The Digital Object Identifier (DOI) is a system for identifying content objects in the digital environment. DOIs are names assigned to any entity for use on Internet digital networks. Scientific data ...sets may be identified by DOIs, and several efforts are now underway in this area. This paper outlines the underlying architecture of the DOI system, and two such efforts which are applying DOIs to content objects of scientific data.
A recurrent problem with the way research data is stored, processed, and accessed is that scientific work environments, as well as research data infrastructures, remain isolated solutions that focus ...on data from specific disciplines or data produced within specific geographic regions, such as the European Union or the United States. Moreover, scientific work environments and research data infrastructures are based on a wide range of different technical architectures; this hampers the exchange of data among systems. In most cases, the research data infrastructures that are currently in operation remain isolated data silos that supply scientists with the data the systems were designed to deliver but do not allow scientists to discover all data that is available world-wide on a topic or to discover data on related topics provided by a different research data infrastructure.
Data Interoperability Pagano, Pasquale; Candela, Leonardo; Castelli, Donatella
Data science journal,
2013, Letnik:
12
Journal Article
Recenzirano
Odprti dostop
In the context of scientific investigations, data have acquired an ever growing leading role while their large scale, cross-community and cross-domain sharing have concurred to identify new ...investigation paradigms (Hey, Tansley, & Tolle, 2009). Unfortunately, data interoperability – a mandatory prerequisite for achieving the above scenarios – is still a difficult open research challenge. Both the “data” and “interoperability” concepts are difficult to be fully perceived and actually lead to different perceptions in diverse communities. This problem is further amplified when considered in the context of (global) research data infrastructures that are expected to serve a plethora of communities of practice (Lave & Wenger, 1991) potentially involved in very diverse application scenarios, each characterised by a specific sharing problem.
Data quality is an issue that touches on every aspect of the research data landscape and is therefore appropriate to examine in the context of planning for future research data infrastructures. As ...producers, researchers want to believe that they produce high quality data; as consumers, they want to obtain data of the highest quality. Data centres typically have stringent controls to ensure that they only acquire and disseminate data of the highest quality. Data managers will usually say that they improve the quality of the data they are responsible for. Much of the infrastructure that will emit, transform, integrate, visualise, manage, analyse, and disseminate data during its life will have dependencies, explicit or implicit, on the quality of the data it is dealing with.
Data Policy Parsons, Mark A
Data science journal,
2013, Letnik:
12
Journal Article
Recenzirano
Odprti dostop
The first purpose of data policy should be to serve the objectives of the organization or project sponsoring the collection of the data. With research data, data policy should also serve the broader ...goals of advancing scientific and scholarly inquiry and society at large. This is especially true with government-funded data, which likely comprise the vast majority of research data. Data policy should address multiple issues, depending on the nature and objectives of the data. These issues include data access requirements, data preservation and stewardship requirements, standards and compliance mechanisms, data security issues, privacy and ethical concerns, and potentially even specific collection protocols and defined data flows. The specifics of different policies can vary dramatically, but all data policies need to address data access and preservation. Research data gain value with use and must therefore be accessible and preserved for future access. This article focuses on data access. While policy might address multiple issues, at a first level it must address where the data stand on what Lyon (2009) calls the continuum of openness. Making data as openly accessible as possible provides the greatest societal benefit, and a central purpose of data policy is to work toward ethically open data access. An open data regime not only maximizes the benefit of the data, it also simplifies most of the other issues around effective research data stewardship and infrastructure development.
Data Discovery Weikum, Gerhard
Data science journal,
2013, Letnik:
12
Journal Article
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Discovery of documents, data sources, facts, and opinions is at the very heart of digital information and knowledge services. Being able to search, discover, compile, and analyse relevant information ...for a user’s specific tasks is of utmost importance in science (e.g., computational life sciences, digital humanities, etc.), business (e.g., market and media analytics, customer relationship management, etc.) , and society at large (e.g., consumer information, traffic logistics, health discussions, etc.).
The Oxford Dictionary defines provenance as “the place of origin, or earliest known history of something.” The term, when transferred to its digital counterpart, has morphed into a more general ...meaning. It is not only used to refer to the origin of a digital artefact but also to its changes over time. By changes in this context we may not only refer to its digital snapshots but also to the processes that caused and materialised the change. As an example, consider a database record r created at point in time t0; an update u to that record at time t1 causes it to have a value r’. In terms of provenance, we do not only want to record the snapshots (t0, r) and (t1, r’) but also the transformation u that when applied to (t0, r) results in (t1, r’), that is u(t0, r) = (t1, r’).
Data Security Lopez, Diego
Data science journal,
2013, Letnik:
12
Journal Article
Recenzirano
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There is a range of reasons for securing data and systems, from those already common nowadays (IPR, licenses, privacy, confidentiality, etc.) to others likely to appear in the future (distributed ...trust, reputation building, social collaboration, etc.). The systems need to manage the data according to these restrictions while maintaining other requirements for data infrastructures (e.g., scalability, interoperability, provenance), and also the systems themselves need to be secured against potential external attacks.
Discussions about funding, sustainability, and governance of any e-infrastructure service tend to be culmination points of the tensions between bottom-up and top-down, static and dynamic, and ...prescriptive and descriptive approaches. Data management brings in additional challenges due to the time aspect. While a computing centre or network technology might be considered obsolete after a few years, data management requirements often extend decades or even centuries into the future. Funding commitments need to be made for long periods of time, and decisions about sustainability and governance models could be seen as setting a precedent that ties down parties for years into the future. At the same time, it is anticipated that the amount of data being produced will continue to exceed the capacity of the managed (curated and otherwise maintained) data services, adding the need to discuss prioritisation of the different datasets.
Data Preservation Meghini, Carlo
Data science journal,
2013, Letnik:
12
Journal Article
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Digital information is a vital resource in our knowledge economy, valuable for research and education, science and the humanities, creative and cultural activities, and public policy (The Blue Ribbon ...Task Force on Sustainable Digital Preservation and Access, 2010). New high-throughput instruments, telescopes, satellites, accelerators, supercomputers, sensor networks, and running simulations are generating massive amounts of data (Thanos, 2011). These data are used by decision makers for improving the quality of life of citizens. Moreover, researchers are employing sophisticated technologies to analyse these data to address questions that were unapproachable just a few years ago (Helbing & Balietti, 2011). Digital technologies have fostered a new world of research characterized by immense datasets, unprecedented levels of openness among researchers, and new connections among researchers, policy makers, and the public (The National Academy of Sciences, 2009).
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