JDIS

Journal of Data and Information Science >

2017 , Vol. 2 >Issue 1: 70 - 88

DOI: https://doi.org/10.1515/jdis-2017-0004

Research Paper

Insight into the Disciplinary Structure of Nanoscience & Nanotechnology

  • Chunjuan Luan , 1, † ,
  • Alan L. Porter 2
Expand
  • 1Faculty of Administration and Law, Dalian University of Technology, Dalian 116085, China
  • 2School of Public Policy, Georgia Institute of Technology, Atlanta, GA 30092, USA
†Corresponding author: Chunjuan Luan (E-mail: ).

Received date: 2016-06-30

  Revised date: 2016-12-12

  Accepted date: 2016-12-14

  Online published: 2016-11-19

Copyright

Open Access
Fold

Abstract

Purpose

This paper aims to gain an insight into the disciplinary structure of nanoscience & nanotechnology (N&N): What is the disciplinary network of N&N like? Which disciplines are being integrated into N&N over time? For a specific discipline, how many other disciplines have direct or indirect connections with it? What are the distinct subgroups of N&N at different evolutionary stages? Such critical issues are to be addressed in this paper.

Design/methodology/approach

We map the disciplinary network structure of N&N by employing the social network analysis tool, Netdraw, identifying which Web of Science Categories (WCs) mediate nbetweenness centrality in different stages of nano development. Cliques analysis embedded in the Ucinet program is applied to do the disciplinary cluster analysis in the study according to the path of “Network-Subgroup-Cliques,” and a tree diagram is selected as the visualizing type.

Findings

The disciplinary network structure reveals the relationships among different disciplines in the N&N developing process clearly, and it is easy for us to identify which disciplines are connected with the core “N&N” directly or indirectly. The tree diagram showing N&N related disciplines provides an interesting perspective on nano research and development (R&D) structure.

Research limitations

The matrices used to draw the N&N disciplinary network are the original ones, and normalized matrix could be tried in future similar studies.

Practical implications

Results in this paper can help us better understand the disciplinary structure of N&N, and the dynamic evolution of N&N related disciplines over time. The findings could benefit R&D decision making. It can support policy makers from government agencies engaging in science and technology (S&T) management or S&T strategy planners to formulate efficient decisions according to a perspective of converging sciences and technologies.

Originality/value

The novelty of this study lies in mapping the disciplinary network structure of N&N clearly, identifying which WCs have a mediating effect in different developmental stages (especially analyzing clusters among disciplines related to N&N, revealing close or distant relationships among distinct areas pertinent to N&N).

Key words： Nanoscience & nanotechnology (N&N); Disciplinary structure; Social network analysis; Cluster analysis; Cliques analysis; Dynamic evolution

Cite this article

Chunjuan Luan , Alan L. Porter . Insight into the Disciplinary Structure of Nanoscience & Nanotechnology[J]. Journal of Data and Information Science, 2017 , 2(1) : 70 -88 . DOI: 10.1515/jdis-2017-0004

1 Introduction

As a typical emerging and converging technology field, nanoscience & nanotechnology (N&N) has attracted tremendous governmental funds and scientific efforts. Articles in the field of N&N have grown explosively. With the rapid development of N&N, studies on N&N have been widely conducted by information scientists worldwide.
Disciplinary analysis of N&N as a major research area has drawn many scholars’ interests. Numerous topics of N&N have been studied, such as impact evaluation of N&N (Bartol & Stopar, 2015; Kostoff, Barth, & Lau, 2008; Leydesdorff, 2013), nano-competition or “nanorace” among countries or regions or institutions (Gorjiara & Baldock, 2014; Guan & Wei, 2015; Leydesdorff & Wagner, 2009; Wong, Ho, & Chan, 2007), technological life cycle of N&N (Anick, 2007; Milanez et al., 2013), and mapping of N&N (Bartol & Stopar, 2015; Kostoff, Koytcheff, & Lau, 2007; Mohammadi, 2012).
Investigations into the interdisciplinarity of N&N have been explored from a wide range of aspects. From the view of author collaboration, hypotheses, such as whether the collaboration in the area of N&N is of an obvious nature, have been proposed (Schummer, 2004), yet the results have not verified the assumption. As far as the toxicology and environmental risks of N&N are concerned, some approaches from interdisciplinary angles have been presented; some examples are an interdisciplinary approach for a comprehensive analysis of the impacts and ethical acceptability of nano technologies (Patenaude et al., 2015); and an interdisciplinary challenge for nanotoxicology has also been pointed out (Krug & Wick, 2011). Actually, various fields related to N&N from the perspective of interdisciplinarity have been studied, such as environmental areas (Bottero et al., 2015), chemistry and physics (Lindquist, 2014), material science (Mody & Choi, 2013), and biotechnology & genomics (Heimeriks, 2013).
Studies concerning the disciplinary structure of N&N are warranted to help set context for analyses of N&N research patterns and knowledge exchange. Porter and Youtie have explored the disciplinary structure of N&N by using Science Citation Index (SCI) journals’ Subject Categories (SCs). They selected nano-related papers by means of a Boolean search in SCI: ‘‘nano*,’’ less exclusions, then plus seven additional modules, detailed by Porter et al. (Porter et al., 2008; Porter & Youtie, 2009). Following this approach, we note that Subject Categories (SCs, WoS version 4) have been supplanted by “Web of Science Categories” (WCs, WoS version 5) launched in August, 2011. We address WCs to accomplish the analysis of the disciplinary structure of N&N in this paper. Compared to SCs, the 222 ISI Subject Categories (SCs) for SCI & Social Sciences Citation Index (SSCI)’s two databases in version 4 of Web of Science (WoS) were renamed and extended to 225 WoS Categories (WCs) (also, a new set of 151 Subject Areas were added, but a higher level of aggregation) (Leydesdorff, Carley, & Rafols, 2013). Thus, we use WCs to detect the disciplinary structure of N&N, further conducting a comparison with conclusions of Porter and Youtie (2009) with the predecessor SCs.
Besides analysis from the perspective of the social network analysis of the disciplinary structure of N&N, cluster analysis by employing cliques embedded in Ucinet software has also been conducted in this paper. This can help understand the disciplinary structure evolution of N&N.
It is of great significance to study the disciplinary structure of N&N both for theory and practice. Theoretically, this study can help us understand the disciplinary and knowledge origins from the beginning of N&N development and trace the trajectory of related subjects’ convergence over time. Practically, it will support research and development (R&D) policy-makers to formulate decisions according to a perspective of converging sciences and technologies.
This paper is organized as follows: Following the introduction, Section 2 introduces data and methods; Section 3 shows the analyses and results; Section 4 states the discussions and conclusions.

2 Data and Methods

2.1 Data

Data in this study are retrieved from the database of Science Citation Index-Expanded, SCI-E. N&N has been listed as a WC in SCI-E nowadays, so it is convenient for us to capture articles belonging to the research area of N&N. Articles in the WC of N&N have been searched in the SCI-E database. Our search strategy is as follows: document types = article; WC for nanoscience nanotechnology; time span: 1900-2014; limited to SCI-E. The date of data search and download is July 1, 2015. We retrieved 249,596 resulting records. The yearly distribution of N&N articles is shown in Figure 1.
The WC called nanoscience nanotechnology is used as the search strategy for N&N in this study. One reason is that we believe this WC employed here is likely to capture a core of N&N publications more crisply than other strategies. Another reason is that WC-based searching has been employed in some recent studies (Herranz & Ruiz-Castillo, 2012; Sweileh et al., 2014). There is possibly a third reason: though a sophisticated “nano” search strategy was presented by Arora and Porter et al. (2013), times change and topical emphases shift. WoS indexers, in associating journals to WCs may have some advantages over topical term based determinations. Readers should recognize that there may be advantages and disadvantages in studying term-based versus WC-based search results. For one, we note that our results are more selective. Applying the Arora et al.’s strategy in September, 2016, we retrieved some 144,000 articles versus about 33,000 articles from our N&N WC-based search. Our results here are more selective; arguably more representative of “core” nano R&D.
In order to gain an insight into the evolution of the disciplinary structure of N&N in different developmental phases, Statistical Product and Service Solutions (SPSS) software is employed to do the phase-dividing work. Three variables, (different years, the amount of N&N articles published in each year, and the number of distinct WCs of N&N articles in each year) are selected according to the method of Hierarchical Cluster Analysis embedded in SPSS, combining significant events during the N&N developing history, such as the Scanning Tunneling Microscope invented in 1981 (Tersoff & Hamann, 1983; Tersoff & Hamann, 1985), the Atomic Force Microscope invented in 1986 (Binnig, Quate, & Gerber, 1986; Martin, Williams, & Wickramasinghe, 1987), and the US National Nanotechnology Initiative (NNI) taken in 2000 (Roco, 2001; Jung & Lee, 2014). Three stages have been obtained: Stage I: 1966-1980, the infancy phase; Stage II: 1981-1999, the preliminary development phase; and Stage III: 2000-2014, the fast development phase (Figure 1).
Figure 1. Development phases of nanoscience and nanotechnology (1966-2014).
We recognize that nanoscience does not really get started in any reasonable way until the advent of the Scanning Tunneling Microscope in 1981 and the Atomic Force Microscope in 1986, so we begin at the second stage timeframe. The evolution of the N&N disciplinary structure during stage II and stage III will be explored, respectively.

