Where To Get Data For Co-citation Analysis

1. Introduction

The term "Sleeping Beauty" has been used to describe an article that is not well cited in the early years after its publication merely experiences a sharp increase in the rate at which it is later on cited (van Raan, 2004). An implication is that the concept presented in such an article is "ahead of its time", and that 93 resistance to its ideas may take delayed its recognition.

Causes for resistance (Barber, 1961; Cole, 1970), and delayed recognition (Garfield, 1970; 1980) accept been postulated that include 1) data overload from the big amount of data bachelor, two) modest communication skills of authors, 3) insufficient promotion of ideas, four) conflict with existing theory and experimental data, 5) the author'due south position in the social hierarchy of science, 6) multiple discovery, 7) the management structures of scientific institutions, eight) and the conservative nature of establishments.

The Sleeping Beauty phenomenon, and variants of it, have been extensively studied in different datasets, and some degree of understanding exists that a fraction of the scientific literature exhibits citation kinetics that suggest delayed but eventual recognition of new ideas (Glänzel et al., 2003; Redner, 2005; Braun et al., 2010; Li, 2014). The size of this fraction has received different estimates also every bit criteria for defining these estimates (Glänzel and Garfield, 2004; Ke et al., 2015; Li and Ye, 2016; van Raan and Winnink, 2019). Imaginative metaphors have also emerged to describe Sleeping Beauty variants that have been subsequently discussed in terms of their precision and impact (Sugimoto and Mostafa, 2018).

While earlier studies examined modest datasets, subsequent ones take considered large samples of the literature, for example, 22 million publications in Ke et al. (2015). In studying the Sleeping Beauty phenomenon, both parameterized and parameter-costless approaches take been used (van Raan, 2004; Costas et al., 2010; Li et al., 2014; Ke et al., 2015; Ye and Bornmann, 2018) with partially overlapping results.

While the research cited above has focused on unmarried publications, new ideas likewise result from combining two previously independent ones. The recognition of such novel ideas tin be examined by co-citation analysis (Marshakova-Shaikevich, 1973; Uzzi et al., 2013; Boyack and Klavans, 2014; Wang et al., 2017; Bradley et al., 2020). Co-citation analysis has as well been used to identify the so-chosen "princes" that awaken Sleeping Beauties by (Teixeira et al., 2017; Zong et al., 2018).

Delayed recognition in co-cited article pairs has been briefly explored (Devarakonda et al., 2020) using simplified criteria derived from prior Sleeping Beauty studies on single publications (van Raan, 2004; Ke et al., 2015; van Raan and Winnink, 2019). The authors of this study (Devarakonda et al., 2020), which examined 33.half-dozen million pairs, reported 24 co-cited pairs exhibiting delayed recognition in the 99th percentile of 33.6 million co-commendation frequencies, and proposed the term delayed co-citations for such cases. While this initial exploration, albeit at calibration, only considered reference pairs where each fellow member of a pair was in the 99th percentile of highly cited articles in Scopus, its results suggest that delayed recognition in co-cited pairs is relatively uncommon.

In this article, we examine a much larger dataset, approximately 940 million pairs of articles. We identify co-cited article pairs that showroom delayed recognition co-ordinate to criteria derived from the work of van Raan (2004); van Raan and Winnink (2019) and Ke et al. (2015). We also ask whether individual articles found in delayed co-commendation pairs can be labeled every bit Sleeping Beauties.

2. Materials and Methods

We accept previously described a dataset of 33.half-dozen 1000000 cited pairs each belonging to the meridian ane% of cited manufactures in the Scopus bibliography (Devarakonda et al., 2020; Figure 2). In the present study, nosotros include all co-cited pairs from references cited by articles published in Scopus in the 11 year period, 1985–1995, not only those fatigued from the meridian 1% of cited articles.

To get together and analyze a working dataset, we first exported 95,524,693 publication records from Scopus (all citation types) equally a citation graph consisting of an edgelist and a nodelist, imported these data into a graph database (Neo4j) treating publications every bit nodes and citations as edges. After creating indexes to improve functioning, we selected all publications of commendation blazon "article" published in the years 1985–1995 (inclusive of both) that had at least five cited references each. In counting references, we only considered references with complete Scopus records. Incomplete references and those with cryptic placeholder identifiers were removed from the dataset. Nosotros also filtered rare cases in the data where a publication cites itself, or if the publication engagement of a cited reference was missing or greater than the publication date of its citing commodity. Option of publications with at least five references was performed after curating references.

