Programmatic access for Altmetrics

Computers are simply better at repetitive tasks

• Makes repetitive tasks take far less time
• Facilitates tool creation by developers
• Allows research questions to be addressed more quickly
• Facilitates reproducibility

REST API

The modern way to serve data to consumers
Makes data consumption easy from any programming language

• Base URI, e.g. http://foo.com
• Media type, e.g., json, xml
• HTTP verbs, like GET, POST, PUT, PATCH, HEAD, etc...

• 1xx - informational
• 2xx - success
• 3xx - redirection
• 4xx - client error
• 5xx - server error

OAuth makes sense for web workflows, but not so much for programmatic workflows.

Having both options is nice.

RAML - http://raml.org/

Programatically create new clients

#%RAML 0.8
–––
title: World Music API
baseUri: http://example.api.com/{version}
version: v1
 /songs:
   get:
   post: 
  ...

Good place to include altmetrics standards...

• Do Altmetrics Work? Twitter and Ten Other... - via Altmetric.com
• Tweeting biomedicine: an analysis of tweets... - via Altmetric.com
• The Spread of Scientific Information... - via PLOS ALM
• Can Tweets Predict Citations? ... - via Twitter Search API
• Altmetrics in the Wild... - via PLOS ALM, various APIs, WebofSci citations
• Social Media Release Increases Dissemination... - via manual collection
• Identifying Audiences of E-Infrastructures... - via Google Analytics
• How the Scientific Community Reacts to... - via Twitter Search API, Google Scholar citations

Obviously

out <- GET("http://alm.plos.org/api/v3/articles?doi=10.1371/journal.pmed.1001361&key=<key>")
stop_for_status(out)
content(out)

{
  doi: "10.1371/journal.pmed.1001361",
  title: "Personalized Prediction of Lifetime Benefits with Statin Therapy for Asymptomatic Individuals: A Modeling Study",
  url: "http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1001361",
  mendeley: "437b07d9-bc40-4c57-b60e-1f60fefe2300",
  pmid: "23300388",
  pmcid: "3531501",
  publication_date: "2012-12-27T08:00:00Z",
  update_date: "2013-10-07T11:06:58Z",
  views: 9329,
  shares: 62,
  bookmarks: 5,
  citations: 1
}

alm(doi = "10.1371/journal.pone.0029797")

An object of class "almtot"
Slot "meta":
$doi
[1] "10.1371/journal.pone.0029797"
...<more metadata>

Slot "summary":
  views shares bookmarks citations
1 29229    237        51         7

Slot "data":
                .id  pdf  html shares groups comments likes citations total
1         bloglines   NA    NA     NA     NA       NA    NA         0     0
2         citeulike   NA    NA      1     NA       NA    NA        NA     1
3          connotea   NA    NA     NA     NA       NA    NA         0     0
4          crossref   NA    NA     NA     NA       NA    NA         7     7
5            nature   NA    NA     NA     NA       NA    NA         4     4
...

Metadata

almmeta("mendeley")

$mendeley
$mendeley$desc
[1] "Mendeley is a reference manager and social bookmarking tool. The Mendeley API returns incomplete API responses for articles where they don't have enough information, and we ignore those."

$mendeley$infourl
[1] "https://github.com/articlemetrics/alm/wiki/Mendeley"

$mendeley$authentication
[1] "OAuth 1.0"

$mendeley$apiurl
[1] "http://api.mendeley.com/oapi/documents/details/DOI/?type=doi&consumer_key=API_KEY"

Combining metrics across aggregators

metaAlm - (doesn't actually exist)

Combine altmetrics data across providers (ImpactStory, Altmetric, etc.)
and across data sources (Twitter, Facebook, etc.)

Get data from three different providers

plos_data <- alm(<doi>)
impactstory_data <- metrics(<doi>)
altmetric_data <- altmetric_data(altmetrics(<doi>))

Easily combine data with a single function, and highlight inconsistencies

alt_combine(plos_data, impactstory_data, altmetric_data)

Warning: Inconsistency in facebookLikes, check metadata

       dataSource fromProvider values
1         twitter      PLOSALM    100
2   facebookLikes  ImpactStory     50
3   facebookLikes    Altmetric     40
4 scopusCitations    Altmetric    150

Load libraries, get 200 DOIs, get ALM data, plot

library(rplos); library(alm); library(plyr)
dois <- searchplos(terms='*:*', fields="id", limit=200)
alm <- ldply( alm(doi=do.call(c,dois$id), total_details=TRUE) )
plot_density(alm, c("counter_pdf","mendeley_shares","pmc_pdf","pmc_total"), c("#83DFB4","#EFA5A5","#CFD470","#B2C9E4"), plot_type="h")

library(rplos); library(alm); library(rCharts)
dois <- c('10.1371/journal.pone.0001543','10.1371/journal.pone.0040117','10.1371/journal.pone.0029797','10.1371/journal.pone.0039395')
dat <- signposts(doi=dois)

plot_signposts(input = dat, type = "multiBarChart", height = 400)

“I’d argue that #opendata today is exactly where open source was some 2 decades ago”-@BenBalter http://t.co/VJ6QiLybUU #oss

— Alex Howard (@digiphile) October 9, 2013

Altmetrics needs checks on

• Consistency (tweets from source A and B should be =)
• Correlation (is metric A strongly corr. with B?)
• Interpretation (open source the interpretation)
• Gaming (security through obscurity doesn't work)

Altmetrics needs checks on

• Consistency (tweets from source A and B should be =)
• Correlation (is metric A strongly corr. with B?)
• Interpretation (open source the interpretation)
• Gaming (security through obscurity doesn't work)

Open data makes all this easier

• Knowledge from research findings
  • Doesn't require open data I suppose :(, but helps facilitate research
  • e.g., think how hard text-mining is - we don't want that in altmetrics
• Open products
  • ReaderMeter
  • ScienceCard
• For-profit products
• Who knows? Making data open allows many experiments, some of which will stick

Are individual altmetrics consistent among data providers?

10.3789/isqv25no2.2013.02