The design principles can be used by public organizations to improve their open data publishing processes. The design principles are derived from practice and discussed with practitioners. The discussions showed that the principles could improve the publication process.

Decreasing the barriers for publishing open government data could result in the publication of more open data. These open data can then be used to stimulate various public values, such as transparency, accountability, innovation, economic growth and informed decision- and policymaking.

Publishing data by public organizations is a complex and ill-understood activity. The lack of suitable business processes and the unclear division of responsibilities block publication of open data. This paper contributes to the literature by presenting design principles which can be used to improve the open data publishing process of public sector organizations.

In recent years, a large number of data repositories have been built and used. However, the extent to which scientific data are re-used in academic publications is still unknown. The purpose of this paper is to explore the functions of re-used scientific data in scholarly publication in different fields.

To address these questions, the authors identified 827 publications citing resources in the Dryad Digital Repository indexed by Scopus from 2010 to 2015.

The results show that: the number of citations to scientific data increases sharply over the years, but mainly from data-intensive disciplines, such as agricultural, biology science, environment science and medicine; the majority of citations are from the originating articles; and researchers tend to reuse data produced by their own research groups.

Dryad data may be re-used without being formally cited.

The conservatism in data sharing suggests that more should be done to encourage researchers to re-use other’s data.

Although current research has investigated how open research data (ORD) are published, researchers' behaviour of ORD sharing on academic social networks (ASNs) remains insufficiently explored. The purpose of this study is to investigate the connections between ORDs publication and social activity to uncover data literacy gaps.

This work investigates whether the ORDs publication leads to social activity around the ORDs and their linked published articles to uncover data literacy needs. The social activity was characterised as reads and citations, over the basis of a non-invasive approach supporting this preliminary study. The eventual associations between the social activity and the researchers' profile (scientific domain, gender, region, professional position, reputation) and the quality of the ORD published were investigated to complete this picture. A random sample of ORD items extracted from ResearchGate (752 ORDs) was analysed using quantitative techniques, including descriptive statistics, logistic regression and K-means cluster analysis.

The results highlight three main phenomena: (1) Globally, there is still an underdeveloped social activity around self-archived ORDs in ResearchGate, in terms of reads and citations, regardless of the published ORDs quality; (2) disentangling the moderating effects over social activity around ORD spots traditional dynamics within the “innovative” practice of engaging with data practices; (3) a somewhat similar situation of ResearchGate as ASN to other data platforms and repositories, in terms of social activity around ORD, was detected.

Although the data were collected within a narrow period, the random data collection ensures a representative picture of researchers' practices.

As per the implications, the study sheds light on data literacy requirements to promote social activity around ORD in the context of open science as a desirable frontier of practice.

Researchers data literacy across digital systems is still little understood. Although there are many policies and technological infrastructure providing support, the researchers do not make an in-depth use of them.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2021-0255.

Entering data about published research results should be implemented as a web application that enables authors to input their own data without the knowledge of the bibliographic standard. The aim of this research is to develop a research management system based on a bibliographic standard and to provide data exchange with other research management systems based on the Common European Research Information Format (CERIF) data model.

Object‐oriented methodology was used for information system modelling. The modelling was carried out using the computer‐aided software engineering (CASE) tool that supports the Unified Modelling Language 2.0 (UML 2.0). The implementation was realised using a set of open‐source solutions written in Java.

The result is a system for managing data about published research results. The main system features are the following: public access via the web; authors input data about their own publications by themselves; data about publications are stored in the MARC 21 format; and the user interface enables authors to input data without the knowledge of the MARC 21 format.

A method of verifying accuracy of entered data has not been considered yet. It is necessary to allow authorised persons to verify the accuracy of the data. After verifying the accuracy the authors cannot change the data.

This software system has been verified and tested on data about published results of researchers employed at the University of Novi Sad in Serbia. This system can be used for evaluation and reporting on scientific research results, generating bibliographies of researchers, research groups and institutions etc.

A part of the research management system for entering data about authors and published results is implemented. Data about publications are stored in a bibliographic format and authors can input data about their own publications without the knowledge of the bibliographic standard. The main feature of the system architecture is mutual independence of the component for interaction with users and the component for persisting and retrieving data from the bibliographic records database.

