'PUBLISH OR PERISH': THE PITFALLS OF DUPLICATE PUBLICATION

Abstract:  Duplication of previously published text or figures in the scientific literature without adequate citation is plagiarism or, in the case of an author's own work, self-plagiarism. It breaches the ethical standards that are expected in science and threatens the integrity of scientific journals. Three examples of duplication are noted, one of which involves Palaeontology . Redundant publication lowers the quality of scientific literature, damages the good standing of journals, and reduces the intellectual impact of a study. Multiple papers on a particular theme are only acceptable if each builds significantly upon previous work and contains only as much background information as necessary to put the new data and observations into perspective.     

Molar absorptivity and A 1 per cent 1 cm values for proteins at selected wavelengths of the ultraviolet and visible regions. XI.

The data in this paper have been compiled from the published scientific literature and, as was done with other papers in this series (1–3), not edited. I have assumed that data cited in the scientific literature have gone through editorial and review processes. The validity and reliability of the values are left to the judgment of the user.     

Text mining for literature review and knowledge discovery in cancer risk assessment and research

Research in biomedical text mining is starting to produce technology which can make information in biomedical literature more accessible for bio-scientists. One of the current challenges is to integrate and refine this technology to support real-life scientific tasks in biomedicine, and to evaluate its usefulness in the context of such tasks. We describe CRAB - a fully integrated text mining tool designed to support chemical health risk assessment. This task is complex and time-consuming, requiring a thorough review of existing scientific data on a particular chemical. Covering human, animal, cellular and other mechanistic data from various fields of biomedicine, this is highly varied and therefore difficult to harvest from literature databases via manual means. Our tool automates the process by extracting relevant scientific data in published literature and classifying it according to multiple qualitative dimensions. Developed in close collaboration with risk assessors, the tool allows navigating the classified dataset in various ways and sharing the data with other users. We present a direct and user-based evaluation which shows that the technology integrated in the tool is highly accurate, and report a number of case studies which demonstrate how the tool can be used to support scientific discovery in cancer risk assessment and research. Our work demonstrates the usefulness of a text mining pipeline in facilitating complex research tasks in biomedicine. We discuss further development and application of our technology to other types of chemical risk assessment in the future.     

Discovering patterns to extract protein-protein interactions from full texts

MOTIVATION: Although there are several databases storing protein-protein interactions, most such data still exist only in the scientific literature. They are scattered in scientific literature written in natural languages, defying data mining efforts. Much time and labor have to be spent on extracting protein pathways from literature. Our aim is to develop a robust and powerful methodology to mine protein-protein interactions from biomedical texts. RESULTS: We present a novel and robust approach for extracting protein-protein interactions from literature. Our method uses a dynamic programming algorithm to compute distinguishing patterns by aligning relevant sentences and key verbs that describe protein interactions. A matching algorithm is designed to extract the interactions between proteins. Equipped only with a dictionary of protein names, our system achieves a recall rate of 80.0% and precision rate of 80.5%. AVAILABILITY: The program is available on request from the authors.     

Writing scientific papers,with special reference to <Emphasis Type="Italic">Evolutionary Ecology</Emphasis>

The advancement of science, as well as scientific careers, depends upon good and clear scientific writing. Science is the most democratic of human endeavours because, in principle, anyone can replicate a scientific discovery. In order for this to continue, writing must be clear enough to be understood well enough to allow replication, either in principle or in fact. In this paper I will present data on the publication process in Evolutionary Ecology, use it to illustrate some of the problems in scientific papers, make some general remarks about writing scientific papers, summarise two new paper categories in the journal which will fill gaps that appear to be expanding in the literature, and summarise new journal policies to help mitigate existing problems. Most of the suggestions about writing would apply to any scientific journal.     

Citation decay in animal science

The half-life of literature in animal science is about 5–6 years, which is short in comparison to many other fields of scientific research. Only about 10% of the citations in animal-science articles refer to articles which were published more than 20 years before the appearance of the papers which cite them. Probably only a small proportion of the older works cited deserve to be called classics. Historical reviews of literature in scientific specialisations are likely to recover many still useful data and ideas.     

The next generation of literature analysis: integration of genomic analysis into text mining

Text-mining systems are indispensable tools to reduce the increasing flux of information in scientific literature to topics pertinent to a particular interest in focus. Most of the scientific literature is published as unstructured free text, complicating the development of data processing tools, which rely on structured information. To overcome the problems of free text analysis, structured, hand-curated information derived from literature is integrated in text-mining systems to improve precision and recall. In this paper several text-mining approaches are reviewed and the next step in development of text-mining systems, which is based on a concept of multiple lines of evidence, is described: results from literature analysis are combined with evidence from experiments and genome analysis to improve the accuracy of results and to generate additional knowledge beyond what is known solely from literature.     

mtDNAmanager: a Web-based tool for the management and quality analysis of mitochondrial DNA control-region sequences

Background  

For the past few years, scientific controversy has surrounded the large number of errors in forensic and literature mitochondrial DNA (mtDNA) data. However, recent research has shown that using mtDNA phylogeny and referring to known mtDNA haplotypes can be useful for checking the quality of sequence data.     

