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Research Article

Measuring novelty in science with word embedding

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations School of Economics and Management, Lund University, Lund, Sweden, Institute for Future Initiative, The University of Tokyo, Tokyo, Japan, National Institute of Science and Technology Policy, Tokyo, Japan

Roles Data curation, Writing – original draft, Writing – review & editing

Affiliations School of Economics and Management, Harbin Institute of Technology, Shenzhen, China, World Intellectual Property Organization, Geneva, Switzerland

Roles Data curation, Formal analysis, Writing – review & editing

Affiliation National Institute of Science and Technology Policy, Tokyo, Japan

• Sotaro Shibayama, 
• Deyun Yin, 
• Kuniko Matsumoto

• Published: July 2, 2021
• https://doi.org/10.1371/journal.pone.0254034
• Reader Comments

Novelty is a core value in science, and a reliable measurement of novelty is crucial. This study proposes a new approach of measuring the novelty of scientific articles based on both citation data and text data. The proposed approach considers an article to be novel if it cites a combination of semantically distant references. To this end, we first assign a word embedding –a vector representation of each vocabulary–to each cited reference on the basis of text information included in the reference. With these vectors, a distance between every pair of references is computed. Finally, the novelty of a focal document is evaluated by summarizing the distances between all references. The approach draws on limited text information (the titles of references) and publicly shared library for word embeddings, which minimizes the requirement of data access and computational cost. We share the code, with which one can compute the novelty score of a document of interest only by having the focal document’s reference list. We validate the proposed measure through three exercises. First, we confirm that word embeddings can be used to quantify semantic distances between documents by comparing with an established bibliometric distance measure. Second, we confirm the criterion-related validity of the proposed novelty measure with self-reported novelty scores collected from a questionnaire survey. Finally, as novelty is known to be correlated with future citation impact, we confirm that the proposed measure can predict future citation.

Citation: Shibayama S, Yin D, Matsumoto K (2021) Measuring novelty in science with word embedding. PLoS ONE 16(7): e0254034. https://doi.org/10.1371/journal.pone.0254034

Editor: Alessandro Muscio, Universita degli Studi di Foggia, ITALY

Received: February 15, 2021; Accepted: June 17, 2021; Published: July 2, 2021

Copyright: © 2021 Shibayama et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files .

Funding: S.S. received a research grant from Lars Erik Lundberg Foundation ( https://www.lundbergsstiftelserna.se ) and Japan Society for the Promotion of Science (19K01830, https://www.jsps.go.jp/english/index.html ). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Novelty constitutes a core value in science, as new discoveries shape the basis of scientific advancement [ 1 , 2 ] and has broader impact on technological innovation [ 3 ]. Accordingly, novelty serves as a key criterion for the evaluation of scientific output as well as decision makings such as funding allocation, employment, and scientific awards [ 1 , 4 – 6 ]. It is therefore critical that scientific novelty can be reliably measured. In practice, novelty is usually assessed through peer review on a small scale [ 7 ], while evaluating novelty on a larger scale remains to be a challenge. Though recent bibliometric techniques have enabled us to measure various qualities of scientific discoveries, including novelty [ 8 – 11 ], the validity and practical utility of the extant measures are debatable [ 12 , 13 ].

Previous bibliometric measures for the novelty of scientific documents draw on roughly two data sources, either citation data or text data. Text data are of obvious use, in that once a scientific discovery is documented, its novelty should be revealed in text information. Nonetheless, due to the ambiguity and complexity of natural languages, previous measures use text data rather superficially without sufficiently exploiting the semantic information [e.g., 14 ]. It is relatively recently that such semantic information got extracted from text data and translated into bibliometric indices [e.g., 15 ]. To circumvent the technical challenges in extracting semantic information from text data, citation data have been extensively utilized in previous novelty measures. As a citation represents information flow from a cited document to a citing document, it can be used to infer certain qualities, including novelty, of a document without scrutinizing the content [ 10 , 16 ]. However, the validity of this approach has been occasionally questioned [ 12 ]. In fact, insufficient validation has been a limitation common to most novelty measures [ 17 ]. Furthermore, a practical limitation common to previous measures is that they require access to a large-scale bibliometric database (often the whole universe of scientific documents), which are usually proprietary and expensive, and high computational power, which potential users of the measures do not always have.

