A new take on altmetrics - the Altmetrics API

Fri, 12 Aug 2016 14:30:38 EDT

1. The altmetric Python code

I previously wrote about altmetrics, and in that post showed how to embed an altmetric badge in html about a paper. Since then, I have learned about the Altmetric API which allows you to gather the altmetric data into tabular form. You will need my scopus Python library, my pycse library, and to follow along with this, and the Python code in Section A new take on altmetrics to do this analysis yourself.

So, here, we get a list of my publications from Scopus (scopusid:7004212771), get the DOI and current citations for each of these that are categorized as "Journal", and then the altmetric data for each of those where there is altmetric data. The altmetric data is returned as json, which we can easily convert to a dictionary for easy analysis.

from scopus.scopus_search import ScopusSearch
from scopus.scopus_api import ScopusAbstract
from altmetric import altmetric
import pycse.orgmode as org

s = ScopusSearch(query='au-id(7004212771)', fields='dc:identifier', refresh=True)

abstracts = [ScopusAbstract(eid) for eid in s.EIDS
             if ScopusAbstract(eid).aggregationType == 'Journal']

DOIs = [ab.doi for ab in abstracts]
cites = [ab.citedby_count for ab in abstracts]
dates = [ab.coverDate for ab in abstracts]

data = [altmetric('doi/' + doi) if doi is not None else None for doi in DOIs ]

results = sorted([[j.get('title', 'No title').replace('\n', ''),
                   '[[{}][doi]]'.format(j['url']),
                   '[[{}][details]]'.format(j['details_url']),
                   j.get('cited_by_tweeters_count', 0),
                   j.get('cited_by_fbwalls_count', 0),
                   int(j['readers'].get('mendeley', 0)),
                   j.get('cited_by_posts_count', 0),
                   cite, date]
                  for j, cite, date in zip(data, cites, dates) if j],
                 key=lambda x: x[3], reverse=True)

results = [['title', 'doi', 'details', 'tweets', 'facebook', 'Mendeley', 'blogs', 'cites', 'date'],
           ['<50>',  '',    '',         '',      '',         '',   '', ''],
           None] + results

org.table(results, caption='Altmetric data for my publications.', name='alt-met')

Table 1: Altmetric data for my publications.
title	doi	details	tweets	facebook	Mendeley	blogs	cites	date
Examples of Effective Data Sharing in Scientific Publishing	doi	details	24	0	36	38	2	2015-06-05
Towards Benchmarking in Catalysis Science:Best Practices, Opportunities, and Challenges	doi	details	15	0	56	18	2	2016-04-01
Neural network and ReaxFF comparison for Au properties	doi	details	9	0	5	12	0	2016-01-01
Estimating Bulk-Composition-Dependent H2 Adsorption Energies on CuxPd1–x Alloy (111) Surfaces	doi	details	4	1	9	8	5	2015-02-06
Relationships between the surface electronic and chemical properties of doped 4d and 5d late transition metal dioxides	doi	details	2	0	17	2	2	2015-03-14
The role of vdW interactions in coverage dependent adsorption energies of atomic adsorbates on Pt(111) and Pd(111)	doi	details	2	0	4	2	0	2016-08-01
Comparative microfluidic screening of amino acid salt solutions for post-combustion CO2 capture	doi	details	2	0	4	2	0	2015-12-01
Alkaline Electrolyte and Fe Impurity Effects on the Performance and Active-phase Structure of NiOOH Thin Films for OER Catalysis Applications	doi	details	2	0	17	2	6	2015-05-28
Property Prediction of Crystalline Solids from Composition and Crystal Structure	doi	details	1	0	4	1	0	2016-01-01
Property Prediction of Crystalline Solids from Composition and Crystal Structure	doi	details	1	0	4	1	0	2016-08-01
High-throughput methods using composition and structure spread libraries	doi	details	1	0	0	1	0	2016-01-01
A Linear Response DFT+U Study of Trends in the Oxygen Evolution Activity of Transition Metal Rutile Dioxides	doi	details	1	0	30	1	9	2015-01-01
Electrocatalytic Oxygen Evolution with an Immobilized TAML Activator	doi	details	1	0	40	1	10	2014-04-16
The outlook for improved carbon capture technology	doi	details	1	0	183	1	150	2012-10-01
The redox mediated separation of carbon dioxide from flue gas	doi	details	1	0	4	1	0	2015-11-19
Core level shifts in Cu–Pd alloys as a function of bulk composition and structure	doi	details	1	0	6	1	0	2015-01-01
Chemical and Molecular Descriptors for the Reactivity of Amines with CO 2	doi	details	0	0	6	0	11	2012-10-24
Simulating Temperature Programmed Desorption of Oxygen on Pt(111) Using DFT Derived Coverage Dependent Desorption Barriers	doi	details	0	0	22	0	6	2014-02-01
Comparisons of amine solvents for post-combustion CO2 capture: A multi-objective analysis approach	doi	details	0	0	30	0	10	2013-10-01
Correlation of Electronic Structure with Catalytic Activity: H 2 –D 2 Exchange across Cu x Pd 1– x Composition Space	doi	details	0	0	3	0	0	2015-05-01
Origin of the Overpotential for Oxygen Reduction at a Fuel-Cell Cathode	doi	details	0	0	743	1	1464	2004-11-18
Interactions in 1-ethyl-3-methyl imidazolium tetracyanoborate ion pair: Spectroscopic and density functional study	doi	details	0	0	27	0	11	2013-04-24
Separation of CO2 from flue gas using electrochemical cells	doi	details	0	0	17	1	26	2010-06-01
Universality in Oxygen Evolution Electrocatalysis on Oxide Surfaces	doi	details	0	0	359	5	373	2011-07-11
Probing the effect of electron donation on CO2 absorbing 1,2,3-triazolide ionic liquids	doi	details	0	0	13	0	7	2014-03-17