2.2 Methods

2.2.1 Construction of Disciplinary Co-occurrence Matrix

Disciplinary co-occurrence matrix reports the relationship among different disciplines of N&N, as operationalized as WCs. The matrix construction is the basic work for analyzing disciplinary network structure and disciplinary cliques here. WCs provide an effective level of measurement of discipline for the study of interdisciplinary processes (National Academies Committee on Facilitating Interdisciplinary Research, 2005). The 225 or so WCs (the number is adjusted slightly over time) reflect sub-disciplines (e.g. Organic Chemistry). WCs have been selected to map science disciplines (Leydesdorff, Carley, & Rafols, 2013), and to do many other bibliometric analyses (Fu & Ho, 2015; Garner, Porter, & Newman, 2014; Lin & Ho, 2015).
An article may involve contributions from two or more disciplines. Keep in mind that the classification into WCs is based on the journal of publication, not on analysis of the individual article. In the SCI-E database, some 40% of journals are associated with multiple WCs; for example, there are six WCs in the following article in the area of N&N.
TI: Thermally stable, efficient polymer solar cells with nanoscale control of the interpenetrating network morphology
•

Chemistry, Multidisciplinary;

•

Chemistry, Physical;

•

Nanoscience & Nanotechnology;

•

Materials Science, Multidisciplinary;

•

Physics, Applied;

•

Physics, Condensed Matter

These six WCs in this record (this is an extreme example; recall that nearly 60% of journals are associated with a single WC) represent the co-occurrence relationship. That is, the record is associated with multiple disciplines (WCs). Bibexcel (Persson & Dastidar, 2013) and Ucinet (Borgatti & Everett, 1999; Freeman, Borgatti, & White, 1991) are jointly employed to get the WC co-occurrence matrix as follows (Table 1). Take the cell crossed by 5 and 8 with value of 1,794 for an example, it means that the co-occurrence frequencies of 5 (Chemistry_Applied) and 8 (Chemistry_Physical) are 1,794 times.
Table 1 Web of Science category, WC co-occurrence matrix (Partial).
1 2 3 4 5 6 7 8 9 10
1 0 22 0 0 0 0 800 0 0 0
2 22 0 1,685 1,685 0 0 0 0 0 1,685
3 0 1,685 0 1,685 0 0 0 0 0 1,685
4 0 1,685 1,685 0 0 0 0 0 0 1,685
5 0 0 0 0 0 0 0 1,794 0 0
6 0 0 0 0 0 0 0 0 0 0
7 800 0 0 0 0 0 0 7,878 0 0
8 0 0 0 0 1,794 0 7,878 0 0 0
9 0 0 0 0 0 0 0 0 0 0
10 0 1,685 1,685 1,685 0 0 0 0 0 0

Note. 1: Biochemical_Research_Methods; 2: Biophysics; 3: Biotechnology_&_Applied_Microbiology; 4: Chemistry_Analytical; 5: Chemistry_Applied; 6: Chemistry_Inorganic_&_Nuclear; 7: Chemistry_Multidisciplinary; 8: Chemistry_Physical; 9: Computer_Science_Hardware_&_Architecture; 10: Electrochemistry.

2.2.2 Analysis of Disciplinary Network Structure and Each Discipline’s Mediating Effect

After obtaining the WC co-occurrence matrix, we can map the disciplinary network by employing Netdraw (Johnson et al., 2009). The WC co-occurrence matrix we used here is the original matrix derived from the bibliographic data, and the Jaccard index method proposed by Leydesdorff (2008) has not been employed here, for the total disciplines (WCs) concerning N&N are not more than 40, and the disciplinary network structure can be visualized clearly by mapping directly from the original WC co-occurrence matrix. The disciplinary networks help us identify the ties among disciplines engaged in N&N and the evolution of the disciplinary network structure over time. It is simple for us to find out which disciplines have connections with a specific discipline in the network (Figure 2).
Figure 2. Ego-network: Disciplines connected with a specific discipline.
The indicator of betweenness centrality (Equation 1) is applied to measure each discipline’s mediating effect according to the path of Network-Centrality-Freeman Betweenness-Node Betweenness, embedded in the Ucinet program, and to further help us understand the mechanism of the evolution of N&N. Betweenness is a centrality measure of a vertex within a graph. Betweenness centrality quantifies the number of times a node acts as a bridge along the shortest path between two other nodes (Freeman, 1977).
If gjk denotes the number of geodesics between j and k, the probability is 1/gjk when all the geodesics are equally selected to be the communicative paths among each node. gjk(ni) represents the number of geodesics between two nodes including ni, and the betweenness centrality of ni can be calculated by Equation (1):
C B = ∑ j < k g jk n i / g jk . (1)
The higher the betweenness centrality of a specific discipline concerning N&N is, the more contribution this discipline has made to the development of N&N.

2.2.3 Analysis of Disciplinary Clusters

The disciplinary network structure of N&N, mapped by employing the social network analysis tool, helps us better understand the dynamic evolution of N&N over time from the perspective of the evolution of the network structure, such as nodes and links added over time. Tree diagrams of N&N disciplines inform the dynamic evolution of N&N from a logical view by showing relationships among different disciplines near or far.

3 Analyses and Results

3.1 Mapping Disciplinary Network Structure and Measuring Each Discipline’s Mediating Effect by Betweenness Centrality

According to the methods illustrated in Section 2.2, we first mapped the disciplinary network structure in two stages: Stage II, the preliminary development phase (1981-1999), and Stage III, the fast development phase (2000-2014); and then we measured each discipline’s mediating effect by selecting the indicator of betweenness centrality in each network.

3.1.1 Stage II: Preliminary Development Phase (1981-1999)

In this technology’s early development phase (1981-1999), there are a total of 22 WCs participating in the N&N related disciplinary network (Figure 3), and these WCs are connected with each other, forming a whole network.
Figure 3. Disciplinary network structure of nanoscience & nanotechnology in Stage II, the preliminary development phase (1981-1999) in terms of betweenness centrality. Tie strength: Minimum line width of 1 and maximum line width of 2. Totally 22 nodes.
The map of the disciplinary network of Stage II, 1981-1999, in Figure 3 seems very dense, and the disciplinary network structure appears to be comparatively obvious, with concentrations relating to Nano-Biotechnology, Nano-Manufacturing, Nano-Materials, Nano-Electronics, Nano-Physics, Nano-Chemistry, Nano-Biomedical, and Nano-Thermodynamics.
In Figure 3, not only can we easily identify those WCs connected with N&N directly or indirectly but also determine if those WCs are also linked to any specific discipline (WC) directly or indirectly. In fact, each WC in Figure 3 can be mapped as an ego-network of disciplinary structure, as shown in Figure 2.
In Figure 3, the relationships among Physics Applied, Engineering Electrical & Electronic, and Nanoscience & Nanotechnology are the strongest ones in the network; the links among Materials Science Multidisciplinary, Metallurgy & Metallurgical Engineering, and Nanoscience & Nanotechnology are much stronger than the rest.
In Stage II, during 1981-1999, a total of seven WCs have played mediating effects (Table 2). The discipline of Nanoscience & Nanotechnology plays the highest mediating effect, with a normalized betweenness centrality of 68.095; the discipline of Physics Applied has the second highest nbetweenness centrality ( nBetweenness refers to normalized betweenness centrality.)of 5.317; Materials Science Multidisciplinary has an nbetweenness centrality of 4.921, the 3rd; and Engineering Electrical & Electronic has an nbetweenness centrality of 1.429; also Instruments Instrumentation has an nbetweenness centrality of 1.429; the other two disciplines with mediating effects are Chemistry Physical (0.476) and Physics Condensed Matter (0.238) (But keep in mind that the set of records was determined by a search on the N&N WC as such).
Table 2 Values of betweenness and nbetweenness centrality over 0 of each discipline in Stage II: 1981-1999.
Rank ID Betweenness nBetweenness
1 Nanoscience_&_Nanotechnology 143.000 68.095
2 Physics_Applied 11.167 5.317
3 Materials_Science_Multidisciplinary 10.333 4.921
4 Engineering_Electrical_&_Electronic 3.000 1.429
5 Instruments_&_Instrumentation 3.000 1.429
6 Chemistry_Physical 1.000 0.476
7 Physics_Condensed_Matter 0.500 0.238

