Journal Profiling Part 5: Competing Journals Analysis¶

This Python notebook shows how to use the Dimensions Analytics API to create a competitive journals analysis report, starting from a specific journal and by using its authors information.

This tutorial is the fifth of a series that uses the data extracted in order to generate a ‘journal profile’ report. See the API Lab homepage for the other tutorials in this series.

In this notebook we are going to

Load the researchers impact metrics data previously extracted (see parts 1-2-3)
Get the full publications history for these researchers
Use this new publications dataset to determine which are the most frequent journals the researchers have also published in
Build some visualizations in order to have a quick overview of the results

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 as progress
import pandas as pd
import plotly.express as px
from plotly.offline import plot
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

Finally, let’s set up a folder to store the data we are going to extract:

[2]:

# create output data folder
FOLDER_NAME = "journal-profile-data"
if not(os.path.exists(FOLDER_NAME)):
    os.mkdir(FOLDER_NAME)

def save(df,filename_dot_csv):
    df.to_csv(FOLDER_NAME+"/"+filename_dot_csv, index=False)

Competing Journals¶

From our researchers master list, we now want to extract the following:

full list of publications for a N year period
full list of journals with counts of how many publications per journal

This new dataset will let us draw up some conclusions re. which are the competing journals of the one we selected at the beginning.

First let’s reload the data obtained in previous steps¶

NOTE If you are using Google Colab or don’t have the data available, just do the following: * open up the ‘Files’ panel in Google Colab and create a new folder journal-profile-data * grab this file, unzip it, open the enclosed folder and upload the file called 2_researchers_impact_metrics.csv to Google Colab (‘Upload’ menu or also by dragging then inside the panel window) * move the file inside the journal-profile-data folder you just created

[3]:

#
researchers = pd.read_csv(FOLDER_NAME+"/2_researchers_impact_metrics.csv")
#
print("Total researchers:", len(researchers))
researchers.head(5)

Total researchers: 19565

[3]:

	researcher_id	pubs	full_name	citations_mean	altmetric_mean	last_pub_year	url
0	ur.0723426172.10	63	Kari Stefansson	129.555556	229.603175	2020	https://app.dimensions.ai/discover/publication...
1	ur.01277776417.51	45	Unnur Thorsteinsdottir	93.088889	160.622222	2019	https://app.dimensions.ai/discover/publication...
2	ur.0641525362.39	35	Gonçalo R Abecasis	134.257143	138.514286	2020	https://app.dimensions.ai/discover/publication...
3	ur.01247426430.47	33	Gudmar Thorleifsson	94.121212	171.878788	2019	https://app.dimensions.ai/discover/publication...
4	ur.01317433110.75	33	Caroline Hayward	157.333333	296.212121	2020	https://app.dimensions.ai/discover/publication...

[4]:

# TIP to speed this up I'm taking only the top 2000 researchers!
# for a full analysis, just comment out the next line
researchers = researchers[:2000]

What the query looks like¶

The approach we’re taking consists in pulling all publications data, so that we can count journals as a second step.

This approach may take some time (as we’re potentially retrieving a lot of publications data), but it will lead to precise results.

The query template to use looks like this (for a couple of researchers only):

[5]:

%%dsldf
search publications where researchers.id in ["ur.01277776417.51", "ur.0637651205.48"]
    and year >= 2015 and journal is not empty
    and journal.id != "jour.1103138"
return publications[id+journal] limit 10

Returned Publications: 10 (total = 161)
Time: 0.94s

[5]:

	id	journal.id	journal.title
0	pub.1126893330	jour.1300829	Communications Biology
1	pub.1124191534	jour.1043282	Nature Communications
2	pub.1123951767	jour.1043282	Nature Communications
3	pub.1127766109	jour.1043282	Nature Communications
4	pub.1125690142	jour.1300829	Communications Biology
5	pub.1130504374	jour.1014075	New England Journal of Medicine
6	pub.1128712754	jour.1018957	Nature
7	pub.1130096425	jour.1040791	Circulation Genomic and Precision Medicine
8	pub.1130077601	jour.1014075	New England Journal of Medicine
9	pub.1128316340	jour.1091325	European Heart Journal

Extracting all publications/journals information¶

This part may take some time to run (depending on how many years back one wants to go) so you may want to get a coffee while you wait..

