Research Data Management Tools and Workflows: Experimental Work at the University of Porto

Abstract

Research datasets include all kinds of objects, from web pages to sensor data, and originate in every domain. Concerns with data generated in large projects and well-funded research areas are centered on their exploration and analysis. For data in the long tail, the main issues are still how to get data visible, satisfactorily described, preserved, and searchable.
 Our work aims to promote data publication in research institutions, considering that researchers are the core stakeholders and need straightforward workflows, and that multi-disciplinary tools can be designed and adapted to specific areas with a reasonable effort. For small groups with interesting datasets but not much time or funding for data curation, we have to focus on engaging researchers in the process of preparing data for publication, while providing them with measurable outputs. In larger groups, solutions have to be customized to satisfy the requirements of more specific research contexts.
 We describe our experience at the University of Porto in two lines of enquiry. For the work with long-tail groups we propose general-purpose tools for data description and the interface to multi-disciplinary data repositories. For areas with larger projects and more specific requirements, namely wind infrastructure, sensor data from concrete structures and marine data, we define specialized workflows. In both cases, we present a preliminary evaluation of results and an estimate of the kind of effort required to keep the proposed infrastructures running. 
 The tools available to researchers can be decisive for their commitment. We focus on data preparation, namely on dataset organization and metadata creation. For groups in the long tail, we propose Dendro, an open-source research data management platform, and explore automatic metadata creation with LabTablet, an electronic laboratory notebook. For groups demanding a domain-specific approach, our analysis has resulted in the development of models and applications to organize the data and support some of their use cases. Overall, we have adopted ontologies for metadata modeling, keeping in sight metadata dissemination as Linked Open Data.

Research data management policies in USA, UK and Australia universities: An online survey

“…Universities required grant applicants to submit DMPs regarding retention, sharing, and dissemination. It was evident in the policies as funders stated that research data should be accessible and shareable (Ribeiro et al 2018). Matusiak and Sposito (2017) reported various types of curation services and new organisational strategies, including shared networks of RDM expertise and multi-purpose research data centres.…”

Getting in touch with metadata: a DDI subset for FAIR metadata production in clinical psychology

“…Our experimental work at the University of Porto, under the TAIL project 2 , focused on the development of an RDM workflow that integrated a set of different tools depending on the requirements of researchers (Ribeiro et al, 2018). The TAIL project regarded researchers as core RDM stakeholders who needed straightforward workflows.…”

“…In this sense, the availability of tools to support data organization and metadata creation at the beginning of research projects can be a determinant to improve data management practices. Thus, and in order to motivate small research groups with no time or funding for data curation, the TAIL team developed Dendro 3 , an open-source platform designed to help researchers describe their data, fully built on Linked Open Data (Rocha da Silva, Ribeiro and Lopes, 2018). Dendro includes domain-specific metadata models to address disciplinary data description requirements (Castro et al, 2017).…”

Institutional Support for Data Management Plans: Five Case Studies

“…At the Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), under the TAIL project [ 19 ], we collaborate with researchers from different scientific domains, analyzing the difficulties they face in RDM activities and DMP creation. This led us to build an RDM workflow, taking into account researchers’ needs and existing RDM and funder requirements, while exploring the integration of the available RDM tools and services, and developing our own.…”