3.1.2 Stage III: Fast Development Phase (2000-2014)

In Stage III, the fast development phase from 2000 to 2014, more disciplines are added into the disciplinary network (Figure 4). The core N&N network expands to 34 WCs. These WCs are connected with each other, forming a whole network.
Figure 4. Disciplinary network structure of nanoscience & nanotechnology in Stage III, the fast development phase (2000-2014) in terms of betweenness centrality. Tie strength: Minimum line width of 2 and maximum line width of 4. Totally 34 nodes.
The density of the disciplinary network in Stage III is much higher than that of Stage II, especially the left part of the network. 12 new WCs are added in Figure 4 compared to Figure 3; they are Biochemical Research Methods, Chemistry Inorganic & Nuclear, Computer Science-Hardware & Architecture, Environmental Sciences, Materials Science-Biomaterials, Medicine-Research & Experimental, Multidisciplinary Sciences, Pharmacology & Pharmacy, Physics- Atomic-Molecular & Chemical, Physics-Fluids & Plasmas, Polymer Science, and Toxicology. A total of 14 WCs have played mediating effects in Stage III: 2000-2014 (Table 3).
Table 3 Values of betweenness and nbetweenness centrality over 0 of each discipline in Stage III: 2000-2014.
Rank ID Betweenness nBetweenness
1 Nanoscience_&_Nanotechnology 394.833 74.779
2 Materials_Science_Multidisciplinary 35.000 6.629
3 Physics_Applied 9.000 1.705
4 Chemistry_Multidisciplinary 6.500 1.231
5 Engineering_Electrical_&_Electronic 5.000 0.947
6 Instruments_&_Instrumentation 4.500 0.852
7 Biophysics 3.000 0.568
8 Physics_Condensed_Matter 3.000 0.568
9 Chemistry_Physical 2.333 0.442
10 Materials_Science_Characterization_&_Testing 2.000 0.379
11 Biotechnology_&_Applied_Microbiology 1.500 0.284
12 Physics_Fluids_&_Plasmas 1.000 0.189
13 Biochemical_Research_Methods 1.000 0.189
14 Engineering_Manufacturing 0.333 0.063
It is notable that Material Science Multidisciplinary plays a very important role with a much higher nbetweenness value than the other WCs—except for Nanoscience & Nanotechnology. It becomes a critical mediating point bridging other disciplines. In Stage III, the discipline of Nanoscience & Nanotechnology still plays the highest mediating effect with an nbetweenness centrality of 74.779; Materials Science Multidisciplinary has an nbetweenness centrality of 6.629; there are two other disciplines with an nbetweenness value exceeding 1.0—Physics Applied with 1.705 and Chemistry Multidisciplinary with 1.231.

3.1.3 Comparisons of Stage II and Stage III

In order to have an overview of the two development stages, Stage II and Stage III, three indicators (density, average distance, and mean nbetweenness (m-nbetweenness)) are selected to do the comparison, and the results are shown in Table 4.
Table 4 Comparison between two stages: Density, avg. distance, and m-nbetweenness.
Density (Avg. value) Avg. distance M-nbetweenness
Stage II 518.636 1.745 3.723
Stage III 804.373 1.836 2.613
Table 4 shows that the value of density is higher in Stage III than in Stage II, indicating closer relationships among N&N related disciplines over time. As far as average distance is concerned, the average distance becomes further as time goes on, which is mainly due to more and more subjects that have appeared. In terms of mean nbetweenness, the value is smaller over time, which is also because of more WCs participating in the arena of N&N over time.

3.2 Cluster Analysis of N&N Related Disciplines

Though the disciplinary networks of N&N in Section 3.1 can tell us what the whole network structure is like, they also help us identify which WCs are connected to a specific discipline. Yet, sometimes the clustering of WCs is not so clear. Thus, clique analysis embedded in the Ucinet program is employed to do cluster analysis, and this will further help us detect the main domains of N&N by selecting the tree diagram display of a subgroup analysis.

3.2.1 Stage II: Preliminary Development Phase (1981-1999)

According to the method illustrated in Section 2.2, and following the path of Network-Subgroup-Cliques of Ucinet, selecting tree diagram as the diagram type, nine cliques (Figure 5) have been found in Stage II, the preliminary development phase (1981-1999), as follows.
A: Chemistry Multidisciplinary, Chemistry Physical Materials Science Multidisciplinary, Nanoscience & Nanotechnology, Physics Applied, Physics Condensed Matter
B: Chemistry Applied, Chemistry Physical, Materials Science Multidisciplinary, Nanoscience & Nanotechnology
C: Engineering Electrical & Electronic, Instruments & Instrumentation, Materials Science Multidisciplinary, Mechanics, Nanoscience & Nanotechnology
D: Engineering Electrical & Electronic, Materials Science Multidisciplinary, Nanoscience & Nanotechnology, Physics Applied, Physics Condensed Matter
E: Materials Science Multidisciplinary, Metallurgy & Metallurgical Engineering, Nanoscience & Nanotechnology
F: Biophysics, Biotechnology & Applied Microbiology, Chemistry Analytical, Electrochemistry, Nanoscience & Nanotechnology
G: Engineering Manufacturing, Engineering Multidisciplinary, Instruments & Instrumentation, Nanoscience & Nanotechnology
H: Engineering Mechanical, Materials Science Characterization & Testing, Nanoscience & Nanotechnology, Physics Applied, Thermodynamics
I: Engineering Electrical & Electronic, Nanoscience & Nanotechnology, Optics, Physics Applied
Figure 5 shows that the cliques of N&N are clearly identified in Stage II: 1981-1999. There are six branches from the root in the tree diagram, and they are (1) Nano-Biomedical, Nano-Biotechnology, (2) Nano-Metallurgy, (3) Nano-Manufacturing, (4) Nano-Material, (5) Nano-Mechanical, and (6) Nano-Thermodynamics. This indicates that in this phase, many branches of N&N have come into being. The linking branch with Nano-Material is the biggest one in the map associated with some other sub-branches, such as Nano-Chemistry, Nano-Electronic, Nano-Physics, and Nano-Optics.
Figure 5. Cliques of nanoscience & nanotechnology in Stage II: 1981-1999.

