Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management. The analysis of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons of models hard, an empirical evaluation seems possible.
@cite_10 , @cite_1 and @cite_7 are alike to @cite_8 in the approach to review existing models and deriving an own lifecycle model based on a gap analysis. None of the three publications offer generic and empirical evaluation criteria or a metamodel for the existing models. Their lifecycle model is designed to supersede the existing approaches for a specific context.
@cite_1 @cite_10 @cite_7 @cite_8
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A huge amount of data is constantly being produced in the world. Data coming from the IoT, from scientific simulations, or from any other field of the eScience, are accumulated over historical data sets and set up the seed for future Big Data processing, with the final goal to generate added value and discover knowledge. In such computing processes, data are the main resource, however, organizing and managing data during their entire life cycle becomes a complex research topic. As part of this, Data LifeCycle (DLC) models have been proposed to efficiently organize large and complex data sets, from creation to consumption, in any field, and any scale, for an effective data usage and big data exploitation. 2. Several DLC frameworks can be found in the literature, each one defined for specific environments and scenarios. However, we realized that there is no global and comprehensive DLC model to be easily adapted to different scientific areas. For this reason, in this paper we describe the Comprehensive Scenario Agnostic Data LifeCycle (COSA-DLC) model, a DLC model which: i) is proved to be comprehensive as it addresses the 6Vs challenges (namely Value, Volume, Variety, Velocity, Variability and Veracity, and ii), it can be easily adapted to any particular scenario and, therefore, fit the requirements of a specific scientific field. In this paper we also include two use cases to illustrate the ease of the adaptation in different scenarios. We conclude that the comprehensive scenario agnostic DLC model provides several advantages, such as facilitating global data management, organization and integration, easing the adaptation to any kind of scenario, guaranteeing good data quality levels and, therefore, saving design time and efforts for the scientific and industrial communities. As science becomes more data-intensive and collaborative, researchers increasingly use larger and more complex data to answer research questions. The capacity of storage infrastructure, the increased sophistication and deployment of sensors, the ubiquitous availability of computer clusters, the development of new analysis techniques, and larger collaborations allow researchers to address grand societ al challenges in a way that is unprecedented. In parallel, research data repositories have been built to host research data in response to the requirements of sponsors that research data be publicly available. Libraries are re-inventing themselves to respond to a growing demand to manage, store, curate and preserve the data produced in the course of publicly funded research. As librarians and data managers are developing the tools and knowledge they need to meet these new expectations, they inevitably encounter conversations around Big Data. This paper explores definitions of Big Data that have coalesced in the last decade around four commonly mentioned characteristics: volume, variety, velocity, and veracity. We highlight the issues associated with each characteristic, particularly their impact on data management and curation. We use the methodological framework of the data life cycle model, assessing two models developed in the context of Big Data projects and find them lacking. We propose a Big Data life cycle model that includes activities focused on Big Data and more closely integrates curation with the research life cycle. These activities include planning, acquiring, preparing, analyzing, preserving, and discovering, with describing the data and assuring quality being an integral part of each activity. We discuss the relationship between institutional data curation repositories and new long-term data resources associated with high performance computing centers, and reproducibility in computational science. We apply this model by mapping the four characteristics of Big Data outlined above to each of the activities in the model. This mapping produces a set of questions that practitioners should be asking in a Big Data project The Semantic Web, especially in the light of the current focus on its nature as a Web of Data, is a data-centric system, and arguably the largest such system in existence. Data is being created, published, exported, imported, used, transformed and re-used, by different parties and for different purposes. Together, these actions form a lifecycle of data on the Semantic Web. Understanding this lifecycle will help to better understand the nature of data on the SW, to explain paradigm shifts, to compare the functionality of different platforms, to aid the integration of previously disparate implementation efforts or to position various actors on the SW and relate them to each other. However, while conceptualisations of many aspects of the SW exist, no exhaustive data lifecycle has been proposed. This paper proposes a data lifecycle model for the Semantic Web by first looking outward, and performing an extensive survey of lifecycle models in other data-centric domains, such as digital libraries, multimedia, eLearning, knowledge and Web content management or ontology development. For each domain, an extensive list of models is taken from the literature, and then described and analysed in terms of its different phases, actor roles and other characteristics. By contrasting and comparing the existing models, a meta vocabulary of lifecycle models for data-centric systems — the Abstract Data Lifecycle Model, or ADLM — is developed. In particular, a common set of lifecycle phases, lifecycle features and lifecycle roles is established, as well as additional actor features and generic features of data and metadata. This vocabulary now provides a tool to describe each individual model, relate them to each other, determine similarities and overlaps and eventually establish a new such model for the Semantic Web.