To address previous limitations, we propose a new approach to compute the novelty of scientific documents by combining both citation and text data (see Fig 1 ). Our approach features recombinant novelty [ 18 – 21 ], considering a document to be novel if it cites a combination of semantically distant documents. This is in line with the previous measures based on citation data [e.g., 8 ]. Unlike previous measures, however, we use text data to quantify the distances between cited documents. Specifically, based on the text information included in cited documents, we map each document to a word embedding –a high-dimensional vector assigned to each vocabulary [ 22 ]–with which to compute distances between cited documents. To the best of our knowledge, this is the first to use the word-embedding technique to measure the novelty of scientific documents.

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https://doi.org/10.1371/journal.pone.0254034.g001

For text information, we test three sources–the abstract, keywords, and the title of cited documents–finding all satisfactory performance. Of the three sources, titles of cited documents are often included in the focal document itself, and the burden of data access is minimized. As a library of word embeddings, we draw on scispaCy [ 23 ], which is publicly available and thus significantly reduces the computational cost. We publicly share the code [ 24 ], with which one can compute the novelty score of a document only with the focal document’s reference list.

We validate the proposed measure in three exercises. First, we confirm that word embeddings from the selected library can be used to quantify semantic distances between documents by comparing with an established bibliometric distance measure. Second, we test the criterion-related validity of the proposed novelty measure based on self-reported novelty scores collected from a questionnaire survey. Third, as novelty is known to be a predictor of future citation impact [ 8 , 11 ], we test whether the proposed measure is correlated with future citation.

This paper is structured as follows. In the next section, we categorize previous novelty measures and discuss their characteristics and limitations. The following section describes our proposed measure and outlines its operationalization. Then, we present the methods and data for the validation exercises. Finally, we present the results and conclude.

Literature review

Previous bibliometric measures for novelty can be categorized based on their conceptualization and operationalization ( Table 1 ). Conceptually, some measures aim to represent the uniqueness of a certain knowledge element (Groups 1 and 4)–for example, a discovery of a new molecule and development of a new material. In contrast, other measures aim to capture a recombination of knowledge elements (Groups 2 and 3), in which a new or rare combination of knowledge is considered to be a sign of novelty. The notion of recombination as a source of novelty has been widely discussed in the literature. The creativity literature argues that associating remote elements is a path to creative solution in general as well as in science [ 18 , 19 ], and the management literature suggests that combining components is a major route to technological innovation [ 20 , 21 ].

https://doi.org/10.1371/journal.pone.0254034.t001

For operationalization, a group of measures exploits citation information to assess novelty indirectly (Group 3), and the other draws on text analysis to assess the content of documents (Groups 1, 2, and 4). Among the latter, the majority uses text information only superficially without using the semantic information of the text (Groups 1 and 2), but recent measures attempt to extract semantic information (Group 4). Studies on novelty measures have been relatively advanced in technology management, in which a patent is used as a unit of document [e.g., 16 , 25 ]. We also refer to these measures because the key idea behind the measures is applicable to scientific documents. In what follows, we discuss four groups of previous measures.

(1) A new word

The first group of novelty measures is based on the first appearance of a word(s) that appears in a document [ 14 , 25 ]. If a document includes or is associated with a certain word or a sequence of words that is new to the world, it can be inferred that the document delivers novel information. For example, if a document contains a previously unknown chemical compound, it suggests that the document is novel. In this category, Azoulay et al . [ 14 ] drew on Medical Subject Heading (MeSH), a controlled keyword dictionary, and operationalized the novelty of a journal article based on the average age of keywords (the number of years since its first appearance). Balsmeier et al . [ 26 ] and Arts et al . [ 25 ] also identified novel inventions based on the first occurrence of a word as well as a sequence of words (bigram and trigram) in patent documents.

(2) Recombination of words

The second group is technically similar to the first group but conceptually different as it is to measure "recombinant" novelty [ 19 , 20 ]. When a document includes a rare combination of knowledge elements, even if each element has been known, the document can be considered to be novel. In this category, Boudreau et al . [ 9 ] measured the novelty of a research grant proposal based on a new combination of MeSH keywords. Similarly, drawing on a controlled dictionary of patent classifications, Verhoeven et al . [ 27 ] measured recombinant novelty by a new combination of IPC codes assigned to the patent. Arts et al . [ 25 ] also measured the novelty of a patent based on a new combination of two words that appeared in the patent.

