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Recordings of musical practices are kept in various public institutions and private depositories around the world. They constitute valuable data for ethnomusicological research and are substantial for the world's musical heritage. At the moment, there are no commonly used systems and standards for organizing, describing or categorizing these data, which makes their use difficult. In this paper, we discuss the required steps to make them findable, accessible, interoperable and reusable (FAIR), and outline action items to reach these goals. We show solutions that help researchers to manage their data over the whole research lifecycle and discuss the benefits of combining technologies from information science, music information retrieval, and linked data, with the aim of giving incentives for the ethnomusicology research community to actively participate in these developments in the future. [ABSTRACT FROM AUTHOR]

Research Data Alliance Austria (RDA-AT) is a national RDA node dedicated to representing emerging research and data management communities throughout Austria. RDA-AT will operate as a formal participant of RDA Europe and RDA Global, linking Austrian data management initiatives and RDA Working and Interest Groups, providing assistance in adoption of RDA recommendations, and allowing Austrian stakeholders to benefit directly from RDA support mechanisms. These new connections will bring key Austrian issues to the global data management table, and global discussions back to Austria. This paper details the goals of the RDA-AT and describes its community and sustainability plans. [ABSTRACT FROM AUTHOR]

Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice. There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others. The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows. This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP. This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves. We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved. [ABSTRACT FROM AUTHOR]

The RDA Europe Workshop „From Planning to Action. Towards the Establishment of an Austrian Research Infrastructure“ was organized in Vienna in November 2017 with the support of RDA Europe, the Vienna University of Technology, and the University of Vienna. The workshop was the first RDA national event in Austria in a series of RDA Europe events dedicated to practical issues surrounding the adoption of RDA recommendations, the implementation of European Open Science Cloud and other key European data initiatives in order to support EU researchers and data centres. The different sections focused on transversal and domain-specific projects and infrastructures in Austria, such as data centres and data repositories that have emerged as a result of initiatives developed at the national level. The presentations also reflected the European perspective on research data management, focusing on the political and scientific work of international organisations. Furthermore there was an analysis of various RDA Europe working group recommendations and their potential for adoption in local contexts. The event also included the announcement of a new dedicated RDA group for Austria. This node will formally represent the Austrian data management community within the new RDA administrative structure as soon as formal procedures are completed. [ABSTRACT FROM AUTHOR]

The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e., machine-actionable Data Management Plan (maDMP). The technology-agnostic approach of the current maDMP specification: (i) does not explicitly link to related data models or ontologies, (ii) has no standardised way to describe controlled vocabularies, and (iii) is extensible but has no clear mechanism to distinguish between the core specification and its extensions.This paper reports on a community effort to create the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts, with a particular focus on a detailed description of the components of the ontology. Our initial result shows that the proposed DCSO can become a suitable candidate for a reference serialisation of the DMP Common Standard. [ABSTRACT FROM AUTHOR]

This article gives an overview of the FAIR Data Austria project objectives and current results. In collaboration with our project partners, we work on the development and establishment of tools for managing the lifecycle of research data, including machine-actionable Data Management Plans (maDMPs), repositories for longterm archiving of research results, RDM training and support services, models, and profiles for Data Stewards and FAIR Office Austria. [ABSTRACT FROM AUTHOR]

At present, accessing and processing Earth Observation (EO) data on different cloud platforms requires users to exercise distinct communication strategies as each backend platform is designed differently. The openEO API (Application Programming Interface) standardises EO-related contracts between local clients (R, Python, and JavaScript) and cloud service providers regarding data access and processing, simplifying their direct comparability. Independent of the providers' data storage system, the API mimics the functionalities of a virtual EO raster data cube. This article introduces the communication strategy and aspects of the data cube model applied by the openEO API. Two test cases show the potential and current limitations of processing similar workflows on different cloud platforms and a comparison of the result of a locally running workflow and its openEO-dependent cloud equivalent. The outcomes demonstrate the flexibility of the openEO API in enabling complex scientific analysis of EO data collections on cloud platforms in a homogenised way. [ABSTRACT FROM AUTHOR]