General Publication Statistics about a Research Organization¶

This Notebook shows how it’s possible to extract basic indicators about a research organization programmatically, using the Dimensions Analytics API and Jupyter Notebooks.

Prerequisites¶

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

[1]:

!pip install dimcli plotly tqdm -U --quiet

import dimcli
from dimcli.utils import *
import os, sys, time, json
from tqdm.notebook import tqdm
import pandas as pd
import plotly.express as px
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()

==
Logging in..
Dimcli - Dimensions API Client (v0.8.2)
Connected to: https://app.dimensions.ai - DSL v1.28
Method: dsl.ini file

Choose a Research Organization¶

For the purpose of this exercise, we will are going to use grid.168010.e (Stanford University). Feel free though to change the parameters below as you want, eg by choosing another GRID organization.

[2]:

GRIDID = "grid.168010.e" #@param {type:"string"}

def grids_url(grids):
    "gen link to Dimensions webapp"
    root = "https://app.dimensions.ai/discover/publication?or_facet_research_org="
    return root + "&or_facet_research_org=".join([x for x in grids])

from IPython.core.display import display, HTML
display(HTML('---<br /><a href="{}">Preview {} Dimensions &#x29c9;</a>'.format(grids_url([GRIDID]), GRIDID)))

---
Preview grid.168010.e Dimensions ⧉

Publications output by year¶

[3]:

tot = dsl.query(f"""search publications where research_orgs.id="{GRIDID}" return publications limit 1""", verbose=False).count_total
print(f"{GRIDID} has a total of {tot} publications in Dimensions")

grid.168010.e has a total of 249953 publications in Dimensions

[4]:

df = dsl.query(f"""search publications where research_orgs.id="{GRIDID}" return year limit 100""").as_dataframe()
df.rename(columns={"id": "year"}, inplace=True)
#
px.bar(df, x="year", y="count",
       title=f"Publications from {GRIDID} - by year")

Returned Year: 100
Time: 0.56s

Publications most cited in last 2 years¶

[5]:

data = dslquery(f"""search publications where research_orgs.id="{GRIDID}"
        return publications[doi+title+recent_citations+category_for+journal]
        sort by recent_citations limit 100""")
df = data.as_dataframe()
df.head(10)[['title', 'doi', 'recent_citations', 'journal.title']]

Returned Publications: 100 (total = 249953)
Time: 0.97s

[5]:

	title	doi	recent_citations	journal.title
0	ImageNet Large Scale Visual Recognition Challenge	10.1007/s11263-015-0816-y	7321	International Journal of Computer Vision
1	A global reference for human genetic variation	10.1038/nature15393	4499	Nature
2	The Elements of Statistical Learning, Data Min...	10.1007/978-0-387-84858-7	4458	NaN
3	An integrated encyclopedia of DNA elements in ...	10.1038/nature11247	4229	Nature
4	Compressed Sensing	10.1109/tit.2006.871582	4082	IEEE Transactions on Information Theory
5	Planck 2015 results - XIII. Cosmological param...	10.1051/0004-6361/201525830	3707	Astronomy & Astrophysics
6	Building Theories from Case Study Research	10.5465/amr.1989.4308385	3559	Academy of Management Review
7	Global, regional, and national prevalence of o...	10.1016/s0140-6736(14)60460-8	3388	The Lancet
8	Regularization and variable selection via the ...	10.1111/j.1467-9868.2005.00503.x	3229	Journal of the Royal Statistical Society Serie...
9	phyloseq: An R Package for Reproducible Intera...	10.1371/journal.pone.0061217	3086	PLoS ONE

Publications most cited - all time¶

[6]:

data = dslquery(f"""search publications
                where research_orgs.id="{GRIDID}"
                return publications[doi+title+times_cited+category_for+journal]
                sort by times_cited limit 1000""")
df = data.as_dataframe()
df.head(10)[['title', 'doi', 'times_cited', 'journal.title']]

Returned Publications: 1000 (total = 249953)
Time: 2.04s

[6]:

	title	doi	times_cited	journal.title
0	Initial sequencing and analysis of the human g...	10.1038/35057062	15997	Nature
1	Compressed Sensing	10.1109/tit.2006.871582	15811	IEEE Transactions on Information Theory
2	The american rheumatism association 1987 revis...	10.1002/art.1780310302	15717	Arthritis & Rheumatology
3	Cluster analysis and display of genome-wide ex...	10.1073/pnas.95.25.14863	12325	Proceedings of the National Academy of Science...
4	Building Theories from Case Study Research	10.5465/amr.1989.4308385	10920	Academy of Management Review
5	The 1982 revised criteria for the classificati...	10.1002/art.1780251101	10912	Arthritis & Rheumatology
6	Atomic Force Microscope	10.1103/physrevlett.56.930	10434	Physical Review Letters
7	An integrated encyclopedia of DNA elements in ...	10.1038/nature11247	10204	Nature
8	Molecular portraits of human breast tumours	10.1038/35021093	9888	Nature
9	ImageNet Large Scale Visual Recognition Challenge	10.1007/s11263-015-0816-y	9464	International Journal of Computer Vision

Publications most cited : which research areas?¶

[28]:

data = dslquery(f"""search publications
                    where research_orgs.id="{GRIDID}"
                    return publications[doi+title+times_cited+category_for+journal]
                    sort by times_cited limit 1000""")

Returned Publications: 1000 (total = 249953)
Time: 1.38s

Most publications have one or more associated Field Of Research (FOR) category, which is represented in the JSON like this:

{'category_for' : [{'id': '3292', 'name': '1402 Applied Economics'},
                    {'id': '3177', 'name': '1117 Public Health and Health Services'}]
                    }`

However since some publications may not have an associated FOR category, the resulting JSON in some cases may not have category_for as a key. Since we want to import the data into pandas we need to ensure the key is always there and has an empty list when no category is available.