Nosotros used a combination of SQL, Cypher, and Python to manage and clarify this volume of information. After initial comparison of SQL vs Cypher, we chose, on the footing of simplicity and performance, to use Cypher queries in Neo4j to generate all pairwise $(n2)$ combinations of an article's cited references. We de-duplicated these pairs across all articles to get together a dataset of $\sim$ 940 million pairs (940,357,633 pairs). Nosotros then calculated the frequency of co-cited pairs.

For efficiency, nosotros divided the data into batches for parallel processing using the Neo4j 4.0 graph database and the GNU Parallel utility. After tuning experiments on a exam set up of one million pairs using Neo4j in a Centos 7.5 virtual machine with 128 Gb of RAM and 16 vCPUs in the Microsoft Azure environs, we set batch size to 1,000 pairs and the degree of parallelization to 15 cores. Nether these atmospheric condition, information technology took roughly 11 min to compute co-citation frequencies for a batch of 1,000 pairs. Nosotros divided these 940 one thousand thousand pairs into 9 subsets of around 100 million pairs each and processed them at the charge per unit of approximately xix h per subset. Our code for parsing and updating Scopus XML data, a PostgreSQL schema for Scopus data, SQL, Cypher, and Python scripts used in this study is freely bachelor from a Github repository (Korobskiy et al., 2019).

The simple Cypher query we used to computing co-citation frequencies of pairs in Neo4j is shown beneath. The input to the query is a csv file containing two columns of article identifiers with each row representing a co-cited pair.

Frequencies thus calculated, were loaded back into PostgreSQL. For kinetic analysis, we selected all pairs with a co-commendation frequency $>=$ 100 and calculated the kinetics of commendation aggregating from the first possible year of co-citation for each pair through the year 2018, once again in Neo4j. Finally, for continuity, we set zero as the frequency for all years between the commencement possible year of co-citation and the last co-cited year (2018), with missing frequency counts. Small-scale differences between the information in Devarakonda et al. (2020) are due to more than electric current data in Scopus in our study, and computing kinetic data through 2018 in this study. We compared small-scale samples between the two datasets and confirmed that these minor differences in co-citation frequencies could exist bridged by including citations from publications in 2019 and later.

After generating a dataset of 940 million pairs, we applied three relatively bourgeois conditions to identify cases of delayed co-citation: 1) a minimum summit (almanac) co-citation frequency for a pair of at least 20; 2) a minimum total co-citation frequency of at to the lowest degree 100; 3) a requirement both members of a co-cited pair should be published no earlier than 1970. We then identified delayed co-citation cases by setting two more atmospheric condition: 1) a minimum sleeping duration of ten years as measured from the start possible year of co-citation (the more recent publication year of the two articles), two) during this sleeping period of 10 years or more, the boilerplate co-citation frequency should exist at most 1 with no more than ii co-citations in any one year.

Nosotros calculated the Beauty Coefficient using the equation below for 1) a single article as described in detail past Ke et al. (2015), and ii) for co-cited pairs every bit described in Devarakonda et al. (2020); we treated the first possible year of co-citation equivalently to the year of publication for a unmarried commodity.

$B={\displaystyle \sum }_{t=0}^{t_m}\frac{\frac{C_{t_m}-C_0}{t_{\mathrm{thou}}}\cdot t+C_0-C_t}{ma\mathrm{10}\left\{1,C_t\right\}}$

where B is the Beauty Coefficient, t is a indicate in fourth dimension describing the age of a publication, and $C_t$ is the number of citations accrued. at fourth dimension t.

We also calculated the slope between the co-citation frequency of the awakening year and the peak frequency. For single publications, we narrowed the criteria of van Raan and Winnink (2019) to consider merely one sleeping period of 10 years or greater; depth of slumber (average commendation rate during sleep) of at well-nigh 1; an enkindling period of 5 years; and an average co-citation frequency during the awakening menses (which is defined as enkindling citation intensity by van Raan) of at to the lowest degree v.

We utilize the term Sleeping Beauty when referring to delayed recognition in private articles that were identified using prior methodology. For co-citations, we use delayed co-citation or delayed recognition.

3. Results and Discussion

In this written report of delayed co-citations, we first examined cited references from 3,433,578 publications in the Scopus database. The criteria for pick of these publications were that they were classified as "commodity," that they were published in the period 1985–1995, and they independent at least five cited references each. We generated all possible co-cited pairs for the references in these articles and de-duplicated them across articles. since the same reference pair can occur in more than one commodity. Then we measured the co-citation frequency of each pair beyond the unabridged Scopus database by counting all co-citation events from the outset possible twelvemonth of co-commendation onwards through 2018 (Figure 1; Table i).

