A connected effort? American Editors Pursue Mathematical Journal Publication, 1804-1878

2019 ◽  
Vol 25 (2) ◽  
pp. 195-233
Author(s):  
Deborah KENT
Keyword(s):  
Mathematical Journal ◽  
Journal Publication

scholarly journals Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database

2020 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Jin Tao ◽  
Kelly Brayton ◽  
Shira Broschat
Keyword(s):  
Language Processing ◽  
Fine Tuning ◽  
Support Vector ◽  
Protein Annotation ◽  
Computing Power ◽  
Journal Publication ◽  
Novel Approach ◽  
Uniprotkb Database ◽  
Public Repositories ◽  
Annotation Errors

Advances in genome sequencing technology and computing power have brought about the explosive growth of sequenced genomes in public repositories with a concomitant increase in annotation errors. Many protein sequences are annotated using computational analysis rather than experimental verification, leading to inaccuracies in annotation. Confirmation of existing protein annotations is urgently needed before misannotation becomes even more prevalent due to error propagation. In this work we present a novel approach for automatically confirming the existence of manually curated information with experimental evidence of protein annotation. Our ensemble learning method uses a combination of recurrent convolutional neural network, logistic regression, and support vector machine models. Natural language processing in the form of word embeddings is used with journal publication titles retrieved from the UniProtKB database. Importantly, we use recall as our most significant metric to ensure the maximum number of verifications possible; results are reported to a human curator for confirmation. Our ensemble model achieves 91.25% recall, 71.26% accuracy, 65.19% precision, and an F1 score of 76.05% and outperforms the Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) model with fine-tuning using the same data.

scholarly journals Cross-sectional study of preprints and final journal publications from COVID-19 studies: discrepancies in results reporting and spin in interpretation

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e051821
Author(s):  
Lisa Bero ◽  
Rosa Lawrence ◽  
Louis Leslie ◽  
Kellia Chiu ◽  
Sally McDonald ◽  
...  
Keyword(s):  
Statistical Significance ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Journal Publication ◽  
Reporting Practices ◽  
Subgroup Analyses ◽  
Journal Publications ◽  
Study Characteristics ◽  
Results Reporting

ObjectiveTo compare results reporting and the presence of spin in COVID-19 study preprints with their finalised journal publications.DesignCross-sectional study.SettingInternational medical literature.ParticipantsPreprints and final journal publications of 67 interventional and observational studies of COVID-19 treatment or prevention from the Cochrane COVID-19 Study Register published between 1 March 2020 and 30 October 2020.Main outcome measuresStudy characteristics and discrepancies in (1) results reporting (number of outcomes, outcome descriptor, measure, metric, assessment time point, data reported, reported statistical significance of result, type of statistical analysis, subgroup analyses (if any), whether outcome was identified as primary or secondary) and (2) spin (reporting practices that distort the interpretation of results so they are viewed more favourably).ResultsOf 67 included studies, 23 (34%) had no discrepancies in results reporting between preprints and journal publications. Fifteen (22%) studies had at least one outcome that was included in the journal publication, but not the preprint; eight (12%) had at least one outcome that was reported in the preprint only. For outcomes that were reported in both preprints and journals, common discrepancies were differences in numerical values and statistical significance, additional statistical tests and subgroup analyses and longer follow-up times for outcome assessment in journal publications.At least one instance of spin occurred in both preprints and journals in 23/67 (34%) studies, the preprint only in 5 (7%), and the journal publications only in 2 (3%). Spin was removed between the preprint and journal publication in 5/67 (7%) studies; but added in 1/67 (1%) study.ConclusionsThe COVID-19 preprints and their subsequent journal publications were largely similar in reporting of study characteristics, outcomes and spin. All COVID-19 studies published as preprints and journal publications should be critically evaluated for discrepancies and spin.

scholarly journals “Open Access” Requires Clarification: Medical Journal Publication Models Evolve

2017 ◽  
Vol 33 (3) ◽  
pp. 497-499 ◽  
Author(s):  
James H. Lubowitz ◽  
Jefferson C. Brand ◽  
Michael J. Rossi ◽  
Matthew T. Provencher
Keyword(s):  
Medical Journal ◽  
Open Access ◽  
Journal Publication

Science or Art? How Aesthetic Standards Grease the Way Through the Publication Bottleneck but Undermine Science

2012 ◽  
Vol 7 (6) ◽  
pp. 562-571 ◽  
Author(s):  
Roger Giner-Sorolla
Keyword(s):  
Information Sharing ◽  
False Positive ◽  
Psychological Research ◽  
Current Crisis ◽  
Journal Publication ◽  
Root Cause ◽  
Methodological Rigor ◽  
Pass Through ◽  
The Relationship ◽  
Positive Results

The current crisis in psychological research involves issues of fraud, replication, publication bias, and false positive results. I argue that this crisis follows the failure of widely adopted solutions to psychology’s similar crisis of the 1970s. The untouched root cause is an information-economic one: Too many studies divided by too few publication outlets equals a bottleneck. Articles cannot pass through just by showing theoretical meaning and methodological rigor; their results must appear to support the hypothesis perfectly. Consequently, psychologists must master the art of presenting perfect-looking results just to survive in the profession. This favors aesthetic criteria of presentation in a way that harms science’s search for truth. Shallow standards of statistical perfection distort analyses and undermine the accuracy of cumulative data; narrative expectations encourage dishonesty about the relationship between results and hypotheses; criteria of novelty suppress replication attempts. Concerns about truth in research are emerging in other sciences and may eventually descend on our heads in the form of difficult and insensitive regulations. I suggest a more palatable solution: to open the bottleneck, putting structures in place to reward broader forms of information sharing beyond the exquisite art of present-day journal publication.

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