It is time for a distributed architecture for scholarly data management
Creators: name: FIdomski, Kim Frieda
Publisher: name: Zenodo
Year: 2022
Subjects: distributed architecture
scholarly data management
FAIR
data exchange

description: Thanks to technological progress, researchers can conduct their studies and disclose their research results and findings faster than ever. As a result, ever greater volumes of data are being generated, which cannot be managed using classical techniques. Efficient analysis and management of these data volumes can greatly improve the level and efficiency of research in many ways. Specifically, proper data management and appropriate data governance are essential to handle large volumes of data and make them usable and discoverable by others. The FAIR data principles were introduced to make data and metadata findable, accessible, interoperable, and reusable. Implementing the FAIR principles aims to improve the ability of applications and computer agents to handle and process data efficiently. These principles not only enable good data management practices but also support knowledge discovery and integration for the research community by, for example, reducing the time and effort required to find and retrieve data. Scholarly data is mostly generated and managed by digital libraries. Given the large volumes of scholarly data, it is becoming increasingly difficult to manage them efficiently and find and retrieve relevant data. An exchange of scholarly data between digital libraries offers numerous possibilities but is, however, hardly manageable by classical techniques. Data exchange between digital libraries, while difficult to manage, enables discovery of new research directions and makes it easier to predict trends and conduct analysis. Efficiently managing and enriching scholarly data with another publisher's scholarly data or with publicly available scholarly data from free and open knowledge bases (e.g., Open Citation Initiative) is therefore a worthwhile option for digital libraries. We aim to improve the efficient management and enrichment of scholarly data by developing a distributed architecture that enables data exchange while respecting FAIR principles. Such an architecture offers several design options in terms of the technology to be used and the services and functionalities to be offered. We differentiate between public data inventories that make scholarly data publicly accessible and private data inventories that do not. Accordingly, the architecture must allow data exchange between both. At the beginning of our research, we will focus on the first two FAIR principles: findability and accessibility. Among other things, this means that the data must be identifiable and findable through a standard identification mechanism. For this purpose, we will analyze and evaluate persistent identifier (PID) systems. PIDs not only uniquely identify (meta-) data but also support the findability and accessibility of it. Accordingly, the idea is to use an architecture’s PID system to uniquely reference and release data. The well-known Crossref initiative as well as DOI and Handle as widely used and accepted PID systems must be the first points of contact as well as existing approaches for distributed PID systems. The start of fulfilling the first two FAIR principles by using a PID system within a distributed architecture, is intended to be a first step toward more efficient management and exchange of scholarly data.