3.2.2 Stage III: Fast Development Phase (2000-2014)

By using the same method, we get 18 cliques (Figure 6) in Stage III, the fast development phase, 2000-2014, as follows.
A: Chemistry Multidisciplinary, Chemistry Physical, Materials Science Multidisciplinary, Nanoscience & Nanotechnology, Physics Applied, Physics Condensed Matter
B: Engineering Electrical & Electronic, Materials Science Multidisciplinary, Nanoscience & Nanotechnology, Optics, Physics Applied, Physics Condensed Matter
C: Materials Science Characterization & Testing, Materials Science Multidisciplinary, Metallurgy & Metallurgical Engineering, Nanoscience & Nanotechnology, Physics Applied, Physics Condensed Matter
D: Chemistry Applied, Chemistry Physical, Materials Science Multidisciplinary, Nanoscience & Nanotechnology
E: Engineering Manufacturing, Instruments & Instrumentation, Materials Science Multidisciplinary, Nanoscience & Nanotechnology
F: Engineering Electrical & Electronic, Instruments & Instrumentation, Materials Science Multidisciplinary, Mechanics, Nanoscience & Nanotechnology
G: Biotechnology & Applied Microbiology, Materials Science Multidisciplinary, Nanoscience & Nanotechnology
H: Chemistry Physical, Materials Science Multidisciplinary, Nanoscience & Nanotechnology, Physics Atomic Molecular & Chemical
I: Chemistry Multidisciplinary, Materials Science Multidisciplinary, Nanoscience & Nanotechnology, Polymer Science
J: Biophysics, Biotechnology & Applied Microbiology, Chemistry Analytical, Electrochemistry, Nanoscience & Nanotechnology
K: Computer Science Hardware & Architecture, Engineering Electrical & Electronic, Nanoscience & Nanotechnology
L: Engineering Biomedical, Materials Science Biomaterials, Nanoscience & Nanotechnology
M: Engineering Mechanical, Materials Science Characterization & Testing, Nanoscience & Nanotechnology, Physics Applied, Thermodynamics
N: Engineering Manufacturing, Engineering Multidisciplinary, Instruments & Instrumentation, Nanoscience & Nanotechnology
O: Chemistry Multidisciplinary, Environmental Sciences, Nanoscience & Nanotechnology
P: Biochemical Research Methods, Biophysics, Nanoscience & Nanotechnology, Physics Fluids & Plasmas
Q: Biochemical Research Methods, Chemistry Multidisciplinary, Nanoscience & Nanotechnology
R: Instruments & Instrumentation, Nanoscience & Nanotechnology, Physics Fluids & Plasmas
Figure 6. Cliques of nanoscience & nanotechnology in Stage III: 2000-2014.
Figure 6 demonstrates two imbalanced domains of N&N: A smaller one is about Nano-Biomedical/Pharmacy; the much bigger one is covering Nano-Manufacturing, Nano-Metallurgy, Nano-Electronic, Nano-Mechanics, Nano-Biotechnology, Nano-Chemistry Analytical, Nano-Electrochemistry, Nano-Biochemical, Nano-Environmental Science, Nano-Chemistry Physical, Nano-Chemistry Applied, Nano-Optics, Nano-Materials, Nano-Physics, Nano-Mechanical, Nano-Thermodynamics, and Nano-Toxicology.

4 Discussion and Conclusion

In this study, some extant studies pertinent to N&N were reviewed, and it was proposed that studies regarding the disciplinary structure of N&N were insufficient. Next, the research purpose and significance were stated, as well as data sources and methods. The data in this paper are from SCI-E with a WC of N&N; a total of 249,596 results of N&N articles are obtained. The methods in this study mainly involve social network analysis and cluster analysis by employing the Ucinet program and Bibexcel software.
The disciplinary network structure reveals relationships among different disciplines in the N&N developing process. We identify the disciplines that are connected with N&N directly or indirectly (and even the disciplines that are linked to a specific discipline). In general, more N&N related disciplines converge into the N&N developing process over time in stages; also, the density of the disciplinary network is closer as time goes on and the average distance is further over time. The value of mean nbetweenness is also smaller. More WCs play a mediating effect with the evolution of different phases of N&N; Materials Science and Physics Applied play a very critical mediating role in the course of development of N&N, besides N&N itself.
The results of N&N cluster analysis show logical relationships among different disciplines related to N&N. The analysis can reveal the original knowledge source at the beginning stage of N&N, the dynamic evolution of N&N over time and also show us relative strength of connections among the different disciplines. With the development of N&N, besides Nano-Engineering and Nano-Manufacturing, more and more other branches have come into being gradually, such as Nano-Materials, Nano-Chemistry Applied, Nano-Polymer, Nano-Optics, Nano-Metallurgy, Nano-Mechanical, and Nano-Thermodynamics, Nano-Electrochemistry, Nano-Biotechnology, Nano-Biomedical, and Nano-Environmental Science.
The novelty of this research lies in mapping the disciplinary network structure of disciplines related to N&N, based on a search using WC in SCI-E. That is also both the strength in focusing on one version of an N&N core, and the limitation in that it does not address the wider swath of R&D that can be identified by a broad, term-based search in such databases. Here, we identify the WCs playing a mediating effect in two stages (especially, analyzing clusters among disciplines related to N&N, revealing close or distant relationships among distinct areas pertinent to N&N). The results help better understand the knowledge sources of N&N at the beginning stage, and also the dynamic evolution of N&N over time.
Compared to similar previous research, core data of the domain of N&N have been selected and analyzed in this paper. There are many studies concerning the interdisciplinary structure of N&N and various subfields and further research could compare results and their implications with such studies to better understand the disciplinary network structure and dynamics (c.f. Porter & Youtie, 2009; Souminen, Li, & Youtie, 2016; Wang & Shapira, 2011).
Another point in this paper is that the WCs (version 5) launched by Thomson Reuters in August 2011 are selected to accomplish the analysis of the disciplinary structure of N&N, supplanting the ISI Subject Categories (SCs) for SCI & SSCI (two databases in version 4 of Web of Science).
Cluster analysis of disciplines related to N&N by employing cliques function embedded in the Ucinet program helped understand the evolutionary mechanics of N&N. The results help illuminate how the area of N&N developed, and which disciplines have converged into N&N over time.

Author Contributions

The study was derived from C.J. Luan’s (julielcj@163.com, corresponding author) curiosity who intended to find out what the disciplinary structure of nanoscience nanotechnology was like. A.L. Porter (alan.porter@isye.gatech.edu) has complemented a number of new thoughts and improved the methodologies in detail, and edited the paper entirely. Both authors contributed to the final manuscript.

The authors have declared that no competing interests exist.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Anick D.J. (2007). The octave potencies convention: A mathematical model of dilution and succussion. Homeopathy, 96(3), 202-208.Several hypothesized explanations for homeopathy posit that remedies contain a concentration of discrete information-carrying units, such as water clusters, nano-bubbles, or silicates. For any such explanation to be sustainable, dilution must reduce and succussion must restore the concentration of these units. Succussion can be modeled by a logistic equation, which leads to mathematical relationships involving the maximum concentration, the average growth of information-carrying units rate per succussion stroke, the number of succussion strokes, and the dilution factor (x, c, or LM). When multiple species of information-carrying units are present, the fastest-growing species will eventually come to dominate, as the potency is increased. An analogy is explored between iterated cycles dilution and succussion, in making homeopathic remedies, and iterated cycles of reseeding and growth, in bacterial cultures. Drawing on this analogy, the active ingredients in low and medium potency remedies may be present at early dilutions but only gradually come to 'dominate', while high potencies may develop from the occurrence of low-probability but faster-growing 'mutations.' Conclusions from this model include: 'x' and 'c' potencies are best compared by the amount of dilution, not the amount of succussion; the minimum number of succussion strokes needed per cycle is proportional to the logarithm of the dilution factor; and a plausible interpretation of why potencies at approximately regular ratios are traditionally used (the octave potencies convention).

DOI PMID

[2]
Arora S.K., Porter A.L., Youtie J., & Shapira P. (2013). Capturing new developments in an emerging technology: An updated search strategy for identifying nanotechnology research outputs. Scientometrics, 95(1), 351-370.Bibliometric analysis of publication metadata is an important tool for investigating emerging fields of technology. However, the application of field definitions to define an emerging technology is complicated by ongoing and at times rapid change in the underlying technology itself. There is limited prior work on adapting the bibliometric definitions of emerging technologies as these technologies change over time. The paper addresses this gap. We draw on the example of the modular keyword nanotechnology search strategy developed at Georgia Institute of Technology in 2006. This search approach has seen extensive use in analyzing emerging trends in nanotechnology research and innovation. Yet with the growth of the nanotechnology field, novel materials, particles, technologies, and tools have appeared. We report on the process and results of reviewing and updating this nanotechnology search strategy. By employing structured text-mining software to profile keyword terms, and by soliciting input from domain experts, we identify new nanotechnology-related keywords. We retroactively apply the revised evolutionary lexical query to 20 years of publication data and analyze the results. Our findings indicate that the updated search approach offers an incremental improvement over the original strategy in terms of recall and precision. Additionally, the updated strategy reveals the importance for nanotechnology of several emerging cited-subject categories, particularly in the biomedical sciences, suggesting a further extension of the nanotechnology knowledge domain. The implications of the work for applying bibliometric definitions to emerging technologies are discussed.

