dataesr/scientific-paragraphs-categorization huggingface dataset:
A different type of EAA, based on the F ST statistic, is usually performed when data come from categorical sampling, that is two or more populations which differ in an environmental variable (e. g. high and low temperature), rather than continuous or random sampling  (Foll et al. 2014; de Villemereuil & Gaggiotti 2015). A limitation of F STbased methodologies is the error associated with the TE frequency estimates. However, recently developed software, such as T-lex2, can estimate TE frequencies with high confidence  (Fiston-Lavier et al. 2015). Some of the best examples where an association between environment and its genetic signature has been shown come from Arabidopsis, where EAAs can also be corroborated with reciprocal transplants or common garden experiments  (Turner et al. 2010; Fournier-Level et al.
In the case of the industrial melanism mutation mentioned above, the age of the allele suggests that selection acted on a de novo mutation. Elucidating whether selection acts more often on standing variation or on de novo mutations is still a major open question in evolutionary biology  (Barrett & Schluter 2008; Peter, Huerta-Sanchez & Nielsen 2012). Several approaches have been developed to estimate the age of an allele both for single time point data and time-series datasets. Most approaches developed for single time point data focus on estimating the age of segregating alleles  (Slatkin & Rannala 2000; Austerlitz, Kalaydjieva & Heyer 2003; Chen & Slatkin 2013, among others) , with some methodologies specifically developed for rare alleles  (Slatkin 2008; Mathieson & McVean 2014). Comparatively fewer approaches are available to estimate the age of fixed mutations  (Przeworski 2003; Ormond et al.
Acknowledgements We thank members of  Gonz alez lab  for comments on the manuscript. We thank  Pleuni Pennings  for sharing the selective sweep figures. JG was funded by the  European Commission  ( H2020-ERC-2014-CoG-647900 ), by the  Ministerio de Econom ıa y Competitividad and Fondo Europeo de Desarollo Regional  ( BFU2014-57779-P  and  RyC-2010-07306 ). MARdC was funded by  Agence Nationale de la Recherche  ( ANR-14-CE02-0003 ).
In crops, optimizing target traits in breeding programs can be fostered by selecting appropriate combinations of architectural traits which determine light interception and carbon acquisition. In apple tree, architectural traits were observed to be under genetic control. However, architectural traits also result from many organogenetic and morphological processes interacting with the environment. The present study aimed at combining a FSPM built for apple tree, MAppleT, with genetic determinisms of architectural traits, previously described in a bi-parental population. We focused on parameters related to organogenesis (phyllochron and immediate branching) and morphogenesis processes (internode length and leaf area) during the first year of tree growth. Two independent datasets collected in 2004 and 2007 on 116 genotypes, issued from a 'Starkrimson' × 'Granny Smith' cross, were used.
This model was fitted on data using the lmer procedure of the lme4 package of R software. The mean broad-sense heritability was calculated as: h 2 b = σ 2 G σ 2 G + σ 2 e n (4) where σ 2 G is the genotypic variance, σ 2 e is the residual variance, and n is the number of replicates per genotype. In order to analyze the genetic variability on the two experiments together, the following linear mixed model using years as fixed effect and genotype as random effect was used. P ijk = µ + A i + G j + I ij + e ijk ( 5 ) where P ijk is the phenotype value of the k th replicate of genotype j in the year i, µ is the overall mean of the progeny over the 2 years, A i is the fixed effect of the year i, G j is the random effect of the genotype j, I ij is the random effect of the interaction between the effects of the year i and the genotype j and e ijk is the gaussian residual.
The mean broad-sense heritability over the 2 years was calculated for the whole data as: h 2 b = σ 2 G σ 2 G + σ 2 i a + σ 2 e n×a (6) where σ 2 G is the genotypic variance, σ 2 I is the interaction variance, σ 2 e is the residual variance, a is the number of years, and n is the number of replicates. The significance of the year effect was evaluated using anova procedure of the lmerTest package of R software. For each heritability value, associated confidence intervals were calculated as defined in  Knapp et al. (1985). The phenotypic and genotypic correlations between MAppleT parameters and architectural traits were calculated for each experiment separately. Correlations between the two experimental dataset were calculated for the three architectural traits, only. The significance of the correlations was tested with a Pearson's test.
