Collaboration Patterns By Year (International, Domestic, Internal)

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Collaboration Patterns By Year (International, Domestic, Internal)#

Using the count capability of the API, Dimensions allows you to quickly identify international, domestic, and inernal Collaboration

This notebook shows how to quickly identify international, domestic, and internal collaboration using the Organizations data source and the Publications data source available via the Dimensions Analytics API.

[1]:

import datetime
print("==\nCHANGELOG\nThis notebook was last run on %s\n==" % datetime.date.today().strftime('%b %d, %Y'))

==
CHANGELOG
This notebook was last run on Sep 10, 2025
==

Prerequisites#

This notebook assumes you have installed the Dimcli library and are familiar with the ‘Getting Started’ tutorial.

[2]:

!pip install dimcli plotly -U --quiet

#
# load libraries
import dimcli
from dimcli.utils import *

import json, sys, time
import pandas as pd
import plotly.express as px  # plotly>=4.8.1
if not 'google.colab' in sys.modules:
  # make js dependecies local / needed by html exports
  from plotly.offline import init_notebook_mode
  init_notebook_mode(connected=True)

print("==\nLogging in..")
# https://digital-science.github.io/dimcli/getting-started.html#authentication
ENDPOINT = "https://app.dimensions.ai"
if 'google.colab' in sys.modules:
  import getpass
  KEY = getpass.getpass(prompt='API Key: ')
  dimcli.login(key=KEY, endpoint=ENDPOINT)
else:
  KEY = ""
  dimcli.login(key=KEY, endpoint=ENDPOINT)
dsl = dimcli.Dsl()

Searching config file credentials for 'https://app.dimensions.ai' endpoint..

==
Logging in..
Dimcli - Dimensions API Client (v1.4)
Connected to: <https://app.dimensions.ai/api/dsl> - DSL v2.12
Method: dsl.ini file

1. Lookup the University that you are interested in#

[3]:

dsl.query("""
search organizations for "melbourne" return organizations
""").as_dataframe()

Returned Organizations: 20 (total = 23)
Time: 0.53s

[3]:
id name city_name country_code country_name types state_name latitude linkout longitude acronym
0 grid.772384.d Trelleborg Marine Systems Melbourne Pty Ltd Victoria AU Australia [Company] NaN NaN NaN NaN NaN
1 grid.746611.3 Noyes Bros Melbourne Pty Ltd NaN AU Australia [Other] NaN NaN NaN NaN NaN
2 grid.631568.f CityLink Melbourne Ltd NaN AU Australia [Other] NaN NaN NaN NaN NaN
3 grid.530408.a Melbourne Institute of Technology Melbourne AU Australia [Nonprofit] Victoria NaN NaN NaN NaN
4 grid.511296.8 Melbourne Genomics Health Alliance Melbourne AU Australia [Nonprofit] Victoria -37.797960 [https://www.melbournegenomics.org.au/] 144.953870 NaN
5 grid.493437.e RMIT Europe Barcelona ES Spain [Education] NaN 41.402576 [https://www.rmit.eu] 2.194333 RMIT
6 grid.490309.7 Melbourne Sexual Health Centre Carlton AU Australia [Healthcare] Victoria -37.803123 [https://www.mshc.org.au/] 144.963840 MSHC
7 grid.477970.a Melbourne Clinic Richmond AU Australia [Healthcare] Victoria -37.815063 [http://www.themelbourneclinic.com.au/] 144.999650 NaN
8 grid.474755.0 Leica Biosystems Melbourne Pty Ltd Mt. Waverley AU Australia [Company] NaN NaN [http://www.danaher.com/] NaN NaN
9 grid.469061.c Ridley College Melbourne AU Australia [Education] Victoria -37.783780 [https://www.ridley.edu.au/] 144.957660 NaN
10 grid.469026.f Melbourne School of Theology Melbourne AU Australia [Education] Victoria -37.859700 [http://www.mst.edu.au/] 145.209410 MBI
11 grid.468079.4 Port of Melbourne Corporation Melbourne AU Australia [Government] Victoria -37.824028 [http://www.portofmelbourne.com/] 144.907070 PoMC
12 grid.468069.5 Melbourne Water Melbourne AU Australia [Government] Victoria -37.814007 [http://www.melbournewater.com.au/Pages/home.a... 144.946700 NaN
13 grid.452643.2 Melbourne Bioinformatics Melbourne AU Australia [Education] Victoria -37.799847 [https://www.melbournebioinformatics.org.au] 144.964460 VLSCI
14 grid.449135.e Melbourne Free University Melbourne AU Australia [Education] Victoria NaN NaN NaN NaN
15 grid.440113.3 Royal Dental Hospital of Melbourne Melbourne AU Australia [Healthcare] Victoria -37.799260 [https://www.dhsv.org.au] 144.964630 RDHM
16 grid.438527.f Royal Melbourne Institute of Technology Univer... Melbourne AU Australia [Other] Victoria NaN NaN NaN NaN
17 grid.429299.d Melbourne Health Melbourne AU Australia [Healthcare] Victoria -37.798940 [http://www.mh.org.au/] 144.955930 NaN
18 grid.416153.4 Royal Melbourne Hospital Melbourne AU Australia [Healthcare] Victoria -37.798756 [http://www.rmh.mh.org.au/] 144.955930 RMH
19 grid.413105.2 St Vincent's Hospital Melbourne AU Australia [Healthcare] Victoria -37.807000 [http://www.svhm.org.au/Pages/Home.aspx] 144.975000 NaN 
[4]:

institution = "grid.1008.9"

2. Publications output by year#

[15]:

allpubs = dsl.query(f"""

        search publications
            where research_orgs.id = "{institution}"
            and type="article"
            and year > 2010
        return year


