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Reprint Author Analysis

Questions

  1. How many records in the Web of Science have affiliation information about reprint authors?

  2. How many records in the Web of Science have more than one reprint author?

  3. How many reprint authors are co-located (institutional level)?

  4. How many reprint authors are internationally co-located (country level)?

  5. What is the collaboration network in terms of country affiliations from reprint authors?

Focus:

  • Database: wos_b_2019
  • Document Types: Articles and Reviews
  • Database Collections: WOS.SCI, WOS.SSCI, WOS.AHCI
  • Publication Period 2014 - 2018

Data analysis

1. How many records in the Web of Science have affiliation information at the country level about reprint authors?

Articles total

select wos_b_2019.items.pubyear, COUNT(DISTINCT(wos_b_2019.items.ut_eid)) as articles_total
    from
        wos_b_2019.items      
    inner join
        wos_b_2019.databasecollection              
            on wos_b_2019.databasecollection.fk_items =  wos_b_2019.items.pk_items        
    where
        wos_b_2019.databasecollection.edition_value in ('WOS.SCI', 'WOS.SSCI', 'WOS.AHCI')                  
        and wos_b_2019.items.doctype in ('Article', 'Review')                   
        and wos_b_2019.items.pubyear in (2014, 2015, 2016, 2017, 2018)
group by wos_b_2019.items.pubyear
articles_total %>% 
  arrange(desc(PUBYEAR))
#>   PUBYEAR ARTICLES_TOTAL
#> 1    2018        1744446
#> 2    2017        1679182
#> 3    2016        1634923
#> 4    2015        1575755
#> 5    2014        1517605

Records with reprint authors

Using wos_b_2019.items_authors_institutions.type = 'RP' to identify reprint authors.

select wos_b_2019.items.pubyear, COUNT(DISTINCT(wos_b_2019.items.ut_eid)) as rp_articles_total
    from
        wos_b_2019.items      
    inner join
        wos_b_2019.databasecollection              
            on wos_b_2019.databasecollection.fk_items =  wos_b_2019.items.pk_items        
    inner join
        wos_b_2019.items_authors_institutions                                        
            on wos_b_2019.items_authors_institutions.fk_items = wos_b_2019.items.pk_items     
    where
        wos_b_2019.databasecollection.edition_value in ('WOS.SCI', 'WOS.SSCI', 'WOS.AHCI')                  
        and wos_b_2019.items.doctype in ('Article', 'Review')                   
        and wos_b_2019.items.pubyear in (2014, 2015, 2016, 2017, 2018)              
        and wos_b_2019.items_authors_institutions.type = 'RP'
    group by wos_b_2019.items.pubyear
rp_articles_total %>% 
  arrange(desc(PUBYEAR))
#>   PUBYEAR RP_ARTICLES_TOTAL
#> 1    2018           1731851
#> 2    2017           1664849
#> 3    2016           1619696
#> 4    2015           1556981
#> 5    2014           1498393

Reprint authors records with country information

Retrieval considerations

An earlier exploration studying how affiliations from reprint authors are represented in the WOS-KB revealed that only one affiliation is tagged with RP in the tablewos_b_2019.items_author_institutions` per author. To obtain all affiliations per reprint author, the following retrieval strategy can be used:

  1. Get all authors tagged as RP per item
  2. Obtain all institutional addresses per rp_author-item combination including countrycode
  3. Summarize observations

Country codes

There are two tables containing country codes, wos_b_2019.institutions.countrycode and wos_b_2019.d_items_authors_institutions.inst_countrycode. First, wos_b_2019.institutions.countrycode is analyzed

select
        d.pubyear, count(distinct(d.ut_eid)) as rp_cc_articles_total
    from
        wos_b_2019.items_authors_institutions 
    inner join
        (
            select
                pk_items,
                fk_authors,
                ut_eid,
                pubyear
            from
                wos_b_2019.items     
            inner join
                wos_b_2019.databasecollection                           
                    on wos_b_2019.databasecollection.fk_items =  wos_b_2019.items.pk_items             
            inner join
                wos_b_2019.items_authors_institutions                                                     
                    on wos_b_2019.items_authors_institutions.fk_items = wos_b_2019.items.pk_items
            where
                wos_b_2019.databasecollection.edition_value in (
                    'WOS.SCI', 'WOS.SSCI', 'WOS.AHCI'
                )                           
                and wos_b_2019.items.doctype in (
                    'Article', 'Review'
                )                            
                and wos_b_2019.items.pubyear in (
                    2014, 2015, 2016, 2017, 2018 
                )                       
                and wos_b_2019.items_authors_institutions.type = 'RP'         
        ) d 
            on (
                wos_b_2019.items_authors_institutions.fk_items = d.pk_items 
                and wos_b_2019.items_authors_institutions.fk_authors = d.fk_authors
            )
         inner join wos_b_2019.institutions on wos_b_2019.institutions.pk_institutions = wos_b_2019.items_authors_institutions.fk_institutions
         where wos_b_2019.institutions.countrycode is not null     
   group by d.pubyear
rp_cc_articles_total %>% 
  arrange(desc(PUBYEAR))
#>   PUBYEAR RP_CC_ARTICLES_TOTAL
#> 1    2018              1731808
#> 2    2017              1664799
#> 3    2016              1619081
#> 4    2015              1556514
#> 5    2014              1498270

