There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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The FAIR Guiding Principles for scientific data management and stewardship

The development and standardization of semantic web technologies has resulted in an unprecedented volume of data being published on the Web as Linked Data (LD). However, we observe widely varying data quality ranging from extensively curated datasets to crowdsourced and extracted data of relatively low quality. In this article, we present the results of a systematic review of approaches for assessing the quality of LD. We gather existing approaches and analyze them qualitatively. In particular, we unify and formalize commonly used terminologies across papers related to data quality and provide a comprehensive list of 18 quality dimensions and 69 metrics. Additionally, we qualitatively analyze the 30 core approaches and 12 tools using a set of attributes. The aim of this article is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused towards data quality, specifically for LD.

http://www.semantic-web-journal.net/system/files/swj773.pdf

Quality assessment for Linked Data: A Survey

Scientific data represents a significant portion of the linked open data cloud and scientists stand to benefit from the data fusion capability this will afford. Publishing linked data into the cloud, however, does not ensure the required reusability. Publishing has requirements of provenance, quality, credit, attribution and methods to provide the reproducibility that enables validation of results. In this paper we make the case for a scientific data publication model on top of linked data and introduce the notion of Research Objects as first class citizens for sharing and publishing. Highlights? We identify and characterise different aspects of reuse and reproducibility. ? We examine requirements for such reuse. ? We propose a scientific data publication model that layers on top of linked data publishing.

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Why linked data is not enough for scientists

State of the nation in data integration for bioinformatics

Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.

A survey towards an integration of big data analytics to big insights for value-creation

What will researchers be publishing in the future? Whilst there is little question that the Web will be the publication platform, as scholars move away from paper towards digital content, there is a need for mechanisms that support the production of self-contained units of knowledge and facilitate the publication, sharing and reuse of such entities. In this paper we discuss the notion of research objects, semantically rich aggregations of resources, that possess some scientifi?c intent or support some research objective. We present a number of principles that we expect such objects and their associated services to follow.

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Research Objects: Towards Exchange and Reuse of Digital Knowledge

https://scholarlypublications.universiteitleiden.nl/access/item%3A2973609/view

Using a suite of ontologies for preserving workflow-centric research objects

Scientific data stands to represent a significant portion of the linked open data cloud and science itself stands to benefit from the data fusion capability that this will afford. However, simply publishing linked data into the cloud does not necessarily meet the requirements of reuse. Publishing has requirements of provenance, quality, credit, attribution, methods in order to provide the \emph{reproducibility} that allows validation of results. In this paper we make the case for a scientific data publication model on top of linked data and introduce the notion of \emph{Research Objects} as first class citizens for sharing and publishing.

/pdf/why-linked-data-is-not-enough-for-scientists-brbabctp82.pdf

Why Linked Data is Not Enough for Scientists

In science, quality is paramount. As scientists increasingly look to the Web to share and discover scientific data, there is a growing need to support the scientist in assessing the quality of that data. However, quality is an ambiguous and overloaded term. In order to support the scientific user in discovering useful data we have systematically examined the nature of "quality" by exploiting three, prevalent properties of scientific data sets: (1) that data quality is commonly defined objectively; (2) the provenance and lineage in its production has a well understood role; and (3) "fitness-for-use" is a definition of utility rather than quality or trust, where the quality and trust-worthiness of the data and the entities that produced that data inform its utility. Our study is presented in two stages. First we review existing information quality dimensions and detail an assessment-oriented classification. We introduce definitions for quality, trust and utility in terms of the entities required in their assessment; producer, provider, consumer, process, artifact and quality standard. Next we detail a novel and experimental approach to assessment by modelling the causal relationships between quality, trust, and utility dimensions through the construction of decision networks informed by provenance graphs. To ground and motivate our discussion throughout we draw on the European Bioinformatics Institute's Gene Ontology Annotations database. We present an initial demonstration of our approach with an example for ranking results from the Gene Ontology Annotation database using an emerging objective quality measure, the Gene Ontology Annotation Quality score.

Matthew Gamble

Papers

Why linked data is not enough for scientists

Research Objects: Towards Exchange and Reuse of Digital Knowledge

Using a suite of ontologies for preserving workflow-centric research objects

Why Linked Data is Not Enough for Scientists

Quality, trust, and utility of scientific data on the web: towards a joint model