The first and second groups are intuitively straightforward but have some limitations. Among others, these measures largely disregard semantic information included in text data. For example, the first group may consider a new synonym of an existing concept to be novel, unless controlled dictionaries are available. Similarly, the second group may consider any recombination equally novel regardless of the semantic distance between combined elements.

(3) Recombination of cited documents

The third group also measures recombinant novelty, but instead of using text information, it draws on citation information. A document citing another document implies that knowledge in the latter is used by the former [ 28 ]. Thus, a document can be characterized by its cited documents, by considering each of cited documents to be a knowledge element that is incorporated into the citing document. Based on the recombinant novelty concept [ 18 , 19 ], a document citing a set of documents that have rarely been cited together can be considered as a sign of novelty. In contrast to the first and second groups, in which a single word is considered a representation of knowledge, considering a cited document as a knowledge element adds semantic richness, making this approach popular in previous studies.

In this group, Dahlin and Behrens [ 16 ] proposed a novelty measure of patents based on a rare combination of cited references. Trapido [ 10 ] applied the same approach to journal articles, specifically in the field of electrical engineering. This approach is extended by Matsumoto et al . [ 17 ] so that it is applicable in any scientific field. A variation of this approach is to draw on journals in which cited documents are published [ 8 , 11 ]. That is, if a focal document cites documents in two journals that have rarely been cited together, it is considered as a sign of novelty. This approach thus consolidates the unit of knowledge further at the journal level. Though considering a document or a journal as a unit of knowledge, without needing to scrutinize the content of documents, is convenient, its validity is under dispute [ 12 , 13 ].

(4) A distant text

The last group quantifies the uniqueness of a document based on text analysis, and relies on more recent development of natural language processing (NLP) to extract semantic information. In particular, drawing on the word embedding technique, Hain et al . [ 15 ] proposed a measure of patent novelty. Word embeddings map each word to a high-dimensional vector (i.e., a list of numbers). It allows us to quantify a semantic relationship between a pair of words by calculating the distance between the vectors–i.e., similar words have close vectors while dissimilar words have remote vectors. Hain et al . [ 15 ] assigned a vector to each patent by aggregating the vectors for a set of words that appear in the patent. Then, they calculated a distance between every pair of patents, with which a patent remote from any other patent is considered to be novel.

Proposed measure of novelty

Measuring novelty with word embedding.

As a new approach, we propose to measure recombinant novelty of scientific documents by applying the combination of the word embedding technique and citation analysis. We consider a cited document as an appropriate unit of knowledge input, as in Group 3. Unlike the previous measures, which disregard the content of cited documents, we draw on the word embedding technique to extract semantic information in cited documents.

The word embedding technique often draws on machine learning algorithms (e.g., word2vec) to calculate a vector representation for each word based on the co-occurrences of words in a text corpus [ 22 ]. The approach is gaining confidence as the performance of machine learning has been improving, and has been recently applied to scientific documents for various purposes. For example, Tshitoyan et al . [ 29 ] captures the knowledge structure in the extant literature in material sciences with which they predict future scientific discoveries in the field. Still, to the best of our knowledge, the technique has not been used to measure the novelty of scientific documents.

Although computing word embeddings is demanding, some algorithms are publicly available, and some well-trained word embedding models (a list of vectors for a set of vocabularies) are also publicly accessible [ 30 ]. In this study, we use scispaCy as an established and publicly available library of word embeddings. ScispaCy builds on a popular spaCy model [ 30 ] and offers vector representations in a 200-dimensional vector space for 600,000 vocabularies specializing in biomedical texts [ 23 , 31 ].

Operationalization

With the selected word embedding library and citation information, the novelty of a document is computed through the following steps ( Fig 1 ). Suppose that a focal document cites N references, and that each of the cited references has some text information. One can use various sources of text information, such as the full text and the abstract. In the following analysis, we construct respective measures from three text sources: the abstract, keywords, and the title of cited documents. Of the three sources, we intend to propose primarily using the title to minimize data requirement and maximize the utility of the measure.

