../../_images/badge-colab.svg ../../_images/badge-github-custom.svg

Benchmarking organizations with the Dimensions API

This Python notebook shows how to use the Dimensions Analytics API in order to perform different benchmarking analyses of Organizations using publications data.

Outline

  1. Quick yet effective benchmarking calculations via built-in API aggregate indicators

  2. Building more complex quality benchmarking indicators

[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 -U --quiet

import dimcli
from dimcli.utils import *
import os, sys, time, json
import pandas as pd

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. Quick benchmarking using the API

Benchmarking by volume is reasonably straight forward if what you want to compare is volume, or one of the available aggregate indicators in the Dimensions API (see https://docs.dimensions.ai/dsl/examples.html#indicators-aggregations).

[3]:

%%dsldf
search publications
return research_orgs[name] aggregate altmetric_median

Returned Research_orgs: 20
Time: 12.29s

[3]:
id name altmetric_median count
0 grid.38142.3c Harvard University 5.292790 715128
1 grid.26999.3d The University of Tokyo 3.000000 570861
2 grid.17063.33 University of Toronto 4.019046 435895
3 grid.214458.e University of Michigan-Ann Arbor 3.968242 412146
4 grid.168010.e Stanford University 4.939072 393415
5 grid.4991.5 University of Oxford 5.104038 387324
6 grid.34477.33 University of Washington 4.304326 385718
7 grid.21107.35 Johns Hopkins University 4.374951 381545
8 grid.19006.3e University of California, Los Angeles 3.871221 373415
9 grid.258799.8 Kyoto University 3.000000 370973
10 grid.11899.38 Universidade de São Paulo 2.778797 367466
11 grid.5335.0 University of Cambridge 4.412618 356990
12 grid.47840.3f University of California, Berkeley 4.103148 353011
13 grid.25879.31 University of Pennsylvania 4.491342 351125
14 grid.17635.36 University of Minnesota Twin Cities 3.252271 324688
15 grid.136593.b Osaka University 3.000000 323974
16 grid.83440.3b University College London 4.154059 320344
17 grid.14003.36 University of Wisconsin-Madison 3.220404 316542
18 grid.410726.6 University of Chinese Academy of Sciences 2.287477 313606
19 grid.47100.32 Yale University 4.602265 305202 
[4]:

%%dsldf
search publications
return research_orgs[name] aggregate citations_total

Returned Research_orgs: 20
Time: 4.16s

[4]:
id name citations_total count
0 grid.38142.3c Harvard University 43542715.0 715128
1 grid.26999.3d The University of Tokyo 12416944.0 570861
2 grid.17063.33 University of Toronto 16896263.0 435895
3 grid.214458.e University of Michigan-Ann Arbor 17899164.0 412146
4 grid.168010.e Stanford University 22857822.0 393415
5 grid.4991.5 University of Oxford 17348878.0 387324
6 grid.34477.33 University of Washington 19245227.0 385718
7 grid.21107.35 Johns Hopkins University 18542871.0 381545
8 grid.19006.3e University of California, Los Angeles 17370426.0 373415
9 grid.258799.8 Kyoto University 8426700.0 370973
10 grid.11899.38 Universidade de São Paulo 6823063.0 367466
11 grid.5335.0 University of Cambridge 16495121.0 356990
12 grid.47840.3f University of California, Berkeley 19445292.0 353011
13 grid.25879.31 University of Pennsylvania 15634591.0 351125
14 grid.17635.36 University of Minnesota Twin Cities 13100152.0 324688
15 grid.136593.b Osaka University 6486832.0 323974
16 grid.83440.3b University College London 13014090.0 320344
17 grid.14003.36 University of Wisconsin-Madison 13060297.0 316542
18 grid.410726.6 University of Chinese Academy of Sciences 8305318.0 313606
19 grid.47100.32 Yale University 14768834.0 305202 
[5]:

%%dsldf
search publications
return research_orgs[name] aggregate recent_citations_total