DOI

[3]
Bartol T., & Stopar K. (2015). Nano language and distribution of article title terms according to power laws. Scientometrics, 103(2), 435-451.Scientometric evaluation of nanoscience/nanotechnology requires complex search strategies and lengthy queries which retrieve massive amount of information. In order to offer some insight based on the most frequently occurring terms our research focused on a limited amount of data, collected on uniform principles. The prefix nano comes about in many different compound words thus offering a possibility for such assessment. The aim is to identify the scatter of nanoconcepts, among and within journals, as well as more generally, in the Web of Science (WOS). Ten principal journals were identified along with all unique nanoterms in article titles. Such terms occur on average in half of all titles. Terms were thoroughly investigated and mapped by lemmatization or stemming to the appropriate roots—nanoconcepts. The scatter of concepts follows the characteristics of power laws, especially Zipf’s law, exhibiting clear inversely proportional relationship between rank and frequency. The same three nanoconcepts are most frequently occurring in as many as seven journals. Two concepts occupy the first and the second rank in six journals. The same six concepts are the most frequently occurring in ten journals as well as full WOS database, representing almost two thirds of all nanotitled articles, in both instances. Subject categories don’t play a decisive role. Frequency falls progressively, quickly producing a long tail of rare concepts. Drop is almost linear on the log scale. The existence of hundreds of different closed-form compound nanoterms has consequences for the retrieval on the Internet search engines (e.g. Google Scholar) which do not permit truncation.

DOI

[4]
Binnig G., Quate C.F., & Gerber C. (1986). Atomic force microscope. Physical Review Letters, 56(9), 930-933.

[5]
Borgatti S.P., & Everett M.G. (1999). Models of core/periphery structures. Social Networks, 21(4), 375-395.A common but informal notion in social network analysis and other fields is the concept of a core/periphery structure. The intuitive conception entails a dense, cohesive core and a sparse, unconnected periphery. This paper seeks to formalize the intuitive notion of a core/periphery structure and suggests algorithms for detecting this structure, along with statistical tests for testing a priori hypotheses. Different models are presented for different kinds of graphs (directed and undirected, valued and nonvalued). In addition, the close relation of the continuous models developed to certain centrality measures is discussed.

DOI

[6]
Bottero J.Y., Auffan M., Borschnek D., Chaurand P., Labille J., Levard C., Masion A., Tella M., Rose J., & Wiesner M.R. (2015). Nanotechnology, global development in the frame of environmental risk forecasting. A necessity of interdisciplinary researches. Comptes Rendus Geoscience, 347(1), 35-42.Interdisciplinarity is of first importance to evaluate the risks associated with nanotechnology. The reasons are that nanomaterials are very new materials that combine nano-sizes and new reactivities. The complexity comes from the very low concentrations of nanomaterials in the environmental medium, the transformations of the nanomaterials due to the reactivity of the surface, the transfer in the environmental media, particularly in the presence of liquid water (soils, sediments, surface water), which implies an association with natural colloids (organic or minerals) and blockage in some compartments. These properties govern the hazard that strongly depends on exposure and speciation.

DOI

[7]
Freeman L. (1977). A set of measures of centrality based upon betweenness. Sociometry, 40(1), 35-41.A Family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced. These measures define centrality in terms of the degree to which a point falls on the shortest path between others and therefore has a potential for control of communication. They may be used to index centrality in any large or small network of symmetrical relations, whether connected or unconnected.

DOI

[8]
Freeman L.C., Borgatti S.P., & White D.R. (1991). Centrality in valued graphs—A measure of betweenness based on network flow. Social Networks, 13(2), 141-154.A new measure of centrality, CF, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman's original measure, CB, the new measure differs from the original in two important ways. First, CF is defined for both valued and non-valued graphs. This makes CF applicable to a wider variety of network datasets. Second, the computation of CF is not based on geodesic paths as is CB but on all the independent paths between all pairs of points in the network.

DOI

[9]
Fu H.Z., & Ho Y.S.(2015). Top cited articles in thermodynamic research. Journal of Engineering Thermophysics, 24(1), 68-85.The 5,320 top cited articles published between 1902 and 2010 in thermodynamic field were identified and characterized using Science Citation Index Expanded. The analyzed aspects covered characteristics of languages, publication years, Web of Science categories, journals, countries/territories, institutions, and authors. These articles were cited a mean number of 210, ranging from 110 to 399 times, with most of the articles in the 1990s and 2000s. Journal of the American Chemical Society was the most productive journal, followed by Journal of Chemical Physics, and Physical Review Letters in 686 journals. Three topmost categories of the 130 Web of Science categories were multidisciplinary chemistry, biochemistry and molecular biology, and physical chemistry. The top cited articles originated from 1,936 institutions of 63 countries. Eight industrial countries: the USA, the UK, Germany, France, Canada, Japan, Italy, and Russia, took the lead with an overwhelming majority (87%), especially about three fifths for the USA. University of California, Harvard University, and Massachusetts Institution of Technology all from the USA led all the institutions. K.S. Pitzer, P.J. Flory (Nobel laureate), and P.A. Kollman advanced the development of thermodynamic field. Moreover, the most influential articles in the history and in the latest year with their citation life cycles were examined to provide some hints for research focuses and trends. Wigner function has been attractive and will probably continue to be popular in the thermodynamic field. Some emerging concerning related to frequency scale factors, OPLS all-atom force field, entanglement between two or more quantum objects, and some softwares including VAMP, NMRPipe, GRASP2, AutoDock, DMol3, and Maxent are likely to receive more attention in the near future.

DOI

[10]
Garner J., Porter A.L., & Newman N.C. (2014). Distance and velocity measures: Using citations to determine breadth and speed of research impact. Scientometrics, 100(3), 687-703.Research that integrates the social and natural sciences is vital to address many societal challenges, yet is difficult to arrange, conduct, and disseminate. This paper compares diffusion of the research supported by a unique U.S. National Science Foundation program on Human and Social Dynamics (“HSD”) with a matched group of heavily cited papers. We offer a measure of the distance of cites between the Web of Science Category (“WoSC”) in which a publication appears and the WoSC of the journal citing it, and find that HSD publications are cited more distantly than are comparison publications. We provide another measure—citation velocity—finding that HSD publications are cited with similar lag times as are the comparison papers. These basic citation distance and velocity measures enrich analyses of research knowledge diffusion patterns.

DOI

[11]
Gorjiara T., & Baldock C.(2014). Nanoscience and nanotechnology research publications: A comparison between Australia and the rest of the world. Scientometrics, 100(1), 121-148.Nanoscience and nanotechnology are research areas of a multidisciplinary nature. Having a good knowledge of the rapidly evolving nature of these research areas is important to understand the research paths, as well as national and global developments in these areas. Accordingly, in this reported study nanoscience and nanotechnology research undertaken globally was compared with that of Australia by way of analyzing research publications. Initially, four different bibliometric Boolean-based search methodologies were used to analyze publications in the Web of Science database (Thomson Reuters ISI Web of Knowledge). These methodologies were (a) lexical query, (b) search in nanoscience and nanotechnology journals, (c) combination of lexical query and journal search and (d) search in the ten nano-journals with the highest impact factors. Based on results obtained, the third methodology was found to be the most comprehensive approach. Consequently, this search methodology was used to compare global and Australian nanoscience and nanotechnology publications for the period 1988-2000. Results demonstrated that depending on the search technique used, Australia ranks fourteenth to seventeenth internationally with a higher than world average number of nanoscience and nanotechnology publications. Over the last decade, Australia showed a relative growth rate in nanoscience and nanotechnology publications of 16% compared to 12% for the rest of the world. Researchers from China, the USA and the UK are from the main countries that collaborate with Australian researchers in nanoscience and nanotechnology publications.