Step 1. First, we vectorize the text information of the i -th reference as v i ∈ℝ 200 ( i ∈{1,…, N }). Since the text information includes multiple words, v i is calculated as the mean of word embeddings of all words included.

The cosine distance ranges from 0 to 2, where a larger value indicates a larger distance.

Computational cost

The aforementioned previous measures of novelty require extensive data access and processing. Text-based approaches ( Table 1 , Groups 1, 2, and 4) require the entire history of word uses, and citation-based approaches ( Table 1 , Group 3) need comprehensive citation network data. This poses two practical challenges for potential users of the novelty measures. First, the required data are usually proprietary, and thus, literally expensive. Second, processing the massive data takes high computational power. Not all users have such rich resources, compromising the utility of the measures.

Our proposed approach addresses these issues and aims to allow anyone to compute and use the novelty measures. Our measure requires only limited data access and little need for proprietary data. The measure can be computed only with the titles of a focal document’s cited references, which is often included in the focal document itself, and a publicly available library of word embeddings. The approach requires only small data processing. Unlike previous measures, our approach does not require extensive citation network analysis unlike Group 3, nor comparison with the whole document universe unlike Group 4. With the publicly shared code, anyone can compute the measure.

Methods and data

Previous novelty measures have been rarely validated with a few exceptions [ 17 ]. To confirm the validity of our proposed measure, we carry out three exercises. The primary analysis is to test the criterion-related validity based on self-reported novelty scores for selected documents. As a preparatory step to this main analysis, we test whether scispaCy word embeddings can be indeed used to measure distances between documents (corresponding to Step 2). Finally, since novelty is known as a predictor of future citation impact [ 8 , 11 ], we run regression analyses to test whether our proposed measure is positively associated with future citation.

To compute the proposed measures, we downloaded bibliometric information from Web of science (WoS). Since scispaCy specializes in the vocabularies in biomedicine, we focus on documents within relevant Subject Categories [ 32 ]. We focus on "article" as a document type and documents written in "English" [ 33 ]. We employ different sets of random samples for each analysis as detailed below.

Validation of distance

Before validating the novelty measure itself, we test if scispaCy word embeddings convey semantic information of a text, and that they can assess the distance between a pair of documents. To this end, we compute distances of pairs of documents in two approaches–one based on scispaCy word embeddings and the other with a previously established approach–and confirm that the two are sufficiently correlated.

For this analysis, we employed the following sampling strategy. First, we randomly sampled 100 authors in the field of biomedicine. Then, we collected all documents authored by these authors [ 34 ]. Finally, we filtered out documents outside of the biomedical field as well as documents missing reference information, resulting in 1,600 documents (16 documents per author on average). We compute the distance measures between documents written by the same author (i.e., we do not compare documents written by different authors). This is because co-citation is rare between a randomly chosen pair of documents written by different authors, which spuriously inflates the correlation.

Validation of novelty

After confirming that the scispaCy word embeddings carry semantic information of text, we test the criterion-related validity of the proposed novelty measure ( Eq 2 ). To this end, we draw on self-reported novelty scores, which we obtained from a questionnaire survey we conducted in 2009–2010 [ 35 , 36 ]. The survey was responded by 2,081 scientists from various scientific fields, of whom this study draws on a subset of 321 respondents in biomedical fields.

The survey included a wide range of questionnaire items, one section of which asked the respondents to assess a randomly selected journal article that they published in 2001–2006. This section includes eight items to characterize the finding reported in the article ( Table 2 ). As novelty is a multifaceted concept [ 37 ], the survey incorporated four aspects (theory, phenomenon, method, and material) in which the article may make scientific contribution. For each aspect, the survey further included two items, one indicating newness and the other indicating improvement over existing literature. We expect that the proposed measure should be correlated more with the newness items but less with the improvement items. Each item was responded in a 5-point scale (1: not relevant at all—5: highly relevant).

https://doi.org/10.1371/journal.pone.0254034.t002

Prediction of future citation

For this analysis, we randomly sampled 2,000 articles published in biomedicine fields in 2010, and evaluated their citation impact as of 2020 (10 years after publication). We oversampled top-1% cited articles, so that the final sample consists of approximately 1,000 top-1% cited articles and 1,000 non-top-1% cited articles.

