Publication Growth in Biological Sub-Fields: Patterns, Predictability and Sustainability

A scoping study of the whole-cell imaging literature: a foundational corpus, potential for mesoscale data synthesis, and implications for standardization of an emerging field

The level of cellular organization bridging the mesoscale and whole-cell scale is coming into focus as a new frontier in cell biology. Great progress has been made in unraveling the complex physical and functional interconnectivity of organelles, but how the entire organelle network is spatially arranged within the cytoplasm is only beginning to be explored. Drawing on cross-disciplinary research synthesis methods, we systematically curated the whole-cell volumetric imaging literature, resulting in a corpus consisting of 89 studies and 118 image datasets. We describe the trajectory and current state of the field between 2004 and 2024. A broad characterization, or "scoping review", of bibliometrics, study design, and reporting practices shows accelerating technological development and research output. We find high variability in study design and reporting practices, including imaging modality, model organism, cellular contexts, organelles imaged, and analyses. Due to the laborious, low-throughput nature of most volumetric imaging methods, we find trends toward small sample sizes (<10 cells) and small cell types. We describe common quantitative analyses across studies, including volumetric ratios of organelles and inter-organelle contact analyses. This work establishes the initial iteration of a growing dataset of whole-cell imaging literature and data, and motivates a call for standardized whole-cell imaging study design, reporting, and data sharing practices in the context of an emerging sub-field of cell biology. Our curated dataset now provides the basis for a plethora of future aggregate and comparative analyses to reveal larger patterns and generalized hypotheses about the systems behavior and regulation of whole-cell organelle networks. More broadly, we showcase the potential of new rigorous secondary research methods to strengthen cell biology's literature review and reproducibility toolkit, create new avenues for discovery, and promote open research practices that support secondary data-reuse and integration.

Global characteristics and trends of researches on watermelon: Based on bibliometric and visualized analysis

Watermelon is an important horticultural plant. A bibliometric analysis of the watermelon literature was carried out in order to analyze the research state, hotspots, and trends, as well as to highlight the overall watermelon research development from a holistic viewpoint. The summary of watermelon research is given via metrological analysis based on a set of indices using a newly built Bibliometrix R-package tool. This study gathered 6,632 documents indexed in the Core Collection of Web of Science (WoS) in the domain of watermelon from 1992 to 2022 using bibliometrix. The results indicated that the number of published articles showed an apparently upward trend. The United States was in the first place, with Plant Disease being the most productive journal. Levi A from the United States Department of Agriculture-Agricultural Research Service is the most prolific author, and Levi A is the most cited; The most frequently used keywords by authors are "growth", "resistance", "identification", "yield", "quality" "plants", "watermelon stomach" and "expression"; The most talked-about issues in this subject are resistance, yield, and quality, which highlight the crucial research areas. To effectively comprehend the turning moments for future research, it is useful to monitor the hotspots and frontiers of watermelon studies. The results highlight the future paths for study in the field of watermelon and provide useful information for researchers interested in the topic.

Biomedical Literature Mining for Repurposing Laboratory Tests

Epidemiological studies identifying biological markers of disease state are valuable, but can be time-consuming, expensive, and require extensive intuition and expertise. Furthermore, not all hypothesized markers will be borne out in a study, suggesting that high-quality initial hypotheses are crucial. In this chapter, we describe a high-throughput pipeline to produce a ranked list of high-quality hypothesized biomarkers for diseases. We review an example use of this approach to generate a large number of candidate disease biomarker hypotheses derived from machine learning models, filter and rank them according to their potential novelty using text mining, and corroborate the most promising hypotheses with further statistical modeling. The example use of the pipeline uses a large electronic health record dataset and the PubMed corpus, to find several promising hypothesized laboratory tests with previously undocumented correlations to particular diseases.

KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications

Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications

Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

Using Manual and Computer-Based Text-Mining to Uncover Research Trends for Apis mellifera

Honey bee research is believed to be influenced dramatically by colony collapse disorder (CCD) and the sequenced genome release in 2006, but this assertion has never been tested. By employing text-mining approaches, research trends were tested by analyzing over 14,000 publications during the period of 1957 to 2017. Quantitatively, the data revealed an exponential growth until 2010 when the number of articles published per year ceased following the trend. Analysis of author-assigned keywords revealed that changes in keywords occurred roughly every decade with the most fundamental change in 1991-1992, instead of 2006. This change might be due to several factors including the research intensification on the Varroa mite. The genome release and CCD had quantitively only minor effects, mainly on honey bee health-related topics post-2006. Further analysis revealed that computational topic modeling can provide potentially hidden information and connections between some topics that might be ignored in author-assigned keywords.

