Abstract
This chapter will present the results of analyzes conducted on scientific publications and patent documents in two different databases and with three different software tools. In total six datasets are analyzed, of which two were compiled on the basis of a keyword-search (with the example of phytosterols) and the last four on the basis of a keyword-independent classification search (with examples from NFF and ICT). First, each sample will be briefly introduced with a short description of its originating database and used software.
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Notes
- 1.
This distinction is attributed to van Raan, who discerns between counts of activity (one-dimensional) and co-occurrence measures (two-dimensional).
- 2.
Signs and indications of trademarks or registered names will be only mentioned during the first appearance of a new name. For easier reading, those signs will be omitted from the following part.
- 3.
By academics it is mostly used in the SciFinder Scholar version, which offers slightly reduced functionalities at a reduced rate for university students and faculty members. Within this study the SciFinder Scholar 2007 version was used.
- 4.
In respect to patents, CAS provides a list of IPCs with either guaranteed or selective coverage.
- 5.
For example, if a set of documents is selected and the number of citations to each of these documents is to be assessed, then this has to be done one by one. Using this selection and then looking for the citing documents will yield all documents citing any from the original sample. But it will not show whether, e.g., one document will cite more than one of the original documents. Thus, any statistical analysis will have to be carefully planned and arranged in order to derive unbiased results.
- 6.
In SciFinder the function “containing at least one of the concepts” was used, yielding all references that contain at least one of the search-terms.
- 7.
Technically, a search in SciFinder only yields references (and direct links) to scientific publications or patent documents, but not the original document. They contain broad information on the document like title, author/inventor/assignee, classifications, patent numbers, dates, journal and abstract. However, they do not contain the full document’s content or even the document itself. Notwithstanding this decisive difference, the text will refer to these references as if they were the original documents for an increased legibility.
- 8.
A repetition of the same search at the beginning of 2010 yielded an additional 1,311 references for the time since 2008 alone, highlighting the still growing interest in phytosterols.
- 9.
For ease of reading, percentages will be mathematically rounded to whole numbers, wherever appropriate.
- 10.
This sample represents a good 36 % of the initial set of patents.
- 11.
There was intentionally no dedicated chemicals cluster arranged, as chemical topics cannot be sensibly separated from the three other industries. A lot of the chemical subject areas may as well be a core area of the chemical industry as of, e.g., the pharmaceutical industry. Therefore, all potentially chemicals’ related subject areas were also assigned to the Rest cluster.
- 12.
That is, each subject area that represented less than 1 % of all documents was omitted for handling reasons.
- 13.
Allowedly, 43 % is not the majority of patents from the pharmaceutical sector, but it demonstrates a similar tendency insofar as it is the single most important subject cluster.
- 14.
The source of significant keywords and concepts for this similarity calculation can be chosen when initially importing the dataset into STN AnaVist. Based on the database, different clustering fields for data and text mining can be chosen. While the patent databases PCTFULL, USPATFULL and WPINDEX offer the possibility to use the IPC codes or (parts of) the claims, CAplus can be visualized on the basis of title and abstract content or CAplus’ own technology indicators. IPC codes can be employed for patent documents in CAplus, but naturally not for scientific articles. Whatever choice is made, the clustering fields are permanent for a certain project and all visualizations created from it. Therefore, visualization of the current sample was based on the contents in title/abstract and the claims (in patents) of the documents.
- 15.
Terms may be included in a maximum of between 10 and 70 % of all documents. This concept frequency cannot be adjusted when using IPC code visualizations, which always use a frequency of 100 %.
- 16.
STN AnaVist, Version 2.0 was used in the course of these analyzes.
- 17.
STN Express, Version 8.4 for Windows was used in the course of these analyzes.
- 18.
The employed search-terms were ‘phytosterol’ and ‘plant(W)sterol’ and their derivatives. The (W) is a proximity operator requiring that answers will have the connected terms adjacent to each other in the specified order.
- 19.
The group of authors from the food area contains researchers from companies or universities/research institutions in areas such as nutrition, animal feed, biology and agriculture.
- 20.
Besides chemistry in a narrower sense, researchers from biochemical institutions and alike were also included.
- 21.
Pharmaceuticals include affiliations within the pharmaceutical sciences as well as medical institutions.
- 22.
Cross-disciplinary in this case means authors from at least two different scientific disciplines.
- 23.
In both decades this cluster is the denser one with more articles (38–16 in 1979–1988 and 52–31 in 1989–1998) focusing on the application of phytosterols.
- 24.
This particularly high percentage of unassigned patents will be discussed in Sect. 6.1.2.1 in greater detail.
- 25.
Due to the small number of patents prior to 1998, outcomes were not expected to be reliable up to then and consequently eliminated from this analysis. In addition, 2 years were added each to one landscape to provide numbers of references above the threshold for a compilation of such landscapes.
- 26.
Time coverage does actually differ considerably from country to country. Even for the few countries with more than 500,000 records in INPADOCDB, this coverage varies between 1790 (USA) and 1985 (France).
- 27.
Food and pharmaceuticals IPCs are both in section A. The cluster’s own IPCs were excluded when calculating the co-classification, of course. Sections have been briefly introduced in Sect. 3.3.1. They are: (A) human necessities; (B) performing operations, transporting; (C) chemistry, metallurgy; (D) textiles, paper; (E) fixed constructions; (F) Mechanical engineering, lighting, heating, weapons, blasting; (G) physics; (H) electricity.
- 28.
Due to the co-classification the complete sample is 7,130 patents smaller than the sum of both groups.
- 29.
Due to the co-classification the complete sample is 1,080 patents smaller than the sum of both groups.
- 30.
Due to the co-classification the complete sample is 18,703 patents smaller than the sum of both groups.
- 31.
Due to the co-classification the complete sample is 31,211 patents smaller than the sum of the five groups.
- 32.
Where they could be calculated without violating mathematical rules, they were 0 % for the first 22 years.
- 33.
For a discussion of convergence patterns in ICT see Sect. 2.3.1.
- 34.
In part with the exception of Japanese patents in the area of NFF.
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Curran, CS. (2013). Analysis and Results. In: The Anticipation of Converging Industries. Springer, London. https://doi.org/10.1007/978-1-4471-5170-8_5
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