Data Summary:

rq_1_df <- articles_total %>%
  left_join(rp_articles_total, by = "PUBYEAR") %>%
  mutate(ARTICLE_RP_RATIO = RP_ARTICLES_TOTAL / ARTICLES_TOTAL) %>%
  left_join(rp_cc_articles_total, by = "PUBYEAR") %>%
  mutate(ARTICLE_CC_RP_RATIO = RP_CC_ARTICLES_TOTAL / ARTICLES_TOTAL) %>%
  arrange(desc(PUBYEAR))
rq_1_df
#>   PUBYEAR ARTICLES_TOTAL RP_ARTICLES_TOTAL ARTICLE_RP_RATIO
#> 1    2018        1744446           1731851             0.99
#> 2    2017        1679182           1664849             0.99
#> 3    2016        1634923           1619696             0.99
#> 4    2015        1575755           1556981             0.99
#> 5    2014        1517605           1498393             0.99
#>   RP_CC_ARTICLES_TOTAL ARTICLE_CC_RP_RATIO
#> 1              1731808                0.99
#> 2              1664799                0.99
#> 3              1619081                0.99
#> 4              1556514                0.99
#> 5              1498270                0.99
rq_1_df %>%
  gather(ARTICLES_TOTAL, RP_ARTICLES_TOTAL, RP_CC_ARTICLES_TOTAL, 
         key = "indicator", value = "wos_articles") %>%
  ggplot(aes(PUBYEAR, wos_articles, fill = indicator)) +
  geom_bar(position="dodge", stat="identity") +
  scale_fill_viridis_d("", option = "C", labels = c("Articles + Reviews", "with RP", "with RP and countrycode")) +
  theme_minimal() +
  labs(y = "wos_b_2019 records", 
       title = "Coverage of reprint authors with country of affiliation",
       subtitle = "wos_b_2019.institutions.countrycode") +
  theme(plot.margin = margin(30, 30, 30, 30)) +
  theme(panel.grid.minor = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(panel.border = element_blank())

Next, wos_b_2019.d_items_authors_institutions.inst_countrycode is analyzed.

