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Beyond the article: tracking other research outputs

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Beyond the article: tracking other research outputs
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8
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CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Release Date2015
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- Josh Borrow (Durham University) & Pedro Russo (Leiden Observatory) – A Blueprint for Assessing Societal Impact Through Public Engagement: Societal impact of research outputs are difficult to measure. Over the past few decades, there has been a huge increase in the amount of public engagement performed by researchers. These initiatives are generally poorly assessed, with abstract searches for learning impact rather than focusing on development of technique. The assessments that are being implemented tend to be motivated by funding bodies’ quest for ‘societal impact’ and require a focus on abstract metrics that are generally irrelevant and difficult to measure. In this presentation, we describe a new method for assessing public engagement events in research. This evaluation framework, as well as supporting researchers in public engagement, will lead to meaningful appraisals of initiatives such that researchers can receive career development for their work. All of this will lead to a greater respect for public engagement within research institutions, fighting the anti-engagement atmosphere and promoting young researchers to engage with the public and have a impact on society. - Robin Haunschild, Max Planck Institute for Solid State Research – Networks of reader and country status: An analysis of Mendeley reader statistics: Mendeley is one of the most important sources for altmetrics (Bornmann, 2014). Here, bookmarks of papers in this popular online reference manager are counted which can be expected to reflect readership of publications. Since Mendeley does not only provide straight reader counts, but also itemized counts for different reader groups, the data can be used for sophisticated analyses. In order to reveal the structure of complex data, social network analysis provides the most suitable methods (de Nooy, Mrvar, & Batagelj, 2011). In this study, we analyze Mendeley readership data of a set of 1,133,224 articles and 64,960 reviews with publication year 2012. In total, we found 9,352,424 reader counts for articles (8.25 reader counts per article on average) and 1,335,764 reader counts for reviews (20.56 reader counts for reviews on average). The Mendeley reader count information was gathered by requests to the Mendeley API. The academic status of the users was available in all cases, the sub-discipline information was available in nearly all cases, but the country information was available only for 49.3% of the articles and 66.1% of the reviews. In total, we observed 1,572,240 reader counts (16.8%) for articles and 212,693 reader counts (15.9%) for reviews where the location information was shared. Three respective networks were generated and groups were distinguished using the community-finding algorithm of Blondel, Guillaume, Lambiotte, and Lefebvre (2008): (1) The network based on disciplinary affiliations of Mendeley readers contains four groups: (i) biology, (ii) social science and humanities (including relevant computer science), (iii) bio-medical sciences, and (iv) natural science and engineering. In all four groups, the category with the addition “miscellaneous” prevails. (2) The network of co-readers in terms of professional status shows that a common interest in papers is mainly shared among PhD students, Master’s students, and postdocs. (3) The country network focusses on global readership patterns: a group of 53 nations is identified as core to the scientific enterprise, including most OECD countries, Russia, and China. - Sünje Dallmeier-Tiessen (CERN) & Merce Crosas (Harvard University) – Comprehensive tracking of research workflows using Altmetrics in Social Sciences and High-Energy Physics: Altmetrics are becoming increasingly pervasive for traditional scholarly objects. However, Open Science and scholarly communication move beyond the sharing of “only” articles and data, but also include code, models, documentations, visualizations etc.. For future reinterpretation, reuse or reproducibility of research results it is indispensable to preserve these scholarly elements to make them accessible and retrievable in the long term. The products that should be considered result from research workflows through the lifetime of (ongoing) experiments or projects and, thus, the objects might be subject to change over time while they should be referenceable for others. Using altmetrics as a tool to track and visualize the use or impact of such materials could provide an important incentive to researchers (and service providers) to engage in this new realm of preservation and publishing activities. However, it is needed to understand how to expand the altmetrics concept to apply to non traditional objects and their preservation and publishing challenges. This presentation highlights two examples from two very different communities, the Social Sciences and High-Energy Physics. At Harvard’s Institute for Quantitative Social Sciences and at CERN preservation and Open Science tools are being built and are (partially) already in use for both communities. We present a comparative study of the different communities, their research processes and how this impacts the resulting scholarly outputs. We will highlight the different needs in terms of preservation and Open Science and underline how this impacts the need, potential use and exposure of altmetrics – for data and other non traditional research outputs. Certainly, there are commonalities between the disciplines and their services, e.g. when using standards or emerging concepts to support data citation. However, when supporting the research workflows more comprehensively, one touches specific challenges like sensitive data or dynamic data which both demand specific solutions. In the presentation we will investigate how this (might) impacts usage and statistics (or not). - Martijn Roelandse, Springer – Bookmetrix: Developed in partnership between Springer and Altmetric, Bookmetrix is the first platform of its kind to offer integrated traditional and non-traditional metrics for books and chapters. Designed to give authors, editors and readers easy access to this combined data all in one place for the first time, Bookmetrix helps to set a new standard for monitoring and reporting the activity surrounding a book post publication. During my talk I will take you on a journey, from inception to launch and beyond.