scholarly journals Concern noted: A descriptive study of editorial expressions of concern in PubMed and PubMed Central

2017 ◽  
Author(s):  
Melissa Vaught ◽  
Diana C. Jordan ◽  
Hilda Bastian
Keyword(s):  
Scientific Community ◽  
Descriptive Study ◽  
Biomedical Literature ◽  
Google Scholar ◽  
Historical Trends ◽  
Pubmed Central ◽  
Potential Problems ◽  
Ongoing Monitoring ◽  
Open Issues

AbstractBackgroundAn editorial expression of concern (EEoC) is issued by editors or publishers to draw attention to potential problems in a publication, without itself constituting a retraction or correction.MethodsWe searched PubMed, PubMed Central (PMC), and Google Scholar to identify EEoCs issued for publications in PubMed and PMC up to 22 August 2016. We also searched the archives of the Retraction Watch blog, some journal and publisher websites, and studies of EEoCs. In addition, we searched for retractions of EEoCs and affected articles in PubMed up to 8 December 2016. We analyzed overall historical trends, as well as reported reasons and subsequent editorial actions related to EEoCs issued between August 2014 and August 2016.ResultsAfter screening 5,076 records, we identified 230 EEoCs that affect 300 publications indexed in PubMed, the earliest issued in 1985. Half of the primary EEoCs were issued between 2014 and 2016 (52%). We found evidence of some EEoCs that had been removed by the publisher without leaving a record and some were not submitted for PubMed or PMC indexing. A minority of publications affected by EEoCs had been retracted by early December 2016 (25%). For the subset of 92 EEoCs issued between August 2014 and August 2016, affecting 99 publications, the rate of retraction was similar (29%). The majority of EEoCs were issued because of concerns with validity of data, methods, or interpretation of the publication (68%), and 31% of cases remained open. Issues with images were raised in 40% of affected publications. Ongoing monitoring after the study identified another 17 EEoCs to year’s end in 2016, increasing the number of EEoCs to 247 and publications in PubMed known to be affected by EEoCs to 320 at the end of 2016.ConclusionsEEoCs have been rare publishing events in the biomedical literature, but their use has been increasing. Most have not led to retractions, and many remain unresolved. Lack of prominence and inconsistencies in management of EEoCs reduce the ability of these notices to alert the scientific community to potentially serious problems in publications. EEoCs will be made identifiable in PubMed in 2017.

GREEN OPEN ACCESS IN KENYA: A REVIEW OF THE CONTENT, POLICIES AND USAGE OF INSTITUTIONAL REPOSITORIES

Mousaion ◽  
2016 ◽  
Vol 33 (3) ◽  
pp. 25-54
Author(s):  
Wanyenda Leonard Chilimo
Keyword(s):  
Developing Countries ◽  
Open Access ◽  
Descriptive Study ◽  
Google Scholar ◽  
Research Institutions ◽  
Journal Articles ◽  
Content Type ◽  
Institutional Repositories ◽  
Current State ◽  
Theses And Dissertations

 There is scant research-based evidence on the development and adoption of open access (OA) and institutional repositories (IRs) in Africa, and in Kenya in particular. This article reports on a study that attempted to fill that gap and provide feedback on the various OA projects and advocacy work currently underway in universities and research institutions in Kenya and in other developing countries. The article presents the findings of a descriptive study that set out to evaluate the current state of IRs in Kenya. Webometric approaches and interviews with IR managers were used to collect the data for the study. The findings showed that Kenya has made some progress in adopting OA with a total of 12 IRs currently listed in the Directory of Open Access Repositories (OpenDOAR) and five mandatory self-archiving policies listed in the Registry of Open Access Repositories Mandatory Archiving Policies (ROARMAP). Most of the IRs are owned by universities where theses and dissertations constitute the majority of the content type followed by journal articles. The results on the usage and impact of materials deposited in Kenyan IRs indicated that the most viewed publications in the repositories also received citations in Google Scholar, thereby signifying their impact and importance. The results also showed that there was a considerable interest in Swahili language publications among users of the repositories in Kenya.

