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      Is Open Access

      The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles

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          Abstract

          Despite growing interest in Open Access (OA) to scholarly literature, there is an unmet need for large-scale, up-to-date, and reproducible studies assessing the prevalence and characteristics of OA. We address this need using oaDOI, an open online service that determines OA status for 67 million articles. We use three samples, each of 100,000 articles, to investigate OA in three populations: (1) all journal articles assigned a Crossref DOI, (2) recent journal articles indexed in Web of Science, and (3) articles viewed by users of Unpaywall, an open-source browser extension that lets users find OA articles using oaDOI. We estimate that at least 28% of the scholarly literature is OA (19M in total) and that this proportion is growing, driven particularly by growth in Gold and Hybrid. The most recent year analyzed (2015) also has the highest percentage of OA (45%). Because of this growth, and the fact that readers disproportionately access newer articles, we find that Unpaywall users encounter OA quite frequently: 47% of articles they view are OA. Notably, the most common mechanism for OA is not Gold, Green, or Hybrid OA, but rather an under-discussed category we dub Bronze: articles made free-to-read on the publisher website, without an explicit Open license. We also examine the citation impact of OA articles, corroborating the so-called open-access citation advantage: accounting for age and discipline, OA articles receive 18% more citations than average, an effect driven primarily by Green and Hybrid OA. We encourage further research using the free oaDOI service, as a way to inform OA policy and practice.

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          Editorial

          The mission of The Journal of General Physiology is to publish articles that elucidate basic biological, chemical, and physical principles of broad physiological significance. Physiological significance usually means mechanistic insights, which often are obtained only after extensive analysis of the experimental results. The significance of the mechanistic insights therefore can be no better than the validity of the theoretical framework used for the analysis—and it is usually better to be vaguely right than precisely wrong. The uncertainties associated with data analysis are well illustrated in the Perspectives on Ion Permeation through membrane-spanning channels (J. Gen. Physiol. 113:761–794) and the related Letters-to-the-Editor in this issue. This exchange moreover identified a particular problem that can be resolved by a change in editorial policy. The problem is the graphic representation of the results of kinetic analyses of ion permeation based on discrete-state rate models—and similar kinetic analyses of other physiological processes. It seems to have become de rigueur to summarize such results in a so-called energy profile (see Fig. 1), where the rate constants (k) deduced from the kinetic analysis are converted into free energies (ΔG ‡)—almost invariably using Eyring's transition state theory (TST): 1 \documentclass[10pt]{article} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \begin{equation*}{\mathrm{{\Delta}}}G^{{\mathrm{{\ddagger}}}}=-k_{{\mathrm{B}}}T{\cdot}{\mathrm{ln}} \left \left[k{\cdot} \left \left({h}/{k}_{{\mathrm{B}}}T\right) \right \right] \right {\mathrm{,}}\end{equation*}\end{document} where k B is Boltzmann's constant, T the temperature in kelvin, and h Planck's constant. The problems arise because will be valid only for elementary transitions; e.g., transitions over distances less than the mean free path in aqueous solutions, ∼0.1 Å. Whether or not one can use a discrete-state rate model to analyze a permeation process, for example, the (in)validity of depends primarily on the distances ions have to traverse in the transitions between the different kinetic states. The limitations inherent in the use of are well known, but energy profiles have taken on a life of their own because they provide a convenient graphic representation of the results, as opposed to the more tedious (albeit more correct) tabulation of the rate constants. Assuming the experimental results justify the use of a discrete-state model, which would entail a demonstration that the model and the deduced rate constants satisfactorily describe the results, the problem becomes, how can one represent the results graphically in a manner that avoids the errors associated with the use of ? One such representation of linear kinetic schemes can be implemented by noting that free energy profiles based on the Eyring TST (i.e., on the use of ) formally can be expressed as: 2 \documentclass[10pt]{article} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \begin{equation*}{\mathrm{{\Delta}}}G \left \left(p\right) \right =-k_{{\mathrm{B}}}T{\cdot}{\mathrm{ln}} \left \frac{{\prod_{{\mathrm{i}}=1,3,{\mathrm{{\ldots}}}}^{p}} \left \left[{k_{{\mathrm{i}}}}/{ \left \left({k_{{\mathrm{B}}}T}/{h}\right) \right }\right] \right }{{\prod_{{\mathrm{i}}=2,4,{\mathrm{{\ldots}}}}^{p}} \left \left[{k_{{\mathrm{i}}}}/{ \left \left({k_{{\mathrm{B}}}T}/{h}\right) \right }\right] \right } \right {\mathrm{,}}\end{equation*}\end{document} where p (= 1, 2,…,n, where n is the total number of rate constants in the scheme) denotes the sequential position of the energy peaks and wells in the kinetic scheme (beginning with the first peak and ending outside the pore on the other side), and k i is the ith rate constant in the scheme (forward rate constants are odd numbered and reverse rate constants are even numbered). That is, ΔG(p) for p = 1, 3,…, n − 1 denotes the peak energies, whereas ΔG(p) for p = 2, 4,…, n denotes the well energies. The interrupted line in Fig. 1 (right-hand ordinate) shows such an energy profile. The generalization of is immediate, as the rate constant “profile” along the kinetic scheme can be represented by the function: 3 \documentclass[10pt]{article} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \begin{equation*}RCR_{{\mathit{ff}}} \left \left(p\right) \right =-{\mathrm{log}} \left \frac{{\prod_{{\mathrm{i}}=1,3,{\mathrm{{\ldots}}}}^{p}} \left \left({k_{{\mathrm{i}}}}/{ff}\right) \right }{{\prod_{{\mathrm{i}}=2,4,{\mathrm{{\ldots}}}}^{p}} \left \left({k_{{\mathrm{i}}}}/{ff}\right) \right } \right {\mathrm{,}}\end{equation*}\end{document} where ff is an arbitrary “frequency factor.” The three lines in Fig. 1 (left-hand ordinate) show rate constant representations (RCR) for ff = 1, 109, and 6 · 1012 s−1 (= k B T/h). (ff = 1 s−1 denotes the simplest version of , ff = 109 s−1 was chosen to approximate the frequency of diffusional transitions over a distance of 1 nm, and ff = k B T/h was chosen for comparison to .) It is instructive to consider briefly some features of and Fig. 1. First, the heights of the “peaks” vary with the choice of ff. The peaks shift in parallel up or down as ff is increased or decreased, which serves to emphasize how arbitrary a “barrier height” is—and to underscore the difficulties inherent in deducing an energy profile from a set of rate constants (compare Fig. 1 and the two different energy profiles deduced for ff = 6 · 1012 and 109 s−1). Second, the differences in height among the peaks are invariant, suggesting that they have mechanistic significance. It is unlikely that the frequency factors associated with each barrier crossing will be identical, however, and one cannot relate differences in peak height to differences in free energy without knowing the variation in ff. Third, the “well” depths relative to the electrolyte solution outside the pore are invariant, again suggesting that they have mechanistic significance. The different behaviors of the peaks and “wells” arise because of the qualitative difference between RCRff (p) for odd and even p: only for odd p does the value of RCRff (p) depend on ff. Visually, the peaks probably should be above the wells; compare the profile for ff = 1 s−1 vs. those for ff = 109 and 6 · 1012 s−1, which justifies the use of physically plausible, albeit arbitrary, frequency factors. applies generally, meaning that it is possible to provide graphic representations of the results of kinetic analyses without invoking the Eyring TST to describe situations where that theory is inapplicable—whether it be ion permeation, channel gating, protein conformational transitions, or other physiological processes. The Journal of General Physiology therefore will publish rate constant representations based on , or some equivalent, but will no longer publish energy profiles deduced from kinetic analyses unless the authors explicitly justify their choice of the underlying model using “generally accepted” physico-chemical reasoning. Olaf Sparre Andersen Editor The Journal of General Physiology
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            Is Open Access

