Digital Identity is indispensable for the promotion of data exchange and the diffusion of digital government. The development and standardization of Decentralized Identifiers (DIDs) based on blockchain technology is underway. Through a comparison between existing centralized IDs and decentralized IDs, we examine the impact of decentralized identity architecture on data exchange.

In recent years, there have been growing expectations for the creation of new businesses and the improvement of the value of existing services by exchanging data in different fields. Data stored in-house within organizations have become a new source of innovation. While there is a high need for the value creation of data, determining the data value is not an easy task, as there is a wide range of factors to be considered, such as data pricing, acquisition cost, usage value, and update frequency. In this study, we observe communication, such as the sharing of know-hows in data exchange and analysis, and discuss the growing process of a community on the data platform. For the experiment, we focused on the data community in the COVID-19 disaster and used a unique dataset from the data platform Kaggle, which is the data analysis competition service. The results suggest that user actions differ in the discussion of the dataset and analysis. Moreover, providing topics, user participation, and activating actions in the early stages after the dataset is released are essential for forming a data community. We argue that the actions on the data analysis, such as comments and votes, are also crucial for fostering a common understanding of the data value.

In recent years, instead of closing data and analysis skills in-house, there has been much interest in widely releasing data analysis knowledge on the web. A data exchange platform is a type of digital platform that exchanges data between stakeholders, e.g., data owners, users, and analysts. However, the datasets handled on such platforms are independently acquired and stored by the data providers for their own purposes. These datasets are not based on the premise of coordination and combination, and there is currently little information available to discuss the systematic organization and combination of these datasets. In this study, we focus on a metadata, summary information of data, and examine the similarity of data on a data exchange platform using natural language processing. In our experiments, we use the metadata from the data exchange platform Kaggle. To compare the similarity of the data, our method employs word2vec and BERT as vectorize methods and converts data descriptions to vectors. Then, our method measures the distances of each vector by calculating cosine similarities between each vector. From experimental results, we found that Kaggle has the same character as other data exchange platforms. Additionally, the results indicated the usability of the natural language processing-based method for extracting similar data pairs.