Hubungan Sanitasi Lingkungan Dengan Kejadian Stunting Pada Balita di Indonesia (Studi Literatur)

2021 ◽  
Vol 2 (2) ◽  
pp. 83-94
Author(s):  
Siti Hasanah ◽  
Sarah Handayani ◽  
Ikhwan Ridha Wilti
Keyword(s):  
Google Scholar ◽  
Pubmed Central ◽  
Central Data

Stunting merupakan suatu keadaan yang menggambarkan status gizi kurang yang bersifat kronik pada masa pertumbuhan dan perkembangan terjadi sejak bayi dalam kandungan sampai bayi dilahirkan. Tujuan penelitian ini adalah untuk mengetahui hubungan sanitasi lingkungan dengan kejadian stunting pada balita di Indonesia. Penelitian ini merupakan penelitian studi literatur. Literatur yang diulas diperoleh dari penelusuran artikel penelitian-penelitian ilmiah dari rentang tahun 2014-2019 dengan menggunakan database Google Scholar, PubMed Central, dan Biomel Central. Data yang digunakan adalah data sekunder, yaitu data yang diperoleh dari hasil penelitian yang telah dilakukan oleh peneliti-peneliti terdahulu. Penelitian ini dilakukan antara bulan Juni hingga Agustus 2020. Berdasarkan penelusuran judul dan abstrak didapatkan 76 artikel namun hanya 20 artikel yang memenuhi kriteria inklusi. Hasil telaah terhadap literatur terdapat hubungan signifikan antara variabel sumber air bersih, akses sanitasi, pengelolaan limbah rumah tangga, pengelolaan sampah rumah tangga, kejadian diare dan kejadian ISPA dengan kejadian stunting pada balita di Indonesia. Kata Kunci   : Balita, Indonesia, Sanitasi Lingkungan, Stunting

scholarly journals Google Scholar Could Be Used as a Stand-Alone Resource for Systematic Reviews

2015 ◽  
Vol 10 (2) ◽  
pp. 147
Author(s):  
Saori Wendy Herman
Keyword(s):  
Systematic Reviews ◽  
Recall Rate ◽  
Biomedical Literature ◽  
Google Scholar ◽  
Cochrane Library ◽  
Bibliographic Database ◽  
Predictive Values ◽  
Advanced Search ◽  
Search Tool ◽  
Meta Analyses

A Review of: Gehanno, J. F., Rollin, L., & Darmoni, S. (2013). Is the coverage of Google Scholar enough to be used alone for systematic reviews. BMC Medical Informatics and Decision Making, 13(1): 7. doi: 10.1186/1472-6947-13-7 Abstract Objective – To determine if Google Scholar (GS) is sensitive enough to be used as the sole search tool for systematic reviews. Design – Citation analysis. Setting – Biomedical literature. Subjects – Original studies included in 29 systematic reviews published in the Cochrane Library or JAMA. Methods – The authors searched MEDLINE for any systematic reviews published in the 2008 and 2009 issues of JAMA or in the July 8, 2009 issue of the Cochrane Database of Systematic Reviews. They chose 29 systematic reviews for the study and included these reviews in a gold standard database created specifically for this project. The authors searched GS for the title of each of the original references for the 29 reviews. They computed and noted the recall of GS for each reference. Main Results – The authors searched GS for 738 original studies with a 100% recall rate. They also made a side discovery of a number of major errors in the bibliographic references. Conclusion – Researchers could use GS as a stand-alone database for systematic reviews or meta-analyses. With a couple improvements to the rate of positive predictive values and advanced search features, GS could become the leading medical bibliographic database. Conclusion – Researchers could use GS as a stand-alone database for systematic reviews or meta-analyses. With a couple improvements to the rate of positive predictive values and advanced search features, GS could become the leading medical bibliographic database.

scholarly journals Mandating access: assessing the NIH’s public access policy

2020 ◽  
Vol 35 (102) ◽  
pp. 269-304 ◽  
Author(s):  
Joseph Staudt
Keyword(s):  
Biomedical Literature ◽  
National Institutes Of Health ◽  
Public Access ◽  
Pubmed Central ◽  
The Public ◽  
Access Policy ◽  
Open Access Journals ◽  
Widespread Access ◽  
Matched Comparison ◽  
Modest Effect

