Friday, March 15, 2013 From rOpenSci (https://ropensci.org/blog/2013/03/15/r-metadata/). Except where otherwise noted, content on this site is licensed under the CC-BY license.
Scholarly metadata - the meta-information surrounding articles - can be super useful. Although metadata does not contain the full content of articles, it contains a lot of useful information, including title, authors, abstract, URL to the article, etc.
One of the largest sources of metadata is provided via the Open Archives Initiative Protocol for Metadata Harvesting or OAI-PMH. Many publishers, provide their metadata through their own endpoint, and implement the standard OAI-PMH methods: GetRecord, Identify, ListIdentifiers, ListMetadataFormats, ListRecords, and ListSets. Many providers use OAI-PMH, including DataCite, Dryad, and PubMed.
Some data-/article-providers provide their metadata via their own APIs. For example, Nature Publishing Group provides their own metadata API here in non OAI-PMH format; you can get PLoS metadata through their search API, and the BHL (see below) provides their own custom metadata service.
In addition, CrossRef provides a number of metadata search services: metadata search and openurl.
What about the other publishers? (please tell me if I’m wrong about these three)
Note that metadata can live in other places:
No, you can’t get metadata via Google Scholar - the don’t allow scraping, and don’t have expose their data via an API.
I have discussed this package in a previous blog post, but have since worked on the code a bit, and thought it deserved a new post.
You can see a tutorial for this package here, and contribute to the code here.
# install_github('rmetadata', 'ropensci') # uncomment to install
library(rmetadata)
Count OAI-PMH identifiers for a data provider.
# For DataCite.
count_identifiers("datacite")
provider count
1 datacite 1216193
Lookup article info via CrossRef with DOI and get a citation.
As Bibtex
print(crossref_citation("10.3998/3336451.0009.101"), style = "Bibtex")
@Article{,
title = {In Google We Trust?},
author = {Geoffrey Bilder},
journal = {The Journal of Electronic Publishing},
year = {2006},
month = {01},
volume = {9},
doi = {10.3998/3336451.0009.101},
}
As regular text
print(crossref_citation("10.3998/3336451.0009.101"), style = "text")
Bilder G (2006). "In Google We Trust?" _The Journal of Electronic
Publishing_, *9*. <URL:
https://doi.org/10.3998/3336451.0009.101>.
Search the CrossRef Metatdata for DOIs using free form references.
Search with title, author, year, and journal
crossref_search_free(query = "Piwowar Sharing Detailed Research Data Is Associated with Increased Citation Rate PLOS one 2007")
text
1 Piwowar Sharing Detailed Research Data Is Associated with Increased Citation Rate PLOS one 2007
match doi score
1 TRUE 10.1038/npre.2007.361 4.905
Get a DOI and get the citation using \code{crossref_search}
# Get a DOI for a paper
doi <- crossref_search_free(query = "Piwowar sharing data PLOS one")$doi
# Get the metadata
crossref_search(doi = doi)[, 1:3]
doi score normalizedScore
1 10.1371/journal.pone.0000308 18.19 100
Get a random set of DOI’s through CrossRef.
# Default search gets 20 random DOIs
crossref_r()
[1] "10.4028/www.scientific.net/MSF.126-128.467"
[2] "10.2139/ssrn.548523"
[3] "10.1016/S0012-821X(02)00562-9"
[4] "10.1093/rsq/13.2-3.167"
[5] "10.5772/55055"
[6] "10.1515/BC.1999.050"
[7] "10.1016/S0020-7292(98)90160-6"
[8] "10.1111/j.1439-0418.1985.tb02788.x"
[9] "10.1089/aid.2012.0115"
[10] "10.1016/0002-9378(95)90155-8"
[11] "10.1001/jama.1949.02900490055028"
[12] "10.1051/jphyscol:1989172"
[13] "10.1016/s0301-2115(03)00298-7"
[14] "10.1007/BF02735292"
[15] "10.1016/0003-4916(65)90026-6"
[16] "10.4156/jdcta.vol5.issue5.12"
[17] "10.1007/s10904-009-9316-2"
[18] "10.1023/A:1021690001832"
[19] "10.1007/s12262-012-0724-0"
[20] "10.1007/bf02192860"
# Specify you want journal articles only
crossref_r(type = "journal_article")
[1] "10.1016/j.jacc.2011.09.055"
[2] "10.1002/dev.420170603"
[3] "10.4315/0362-028X.JFP-10-403"
[4] "10.1016/S0925-4927(98)00016-X"
[5] "10.1111/j.1933-1592.2002.tb00141.x"
[6] "10.1541/ieejfms.127.629"
[7] "10.5539/enrr.v3n1p62"
[8] "10.1016/S0960-9776(96)90038-7"
[9] "10.1016/0925-9635(94)05240-9"
[10] "10.1016/s0929-693x(97)86846-7"
[11] "10.1002/(SICI)1096-9071(199601)48:1<53::AID-JMV9>3.0.CO;2-K"
[12] "10.1016/s0267-7261(01)00016-1"
[13] "10.1111/j.1748-0361.2003.tb00575.x"
[14] "10.1097/00005721-197701000-00011"
[15] "10.1007/s00894-009-0593-z"
[16] "10.1071/AR9830063"
[17] "10.1186/gb-2009-10-4-r39"
[18] "10.2165/00128415-201113540-00038"
[19] "10.1007/BF00522986"
[20] "10.1080/19407963.2011.539385"
Search the CrossRef Metatdata API.