            How open science helps researchers succeed

            Open access, open data, open source and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices. DOI: http://dx.doi.org/10.7554/eLife.16800.001
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              Self-Selected or Mandated, Open Access Increases Citation Impact for Higher Quality Research

              Background Articles whose authors have supplemented subscription-based access to the publisher's version by self-archiving their own final draft to make it accessible free for all on the web (“Open Access”, OA) are cited significantly more than articles in the same journal and year that have not been made OA. Some have suggested that this “OA Advantage” may not be causal but just a self-selection bias, because authors preferentially make higher-quality articles OA. To test this we compared self-selective self-archiving with mandatory self-archiving for a sample of 27,197 articles published 2002–2006 in 1,984 journals. Methdology/Principal Findings The OA Advantage proved just as high for both. Logistic regression analysis showed that the advantage is independent of other correlates of citations (article age; journal impact factor; number of co-authors, references or pages; field; article type; or country) and highest for the most highly cited articles. The OA Advantage is real, independent and causal, but skewed. Its size is indeed correlated with quality, just as citations themselves are (the top 20% of articles receive about 80% of all citations). Conclusions/Significance The OA advantage is greater for the more citable articles, not because of a quality bias from authors self-selecting what to make OA, but because of a quality advantage, from users self-selecting what to use and cite, freed by OA from the constraints of selective accessibility to subscribers only. It is hoped that these findings will help motivate the adoption of OA self-archiving mandates by universities, research institutions and research funders.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                13 February 2018
                2018
                : 6
                : e4375
                Affiliations
                [1 ]Impactstory , Sanford, NC, USA
                [2 ]École de bibliothéconomie et des sciences de l’information, Université de Montréal , Montréal, QC, Canada
                [3 ]Observatoire des Sciences et des Technologies (OST), Centre Interuniversitaire de Recherche sur la Science et la Technologie (CIRST), Université du Québec à Montréal , Montréal, QC, Canada
                [4 ]Canadian Institute for Studies in Publishing, Simon Fraser University , Vancouver, BC, Canada
                [5 ]Public Knowledge Project , Canada
                [6 ]Scholarly Communications Lab, Simon Fraser University , Vancouver, Canada
                [7 ]Information School, University of Washington , Seattle, USA
                [8 ]FlourishOA , USA
                [9 ]School of Information Studies, University of Ottawa , Ottawa, ON, Canada
                Article
                4375
                10.7717/peerj.4375
                5815332
                29456894
                67248dc9-d1f8-4df6-b688-1e93a686170e
                ©2018 Piwowar et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 9 August 2017
                : 25 January 2018
                Funding
                The authors received no funding for this work.
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                Legal Issues
                Science Policy
                Data Science

                open access,open science,scientometrics,publishing,libraries,scholarly communication,bibliometrics,science policy

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