DOI

[12]
Guan J.C., & Wei H. (2015). A bilateral comparison of research performance at an institutional level. Scientometrics,104(1), 147-173.An extensive body of research indicated that the USA and China were the first two largest producers in the nanoscience and nanotechnology field while China performed better than USA in terms of quantity; it had produced inferior quality publications. Yet, no studies investigated whether the specific institutions are consistent with these conclusions or not. In this study, we identify two institutions National Center for Nanoscience and Technology (NCNST) from China and University of California Los Angeles-California Nanosystems Institute (CNSI) from the USA) and compare their scientific research. Further, we develop and exploit a novel and updated dataset on paper co-authorship to assess their scientific research. Our analysis reveals NCNST has many advantages in regards to author and paper quantities, growth rate and the strength of collaborations but loses dominance with respect to research quality. We do find that the collaboration networks of both NCNST and CNSI have small-world and scale-free properties. Besides, the analysis of knowledge networks shows that they have similar research interests or hotspots. Using statistical models, we test and discover that degree centrality has a significant inverted-U shape effect on scientific output and influence. However, we fail to find any significant effect of structural holes.

DOI

[13]
Heimeriks G. (2013). Interdisciplinarity in biotechnology, genomics and nanotechnology. Science and Public Policy, 40(1), 97-112.In this paper we study developments in biotechnology, genomics and nanotechnology in the period 1998–2008. The fields show changing interdisciplinary characteristics in relation to distinct co-evolutionary dynamics in research, science and society. Biotechnology emerged as a discipline in publication patterns at the same time as the number of biotechnology departments increased, whereas genomics emerged as a stable discipline, while the number of genomics departments declined. Nanotechnology maintains an interdisciplinary journal citation pattern while the number of nanotechnology departments increased. In all three fields the importance of industry–university collaborations increased, albeit to different degrees. Patterns of interdisciplinarity can thus be distinguished, as different ways in which the three dynamics co-evolve. From a governance perspective, this conceptualization provides distinct rationales for policy interventions in relation to interdisciplinarity in research, science and society.

DOI

[14]
Herranz N., & Ruiz-Castillo J. (2012). Sub-field normalization in the multiplicative case: Average-based citation indicators. Journal of Informetrics, 6(4), 543-556.This paper investigates the citation impact of three large geographical areas – the U.S., the European Union (EU), and the rest of the world (RW) – at different aggregation levels. The difficulty is that 42% of the 3.6 million articles in our Thomson Scientific dataset are assigned to several sub-fields among a set of 219 Web of Science categories. We follow a multiplicative approach in which every article is wholly counted as many times as it appears at each aggregation level. We compute the crown indicator and the Mean Normalized Citation Score ( MNCS ) using for the first time sub-field normalization procedures for the multiplicative case. We also compute a third indicator that does not correct for differences in citation practices across sub-fields. It is found that: (1) No geographical area is systematically favored (or penalized) by any of the two normalized indicators. (2) According to the MNCS , only in six out of 80 disciplines – but in none of 20 fields – is the EU ahead of the U.S. In contrast, the normalized U.S./EU gap is greater than 20% in 44 disciplines, 13 fields, and for all sciences as a whole. The dominance of the EU over the RW is even greater. (3) The U.S. appears to devote relatively more – and the RW less – publication effort to sub-fields with a high mean citation rate, which explains why the U.S./EU and EU/RW gaps for all sciences as a whole increase by 4.5 and 5.6 percentage points in the un-normalized case. The results with a fractional approach are very similar indeed.

DOI

[15]
Johnson J.C., Luczkovich J.J., Borgatti S.P., & Snijders T.A.B. (2009). Using social network analysis tools in ecology: Markov process transition models applied to the seasonal trophic network dynamics of the Chesapeake Bay. Ecological Modelling, 220(22), 3133-3140.Ecosystem components interact in complex ways and change over time due to a variety of both internal and external influences (climate change, season cycles, human impacts). Such processes need to be modeled dynamically using appropriate statistical methods for assessing change in network structure. Here we use visualizations and statistical models of network dynamics to understand seasonal chan...

DOI

[16]
Jung H.J., & Lee J. (2014). The impacts of science and technology policy interventions on university research: Evidence from the US National Nanotechnology Initiative. Research Policy, 43(1), 74-91.We examine how the National Nanotechnology Initiative (NNI), a recent U.S. government science and technology (S&T) program launched in 2000, affects the nature of university research in nanotechnology. We characterize the NNI as a policy intervention that targets the commercialization of technology and a focused research direction to promote national economic growth. As such, we expect that the NNI has brought about unintended consequences in the direction of university-industry knowledge flows and the characteristics of university research output in nanotechnology. Using a difference-in-differences analysis of U.S. nanotechnology patents filed between 1996 and 2007, we find that, after the NNI, U.S. universities have significantly increased knowledge inflows from the industry, reduced the branching-out to novel technologies, narrowed down the research scope, and become less likely to generate technological breakthroughs, as compared to other U.S. and non-U.S. research institutions. Our findings suggest that, at least in the case of the NNI, targeted government S&T programs may increase the efficiency of university research, but potentially do so at a price.

DOI

[17]
Kostoff R.N., Barth R.B., & Lau C.G.Y. (2008). Relation of seminal nanotechnology document production to total nanotechnology document production—South Korea. Scientometrics, 76(1), 43-67.This study evaluates trends in quality of nanotechnology and nanoscience papers produced by South Korean authors. The metric used to gauge quality is ratio of highly cited nanotechnology papers to total nanotechnology papers produced in sequential time frames. In the first part of this paper, citations (and publications) for nanotechnology documents published by major producing nations and major producing global institutions in four uneven time frames are examined. All nanotechnology documents in the Science Citation Index [SCI, 2006] for 1998, 1999-2000, 2001-2002, 2003 were retrieved and analyzed in March 2007. In the second part of this paper, all the nanotechnology documents produced by South Korean institutions were retrieved and examined. All nanotechnology documents produced in South Korea (each document had at least one author with a South Korea address) in each of the above time frames were retrieved and analyzed. The South Korean institutions were extracted, and their fraction of total highly cited documents was compared to their fraction of total published documents. Non-Korean institutions that co-authored papers were included as well, to offer some perspective on the value of collaboration.

DOI

[18]
Kostoff R.N., Koytcheff R.G., & Lau C.G.Y. (2007). Global nanotechnology research metrics. Scientometrics, 70(3), 565-601.

[19]
Krug H.F., & Wick P. (2011). Nanotoxicology: An interdisciplinary challenge. Angewandte Chemie-International Edition, 50(6), 1260-1278.

[20]
Leydesdorff L. (2008). On the normalization and visualization of author co-citation data: Salton’s cosine versus the Jaccard index. Journal of the American Society for Information Science and Technology, 59(1), 77-85.The debate about which similarity measure one should use for the normalization in the case of Author Co-citation Analysis (ACA) is further complicated when one distinguishes between the symmetrical co-citation--or, more generally, co-occurrence--matrix and the underlying asymmetrical citation--occurrence--matrix. In the Web environment, the approach of retrieving original citation data is often not feasible. In that case, one should use the Jaccard index, but preferentially after adding the number of total citations (occurrences) on the main diagonal. Unlike Salton's cosine and the Pearson correlation, the Jaccard index abstracts from the shape of the distributions and focuses only on the intersection and the sum of the two sets. Since the correlations in the co-occurrence matrix may partially be spurious, this property of the Jaccard index can be considered as an advantage in this case.

DOI

[21]
Leydesdorff L. (2013). An evaluation of impacts in “nanoscience & nanotechnology”: Steps towards standards for citation analysis. Scientometrics, 94(1), 35-55.

[22]
Leydesdorff L., Carley S., & Rafols I. (2013). Global maps of science based on the new Web-of-Science categories. Scientometrics, 94(2), 589-593.In August 2011, Thomson Reuters launched version 5 of the Science and Social Science Citation Index in the Web of Science (WoS). Among other things, the 222 ISI Subject Categories (SCs) for these two databases in version 4 of WoS were renamed and extended to 225 WoS Categories (WCs). A new set of 151 Subject Areas was added, but at a higher level of aggregation. Perhaps confusingly, these Subject Areas are now abbreviated “SC” in the download, whereas “WC” is used for WoS Categories. Since we previously used the ISI SCs as the baseline for a global map in Pajek (Pajek is freely available at http://vlado.fmf.uni-lj.si/pub/networks/pajek/) (Rafols et al., Journal of the American Society for Information Science and Technology 61:1871–1887, 2010) and brought this facility online (at http://www.leydesdorff.net/overlaytoolkit ), we recalibrated this map for the new WC categories using the Journal Citation Reports 2010. In the new installation, the base maps can also be made using VOSviewer (VOSviewer is freely available at http://www.VOSviewer.com/) (Van Eck and Waltman, Scientometrics 84:523–538, 2010).