Systematic review of descriptions of novel bacterial species: evaluation of the twenty-first century taxonomy through text mining

Although described bacterial species increased in the twenty-first century, they correspond to a tiny fraction of the actual number of species living on our planet. The volume of textual data of these descriptions constitutes valuable information for revealing trends that in turn could support strategies for improvement of bacterial taxonomy. In this study, a text mining approach was used to generate bibliometric data to verify the state-of-art of bacterial taxonomy. Around 9700 abstracts of bacterial classification containing the expression 'sp. nov.' and published between 2001 and 2018 were downloaded from PubMed and analysed. Most articles were from PR China and the Republic of Korea, and published in the International Journal of Systematic and Evolutionary Microbiology. From about 10 800 species names detected, 93.33 % were considered valid according to the rules of the Bacterial Code, and they corresponded to 82.98 % of the total number of species validated between 2001 and 2018. Streptomyces, Bacillus and Paenibacillus each had more than 200 species described in the period. However, almost 40 % of all species were from the phylum Proteobacteria. Most bacteria were Gram-stain-negative, bacilli and isolated from soil. Thirteen species and one genus homonyms were found. With respect to methodologies of bacterial characterization, the use of terms related to 16S rRNA and polar lipids increased along these years, and terms related to genome metrics only began to appear from 2009 onward, although at a relatively lower frequency. Bacterial taxonomy is known as a conservative discipline, but it gradually changed in terms of players and practices. With the advent of the mandatory use of genomic analyses for species description, we are probably witnessing a turning point in the evolution of bacterial taxonomy.

Survey and analysis of the quantitative methods used in electricity research on GCC countries: 1983-2018

This study provides a systematic analysis of research on the electricity sector in Gulf Cooperation Council (GCC) countries in the period 1983–2018. GCC countries have experienced tremendous economic growth in the past few decades. This was accompanied by a corresponding increase in electricity consumption. Therefore, a thorough review is needed to understand the research conducted on the electricity sector in GCC countries. This study reviewed articles published in five well-known energy journals: Applied Energy, Energy, Energy Economics, Energy Policy, and Renewable and Sustainable Energy Reviews. The articles were classified into seven categories based on the analysis tools implemented in the papers: 1. Simulation tools, 2. Scenarios tools, 3. Equilibrium tools, 4. Top-down tools, 5. Bottom-up tools, 6. Operations optimization tools, and 7. Investment optimization tools. This study also provides an overview of the research, including the increase in publications over time, an authorship analysis, a keywords analysis, and an analysis of the length of the publications.

The intriguing evolution of effect sizes in biomedical research over time: smaller but more often statistically significant

Background

In medicine, effect sizes (ESs) allow the effects of independent variables (including risk/protective factors or treatment interventions) on dependent variables (e.g., health outcomes) to be quantified. Given that many public health decisions and health care policies are based on ES estimates, it is important to assess how ESs are used in the biomedical literature and to investigate potential trends in their reporting over time.

Results

Through a big data approach, the text mining process automatically extracted 814 120 ESs from 13 322 754 PubMed abstracts. Eligible ESs were risk ratio, odds ratio, and hazard ratio, along with their confidence intervals. Here we show a remarkable decrease of ES values in PubMed abstracts between 1990 and 2015 while, concomitantly, results become more often statistically significant. Medians of ES values have decreased over time for both “risk” and “protective” values. This trend was found in nearly all fields of biomedical research, with the most marked downward tendency in genetics. Over the same period, the proportion of statistically significant ESs increased regularly: among the abstracts with at least 1 ES, 74% were statistically significant in 1990–1995, vs 85% in 2010–2015.

Conclusions

whereas decreasing ESs could be an intrinsic evolution in biomedical research, the concomitant increase of statistically significant results is more intriguing. Although it is likely that growing sample sizes in biomedical research could explain these results, another explanation may lie in the “publish or perish” context of scientific research, with the probability of a growing orientation toward sensationalism in research reports. Important provisions must be made to improve the credibility of biomedical research and limit waste of resources.

