In order to integrate distributed and isomerous earth system scientific data resources and provide one stop data sharing services for different users, a distributed data sharing platform is needed urgently in China. In this paper, we analyze interoperability requirements of the distributed earth system scientific data sharing platform (ESSDSP) in the first place. Then the general framework of the platform based on service-oriented architecture (SOA) is designed. This platform comprises one main center, one authentication center and several sub-centers in which core functions are encapsulated into web services. Through this incompact architecture, it can not only implement the interoperability among distributed data centers, but also easily integrate other software systems. Furthermore, these web services can be accessed by third party software such as e-business, egovernment and so on to achieve data sharing function. Finally we introduce the design, implementation and deployment of some core web services in detail, i.e. data centers' information synchronization service, distributed metadata synchronization management service and multiresource data access service. This platform was deployed in Earth System
Geospatial data is an indispensable data resource for research and applications in many fields. The technologies and applications related to geospatial data are constantly advancing and updating, so identifying the technologies and applications among them will help foster and fund further innovation. Through topic analysis, new research hotspots can be discovered by understanding the whole development process of a topic. At present, the main methods to determine topics are peer review and bibliometrics, however they just review relevant literature or perform simple frequency analysis. This paper proposes a new topic discovery method, which combines a word embedding method, based on a pre-trained model, Bert, and a spherical k-means clustering algorithm, and applies the similarity between literature and topics to assign literature to different topics. The proposed method was applied to 266 pieces of literature related to geospatial data over the past five years. First, according to the number of publications, the trend analysis of technologies and applications related to geospatial data in several leading countries was conducted. Then, the consistency of the proposed method and the existing method PLSA (Probabilistic Latent Semantic Analysis) was evaluated by using two similar consistency evaluation indicators (i.e., U-Mass and NMPI). The results show that the method proposed in this paper can well reveal text content, determine development trends, and produce more coherent topics, and that the overall performance of Bert-LSA is better than PLSA using NPMI and U-Mass. This method is not limited to trend analysis using the data in this paper; it can also be used for the topic analysis of other types of texts.
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