RIRO v.1.2 (eng)

riro ror wikidata microsoft academic scopus scival web of science incites elibrary

It’s the Russian Index of Research Organizations (RIRO) project’s release v.1.2, with 400+ new organizations added and significantly expanded tables of the Scopus and Wikidata identifiers matched to the Russian organizations.

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09-19-2021

IN RUSSIAN

RIRO (версия 1.2)

This releases of RIRO (v.1.2) introduces the following improvements:

An UpSet diagram below shows the sets of organizations listen in RIRO (v.1.2) and matched to the various identifiers. Only the head and active (existing) organizations are counted here, so the identifiers referring to the branches or to the predecessors liquidated via acquisition, are not counted.

Show code 
filename <- paste0(img.dir, "chart_upset_v1.2.eng.png")
knitr::include_graphics(filename)

In addition to matching and updating the records, we also published the materials highlighting a practical value of RIRO tables (in Russian only).

Special Notes

RIRO dataset

All versions of RIRO dataset (CSV tables) can be downloaded from Zenodo community, via OAI-PMH Harvesting API or by using REST API. Zenodo – is one of the leading data repositories supported by CERN (details).

To cite RIRO dataset (without version):

To cite the latest version of RIRO (v.1.2):

The CSV tables share a primary key named code, so one can easily join the tables or build a database.

Show code 
riro_versions <- list.dirs(paste0(dir, "/final_tables/"), recursive =  FALSE) %>% 
   sort(., decreasing = TRUE) %>% .[grepl("1.2",.)]

riro_tables <- list.files(riro_versions, full.names = TRUE)

Below are the parts of each table corrsponding to the 3 organizations selected as an example:

Table 1 - Official Info

This table comprises the basic organization details - OGRN (Primary State Registration Number), INN (Taxpayer Identification Number), KPP (Tax Registration Reason Code), full & short names, status {active, liquidated, in reorganization process}, and the branch type {head or branch}, and also since v.1.1. - OKOGU (the state governing body identificator).

A table below lists the legal entities found by OGRN and their branches (the branch organization and its head entity share same OGRN and INN). To save some space a column with the short names column is not included.

Show code 
t1 <- riro_tables %>% .[grepl("table1_",.)] %>% 
  read_csv(col_types = cols(.default = col_character()))

test_group <- t1 %>% filter(ogrn %in% c("1025900517378", "1022402137460", "1026103165241"))

test_group %>% 
  select(code, level, status, name_full, full_okogu, ogrn, inn, kpp) %>% 
datatable(rownames = FALSE, filter = "none", escape = FALSE, 
      class = "row-border", 
      options = list(columnDefs = list(
              list(width = '600px', targets = c(3)))))

Each row has its unique code whih serves as a primary key for all the tables of the RIRO database.

Table 2 - Locations and Geodata

Table 2 comprises the full address and its separate parts (in Russian), accompanied with the geocode, geo coordinates and time zone. Table 2 is the only table in RIRO having 1:1 correspondance to Table 1 via code. The other tables can have few rows for one code.

Show code 
t2 <- riro_tables %>% .[grepl("table2_",.)] %>% 
  read_csv(col_types = cols(.default = col_character()))

t2 %>% filter(code %in% test_group$code) %>% 
  datatable(rownames = FALSE, filter = "none", escape = FALSE, 
            class = "row-border", 
            options = list(columnDefs = list(
              list(width = '450px', targets = c(6)))))

Table 3 - Hierarchy

This is a very important table, as it links the parent organizations not only with its current branches, but also with the predecessors (for convenience, in this document both will be referred as “children accounts”).

Table 3 does not pretend to be complete for few reasons:

  1. the list includes only the last predecessors of the current organizations, so there is no historical perspective from 2000s or from USSR.

  2. some organizations have many branch offices (e.g. hospitals), but information about their hierarchy has little or no value from research assessment perspective. Therefore, for some organizations RIRO does not show the branches. The orgs including the branches are mainly the federal organizations (subdued to the ministries and the federal agencies).

The “child_code” is a code for the children account, and the values in the “relation” column reflect nature of the subordination (it can be a branch or a predecessor).