Genomic Prediction of MAppleT Parameter Values Following  Xu et al. (2011) , QTL were detected on BLUPs for the four studied parameters using the interval mapping method within the MapQTL software  (Van Ooijen et al. , 2002). The logarithm of the odds (LOD) at which QTL was declared significant was determined according to a genome wide error of 0. 05 over 2000 permutations of the data. A QTL was considered as significant when the associated LOD was higher than 4. As polygenic determinisms were expected, a genomic prediction method based on Ridge Regression was used after this QTL analysis  (Whittaker et al. , 2000; Meuwissen et al. , 2001). More precisely, this method estimates infinitesimal loci effects assuming a shrinkage parameter equals to the ratio of the residual to the environmental variance using the following mixed effect model: Y = µ + Xg + e (7) where Y are the phenotypic values (232 × 1; two replicates per genotype), µ is the overall mean of phenotypic value, X (232 × 2160) is the matrix of markers for each individual, g (2160 × 1) is the marker effects which are assumed to be normally distributed (g ∼ N(0, Iσ 2 g )), and e (232 × 1) is the residual effects (g ∼ N(0, Iσ 2 e )).
In this study, we only kept 2160 markers among the 3123 SNPs available in the genetic map. These 2160 markers correspond to the markers that were heterozygous for one of the two parents, only. As a consequence, the matrix of markers includes one kind of heterozygous (A/B) and one kind of homozygous markers (A/A), only. In the matrix of markers, a locus was then coded -1 when the two alleles were different (A/B), and was coded 1 when the two alleles were similar (A/A), whatever the heterozygous parent. Marker effect estimations were performed on phenotypic values collected in Experiment 1 data using the mixed. solve procedure of the rrBLUP package of R software  (Endelman, 2011). We estimated the genetic values of each parameter (G, (116×1)) for all genotypes in the population using the estimated marker effects (ĝ): G = µ + W ĝ (8) with W the matrix of markers for each genotype (116 × 2160).
82 for RLE GDD , 0. 98 for IN_length, 0. 87 for loga syll , and 0. 80 for Leaf_area, respectively (Figure  2 ). All these correlations were significant according to Pearson's test (P < 0. 05) showing the For analysis on the whole dataset, heritability were estimated using the mixed model with year as fixed effect. Stars indicated the significance of year effect ( * if p-value < 0. 05; * * if p-value < 0. 01, and * * * if p-value < 0. 001). The significance of the year effect is indicated for the three variables that were observed in a second experiment (Experiment 2). See text for abbreviation meaning. ability of the genome wide model to estimate parameter values from SNP markers. However, the prediction accuracies were notably lower using the cross validation methods (Figure  2 ). The mean accuracy from the 10-fold cross validation was 0.
DISCUSSION This paper presents a proposition for linking a FSPM, MAppleT, with genetic information on growth and branching model parameters, estimated on an apple tree segregating population. Mean heritability values of architectural traits (trunk length, number of leaves, and number of sylleptic laterals) were estimated with two independent datasets which provided similar results. However, heritability values were lower than those obtained by  Segura et al. (2006) , on a subsample of 50 hybrids of the same progeny, observed at the same age (1-year-old) but planted in a different year  (2003)  and with three replicates per genotype  (Segura et al. , 2006). Even though heritability values were similar in Experiments 1 and 2, both phenotypic and genetic correlations between these experiments were low.
