Scientometric and Patentometric Analyses to Determine the Knowledge Landscape in Innovative Technologies: the Case of 3D Bioprinting

This research proposes an innovative data model to determine the landscape of emerging technologies. It is based on a competitive technology intelligence methodology that incorporates the assessment of scientific publications and patent analysis production, and is further supported by experts' feedback. It enables the definition of the growth rate of scientific and technological output in terms of the top countries, institutions and journals producing knowledge within the field as well as the identification of main areas of research and development by analyzing the International Patent Classification codes including keyword clusterization and co-occurrence of patent assignees and patent codes. This model was applied to the evolving domain of 3D bioprinting. Scientific documents from the Scopus and Web of Science data-bases, along with patents from 27 authorities and 140 countries, were retrieved. In total, 4782 scientific publications and 706 patents were identified from 2000 to mid-2016. The number of scientific documents published and patents in the last five years showed an annual average growth of 20% and 40%, respectively. Results indicate that the most prolific nations and institutions publishing on 3D bioprinting are the USA and China, including the Massachusetts Institute of Technology (USA), Nanyang Technological University (Singapore) and Tsinghua University (China), respectively. Biomaterials and Biofabrication are the predominant journals. The most prolific patenting countries are China and the USA; while Organovo Holdings Inc. (USA) and Tsinghua University (China) are the institutions leading. International Patent Classification codes reveal that most 3D bioprinting inventions intended for medical purposes apply porous or cellular materials or biologically active materials. Knowledge clusters and expert drivers indicate that there is a research focus on tissue engineering including the fabrication of organs, bioinks and new 3D bioprinting systems. Our model offers a guide to researchers to understand the knowledge production of pioneering technologies, in this case 3D bioprinting.This work was funded by Tecnologico de Monterrey through the Escuela de Ingenieria y Ciencias and also supported by a grant from the National Council for Science and Technology (CONACYT), Mexico (Grant number:261683).The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript

The last five years of Big Data Research in Economics, Econometrics and Finance: Identification and conceptual analysis

Today, the Big Data term has a multidimensional approach where five main characteristics stand out: volume, velocity, veracity, value and variety. It has changed from being an emerging theme to a growing research area. In this respect, this study analyses the literature on Big Data in the Economics, Econometrics and Finance field. To do that, 1.034 publications from 2015 to 2019 were evaluated using SciMAT as a bibliometric and network analysis software. SciMAT offers a complete approach of the field and evaluates the most cited and productive authors, countries and subject areas related to Big Data. Lastly, a science map is performed to understand the intellectual structure and the main research lines (themes)

Additive manufacturing knowledge incursion on orthopaedic devices : the case of hand orthoses

Additive manufacturing is growing rapidly in the health industry because of the superior advantages it has over conventional methods such as customization. From the vast applications of this technology, the development of orthopaedic devices represents an attractive opportunity. The orthopaedic field in particular is in dire need of new ways to adapt orthoses to individuals for hand rehabilitation, a common and growing problem around the world. This study aims to identify additive manufacturing research efforts for hand orthoses development. Scientific production from 2000 to 2016 in this domain was assessed. The use of additive manufacturing for hand orthoses creation was found to be scarce. Scientometrics tools were applied to identify the principal research focus, materials, additive manufacturing processes, and main institutions involved. Plastic materials, fuse deposition modelling (FDM) processes, and Solidworks software predominate in the scientific documents analysed. Loughborough University from the UK was found to be the institution with more publications. This research adds value to decision-making regarding R&D on this field.Published versio

Publication rates and geographical distribution of 3D bioprinting patents.

<p>(A) The number of 3D bioprinting patent families (PFs) by year from 2000 to 2015. (B) The number of 3D bioprinting patents applied for in each of the top 10 most prolific priority countries (i.e. countries in which the first patent of a PF was applied for). Four countries (Belgium, Canada, India and Japan) were tied for the tenth position. (C) The number of PFs ranked according to assignee institutions by year, from January 1, 2000 until July 1, 2016. Three institutions (Shandong University, Tongji University and Wuhan University) were tied for the tenth position. In all three graphs, yellow indicates grants that were applied for, green indicates PFs that were granted, and red indicates inactive PFs.</p

Linkage between 3D bioprinting drivers identified by experts and knowledge clusters generated through text mining software.

<p>Linkage between 3D bioprinting drivers identified by experts and knowledge clusters generated through text mining software.</p

Competitive technology intelligence (CTI) and expert perspective hybrid data model.

<p>This flow chart outlines the 10 main steps of the methodology implemented in the present study. The steps are indicated in boxes and sub-steps are indicated in ovals. The methodology begins with step one (the planning process) and continues through step 10 (decision making). The steps are repeated iteratively until the desired result has been obtained.</p

Past Child Abuse and Neglect in Adults From Northern Mexico: Development of a Scale and Prevalence

Child abuse has been present in Mexico but there have been few studies that analyze its effects in adults. There are no Mexican validated scales that measure the relationship between abuse experienced in childhood and its effects into adulthood. The purpose of this study is to develop a past child abuse and neglect scale to measure these phenomena in adults and also to analyze the relationship the effects have with other psychological variables (e.g., anxiety, depression, self-esteem, partner-violence, personality, and fatalism). There were 763 participants from Juarez City, located on the northern border of Mexico. All participants were above the age of 18 years. The scale was developed, and its psychometric properties were analyzed. A first analysis consisted of analyzing the factor structure of the scale ítems with an Exploratory Factor Analysis (EFA), and then a Confirmatory Factor Analysis (CFA) was used to corroborate the factor structure. The resulting factors were guilt, relationship with parents, strong physical abuse, sexualabuse, mild physical and verbal abuse, and basic care. The internal reliabilities for all factors in both analyses were between Cronbach’s alpha values of .77 and .92. Correlations of these factors with psychological variables were analyzed, and several statistically significant correlations were found. The scale has a good factor structure that correctly reflects the indicators of child abuse and neglect with good internal reliability values. The análisis showed that the prevalence rates of child abuse and neglect in Juarez were higher than those reported by the World Health Organization (WHO) in other locations worldwide. Actions by governments, universities, and civil associations should take place to reduce these rates, especially because of their long-term physical, emotional, and psychological consequences

Knowledge cluster breakdown.

<p>Knowledge cluster breakdown.</p

Main query terminology for the database searches.

<p>Words marked with an asterisk (*) are root words, indicating that all possible suffixes are covered under the query.</p