On the relationship between body condition and parasite infection in wildlife: a review and meta-analysis

Body condition metrics are widely used to infer animal health and to assess costs of parasite infection. Since parasites harm their hosts, ecologists might expect negative relationships between infection and condition in wildlife, but this assumption is challenged by studies showing positive or null condition-infection relationships. Here, we outline common condition metrics used by ecologists in studies of parasitism, and consider mechanisms that cause negative, positive, and null condition-infection relationships in wildlife systems. We then perform a meta-analysis of 553 condition-infection relationships from 187 peer-reviewed studies of animal hosts, analysing observational and experimental records separately, and noting whether authors measured binary infection status or intensity. Our analysis finds substantial heterogeneity in the strength and direction of condition-infection relationships, a small, negative average effect size that is stronger in experimental studies, and evidence for publication bias towards negative relationships. The strongest predictors of variation in study outcomes are host thermoregulation and the methods used to evaluate body condition. We recommend that studies aiming to assess parasite impacts on body condition should consider host-parasite biology, choose condition measures that can change during the course of infection, and employ longitudinal surveys or manipulate infection status when feasible.

Words by the tail: Assessing lexical diversity in scholarly titles using frequency-rank distribution tail fits

This research assesses the evolution of lexical diversity in scholarly titles using a new indicator based on zipfian frequency-rank distribution tail fits. At the operational level, while both head and tail fits of zipfian word distributions are more independent of corpus size than other lexical diversity indicators, the latter however neatly outperforms the former in that regard. This benchmark-setting performance of zipfian distribution tails proves extremely handy in distinguishing actual patterns in lexical diversity from the statistical noise generated by other indicators due to corpus size fluctuations. From an empirical perspective, analysis of Web of Science (WoS) article titles from 1975 to 2014 shows that the lexical concentration of scholarly titles in Natural Sciences & Engineering (NSE) and Social Sciences & Humanities (SSH) articles increases by a little less than 8% over the whole period. With the exception of the lexically concentrated Mathematics, Earth & Space, and Physics, NSE article titles all increased in lexical concentration, suggesting a probable convergence of concentration levels in the near future. As regards to SSH disciplines, aggregation effects observed at the disciplinary group level suggests that, behind the stable concentration levels of SSH disciplines, a cross-disciplinary homogenization of the highest word frequency ranks may be at work. Overall, these trends suggest a progressive standardization of title wording in scientific article titles, as article titles get written using an increasingly restricted and cross-disciplinary set of words.

Instant taxonomy: choosing adequate characters for species delimitation and description through congruence between molecular data and quantitative shape analysis

The lack of university funding is one of the major impediments to taxonomy, partly because traditional taxonomic training takes longer than a PhD course. Understanding ranges of phenotypic variability for different morphological structures, and their use as characters for delimitation and description of taxa, is a tedious task. We argue that the advent of molecular barcoding and quantitative shape analysis makes it unnecessary. As an example, we tackle a problematic species-complex of marine copepods from Korea and Japan, approaching it as a starting taxonomist might. Samples were collected from 14 locations and the mitochondrial COI gene was sequenced from 42 specimens. Our phylogenetic analyses reveal four distinct clades in Korea and Japan, and an additional nine belonging to a closely related complex from other parts of the Northern Pacific. Twenty different morphological structures were analysed for one Japanese and two Korean clades using landmark-based two-dimensional geometric morphometrics. Although there is no single morphological character that can distinguish with absolute certainty all three cryptic species, most show statistically significant interspecific differences in shape and size. We use five characters to describe two new species from Korea and to re-describe Tigriopus japonicus Mori, 1938 from near its type locality.

Large-scale online semantic indexing of biomedical articles via an ensemble of multi-label classification models

Background

In this paper we present the approach that we employed to deal with large scale multi-label semantic indexing of biomedical papers. This work was mainly implemented within the context of the BioASQ challenge (2013–2017), a challenge concerned with biomedical semantic indexing and question answering.

Methods

Our main contribution is a MUlti-Label Ensemble method (MULE) that incorporates a McNemar statistical significance test in order to validate the combination of the constituent machine learning algorithms. Some secondary contributions include a study on the temporal aspects of the BioASQ corpus (observations apply also to the BioASQ’s super-set, the PubMed articles collection) and the proper parametrization of the algorithms used to deal with this challenging classification task.

Results

The ensemble method that we developed is compared to other approaches in experimental scenarios with subsets of the BioASQ corpus giving positive results. In our participation in the BioASQ challenge we obtained the first place in 2013 and the second place in the four following years, steadily outperforming MTI, the indexing system of the National Library of Medicine (NLM).

Conclusions

The results of our experimental comparisons, suggest that employing a statistical significance test to validate the ensemble method’s choices, is the optimal approach for ensembling multi-label classifiers, especially in contexts with many rare labels.

