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PlosOpenR – Exploring FP7 funded PLOS publications

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      This case study explores alternative science metrics on grant-supported research publications. The study is based on plosOpenR, a software package for the statistical computing environment R. plosOpenR facilitates access to the application programming interfaces (API) provided by Open Access publisher Public Library of Science (PLOS) and OpenAIRE – Open Access Infrastructure for Research in Europe.

      We report 1,166 PLOS articles that acknowledge grant support from 624 different research projects funded by the European Union's 7th Framework Programme (FP7). plosOpenR allows the exploration of PLOS Article-Level Metrics (PLOS ALM), including citations, usage and social media events as well as collaboration patterns on these articles. Findings reveal the potential of reusing data, that are made openly and automatically available by publishers, funders and the repository community.

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      Most cited references 4

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      The structure of scientific collaboration networks

      We investigate the structure of scientific collaboration networks. We consider two scientists to be connected if they have authored a paper together, and construct explicit networks of such connections using data drawn from a number of databases, including MEDLINE (biomedical research), the Los Alamos e-Print Archive (physics), and NCSTRL (computer science). We show that these collaboration networks form "small worlds" in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. We further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied.
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        The Altmetrics Collection

        Introduction What paper should I read next? Who should I talk to at a conference? Which research group should get this grant? Researchers and funders alike must make daily judgments on how to best spend their limited time and money–judgments that are becoming increasingly difficult as the volume of scholarly communication increases. Not only does the number of scholarly papers continue to grow, it is joined by new forms of communication from data publications to microblog posts. To deal with incoming information, scholars have always relied upon filters. At first these filters were manually compiled compendia and corpora of the literature. But by the mid-20th century, filters built on manual indexing began to break under the weight of booming postwar science production. Garfield [1] and others pioneered a solution: automated filters that leveraged scientists own impact judgments, aggregating citations as “pellets of peer recognition.” [2]. These citation-based filters have dramatically grown in importance and have become the tenet of how research impact is measured. But, like manual indexing 60 years ago, they may today be failing to keep up with the literature’s growing volume, velocity, and diversity [3]. Citations are heavily gamed [4]–[6] and are painfully slow to accumulate [7], and overlook increasingly important societal and clinical impacts [8]. Most importantly, they overlook new scholarly forms like datasets, software, and research blogs that fall outside of the scope of citable research objects. In sum, citations only reflect formal acknowledgment and thus they provide only a partial picture of the science system [9]. Scholars may discuss, annotate, recommend, refute, comment, read, and teach a new finding before it ever appears in the formal citation registry. We need new mechanisms to create a subtler, higher-resolution picture of the science system. The Quest for Better Filters The scientometrics community has not been blind to the limitations of citation measures, and has collectively proposed methods to gather evidence of broader impacts and provide more detail about the science system: tracking acknowledgements [10], patents [11], mentorships [12], news articles [8], usage in syllabuses [13], and many others, separately and in various combinations [14]. The emergence of the Web, a “nutrient-rich space for scholars” [15], has held particular promise for new filters and lenses on scholarly output. Webometrics researchers have uncovered evidence of informal impact by examining networks of hyperlinks and mentions on the broader Web [16]–[18]. An important strand of webometrics has also examined the properties of article download data [7], [19], [20]. The last several years, however, have presented a promising new approach to gathering fine-grained impact data: tracking large-scale activity around scholarly products in online tools and environments. These tools and environments include, among others: social media like Twitter and Facebook online reference managers like CiteULike, Zotero, and Mendeley collaborative encyclopedias like Wikipedia blogs, both scholarly and general-audience scholarly social networks, like ResearchGate or conference organization sites like Growing numbers of scholars are using these and similar tools to mediate their interaction with the literature. In doing so, they are leaving valuable tracks behind them–tracks with potential to show informal paths of influence with unprecedented speed and resolution. Many of these tools offer open APIs, supporting large-scale, automated mining of online activities and conversations around research objects [21]. Altmetrics [22], [23] is the study and use of scholarly impact measures based on activity in online tools and environments. The term has also been used to describe the metrics themselves–one could propose in plural a “set of new altmetrics.” Altmetrics is in most cases a subset of both scientometrics and webometrics; it is a subset of the latter in that it focuses more narrowly on scholarly influence as measured in online tools and environments, rather than on the Web more generally. Altmetrics may support finer-grained maps of science, broader and more equitable evaluations, and improvements to the peer-review system [24]. On the other hand, the use and development of altmetrics should be pursued with appropriate scientific caution. Altmetrics may face attempts at manipulation similar to what Google must deal with in web search ranking. Addressing such manipulation may, in-turn, impact the transparency of altmetrics. New and complex measures may distort our picture of the science system if not rigorously assessed and correctly understood. Finally, altmetrics may promote an evaluation system for scholarship that many argue has become overly focused on metrics. Scope of this Collection The goal of this collection is to gather an emerging body of research for the further study and use of altmetrics. We believe it is greatly needed, as important questions regarding altmetrics’ prevalence, validity, distribution, and reliability remain incompletely answered. Importantly, the present collection, which has the virtue of being online and open access, allows altmetrics researchers to experiment on themselves. The collection’s scope includes: Statistical analysis of altmetrics data sources, and comparisons to established sources Metric validation, and identification of biases in measurements Validation of models of scientific discovery/recommendation based on altmetrics Qualitative research describing the scholarly use of online tools and environments Empirically-supported theory guiding altmetrics’ use Other research relating to scholarly impact in online tools and environments. The current collection includes articles that address many of these areas. It will publish new research on an ongoing basis, and we hope to see additional contributions appear in the coming months. We look forward to building a foundation of early research to support this new field.
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          rfishbase: exploring, manipulating and visualizing FishBase data from R.

          This article introduces a package that provides interactive and programmatic access to the FishBase repository. This package allows interaction with data on over 30 000 fish species in the rich statistical computing environment, R. This direct, scriptable interface to FishBase data enables better discovery and integration essential for large-scale comparative analyses. This article provides several examples to illustrate how the package works, and how it can be integrated into phylogenetics packages such as ape and geiger. © 2012 The Authors. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.

            Author and article information

            Bielefeld University Library, Bielefeld University, Bielefeld, Germany
            Hannover Medical School, Hannover, Germany
            Technical Lead PLOS Article Level Metrics, Public Library of Science, San Francisco, CA, USA
            Author notes

            Corresponding author: Najko Jahn, Bielefeld University Library, Bielefeld University, Universitaetsstrasse 25, 33602 Bielefeld, Germany. E-mail:

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            This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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