Returned Research_orgs: 20
Time: 5.11s

[5]:
id name count recent_citations_total
0 grid.38142.3c Harvard University 715128 5657002.0
1 grid.26999.3d The University of Tokyo 570861 1498274.0
2 grid.17063.33 University of Toronto 435895 2557162.0
3 grid.214458.e University of Michigan-Ann Arbor 412146 2411193.0
4 grid.168010.e Stanford University 393415 3172519.0
5 grid.4991.5 University of Oxford 387324 2687354.0
6 grid.34477.33 University of Washington 385718 2508430.0
7 grid.21107.35 Johns Hopkins University 381545 2441471.0
8 grid.19006.3e University of California, Los Angeles 373415 2151381.0
9 grid.258799.8 Kyoto University 370973 966227.0
10 grid.11899.38 Universidade de São Paulo 367466 1207947.0
11 grid.5335.0 University of Cambridge 356990 2258714.0
12 grid.47840.3f University of California, Berkeley 353011 2404905.0
13 grid.25879.31 University of Pennsylvania 351125 2063182.0
14 grid.17635.36 University of Minnesota Twin Cities 324688 1575033.0
15 grid.136593.b Osaka University 323974 691161.0
16 grid.83440.3b University College London 320344 2241297.0
17 grid.14003.36 University of Wisconsin-Madison 316542 1508661.0
18 grid.410726.6 University of Chinese Academy of Sciences 313606 2620498.0
19 grid.47100.32 Yale University 305202 1861426.0

Aside: Recent Citations

[6]:

%%dsldf
search publications
return year aggregate recent_citations_total

Returned Year: 20
Time: 5.16s

[6]:
id count recent_citations_total
0 2024 7882763 13641369.0
1 2023 7755821 24571792.0
2 2022 7279681 26740821.0
3 2021 7016199 27057034.0
4 2020 6831663 25211581.0
5 2019 6004300 20340055.0
6 2018 5550132 17327713.0
7 2017 5171177 15030351.0
8 2025 5064609 1745079.0
9 2016 4775828 13029942.0
10 2015 4534568 11396769.0
11 2014 4382421 9976449.0
12 2013 4194614 8834241.0
13 2012 3898366 7799039.0
14 2011 3761002 7104082.0
15 2010 3327367 6409071.0
16 2009 3198543 5733537.0
17 2008 3001138 5037413.0
18 2007 2986096 4665167.0
19 2006 2688556 4263341.0 
[7]:

dsl_last_results.sort_values(by='id').plot(x='id', y='recent_citations_total', figsize=(20,10))

Matplotlib is building the font cache; this may take a moment.

[7]:

<Axes: xlabel='id'>
../../_images/cookbooks_8-organizations_7-benchmarking-organizations_11_2.png 
[8]:

recent_citations = dsl_last_results

[9]:

recent_citations['recent_ratio'] = recent_citations['recent_citations_total']/recent_citations['count']
recent_citations['year'] = recent_citations['id']

[10]:

recent_citations.sort_values(by='year').\
    plot(x='year',y='recent_ratio', figsize=(20,10))

[10]:

<Axes: xlabel='year'>
../../_images/cookbooks_8-organizations_7-benchmarking-organizations_14_1.png

End Aside:

2. Calculating more complex ‘Quality’ Benchmarking indicators: Number of articles in the top X percent of research their category

Step 1. retrieve the total volume of publications by volume. (focusing on Fields of Research)

[11]:

%%dsldf

search publications
where year=2018
return category_for limit 1000

Returned Category_for: 193
Time: 1.30s

[11]:
id name count
0 80003 32 Biomedical and Clinical Sciences 1094225
1 80011 40 Engineering 833052
2 80045 3202 Clinical Sciences 510399
3 80017 46 Information and Computing Sciences 425860
4 80002 31 Biological Sciences 365542
... ... ... ...
188 80201 4802 Environmental and Resources Law 6659
189 80129 4101 Climate Change Impacts and Adaptation 6626
190 80091 3702 Climate Change Science 6401
191 80131 4103 Environmental Biotechnology 5084
192 80088 3606 Visual Arts 691

193 rows × 3 columns

Step 1.2. … Need to filter for level 2 codes

[12]:

result = dsl.query("""
      search publications
      where year=2018
      return category_for limit 1000

""").as_dataframe()

Returned Category_for: 193
Time: 0.70s

[13]:

result['level'] = result.name.apply(lambda n: len(n.split(' ')[0]))

[14]:

result

[14]:
id name count level
0 80003 32 Biomedical and Clinical Sciences 1094225 2
1 80011 40 Engineering 833052 2
2 80045 3202 Clinical Sciences 510399 4
3 80017 46 Information and Computing Sciences 425860 2
4 80002 31 Biological Sciences 365542 2
... ... ... ... ...
188 80201 4802 Environmental and Resources Law 6659 4
189 80129 4101 Climate Change Impacts and Adaptation 6626 4
190 80091 3702 Climate Change Science 6401 4
191 80131 4103 Environmental Biotechnology 5084 4
192 80088 3606 Visual Arts 691 4

