Abstract. The rapid growth of diverse data types and greater volumes available to environmental sciences prompts the scientists to seek knowledge in data from multiple places, times, and scales. To facilitate such need, ONEMercury has recently been implemented as part of the DataONE project to serve as a portal for accessing environmental and observational data across the globe. ONEMercury harvests metadata from the data hosted by multiple repositories and makes it searchable. However, harvested metadata records sometimes are poorly annotated or lacking meaningful keywords, and hence would unlikely be retrieved during the search process. In this paper, we develop an algorithm for automatic metadata annotation. We transform the problem into a tag recommendation problem, and propose a score propagation style algorithm for tag recommendation. Our experiments on four data sets of environmental science metadata records not only show great promises on the performance of our method, but also shed light on the different natures of the data sets.
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