SCOPUS 2024

Author

Sergio Uribe - sergio.uribe@rsu.lv

CREATED

September 19, 2024

UPDATED

September 19, 2024

Dataset

Ioannidis, John P.A. (2024), “August 2024 data-update for”Updated science-wide author databases of standardized citation indicators””, Elsevier Data Repository, V7, doi: 10.17632/btchxktzyw.7

Data Dictionary

“authfull”: Full name of the author.
“inst_name”: The name of the institution the author is affiliated with.
“cntry”: The country where the author’s institution is located.
“np6023”: Number of papers published by the author from 1960 to 2023.
“firstyr”: The first year the author published a paper.
“lastyr”: The most recent year the author published a paper.
“rank (ns)”: Author’s rank in terms of citations or papers within their subject or subfield (non-standardized).
“nc2323 (ns)”: Number of citations received by the author from 2023 (non-standardized).
“h23 (ns)”: The author’s H-index (non-standardized) as of 2023 (a measure that combines productivity and citation impact).
“hm23 (ns)”: The author’s H-metric (non-standardized) as of 2023 (similar to H-index but adjusted for author position in publications).
“nps (ns)”: Number of papers the author has published (non-standardized).
“ncs (ns)”: Number of citations the author has received (non-standardized).
“cpsf (ns)”: Citations per standardized fractional paper count (non-standardized).
“ncsf (ns)”: Number of citations per fractional paper (non-standardized).
“npsfl (ns)”: Number of papers with fractional authorship (non-standardized).
“ncsfl (ns)”: Number of citations for fractional papers (non-standardized).
“c (ns)”: Number of citations (non-standardized).
“npciting (ns)”: Number of citing papers (non-standardized).
“cprat (ns)”: Citation to publication ratio (non-standardized).
“np6023 cited2323 (ns)”: Number of papers from 1960 to 2023 that were cited in 2023 (non-standardized).
“self%”: Percentage of self-citations.
“rank”: Author’s rank in the dataset (standardized).
“nc2323”: Number of citations received by the author as of 2023 (standardized).
“h23”: Author’s H-index as of 2023 (standardized).
“hm23”: Author’s H-metric as of 2023 (standardized).
“nps”: Number of papers published by the author (standardized).
“ncs”: Number of citations received by the author (standardized).
“cpsf”: Citations per standardized fractional paper count (standardized).
“ncsf”: Number of citations per fractional paper (standardized).
“npsfl”: Number of fractional papers (standardized).
“ncsfl”: Number of citations for fractional papers (standardized).
“c”: Total number of citations (standardized).
“npciting”: Number of citing papers (standardized).
“cprat”: Citation to publication ratio (standardized).
“np6023 cited2323”: Number of papers from 1960 to 2023 that were cited in 2023 (standardized).
“np6023_rw”: Number of papers from 1960 to 2023 that were reweighted (a measure adjusting for factors like field or co-authorship).
“nc2323_to_rw”: Number of citations from 2023 reweighted.
“nc2323_rw”: Reweighted number of citations in 2023.
“sm-subfield-1”: Primary subfield of research for the author.
“sm-subfield-1-frac”: Fraction of work the author has published in the primary subfield.
“sm-subfield-2”: Secondary subfield of research for the author.
“sm-subfield-2-frac”: Fraction of work published in the secondary subfield.
“sm-field”: The broad field of research the author works in.
“sm-field-frac”: Fraction of work published in the broad field.
“rank sm-subfield-1”: Rank within the primary subfield (standardized).
“rank sm-subfield-1 (ns)”: Rank within the primary subfield (non-standardized).
“sm-subfield-1 count”: Number of papers in the primary subfield.