[6]:

#
journal_id = "jour.1103138" # = Nature Genetics
start_year = 2018

# our list of researchers
llist = list(researchers['researcher_id'])
#
# the query
q2 = """search publications
            where researchers.id in {}
            and year >= {} and journal is not empty and journal.id != "{}"
    return publications[id+journal+year]"""

[8]:

VERBOSE = True
RESEARCHER_ITERATOR_NO = 400

pubs = pd.DataFrame
for chunk in progress(list(chunks_of(llist, RESEARCHER_ITERATOR_NO))):
    # get all pubs
    query = q2.format(json.dumps(chunk), start_year, journal_id)
    res = dsl.query_iterative(query, verbose=VERBOSE)
    if pubs.empty:
        # first time, init the dataframe
        pubs = res.as_dataframe()
    else:
        pubs.append(res.as_dataframe())

Starting iteration with limit=1000 skip=0 ...
0-1000 / 15127 (1.67s)
1000-2000 / 15127 (1.73s)
2000-3000 / 15127 (1.01s)
3000-4000 / 15127 (1.52s)
4000-5000 / 15127 (1.53s)
5000-6000 / 15127 (1.76s)
6000-7000 / 15127 (0.95s)
7000-8000 / 15127 (0.95s)
8000-9000 / 15127 (1.58s)
9000-10000 / 15127 (0.86s)
10000-11000 / 15127 (1.57s)
11000-12000 / 15127 (1.56s)
12000-13000 / 15127 (1.98s)
13000-14000 / 15127 (0.94s)
14000-15000 / 15127 (0.97s)
15000-15127 / 15127 (1.17s)
===
Records extracted: 15127
Starting iteration with limit=1000 skip=0 ...
0-1000 / 13961 (0.92s)
1000-2000 / 13961 (1.04s)
2000-3000 / 13961 (0.96s)
3000-4000 / 13961 (0.85s)
4000-5000 / 13961 (0.86s)
5000-6000 / 13961 (0.98s)
6000-7000 / 13961 (0.92s)
7000-8000 / 13961 (0.96s)
8000-9000 / 13961 (0.86s)
9000-10000 / 13961 (1.50s)
10000-11000 / 13961 (1.73s)
11000-12000 / 13961 (1.05s)
12000-13000 / 13961 (1.57s)
13000-13961 / 13961 (1.06s)
===
Records extracted: 13961
Starting iteration with limit=1000 skip=0 ...
0-1000 / 13285 (1.56s)
1000-2000 / 13285 (0.95s)
2000-3000 / 13285 (0.94s)
3000-4000 / 13285 (0.97s)
4000-5000 / 13285 (1.48s)
5000-6000 / 13285 (1.66s)
6000-7000 / 13285 (0.95s)
7000-8000 / 13285 (0.91s)
8000-9000 / 13285 (2.01s)
9000-10000 / 13285 (0.84s)
10000-11000 / 13285 (1.53s)
11000-12000 / 13285 (2.03s)
12000-13000 / 13285 (1.44s)
13000-13285 / 13285 (1.57s)
===
Records extracted: 13285
Starting iteration with limit=1000 skip=0 ...
0-1000 / 14285 (2.17s)
1000-2000 / 14285 (3.39s)
2000-3000 / 14285 (2.15s)
3000-4000 / 14285 (1.82s)
4000-5000 / 14285 (2.10s)
5000-6000 / 14285 (1.56s)
6000-7000 / 14285 (1.74s)
7000-8000 / 14285 (1.87s)
8000-9000 / 14285 (1.67s)
9000-10000 / 14285 (1.57s)
10000-11000 / 14285 (1.67s)
11000-12000 / 14285 (1.74s)
12000-13000 / 14285 (1.73s)
13000-14000 / 14285 (1.93s)
14000-14285 / 14285 (1.61s)
===
Records extracted: 14285
Starting iteration with limit=1000 skip=0 ...
0-1000 / 12741 (2.33s)
1000-2000 / 12741 (2.16s)
2000-3000 / 12741 (1.54s)
3000-4000 / 12741 (2.81s)
4000-5000 / 12741 (1.64s)
5000-6000 / 12741 (1.67s)
6000-7000 / 12741 (1.46s)
7000-8000 / 12741 (1.90s)
8000-9000 / 12741 (1.45s)
9000-10000 / 12741 (1.65s)
10000-11000 / 12741 (1.89s)
11000-12000 / 12741 (1.57s)
12000-12741 / 12741 (1.78s)
===
Records extracted: 12741