193 rows × 4 columns

[15]:

result[result['level']==2]

[15]:
id name count level
0 80003 32 Biomedical and Clinical Sciences 1094225 2
1 80011 40 Engineering 833052 2
3 80017 46 Information and Computing Sciences 425860 2
4 80002 31 Biological Sciences 365542 2
5 80013 42 Health Sciences 304968 2
6 80005 34 Chemical Sciences 301217 2
7 80022 51 Physical Sciences 266544 2
8 80015 44 Human Society 227080 2
9 80006 35 Commerce, Management, Tourism and Services 191148 2
10 80020 49 Mathematical Sciences 179411 2
11 80001 30 Agricultural, Veterinary and Food Sciences 165669 2
13 80008 37 Earth Sciences 141809 2
14 80018 47 Language, Communication and Culture 138186 2
15 80023 52 Psychology 136222 2
17 80021 50 Philosophy and Religious Studies 117551 2
19 80010 39 Education 114877 2
21 80012 41 Environmental Sciences 99713 2
27 80019 48 Law and Legal Studies 78815 2
29 80009 38 Economics 77972 2
30 80014 43 History, Heritage and Archaeology 75539 2
32 80004 33 Built Environment and Design 73851 2
35 80007 36 Creative Arts and Writing 69695 2

Step 2. calculate 1% of the total number of records by category. This will be used to retrieve the 1% boundary record..

What is the boundary record?

[16]:

result['cutoff'] = (result['count'] * .01).astype('int')

[17]:

result[result['level']==2]

[17]:
id name count level cutoff
0 80003 32 Biomedical and Clinical Sciences 1094225 2 10942
1 80011 40 Engineering 833052 2 8330
3 80017 46 Information and Computing Sciences 425860 2 4258
4 80002 31 Biological Sciences 365542 2 3655
5 80013 42 Health Sciences 304968 2 3049
6 80005 34 Chemical Sciences 301217 2 3012
7 80022 51 Physical Sciences 266544 2 2665
8 80015 44 Human Society 227080 2 2270
9 80006 35 Commerce, Management, Tourism and Services 191148 2 1911
10 80020 49 Mathematical Sciences 179411 2 1794
11 80001 30 Agricultural, Veterinary and Food Sciences 165669 2 1656
13 80008 37 Earth Sciences 141809 2 1418
14 80018 47 Language, Communication and Culture 138186 2 1381
15 80023 52 Psychology 136222 2 1362
17 80021 50 Philosophy and Religious Studies 117551 2 1175
19 80010 39 Education 114877 2 1148
21 80012 41 Environmental Sciences 99713 2 997
27 80019 48 Law and Legal Studies 78815 2 788
29 80009 38 Economics 77972 2 779
30 80014 43 History, Heritage and Archaeology 75539 2 755
32 80004 33 Built Environment and Design 73851 2 738
35 80007 36 Creative Arts and Writing 69695 2 696

Step 3. Use the cutoff value to get the indicator value for the 1% boundary

Note: Here we use:

‘sort by’ , limit, and skip!

  • ‘sort by’: return results in order of field_citation_ratio

  • ‘limit’: we are only interested in the first result returned

  • ‘skip’ we are ‘skipping’ to the boundary record

Double Note: this strategy won’t work when the boundary record is > 50,000…

[18]:

dfl = []

for r in result[result['level']==2].iterrows():

    result = dsl.query(f"""

           search publications
           where category_for.id = "{r[1]['id']}"
           and year = 2018
           return publications[field_citation_ratio]
               sort by field_citation_ratio
               limit 1
               skip {r[1]['cutoff']}

      """).as_dataframe()

    result['name'] = r[1]['name']
    result['id'] = r[1]['id']
    dfl.append(result)