Data preparing

Normalize the names

Clean the dataset

Dimensions

x
1885
48

Institutions

Countries

Only active authors

Active authors

the institutions that cite themselves

# A tibble: 6 × 2
  inst_name                                                    avg_self_citation
  <chr>                                                                    <dbl>
1 University of Pennsylvania Perelman School of Medicine                    70.1
2 National Research Centre                                                  60.9
3 Università degli Studi di Salerno                                         59.4
4 Dipartimento di Medicina, Chirurgia e Odontoiatria “Scuola …              55.0
5 Hôpital Henri Mondor                                                      51.6
6 East Carolina University                                                  51.0

# A tibble: 1 × 2
   mean    sd
  <dbl> <dbl>
1  9.47  7.83

Correlation between productivity and self citation

Impact per institution

# A tibble: 429 × 2
   inst_name                              mean_h_index
   <chr>                                         <dbl>
 1 Vietnam National University, Hanoi             39  
 2 Università di Pisa                             21  
 3 La Trobe University                            20  
 4 Queen Mary University of London                19  
 5 Ludwig-Maximilians-Universität München         18.3
 6 King Faisal University                         18  
 7 National Taiwan University                     18  
 8 Obafemi Awolowo University                     18  
 9 Universidade Estadual de Maringá               18  
10 The University of Queensland                   17.6
# ℹ 419 more rows