Description of the measure

The same sample for the third validation study (prediction of future citation) is used, except that oversampled highly-cited documents are excluded. The 947 selected documents include in total approximately 230,000 combinations of cited references, for which the distance ( Eq 1 ) is computed (A). The distances are summarized at the focal document level ( Eq 2 ), and Novel 100 is displayed as an example (B). Novelty measures with different q values are illustrated in S1 Appendix . Since abstracts and keywords are not available for all documents, the sample sizes are smaller.

https://doi.org/10.1371/journal.pone.0254034.g002

https://doi.org/10.1371/journal.pone.0254034.t003

Table 4 reports the correlation between the series of the proposed bibliometric measures (on the vertical axis) and the self-reported questionnaire scores (on the horizontal axis). On top of the eight scores from the questionnaire, we added two summary scores by taking the mean of the four newness scores (Column 9) and the mean of the four improvement scores (Column 10) respectively. We expect that our proposed measure should be correlated with the newness scores (Columns 1, 3, 5, 7, and 9) rather than the improvement scores (Columns 2, 4, 6, 8, and 10). Focusing on the newness summary score (Column 9), Fig 3 illustrates the correlation coefficients with novelty measures from three different text sources and with different q values.

Pearson’s correlation coefficient. Novel q ( q ∈{100,99,95,90,80,50}) is correlated with the mean of four self-reported newness scores (Column 9 in Table 4 ). † p<0.1, *p<0.05, **p<0.01, ***p<0.001.

https://doi.org/10.1371/journal.pone.0254034.g003

https://doi.org/10.1371/journal.pone.0254034.t004

https://doi.org/10.1371/journal.pone.0254034.t005

The probability of a focal document falling within the top 1 percentile is predicted. For easier interpretation and comparison, the horizontal axis takes the percentile of the novelty measures. (A) based on Row 1 in Table 5 . (B) and (C) based on curvilinear models incorporating the quadratic term of the novelty measures ( S1 Appendix ).

https://doi.org/10.1371/journal.pone.0254034.g004

We find that adding the quadratic term increases a model fit for the novelty measures with smaller q ’s. Fig 4B and 4C illustrate the curvilinear associations for Novel 90 and Novel 50 , showing that the optimal level of novelty scores decreases for lower q ’s. This also suggests that a document with too many recombinations does not attract citation.

Alternative measure of recombination within a document

Although the proposed measure utilizes recombination between cited documents, it is plausible to find recombination within a focal document itself. By decomposing the text information (the title, the abstract, or keywords) of a focal document into words, assigning word embeddings to them, and measuring the distance of every pair of words, we additionally constructed similar sets of novelty measures. This is in line with a category of previous measures [ 25 ] except that we use word embeddings to compute word distances.

We tested the validity of this additional set of measures for the correlation with self-reported novelty as well as for the prediction of future citation ( S1 Appendix ). The result is overall unsatisfactory. Correlations with the self-reported scores are mostly insignificant and sometimes negatively significant. Similarly, correlations with future citation impact are insignificant or negatively significant. Thus, the proposed approach to quantify recombinant novelty does not work with the text information within a focal document itself. This contrasts with the previous measures of recombination within a document [ 9 , 25 ], which may be attributable to a different operationalization that the previous measures are based on the first appearance of a combined use of two words rather than their distance.

Discussion and conclusion

Novelty is a core value in science [ 1 , 2 ], and thus, a reliable approach to measure the novelty of scientific documents in a large scale is crucial. This study is the first to propose measuring the recombinant novelty of scientific documents based on the word-embedding technique. Most previous measures for recombinant novelty in science have been based solely on citation data [ 8 , 10 , 11 , 16 , 17 ]. Although citation network data is an effective tool to indirectly retrieve semantic information, recent advancement in text analysis allows us to extract it more directly and possibly more accurately [ 39 , 40 ]. Combining citation data and text data, we provide a well-validated and user-friendly measure of scientific novelty.

