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 Jan 25, 2022
==

Prerequisites¶

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

[16]:

!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 (v0.9.6)
Connected to: <https://app.dimensions.ai/api/dsl> - DSL v2.0
Method: dsl.ini file

1. Lookup the University that you are interested in¶

[17]:

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

Returned Organizations: 16 (total = 16)
Time: 0.57s

[17]:

	city_name	country_name	id	latitude	linkout	longitude	name	state_name	types	acronym
0	Melbourne	Australia	grid.511296.8	-37.797960	[https://www.melbournegenomics.org.au/]	144.953870	Melbourne Genomics Health Alliance	Victoria	[Nonprofit]	NaN
1	Barcelona	Spain	grid.493437.e	41.402576	[https://www.rmit.eu]	2.194333	RMIT Europe	NaN	[Education]	RMIT
2	Carlton	Australia	grid.490309.7	-37.803123	[https://www.mshc.org.au/]	144.963840	Melbourne Sexual Health Centre	Victoria	[Healthcare]	MSHC
3	Richmond	Australia	grid.477970.a	-37.815063	[http://www.themelbourneclinic.com.au/]	144.999650	Melbourne Clinic	Victoria	[Healthcare]	NaN
4	Melbourne	Australia	grid.469061.c	-37.783780	[https://www.ridley.edu.au/]	144.957660	Ridley College	Victoria	[Education]	NaN
5	Melbourne	Australia	grid.469026.f	-37.859700	[http://www.mst.edu.au/]	145.209410	Melbourne School of Theology	Victoria	[Education]	MBI
6	Melbourne	Australia	grid.468079.4	-37.824028	[http://www.portofmelbourne.com/]	144.907070	Port of Melbourne Corporation	Victoria	[Government]	PoMC
7	Melbourne	Australia	grid.468069.5	-37.814007	[http://www.melbournewater.com.au/Pages/home.a...	144.946700	Melbourne Water	Victoria	[Government]	NaN
8	Melbourne	Australia	grid.452643.2	-37.799847	[https://www.vlsci.org.au/]	144.964460	Victorian Life Sciences Computation Initiative	Victoria	[Education]	NaN
9	Melbourne	Australia	grid.449135.e	NaN	NaN	NaN	Melbourne Free University	Victoria	[Education]	NaN
10	Melbourne	Australia	grid.440113.3	-37.799260	[https://www.dhsv.org.au]	144.964630	Royal Dental Hospital of Melbourne	Victoria	[Healthcare]	RDHM
11	Melbourne	Australia	grid.429299.d	-37.798940	[http://www.mh.org.au/]	144.955930	Melbourne Health	Victoria	[Healthcare]	NaN
12	Melbourne	Australia	grid.416153.4	-37.798756	[http://www.rmh.mh.org.au/]	144.955930	Royal Melbourne Hospital	Victoria	[Healthcare]	RMH
13	Clayton	Australia	grid.410660.5	-37.915775	[http://nanomelbourne.com/]	145.143660	Melbourne Centre for Nanofabrication	Victoria	[Facility]	MCN
14	Melbourne	Australia	grid.1017.7	-37.806747	[https://www.rmit.edu.au/]	144.962570	RMIT University	Victoria	[Education]	RMIT
15	Melbourne	Australia	grid.1008.9	-37.797115	[http://www.unimelb.edu.au/]	144.959980	University of Melbourne	Victoria	[Education]	NaN

[18]:

institution = "grid.1008.9"

2. Publications output by year¶

[19]:

allpubs = dsl.query(f"""

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


    """).as_dataframe()

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

Returned Year: 12
Time: 0.57s

3. International publications¶

[20]:

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

Returned Year: 12
Time: 0.64s

4. Domestic¶

[21]:

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

Returned Year: 12
Time: 5.68s

5. Internal¶

[22]:

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

Returned Year: 12
Time: 0.56s

6. Joining up All metrics together¶

[23]:

jdf = allpubs.set_index('year'). \
      join(international.set_index('year')). \
      join(domestic.set_index('year')). \
      join(internal.set_index('year'))

jdf

[23]:

	pubs	international_count	domestic_count	internal_count
year
2021	13015	7212	5803	1585
2020	12183	6794	5389	1589
2019	11039	5948	5091	1584
2018	9954	5335	4619	1577
2017	9198	4669	4529	1541
2016	8281	4041	4240	1465
2015	7720	3697	4023	1450
2014	7184	3250	3934	1482
2013	6779	3000	3779	1480
2012	6030	2621	3409	1427
2011	5742	2367	3375	1494
2022	816	494	322	88

[24]:

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

7. How does this compare to Australia?¶

[27]:

auallpubs = dsl.query("""

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

    """).as_dataframe()

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

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 = ['all_int_count', 'year', ]


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 = [ 'all_dom_count', 'year',]

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 = ['all_internal_count', 'year', ]

audf = auallpubs.set_index('year'). \
      join(auintpubs.set_index('year')). \
      join(audompubs.set_index('year')). \
      join(auinternalpubs.set_index('year')). \
      sort_values(by=['year'])

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

Returned Year: 12
Time: 0.73s
Returned Year: 12
Time: 0.72s
Returned Year: 12
Time: 0.58s
Returned Year: 12
Time: 0.60s

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

[28]:

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 = ['pubs', 'year', ]



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 = ['international_count', 'year', ]


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 = ['domestic_count', 'year', ]

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 = ['internal_count', 'year', ]


jdf = allpubs.set_index('year'). \
      join(international.set_index('year')). \
      join(domestic.set_index('year')). \
      join(internal.set_index('year'))

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

Returned Year: 12
Time: 0.56s
Returned Year: 12
Time: 0.59s
Returned Year: 12
Time: 0.61s
Returned Year: 12
Time: 0.56s

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.