So instead of trying to ignore the metrics’ importance, this article introduces an increasingly powerful tool for a publishing academic to track one’s academic impact. Of course, you will soon see that the method is centered on the citation, but there is also more than the citation count alone. The tool is called Dimensions

La, Viet-Phuong

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 Mindsponge https://mindsponge.info/posts/139 Lastupdated: 01/12/2023A note on using DigitalScience’s Dimensions to learnabout individual researchimpactViet-Phuong La, AISDLhttps://orcid.org/0000-0002-4301-9292January 12, 2023In today’s academia, the metrics mania has continued. It will certainlycontinue for many years to come, regardless of the urgent call for correctingthe usage of metrics for university rankings, reducing the addiction to metrics,or institutionalizing “better” methods of evaluating an academic’scontribution and merits. It is because for publishing scientists, citations andtheir related impacts have a far more important influence on their lives,recognition, and perceived achievements than what a person outside of theacademe usually thinks of.So instead of trying to ignore the metrics’ importance, this article introducesan increasingly powerful tool for a publishing academic to track one’sacademic impact. Of course, you will soon see that the method is centered onthe citation, but there is also more than the citation count alone. The tool iscalled Dimensions.Dimensions is a product in Digital Science’s portfolio,https://www.dimensions.ai/. It works on a very large dataset, containing over133 million research publications as of today. But there are other numbersconcerning their databases, as shown in Figure 1.Figure 1. Dimensions’ research databasesThe good news is for academics; basic information is provided for free. Onecan easily sign up to have a personal account, then start playing around withtheir provided data. A concise but useful article in 2018 by Catherine Williams[1] provides us with some basic information concerning Dimensions’ startingpoint and early functions. The author wrote:“Researchers themselves benefit enormously, too. Whether or not theirinstitution has chosen to license the Plus or Analytics version of the platform,they can conduct advanced literature searches using the openly availableapplication at https://app.dimensions.ai – and explore the grants, clinicaltrials, patents and policy records directly associated with the publications thatappear in their search results. Optionally, they can create a free account tosave searches and export data, and an integration with ORCID makes itpossible to add publications from Dimensions to an individual ORCID profilein just one click...”And I can reconfirm that these utilities are still available for academics to useFOC today. In the remaining space of this article, I will show some uses ofDimensions’ metrics for one’s own evaluation of academic impact. I’ll be usingthe data provided by Dimensions for my colleague, the founder of this scienceportal. The data subset for his published articles is provided here [URL].Dimensions allows its users to sort their research data using one of its impactmetrics, namely citations, RCR, FCR, and Altmetrics. Of the four metrics, threefirst are centered around citation, and the last is on media mentions. Thus, wecan again see the stress on the citation even though Dimensions itself has aninherent motivation to promote Altmetrics because it is also another productin Digital Science’s portfolio.This sorting tool is useful, although it looks quite simple. It is because whilethe citation count for an individual research publication is meaningful, it is notenough to tell “how big is big”. Therefore, the citation ranking can only beunderstood correctly if further look at an additional, or two, metric measure ispossible. And Dimensions has that on offer. The FCR (Field Citation Ratio)measure is a particularly useful and easy-to-communicate one.The definition of FCR from Dimensions follows. “The Field Citation Ratio (FCR)indicates the relative citation performance of an article when compared tosimilarly-aged articles in its subject area. The FCR is normalized to 1.0 for thisselection of articles. An FCR value of more than 1.0 shows that the publicationhas a higher-than-average number of citations for its group (defined by its FoRsubject code and publication year). Articles that are less than 2 years old donot have an FCR. An article with zero citations has an FCR of 0.”Figure 2 shows our colleague’s data for the first five publications with thehighest FCR metrics. I will show you why this additional look at FCR has value.Figure 2. Top 5 highest FCR documentsThe first insight is that this ranking is not