--- title: "SCOPUS 2024" author: "Sergio Uribe - sergio.uribe@rsu.lv" date: 2024-09-19 date-modified: last-modified language: title-block-published: "CREATED" title-block-modified: "UPDATED" format: html: toc: true toc-expand: 3 code-fold: true code-tools: true editor: visual execute: echo: false cache: true warning: false message: false --- ```{r} # Load required libraries with pacman; installs them if not already installed pacman::p_load(tidyverse, # tools for data science # visdat, #NAs janitor, # for data cleaning and tables here, # for reproducible research gtsummary, # for tables countrycode, # to normalize country data # easystats, # check https://easystats.github.io/easystats/ scales, lubridate ) ``` ## Dataset Ioannidis, John P.A. (2024), “August 2024 data-update for "Updated science-wide author databases of standardized citation indicators"”, Elsevier Data Repository, V7, doi: 10.17632/btchxktzyw.7 ```{r} df <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vRUaeUNVQaq1A_uiKc9vKJTf3KdgsMrGkSaPWZBJYNsgBWs4Jv1i_20TEQqSqsT40OhUmmwXCjK9Bvc/pub?gid=1127167516&single=true&output=csv") ``` # Data Dictionary - **"authfull"**: Full name of the author. - **"inst_name"**: The name of the institution the author is affiliated with. - **"cntry"**: The country where the author's institution is located. - **"np6023"**: Number of papers published by the author from 1960 to 2023. - **"firstyr"**: The first year the author published a paper. - **"lastyr"**: The most recent year the author published a paper. - **"rank (ns)"**: Author's rank in terms of citations or papers within their subject or subfield (non-standardized). - **"nc2323 (ns)"**: Number of citations received by the author from 2023 (non-standardized). - **"h23 (ns)"**: The author’s **H-index** (non-standardized) as of 2023 (a measure that combines productivity and citation impact). - **"hm23 (ns)"**: The author’s **H-metric** (non-standardized) as of 2023 (similar to H-index but adjusted for author position in publications). - **"nps (ns)"**: Number of papers the author has published (non-standardized). - **"ncs (ns)"**: Number of citations the author has received (non-standardized). - **"cpsf (ns)"**: Citations per standardized fractional paper count (non-standardized). - **"ncsf (ns)"**: Number of citations per fractional paper (non-standardized). - **"npsfl (ns)"**: Number of papers with fractional authorship (non-standardized). - **"ncsfl (ns)"**: Number of citations for fractional papers (non-standardized). - **"c (ns)"**: Number of citations (non-standardized). - **"npciting (ns)"**: Number of citing papers (non-standardized). - **"cprat (ns)"**: Citation to publication ratio (non-standardized). - **"np6023 cited2323 (ns)"**: Number of papers from 1960 to 2023 that were cited in 2023 (non-standardized). - **"self%"**: Percentage of self-citations. - **"rank"**: Author's rank in the dataset (standardized). - **"nc2323"**: Number of citations received by the author as of 2023 (standardized). - **"h23"**: Author’s H-index as of 2023 (standardized). - **"hm23"**: Author’s H-metric as of 2023 (standardized). - **"nps"**: Number of papers published by the author (standardized). - **"ncs"**: Number of citations received by the author (standardized). - **"cpsf"**: Citations per standardized fractional paper count (standardized). - **"ncsf"**: Number of citations per fractional paper (standardized). - **"npsfl"**: Number of fractional papers (standardized). - **"ncsfl"**: Number of citations for fractional papers (standardized). - **"c"**: Total number of citations (standardized). - **"npciting"**: Number of citing papers (standardized). - **"cprat"**: Citation to publication ratio (standardized). - **"np6023 cited2323"**: Number of papers from 1960 to 2023 that were cited in 2023 (standardized). - **"np6023_rw"**: Number of papers from 1960 to 2023 that were reweighted (a measure adjusting for factors like field or co-authorship). - **"nc2323_to_rw"**: Number of citations from 2023 reweighted. - **"nc2323_rw"**: Reweighted number of citations in 2023. - **"sm-subfield-1"**: Primary subfield of research for the author. - **"sm-subfield-1-frac"**: Fraction of work the author has published in the primary subfield. - **"sm-subfield-2"**: Secondary subfield of research for the author. - **"sm-subfield-2-frac"**: Fraction of work published in the secondary subfield. - **"sm-field"**: The broad field of research the author works in. - **"sm-field-frac"**: Fraction of work published in the broad field. - **"rank sm-subfield-1"**: Rank within the primary subfield (standardized). - **"rank sm-subfield-1 (ns)"**: Rank within the primary subfield (non-standardized). - **"sm-subfield-1 count"**: Number of papers in the primary subfield. # Data preparing Normalize the names ```{r} df <- df |> janitor::clean_names() ``` Clean the dataset ```{r} df <- df %>% mutate(self_percent = str_trim(str_remove(self_percent, "%"))) %>% mutate(self_percent = as.numeric(self_percent)) ``` ## Dimensions ```{r} # glimpse(df) dim(df) |> knitr::kable() ``` ## Institutions ```{r} df |> mutate(inst_name = fct_lump_prop(inst_name, prop = .005)) |> filter(inst_name != "School of Dentistry") |> filter(inst_name != "Other") |> group_by(inst_name) |> count() |> arrange(desc(n)) |> ggplot(aes(x = fct_reorder(inst_name, n), y = n)) + geom_col() + # scale_y_log10() + geom_text(aes(label = n), color = "white", hjust = 1.2) + coord_flip() + labs(title = "Top Dental Institutions by number of scientists in Scopus Index", caption = "Source: Ioannidis, John P.A. (2024), “August 2024 data-update for \"Updated science-wide author databases\nof standardized citation indicators\"”, Elsevier Data Repository, V7, doi: 10.17632/btchxktzyw.7\n Figure by Sergio.Uribe@rsu.lv", x = "", y = "N of Scientists") ``` ## Countries ```{r} df |> mutate(cntry = fct_lump_prop(cntry, prop = .005)) |> filter(cntry != "School of Dentistry") |> filter(cntry != "Other") |> group_by(cntry) |> count() |> arrange(desc(n)) |> ggplot(aes(x = fct_reorder(cntry, n), y = n)) + geom_col() + # scale_y_log10() + geom_text(aes(label = n), color = "white", hjust = 1.2) + coord_flip() + labs(title = "Top countries by number of dental scientists in Scopus Index", caption = "Source: Ioannidis, John P.A. (2024), “August 2024 data-update for \"Updated science-wide author databases\nof standardized citation indicators\"”, Elsevier Data Repository, V7, doi: 10.17632/btchxktzyw.7\n Figure by Sergio.Uribe@rsu.lv", x = "", y = "N of Scientists") ``` # Only active authors ## Active authors ```{r} df_active <- df %>% filter(firstyr >=1994 & lastyr >= 2024) ``` ```{r} ``` ```{r} df_active |> mutate(inst_name = fct_lump_prop(inst_name, prop = .005)) |> filter(inst_name != "School of Dentistry") |> filter(inst_name != "Other") |> group_by(inst_name) |> count() |> arrange(desc(n)) |> ggplot(aes(x = fct_reorder(inst_name, n), y = n)) + geom_col() + # scale_y_log10() + geom_text(aes(label = n), color = "white", hjust = 1.2) + coord_flip() + labs(title = "Top Dental Institutions by number of scientists in Scopus Index,\n wfirst publication 1994 and last in 2023", caption = "Source: Ioannidis, John P.A. (2024), “August 2024 data-update for \"Updated science-wide author databases\nof standardized citation indicators\"”, Elsevier Data Repository, V7, doi: 10.17632/btchxktzyw.7\n Figure by Sergio.Uribe@rsu.lv", x = "", y = "N of Scientists") ``` ## the institutions that cite themselves ```{r} # Calculate the average self-citation percentage per institution df_active %>% group_by(inst_name) %>% summarise(avg_self_citation = mean(self_percent, na.rm = TRUE)) %>% filter(avg_self_citation > 50) %>% arrange(desc(avg_self_citation)) ``` ```{r} df_active |> filter(self_percent > 25.14) |> arrange(desc(self_percent)) |> ggplot(aes(x = fct_reorder(authfull, self_percent), y = self_percent)) + geom_jitter(width = 0.2, height = 0) + # Adding some width to the jitter coord_flip() + labs( title = "Authors with More Than 2SD self-citation percentage", subtitle = "(average = 9% SD 7.8)", x = "Author (Sorted by Self-Citation Percentage)", y = "Self-Citation Percentage", color = "Country" ) + theme_minimal() # Apply a clean theme ``` ```{r} df |> summarise(mean = mean(self_percent), sd = sd(self_percent)) ``` Correlation between productivity and self citation ```{r} df_active |> ggplot(aes(x = self_percent, y = np6023)) + geom_point() + # scale_x_log10() + # scale_y_log10() + geom_smooth() ``` ```{r} # By country df_active %>% group_by(cntry) %>% summarise(avg_self_citation = mean(self_percent, na.rm = TRUE)) %>% filter(avg_self_citation > 20) %>% ggplot(aes(x = fct_reorder(cntry, avg_self_citation), y = avg_self_citation)) + geom_jitter(width = 0.2, height = 0) + coord_flip() + labs( title = "Countries with More Than 20% Self Citations", # subtitle = "(average = 9%, SD = 7.8)", x = "Country (Sorted by Average Self-Citation Percentage)", y = "Average Self-Citation Percentage", color = "Country" ) + theme_minimal() ``` ```{r} # One-step calculation and plot for institution df_active %>% group_by(inst_name) %>% summarise(avg_self_citation = mean(self_percent, na.rm = TRUE)) %>% filter(avg_self_citation > 30) %>% ggplot(aes(x = fct_reorder(inst_name, avg_self_citation), y = avg_self_citation)) + geom_jitter(width = 0.2, height = 0) + coord_flip() + labs( title = "Institutions with More 30% Self Citations", # subtitle = "(average = 9%, SD = 7.8)", x = "Institution (Sorted by Average Self-Citation Percentage)", y = "Average Self-Citation Percentage", color = "Institution" ) + theme_minimal() ``` ## Impact per institution ```{r} df_active %>% group_by(inst_name) %>% summarise(mean_h_index = mean(h23, na.rm = TRUE)) %>% arrange(desc(mean_h_index)) ```