One limitation common to most previous measures is insufficient validation [ 17 ]. To address this issue, we investigated our proposed measure from multiple angles. First, we show that the word embeddings, with which the novelty measure is computed, can be used to gauge the distance between scientific documents. Second, the novelty measures are significantly positively correlated with self-reported scores for various dimensions of newness but not with those for improvement, suggesting that the proposed measure can distinguish novel discoveries from mere improvements. Third, the novelty measure is found to be a significant predictor of citation impact in 10 years. Overall, these results confirm the validity of the proposed measure.

We examined several variations of novelty measures. First, we tested different percentile values ( q ) in aggregating the distance scores across all pairs of cited references. The result shows greater performance with higher q ’s both in the correlation with self-reported novelty measures and in the prediction of future citation. Thus, the novelty of scientific documents is determined by a small number of distant recombination. This contrasts with the previous recombinant novelty measures based on more average distances [ 9 ].

Another limitation common to previous measures is their computational cost for expensive data access as well as processing of massive data. Many potential users of the novelty measure cannot afford to it, which has substantially compromised the utility of the measures and delayed the progress of studies on scientific novelty. Our proposed approach overcomes these challenges. Drawing on limited text information (titles of cited references) and publicly shared library of word embeddings (scispaCy), our approach minimizes data access requirement as well as computational cost. Using the shared code, one can compute the novelty score of a document of interest only with the reference list of the document. Thus, we encourage the application of the approach for various purposes.

The approach has two limitations that future work needs to address. First, it depends on publicly available word-embedding libraries. ScispaCy specializes in biomedicine. Similar libraries are available in some fields but not in others, in which one needs to start with computing word embeddings. When a different library is used, the external validity of our approach needs to be tested. Second, we disregard the time dependency of word embeddings. The semantic distances between words change over time. Iterated computation of word embeddings may be required, for example, when novelty scores across different time points are to be compared.

Supporting information

S1 appendix. supplementary analysis..

https://doi.org/10.1371/journal.pone.0254034.s001

S1 Dataset.

https://doi.org/10.1371/journal.pone.0254034.s002

S2 Dataset.

https://doi.org/10.1371/journal.pone.0254034.s003

S3 Dataset.

https://doi.org/10.1371/journal.pone.0254034.s004

S4 Dataset.

https://doi.org/10.1371/journal.pone.0254034.s005

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What has been is what will be, and what has been done is what will be done, and there is nothing new under the sun. — Ecclesiastes

Novelty can be described as the quality of being new, original or unusual . Novelty in scientific publishing is crucial, because journal editors and peer reviewers greatly prize novel research over and above confirmatory papers or research with negative results . After all, why give precious and limited journal space to something previously reported when authors submit novel, unreported discoveries?

How do you know what constitutes as novel? How can you as an academic author enhance the novelty effect with your research submissions ? Below we explore ideas that will help you maximise the novelty effect in your submissions.

a. New discovery

This comprises research on and reports of completely new discoveries. These can be new chemical elements, planets or other astrological phenomena, new species of flora or fauna, previously undiagnosed diseases, viruses etc. These are things never seen or reported before. Often such new discoveries serve as a seedbed for multiple reports or even completely new avenues of research. Journals prize submissions on new discoveries and often tout them in media reports.

b. The exceptionally rare

Not quite as exciting as new discoveries are reports on things not new, but seen or encountered exceptionally rarely, or not for a long time. An example is the sighting of the rare pink handfish, recently spotted in Australia for the first time in decades. In biomedical publishing , rare case reports of a near-unique condition (such as the separation of conjoined twins) are occasionally published and make the nightly news.

c. New theories

Typically, these papers provide substantial data which supports the novel thesis. Reports of new theories must have rigorous logic and need to stand on clear and well-documented foundations. They can’t be simple flights of theoretical fancy. As with new discoveries, new theories can spawn whole new branches of scientific inquiry.

d. New or significantly improved diagnostic/laboratory techniques

Reports on novel techniques don’t usually receive coverage from the mass media, but can often garner huge numbers of references if the new technique is adopted by the scientific community. Publication-worthy techniques include those which are more efficient, less time-consuming or more reliable than currently existing techniques or diagnostic procedures. Anything that is truly new or improves significantly on an established technique is potentially worthy of publication. In medicine, new surgical techniques are very important, but here’s a tip : try to provide a large prospective case series with long-term follow-up instead of a just a single case report.