Show code 
t3 <- riro_tables %>% .[grepl("table3_",.)] %>% 
  read_csv(col_types = cols(.default = col_character())) 

full_test <- t3 %>% filter(code %in% test_group$code) %>% arrange(desc(code))
full_test %>% datatable(rownames = FALSE, filter = "none", escape = FALSE, class = "row-border", 
            options = list(autoWidth = FALSE, 
                           columnDefs = list(list(className = 'dt-center', targets = c(0:2)))))

So, using a list of OGRNs for 3 selected organizations, we extracted from Table 1 18 entities with unique codes (head & branch organizations), further used to retrieve a list of all the predecessors. As a result we get a list of 54 entities with unique code values.

Let’s look into this list:

Show code 
hierarchy <- full_test %>% 
  add_row(code = unique(full_test$code), 
          child_code = unique(full_test$code),
          relation = "Головная") %>% 
  rename(parent_code = code, code = child_code) %>% 
  arrange(parent_code, relation)

hierarchy %>% count(parent_code, relation) %>% 
  pivot_wider(names_from = relation, 
              values_from = n, values_fill = 0) %>%
  select(parent_code, parent_org = `Головная`, 
           branch = `Филиал`, predecessors = `Правопредшественники`) %>% 
  datatable(rownames = FALSE, filter = "none", 
            escape = FALSE, class = "row-border", 
            options = list(autoWidth = FALSE, 
  columnDefs = list(list(className = 'dt-center', 
                         targets = c(0:3)))))

One may note that a list of both head and branch organizations can be retrieved from Table 1, so why to use the table 3?

Fair question, let me show you why the RIRO table 3 should not be ignored.

Table 4 - ROR

Research Organizations Registry (ROR) is an international project launched in 2019 with an ambitious goal to create a public ORCID-like registry for the research organizations. It inherits a lot from GRID (Global Research Identifier Database). The ROR organization info can be downloaded as a JSON dump or retrieved via API.

Table 4 contains not all the Russian-related records from ROR v.9, but only those matched to the organizations present in RIRO.

Show code 
t4 <- riro_tables %>% .[grepl("table4_",.)] %>% 
  read_csv(col_types = cols(.default = col_character())) 

hierarchy %>% inner_join(t4, by = "code") %>% 
  arrange(parent_code) %>% select(-parent_code, - relation) %>% 
  datatable(rownames = FALSE, filter = "none", 
            escape = FALSE, class = "row-border", 
            options = list(columnDefs = list(
              list(width = '250px', targets = c(3:5)),
              list(width = '400px', targets = c(8)))))

The column “Relationships” comprise the composite values of following structure:

label:xxxx|type:yyyyyy|id:https://ror.org/zzzzz,

with 3 units (label, type, id) for the relative (according to ROR best judgements) organizations.

In cases like ours such references can be misleading - according to ROR the research institutes of the Perm Federal Research Center have different parents:

So for a single organization ROR shows 4 accounts subordinating to 4 different RAS structures (which is a total confusion, of course).

The truth is that the found 4 research institutes of the Perm Federal Research Center ceased to exist as individual legal entities few years ago, they were acquired by the newly formed federal centre and are now its branches.

That’s the value of Table 3 in RIRO - it helps to gather the related identifiers and qualify them as corresponding to a branch or a predecessor.

The Table 4 is the same as it was in RIRO v.1.1.

Table 5 - Wikidata

WikiData is a public repository of structured data originating from multiple sources. Some sources are more or less consistent (like CrossRef or ISSN), but there’s also a lot of Wikidata records that are created and modified by people. As a result, even though Wikidata offers a pre-defined templates for the profiles of universities and research organizations, many profiles have unpopulated fields.

The table 5 comprises just a part of fields present in the Wikidata organization items and it lists only those Russian research organizations that we matched against the RIRO organizations.

