- Dettagli della rivista
- Pubblicato per la prima volta
- 30 Mar 2017
- Periodo di pubblicazione
- 4 volte all'anno
- Accesso libero
Pagine: 1 - 9
In the current data-intensive era, the traditional hands-on method of conducting scientific research by exploring related publications to generate a testable hypothesis is well on its way of becoming obsolete within just a year or two. Analyzing the literature and data to automatically generate a hypothesis might become the
The Panama Canal, the 77-kilometer waterway connecting the Atlantic and Pacific oceans, has played a crucial role in international trade for more than a century. However, digging the Panama Canal was an exceedingly challenging process. A French effort in the late 19th century was abandoned because of equipment issues and a significant loss of labor due to tropical diseases transmitted by mosquitoes. The United States officially took control of the project in 1902. The United States replaced the unusable French equipment with new construction equipment that was designed for a much larger and faster scale of work. Colonel William C. Gorgas was appointed as the chief sanitation officer and charged with eliminating mosquito-spread illnesses. After overcoming these and additional trials and tribulations, the Canal successfully opened on August 15, 1914. The triumphant completion of the Panama Canal demonstrates that using the right tools and eliminating significant threats are critical steps in any project.
More than 100 years later, a paradigm shift is occurring, as we move into a data-centered era. Today, data are extremely rich but overwhelming, and extracting information out of data requires not only the right tools and methods but also awareness of major threats. In this data-intensive era, the traditional method of exploring the related publications and available datasets from previous experiments to arrive at a testable hypothesis is becoming obsolete. Consider the fact that a new article is published every 30 seconds (
Scouring the literature and data to generate a hypothesis might become the
Research communities in many disciplines are finally recognizing that with advances in information technology there needs to be new ways to extract entities from increasingly data-intensive publications and to integrate and analyze large-scale datasets. This provides a compelling opportunity to improve the process of knowledge discovery from the literature and datasets through use of knowledge graphs and an associated framework that integrates scholars, domain knowledge, datasets, workflows, and machines on a scale previously beyond our reach (
- Accesso libero
Pagine: 10 - 32
To address the under-reporting of research results, with emphasis on the under-reporting/distorted reporting of adverse events in the biomedical research literature.
A four-step approach is used: (1) To identify the characteristics of literature that make it adequate to support policy; (2) to show how each of these characteristics becomes degraded to make inadequate literature; (3) to identify incentives to prevent inadequate literature; and (4) to show policy implications of inadequate literature.
This review has provided reasons for, and examples of, adverse health effects of myriad substances (1) being under-reported in the premiere biomedical literature, or (2) entering this literature in distorted form. Since there is no way to gauge the extent of this under/distorted-reporting, the quality and credibility of the ‘premiere’ biomedical literature is unknown. Therefore, any types of meta-analyses or scientometric analyses of this literature will have unknown quality and credibility. The most sophisticated scientometric analysis cannot compensate for a highly flawed database.
The main limitation is in identifying examples of under-reporting. There are many incentives for under-reporting and few dis-incentives.
Almost all research publications, addressing causes of disease, treatments for disease, diagnoses for disease, scientometrics of disease and health issues, and other aspects of healthcare, build upon previous healthcare-related research published. Many researchers will not have laboratories or other capabilities to replicate or validate the published research, and depend almost completely on the integrity of this literature. If the literature is distorted, then future research can be misguided, and health policy recommendations can be ineffective or worse.
This review has examined a much wider range of technical and non-technical causes for under-reporting of adverse events in the biomedical literature than previous studies.
- Publication bias
- Reporting bias
- Manufactured research
- Research misconduct
- Research malfeasance
- Biomedical literature
- Accesso libero
Pagine: 81 - 101
Based on the weak tie theory, this paper proposes a series of connection indicators of weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolution.
First, keywords are extracted from article titles and preprocessed. Second, high-frequency keywords are selected to generate weak tie co-occurrence networks. By removing the internal lines of clustered sub-topic networks, we focus on the analysis of weak tie subnets’ composition and functions and the weak tie nodes’ roles.
The research topics’ clusters and themes changed yearly; the subnets clustered with technique-related and methodology-related topics have been the core, important subnets for years; while close subnets are highly independent, research topics are generally concentrated and most topics are application-related; the roles and functions of nodes and weak ties are diversified.
The parameter values are somewhat inconsistent; the weak tie subnets and nodes are classified based on empirical observations, and the conclusions are not verified or compared to other methods.
The research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends.
To contribute to the strength of weak tie theory, the research translates weak and strong ties concepts to co-occurrence strength, and analyzes weak ties’ functions. Also, the research proposes a quantitative method to classify and measure the topics’ clusters and nodes.
- Research topics
- Weak tie network
- Weak tie theory
- Weak tie nodes
- Library and Information Science (LIS)
- Accesso libero
Pagine: 60 - 80
To understand how authors and reviewers are accepting and embracing Open Peer Review (OPR), one of the newest innovations in the Open Science movement.
This research collected and analyzed data from the Open Access journal
This research is constrained by the availability of the peer review history data. Some peer reviews were not available when the authors opted out of publishing their review histories. The anonymity of reviewers made it impossible to give an accurate count of reviewers who contributed to the review process.
These findings shed light on the current characteristics of OPR. Given the policy that authors are encouraged to make their articles’ review history public and referees are encouraged to sign their review reports, the three years of
This is the first study to closely examine
- Open Peer Review (OPR
- Adoption of OPR
- Open Access
- Open Science
- Open research
- Scientific communication
- Accesso libero
Mapping Diversity of Publication Patterns in the Social Sciences and Humanities: An Approach Making Use of Fuzzy Cluster Analysis
Pagine: 33 - 59
To present a method for systematically mapping diversity of publication patterns at the author level in the social sciences and humanities in terms of publication type, publication language and co-authorship.
In a follow-up to the hard partitioning clustering by Verleysen and Weeren in 2016, we now propose the complementary use of fuzzy cluster analysis, making use of a membership coefficient to study gradual differences between publication styles among authors within a scholarly discipline. The analysis of the probability density function of the membership coefficient allows to assess the distribution of publication styles within and between disciplines.
As an illustration we analyze 1,828 productive authors affiliated in Flanders, Belgium. Whereas a hard partitioning previously identified two broad publication styles, an international one
The dataset used is limited to one country for the years 2000–2011; a cognitive classification of authors may yield a different result from the affiliation-based classification used here.
Our method is applicable to other bibliometric and research evaluation contexts, especially for the social sciences and humanities in non-Anglophone countries.
The method proposed is a novel application of cluster analysis to the field of bibliometrics. Applied to publication patterns at the author level in the social sciences and humanities, for the first time it systematically documents intra-disciplinary diversity.
- Social sciences and humanities
- Publication patterns
- Cluster analysis