Over the past two decades, databases and the tools to access them in a simple manner have become increasingly available, allowing historical and modern-day topics to be merged and studied. Throughout the recent COVID-19 pandemic, for example, many researchers have reflected on whether any lessons learned from the Spanish flu pandemic of 1918 could have been helpful in the present pandemic. Most studies using text-mining applications rarely use full-text journal articles. This article provides a methodology used to develop a full-text journal article corpus using the R fulltext package. Using the proposed methodology, 2743 full-text journal articles were obtained. The aim of this article is to provide a methodology and supplementary codes for researchers to use the R fulltext package to curate a full-text journal corpus.
KobayashiVBMolSTBerkersHA, et al. Text classification for organizational researchers: a tutorial. Organ Res Methods2018; 21: 766–799.
2.
AntonsDGrünwaldECichyP, et al. The application of text mining methods in innovation research: current state, evolution patterns, and development priorities. RD Manage2020; 50: 329–351.
3.
MüllerHMVan AukenKMLiY, et al. Textpresso Central: a customizable platform for searching, text mining, viewing, and curating biomedical literature. BMC Bioinform2018; 19: 94.
4.
LandhuisE. Scientific literature: information overload. Nature2016; 535: 457–458.
5.
FraserNBrierleyLDeyG, et al. The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscape. PLoS Biol2021; 19: e3000959.
6.
WangLLoK. Text mining approaches for dealing with the rapidly expanding literature on COVID-19. Brief Bioinform2021; 22: 781–799.
7.
ThakurKKumarV. Application of text mining techniques on scholarly research articles: methods and tools. New Rev Acad Libr2022; 28: 1–25.
8.
SalloumSAl-EmranMMonemA, et al. Using text mining techniques for extracting information from research articles. In: ShaalanKHassanienATolbaF (eds) Intelligent natural language processing: trends and applications. Cham: Springer, 2018, pp. 373–397.
9.
KatsaliakiKMustafeeNDwivediYK, et al. A profile of OR research and practice published in the Journal of the Operational Research Society. J Oper Res Soc2010; 61: 82–94.
10.
AbdelrahmanMMZhanSMillerC, et al. Data science for building energy efficiency: a comprehensive text-mining driven review of scientific literature. Energ Build2021; 242: 110885.
11.
WestergaardDStærfeldtHHTønsbergC, et al. A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts. PLoS Comput Biol2018; 14: e1005962.
12.
TudorCRossKLiG, et al. Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system. Database2015; 2015: bav020.
13.
LandeghemSVBjörneJWeiCH, et al. Large-scale event extraction from literature with multi-level gene normalization. PLoS ONE2013; 8: e55814.
14.
LiechtiRGeorgeNGötzL, et al. SourceData: a semantic platform for curating and searching figures. Nat Methods2017; 14: 1021–1022.
15.
AllotAPengYWeiCH, et al. LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC. Nucl Acid Res2018; 46: W530–W536.
16.
MorkJAronsonADemner-FushmanD. 12 years on – is the NLM medical text indexer still useful and relevant?J Biomed Semant2017; 8: 8.
17.
BlakeC. Beyond genes, proteins, and abstracts: identifying scientific claims from full-text biomedical articles. J Biomed Inform2010; 43: 173–189.
18.
LinJ. Is searching full text more effective than searching abstracts?BMC Bioinform2009; 10: 46
19.
CohenKBJohnsonHLVerspoorK, et al. The structural and content aspects of abstracts versus bodies of full text journal articles are different. BMC Bioinform2010; 11: 492.
20.
LeeSKimDLeeK, et al. BEST: next-generation biomedical entity search tool for knowledge discovery from biomedical literature. PLoS ONE2016; 11: e0164680.
21.
Rosário-FerreiraNMarques-PereiraCPiresM, et al. The treasury chest of text mining: piling available resources for powerful biomedical text mining. BioChem2021; 1: 60–80.
22.
BaillargeonTKowalikECookJ. Collaborative digital research: case study of text mining a corpus of academic journals. New Rev Acad Libr2021; 27: 230–242.
23.