Show code 
t5 <- riro_tables %>% .[grepl("table5_",.)] %>% 
  read_csv(col_types = cols(.default = col_character())) 

hierarchy %>% inner_join(t5, by = "code") %>% 
  arrange(parent_code) %>% 
  select(-parent_code, - relation, -wd_item) %>%  
  mutate_at(c("wikipedia_eng", "wikipedia_rus", "wd_altlabel_eng", "wd_altlabel_rus"),
            ~ifelse(is.na(.x),.x, paste0(substr(.x,1,40),"...")))%>% 
  datatable(rownames = FALSE, filter = "none", escape = FALSE, class = "row-border", 
            options = list(columnDefs = list(
              list(width = '250px', targets = c(2:4)),
              list(width = '150px', targets = c(6:9)))))

Starting from RIRO v.1.2, the table 5 contains only a set of fields that we think are crucial for understanding how the organization is present in Wikidata. Those are a pair of parent/subsidiary properties (used to describe the relations between the parent organizations and its branches), the pairs replaced_by/replaces, followed_by/follows, is_part_of/has_a_part, and also the properties disstime and endtime (used to indicate that an organization does not longer exist).

Table 6 - Scopus

Scopus is a (one of leading) citation index accumulating the metadata from 20k+ journal titles, selected conference sources, and some academic book titles. Table 6 lists the Scopus affiliation profiles matched to the organizations in RIRO, and also a number of publications under Scopus affiliation profile (on a data of request, August 2021).

Please also notice that some Scopus profiles in the table 6 have just the matched RIRO code and the orther fields are empty. Most likely the Scopus records that were present in those profiles in April 2021 were assigned to another Scopus profile, so they do not exists any longer. In RIRO v.1.2 more than 800 organizations have more than one Scopus profile - we hope that RIRO will help to those organizations and Elsevier to fix it one day.

Over 1000 Russian research organizations (and universities) have an access to Scopus under the state-funded centralized subscription, and can use the matched IDs via the online UI or the API-service. The latter has few wrappers for python and R that make working with API more comfortable.

Please note that matching the affiliation profiles to RIRO organizations is based on the affiliation name and city. It does not guarantee that all the publications in the profile are assigned to it correctly. More details on how to edit the affiliation profiles in Scopus can be found on Elsevier web site.

Show code 
t6 <- riro_tables %>% .[grepl("table6_",.)] %>% 
  read_csv(col_types = cols(.default = col_character())) 

hierarchy %>% inner_join(t6, by = "code") %>% 
  arrange(parent_code) %>% select(-parent_code, - relation) %>% 
  datatable(rownames = FALSE, filter = "none", escape = FALSE, class = "row-border", 
            options = list(columnDefs = list(
              list(className = 'dt-center', targets = c(0,6)),
              list(width = '350px', targets = c(2:3)))))

Table 7 - Microsoft Academic

Microsoft Academic Graph (MAG) is a database created based on the information extracted with Bing-parsers from the publisher web sites and PDF files details). This approach is differen from the one utilized by Web of Science and Scopus that receive a large part of information for indexation directly from the publishers.

MAG is also a source of information for many novel solutions like Lens, Semantic Scholar, Open Academic Graph, Unsub.

Even though the last news about MAG shocked us too, we decided to include the MAG Organization IDs into RIRO. Few international companies committed to launching a new tool that may substitute MAG:

ANNOUNCING: We’re building a replacement for Microsoft Academic Graph. https://t.co/GXelkpt6Zc

— Our Research (@our_research) May 8, 2021

Thanks to @MSFTResearch for providing the Microsoft Academic Graph (MAG). We’ve been working with MSR and MAG since 2018, and we’ve been collaborating on this transition for some time. 1/3https://t.co/7aHTLio8uK

— Semantic Scholar (@SemanticScholar) May 11, 2021

Table 7 lists just the MAG organization IDs and names agains the RIRO codes.

The Table 7 is the same as it was in RIRO v.1.1.

Show code 
t7 <- riro_tables %>% .[grepl("table7_",.)] %>% 
  read_csv(col_types = cols(.default = col_character())) 

hierarchy %>% inner_join(t7, by = "code") %>% 
  arrange(parent_code) %>% select(-parent_code, - relation) %>% 
  datatable(rownames = FALSE, filter = "none", escape = FALSE, class = "row-border", 
            options = list(autoWidth = FALSE,
              columnDefs = list(
                list(className = 'dt-center', targets = c(0:1)),
                list(width = '450px', targets = c(2)))))

Table 8 - InCites

InCites is an analytical solution build over Web of Science Core Collection. It allows to export the records, so the matched names can be used for further analysis.

The table 8 lists the official organization names in InCite and Web of Science Core Collection against the RIRO codes.

The Table 8 is the same as it was in RIRO v.1.1.