Returned Publications: 1 (total = 1094225)
Time: 6.21s
Returned Publications: 1 (total = 833052)
Time: 0.90s
Returned Publications: 1 (total = 425860)
Time: 5.81s
Returned Publications: 1 (total = 365542)
Time: 0.74s
Returned Publications: 1 (total = 304968)
Time: 0.66s
Returned Publications: 1 (total = 301217)
Time: 5.97s
Returned Publications: 1 (total = 266544)
Time: 6.07s
Returned Publications: 1 (total = 227080)
Time: 0.81s
Returned Publications: 1 (total = 191148)
Time: 5.18s
Returned Publications: 1 (total = 179411)
Time: 6.12s
Returned Publications: 1 (total = 165669)
Time: 6.00s
Returned Publications: 1 (total = 141809)
Time: 6.97s
Returned Publications: 1 (total = 138186)
Time: 0.59s
Returned Publications: 1 (total = 136222)
Time: 0.62s
Returned Publications: 1 (total = 117551)
Time: 6.13s
Returned Publications: 1 (total = 114877)
Time: 0.55s
Returned Publications: 1 (total = 99713)
Time: 0.57s
Returned Publications: 1 (total = 78815)
Time: 4.73s
Returned Publications: 1 (total = 77972)
Time: 0.82s
Returned Publications: 1 (total = 75539)
Time: 5.24s
Returned Publications: 1 (total = 73851)
Time: 6.10s
Returned Publications: 1 (total = 69695)
Time: 0.63s

[19]:

cutoffs = pd.concat(dfl)

[20]:

cutoffs

[20]:
field_citation_ratio name id
0 37.05 32 Biomedical and Clinical Sciences 80003
0 28.18 40 Engineering 80011
0 45.07 46 Information and Computing Sciences 80017
0 28.34 31 Biological Sciences 80002
0 33.82 42 Health Sciences 80013
0 25.02 34 Chemical Sciences 80005
0 38.91 51 Physical Sciences 80022
0 38.81 44 Human Society 80015
0 44.68 35 Commerce, Management, Tourism and Services 80006
0 34.54 49 Mathematical Sciences 80020
0 22.36 30 Agricultural, Veterinary and Food Sciences 80001
0 23.26 37 Earth Sciences 80008
0 39.98 47 Language, Communication and Culture 80018
0 33.78 52 Psychology 80023
0 39.12 50 Philosophy and Religious Studies 80021
0 35.81 39 Education 80010
0 29.06 41 Environmental Sciences 80012
0 37.09 48 Law and Legal Studies 80019
0 48.26 38 Economics 80009
0 31.37 43 History, Heritage and Archaeology 80014
0 38.82 33 Built Environment and Design 80004
0 37.37 36 Creative Arts and Writing 80007

We can only filter on integers in the DSL, so we will round up the values

[21]:

cutoffs.field_citation_ratio =  cutoffs.field_citation_ratio.astype('int')

[22]:

cutoffs

[22]:
field_citation_ratio name id
0 37 32 Biomedical and Clinical Sciences 80003
0 28 40 Engineering 80011
0 45 46 Information and Computing Sciences 80017
0 28 31 Biological Sciences 80002
0 33 42 Health Sciences 80013
0 25 34 Chemical Sciences 80005
0 38 51 Physical Sciences 80022
0 38 44 Human Society 80015
0 44 35 Commerce, Management, Tourism and Services 80006
0 34 49 Mathematical Sciences 80020
0 22 30 Agricultural, Veterinary and Food Sciences 80001
0 23 37 Earth Sciences 80008
0 39 47 Language, Communication and Culture 80018
0 33 52 Psychology 80023
0 39 50 Philosophy and Religious Studies 80021
0 35 39 Education 80010
0 29 41 Environmental Sciences 80012
0 37 48 Law and Legal Studies 80019
0 48 38 Economics 80009
0 31 43 History, Heritage and Archaeology 80014
0 38 33 Built Environment and Design 80004
0 37 36 Creative Arts and Writing 80007

Step 4. Now get the number of publications by organisation, filtered by category that have a field_citation_ratio > the boundary score

[23]:

dfl = []

for r in cutoffs.iterrows():

  result = dsl.query(f"""

     search publications
     where
         year=2018
         and category_for.id = "{r[1]['id']}"
         and field_citation_ratio >= {int(r[1]['field_citation_ratio'])}
    return research_orgs limit 1000

  """).as_dataframe()

  result['for_name'] = r[1]['name']
  result['for_id'] = r[1]['id']
  dfl.append(result)