DOI PMID

[23]
Leydesdorff L., & Wagner C. (2009). Is the United States losing ground in science? A global perspective on the world science system. Scientometrics, 78(1), 23-36.

PMID

[24]
Lin C.S.L., & Ho Y.S. (2015). A bibliometric analysis of publications on pluripotent stem cell research. Cell Journal, 17(1), 59-70.OBJECTIVE: pluripotent stem cells are self-renewing cells with the ability to differentiate into a variety of cells and are viewed to have great potential in the field of regenerative medicine. Research in pluripotent stem cells holds great promise for patient specific therapy in various diseases. In this study, pluripotent stem cell articles published from 1991 to 2012 were screened and retrieved from Science Citation Index Expanded (SCI-EXPANDED).: In this retrospective study, the publication trend, citation trends for top articles, distributions of journals and Web of Science categories were analyzed. Five bibliometric indicators including total articles, independent articles, collaborative articles, first author articles, and corresponding author articles were applied to compare publications between countries and institutions.: The impact of top articles changed from year to year. Top cited articles in previous publication years were not the same as recent years. "Induced pluripotent stem cell (s)" and "stem cell (s)" were the most used author keywords in pluripotent stem cell research. In addition, the winner of the Nobel Prize in physiology or medicine in 2012, Prof. Shinya Yamanaka, published four of the top ten most frequently cited articles.: The comprehensive analysis of highly cited articles in the stem cell field could identify milestones and important contributors, giving a historic perspective on scientific progress.

DOI PMID

[25]
Lindquist N.C. (2014). Interdisciplinary chemistry and physics research and advanced nanotechnology labs. Abstracts of Papers of the American Chemical Society, 247.

[26]
Martin Y., Williams C.C., & Wickramasinghe H.K.(1987). Atomic force microscope foce mapping and profiling on a sub 100-A scale. Journal of Applied Physics, 61(10), 4723-4729.

[27]
Milanez D.H., do Amaral R.M., Faria L.I.L. de, & Gregolin J.A.R. (2013). Assessing nanocellulose developments using science and technology indicators. Materials Research-Ibero-American Journal of Materials, 16(3), 635-641.

[28]
Mody C.C.M., & Choi H. (2013). From materials science to nanotechnology: Interdisciplinary center programs at Cornell University, 1960-2000. Historical Studies in the Natural Sciences, 43(2), 121-161.During the last several decades, interdisciplinary research centers have emerged as a standard, powerful tool for federal funding of university research. This paper contends that this organizational model can be traced to the "Interdisciplinary Laboratories" program funded by the Advanced Research Projects Agency in 1960. The novelty of the IDL program was that it created a peer group of university laboratories with sustained funding to ensure their institutional stability. The Cornell Materials Science Center, one of the first three Interdisciplinary Laboratories, served as a breeding ground for a new community of engineering faculty members, who subsequently helped establish a series of interdisciplinary research centers at Cornell, including the National Research and Resource Facility for Submicron Structures (or National Submicron Facility) in 1977. The Materials Science Center and National Submicron Facility provided explicit models for the expansion and coordination of networks of interdisciplinary centers, both within single universities (such as Cornell) and across multiple campuses (through programs such as the National Nanotechnology Infrastructure Network and the Nanoscale Science and Engineering Centers). The center model has proved both flexible and durable in the face of changing demands on universities. By examining the Materials Science Center and the National Submicron Facility, we show that recent institutional developments perceived as entirely novel have their roots in the high Cold War years.

DOI

[29]
Mohammadi E. (2012). Knowledge mapping of the Iranian nanoscience and technology: A text mining approach. Scientometrics, 92(3), 593-608.Abstract<br/>Nanoscience and technology (NST) is a relatively new interdisciplinary scientific domain, and scholars from a broad range of different disciplines are contributing to it. However, there is an ambiguity in its structure and in the extent of multidisciplinary scientific collaboration of NST. This paper investigates the multidisciplinary patterns of Iranian research in NST based on a selection of 1,120 ISI—indexed articles published during 1974–2007. Using text mining techniques, 96 terms were identified as the main terms of the Iranian publications in NST. Then the scientific structure of the Iranian NST was mapped through multidimensional scaling, based upon the co-occurrence of the main terms in the academic publications. The results showed that the NST domain in Iranian publications has a multidisciplinary structure which is composed of different fields, such as pure physics, analytical chemistry, chemistry physics, material science and engineering, polymer science, biochemistry and new emerging topics.<br/>

DOI

[30]
National Academies Committee on Facilitating Interdisciplinary Research, Committee on Science, Engineering, Public Policy (COSEPUP). (2005). Facilitating interdisciplinary research. Washington, D.C.: National Academies Press.

[31]
Patenaude J., Legault G.A., Beauvais J., Bernier L., Beland J.P., Boissy P., Chenel V., Daniel C.E., Genest J., Poirier M.S., & Tapin D. (2015). Framework for the analysis of nanotechnologies’ impacts and ethical acceptability: Basis of an interdisciplinary approach to assessing novel technologies. Science and Engineering Ethics, 21(2), 293-315.The genetically manipulated organism (GMO) crisis demonstrated that technological development based solely on the law of the marketplace and State protection against serious risks to health and safety is no longer a warrant of ethical acceptability. In the first part of our paper, we critique the implicitly individualist social-acceptance model for State regulation of technology and recommend an interdisciplinary approach for comprehensive analysis of the impacts and ethical acceptability of technologies. In the second part, we present a framework for the analysis of impacts and acceptability, devised-with the goal of supporting the development of specific nanotechnological applications-by a team of researchers from various disciplines. At the conceptual level, this analytic framework is intended to make explicit those various operations required in preparing a judgement about the acceptability of technologies that have been implicit in the classical analysis of toxicological risk. On a practical level, we present a reflective tool that makes it possible to take into account all the dimensions involved and understand the reasons invoked in determining impacts, assessing them, and arriving at a judgement about acceptability.

DOI PMID

[32]
Persson O., & Dastidar P.G.(2013). Citation analysis to reconstruct the dynamics of Antarctic ozone hole research and formulation of the Montreal Protocol. Current Science, 104(7), 835-840.

[33]
Porter A.L., & Youtie J. (2009). How interdisciplinary is nanotechnology? Journal of Nanoparticle Research, 11(5), 1023-1041.<a name="Abs1"></a>Facilitating cross-disciplinary research has attracted much attention in recent years, with special concerns in nanoscience and nanotechnology. Although policy discourse has emphasized that nanotechnology is substantively integrative, some analysts have countered that it is really a loose amalgam of relatively traditional pockets of physics, chemistry, and other disciplines that interrelate only weakly. We are developing empirical measures to gauge and visualize the extent and nature of interdisciplinary interchange. Such results speak to research organization, funding, and mechanisms to bolster knowledge transfer. In this study, we address the nature of cross-disciplinary linkages using “science overlay maps” of articles, and their references, that have been categorized into subject categories. We find signs that the rate of increase in nano research is slowing, and that its composition is changing (for one, increasing chemistry-related activity). Our results suggest that nanotechnology research encompasses multiple disciplines that draw knowledge from disciplinarily diverse knowledge sources. Nano research is highly, and increasingly, integrative—but so is much of science these days. Tabulating and mapping nano research activity show a dominant core in materials sciences, broadly defined. Additional analyses and maps show that nano research draws extensively upon knowledge presented in other areas; it is not constricted within narrow silos.

DOI

[34]
Porter A.L., Youtie J., Shapira P., & Schoeneck D.J. (2008). Refining search terms for nanotechnology. Journal of Nanoparticle Research, 10(5), 715-728.The ability to delineate the boundaries of an emerging technology is central to obtaining an understanding of the technology’s research paths and commercialization prospects. Nowhere is this more relevant than in the case of nanotechnology (hereafter identified as “nano”) given its current rapid growth and multidisciplinary nature. (Under the rubric of nanotechnology, we also include nanoscience and nanoengineering.) Past efforts have utilized several strategies, including simple term search for the prefix nano, complex lexical and citation-based approaches, and bootstrapping techniques. This research introduces a modularized Boolean approach to defining nanotechnology which has been applied to several research and patenting databases. We explain our approach to downloading and cleaning data, and report initial results. Comparisons of this approach with other nanotechnology search formulations are presented. Implications for search strategy development and profiling of the nanotechnology field are discussed.