e. Existing data combined into new knowledge

There is a profound novelty effect when researchers combine existing data/knowledge into something new. Ideas from disparate, previously unrelated fields of research can lead to completely novel discoveries with untold potential applications. Translational or applied research (particularly in the biomedical sciences) has borne abundant fruit over the last many decades. Translational applications of chemistry and physics to medicine have seen enormous advances in the diagnosis and treatment of numerous diseases.

f. Incremental additions to the literature

Not all research or publications will report on truly novel discoveries; in fact, very few will. But that doesn’t necessarily diminish the novelty effect of your work. The vast majority of published research adds incrementally to what is already known, nudging scientific knowledge forward. The accumulation of incremental discovery leads, over time, to large gains in understanding and knowledge.

How to ensure and verify the novelty effect

Whether your research reports something completely new or furthers an existing field in a new way, you need to make sure the contribution is indeed new.

• Do your homework : Pore through the literature (in as many languages as possible) to make sure your idea is indeed new, or significantly different enough to be considered new.
• To the degree possible, provide the ‘idea genealogy’ for your concept : Reference the major sources of those who have come before you. Through references and by describing your thought processes, describe clearly how you came up with the new idea or combination of ideas.
• Disclose your sources of inspiration and new application : Doing so constitutes academic honesty, gives credit to those upon whose shoulders your research rests and provides intellectual fertiliser for other scientists who may, in turn, be able to build upon your own ideas.

All the best for your (novel) submission!

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• Indian J Anaesth
• v.67(3); 2023 Mar
• PMC10220166

Novelty in research: A common reason for manuscript rejection!

Nishant kumar.

Department of Anaesthesia, Lady Hardinge Medical College and Associated Hospitals, New Delhi, India

Zulfiqar Ali

1 Department of Anesthesiology, Sher-i-Kashmir Institute of Medical Sciences, Srinagar, Jammu and Kashmir, India

Rudrashish Haldar

2 Department of Anesthesiology, Sanjay Gandhi Post Graduate Institute of Medical Science, Lucknow, Uttar Pradesh, India

We often hear back from reviewers and editors of scientific journals that a particular manuscript (original research, case report, series or letter to the editor) has not been accepted because it lacks novelty. Though disheartening, the reason for such a response from said reviewers needs proper elucidation, as a moral obligation from the editorial board towards the authors of the manuscripts.

Research, as defined by the Cambridge Dictionary is ‘a detailed study of a subject, especially in order to discover (new) information or reach a (new) understanding’. [ 1 ] Novelty on the other hand is defined as ‘the quality of being new, original, or unusual’ or a ‘new or unfamiliar thing or experience’. Therefore, adding the adjective novel along with research is actually one of the most common redundancies that is similar to ‘return back’ or ‘revert back’ and denotes one and the same thing! [ 1 ]

Without delving into the nitty-gritty of the English language, novel research can be best described as one or more elements of research that are unique, such as a new methodology or a new observation that leads to the acquisition of new knowledge. It is this novelty that contributes to scientific progress. Since the main aim of research is to unravel what is unknown or to challenge views or ideas that may or may not be based on sound scientific principles, this exclusivity of novel research therefore allows us to expand our horizon beyond the realms of known domains. [ 2 ]

Having defined novelty in research, one of the most common mistakes that researchers commit is confusing novelty with originality. These terms are often used interchangeably. Originality implies the genuineness of the work and signifying that the said work has not been copied from any other source. Originality can always be examined by plagiarism checkers, and data is often analysed for duplication or fabrication only if there exists a certain doubt regarding its factuality. A study, therefore, can be mutually exclusive i.e. novel, but not original, or it can be original but not novel. It is the latter that reviewers and editors encounter most often.

The most common scenario encountered in anaesthesia related manuscripts that lacks novelty is the substitution of the same anaesthetic technique to different surgical procedures or patient populations (based on gender or age), with no expected change in the result. Here, the hypothesis and study designs are almost identical; however, the agents are replaced with different ones. A classic example is the comparison of the duration of analgesia with a longer acting analgesic or that of a local anaesthetic with a shorter one. The intrinsic properties of a drug are already well known, and, irrespective of it being an abdominal surgery or a limb surgery, the drugs are going to behave according to their pharmacological properties. Similarly, modern airway devices, such as video laryngoscopes, have conclusively been proven to be better aids than the conventional ones. A comparison of any new laryngoscope would definitely be a novel idea, in terms of whether it outperforms the existing device. If a certain number of studies, systematic reviews, or metanalyses have already been published on that particular device or drug, the study undertaken cannot be considered novel unless the results of the aforementioned study, utilising sound scientific principles, actually challenge or contradict the existing ideas.