Returned Research_orgs: 1000
Time: 6.15s
Returned Research_orgs: 1000
Time: 1.83s
Returned Research_orgs: 1000
Time: 5.51s
Returned Research_orgs: 1000
Time: 5.82s
Returned Research_orgs: 1000
Time: 1.56s
Returned Research_orgs: 1000
Time: 6.63s
Returned Research_orgs: 1000
Time: 1.82s
Returned Research_orgs: 1000
Time: 1.30s
Returned Research_orgs: 1000
Time: 1.37s
Returned Research_orgs: 1000
Time: 3.49s
Returned Research_orgs: 1000
Time: 1.73s
Returned Research_orgs: 1000
Time: 1.37s
Returned Research_orgs: 792
Time: 6.18s
Returned Research_orgs: 1000
Time: 1.29s
Returned Research_orgs: 764
Time: 1.06s
Returned Research_orgs: 953
Time: 4.22s
Returned Research_orgs: 1000
Time: 1.49s
Returned Research_orgs: 713
Time: 1.15s
Returned Research_orgs: 773
Time: 1.28s
Returned Research_orgs: 827
Time: 4.86s
Returned Research_orgs: 802
Time: 6.49s
Returned Research_orgs: 553
Time: 1.32s

ok, can only filter on Integrers

[24]:

top_insts = pd.concat(dfl)

Step 5. Rank the results

[25]:

top_insts['rank'] = top_insts.groupby('for_name')['count'].rank(ascending=False)

[26]:

top_insts[top_insts['name']=='University of Melbourne'][['for_name','rank']]

[26]:
for_name rank
15 32 Biomedical and Clinical Sciences 16.0
173 40 Engineering 177.0
114 46 Information and Computing Sciences 121.0
18 31 Biological Sciences 19.0
12 42 Health Sciences 12.5
192 34 Chemical Sciences 212.0
142 51 Physical Sciences 148.0
10 44 Human Society 12.0
81 35 Commerce, Management, Tourism and Services 90.5
125 49 Mathematical Sciences 142.0
21 30 Agricultural, Veterinary and Food Sciences 23.5
96 37 Earth Sciences 108.5
10 47 Language, Communication and Culture 12.0
6 52 Psychology 7.5
80 50 Philosophy and Religious Studies 106.5
8 39 Education 10.0
19 41 Environmental Sciences 21.5
13 48 Law and Legal Studies 18.5
69 38 Economics 87.5
51 43 History, Heritage and Archaeology 65.5
18 33 Built Environment and Design 21.0
6 36 Creative Arts and Writing 10.0

We should probably control for Volume though…

Step 6. Get the total paper counts for each organisation

[27]:

dfl = []

for r in cutoffs.iterrows():

  result = dsl.query(f"""

     search publications
     where
         year=2018
         and category_for.id = "{r[1]['id']}"
    return research_orgs limit 1000

  """).as_dataframe()

  result['for_name'] = r[1]['name']
  result['for_id'] = r[1]['id']
  dfl.append(result)


Returned Research_orgs: 1000
Time: 4.39s
Returned Research_orgs: 1000
Time: 6.05s
Returned Research_orgs: 1000
Time: 1.88s
Returned Research_orgs: 1000
Time: 1.80s
Returned Research_orgs: 1000
Time: 4.84s
Returned Research_orgs: 1000
Time: 1.92s
Returned Research_orgs: 1000
Time: 3.19s
Returned Research_orgs: 1000
Time: 3.04s
Returned Research_orgs: 1000
Time: 3.34s
Returned Research_orgs: 1000
Time: 2.38s
Returned Research_orgs: 1000
Time: 5.36s
Returned Research_orgs: 1000
Time: 1.38s
Returned Research_orgs: 1000
Time: 1.30s
Returned Research_orgs: 1000
Time: 6.38s
Returned Research_orgs: 1000
Time: 1.41s
Returned Research_orgs: 1000
Time: 4.51s
Returned Research_orgs: 1000
Time: 1.98s
Returned Research_orgs: 1000
Time: 1.47s
Returned Research_orgs: 1000
Time: 6.03s
Returned Research_orgs: 1000
Time: 1.30s
Returned Research_orgs: 1000
Time: 4.47s
Returned Research_orgs: 1000
Time: 1.25s

[28]:

all_publications = pd.concat(dfl)[['id','for_id','count']]

[29]:

top_insts_all = all_publications.rename(columns={'count':'count all'}).merge(top_insts, on =['id','for_id'])

[30]:

top_insts_all[['for_name','name','count','count all']]