Methods This article provides a general review of conventional journal article peer review and evaluation of recent and current innovations in the field. It is not a systematic review or meta-analysis of the empirical literature (i. e. , we did not perform a formal search strategy undertaken with specific keywords). Rather, a team of researchers with diverse expertise in the sciences, scholarly publishing and communication, and libraries pooled their knowledge to collaboratively and iteratively analyze and report on the present literature and current innovations. The reviewed and cited articles within were identified and selected through searches of general research databases (e. g. , Web of Science, Google Scholar, and Scopus) as well as specialized research databases (e. g. , Library & Information Science Abstracts (LISA) and PubMed).
Initiatives such as the San Francisco Declaration on Research Assessment (ascb. org/dora/; DORA), that called for systemic changes in the way that scientific research outputs are evaluated, and advances in Web-based technologies, are likely catalysts for such innovation. Born-digital journals, such as the PLOS series, introduced commenting on published papers, and Rapid Responses by BMJ has been highly successful in providing a platform for formalised comments (bmj. com/rapid-responses). Such initiatives spurred developments in cross-publisher annotation platforms like PubPeer (pubpeer. com/) and PaperHive (paperhive. org/). Some journals, such as F1000 Research (f1000research. com/) and The Winnower (thewinnower. com/), rely exclusively on a model where peer review is conducted after the manuscripts are made publicly available.
Proprietary reviewer databases that enable high selectivity are seen as a good business asset. In fact, the attribution is based on the false assumption that peer review requires careful selection of specific reviewers to assure a definitive level of adequate quality, termed the "Fallacy of Misplaced Focus" by  Kelty et al. (2008). In addition to being used to judge submitted material for acceptance at a journal, review comments provided to the authors serve to improve the work and the writing and analysis skills of the authors. This feedback can lead to improvements to the submitted work that are iterated between the authors, reviewers, and editor, until the work is either accepted or the editor decides that it cannot be made acceptable for their specific scientific journal. In other cases, it allows the authors to improve their work to prepare for a new submission to another venue.
Once the research has been finished and written-up, completed manuscripts are then subject to a second-stage of peer review which, in addition to affirming the soundness of the results, also confirms that data collection and analysis occurred in accordance with the originally described methodology. The format, originally introduced by the psychology journals Cortex and Perspectives in Psychological Science in 2013, is now used in some form by more than 70 journals (Nature Human Behaviour, 2017) (see cos. io/rr/ for an up-to-date list of participating journals). Registered Reports are designed to boost research integrity by ensuring the publication of all research results, which helps reduce publication bias. As opposed to the traditional model of publication, where "positive" results are more likely to be published, results remain unknown at the time of the first review stage and therefore even "negative" results are equally as likely to be published.
Peer evaluation in this form emphasizes progress and research as a discourse over piecemeal publications or objects as part of a lengthier process. Such a system could be applied to other forms of scientific work, which includes code, data and images, thereby allowing contributors to claim credit for their full range of research outputs. Comments could be signed by default, pseudonymous, or anonymized until a contributor chooses to reveal their identity. If required, anonymized comments could be filtered out automatically by users. A key to this could be peer identity verification, which can be done at the back-end via email or integrated via ORCID.
Translating engagement into prestige. Reddit karma points are awarded for sharing links and comments, and having these upvoted or downvoted by other registered members. The simplest implementation of such a voting system for peer review would be through interaction with any article in the database with a single click. This form of field-specific social recommendation for content simultaneously creates both a filter and a structured feed, similar to Facebook and Google+, and can easily be automated. With this, contributions get a rating, which accumulate to form a peer-based rating as a form of reputation and could be translated into a quantified level of community-granted prestige. Ratings are transparent and contributions and their ratings can be viewed on a public profile page. More sophisticated approaches could include graded ratings-e.
g. , five-point responses, like those used by Amazon-or separate rating dimensions providing peers with an immediate snapshot of the strengths and weaknesses of each article. Such a system is already in place at ScienceOpen, where referees evaluate an article for each of its importance, validity, completeness, and comprehensibility using a five-star system. For any given set of articles retrieved from the database, a ranking algorithm could be used to dynamically order articles on the basis of a combination of quality (an article's aggregate rating within the system, like at Stack Exchange), relevance (using a recommendation system akin to Amazon), and recency (newly added articles could receive a boost). By default, the same algorithm would be implemented for all peers, as on Reddit. The issue here is making any such karma points equivalent to the amount of effort required to obtain them, and also ensuring that they are valued by the broader research community and assessment bodies.
