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This paper aims to analyse a set of converging trends underpinning a larger phenomenon called science 2.0 and to assess what are the most important implications for scientific method and research institutions.

It is based on a triangulation of exploratory methods which include a wide-ranging literature review, Web-based mapping and in-depth interviews with stakeholders.

The main implications of science 2.0 are enhanced efficiency, transparency and reliability; raise of data-driven science; microcontributions on a macroscale; multidimensional, immediate and multiform evaluation of science; disaggregation of the value chain of service providers for scientists; influx of multiple actors and the democratisation of science.

The paper rejects the notion of science 2.0 as the mere adoption of Web 2.0 technologies in science and puts forward an original integrated definition covering three trends that have not yet been analysed together: open science, citizens science and data-intensive science. It argues that these trends are mutually reinforcing and puts forward their main implications. It concludes with the identification of three enablers of science 2.0 – policy measures, individual practice of scientists and new infrastructure and services and sees the main bottleneck in lack of incentives on the individual level.

It is widely recognized that sharing data is beneficial not only for science but also for the common good, and researchers are increasingly expected to share their data. However, many researchers are still not making their data available, one of the reasons being that this activity is not adequately recognized in the current reward system of science. Since the attribution of data sets to individual researchers is necessary if we are to include them in research evaluation processes, the purpose of this paper is to explore the feasibility of linking data set records from DataCite to the authors of articles indexed in the Web of Science.

DataCite and WoS records are linked together based on the similarity between the names of the data sets’ creators and the articles’ authors, as well as the similarity between the noun phrases in the titles of the data sets and the titles and abstract of the articles.

The authors report that a large number of DataCite records can be attributed to specific authors in WoS, and the authors demonstrate that the prevalence of data sharing varies greatly depending on the research discipline.

It is yet unclear how data sharing can provide adequate recognition for individual researchers. Bibliometric indicators are commonly used for research evaluation, but to date no large-scale assessment of individual researchers’ data sharing activities has been carried out.