  select
        d.pubyear, count(distinct(d.ut_eid)) as rp_d_cc_articles
    from
        wos_b_2019.d_items_authors_institutions 
    inner join
        (
            select
                pk_items,
                fk_authors,
                ut_eid,
                pubyear
            from
                wos_b_2019.items     
            inner join
                wos_b_2019.databasecollection                           
                    on wos_b_2019.databasecollection.fk_items =  wos_b_2019.items.pk_items             
            inner join
                wos_b_2019.items_authors_institutions                                                     
                    on wos_b_2019.items_authors_institutions.fk_items = wos_b_2019.items.pk_items
            where
                wos_b_2019.databasecollection.edition_value in (
                    'WOS.SCI', 'WOS.SSCI', 'WOS.AHCI'
                )                           
                and wos_b_2019.items.doctype in (
                    'Article', 'Review'
                )                            
                and wos_b_2019.items.pubyear in (
                    2014, 2015, 2016, 2017, 2018
                )                       
                and wos_b_2019.items_authors_institutions.type = 'RP'         
        ) d 
            on (
                wos_b_2019.d_items_authors_institutions.fk_items = d.pk_items 
                and wos_b_2019.d_items_authors_institutions.fk_authors = d.fk_authors
            )
         where wos_b_2019.d_items_authors_institutions.inst_countrycode is not null     
   group by d.pubyear
rp_d_cc_articles_total
#>   PUBYEAR RP_D_CC_ARTICLES
#> 1    2018          1731808
#> 2    2014          1498271
#> 3    2017          1664800
#> 4    2015          1556568
#> 5    2016          1619163
rp_df_d <- articles_total %>%
  left_join(rp_articles_total, by = "PUBYEAR") %>%
  mutate(ARTICLE_RP_RATIO = RP_ARTICLES_TOTAL / ARTICLES_TOTAL) %>%
  left_join(rp_d_cc_articles_total, by = "PUBYEAR") %>%
  mutate(RP_D_CC_ARTICLES_RATIO = RP_D_CC_ARTICLES / ARTICLES_TOTAL) %>%
  arrange(desc(PUBYEAR))
rp_df_d
#>   PUBYEAR ARTICLES_TOTAL RP_ARTICLES_TOTAL ARTICLE_RP_RATIO
#> 1    2018        1744446           1731851             0.99
#> 2    2017        1679182           1664849             0.99
#> 3    2016        1634923           1619696             0.99
#> 4    2015        1575755           1556981             0.99
#> 5    2014        1517605           1498393             0.99
#>   RP_D_CC_ARTICLES RP_D_CC_ARTICLES_RATIO
#> 1          1731808                   0.99
#> 2          1664800                   0.99
#> 3          1619163                   0.99
#> 4          1556568                   0.99
#> 5          1498271                   0.99
rp_df_d %>%
  gather(ARTICLES_TOTAL, RP_ARTICLES_TOTAL, RP_D_CC_ARTICLES, 
         key = "indicator", value = "wos_articles") %>%
  ggplot(aes(PUBYEAR, wos_articles, fill = indicator)) +
  geom_bar(position="dodge", stat="identity") +
  scale_fill_viridis_d("", option = "C", labels = c("Articles + Reviews", "with RP", "with RP and countrycode")) +
  theme_minimal() +
  labs(y = "wos_b_2019 records", 
       title = "Coverage of reprint authors with country of affiliation",
       subtitle = "wos_b_2019.d_items_authors_institutions.inst_countrycode") +
  theme(plot.margin = margin(30, 30, 30, 30)) +
  theme(panel.grid.minor = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(panel.border = element_blank())

In conclusion, joining with both tables seems feasible, because it results in a thorough countries of affiliation coverage for reprint authors. Overall, 99 % of records representing original articles and reviews in the period 2014 - 2017 provide affiliation information for reprint authors at the country-level. In the following, wos_b_2019.d_items_authors_institutions.inst_countrycode is used.

2. How many records in the Web of Science have more than one reprint author?

Query

select
        distinct         
        d.pubyear,
        d.ut_eid,
        wos_b_2019.d_items_authors_institutions.fk_authors,
        wos_b_2019.d_items_authors_institutions.inst_per_auth_cnt,
        wos_b_2019.d_items_authors_institutions.country_per_auth_cnt     
    from
        wos_b_2019.d_items_authors_institutions      
    inner join
        (
            select
                pk_items,
                fk_authors,
                ut_eid,
                pubyear             
            from
                wos_b_2019.items                  
            inner join
                wos_b_2019.databasecollection                                                
                    on wos_b_2019.databasecollection.fk_items =  wos_b_2019.items.pk_items                          
            inner join
                wos_b_2019.items_authors_institutions                                                                          
                    on wos_b_2019.items_authors_institutions.fk_items = wos_b_2019.items.pk_items                               
            where
                wos_b_2019.databasecollection.edition_value in (
                    'WOS.SCI', 'WOS.SSCI', 'WOS.AHCI'                 
                )                                            
                and wos_b_2019.items.doctype in (
                    'Article', 'Review'                 
                )                                             
                and wos_b_2019.items.pubyear in (
                    2014, 2015, 2016, 2017, 2018                 
                )                                        
                and wos_b_2019.items_authors_institutions.type = 'RP'                  
        ) d              
            on (
                wos_b_2019.d_items_authors_institutions.fk_items = d.pk_items                  
                and wos_b_2019.d_items_authors_institutions.fk_authors = d.fk_authors             
            )          
    where
        wos_b_2019.d_items_authors_institutions.inst_countrycode is not null

Backup in KB table space

dbWriteTable(conn = jdbcConnection, 
             name = "rp_ind_df", 
             value = rp_ind_df,
             overwrite = TRUE)
#> [1] TRUE

Analysis: Distribution of the number of reprint authors

select
        pubyear,
        ut_eid,
        count(fk_authors) as n_au
    from
        rp_ind_df 
    group by
        ut_eid,
        pubyear

Summary statistics

summary(rp_au$N_AU)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>       1       1       1       1       1      32

Frequency table of the number of reprint authors per publication. Publications with more than five reprint authors per publication were reduced to the residual category “> 5”.