[30]:
for_name name count count all
0 32 Biomedical and Clinical Sciences Harvard University 767 15967
1 32 Biomedical and Clinical Sciences Johns Hopkins University 356 9182
2 32 Biomedical and Clinical Sciences University of Toronto 338 8932
3 32 Biomedical and Clinical Sciences Mayo Clinic 380 8507
4 32 Biomedical and Clinical Sciences University of California, San Francisco 339 7477
... ... ... ... ...
13171 36 Creative Arts and Writing Adobe Inc 3 7
13172 36 Creative Arts and Writing Polytechnic University of Turin 1 7
13173 36 Creative Arts and Writing University of Electronic Science and Technolog... 1 7
13174 36 Creative Arts and Writing University of Cyprus 1 7
13175 36 Creative Arts and Writing Broad Institute 1 7

13176 rows × 4 columns

Step 7. calculate the percentage of local papers in the top 1% of global publications (in 2018)

[31]:

top_insts_all['percentage top 1'] = (100 * top_insts_all['count']/top_insts_all['count all']).round(2)

[32]:

top_insts_all['percent rank'] = top_insts_all.groupby('for_name')['percentage top 1'].rank(ascending=False)

Now the results are going to look a little strange…

[33]:

top_insts_all[top_insts_all['name']=='University of Cambridge'][['for_name','percent rank']]

[33]:
for_name percent rank
73 32 Biomedical and Clinical Sciences 54.5
842 40 Engineering 205.0
1592 46 Information and Computing Sciences 191.5
2167 31 Biological Sciences 71.0
2916 42 Health Sciences 31.0
3577 34 Chemical Sciences 47.0
4211 51 Physical Sciences 315.0
4992 44 Human Society 115.5
5642 35 Commerce, Management, Tourism and Services 241.0
6253 49 Mathematical Sciences 87.0
7112 30 Agricultural, Veterinary and Food Sciences 57.0
7557 37 Earth Sciences 285.5
8174 47 Language, Communication and Culture 422.0
8696 52 Psychology 201.0
9362 50 Philosophy and Religious Studies 422.0
9857 39 Education 255.0
10448 41 Environmental Sciences 190.0
11006 48 Law and Legal Studies 291.5
11405 38 Economics 301.0
11884 43 History, Heritage and Archaeology 255.0
12409 33 Built Environment and Design 428.0
12841 36 Creative Arts and Writing 285.0 
[34]:

top_insts_all[top_insts_all['for_name']=='11 Medical and Health Sciences'][['name','percent rank']]

[34]:
name percent rank

Smaller institutions are being preferenced too much…

Need to control for size…

[35]:

reference_institutions = top_insts_all[['id','name','for_id','count all']].\
     rename(columns={
            'id':'reference id',
            'name':'reference name',
           'count all':'reference count all'
           })

[36]:

relative_ranking = reference_institutions.merge(top_insts_all, on='for_id')

[37]:

relative_ranking[relative_ranking['reference name']=='University of Melbourne']