Systematic review on the conservation genetics of African savannah elephants

Background

In this paper we review the conservation genetics of African savannah elephants, aiming to understand the spatio-temporal research trends and their underlying factors. As such, we explore three questions associated to the conservation genetics and molecular ecology of these elephants: (1) what are the research trends concerning the conservation genetics of Loxodonta africana? (2) Do richer countries conduct more research on the genetics of African elephants? (3) Which attributes influence where scholars conduct their research?

Materials and Methods

We examined available peer-reviewed publications from 1993 to 2014 in complementary online databases, including the ISI/Web of Science (WoS), Scopus and Google Scholar (GS), and searched for publications in scientific journals as well as in the reference section of these publications. We analyzed the annual trend of publications in this field of research, including the number of authors, levels of collaboration among authors, year of publication, publishing journal and the countries from where genetic samples were collected. Additionally, we identified main research clusters, authors, and institutional collaborations, based on co-citation and co-occurrence networks.

Results

We found that during the study period there was a positive trend in the number of publications and a reduction in the number of authors per paper. Twenty-five countries contributed, with the majority of publications authored by researchers in the USA, Kenya and South Africa. The majority of samples were collected in Kenya, Tanzania and South Africa. Research outputs are associated with the existence of long-term conservation/research projects and research potential as measured by the literacy rate and the number of higher education institutions in a country. Five research clusters were identified, focusing on the origin and evolution of the species, methodological issues and the relatedness among elephant species.

Conclusions

Research in this field should be expanded to additional countries harboring elephant populations to enable a more comprehensive understanding of the population structure and genetic differentiation of the species, and to cope with challenges associated with the conservation of the species such as illegal hunting, habitat fragmentation, species reintroduction and climate change.

Citations increase with manuscript length, author number, and references cited in ecology journals

Most top impact factor ecology journals indicate a preference or requirement for short manuscripts; some state clearly defined word limits, whereas others indicate a preference for more concise papers. Yet evidence from a variety of academic fields indicates that within journals longer papers are both more positively reviewed by referees and more highly cited. We examine the relationship between citations received and manuscript length, number of authors, and number of references cited for papers published in 32 ecology journals between 2009 and 2012. We find that longer papers, those with more authors, and those that cite more references are cited more. Although paper length, author count, and references cited all positively covary, an increase in each independently predicts an increase in citations received, with estimated relationships positive for all the journals we examined. That all three variables covary positively with citations suggests that papers presenting more and a greater diversity of data and ideas are more impactful. We suggest that the imposition of arbitrary manuscript length limits discourages the publication of more impactful studies. We propose that journals abolish arbitrary word or page limits, avoid declining papers (or requiring shortening) on the basis of length alone (irrespective of content), and adopt the philosophy that papers should be as long as they need to be.

Improving the peer review of narrative literature reviews

As the size of the published scientific literature has increased exponentially over the past 30 years, review articles play an increasingly important role in helping researchers to make sense of original research results. Literature reviews can be broadly classified as either “systematic” or “narrative”. Narrative reviews may be broader in scope than systematic reviews, but have been criticised for lacking synthesis and rigour. The submission of more scientific manuscripts requires more researchers acting as peer reviewers, which requires adding greater numbers of new reviewers to the reviewing population over time. However, whereas there are many easily accessible guides for reviewers of primary research manuscripts, there are few similar resources to assist reviewers of narrative reviews. Here, I summarise why literature reviews are valued by their diverse readership and how peer reviewers with different levels of content expertise can improve the reliability and accessibility of narrative review articles. I then provide a number of recommendations for peer reviewers of narrative literature reviews, to improve the integrity of the scientific literature, while also ensuring that narrative review articles meet the needs of both expert and non-expert readers.

On the benefits of systematic reviews for wildlife parasitology

Systematic reviews and meta-analyses are widely accepted as the best means to synthesise quantitative or qualitative scientific evidence. Many scientific fields have embraced these more rigorous review techniques as a means to bring together large and complex bodies of literature and their data. Unfortunately, due to perceived difficulties and unfamiliarity with processes, other fields are not using these options to review their literature. One way to provide guidance for a specific field is to examine critically recent reviews and meta-analyses and to explain the advantages and disadvantages of the various review techniques. In this paper, we examine review papers in the emerging field of wildlife parasitology and compare five different literature review types-configurative narrative review, aggregative scoping review, aggregative literature review, aggregative meta-analysis, and aggregative systematic review. We found that most literature reviews did not adequately explain the methodology used to find the literature under review. We also found that most literature reviews were not comprehensive nor did they critically appraise the literature under review. Such a lack severely reduces the reliability of the reviews. We encourage all authors to consider using systematic reviews in the future, and for authors and peer-reviewers to be aware of the limitations of non-systematic reviews.

