Open access editorial policies of SciELO health sciences journals

Abstract

SciELO promotes open access and cooperative publication of scholarly journals, based mainly in Latin America, the Caribbean, Spain and Portugal. SciELO was created to offer solutions to increase the visibility of participating journals and facilitate free access to their full texts. This work aims to analyse the open access editorial policies implemented by the health sciences journals of the SciELO network (411 journals at the time of this study) in terms of authors’ rights, copyright issues, self-archiving policies and openness. From SciELO health sciences journals network, 92% of the 411 journals use a Creative Commons licence, 89% require transfer of author copyright and 14% apply author processing charges. According to the past SHERPA/RoMEO taxonomy of self-archiving policies, 8.5% of the journals were classified as white, 81.5% blue and 10% green. The openness of journals calculated through the Open Access Spectrum approach was higher than 60% in more than 80% of the total journals. Out of the 411 journals in SciELO portals, 380 have their own website. Discrepancies were found between licences stated in SciELO compared with the ones used in their websites, mainly due to the lack of declared licences in either of the two sources or because the licences did not match. The licences used on the websites and in SciELO were also compared with their corresponding records in the Directory of Open Access Journals and Crossref, and again the differences were narrowly related to the data supplier.

Open Access Author Contracts and Alignment with the Open Ethos: A Global Study

“…29 Findings from this study more strongly resemble trends in European journals than the findings on SciELO journals in Latin America and the Caribbean, where it was found that 89 percent of such open access journals required copyright transfer. 30 An important difference, however, is that our study recorded a much higher instance of the copyright status for authors as unknown-22 percent as compared to a negligible amount in the Morrison et al study.…”

Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes

“…The dataset used in this work results from merging four distinct datasets that share a common set of documents. These datasets correspond to annotated versions of the 1000 clinical cases making up the Spanish Clinical Case Corpus [ 77 ], a collection of clinical case reports obtained from Spanish medical publications [ 78 ], encompassing a total of 16 504 sentences with an average of 16.5 sentences per clinical case. Each of these documents was verified to contain the same text.…”