SUMMARY In April 2008, the National Institutes of Health (NIH) implemented the Public Access Policy (PAP), which mandated that the full text of NIH-supported articles be made freely available on PubMed Central – the NIH’s repository of biomedical research. This paper uses 600,000 NIH articles and a matched comparison sample to examine how the PAP impacted researcher access to the biomedical literature and publishing patterns in biomedicine. Though some estimates allow for large citation increases after the PAP, the most credible estimates suggest that the PAP had a relatively modest effect on citations, which is consistent with most researchers having widespread access to the biomedical literature prior to the PAP, leaving little room to increase access. I also find that NIH articles are more likely to be published in traditional subscription-based journals (as opposed to ‘open access’ journals) after the PAP. This indicates that any discrimination the PAP induced, by subscription-based journals against NIH articles, was offset by other factors – possibly the decisions of editors and submission behaviour of authors.

scholarly journals Phytochemical and Pharmacological Review of Cryptolepis sanguinolenta (Lindl.) Schlechter

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Newman Osafo ◽  
Kwesi Boadu Mensah ◽  
Oduro Kofi Yeboah
Keyword(s):  
Medicinal Plant ◽  
West Africa ◽  
Google Scholar ◽  
Pubmed Central ◽  
Detailed Review ◽  
Lead Compounds ◽  
Cryptolepis Sanguinolenta ◽  
Therapeutic Benefits ◽  
Continue Research ◽  
Respiratory Conditions

Ethnopharmacological Relevance. Cryptolepis sanguinolenta is a scrambling thin-stemmed shrub found in Africa. Traditionally in West Africa, it is employed in the treatment of malaria, diarrhea, and respiratory conditions. This review discusses the traditional importance as well as the phytochemical, ethnomedical, pharmacological, and toxicological importance of this plant. Materials and Methods. Excerpta Medica Database, Google Scholar, Springer, and PubMed Central were the electronic databases used to search for and filter primary studies on Cryptolepis sanguinolenta. Results. The detailed review of various studies conducted on C. sanguinolenta and some of its constituents gives an important body of proof of its potential therapeutic benefits and also of its use as a source of lead compounds with therapeutic potentials. Conclusion. The review on C. sanguinolenta is important in identifying grey areas in the research on this medicinal plant and also provides comprehensive data thus far to continue research on this plant.

scholarly journals Biotea, semantics for Pubmed Central

2017 ◽  
Author(s):  
Alexander Garcia ◽  
Federico Lopez ◽  
Leyla Garcia ◽  
Olga Giraldo ◽  
Victor Bucheli ◽  
...  
Keyword(s):  
Linked Data ◽  
Scientific Literature ◽  
Query Language ◽  
Biomedical Ontology ◽  
Sparql Query ◽  
Biomedical Literature ◽  
Automatic Annotation ◽  
Pubmed Central ◽  
Web Technologies ◽  
Aggregate Content

A significant portion of biomedical literature is represented in a manner that makes it difficult for consumers to find or aggregate content through a computational query. One approach to facilitate reuse of the scientific literature is to structure this information as linked data using standardized web technologies. In this paper we present the second version of Biotea, a semantic, linked data version of the open-access subset of PubMed Central that has been enhanced with specialized annotation pipelines that uses existing infrastructure from the National Center for Biomedical Ontology. We expose our models, services, software and datasets. Our infrastructure enables manual and semi-automatic annotation, resulting data are represented as RDF-based linked data and can be readily queried using the SPARQL query language. We illustrate the utility of our system with several use cases. Availability: Our datasets, methods and techniques are available at http://biotea.github.io

scholarly journals Cell-Line Annotation on Europe PubMed Central

2014 ◽  
Author(s):  
Jee-Hyub Kim
Keyword(s):  
Cell Culture ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Biomedical Literature ◽  
Pubmed Central ◽  
Preliminary Results ◽  
Data Citation ◽  
A Cell

A cell line is a cell culture developed from a single cell and therefore consisting of cells with a uniform genetic make-up. A cell line has an important role as a research resource such as organisms, antibodies, constructs, knockdown reagents, etc. Unique identification of cell lines in the biomedical literature is important for the reproducibility of science. As data citation, resource citation is also important for resource re-use. In this paper, we mention the challenges of identifying cell lines and describe a system for cell line annotation with preliminary results.