WendtCJAndersonGB. Ten simple rules for finding and selecting R packages. PLoS Comput Biol2022; 18: e1009884.
BlairDCMaronME. An evaluation of retrieval effectiveness for a full-text document-retrieval system. Commun ACM1985; 28: 289–299.
27.
SwansonDR. Searching natural language text by computer. Science1960; 132: 1099–1104.
28.
SaltonG. Automatic text analysis. Science1970; 168: 335–343.
29.
TongRMAskmanVNCunninghamJF, et al. RUBRIC: an environment for full text information retrieval. In: Proceedings of the 8th annual international ACM SIGIR conference on research and development in information retrieval – SIGIR ’85, Montreal, QC, Canada, 5–7 June 1985, pp. 243–251. New York: ACM Press.
30.
BjörkBCWellingPLaaksoM, et al. Open access to the scientific journal literature: situation 2009. PLoS ONE2010; 5: e11273.
31.
TenopirCKingDCampbellR. Towards electronic journals: realities for scientists, librarians & publishers. J Scholar Publ2000; 31: 214.
32.
LaaksoMBjörkBC. Anatomy of open access publishing: a study of longitudinal development and internal structure. BMC Med2012; 10: 124.
33.
LoKWangLLNeumannM, et al. S2ORC: the semantic scholar open research corpus, http://arxiv.org/abs/1911.02782 (2020, accessed 26 April 2022).
34.
GartenYAltmanRB. Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text. BMC Bioinform2009; 10: S6.
35.
ComeauDWeiCIslamaj DoğanR, et al. PMC text mining subset in BioC: about three million full-text articles and growing. Bioinformatics2019; 35: 3533–3535.
36.
LuZHirschmanL. Biocuration workflows and text mining: overview of the BioCreative 2012 Workshop Track II. Database2012; 2012: bas043.
37.
Van AukenKSchaefferMMcQuiltonP, et al. BC4GO: a full-text corpus for the BioCreative IV GO task. Database2014; 2014: bau074.
PearseWChamberlainS. Suppdata: downloading supplementary data from published manuscripts. J Open Sour Softw2018; 3: 721.
40.
HarrisonHGriffinSJKuhnI, et al. Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation. BMC Med Res Methodol2020; 20: 7.
41.
BozadaTBordenJWorkmanJ, et al. Sysrev: a FAIR platform for data curation and systematic evidence review. Front Artif Intell2021; 4: 685298.
KononovaOHeTHuoH, et al. Opportunities and challenges of text mining in materials research. iScience2021; 24: 102155.
50.
ConstantinAPettiferSVoronkovA. PDFX: fully-automated PDF-to-XML conversion of scientific literature. In: Proceedings of the 2013 ACM symposium on document engineering, Florence, 10–13 September 2013, pp. 177–180. New York: ACM.
51.
MemonJSamiMKhanRA, et al. Handwritten optical character recognition (OCR): a comprehensive systematic literature review (SLR). IEEE Access2020; 8: 142642–142668.
52.
MilosevicNGregsonCHernandezR, et al. A framework for information extraction from tables in biomedical literature. IJDAR2019; 22: 55–78.
53.
IveyCCrumJ. Choosing the right citation management tool: EndNote, Mendeley, RefWorks, or Zotero. J Med Libr Assoc2018; 106: 399–403.
54.
Bannach-BrownAHairKBahorZ, et al. Technological advances in preclinical meta-research. BMJ Open Sci2021; 5: e100131.
55.
PullaP. The plan to mine the world’s research papers. Nature2019; 571: 316–318.
MooreSA. Individuation through infrastructure: get full text research, data extraction and the academic publishing oligopoly. J Document2020; 77: 129–141.
58.
TayA. Improving authentication and authorization: seamlessaccess and GetFTR. Libr Technol Rep2022; 58: 31–39.
59.
GengYCaoRMHanXP, et al. Scientists are working overtime and at the weekends: comparison of publication downloading from copyrighted and pirated platforms, http://arxiv.org/abs/2111.02664 (2021, accessed 28 April 2022).
60.
González-SolarLFernández-MarcialS. Sci-Hub, a challenge for academic and research libraries. Profes Inform2019; 28: e280112.