Data science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different disciplines including mathematics and statistics, computer science and information science. The purpose of this paper is to present the results of the study that explored the field of data science from the library and information science (LIS) perspective.

Analysis of research publications on data science was made on the basis of papers published in the Web of Science database. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, authors of publications, affiliations of the article authors and the most cited articles related to data science in the field of LIS? What are the main themes discussed in the publications from the LIS perspective?

The highest contribution to data science comes from the computer science research community. The contribution of information science and library science community is quite small. However, there has been continuous increase in articles from the year 2015. The main document types are journal articles, followed by conference proceedings and editorial material. The top three journals that publish data science papers from the LIS perspective are the Journal of the American Medical Informatics Association, the International Journal of Information Management and the Journal of the Association for Information Science and Technology. The top five countries publishing are USA, China, England, Australia and India. The most cited article has got 112 citations. The analysis revealed that the data science field is quite interdisciplinary by nature. In addition to the field of LIS the papers belonged to several other research areas. The reviewed articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences.

The limitations of this research are that this study only analyzed research papers in the Web of Science database and therefore only covers a certain amount of scientific papers published in the field of LIS. In addition, only publications with the term “data science” in the topic area of the Web of Science database were analyzed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords such as “e-science,” “e-research,” “data service,” “data curation” or “research data management.”

The field of data science has not been explored using bibliographic analysis of publications from the perspective of the LIS. This paper helps to better understand the field of data science and the perspectives for information professionals.

The purpose of this paper is to propose an extensible framework for extracting data set usage from research articles.

The framework uses a training set of manually labeled examples to identify word features surrounding data set usage references. Using the word features and general entity identifiers, candidate data sets are extracted and scored separately at the sentence and document levels. Finally, the extracted data set references can be verified by the authors using a web-based verification module.

This paper successfully addresses a significant gap in entity extraction literature by focusing on data set extraction. In the process, this paper: identified an entity-extraction scenario with specific characteristics that enable a multiphase approach, including a feasible author-verification step; defined the search space for word feature identification; defined scoring functions for sentences and documents; and designed a simple web-based author verification step. The framework is successfully tested on 178 articles authored by researchers from a large research organization.

Whereas previous approaches focused on completely automated large-scale entity recognition from text snippets, the proposed framework is designed for a longer, high-quality text, such as a research publication. The framework includes a verification module that enables the request validation of the discovered entities by the authors of the research publications. This module shares some similarities with general crowdsourcing approaches, but the target scenario increases the likelihood of meaningful author participation.

With the aid of seed money from a federal grant, librarians at the University of Colorado at Boulder (CU Boulder) developed an online statistical abstract called Colorado by the Numbers (CBN). The last print version of the Colorado Statistical Abstract was published in 1987. CBN provides updated socio‐economic data about the state and its counties on the Web. Librarians have gone beyond the acquisition and maintenance of traditional printed information sources to producing tailor made resources that meet the information needs of their local community. The CBN design and management model is discussed.

This study aimed to examine how Medical Expenditure Panel Survey (MEPS) data have been used to support scientific discoveries in biomedical and health sciences, and provide insight to researchers who are interested in using MEPS regarding collaborations and dissemination of research output.

A bibliometric approach was used to systematically examine the publications that used MEPS data and were indexed by PubMed and Web of Science (WoS). Microsoft Excel and bibliometric tools (WoS and VOSviewer) were utilized for quantitative and bibliometric network analysis. The measures were investigated on the total number of publications by year, research categories, source journals, other datasets/databases co-used with MEPS, funding sources, collaboration patterns, and research topics.

A total of 1,953 eligible publications were included in this study with the numbers growing significantly over time. MEPS data were primarily used in healthcare services, public environmental and occupational health research. The journals that published the most papers using MEPS were all in the healthcare research area. Twenty-four other databases were found to be used along with MEPS. Over 3,200 researchers from 1,074 institutions in 25 countries have contributed to the publications. Research funding was supported from federal, private, local, and international agencies. Three clusters of research topics were identified among 235 key terms extracted from titles and abstracts.

Our results illustrated the broad landscape of the research efforts that MEPS data have supported and substantiated the value of AHRQ's effort of providing MEPS to the public.