tp_au_count <- rp_au %>% 
  mutate(N_AU = fct_other(factor(N_AU), keep = c("1", "2", "3", "4", "5"),
                          other_level = "> 5")) %>% 
  group_by(N_AU, PUBYEAR) %>% 
  summarize(N = n()) %>% 
  ungroup() %>% 
  group_by(PUBYEAR) %>% 
  mutate(PROP = N / sum(N))
tp_au_count %>%
  ggplot(aes(N_AU, PROP, fill = factor(PUBYEAR))) +
  geom_bar(stat = "identity") +
  scale_fill_brewer(type = "qual", palette = "Set1") +
  facet_grid( ~ factor(PUBYEAR)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 5L)) +
  labs(
    x = "Number of reprint authors per record",
    y = "Proportion",
    title = "Number of reprint authors per Web of Science record",
    subtitle = "Articles and reviews 2014 - 2018"
  ) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(plot.margin = margin(30, 30, 30, 30)) +
  theme(panel.grid.minor = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(panel.border = element_blank())

SQL statement to retrieve all publications with more than one reprint author (incl. country info)

select
        pubyear,
        ut_eid,
        count(fk_authors)  
    from
        rp_ind_df 
    group by
        ut_eid,
        pubyear 
    having
        count(fk_authors) > 1

In total, 497,230 out of 8,070,610 records indexed in the Web of Science for the period 2014 - 2017 have more than one reprint author, representing a percentage of 6.2 %. Furthermore, data exploration suggests that starting from 2016 the Web of Science has begun to systematically keep track of more than one reprint author.

3. How many reprint authors are co-located (institutional level)?

Query

select
        inst_per_auth_cnt,
        pubyear,
        count(*) as inst_n
    from
        rp_ind_df 
    group by
        pubyear,
        inst_per_auth_cnt

Analysis

Distribution of the number of institutional affiliations per reprint author and year

rp_inst_au_df <- rp_inst_au_count %>%
  mutate(INST_PER_AUTH_CNT = fct_other(
    factor(INST_PER_AUTH_CNT),
    keep = c("1", "2", "3", "4", "5"),
    other_level = "> 5"
  )) %>%
  ungroup() %>%
  group_by(PUBYEAR) %>%
  mutate(PROP = INST_N / sum(INST_N)) %>%
  arrange(INST_PER_AUTH_CNT, PUBYEAR)

rp_inst_au_df %>%
  ggplot(aes(
    x = factor(INST_PER_AUTH_CNT),
    PROP,
    fill = factor(PUBYEAR)
  )) +
  geom_bar(stat = "identity") +
  scale_fill_brewer(type = "qual", palette = "Set1") +
  facet_grid( ~ PUBYEAR) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 5L)) +
  labs(
    x = "Number of Institutions",
    y = "Proportion",
    title = "Institutional affiliations per reprint author",
    caption = "Derived from wos_b_2019.D_ITEMS_AUTHORS_INSTITUTIONS.INST_PER_AUTH_CNT"
  ) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(plot.margin = margin(30, 30, 30, 30)) +
  theme(panel.grid.minor = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(panel.border = element_blank())

Overall, around half of the reprint authors indexed listed more than one institutional affiliation (N = 4,148,308, Proportion = 48 %).

4. How many reprint authors are internationally co-located (country level)?

Query

select
        country_per_auth_cnt,
        pubyear,
        count(*) as country_n
    from
        rp_ind_df 
    group by
        pubyear,
        country_per_auth_cnt

Frequency table of the number of country affiliation per reprint author and publication. Reprint authors with more than three countries of affiliation at the time of publication were reduced to the residual category “Other”.

