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Making Data Count and the Value of Research Data

May 29, 2013 in Featured, Open Research

Last month in Berlin, the Knowledge Exchange gathered around 80 representatives from funder agencies, research institutions, universities and scholarly societies in the Making Data Count workshop with the aim “to discuss and build on possibilities to implement the culture of sharing and to integrate publication of data into research assessment procedures.”

The report “The Value of Research Data: Metrics for datasets from a cultural and technical point of view”, which was presented during the workshop argued that while data sharing between scientists in not a common practice, the development of data metrics should serve as one of the incentives for researchers, being incorporated in the professional and career reward structures and making data more visible and establishing a better practice of data citation and data re-use.

Some of the conclusions of the report also emphasise that data sharing has many important functions. One of them is serving as “a potential source for scientific recognition”, where the creation and curation of datasets may be seen an important contribution to be considered in promotions and the allocation of research funding. Another function of making data openly available to the research community is providing the possibility to verify and reproduce research findings as part of good scientific practice, “protecting against fraud and faulty data”.

Additionally, data sharing allows a more efficient use of research resources where repeated collection of data is avoided and new opportunities emerge for the re-use of the data and for new scientific collaborations. Data sharing is also mentioned as tool enabling new research agendas, international research collaborations and interdisciplinary research. Then, the availability of research data provides training material and supports the work of educators.

The report also discusses the current data metrics models, the opportunities and limitations of data publications, which the authors point out as the most developed model of all. The recommendations include bringing down the costs of data publications and making the process more efficient, incorporating data metrics in the scholarly award structures, reducing the dispersion of data repositories, developing standards and interoperability protocols across the different actors, etc.

The report was written by Rodrigo Costas, Ingeborg Meijer, Zohreh Zahedi and Paul Wouters of Leiden University. Read the report

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