FIGURE i. Frequencies of $\sim$ 940 million co-cited pairs drawn from Scopus 1985–1995. Pairwise combinations, $(n2)$ , of references from articles indexed in Scopus (1985–1995), were generated every bit described in Materials and Methods. Full co-commendation frequencies for these pairs, ranged from 1 to 52,471 with a median frequency of i. The empirical cumulative distribution function (ECDF) was calculated from 940,357,633 co-citation frequencies and plotted against co-commendation frequencies on a log₂ scale.

TABLE ane. Distribution of 940 million Co-citation Frequencies. The count of co-cited pairs in each frequency class likewise as the percentage relative to the full number of 940,357,633 is shown. Counts include the lower bound in each grade and exclude the upper spring.

The data in Figure 1 bear witness a highly skewed distribution of co-citation frequencies across a large dataset. Roughly 84% of the pairs take a total co-citation frequency of ii or less, and the 99th percentile is 16 although each pair had at least ten years to accumulate co-citations. Even for a pair of articles from the near recent year in our data, 1995, this frequency of 16 corresponds to less than i co-commendation per year on average. Thus, only a small fraction of pairs in these information have co-citation frequencies greater than two per year. One might consider that the reasons advanced for delayed recognition described in the Introduction could also contribute to such minor recognition or even acknowledgment of non-merit.

Beyond a high level understanding of the distribution of co-citation frequencies, however, nosotros are interested in frequently co-cited publications, which are derived from highly cited publications (Small, 1973), and are of involvement to the customs. Thus, we subset the data using a conservative threshold of 100 for total co-citation frequency along with a peak annual co-citation frequency of at least xx. These criteria are analogous to those proposed past van Raan (2004) and Redner (2005). Subsequently applying these two further restrictions, the number of co-cited pairs in consideration was reduced to 51,613 (approximately 0.055% of the total number of pairs).

We practical farther conditions to these 51,613 pairs to determine whether they qualified equally cases of delayed co-citation: 1) a co-cited pair should have experienced dormancy in citation (a period of "sleep") for at least 10 years during which it should have received no more than two co-citations per year. This period of dormancy ended in the start year that the pair received more than two co-citations. To exist labeled a example of delayed recognition, nosotros also required that the awakening period that follows the sleeping flow was characterized by 2) a peak almanac co-commendation frequency of at least 20. These criteria when collectively applied, identified 1,196 cases of delayed co-citation, whose characteristics are summarized in Table two. We also note that roughly xviii% (223/1,196) pairs were continued past direct citation to each other.

TABLE 2. Summary Statistics of 1,196 Delayed Co-citation Pairs. Criteria for selection were a minimum sleeping period of 10 years and a minimum elevation of 20 citations in whatsoever twelvemonth. Q1 and Q3 refer to the first and third quartile respectively.

Interestingly, these 1,196 pairs are derived from merely 1,267 of a possible ii,392 private publications indicating that some members of frequently co-cited pairs are plant in multiple pairs. This observation is consequent with a pair of manufactures concerning methods in biochemistry, contributing to over 40,000 different co-cited pairs with frequencies of at least x (Devarakonda et al., 2020).

A logical question is whether whatever of these 1,267 individual publications would exhibit delayed recognition (be classified every bit Sleeping Beauties). Applying van Raan'south criteria (Department 2), we identify 128 of these 1,267 publications. Interestingly, 27 of the i,196 delayed co-citation pairs were cases where both members of a delayed co-citation pair would qualify as Sleeping Beauties. Thus, delayed recognition can occur without a requirement that at least one member of a co-cited pair with delayed recognition should accept Sleeping Beauty characteristics. These observations also advise that while high-referencing fields such as biology (Small and Greenlee, 1980) might be advantaged past our choice criteria, the thresholds we set do not entirely exclude other fields. Accordingly, continuing this work with field normalization of co-citation frequencies, to the extent possible, is warranted.

In contrast to co-citation frequencies for delayed co-citations (Figure 2), which range from 20 to 260; citation counts for the one,267 publications that contribute to these one,196 delayed co-citations range from 121 to 190,832 with 72 of these publications having citation counts of greater than 10,000.