Another common scenario faced by the reviewers or editors is the anaesthetic management of common or uncommon syndromes or diseases. They are often well described in literature, but when managed as per the existing guidelines and expected challenges they do not constitute novelty. A case report is novel and worth publishing if an unforeseen or unanticipated event has occurred or the case has been managed in a unique or unconventional manner or significant innovative skills or equipment have been employed. However, due caution has to be exercised as this should not lead the researcher to be overtly adventurous or show undue bravado by going against the principles of patient safety.

Now here lies the contradiction. We have been harping on novelty, introducing new ideas, and challenging old fixed ideas when conducting research and reporting cases. However, at the same time, due caution must be exercised, and one must not to be adventurous, unconventional, or bold. There is a fine line of distinction between these two. Herein comes the role of ethics, a separate topic of discussion altogether.

Research or advancement may not always be novel just by intervention or experimentation. Theoretical or hypothesis testing may also contribute paradigm-changing findings. Some of these may include thought-based experiments, rectifying or logical rearrangement of existing knowledge, re-evaluating space and time, utilising principles of philosophy, and analysing already existing data from a new and different perspective. [ 3 ] A thorough literature search is pivotal for designing a novel research project as it helps to understand known facts and gaps. An attempt at bridging identified research gaps adds to the novelty of the study. [ 2 ]

Another aspect of novel research is technological advancement. Most research starts from an idea, a thought, or an observation that further leads to hypothesis building, experimentation, data collection, analysis, and, finally, principle building. Technological advancement may stem from any of these phases. Novelty in research propels the industry to excel and outdo itself. [ 4 ]

Can novelty in research be measured? The answer is a resounding yes. Traditionally, it has been measured through peer reviews and by applying bibliometric measures such as citation or text data, keeping in mind their inherent limitations. However, word embedding is a new technique that can reliably measure novelty and even predict future citations. However, this is currently limited by publicly available word-embedding libraries and its high costs. [ 5 ]

To the average author and reader, novelty adds to their knowledge and makes them aware of complications that they may encounter. It offers a way out by conventional or different measures, within the realm of scientific, ethical and principles of social justice, should they get stuck, keeping in mind the quote of Hippocrates: ‘ Primum non nocere’ ( First, do no harm ).

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• First Online: 13 June 2020
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• Maria Regina Brioschi 2  

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Novelty, defined as the quality of being new, has always been associated with a vast range of phenomena which had never existed before. The concept is of particular relevance especially in the fields of science, technology, and art, because they mutually deal with discovery , innovation , and invention . As it is apparent, the three disciplines mutually share the importance of creative processes, which lie at their very core. For this reason, the main investigations on novelty, both theoretical and empirical, from the twentieth century onward have mainly been carried out by philosophical and psychological studies related to creativity. Novelty indeed represents one of the fundamental criteria of creativity . Accordingly, this entry (1) begins with a general, phenomenological description of novelty, (2) points out the current relevance of novelty, (3) focuses on its history by providing a theoretical framework for its understanding, (4) explores different empirical researches and cognitive models of novelty elaborated hitherto, and (5) illustrates the fundamental relation between novelty and the possible.

This research was founded by the Department of Philosophy “Piero Martinetti” of the University of Milan under the Project “Department of Excellence 2018-2022” awarded by the Ministry of Education, University and Research (MIUR).

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Maria Regina Brioschi

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Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy

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Brioschi, M.R. (2020). Novelty. In: The Palgrave Encyclopedia of the Possible. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-98390-5_116-1

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DOI : https://doi.org/10.1007/978-3-319-98390-5_116-1

Received : 27 April 2020

Accepted : 22 May 2020

Published : 13 June 2020

Publisher Name : Palgrave Macmillan, Cham

Print ISBN : 978-3-319-98390-5

Online ISBN : 978-3-319-98390-5

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