[9]:

# remove duplicate publications, if they have the same PUB_ID
pubs = pubs.drop_duplicates(subset="id")
# preview the data
pubs

[9]:

	id	year	journal.id	journal.title
0	pub.1124795380	2020	jour.1045059	European Journal of Psychotraumatology
1	pub.1130479619	2020	jour.1041075	Global Health Action
2	pub.1125404225	2020	jour.1045337	Scientific Reports
3	pub.1125109477	2020	jour.1023439	Genome Biology
4	pub.1127505679	2020	jour.1043282	Nature Communications
...	...	...	...	...
15122	pub.1100301829	2018	jour.1048149	European Thyroid Journal
15123	pub.1111681430	2018	jour.1011409	Human Heredity
15124	pub.1101241166	2018	jour.1049794	Oncology Research and Treatment
15125	pub.1101248219	2018	jour.1100504	Dementia and Geriatric Cognitive Disorders
15126	pub.1109951444	2018	jour.1276748	SSRN Electronic Journal

15111 rows × 4 columns

Now we can create a journals-only dataset that includes counts per year, and grant total.

[10]:

journals = pubs.copy()
# drop pub_id column
journals = journals.drop(['id'], axis=1)
#
# add total column
journals['total'] = journals.groupby('journal.id')['journal.id'].transform('count')
journals['total_year'] = journals.groupby(['journal.id', 'year'])['journal.id'].transform('count')
#
# remove multiple counts for same journal
journals = journals.drop_duplicates()
journals.reset_index(drop=True)
#
# sort by total count
journals = journals.sort_values('total', ascending=False)
# #
# # save
save(journals, "5.competing_journals.csv" )
print("======\nDone")

#preview the data
journals.head(10)

======
Done

[10]:

	year	journal.id	journal.title	total	total_year
9998	2018	jour.1293558	bioRxiv	1341	486
4226	2019	jour.1293558	bioRxiv	1341	583
409	2020	jour.1293558	bioRxiv	1341	272
4406	2019	jour.1101548	European Neuropsychopharmacology	480	465
1009	2020	jour.1101548	European Neuropsychopharmacology	480	7
11482	2018	jour.1101548	European Neuropsychopharmacology	480	8
4420	2019	jour.1043282	Nature Communications	353	136
10151	2018	jour.1043282	Nature Communications	353	123
4	2020	jour.1043282	Nature Communications	353	94
386	2020	jour.1369542	medRxiv	318	252

Visualizations¶

[11]:

threshold = 100
temp = journals.sort_values("total", ascending=False)[:threshold]

px.bar(journals[:threshold],
       x="journal.title", y="total_year",
       color="year",
       hover_name="journal.title",
       hover_data=['journal.id', 'journal.title', 'total' ],
       title=f"Top {threshold} competitors for {journal_id} (based on publications data from {start_year})")

[12]:

threshold = 200
temp = journals.sort_values("year", ascending=True).groupby("year").head(threshold)

px.bar(journals[:threshold],
       x="journal.title", y="total_year",
       color="year",
       facet_row="year",
       height=900,
       hover_name="journal.title",
       hover_data=['journal.id', 'journal.title', 'total' ],
       title=f"Top {threshold} competitors for {journal_id} - segmented by year")

NOTE the European Neuropsychopharmacology journal has a massive jump in 2019 cause they published a lot of conference proceedings! See also the journal Dimensions page for comparison..

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.