scholarly journals The Biological and Chemical Oceanography Data Management Office: Accelerating Scientific Discovery Through Responsive Management of Observational Oceanographic Data

2019 ◽  
Author(s):  
Danie Kinkade ◽  
Adam Shepherd ◽  
Matt Biddle ◽  
Nancy Copley ◽  
Christina Haskins ◽  
...  
Author(s):  
D T Penny ◽  
R S Adams ◽  
J Andrea ◽  
M R Lewis ◽  
P Lingras

2019 ◽  
Author(s):  
Tom Peterka ◽  
Deborah Bard ◽  
Janine Bennett ◽  
E. Wes Bethel ◽  
Ron Oldfield ◽  
...  

2018 ◽  
Vol 52 (3) ◽  
pp. 28-32 ◽  
Author(s):  
Chris Turner ◽  
Ian Gill

AbstractThe management of oceanographic data is particularly challenging given the variety of protocols for the analysis of data collection and model output, the vast range of environmental conditions studied, and the potentially enormous extent and volume of the resulting data sets and model results. Here, we describe the Research Workspace (the Workspace), a web platform designed around data management best practices to meet the challenges of managing oceanographic data throughout the research life cycle. The Workspace features secure user accounts and automatic file versioning to assist with the early stages of project planning and data collection. Jupyter Notebooks have been integrated into the Workspace to support reproducible numerical analysis and data visualization while making use of high-performance computer resources collocated with data assets. An ISO-compliant metadata editor has also been integrated into the Workspace to support data synthesis, publication, and reuse. The Workspace currently supports stakeholders across the ocean science community, from funding agencies to individual investigators, by providing a data management platform to meet the needs of big ocean data.


Author(s):  
Tom Peterka ◽  
Deborah Bard ◽  
Janine C Bennett ◽  
E Wes Bethel ◽  
Ron A Oldfield ◽  
...  

In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.


2019 ◽  
Vol 13 (2) ◽  
pp. 509-521 ◽  
Author(s):  
Adam Leadbetter ◽  
Ramona Carr ◽  
Sarah Flynn ◽  
Will Meaney ◽  
Siobhan Moran ◽  
...  

AbstractThe 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.


2020 ◽  
Author(s):  
Adam Leadbetter ◽  
Andrew Conway ◽  
Sarah Flynn ◽  
Tara Keena ◽  
Will Meaney ◽  
...  

<p>The ability to access and search metadata for marine science data is a key requirement for answering fundamental principles of data management (making data Findable, Accessible, Interoperable and Reusable) and also in meeting domain-specific, community defined standards and legislative requirements placed on data publishers. Therefore, in the sphere of oceanographic data management, the need for a modular approach to data cataloguing which is designed to meet a number of requirements can be clearly seen. In this paper we describe a data cataloguing system developed at and in use at the Marine Institute, Ireland to meet the needs of legislative requirements including the European Spatial Data Infrastructure (INSPIRE) and the Marine Spatial Planning directive.</p><p>The data catalogue described here makes use of a metadata model focussed on oceanographic-domain. It comprises a number of key classes which will be described in detail in the paper, but which include:</p><ul><li><strong>Dataset</strong> - combine many different parameters, collected at multiple times and locations, using different instruments</li> <li><strong>Dataset Collection</strong> - provides a link between a Dataset Collection Activity and a Dataset, as well as linking to the Device(s) used to sample the environment for a given range of parameters. An example of a Dataset Collection may be the Conductivity-Temperature-Depth profiles taken on a research vessel survey allowing the individual sensors to be connected to the activity and the calibration of those sensors to be connected with the associated measurements. </li> <li><strong>Dataset Collection Activity</strong> - a specialised dataset to cover such activities as research vessel cruises; or the deployments of  moored buoys at specific locations for given time periods</li> <li><strong>Platform</strong> - an entity from which observations may be made, such as a research vessel or a satellite</li> <li><strong>Programme</strong> - represents a formally recognized scientific effort receiving significant funding, requiring large scale coordination</li> <li><strong>Device</strong> - aimed at providing enough metadata for a given instance of an instrument to provide a skeleton SensorML record</li> <li><strong>Organisation</strong> - captures the details of research institutes, data holding centres, monitoring agencies, governmental and private organisations, that are in one way or another engaged in oceanographic and marine research activities, data & information management and/or data acquisition activities</li> </ul><p>The data model makes extensive use of controlled vocabularies to ensure both consistency and interoperability in the content of attribute fields for the Classes outlined above.</p><p>The data model has been implemented in a module for the Drupal open-source web content management system, and the paper will provide details of this application.</p>


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