FIGURE 2. Kinetics of Co-commendation Frequencies for Delayed Co-citations. Three sample plots are shown from 1,196 delayed co-citations selected for maximum slope (left console). mean slope (middle panel), and minimum slope (right panel) of a line connecting the co-citation frequency of the enkindling year to the co-commendation frequency of the superlative year. Total co-commendation frequencies for these three plots were 131, 174, and 254, with peaks of 22, 22, and 23, and slopes of NA, 2.38, and 0.21, respectively. The carmine triangle marks the awakening year and the dotted bluish line, the slope. The slope in the left panel is NA since the peak year is the enkindling yr. The article pairs shown higher up are 1) Spacetime as a membrane in higher dimensions (Gibbons 1987) and An exotic class of Kaluza-Klein models (Visser, 1985), 2) Conception of the reaction coordinate (Fukui 1970) and Ab initio effective core potentials for molecular calculations. Potentials for main group elements Na to Bi (Wadt and Hay, 1985), 3) A proposed grading system for arteriovenous malformations (Spetzler, 1986) and arteriovenous malformations of the brain: Natural history in unoperated patients (Crawford et al., 1986).

Yet, other co-citation frequencies do exceed the seemingly pocket-size frequencies noted for delayed co-citations. For example, Becke (1993) and Lee et al. (1988), a pair of articles from the field of physical chemistry, accept been co-cited over 51,000 times only do non exhibit delayed citation kinetics. It should also exist noted that these articles have individually been cited over 70,000 times each. Similarly, i,357 pairs from the data shown in Effigy 2 have co-citation frequencies greater than one,000.

We notice (Figure 1), that the 90th, 95th, and 99th percentiles of co-citation frequencies in our dataset are four, 6, and xvi respectively. In comparing. the 90th, 95th, and 99th percentile of citation frequencies of $\sim$ 10.vii 1000000 publications of blazon "article" in Scopus, published in the years 1970–1995, are 58, 96, and 254 respectively (roughly ten fold greater). What emerges is that delayed co-citations tend to have frequency profiles that are lower than those of other co-cited pairs, and single publications. This is non unexpected since co-cited frequencies cannot exceed the citation frequencies of the publications in these pairs merely information technology does propose that seemingly low co-citation frequencies should not be overlooked.

To examine rates of awakening, we also calculated the slope between the co-commendation frequency in the beginning awakening yr and the frequency of the peak year and noted a fairly broad range of slopes with a hateful of 2.4 (Table 2). The kinetics of co-citation are visualized in Figure ii, for three examples with the maximum slope, the hateful slope, and the minimum slope observed.

Of i,196 delayed co-citations, the slope could not exist computed for ten pairs because the superlative yr was the year of enkindling. This small number of cases, suggest sudden recognition of the concepts represented past these pairs (Table three. These x pairs span the areas of LED technology, cosmology, immunology, psychology, and computational science. One publication from 1985 titled, "An exotic course of Kaluza-Klein models" appears in 3 of 10 pairs and the writer himself refers, in 1999, to "renewed interest due to the explosion of activity in the non compact extra dimensions variant of the Kaluza Klein model" (Visser, 1999).

TABLE 3. Co-cited pairs with peak frequency in the first year of awakening.

We also examined lesser co-citation frequencies, between twenty and 100, and observed 5,928,815 pairs. After removing pairs with 1) less than 10 years of kinetic data (the deviation between publication year and top year is less than 10 years) two) a negative Beauty Coefficient, which describes manufactures whose citations growing linearly with time or with a citation trajectory that is a concave office of time, 3) without at least one peak of frequency 20, so the number reduced to 13,057 pairs. Of these 12,920 had but a single acme of 20 or greater and may exist similar to "flash in the pan" citations (Costas et al., 2010; Li, 2014). Given our focus on frequently co-cited pairs, we did non written report these further.

An appealing alternative approach for delayed co-citations and Sleeping Beauties is the Beauty Coefficient. Nosotros computed the Dazzler Coefficient (Materials and Methods) for these 1,196 pairs observing a range of 34.21–1678.62. These data are summarized in Tabular array 2. Given co-citation frequencies beingness mostly lower than commendation frequencies, the top 15 Beauty Coefficient values of the 1,196 delayed co-citations range from 712.47 to 1678.62, which appear comparable given lower co-citation frequencies to the top 15 unmarried manufactures described by Ke et al. (2015), all in a higher place two,000.

Ke and colleagues annotate that parameterized approaches in preceding studies have suffered from being somewhat arbitrary. Arbitrariness may non accept impeded discovery, for example Redner's work on the physics literature (Redner, 2005) with its selection threshold of 250 citations. Farther, while the Beauty Coefficient is parameter free, the selection of selection threshold is left to the user leaving the door open up for arbitrary selection thresholds. Nosotros consider this a strength of the measure since it tin exist used in contextual studies. The approach of van Raan is also intuitive and flexible just does not consider the maximum number of citations received equally an important parameter to be tuned. The cases with a sleeping flow of ten years, and a citation rate of five for the next 5 years, would satisfy requirements for delayed recognition but are perhaps less noteworthy.