Obviously not all of my papers (67 now) have altmetric data, and most of the ones that do have very little altmetric data ;(. The Mendeley data is interesting, and this next analysis shows it is reasonably correlated with the number of citations, at least for the few highly cited papers I have. For my typical papers though, it is not clear the correlation is that high.

import matplotlib.pyplot as plt
import pycse.orgmode as org

mendeley = [x[5] for x in data[2:]]
cites = [x[7] for x in data[2:]]

plt.plot(mendeley, cites, 'bo')
plt.xlabel('Mendeley readers')
plt.ylabel('Citation count')
org.figure(plt.savefig('images/alt-metrics.png'))

In summary, the Altmetric API gives you access to data, and (in conjunction with Scopus) an ability to systematically compare metrics and altmetrics across any set of documents you can create via queries.

1 The altmetric Python code

You may want to look at https://pypi.python.org/pypi/pyaltmetric and https://github.com/ropensci/rAltmetric for more advance Python code to work with the Altmetric API. This one has some features they do not, and was easy to write.

import hashlib
import os
import json
import requests
import time

def altmetric(query, version='v1', refresh=False, throttle=1):
    """Return dictionary of altmetric data for query.
    query is a string like: doi/10.1007/s00799-016-0173-7

    Results are cached in ~/.altmetric_data, unless refresh is True.
    throttle is a delay in seconds to avoid too many requests too fast.

    see http://api.altmetric.com

    """
    m = hashlib.md5()
 
    url = 'http://api.altmetric.com/' + version + '/' + query
    m.update(url.encode('utf-8'))
    md5 = m.hexdigest()

    datafile = os.path.join('/Users/jkitchin/.altmetric_data', md5)

    if os.path.exists(datafile) and not refresh:
        with open(datafile) as f:
            results = json.loads(f.read())
    else:
        req = requests.get(url)
        if req.status_code != 404:
            results = req.json()
        else:
            results = {}
        with open(datafile, 'w') as f:
            f.write(json.dumps(results))
        if throttle:
            time.sleep(throttle)  # throttle how many requests we make.

    return results

org-mode source

Org-mode version = 8.3.4

The Kitchin Research Group

A new take on altmetrics - the Altmetrics API

Table of Contents

1 The altmetric Python code