Ethnobotany and herbal medicine in modern complementary and alternative medicine: An overview of publications in the field of I&C medicine 2001-2013

ETHNOPHARMACOLOGICAL RELEVANCE

Ethnobotanical knowledge and traditional medicinal practices from different parts of the world are of global importance and documentation of ethnobotanical, and ethnopharmacological data is a key prerequisite for further research in the area of herbal medicine and its implementation in clinical practice.

AIMS

An attempt was made to evaluate the scientific output of research related to ethnobotany and herbal medicine in journals indexed in the subject area "Integrative and Complementary Medicine" in the period 2001-2013, in order to ascertain research trends in both subdisciplines.

MATERIALS AND METHODS

All articles related to ethnobotany and herbal medicine, extracted from journals included in the field of I&C Medicine and published in the period 2001-2013, have been analyzed for general bibliometric data, and specific data: ethnobotanical data (geographic, floristic, pharmacological, sociological and other relevant data) and phytotherapeutic data (type of applied herbal medicine, plant species studied, pharmacological activity of studied plant species and disease and disorder type studied on a particular model).

RESULTS

In the studied period, the number of articles dealing with ethnobotany and herbal medicine increased 6.3-fold. Articles related to ethnobotanical studies documented medicinal flora from 81 countries, either giving an overview of overall medicinal flora, or presenting the ethnomedicinal aspect of the use of plants for the treatment of ailments typical to the studied area. Additionally, the authors provided significant information on the methods of use and herbal preparations. In herbal medicine studies, plants, traditional plant remedies, herbal medicinal products and active herbal compounds were tested for many of pharmacological activities (146), with the curative activity emerging as most frequently tested. Out of 39 model systems, most of the studies were carried out under controlled in vitro conditions (4589 articles), followed by rat in vivo (2320), human in vivo (1285), mouse in vivo (955), and on agents of pathogenic diseases (887); more than 800 medical disorders were treated.

CONCLUSIONS

The study revealed the regions most studied for new records of floristic and ethnomedicinal diversity, the most frequently studied plant species, and the most promising therapeutic indications for the integration of herbal remedies in the curative process, as ascertained from the selected bibliographic databases.

"One Health" or Three? Publication Silos Among the One Health Disciplines

The One Health initiative is a global effort fostering interdisciplinary collaborations to address challenges in human, animal, and environmental health. While One Health has received considerable press, its benefits remain unclear because its effects have not been quantitatively described. We systematically surveyed the published literature and used social network analysis to measure interdisciplinarity in One Health studies constructing dynamic pathogen transmission models. The number of publications fulfilling our search criteria increased by 14.6% per year, which is faster than growth rates for life sciences as a whole and for most biology subdisciplines. Surveyed publications clustered into three communities: one used by ecologists, one used by veterinarians, and a third diverse-authorship community used by population biologists, mathematicians, epidemiologists, and experts in human health. Overlap between these communities increased through time in terms of author number, diversity of co-author affiliations, and diversity of citations. However, communities continue to differ in the systems studied, questions asked, and methods employed. While the infectious disease research community has made significant progress toward integrating its participating disciplines, some segregation--especially along the veterinary/ecological research interface--remains.

Researchers' Individual Publication Rate Has Not Increased in a Century

Debates over the pros and cons of a “publish or perish” philosophy have inflamed academia for at least half a century. Growing concerns, in particular, are expressed for policies that reward “quantity” at the expense of “quality,” because these might prompt scientists to unduly multiply their publications by fractioning (“salami slicing”), duplicating, rushing, simplifying, or even fabricating their results. To assess the reasonableness of these concerns, we analyzed publication patterns of over 40,000 researchers that, between the years 1900 and 2013, have published two or more papers within 15 years, in any of the disciplines covered by the Web of Science. The total number of papers published by researchers during their early career period (first fifteen years) has increased in recent decades, but so has their average number of co-authors. If we take the latter factor into account, by measuring productivity fractionally or by only counting papers published as first author, we observe no increase in productivity throughout the century. Even after the 1980s, adjusted productivity has not increased for most disciplines and countries. These results are robust to methodological choices and are actually conservative with respect to the hypothesis that publication rates are growing. Therefore, the widespread belief that pressures to publish are causing the scientific literature to be flooded with salami-sliced, trivial, incomplete, duplicated, plagiarized and false results is likely to be incorrect or at least exaggerated.