Finally, to inquire which fields these 1,196 delayed co-citations are constitute in, we mapped them to the All Science Journal Nomenclature (ASJC) maintained by Scopus, which consists of 27 major subject area categories. The data are represented in Effigy three simply should exist interpreted in the light of these field of study area labels being derived from journals and that an article may have more than one label. Fifty-fifty so, the data suggest that delayed co-citations, equally we define them in our dataset are largely drawn from the domain of biochemistry, genetics, and molecular biology followed past physics, computer science, chemistry, and technology. These observations are slightly dissimilar from (Ke et al., 2015; Effigy 4) with Biochemistry, Genetics, and Molecular Biology dominating in our set but those authors studied single publications from a different data source, and a different time flow.

FIGURE three. Disciplinary composition of 1,196 Delayed Co-citations. Each node represents a major subject field area in the Scopus ASJC classification. Node size is scaled to the number of articles in a given field of study expanse. Edge thickness indicates the number of pairs with one member in each of the 2 nodes connected past the edge. Major subject areas are abbreviated in the graphic: BGMB, Biochemistry, Genetics and Molecular Biology; SS, Social Sciences; PSY, Psychology; PHY, Physics and Astronomy; NEU, Neuroscience; EPS, Earth and Planetary Sciences; ENS, Environmental Science; ENG, Engineering; DCS, Determination Sciences; CS, Computer science; BMA, Business, Management, and Accounting; ABS, Agricultural and Biological Sciences; A&H, Arts and Humanities; EGY, Energy; EEF, Economic science, Econometrics and Finance; PTP, Pharmacology, Toxicology and Pharmaceutics; MAT, Material Sciences; CHE, Chemistry; CEN, Chemical Engineering science; MTH, Mathematics; MED, Medicine; IMM, Immunology and Microbiology; HP, Health Professions; GEN, Full general.

4. Decision

In a large-calibration exploration of the kinetics of co-citation (more than 940 million unique commodity pairs), nosotros have identified one,196 cases of delayed co-commendation using criteria largely derived from the work of van Raan and Ke. We acknowledge that our selection criteria, while guided by positional statistics and intuitive preference, suffers from some caste of arbitrariness. As with all bibliometric data, coverage and data quality also influence discovery. Thus, we have tried to identify co-cited pairs of college frequency since the trends in such cases are more likely to be reproducible across other data sources. Relaxing these weather condition, will identify additional cases. Our goal was to identify a set up of delayed co-cited pairs that can be studied, in the longer term, to sympathise the reasons for the patterns of citation. This future task will require a greater understanding of the fields in which such delayed co-citations occurred and ideally should be coupled to qualitative techniques. Resolving these observations in a finer-grained manner with respect to kinetics and field of study would also be informative.

Information Availability Statement

The datasets presented in this commodity are non readily available because Scopus data has licensing restrictions and cannot exist redistributed. The studies are reproducible to persons with a license from Elsevier, the vendor of Scopus. Requests to access the datasets should exist directed to netelabs@nete.com

Author Contributions

WZ: Conceptualization; Methodology; Investigation; Writing—Review and Editing. DK: Methodology; Writing–Review and Editing; GC: Conceptualization; Methodology; Investigation; Writing—Original Draft; Writing—Review and Editing; Funding Acquisition, Resources; Supervision.

Funding

Research and development reported in this publication was partially supported by federal funds from the National Institute on Drug Abuse (NIDA), National Institutes of Wellness, U.S. Department of Health and Human Services, under Contract Nos. HHSN271201700053C (N43DA-17-1216) and HHSN271201800040C (N44DA-18-1216).

Disclaimer

Data used in this study derive from the ERNIE project, which involves a collaboration with Elsevier. The content of this publication is solely the responsibility of the authors and does non necessarily stand for the official views of the National Institutes of Wellness or Elsevier. Elsevier staff did not accept a role in pattern, manuscript-writing, or review and interpretation of results.

Disharmonize of Involvement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed every bit a potential conflict of interest.

Acknowledgments

We thank our Elsevier colleagues for their back up of the ERNIE projection.

Supplementary Material

The Supplementary Material for this article can be establish online at: https://www.frontiersin.org/articles/10.3389/frma.2020.577131/full#supplementary-material.

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Where To Get Data For Co-citation Analysis,

Source: https://www.frontiersin.org/articles/10.3389/frma.2020.577131/full

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