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The NERC Science Information Strategy Data Citation and Publication project aims to develop and formalise a method for formally citing and publishing the datasets stored in its environmental data centres. It is believed that this will act as an incentive for scientists, who often invest a great deal of effort in creating datasets, to submit their data to a suitable data repository where it can properly be archived and curated. Data citation and publication will also provide a mechanism for data producers to receive credit for their work, thereby encouraging them to share their data more freely.

Part 2: Information Systems, Information Modelling and SemanticsInternational audiencenetCDF is a well-known and widely used format to exchange array-oriented scientific data such as grids and time-series. We describe a new convention for encoding netCDF based on Linked Data principles called netCDF-LD. netCDF-LD allows metadata elements, given as string values in current netCDF files, to be given as Linked Data objects. netCDF-LD allows precise semantics to be used for elements and expands the type options beyond lists of controlled terms. Using Uniform Resource Identifiers (URIs) for elements allows them to refer to other Linked Data resources for their type and descriptions. This enables improved data discovery through a generic mechanism for element type identification and adds element type expandability to new Linked Data resources as they become available. By following patterns already established for extending existing formats, netCDF-LD applications can take advantage of existing software for processing Linked Data and supporting more effective data discovery and integration across systems

Producing new generation of digital public services from open data is of major interest to policymakers, practitioners and academia in the digital government community. Recent efforts in the area of Linked Statistical Data suggest that the associated multidimensional data cubes are excellent resources that could underpin data-driven digital public services. We describe in this paper a set of tools and approach to exploiting linked statistical data produced from the integration of streams of open marine datasets for developing digital services to support Marine Rescue Operations. We also highlight the opportunities enabled through co-creation activities as well as the benefit and challenges for scaling and sustaining the initiative.

The International Oceanographic Data and Information Exchange of UNESCO's Intergovernmental Oceanographic Commission (IOC-IODE) released a quality management framework for its National Oceanographic Data Centre (NODC) network in 2013. This document is intended, amongst other goals, to provide a means of assistance for NODCs to establish organisational data management quality management systems. The IOC-IODE's framework also promotes the accreditation of NODCs which have implemented a Data Management Quality Management Framework adhering to the guidelines laid out in the IOC-IODE's framework. In its submission for IOCE-IODE accreditation, Ireland's National Marine Data Centre (hosted by the Marine Institute) included a Data Management Quality Management model; a manual detailing this model and how it is implemented across the scientific and environmental data producing areas of the Marine Institute; and, at a more practical level, an implementation pack consisting of a number of templates to assist in the compilation of the documentation required by the model and the manual.