The AMIDER, Advanced Multidisciplinary Integrated-Database for Exploring new Research, is a newly developed research data catalog to demonstrate an advanced database application. AMIDER is characterized as a multidisciplinary database equipped with a user-friendly web application. Its catalog view displays diverse research data at once beyond any limitation of each individual discipline. Some useful functions, such as a selectable data download, data format conversion, and display of data visual information, are also implemented. Further advanced functions, such as visualization of dataset mutual relationship, are also implemented as a preliminary trial. These characteristics and functions are expected to enhance the accessibility to individual research data, even from non-expertized users, and be helpful for collaborations among diverse scientific fields beyond individual disciplines. Multidisciplinary data management is also one of AMIDER's uniqueness, where various metadata schemas can be mapped to a uniform metadata table, and standardized and self-describing data formats are adopted. AMIDER website (https://amider.rois.ac.jp/) had been launched in April 2024. As of July 2024, over 15,000 metadata in various research fields of polar science have been registered in the database, and approximately 500 visitors are viewing the website every day on average. Expansion of the database to further multidisciplinary scientific fields, not only polar science, is planned, and advanced attempts, such as applying Natural Language Processing (NLP) to metadata, have also been considered.

翻译：AMIDER（先进多学科集成研究探索数据库）是一个新开发的研究数据目录，旨在展示先进的数据库应用。该数据库的特点在于其多学科属性，并配备了用户友好的网络应用程序。其目录视图能够一次性展示跨越单一学科限制的多样化研究数据。同时，系统还实现了一些实用功能，例如可选择的数据下载、数据格式转换以及数据可视化信息的显示。此外，作为初步尝试，还实现了数据集相互关系可视化等更高级的功能。这些特性和功能有望增强非专业用户对个体研究数据的可访问性，并有助于促进跨学科的科学领域合作。多学科数据管理也是AMIDER的独特之处，它能够将各种元数据模式映射到统一的元数据表中，并采用标准化且自描述的数据格式。AMIDER网站（https://amider.rois.ac.jp/）已于2024年4月上线。截至2024年7月，数据库中已注册了涵盖极地科学等多个研究领域的超过15,000条元数据，平均每天约有500名访客浏览该网站。计划将数据库进一步扩展到包括极地科学在内的更多多学科科学领域，同时也在考虑应用自然语言处理（NLP）技术处理元数据等高级尝试。