Abstract
Research data management is acknowledged as an important concern for institutions and several platforms to support data deposits have emerged. In this paper we start by overviewing the current practices in the data management workflow and identifying the stakeholders in this process. We then compare four recently proposed data repository platforms-DSpace, CKAN, Zenodo and Figshare-considering their architecture, support for metadata, API completeness, as well as their search mechanisms and community acceptance. To evaluate these features, we take into consideration the identified stakeholders' requirements. In the end, we argue that, depending on local requirements, different data repositories can meet some of the stakeholders requirements. Nevertheless, there is still room for improvements, mainly regarding the compatibility with the description of data from different research domains, to further improve data reuse.

Abstract
Depositing and sharing research data is at the core of open science practices. However, institutions in the long tail of science are struggling to properly manage large amounts of data. Support for research data management is still fragile, and most existing solutions adopt generic metadata schemas for data description. These might be unable to capture the production contexts of many datasets, making them harder to interpret. EUDAT is a large ongoing EU-funded project that aims to provide a platform to help researchers manage their datasets and share them when they are ready to be published. Data-Publication@U. Porto is an EUDAT Data Pilot proposing the integration between Dendro, a prototype research data management platform, and the EUDAT B2Share module. The goal is to offer researchers a streamlined workflow: they organize and describe their data in Dendro as soon as they are available, and decide when to deposit in a data repository. Dendro integrates with the API of B2Share, automatically filling the standard metadata descriptors and complementing the data package with additional files for domain-specific descriptors. Our integration offers researchers a simple but complete workflow, from data preparation and description to data deposit.

Abstract
In the recent trend of data-intensive science, data publication is essential and institutions have to promote it with the researchers. For the past decade, institutional repositories have been widely established for publications, and the motivations for deposit are well established. The situation is quite different for data, as we argue on the basis of a 5-year experience with research data management at the University of Porto. We address research data management from a disciplined yet flexible point of view, focusing on domain-specific metadata models embedded in intuitive tools, to make it easier for researchers to publish their datasets. We use preliminary data from a recent experiment in data publishing to identify motivators and deterrents for data publishing.

Abstract
Data description is an essential part of research data management, and it is easy to argue for the importance of describing data early in the research workflow. Specific metadata schemas are often proposed to support description. Given the diversity of research domains, such schemas are often missing, and when available they may be too generic, too complex or hard to incorporate in a description platform. In this paper we present a method used to design metadata models for research data description as ontologies. Ontologies are gaining acceptance as knowledge representation structures, and we use them here in the scope of the Dendro platform. The ontology design process is illustrated with a case study from Vehicle Simulation. According to the design process, the resulting model was validated by a domain specialist.

Abstract
Research institutions are considering data repositories to manage their outputs and ensure their visibility. In many domains, purpose-built tools can help collect data and metadata as they are created. LabTablet is such a tool, designed to provide the functions of a laboratory notebook, and being able to accompany users in either experimental sessions or field trips. In these contexts, the interaction with the device can be problematic, so we experimented with a speech recognition extension for two purposes: to provide commands, such as requesting readings from the built-in sensors, and to record observations such as a dictated note in a field trip. Copyright 2016 ACM.

Abstract
The description of data is a central task in research data management. Describing datasets requires deep knowledge of both the data and the data creation process to ensure adequate capture of their meaning and context. Metadata schemas are usually followed in resource description to enforce comprehensiveness and interoperability, but they can be hard to understand and adopt by researchers. We propose to address data description using ontologies, which can evolve easily, express semantics at different granularity levels and be directly used in system development. Considering that existing ontologies are often hard to use in a cross domain research data management environment, we present an approach for creating lightweight ontologies to describe research data. We illustrate our process with two ontologies, and then use them as configuration parameters for Dendro, a software platform for research data management currently being developed at the University of Porto.