[29]:

# dimcli.shortcuts.normalize_key takes: field name / json list / value to add when the field is not found
normalize_key("category_for", data.publications, [])

[30]:

df = pd.json_normalize(data.publications, record_path='category_for', meta=['doi', 'title', 'times_cited', ], errors='ignore' )
df.head()

[30]:

	id	name	doi	title	times_cited
0	2620	0604 Genetics	10.1038/35057062	Initial sequencing and analysis of the human g...	15997
1	2206	06 Biological Sciences	10.1038/35057062	Initial sequencing and analysis of the human g...	15997
2	2208	08 Information and Computing Sciences	10.1109/tit.2006.871582	Compressed Sensing	15811
3	2209	09 Engineering	10.1109/tit.2006.871582	Compressed Sensing	15811
4	2210	10 Technology	10.1109/tit.2006.871582	Compressed Sensing	15811

[31]:

px.scatter(df,
           x="times_cited", y="name",
           marginal_x="histogram",
           marginal_y="histogram",
           hover_data=["doi", "title"],
           height=600,
           title=f"Publications from {GRIDID} - Research Areas VS Citations")

Publications most cited : which journals?¶

[32]:

data = dslquery(f"""search publications
                    where research_orgs.id="{GRIDID}"
                    return publications[doi+title+times_cited+category_for+journal]
                    sort by times_cited limit 1000""")

df = data.as_dataframe()
#
px.scatter(df,
           x="times_cited", y="journal.title",
           marginal_x="histogram",
           marginal_y="histogram",
           height=600,
           title=f"Publications from {GRIDID} - Journals VS Citations")

Returned Publications: 1000 (total = 249953)
Time: 1.45s

Top Funders (by aggregated funding amount)¶

[33]:

fundersdata = dsl.query(f"""search grants
                        where research_orgs.id="{GRIDID}"
                        return funders aggregate funding
                        sort by funding""")
df = fundersdata.as_dataframe()
df.head(10)

Returned Funders: 20
Time: 0.53s

[33]:

	id	count	funding	country_name	acronym	state_name	city_name	name	types	longitude	linkout	latitude
0	grid.48336.3a	788	1.059476e+09	United States	NCI	Maryland	Rockville	National Cancer Institute	[Government]	-77.101190	[http://www.cancer.gov/]	39.004326
1	grid.280785.0	770	1.040659e+09	United States	NIGMS	Maryland	Bethesda	National Institute of General Medical Sciences	[Facility]	-77.099380	[http://www.nigms.nih.gov/Pages/default.aspx]	38.997833
2	grid.419681.3	567	7.449747e+08	United States	NIAID	Maryland	Bethesda	National Institute of Allergy and Infectious D...	[Facility]	-77.111830	[http://www.niaid.nih.gov/Pages/default.aspx]	39.066647
3	grid.279885.9	609	7.437638e+08	United States	NHLBI	Maryland	Bethesda	National Heart Lung and Blood Institute	[Facility]	-77.100945	[http://www.nhlbi.nih.gov/]	39.004280
4	grid.457875.c	1292	6.756204e+08	United States	NSF MPS	Virginia	Arlington	Directorate for Mathematical & Physical Sciences	[Government]	-77.110990	[http://www.nsf.gov/dir/index.jsp?org=MPS]	38.880566
5	grid.280128.1	140	6.005008e+08	United States	NHGRI	Maryland	Bethesda	National Human Genome Research Institute	[Facility]	-77.096930	[https://www.genome.gov/]	38.996967
6	grid.416870.c	523	5.996880e+08	United States	NINDS	Maryland	Bethesda	National Institute of Neurological Disorders a...	[Facility]	-77.101180	[http://www.ninds.nih.gov/]	39.003826
7	grid.416868.5	521	5.463138e+08	United States	NIMH	Maryland	Bethesda	National Institute of Mental Health	[Facility]	-77.104570	[https://www.nimh.nih.gov/index.shtml]	39.003693
8	grid.418557.a	137	4.777585e+08	United States	CIRM	California	San Francisco	California Institute for Regenerative Medicine	[Government]	-122.392340	[https://www.cirm.ca.gov/]	37.778034
9	grid.457799.1	131	4.298322e+08	United States	NSF EHR	Virginia	Arlington	Directorate for Education & Human Resources	[Government]	-77.111000	[http://www.nsf.gov/dir/index.jsp?org=EHR]	38.880580

Top funders split by country of the funder¶

[34]:

px.bar(df,
       x="name", y="funding",
       color="country_name",
       title=f"Funding for {GRIDID} - by year")

Correlation between No of Publications VS Funding¶

[35]:

px.scatter(df,
           x="funding", y="count",
           color="name",
           height=600,
           title=f"Funding for {GRIDID} - Publications VS Aggregated Funding Amount")

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.