Social functions of GitHub. Software review is an important part of software development, particularly for collaborative efforts. It is important that contributions are reviewed before they are merged into a code base, and GitHub provides this functionality. In addition, GitHub offers the ability to discuss specific issues, where multiple people can contribute to such a discussion, and discussions can refer to code segments or code changes and vice versa (but note that GitHub can also be used for non-code content). GitHub also includes a variety of notification options for both users and project repositories. Users can watch repositories or files of interest and be notified of any new issues or commits (updates), and someone who has discussed an issue can also be notified of any new discussions of that same issue.
Peer review would operate more like a social network, with specific tasks (or repositories) being developed, distributed, and promoted through GitHub. As all code, data, and other content are supplied, and peers would be able to assess methods and results comprehensively, which in turn increases rigor, transparency, and replicability. Reviewers would also be able to claim credit and be acknowledged for their tracked contributions, and thereby quantify their impact on a project as a supply of individual prestige. This in turn facilitates the assessment of quality of reviews and reviewers. As such, evaluation becomes an interactive and dynamic process, with version control facilitating this all in a post-publication environment  (Ghosh et al. , 2012). The potential issue of proliferating non-significant work here is minimal, as projects that are not deemed to be interesting or of a sufficient standard of quality are simply never paid attention to in terms of follows, contributions, and re-use.
Current use of GitHub for peer review. Two example uses of GitHub for peer review already exist in The Journal of Open Source Software (JOSS; joss. theoj. org), created to give software developers a lightweight mechanism for software developers to quickly supplement their code with metadata and a descriptive paper, and then to submit this package for review and publication, and ReScience (rescience. github. io), created to publish replication efforts in computational science. The JOSS submission portal converts a submission into a new GitHub issue of type "pre-review" in the JOSS-review repository (github. com/openjournals/joss-reviews). The editorin-chief checks a submission, and if deemed suitable for review, assigns it to a topic editor who in turn assigns it to one or more reviewers. The topic editor then issues a command that creates a new issue of type "review", with a check-list of required elements for the review.
This model of engagement also benefits from the "many eyes" hypothesis, where if something is visible to multiple people then, collectively, they are more likely to detect any errors in it, and tasks become more spread out as the size of a group increases. In Wikipedia, and to a larger extent Wikidata, automation or semi-automation through bots helps to maintain and update information on a large scale. For example, Wikidata is used as a centralized microbial genomics database  (Putman et al. , 2016) , which uses bots to aggregate information from structured data sources. As such, Wikipedia represents a fairly extreme alternative to peer review where traditionally the barriers to entry are very high (based on expertise), to one where the pool of potential peers is relatively large  (Kelty et al.
Another publishing platform being developed that leverages blockchain is Aletheia, which uses the technology to "achieve a distributed and tamper proof database of information, storing document metadata, vote topics, vote results and information specific to users such as reputation and certifications" (github. com/aletheia-foundation/aletheia-whitepaper/blob/master/ WHITE-PAPER. md#a-blockchain-journal). Furthermore, blockchain-based models offer the potential to go well beyond peer review, possibly integrating all functions of publication in general. They could be used to support data publication, research evaluation, incentivization, and research fund distribution. A relevant example is a proposed decentralized peer review group as a way of managing quality control in peer review via blockchain through a system of cohort-based training  (Dhillon, 2016).