scholarly journals Brief chronological progress in the management of intussusception: 1674 to 2021

2021 ◽  
Vol 9 (10) ◽  
pp. 3222
Author(s):  
Rajendra K. Ghritlaharey
Keyword(s):  
Literature Search ◽  
Google Scholar ◽  
Review Of The Literature ◽  
Historical Events ◽  
Pubmed Central

This is a review of the literature related to the progress in the management of intussusception. Literature and other publication on the topic of “intussusception” were retrieved from 1674 to August 2021. The online literature search was – performed using various websites, i e, PubMed, PubMed central, ResearchGate, and Google - eBooks, Google Scholar, and Google Images. The important historical events that occurred in the management of intussusception were briefly presented in this manuscript.

scholarly journals Auto-CORPus: Automated and Consistent Outputs from Research Publications

2021 ◽  
Author(s):  
Yan Hu ◽  
Shujian Sun ◽  
Thomas Rowlands ◽  
Tim Beck ◽  
Joram Matthias Posma
Keyword(s):  
Language Processing ◽  
Association Studies ◽  
Biomedical Literature ◽  
Pubmed Central ◽  
Manual Curation ◽  
Genome Wide ◽  
Ontology Extraction ◽  
Table Data ◽  
Machine Readable ◽  
Readable Format

Motivation: The availability of improved natural language processing (NLP) algorithms and models enable researchers to analyse larger corpora using open source tools. Text mining of biomedical literature is one area for which NLP has been used in recent years with large untapped potential. However, in order to generate corpora that can be analyzed using machine learning NLP algorithms, these need to be standardized. Summarizing data from literature to be stored into databases typically requires manual curation, especially for extracting data from result tables. Results: We present here an automated pipeline that cleans HTML files from biomedical literature. The output is a single JSON file that contains the text for each section, table data in machine-readable format and lists of phenotypes and abbreviations found in the article. We analyzed a total of 2,441 Open Access articles from PubMed Central, from both Genome-Wide and Metabolome-Wide Association Studies, and developed a model to standardize the section headers based on the Information Artifact Ontology. Extraction of table data was developed on PubMed articles and fine-tuned using the equivalent publisher versions. Availability: The Auto-CORPus package is freely available with detailed instructions from Github at https://github.com/jmp111/AutoCORPus/.

scholarly journals BiDI: Using Machine Learning to collect and facilitate remote access to biomedical databases (Preprint)

2020 ◽  
Author(s):  
Eduardo Rosado ◽  
Miguel Garcia-Remesal Sr ◽  
Sergio Paraiso-Medina Sr ◽  
Alejandro Pazos Sr ◽  
Victor Maojo Sr
Keyword(s):  
Deep Learning ◽  
Text Processing ◽  
Biomedical Literature ◽  
Remote Access ◽  
Pubmed Central ◽  
Processing Pipeline ◽  
Learning Techniques ◽  
Different Types ◽  
Automatic Text ◽  
Biomedical Databases

BACKGROUND Currently, existing biomedical literature repositories do not commonly provide users with specific means to locate and remotely access biomedical databases. OBJECTIVE To address this issue we developed BiDI (Biomedical Database Inventory), a repository linking to biomedical databases automatically extracted from the scientific literature. BiDI provides an index of data resources and a path to access them in a seamless manner. METHODS We designed an ensemble of Deep Learning methods to extract database mentions. To train the system we annotated a set of 1,242 articles that included mentions to database publications. Such a dataset was used along with transfer learning techniques to train an ensemble of deep learning NLP models based on the task of database publication detection. RESULTS The system obtained an f1-score of 0.929 on database detection, showing high precision and recall values. Applying this model to the PubMed and PubMed Central databases we identified over 10,000 unique databases. The ensemble also extracts the web links to the reported databases, discarding the irrelevant links. For the extraction of web links the model achieved a cross-validated f1-score of 0.908. We show two use cases, related to “omics” and the COVID-19 pandemia. CONCLUSIONS BiDI enables the access of biomedical resources over the Internet and facilitates data-driven research and other scientific initiatives. The repository is available at (http://gib.fi.upm.es/bidi/) and will be regularly updated with an automatic text processing pipeline. The approach can be reused to create repositories of different types (biomedical and others).

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