Extracting causal relations on HIV drug resistance from literature

Background  

In HIV treatment it is critical to have up-to-date resistance data of applicable drugs since HIV has a very high rate of mutation. These data are made available through scientific publications and must be extracted manually by experts in order to be used by virologists and medical doctors. Therefore there is an urgent need for a tool that partially automates this process and is able to retrieve relations between drugs and virus mutations from literature.     

Taxonomy and species delimitation in Cryptosporidium

Amphibians, reptiles, birds and mammals serve as hosts for 19 species of Cryptosporidium. All 19 species have been confirmed by morphological, biological, and molecular data. Fish serve as hosts for three additional species, all of which lack supporting molecular data. In addition to the named species, gene sequence data from more than 40 isolates from various vertebrate hosts are reported in the scientific literature or are listed in GenBank. These isolates lack taxonomic status and are referred to as genotypes based on the host of origin. Undoubtedly, some will eventually be recognized as species. For them to receive taxonomic status sufficient morphological, biological, and molecular data are required and names must comply with the rules of the International Code for Zoological Nomenclature (ICZN). Because the ICZN rules may be interpreted differently by persons proposing names, original names might be improperly assigned, original literature might be overlooked, or new scientific methods might be applicable to determining taxonomic status, the names of species and higher taxa are not immutable. The rapidly evolving taxonomic status of Cryptosporidium sp. reflects these considerations.     

Dating of sedimentary rock intervals using visual comparison of carbon isotope records: a comment on the recent paper by Bergström et al. concerning the age of the Winneshiek Shale

The recently published Lethaia paper by Bergström et al. ( https://doi.org/10.1111/let.12269 ) on the age of the Ordovician Winneshiek Shale (Iowa, USA), and the impact that formed the Decorah crater which hosts this rock unit, is an interesting scientific contribution, although there are a number of problems with the interpretations and data presentation that merit comment. Due mainly to a lack of adequate critical assessment of δ¹³C data and biostratigraphical control, we contend that the conclusions of Bergström et al. are poorly founded and should not be cursorily accepted and propagated in future scientific literature.     

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO)

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.     

Open by default: a proposed copyright license and waiver agreement for open access research and data in peer-reviewed journals

ABSTRACT: Copyright and licensing of scientific data, internationally, are complex and present legal barriers to data sharing, integration and reuse, and therefore restrict the most efficient transfer and discovery of scientific knowledge. Much data are included within scientific journal articles, their published tables, additional files (supplementary material) and reference lists. However, these data are usually published under licenses which are not appropriate for data. Creative Commons CC0 is an appropriate and increasingly accepted method for dedicating data to the public domain, to enable data reuse with the minimum of restrictions. BioMed Central is committed to working towards implementation of open data-compliant licensing in its publications. Here we detail a protocol for implementing a combined Creative Commons Attribution license (for copyrightable material) and Creative Commons CC0 waiver (for data) agreement for content published in peer-reviewed open access journals. We explain the differences between legal requirements for attribution in copyright, and cultural requirements in scholarship for giving individuals credit for their work through citation. We argue that publishing data in scientific journals under CC0 will have numerous benefits for individuals and society, and yet will have minimal implications for authors and minimal impact on current publishing and research workflows. We provide practical examples and definitions of data types, such as XML and tabular data, and specific secondary use cases for published data, including text mining, reproducible research, and open bibliography. We believe this proposed change to the current copyright and licensing structure in science publishing will help clarify what users -- people and machines -- of the published literature can do, legally, with journal articles and make research using the published literature more efficient. We further believe this model could be adopted across multiple publishers, and invite comment on this article from all stakeholders in scientific research.     

Concept-based query expansion for retrieving gene related publications from MEDLINE

Background  

Advances in biotechnology and in high-throughput methods for gene analysis have contributed to an exponential increase in the number of scientific publications in these fields of study. While much of the data and results described in these articles are entered and annotated in the various existing biomedical databases, the scientific literature is still the major source of information. There is, therefore, a growing need for text mining and information retrieval tools to help researchers find the relevant articles for their study. To tackle this, several tools have been proposed to provide alternative solutions for specific user requests.     

The Extent and Consequences of P-Hacking in Science

A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as “p-hacking,” occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. Here, we use text-mining to demonstrate that p-hacking is widespread throughout science. We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.     

Literature mining for the biologist: from information retrieval to biological discovery

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.     

An ontology for <Emphasis Type="Italic">Xenopus</Emphasis> anatomy and development

Background  

The frogs Xenopus laevis and Xenopus (Silurana) tropicalis are model systems that have produced a wealth of genetic, genomic, and developmental information. Xenbase is a model organism database that provides centralized access to this information, including gene function data from high-throughput screens and the scientific literature. A controlled, structured vocabulary for Xenopus anatomy and development is essential for organizing these data.     

Literature mining in support of drug discovery

The drug discovery enterprise provides strong drivers for data integration. While attention in this arena has tended to focus on integration of primary data from omics and other large platform technologies contributing to drug discovery and development, the scientific literature remains a major source of information valuable to pharmaceutical enterprises, and therefore tools for mining such data and integrating it with other sources are of vital interest and economic impact. This review provides a brief overview of approaches to literature mining as they relate to drug discovery, and offers an illustrative case study of a 'lightweight' approach we have implemented within an industrial context.