As such, it can be leveraged for a decoupled peer review process by combining certification with distribution and communication. It is entirely feasible for this to be implemented on a system-wide scale, with researcher databases such as ORCID becoming increasingly widely adopted. However, as the scale of such an initiative increases, the risk of over-fitting also increases due to the inherent complexity in modelling the diversity of research communities, although there are established techniques to avoid this. Questions have been raised about the impact of such systems on the practice of scholarly writing, such as how authors may change their approach when they know their manuscript is being evaluated by a machine (Hukkinen, 2017), or how machine assessment could discover unfounded authority in statements by authors through analysis of citation networks  (Greenberg, 2009).
Using multiple peer review models While individual publishers may use specific methods when peer review is controlled by the author of the document to be reviewed, multiple peer review models can be used either in series or in parallel. For example, the FORCE11 Software Citation Working Group used three different peer review models and methods to iteratively improve their principles document, leading to a journal publication  (Smith et al. , 2016). Initially, the document that was produced was made public and reviewed by GitHub issues (github. com/force11/force11-scwg [see Section 3. 4]). The next version of the document was placed on a website, and new reviewers commented on it both through additional GitHub issues and through Hypothesis (via. hypothes. is/https://www. force11. org/software-citation-principles [see Section 3.
General Comments This is a wide ranging, timely paper and will be a useful resource. My main comment is that this is a mix of opinion, review, and thought experiment of future models. While all of these are needed in this area, for the review part of the paper, it would be much strengthened with a description of the methodology used for the review, including databases searched for information and keywords used to search, etc. The paper is very long and there is a substantial amount of repetition. I think the introduction in particular could be much shortened -especially as it contains a lot of opinion, and repetition of issues dealt with elsewhere in the paper. The language of the paper is also quite emotive in places and though I would personally agree with some of the sentiments I don't think they are helpful in making the authors' case eg in Table  2  assessment of pre publication peer review is listed as Non-transparent,impossible to evaluate,biased, secretive, exclusive Or The entrenchment of the ubiquitously practiced and much more favored traditional model (which, as.
Referee The authors report on many aspects of peer review and devote considerable attention to some challenges in the field and the enormous innovation the field is witnessing. I think the paper can be improved: 1. It is missing a Methods section. It was unclear to me whether the authors conducted a systematic review or whether they used a snowballing technique (starting with seed articles) to identify the content discussed in the paper? Did the authors search electronic databases (and if so which ones?) What were their search strategies and/or did they rely on their own file drawers? Are all the peer review innovations/systems/approaches identified by the authors discussed or did they only discuss some (i. e. , was a filter applied?)? With a focus on reproducibility I think the authors need to document their methods.
Acknowledgements CKP, CRM, DG, DM, DSK, DPOD, JNK, KEN, MP, MS, SK, SR, and YE thank those who posted on Twitter for making them aware of this project. During the writing of this manuscript, we also received numerous refinements, edits, suggestions, and comments from an enormous external community. DG has been supported by the  Alexander von Humboldt (AvH) Foundation. We also received significant input during two "MozSprint" (mozilla. github. io/global-sprint/)events in June 2016 and June 2017 from a range of non-authors. We would like to extend our deepest thanks to all of those who contributed throughout these times. Virginia Barbour  and  David Moher  are especially thanked for their constructive and thoughtful reviews on early versions of this manuscript, which we recommend readers to look at below.
Characteristics of papers published in journals: an analysis of open access journals, country of publication, and languages used. Scientometrics. 2017; 112(2): 1007-1023. ISSN: 1588-2861. Publisher Full Text Fyfe A, Coate K, Curry S, et al. : Untangling Academic Publishing: A history of the relationship between commercial interests, academic prestige and the circulation of research. Zenodo. 2017. Publisher Full Text Galipeau J, Moher D, Campbell C, et al. : A systematic review highlights a knowledge gap regarding the effectiveness of health-related training programs in journalology. J Clin Epidemiol. 2015; 68(3): 257-65. PubMed Abstract | Publisher Full Text Gashler M: GPeerReview -a tool for making digital-signatures using data mining. KDnuggets. 2008; Date accessed: 2017-06-12. experiment in a journal's transparent peer review on the web.