rp_country_au_df <- rp_country_au_count %>%
  mutate(COUNTRY_PER_AUTH_CNT = fct_other(
    factor(COUNTRY_PER_AUTH_CNT),
    keep = c("1", "2", "3"),
    other_level = "> 3"
  )) %>%
  ungroup() %>%
  group_by(PUBYEAR) %>%
  mutate(PROP = COUNTRY_N / sum(COUNTRY_N)) %>%
  arrange(COUNTRY_PER_AUTH_CNT, PUBYEAR)
rp_country_au_df
#> # A tibble: 39 x 4
#> # Groups:   PUBYEAR [5]
#>    COUNTRY_PER_AUTH_CNT PUBYEAR COUNTRY_N   PROP
#>    <fct>                  <dbl>     <dbl>  <dbl>
#>  1 1                       2014   1441013 0.962 
#>  2 1                       2015   1495351 0.960 
#>  3 1                       2016   1709133 0.945 
#>  4 1                       2017   1740170 0.944 
#>  5 1                       2018   1828802 0.942 
#>  6 2                       2014     55434 0.0370
#>  7 2                       2015     59977 0.0385
#>  8 2                       2016     96786 0.0535
#>  9 2                       2017     99119 0.0538
#> 10 2                       2018    107512 0.0554
#> # … with 29 more rows
rp_country_au_df %>%
  ggplot(aes(
    x = factor(COUNTRY_PER_AUTH_CNT),
    PROP,
    fill = factor(PUBYEAR)
  )) +
  geom_bar(stat = "identity") +
  scale_fill_brewer(type = "qual", palette = "Set1") +
  facet_grid( ~ PUBYEAR) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 5L)) +
  labs(
    x = "Number of Countries",
    y = "Proportion",
    title = "Country affiliations per reprint author",
    caption = "Derived from wos_b_2019.D_ITEMS_AUTHORS_INSTITUTIONS.COUNTRY_PER_AUTH_CNT"
  ) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(plot.margin = margin(30, 30, 30, 30)) +
  theme(panel.grid.minor = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(panel.border = element_blank())

Although the number of co-located reprint authors is growing, only a small proportion of reprint author is internationally co-located (N = 434,331, Proportion = 5 %).

5. What is the collaboration network in terms of country affiliations from reprint authors?

Query

Obtain publications with country affiliations

select  distinct wos_b_2019.d_items_authors_institutions.INST_COUNTRYCODE,       
        d.ut_eid
    from
        wos_b_2019.d_items_authors_institutions      
    inner join
        (
            select
                pk_items,
                fk_authors,
                ut_eid,
                pubyear             
            from
                wos_b_2019.items                  
            inner join
                wos_b_2019.databasecollection                                                
                    on wos_b_2019.databasecollection.fk_items =  wos_b_2019.items.pk_items                          
            inner join
                wos_b_2019.items_authors_institutions                                                                          
                    on wos_b_2019.items_authors_institutions.fk_items = wos_b_2019.items.pk_items                               
            where
                wos_b_2019.databasecollection.edition_value in (
                    'WOS.SCI', 'WOS.SSCI', 'WOS.AHCI'                 
                )                                            
                and wos_b_2019.items.doctype in (
                    'Article', 'Review'                 
                )                                             
                and wos_b_2019.items.pubyear in (
                    2014, 2015, 2016, 2017, 2018                 
                )                                        
                and wos_b_2019.items_authors_institutions.type = 'RP'                  
        ) d              
            on (
                wos_b_2019.d_items_authors_institutions.fk_items = d.pk_items                  
                and wos_b_2019.d_items_authors_institutions.fk_authors = d.fk_authors             
            )          
    where
        wos_b_2019.d_items_authors_institutions.inst_countrycode is not null

Analysis

Publications with internationally co-located reprint author(s)

co_pubs <- rp_country_mat %>% 
  group_by(UT_EID) %>%
  filter(n() > 1)

Country share

all_count <- rp_country_mat %>%
  count(INST_COUNTRYCODE, sort = TRUE)
co_count <- co_pubs %>%
  ungroup() %>%
  count(INST_COUNTRYCODE, sort = TRUE, name = "n_co")
left_join(all_count, co_count, by = "INST_COUNTRYCODE") %>%
  mutate(prop = n_co / n * 100) 
#> # A tibble: 249 x 4
#>    INST_COUNTRYCODE       n   n_co  prop
#>    <chr>              <int>  <int> <dbl>
#>  1 USA              1675266 135429  8.08
#>  2 CHN              1492259  95259  6.38
#>  3 GBR               410762  63330 15.4 
#>  4 DEU               391561  52884 13.5 
#>  5 JPN               332902  20692  6.22
#>  6 IND               292829  12520  4.28
#>  7 KOR               259593  13445  5.18
#>  8 ITA               258881  22743  8.79
#>  9 FRA               253486  35473 14.0 
#> 10 CAN               246357  27937 11.3 
#> # … with 239 more rows

Create bi-partite matrix

#co_pubs_mat <- as.matrix(table(co_pubs$UT_EID, co_pubs$INST_COUNTRYCODE))

calculate unipartite matrix (country level)

#co_pubs_mat_t <- t(co_pubs_mat) %*% co_pubs_mat

visualise network (tbc)

# library(sna)
# sna::gplot(co_pubs_mat_t, 
#            gmode = "graph", 
#            vertex.cex = log(diag(co_pubs_mat_t)^0.3),
#            usearrows = FALSE)