Tag: Altmetrics

  • Research Intelligence

    Research Intelligence

    This presentation reflects upon the report “Users, narcissism and control” from Paul Wouters & Rodrigo Costas (CWTS) [download Users narcissism and control]

    In this presentation we make the bold statement that research policy and reward system is build upon a very small and thin layer of information. Yet the full spectrum contains much richer information. This presentation explains how this rich layer looks like and what information it contains in different layers of abstraction, and how it connects with policy and decision making in the end. This is the business case for the Dutch goverment to introduce performance indicators.

    This presentation suggests that one can do more with these indicators, such as discovering trends, and making analytics. For example making correlations between the downloads and mentions about publications or scientific instruments increases the Shanhai-index by x% in two years time. This can make a shift policy for universities to invest more in open access and in marketing their projects better.

    To support this abundance of information and making cross-domain analysis, one needs a cluster of computing power, storage, and has to arrange licences. This can be done with an array of partners in the Netherlands, creating a middelware infrastructure for Research Information (RI). Universities and research institutes who want to use this RI-middelware infrastructure (RIMI) can subscribe. The RIMI provides raw output and calculated output for services to draw from, services used by the subscribers. These services can be for example, a research information dashboard for individuals, for institutions, for funders and for ministries. It can be used in services that want to visualise semantic-web like relations of the academic information domain. It can be use for resolution mechanisms, research portals, catalogue search systems, etc. The main point is that all these services easily can be build, because the information openly available, in machine reabable formats based on internationally accepted standards so that developers can work with it right away. The information comes from trusted sources, and is as clean as possible, to reduce the noise amplification in the processes later on when the data is compared, combined, calculated and correlated.

    2012 12-12 research analytics – idea from maurice.vanderfeesten

    Disclaimer: This presentation is just an idea, and it contains organisations in fictional situations. It is a proposal, and does not reflect the current situation.

  • Article Level Metrics

    Article Level Metrics

    In response to Martin Fenner’s interview with himself.

    Hi Martin, I’ve got the link of you Blog Post from Najko Jahn – Bielefeld University. I wish you all the best at Plos-One ALM.

    Just for you to know as technical lead: DFG (DE), JISC (UK), DEFF (DK) and SURF (NL) worked together on guidelines for exchanging article level metrics in a transparent manner. The reason to do this is to be able to compare the statistical usage data that comes from various distributed locations, eg. repositories, but also publishers in the future. You might want to look at these KE Guidelines for the aggregation and exchange of Usage Data .
    Dutch repositories already aggregate the Article Level Metics from each repository, and can create a National overview of this data. The EU project OpenAIRE also use these guidelines for Europe-wide data exchange on these metrics. A neat thing is that the Article data can be aggregated on Author level once you know the Author ID. This is being tested in NARCIS right now, results look great.

    Plos-One ALM has been a great source of inspiration and motivation for these kind of projects, so keep up the good work!

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