# Search for two different query terms
crossref_search(query = c("renear", "palmer"), rows = 4)[, 1:3]
doi score normalizedScore
1 10.1126/science.1157784 3.253 100
2 10.1002/meet.2009.1450460141 2.169 66
3 10.4242/BalisageVol3.Renear01 2.102 64
4 10.4242/BalisageVol5.Renear01 2.102 64
# Get results for a certain year
crossref_search(query = c("renear", "palmer"), year = 2010)[, 1:3]
doi score normalizedScore
1 10.1002/meet.14504701218 1.0509 100
2 10.1002/meet.14504701240 1.0509 100
3 10.5270/OceanObs09.cwp.68 1.0442 99
4 10.1353/mpq.2010.0003 0.6890 65
5 10.1353/mpq.0.0041 0.6890 65
6 10.1353/mpq.0.0044 0.6890 65
7 10.1353/mpq.0.0057 0.6890 65
8 10.1386/fm.1.1.2 0.6890 65
9 10.1386/fm.1.2.2 0.6890 65
10 10.1386/fm.1.3.2 0.6890 65
11 10.1097/ALN.0b013e3181f09404 0.6090 57
12 10.1016/j.urology.2010.02.033 0.6090 57
13 10.1353/ect.2010.0025 0.6090 57
14 10.1117/2.4201001.04 0.6090 57
15 10.1111/j.1835-9310.1977.tb01159.x 0.6090 57
16 10.4067/S0717-69962010000100001 0.6090 57
17 10.4067/S0717-69962010000200001 0.6090 57
18 10.2105/AJPH.2009.191098 0.6029 57
19 10.1353/mpq.2010.0004 0.5167 49
20 10.1353/mpq.0.0048 0.5167 49
Get a short DOI from shortdoi.org.
# Geta a short DOI, just the short DOI returned
short_doi(doi = "10.1371/journal.pone.0042793")
[1] "10/f2bfz9"
# Geta a short DOI, all data returned
short_doi(doi = "10.1371/journal.pone.0042793", justshort = FALSE)
$DOI
[1] "10.1371/journal.pone.0042793"
$ShortDOI
[1] "10/f2bfz9"
$IsNew
[1] FALSE
Get a record from a OAI-PMH data provider.
# Single provider, one identifier
md_getrecord(provider = "pensoft", identifier = "10.3897/zookeys.1.10")
title
1 A new candidate for a Gondwanaland distribution in the Zodariidae (Araneae): Australutica in Africa
creator date type
1 Jocqué,Rudy 2008 Research Article
# Single provider, multiple identifiers
md_getrecord(provider = "pensoft", identifier = c("10.3897/zookeys.1.10", "10.3897/zookeys.4.57"))
title
1 A new candidate for a Gondwanaland distribution in the Zodariidae (Araneae): Australutica in Africa
2 Studies of Tiger Beetles. CLXXVIII. A new Lophyra (Lophyra) from Somaliland (Coleoptera, Cicindelidae)
creator date type
1 Jocqué,Rudy 2008 Research Article
2 Cassola,Fabio 2008 Research Article
List available metadata formats from various providers.
# List metadata formats for a provider
md_listmetadataformats(provider = "dryad")
metadataPrefix
1 oai_dc
2 rdf
3 ore
4 mets
schema
1 http://www.openarchives.org/OAI/2.0/oai_dc.xsd
2 http://www.openarchives.org/OAI/2.0/rdf.xsd
3 http://tweety.lanl.gov/public/schemas/2008-06/atom-tron.sch
4 http://www.loc.gov/standards/mets/mets.xsd
metadataNamespace
1 http://www.openarchives.org/OAI/2.0/oai_dc/
2 http://www.openarchives.org/OAI/2.0/rdf/
3 http://www.w3.org/2005/Atom
4 http://www.loc.gov/METS/
# List metadata formats for a specific identifier for a provider
md_listmetadataformats(provider = "pensoft", identifier = "10.3897/zookeys.1.10")
identifier metadataPrefix
1 10.3897/zookeys.1.10 oai_dc
2 10.3897/zookeys.1.10 mods
schema
1 http://www.openarchives.org/OAI/2.0/oai_dc.xsd
2 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd
metadataNamespace
1 http://www.openarchives.org/OAI/2.0/oai_dc/
2 http://www.loc.gov/mods/v3
Some plotting - mean number of authors per paper
Okay, so this isn’t a super useful visualization, but you can surely think of something better.
library(ggplot2)
library(ggthemes)
library(reshape)
temp <- md_listrecords(provider = "pensoft", from = "2011-10-01", until = "2012-01-01")
temp2 <- ldply(temp)[, -1]
auths <- sapply(temp2$creator, function(x) length(strsplit(as.character(x),
";")[[1]]))
toplot <- data.frame(authors = auths, articletype = temp2$type)
toplot_ <- ddply(toplot, .(articletype), summarise, authors = mean(authors))
toplot_$articletype <- reorder(toplot_$articletype, toplot_$authors)
ggplot(toplot_, aes(articletype, authors)) + theme_tufte(base_size = 16) + geom_bar(stat = "identity") +
coord_flip()