Show code 
t8 <- riro_tables %>% .[grepl("table8_",.)] %>% 
  read_csv(col_types = cols(.default = col_character()))  

hierarchy %>% inner_join(t8, by = "code") %>% 
  arrange(parent_code) %>% select(-parent_code, - relation) %>% 
  datatable(rownames = FALSE, filter = "none", escape = FALSE, class = "row-border", 
            options = list(autoWidth = FALSE,
              columnDefs = list(
                list(className = 'dt-center', targets = c(0,3)),
                list(width = '450px', targets = c(1:2)))))

Table 9 - SciVal

SciVal is an analytical tool build over Scopus. Some Russian organizations have an access to SciVal API and could use the IDs matched against the RIRO codes in the table 9.

The Table 9 is the same as it was in RIRO v.1.1.

Show code 
t9 <- riro_tables %>% .[grepl("table9_",.)] %>% 
  read_csv(col_types = cols(.default = col_character())) 

hierarchy %>% inner_join(t9, by = "code") %>% 
  arrange(parent_code) %>% select(-parent_code, - relation) %>% 
  datatable(rownames = FALSE, filter = "none", escape = FALSE, class = "row-border", 
            options = list(autoWidth = FALSE,
              columnDefs = list(
                list(className = 'dt-center', targets = c(0:1)),
                list(width = '450px', targets = c(2)))))

Table 10 - Russian Universities Assessment System (by the Ministry of Science & Higher Education)

The system gathers the university statistical reports from all Russian higher education institutions (except some schools under the Ministries of Defence, etc). The reports contain a lot of useful information - from financial to enrollment data.

Table 10 lists the IDs that corresponds to the university’s web page on the portal, matched to the RIRO codes.

The Table 10 is the same as it was in RIRO v.1.1.

Show code 
t10 <- riro_tables %>% .[grepl("table10_",.)] %>% 
  read_csv(col_types = cols(.default = col_character())) 

hierarchy %>% inner_join(t10, by = "code") %>% 
  arrange(parent_code) %>% select(-parent_code, - relation) %>% 
  datatable(rownames = FALSE, filter = "none", escape = FALSE, class = "row-border", 
            options = list(autoWidth = FALSE,
              columnDefs = list(list(className = 'dt-center', 
                                     targets = c(0:1)))))

Table 11 - Web of Science

Web of Science is by far the world’s oldest and most prominent citation index. At this moment Web of Science does not provide the organization IDs that could be used for search or data retrieval, but the search results have the orgaization names. The table 11 lists almost 4000 such names matched to the organizations in RIRO. This is not a complete list of known affiliation names for the Russian research organizations, but we hope to adjust this table in future releases of RIRO.

The Table 11 is the same as it was in RIRO v.1.1.

Show code 
t11 <- riro_tables %>% .[grepl("table11_",.)] %>% 
  read_csv(col_types = cols(.default = col_character())) 

hierarchy %>% inner_join(t11, by = "code") %>% 
  arrange(parent_code) %>% select(-parent_code, - relation) %>% 
  datatable(rownames = FALSE, filter = "none", 
            escape = FALSE, class = "row-border", 
            options = list(autoWidth = FALSE,
              columnDefs = list(
                list(className = 'dt-center', targets = c(0)),
                list(width = '450px', targets = c(1,3)))))

Table 12. eLIRBARY.ru

eLIBRARY.ru is the largest Russian aggregator of the scholarly contents. In the last years the company actively invests into the online services, including API. The Org ID is used in the URLs of the organization profiles (e.g. https://www.elibrary.ru/org_about.asp?orgsid=17548) and by some API.

Table 12 lists the organization ids of 1827 largest Russian research organizations. We cooperate with the proprietor to extend the list in the next RIRO releases.

The Table 12 is the same as it was in RIRO v.1.1.

Show code 
t12 <- riro_tables %>% .[grepl("table12_",.)] %>% 
  read_csv(col_types = cols(.default = col_character())) 

hierarchy %>% inner_join(t12, by = "code") %>% 
  arrange(parent_code) %>% select(-parent_code, - relation) %>% 
  datatable(rownames = FALSE, filter = "none", 
            escape = FALSE, class = "row-border", 
            options = list(autoWidth = FALSE,
              columnDefs = list(
                list(className = 'dt-center', targets = c(0,1)),
                list(width = '450px', targets = c(2)))))

All IDs for 3 organizations

An illustration below shows the identifiers (ROR, GRID, Scopus Affiliation ID, InCites ID, MAG, Wikidata) matched to 3 organizations selected as an example. Some IDs correspond to the head organizations, the others either to the branches or to the predecessors.