DOI

[35]
Roco M.C. (2001). From vision to the implementation of the US National Nanotechnology Initiative. Journal of Nanoparticle Research, 3(1), 5-11.All natural and living systems are governed by atomic and molecular behavior at the nanoscale. Research is now seeking systematic approaches to create revolutionary new products and technologies by control of matter at the same scale. Fundamental discoveries and potential implications of nanotechnology to wealth, health and peace have captured the imagination of scientists, industry and government experts. The National Nanotechnology Initiative (NNI) has become a top national priority in science and technology in U.S. for fiscal year 2001, with a Federal nanotechnology investment portfolio of $422 million. Nanotechnology is expected to have a profound impact on our economy and society in the earlier 21st century. The vision, research and development strategy, and timeline of NNI are presented by using several recent scientific discoveries and results from industry.

DOI

[36]
Schummer J. (2004). Multidisciplinarity, interdisciplinarity, and patterns of research collaboration in nanoscience and nanotechnology. Scientometrics, 59(3), 425-465.This paper first describes the recent development that scientists and engineers of many disciplines, countries, and institutions increasingly engage in nanoscale research at breathtaking speed. By co-author analysis of over 600 papers published in “nano journals” in 2002 and 2003, I investigate if this apparent concurrence is accompanied by new forms and degrees of multi- and interdisciplinarity as well as of institutional and geographic research collaboration. Based on a new visualization method, patterns of research collaboration are analyzed and compared with those of classical disciplinary research. I argue that current nanoscale research reveals no particular patterns and degrees of interdisciplinarity and that its apparent multidisciplinarity consists of different largely mono-disciplinary fields which are rather unrelated to each other and which hardly share more than the prefix “nano”.

DOI

[37]
Souminen A., Li Y., & Youtie J. (2016). A bibliometric analysis of the development of next generation active nanotechnologies. Journal of Nanoparticle Research, 18(9), 270.Delineating the emergence of nanotechnologies that offer new functionalities is an important element in an anticipatory approach to the governance of nanotechnology and its potential impacts. This paper examines the transition to next generation active nanotechnologies which incorporate functions that respond to the environment or systems concepts that combine devices and structures that are dynamic and which may change their states in use. We develop an approach to identifying these active nanotechnologies and then use bibliometric analysis to examine the extent of research papers and patents involving these concepts. We also examine references to environmental, health, and safety concepts in these papers, given that these next generation nanotechnologies are likely to have risk profiles that are different from those of first-generation passive nanomaterials. Our results show a steady growth overall in focus on active nanotechnologies in the research literature and in patents over the study period of 1990-2010. We also find an increase in consideration given to environmental, health, and safety topics. While gaps are highlighted in our understanding of research and innovation in active nanotechnologies, the results suggest that there is beginning to be a shift to active nanotechnologies, with the implication that governance processes need to be conscious of this shift and to prepare for it.

DOI

[38]
Sweileh W.M., Al-Jabi S.W., Sawalha A.F., & Zyoud S.H. (2014). Bibliometric analysis of nutrition and dietetics research activity in Arab countries using ISI Web of Science database. Springerplus, 3(1), 718.Reducing nutrition-related health problems in Arab countries requires an understanding of the performance of Arab countries in the field of nutrition and dietetics research. Assessment of research activity from a particular country or region could be achieved through bibliometric analysis. This study was carried out to investigate research activity in "nutrition and dietetics" in Arab countries. Original and review articles published from Arab countries in "nutrition and dietetics" Web of Science category up until 2012 were retrieved and analyzed using the ISI Web of Science database. The total number of documents published in "nutrition and dietetics" category from Arab countries was 2062. This constitutes 1% of worldwide research activity in the field. Annual research productivity showed a significant increase after 2005. Approximately 60% of published documents originated from three Arab countries, particularly Egypt, Kingdom of Saudi Arabia, and Tunisia. However, Kuwait has the highest research productivity per million inhabitants. Main research areas of published documents were in "Food Science/Technology" and "Chemistry" which constituted 75% of published documents compared with 25% for worldwide documents in nutrition and dietetics. A total of 329 (15.96%) nutrition - related or or documents were published from Arab countries compared with 21% for worldwide published documents. Interest in nutrition and dietetics research is relatively recent in Arab countries. Focus of nutrition research is mainly toward food technology and chemistry with lesser activity toward nutrition-related health research. International cooperation in nutrition research will definitely help Arab researchers in implementing nutrition research that will lead to better national policies regarding nutrition.

DOI PMID

[39]
Tersoff J., & Hamann D.R. (1983). Theory and application for the scanning tunneling microscope. Physical Review Letters, 50(25), 1998-2001.A theory is presented for vacuum tunneling between a real solid surface and a model probe with a locally spherical tip, applicable to the recently developed "scanning tunneling microscope." Calculations for 2×1 and 3×1 reconstructions of Au(110) are in excellent agreement with recent experimental results, if an effective radius of curvature of 9 03 is assumed for the tip.

DOI

[40]
Tersoff J., & Hamann D.R. (1985). Theory of the scanning tunneling microscope. Physical Review B, 31(2), 805-813.

[41]
Wang J., & Shapira P. (2011). Funding acknowledgement analysis: An enhanced tool to investigate research sponsorship impacts: The case of nanotechnology. Scientometrics, 87(3), 563-586.There is increasing interest in assessing how sponsored research funding influences the development and trajectory of science and technology. Traditionally, linkages between research funding and subsequent results are hard to track, often requiring access to separate funding or performance reports released by researchers or sponsors. Tracing research sponsorship and output linkages is even more challenging when researchers receive multiple funding awards and collaborate with a variety of differentially-sponsored research colleagues. This article presents a novel bibliometric approach to undertaking funding acknowledgement analysis which links research outputs with their funding sources. Using this approach in the context of nanotechnology research, the article probes the funding patterns of leading countries and agencies including patterns of cross-border research sponsorship. We identify more than 91,500 nanotechnology articles published worldwide during a 12-month period in 2008-2009. About 67% of these publications include funding acknowledgements information. We compare articles reporting funding with those that do not (for reasons that may include reliance on internal core-funding rather than external awards as well as omissions in reporting). While we find some country and field differences, we judge that the level of reporting of funding sources is sufficiently high to provide a basis for analysis. The funding acknowledgement data is used to compare nanotechnology funding policies and programs in selected countries and to examine their impacts on scientific output. We also examine the internationalization of research funding through the interplay of various funding sources at national and organizational levels. We find that while most nanotechnology funding is nationally-oriented, internationalization and knowledge exchange does occur as researchers collaborate across borders. Our method offers a new approach not only in identifying the funding sources of publications but also in feasibly undertaking large-scale analyses across scientific fields, institutions and countries.

DOI

[42]
Wong P.K., Ho Y.P., & Chan C.K. (2007). Internationalization and evolution of application areas of an emerging technology: The case of nanotechnology. Scientometrics, 70(3), 715-737.Nanotechnology patenting has grown rapidly in recent years as an increasing number of countries are getting into the global nanotechnology race. Using a refined methodology to identify and classify nanotechnology patents, this paper analyses the changing pattern of internationalization of nanotechnology patenting activities from 1976–2004. We show that the dominance of the G5 countries have declined in recent years, not only in terms of quantity, but also in terms of quality as measured by citation indicators. In addition, using a new approach to classifying the intended areas of commercial applications, we show that nanotechnology patenting initially emphasized instrumentation, but exhibited greater diversification to other application areas in recent years. Significant differences in application area specialization are also found among major nanotechnology nations. Moreover, universities are found to play a significant and increasing role in patenting, particularly in US, UK and Canada.

DOI

Options
Outlines

/

〈 〉

京ICP备05002861号-43

Copyright © 2023 All rights reserved Journal of Data and Information Science

E-mail: jdis@mail.las.ac.cn　Add:No.33, Beisihuan Xilu, Haidian District, Beijing 100190, China

Support by Beijing Magtech Co.ltd　E-mail: support@magtech.com.cn