[37]:
reference id reference name for_id reference count all id count all name city_name count country_code ... latitude linkout longitude state_name types acronym for_name rank percentage top 1 percent rank
17756 grid.1008.9 University of Melbourne 80003 4797 grid.38142.3c 15967 Harvard University Cambridge 767 US ... 42.377052 [http://www.harvard.edu/] -71.116650 Massachusetts [Education] NaN 32 Biomedical and Clinical Sciences 1.0 4.80 63.5
17757 grid.1008.9 University of Melbourne 80003 4797 grid.21107.35 9182 Johns Hopkins University Baltimore 356 US ... 39.328888 [https://www.jhu.edu/] -76.620280 Maryland [Education] JHU 32 Biomedical and Clinical Sciences 4.0 3.88 195.5
17758 grid.1008.9 University of Melbourne 80003 4797 grid.17063.33 8932 University of Toronto Toronto 338 CA ... 43.661667 [http://www.utoronto.ca/] -79.395000 Ontario [Education] NaN 32 Biomedical and Clinical Sciences 8.0 3.78 220.5
17759 grid.1008.9 University of Melbourne 80003 4797 grid.66875.3a 8507 Mayo Clinic Rochester 380 US ... 44.024070 [http://www.mayoclinic.org/patient-visitor-gui... -92.466310 Minnesota [Healthcare] NaN 32 Biomedical and Clinical Sciences 3.0 4.47 96.5
17760 grid.1008.9 University of Melbourne 80003 4797 grid.266102.1 7477 University of California, San Francisco San Francisco 339 US ... 37.762800 [https://www.ucsf.edu/] -122.457670 California [Education] UCSF 32 Biomedical and Clinical Sciences 6.5 4.53 89.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
8082796 grid.1008.9 University of Melbourne 80007 126 grid.467212.4 7 Adobe Inc San Jose 3 US ... NaN [https://www.adobe.com/] NaN California [Company] NaN 36 Creative Arts and Writing 59.0 42.86 1.0
8082797 grid.1008.9 University of Melbourne 80007 126 grid.4800.c 7 Polytechnic University of Turin Turin 1 IT ... 45.063095 [http://www.polito.it/] 7.661075 Piemonte [Education] NaN 36 Creative Arts and Writing 350.5 14.29 34.5
8082798 grid.1008.9 University of Melbourne 80007 126 grid.54549.39 7 University of Electronic Science and Technolog... Chengdu 1 CN ... 30.675713 [http://en.uestc.edu.cn/] 104.100270 NaN [Education] UESTC 36 Creative Arts and Writing 350.5 14.29 34.5
8082799 grid.1008.9 University of Melbourne 80007 126 grid.6603.3 7 University of Cyprus Nicosia 1 CY ... 35.160270 [http://www.ucy.ac.cy/en/] 33.376976 NaN [Education] UCY 36 Creative Arts and Writing 350.5 14.29 34.5
8082800 grid.1008.9 University of Melbourne 80007 126 grid.66859.34 7 Broad Institute Cambridge 1 US ... 42.367890 [http://www.broadinstitute.org/] -71.087030 Massachusetts [Nonprofit] NaN 36 Creative Arts and Writing 350.5 14.29 34.5

13176 rows × 21 columns

[38]:

filtered_relative_ranking = relative_ranking[relative_ranking[
                                      'reference count all'] <= relative_ranking['count all']
                                      ].copy()

[39]:

filtered_relative_ranking['filtered percent rank'] = filtered_relative_ranking.\
                                                   groupby(['reference id','for_name'])['percentage top 1'].\
                                                   rank(ascending=False)

[40]:

inst = 'University of Melbourne'

filtered_relative_ranking[

                          (filtered_relative_ranking['reference name'] == inst) &
                          (filtered_relative_ranking['name'] == inst)

                         ][['id', 'for_id', 'name','for_name','filtered percent rank']]

[40]:
id for_id name for_name filtered percent rank
17779 grid.1008.9 80003 University of Melbourne 32 Biomedical and Clinical Sciences 6.0
733648 grid.1008.9 80011 University of Melbourne 40 Engineering 85.0
1168804 grid.1008.9 80017 University of Melbourne 46 Information and Computing Sciences 51.0
1578085 grid.1008.9 80002 University of Melbourne 31 Biological Sciences 11.0
2046557 grid.1008.9 80013 University of Melbourne 42 Health Sciences 5.0
2644004 grid.1008.9 80005 University of Melbourne 34 Chemical Sciences 98.0
3128285 grid.1008.9 80022 University of Melbourne 51 Physical Sciences 46.0
3570941 grid.1008.9 80015 University of Melbourne 44 Human Society 4.0
3958749 grid.1008.9 80006 University of Melbourne 35 Commerce, Management, Tourism and Services 22.0
4403670 grid.1008.9 80020 University of Melbourne 49 Mathematical Sciences 37.0
4832701 grid.1008.9 80001 University of Melbourne 30 Agricultural, Veterinary and Food Sciences 8.0
5224547 grid.1008.9 80008 University of Melbourne 37 Earth Sciences 44.0
5606498 grid.1008.9 80018 University of Melbourne 47 Language, Communication and Culture 4.0
5864420 grid.1008.9 80023 University of Melbourne 52 Psychology 2.0
6341779 grid.1008.9 80021 University of Melbourne 50 Philosophy and Religious Studies 25.0
6535541 grid.1008.9 80010 University of Melbourne 39 Education 2.0
6853435 grid.1008.9 80012 University of Melbourne 41 Environmental Sciences 5.0
7239785 grid.1008.9 80019 University of Melbourne 48 Law and Legal Studies 2.5
7398600 grid.1008.9 80009 University of Melbourne 38 Economics 24.5
7643598 grid.1008.9 80014 University of Melbourne 43 History, Heritage and Archaeology 14.0
7880154 grid.1008.9 80004 University of Melbourne 33 Built Environment and Design 8.0
8082459 grid.1008.9 80007 University of Melbourne 36 Creative Arts and Writing 2.0 
[ ]:

Final step. Show me the institutions that I should be most interested in (Five above)

[41]:

rank_cutoffs = filtered_relative_ranking[

                          (filtered_relative_ranking['reference name'] == filtered_relative_ranking['name'] )

                         ][['id', 'for_id', 'filtered percent rank']].\
                         rename(columns={'id':'reference id',
                                         'filtered percent rank':'reference filtered percent rank'})

[42]:

filtered_relative_ranking_final = rank_cutoffs.merge(filtered_relative_ranking, on=['reference id','for_id'])

[43]:

filtered_relative_ranking_final['rank_difference'] = filtered_relative_ranking_final['filtered percent rank'] - filtered_relative_ranking_final['reference filtered percent rank']

[44]:

inst = 'Monash University'
forname = '11 Medical and Health Sciences'

filtered_relative_ranking_final[

                                 (filtered_relative_ranking_final['rank_difference'].between(-5, 5)) &
                                 (filtered_relative_ranking_final['reference name'] == inst) &
                                 (filtered_relative_ranking_final['for_name'] == forname)

                                 ][['name','filtered percent rank']].sort_values(by='filtered percent rank')



[44]:
name filtered percent rank 
[45]:

filtered_relative_ranking_final

[45]:
reference id for_id reference filtered percent rank reference name reference count all id count all name city_name count ... longitude state_name types acronym for_name rank percentage top 1 percent rank filtered percent rank rank_difference
0 grid.38142.3c 80003 1.0 Harvard University 15967 grid.38142.3c 15967 Harvard University Cambridge 767 ... -71.116650 Massachusetts [Education] NaN 32 Biomedical and Clinical Sciences 1.0 4.80 63.5 1.0 0.0
1 grid.21107.35 80003 2.0 Johns Hopkins University 9182 grid.38142.3c 15967 Harvard University Cambridge 767 ... -71.116650 Massachusetts [Education] NaN 32 Biomedical and Clinical Sciences 1.0 4.80 63.5 1.0 -1.0
2 grid.21107.35 80003 2.0 Johns Hopkins University 9182 grid.21107.35 9182 Johns Hopkins University Baltimore 356 ... -76.620280 Maryland [Education] JHU 32 Biomedical and Clinical Sciences 4.0 3.88 195.5 2.0 0.0
3 grid.17063.33 80003 3.0 University of Toronto 8932 grid.38142.3c 15967 Harvard University Cambridge 767 ... -71.116650 Massachusetts [Education] NaN 32 Biomedical and Clinical Sciences 1.0 4.80 63.5 1.0 -2.0
4 grid.17063.33 80003 3.0 University of Toronto 8932 grid.21107.35 9182 Johns Hopkins University Baltimore 356 ... -76.620280 Maryland [Education] JHU 32 Biomedical and Clinical Sciences 4.0 3.88 195.5 2.0 -1.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4134095 grid.66859.34 80007 34.5 Broad Institute 7 grid.467212.4 7 Adobe Inc San Jose 3 ... NaN California [Company] NaN 36 Creative Arts and Writing 59.0 42.86 1.0 1.0 -33.5
4134096 grid.66859.34 80007 34.5 Broad Institute 7 grid.4800.c 7 Polytechnic University of Turin Turin 1 ... 7.661075 Piemonte [Education] NaN 36 Creative Arts and Writing 350.5 14.29 34.5 34.5 0.0
4134097 grid.66859.34 80007 34.5 Broad Institute 7 grid.54549.39 7 University of Electronic Science and Technolog... Chengdu 1 ... 104.100270 NaN [Education] UESTC 36 Creative Arts and Writing 350.5 14.29 34.5 34.5 0.0
4134098 grid.66859.34 80007 34.5 Broad Institute 7 grid.6603.3 7 University of Cyprus Nicosia 1 ... 33.376976 NaN [Education] UCY 36 Creative Arts and Writing 350.5 14.29 34.5 34.5 0.0
4134099 grid.66859.34 80007 34.5 Broad Institute 7 grid.66859.34 7 Broad Institute Cambridge 1 ... -71.087030 Massachusetts [Nonprofit] NaN 36 Creative Arts and Writing 350.5 14.29 34.5 34.5 0.0

4134100 rows × 24 columns


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.

../../_images/badge-dimensions-api.svg