The identifiers are placed along X-axis, organized in three sections (by organization). The RIRO entities are place along Y-axis, the existing organization are shown as squares (the head organizations are also marked with a special sign), the predecessors as circles.

Show code 
filename <- paste0(img.dir, "chart_examples_v1.2_eng.png")
knitr::include_graphics(filename)

Feedback

For RIRO v.1.2 we have a google form (in Russian) with the following scenarios of the change requests:

We do hope that feedback will help us to make RIRO a more valuable tool.

Acknowledgments

Allaire J, Xie Y, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2021). rmarkdown: Dynamic Documents for R. R package version 2.7, <URL: https://github.com/rstudio/rmarkdown>.

Blondel E (2021). zen4R: Interface to ‘Zenodo’ REST API. R package version 0.4-3, <URL: https://github.com/eblondel/zen4R>.

Chang, W (2014). extrafont: Tools for using fonts. R package version 0.17, <URL: https://CRAN.R-project.org/package=extrafont>.

Henry L, Wickham H (2020). purrr: Functional Programming Tools. R package version 0.3.4, <URL: https://CRAN.R-project.org/package=purrr>.

Krassowski M (2020). “ComplexUpset.” doi: 10.5281/zenodo.3700590 (URL: https://doi.org/10.5281/zenodo.3700590), <URL: https://doi.org/10.5281/zenodo.3700590>.

Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H (2014). “UpSet: Visualization of Intersecting Sets,.” IEEE Transactions on Visualization and Computer Graphics, 20(12), 1983–1992. doi: 10.1109/TVCG.2014.2346248 (URL: https://doi.org/10.1109/TVCG.2014.2346248), <URL: https://doi.org/10.1109/TVCG.2014.2346248>.

Wickham H (2020). tidyr: Tidy Messy Data. R package version 1.1.2, <URL: https://CRAN.R-project.org/package=tidyr>.

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, <URL: https://ggplot2.tidyverse.org>.

Wickham H (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0, <URL: https://CRAN.R-project.org/package=stringr>.

Wickham H, Francois R, Henry L, Muller K (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.3, <URL: https://CRAN.R-project.org/package=dplyr>.

Wickham H, Hester J (2020). readr: Read Rectangular Text Data. R package version 1.4.0, <URL: https://CRAN.R-project.org/package=readr>.

Wickham H, Seidel D (2020). scales: Scale Functions for Visualization. R package version 1.1.1, <URL: https://CRAN.R-project.org/package=scales>.

Xie Y (2020). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.30, <URL: https://yihui.org/knitr/>.

Xie Y (2015). Dynamic Documents with R and knitr, 2nd edition. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 978-1498716963, <URL: https://yihui.org/knitr/>.

Xie Y (2014). “knitr: A Comprehensive Tool for Reproducible Research in R.” In Stodden V, Leisch F, Peng RD (eds.), Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595, <URL: http://www.crcpress.com/product/isbn/9781466561595>.

Xie Y, Allaire J, Grolemund G (2018). R Markdown: The Definitive Guide. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 9781138359338, <URL: https://bookdown.org/yihui/rmarkdown>.

Xie Y, Cheng J, Tan X (2021). DT: A Wrapper of the JavaScript Library ‘DataTables’. R package version 0.17, <URL: https://CRAN.R-project.org/package=DT>.

Xie Y, Dervieux C, Riederer E (2020). R Markdown Cookbook. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 9780367563837, <URL: https://bookdown.org/yihui/rmarkdown-cookbook>.

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

team (2021, Sept. 19). Russian Index of Research Organizations: RIRO v.1.2 (eng). Retrieved from https://openriro.github.io/posts/rirov12eng/

BibTeX citation

@misc{team2021riro,
  author = {team, RIRO},
  title = {Russian Index of Research Organizations: RIRO v.1.2 (eng)},
  url = {https://openriro.github.io/posts/rirov12eng/},
  year = {2021}
}