    """).as_dataframe()

allpubs.columns = ['year', 'pubs']
px.bar(allpubs, x="year", y="pubs")

Returned Year: 16
Time: 0.55s

3. International publications#

[16]:

international = dsl.query(f"""

        search publications
            where research_orgs.id = "{institution}"
            and type="article"
            and count(research_org_countries) > 1
                 and year > 2010
        return year


    """).as_dataframe()

international.columns = ['year','international_count']
px.bar(international, x="year", y="international_count")

Returned Year: 16
Time: 0.58s

4. Domestic#

[17]:

domestic = dsl.query(f"""

        search publications
            where research_orgs.id = "{institution}"
            and type="article"
            and count(research_org_countries) = 1
                 and year > 2010
        return year


    """).as_dataframe()

domestic.columns = ['year','domestic_count']
px.bar(domestic, x="year", y="domestic_count")

Returned Year: 16
Time: 0.68s

5. Internal#

[18]:

internal = dsl.query(f"""

        search publications
            where research_orgs.id = "{institution}"
            and type="article"
            and count(research_orgs) = 1
                 and year > 2010
        return year


    """).as_dataframe()

internal.columns = ['year','internal_count']
px.bar(internal, x="year", y="internal_count")

Returned Year: 16
Time: 0.63s

6. Joining up All metrics together#

[22]:

jdf = allpubs.set_index('year'). \
      merge(international, how='left', on='year'). \
      merge(domestic, how='left', on='year'). \
      merge(internal, how='left', on='year')

jdf

[22]:
year pubs international_count domestic_count internal_count
0 2021 19702 10330 9372 2920
1 2024 19104 10846 8258 2404
2 2023 18827 10338 8489 2641
3 2022 18677 10200 8477 2552
4 2020 18657 9649 9008 2782
5 2019 16617 8557 8060 2612
6 2018 15865 7934 7931 2538
7 2017 14873 7194 7679 2681
8 2016 13934 6563 7371 2608
9 2025 13886 8083 5803 1666
10 2015 13379 6285 7094 2624
11 2014 12569 5684 6885 2620
12 2013 12255 5310 6945 2794
13 2012 11133 4746 6387 2691
14 2011 10345 4328 6017 2720
15 2026 15 9 6 2 
[23]:

px.bar(jdf, title="University of Melbourne: publications collaboration")

7. How does this compare to Australia?#

[25]:

auallpubs = dsl.query("""

        search publications
            where research_org_countries.name= "Australia"
            and type="article"
            and year > 2010
        return year

    """).as_dataframe()

auallpubs.columns = ['year', 'all_count']

auintpubs = dsl.query("""

        search publications
            where research_org_countries.name= "Australia"
            and type="article"
            and year > 2010
            and count(research_org_countries) > 1
        return year

    """).as_dataframe()

auintpubs.columns = ['year', 'all_int_count']


audompubs = dsl.query("""

        search publications
            where research_org_countries.name= "Australia"
            and type="article"
            and year > 2010
            and count(research_org_countries) = 1
        return year

    """).as_dataframe()

audompubs.columns = ['year', 'all_dom_count']

auinternalpubs = dsl.query("""

        search publications
            where
            research_org_countries.name= "Australia"
            and count(research_orgs) = 1
            and type="article"
            and year > 2010
        return year

    """).as_dataframe()

auinternalpubs.columns = ['year', 'all_internal_count']

audf = auallpubs.set_index('year'). \
      merge(auintpubs, how='left', on='year'). \
      merge(audompubs, how='left', on='year'). \
      merge(auinternalpubs, how='left', on='year'). \
      sort_values(by=['year'])

px.bar(audf, title="Australia: publications collaboration")

Returned Year: 16
Time: 0.63s
Returned Year: 16
Time: 0.52s
Returned Year: 16
Time: 0.48s
Returned Year: 16
Time: 5.60s

8. How does this compare to a different Institution (University of Toronto)?#

[27]:

institution = "grid.17063.33"

allpubs = dsl.query(f"""

        search publications
            where research_orgs.id = "{institution}"
            and type="article"
            and year > 2010
        return year


    """).as_dataframe()

allpubs.columns = ['year', 'pubs']



international = dsl.query(f"""

        search publications
            where research_orgs.id = "{institution}"
            and type="article"
            and count(research_org_countries) > 1
                 and year > 2010
        return year


    """).as_dataframe()

international.columns = ['year', 'international_count']


domestic = dsl.query(f"""

        search publications
            where research_orgs.id = "{institution}"
            and type="article"
            and count(research_org_countries) = 1
                 and year > 2010
        return year


    """).as_dataframe()

domestic.columns = ['year', 'domestic_count']

internal = dsl.query(f"""

        search publications
            where research_orgs.id = "{institution}"
            and type="article"
            and count(research_orgs) = 1
                 and year > 2010
        return year


    """).as_dataframe()

internal.columns = ['year', 'internal_count']


jdf = allpubs.set_index('year'). \
      merge(international, how='left', on='year'). \
      merge(domestic, how='left', on='year'). \
      merge(internal, how='left', on='year')

px.bar(jdf, title="Univ. of Toronto: publications collaboration")

Returned Year: 16
Time: 0.49s
Returned Year: 16
Time: 6.01s
Returned Year: 16
Time: 0.63s
Returned Year: 16
Time: 0.49s

Want to learn more?#

Check out the Dimensions API Lab website, which contains many tutorials and reusable Jupyter notebooks for scholarly data analytics.


Note

The Dimensions Analytics API allows to carry out sophisticated research data analytics tasks like the ones described on this website. Check out also the associated Github repository for examples, the source code of these tutorials and much more.

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