Making literature reviews more reliable through application of lessons from systematic reviews

Review articles can provide valuable summaries of the ever-increasing volume of primary research in conservation biology. Where findings may influence important resource-allocation decisions in policy or practice, there is a need for a high degree of reliability when reviewing evidence. However, traditional literature reviews are susceptible to a number of biases during the identification, selection, and synthesis of included studies (e.g., publication bias, selection bias, and vote counting). Systematic reviews, pioneered in medicine and translated into conservation in 2006, address these issues through a strict methodology that aims to maximize transparency, objectivity, and repeatability. Systematic reviews will always be the gold standard for reliable synthesis of evidence. However, traditional literature reviews remain popular and will continue to be valuable where systematic reviews are not feasible. Where traditional reviews are used, lessons can be taken from systematic reviews and applied to traditional reviews in order to increase their reliability. Certain key aspects of systematic review methods that can be used in a context-specific manner in traditional reviews include focusing on mitigating bias; increasing transparency, consistency, and objectivity, and critically appraising the evidence and avoiding vote counting. In situations where conducting a full systematic review is not feasible, the proposed approach to reviewing evidence in a more systematic way can substantially improve the reliability of review findings, providing a time- and resource-efficient means of maximizing the value of traditional reviews. These methods are aimed particularly at those conducting literature reviews where systematic review is not feasible, for example, for graduate students, single reviewers, or small organizations.

The Role of Google Scholar in Evidence Reviews and Its Applicability to Grey Literature Searching

Google Scholar (GS), a commonly used web-based academic search engine, catalogues between 2 and 100 million records of both academic and grey literature (articles not formally published by commercial academic publishers). Google Scholar collates results from across the internet and is free to use. As a result it has received considerable attention as a method for searching for literature, particularly in searches for grey literature, as required by systematic reviews. The reliance on GS as a standalone resource has been greatly debated, however, and its efficacy in grey literature searching has not yet been investigated. Using systematic review case studies from environmental science, we investigated the utility of GS in systematic reviews and in searches for grey literature. Our findings show that GS results contain moderate amounts of grey literature, with the majority found on average at page 80. We also found that, when searched for specifically, the majority of literature identified using Web of Science was also found using GS. However, our findings showed moderate/poor overlap in results when similar search strings were used in Web of Science and GS (10–67%), and that GS missed some important literature in five of six case studies. Furthermore, a general GS search failed to find any grey literature from a case study that involved manual searching of organisations’ websites. If used in systematic reviews for grey literature, we recommend that searches of article titles focus on the first 200 to 300 results. We conclude that whilst Google Scholar can find much grey literature and specific, known studies, it should not be used alone for systematic review searches. Rather, it forms a powerful addition to other traditional search methods. In addition, we advocate the use of tools to transparently document and catalogue GS search results to maintain high levels of transparency and the ability to be updated, critical to systematic reviews.

An integrated modeling approach to assessing linkages between environment, organism, and phenotypic plasticity

Many of the most interesting questions in organismal biology, especially those involving the functional and adaptive significance of organismal characteristics, intrinsically transcend levels of biological organization. These organismal functions typically involve multiple interacting biological mechanisms. We suggest that subdisciplinary advances have led both to the opportunity and to the necessity to reintegrate knowledge into a new understanding of the whole organism. We present a conceptual framework for a modeling approach that addresses the functioning of organisms in an integrative way, incorporating elements from environments, populations, individuals, and intra-organismal dynamics such as physiology and behavior. To give substance to our conceptual framework, we provide a preliminary focal case study using phenotypic plasticity in the tooth morphology of snails in the genus Lacuna. We use this case study to illustrate ways in which questions about the evolution and ecology of organismal function intrinsically span all organizational levels. In this case, and in many others, quantitative approaches that integrate across mechanisms and scales can suggest new hypotheses about organismal function, and provide new tools to test those hypotheses. Integrative quantitative models also provide roadmaps for the large-scale collaborations among diverse disciplinary specialists that are needed to gain deeper insights into organismal function.

Why not connectomics?

Opinions diverge on whether mapping the synaptic connectivity of the brain is a good idea. Here we argue that albeit their limitations, such maps will reveal essential characteristics of neural circuits that would otherwise be inaccessible.