identical to the citation measureranking. His highest FCR publication [2], representing the most impactfulresearch document among those being published around the same time, is, inreality, NOT the top-cited one, i.e., [3]. This is not quite obvious because thetop-cited one is a “newer” one, published in 2020 and has received tons ofcitations since. (For transparency, I am the paper’s first author for somehistorical reasons.)Regarding total citations, [3] has twice as many citations as [2] has received.But in terms of FCR, the metric for [2] is twice as large as that of [3]. The twodocuments swap their positions when we move from one metric to another.In addition, Dimensions also provides some further insights learned from theirdatabases for a research item. Figure 3 presents the summary for one of thehighest-CFR publications [4]. It is ready to see that, although the citationcount is below 90, the FCR measure is 170, meaning that the item has received170 times more citations than the field’s average (for any research item withthe same age).Figure 3. Dimensions’ summary for research item [4]In contrast, item [5] in the top-cited list has received 115 citations, but its FCR isonly 102, much lower than [4], as seen in Fig. 3. The contrast is even deeper forresearch publication [6], published in 2020. Item [6] has received only 35citations, which is 1/3 of item [4]; however, its FCR is 173, higher than that of[4]. Both articles were published in the same year, 2020.It is also noteworthy that the RCR measure for [4] is unavailable. I think N/Ameans the system has not collected sufficient data to calculate the number. Inaddition, reading RCR measure is less straightforward than FCR, due largely toits official definition as follows (exact quotation from Dimensions):“The Relative Citation Ratio (RCR) indicates the relative citation performanceof an article when compared to other articles in its area of research. The RCR isnormalized to 1.0 and calculated for all articles funded by the NIH in theDimensions catalog. An RCR of more than 1.0 shows that a publication has anabove-average citation rate for its group, when defined by the subject areacitation rates of the articles that have been cited with it.”From the law of large numbers point of view, I have seen that many individualpublication records have an FCR but not an RCR. So, it seems that calculatingan RCR requires more data and is not so straightforward.But there are situations where the reverse is true, i.e., a record containing anRCR but not an FCR. Two examples are records [7] and [8]. Both records havehigh citation counts, 156 and 155, respectively. Their RCR metrics are also high(compared to hundreds of other well-cited research items), 8.8 and 4.2,respectively. But none of the two has an FCR for some mysterious reason. Forthis very “unknown” reason, these two are not in the top-cited list; otherwise,they may well kick some other records out of the top 5, given their highcitation counts.Of course, there are more aspects and functions of Dimensions that aresearcher can study and try out. However, as presented above, some earlyinsights may be of direct and instant interest to a novice to Dimensions.References[1] Williams C. (2018). Dimensions from Digital Science. Insights, 31,33.https://insights.uksg.org/articles/10.1629/uksg.420/[2] Vuong QH, et al. (2018). Cultural additivity: behavioural insights from theinteraction of Confucianism, Buddhism and Taoism in folktales. PalgraveCommunications, 4, 143.[3] La VP, et al. (2020). Policy response, social media and science journalismfor the sustainability of the public health system amid the COVID-19 outbreak:the Vietnam lessons. Sustainability, 12(7), 2931.[4] Vuong QH. (2020). Reform retractions to make them more transparent.Nature, 582(7811), 149.[5] Vuong QH, Napier NK. (2015). Acculturation and global mindsponge: anemerging market perspective. International Journal of Intercultural Relations,49, 354-367.[6] Vuong QH, et al. (2020). On how religions could accidentally incite lies andviolence: folktales as a cultural transmitter. Palgrave Communications, 6, 82.[7] Tran BX, et al. (2019). Global evolution of research in artificial intelligence inhealth and medicine: a bibliometric study. Journal of Clinical Medicine, 8(3),360.[8] Vuong QH. (2018). The (ir)rational consideration of the cost of science intransition economies. Nature Human Behaviour, 2(1), 5. ©2023 AISDL - Science Portal for the SM3D Knowledge Management Theory

A note on using Digital Science’s Dimensions to learn about individual research impact

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A note on using Digital Science’s Dimensions to learn about individual research impact

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