Interoperability in physical model testing

The funders of research programmes, such as Horizon 2020 are increasingly requiring that the resulting publications and data are made openly available. The EC, for example, requires FAIR (Findable, Accessible, Interoperable, Reusable) data management. This is promoted through a set of principles and guidelines for experimenters to follow. These respect the technologies and intentions at each organisation, whilst providing positive practices to ensure that the experimental data produced is interoperable and reusable. The recommendations respect the wide variety of data management options for formats and structures; metadata, vocabularies and ontologies; and licenses and embargo periods. Where appropriate, specific technologies have been offered. They do not seek to impose an unrealistic set of rules and regulations which must be followed, rather they offer a set of sensible, modern principles and resources to move the community forwards together and bring it in line with other similar communities currently iterating their own data management practices. They also dovetail with the use of data repositories for the storage of data and papers

An ECOOP web portal for visualising and comparing distributed coastal oceanography model and in situ data

As part of a large European coastal operational oceanography project (ECOOP), we have developed a web portal for the display and comparison of model and in situ marine data. The distributed model and in situ datasets are accessed via an Open Geospatial Consortium Web Map Service (WMS) and Web Feature Service (WFS) respectively. These services were developed independently and readily integrated for the purposes of the ECOOP project, illustrating the ease of interoperability resulting from adherence to international standards. The key feature of the portal is the ability to display co-plotted timeseries of the in situ and model data and the quantification of misfits between the two. By using standards-based web technology we allow the user to quickly and easily explore over twenty model data feeds and compare these with dozens of in situ data feeds without being concerned with the low level details of differing file formats or the physical location of the data. Scientific and operational benefits to this work include model validation, quality control of observations, data assimilation and decision support in near real time. In these areas it is essential to be able to bring different data streams together from often disparate locations

From integration to fusion: the challenges ahead

The increasing complexity of numerical modelling systems in environmental sciences has led to the development of different supporting architectures. Integrated environmental modelling can be undertaken by building a ‘super model’ simulating many processes or by using a generic coupling framework to dynamically link distinct separate models during run-time. The application of systemic knowledge management to integrated environmental modelling indicates that we are at the onset of the norming stage, where gains will be made from consolidation in the range of standards and approaches that have proliferated in recent years. Consolidation is proposed in six topics: metadata for data and models; supporting information; Software-as-a-service; linking (or interface) technologies; diagnostic or reasoning tools; and the portrayal and understanding of integrated modelling. Consolidation in these topics will develop model fusion: the ability to link models, with easy access to information about the models, interface standards such as OpenMI and software tools to make integration easier. For this to happen, an open software architecture will be crucial, the use of open source software is likely to increase and a community must develop that values openness and the sharing of models and data as much as its publications and citation records

Improving the accessibility and re-use of environmental models through provision of model metadata : a scoping study

This poster presents the results of a scoping study funded under a recent Natural Environment Research Council (NERC) Environmental Data Call. The work was undertaken by the British Geological Survey (Nottinghamshire, UK) in collaboration with HR Wallingford (Oxfordshire, UK). This investigation was designed to better understand the problem that whilst the input data used for modelling frequently has metadata data available, and metadata is often routinely created for the datasets created by modelling, there was perceived to be a lack of schemes and systems to record metadata about the modelling process itself. From this analysis gaps in metadata provision were identified, and recommendations for further work to address these were identified

Open data from physical model tests: Lessons learned from related initiatives

The HYDRALAB network of European physical model laboratories (www.hydralab.eu) has a range of facilities that includes flumes, basins, ice facilities, rotating tanks and environmental facilities. Each institution had its own data collection system, there are many proprietorial data formats, a shortage of meta-data and no central effort to curate or preserve this data in a findable, accessible, interoperable and reusable (FAIR) way. HYDRALAB+ (2015-2019) is a European Commission Horizon 2020 project to support this network, which requires FAIR data management. HYDRALAB is reviewing the steps taken to make data openly accessible in related disciplines, so that lessons learned can be applied to HDRALAB+. The chosen communities were: (i) the University of Hull’s digital repository, (ii) EMODnet Baltic Checkpoint, (iii) OpenEarth and (iv) the FP7 projects PEGASO and MEDINA and the EU MED project COASTGAP. It is clear that no one solution can deal with all situations: different data types and requirements can best be dealt with by different approaches. Standards for meta-data should be applied, but no existing standard covers the range of situations faced by HYDRALAB. All can be extended in a bespoke manner (which can potentially be included in an update of the standard) but it is highly likely that more than one standard (and none) will be used in such a diverse community. This is perfectly acceptable, so long as the standard is published. There is also a clear need for guidance on the development of repositories where large volumes of data are collected and an understanding of how much needs to be made available on-line. Although there can be conflicts of interest between institutions that are developing policies for data management and projects that want a uniform approach to data management across all partners, systems today can generally accommodate this

Selection and integration of earth observation-based data for an operational disease forecasting system

The current increase in the volume and quality of Earth Observation (EO) data being collected by satellites offers the potential to contribute to applications across a wide range of scientific domains. It is well established that there are correlations between characteristics that can be derived from EO satellite data, such as land surface temperature or land cover, and the incidence of some diseases. Thanks to the reliable frequent acquisition and rapid distribution of EO data it is now possible for this field to progress from using EO in retrospective analyses of historical disease case counts to using it in operational forecasting systems. However, bringing together EO-based and non-EO-based datasets, as is required for disease forecasting and many other fields, requires carefully designed data selection, formatting and integration processes. Similarly, it requires careful communication between collaborators to ensure that the priorities of that design process match the requirements of the application. Here we will present work from the D-MOSS (Dengue forecasting MOdel Satellite-based System) project. D-MOSS is a dengue fever early warning system for South and South East Asia that will allow public health authorities to identify areas at high risk of disease epidemics before an outbreak occurs in order to target resources to reduce spreading of epidemics and improve disease control. The D-MOSS system uses EO, meteorological and seasonal weather forecast data, combined with disease statistics and static layers such as land cover, as the inputs into a dengue fever model and a water availability model. Water availability directly impacts dengue epidemics due to the provision of mosquito breeding sites. The datasets are regularly updated with the latest data and run through the models to produce a new monthly forecast. For this we have designed a system to reliably feed standardised data to the models. The project has involved a close collaboration between remote sensing scientists, geospatial scientists, hydrologists and disease modelling experts. We will discuss our approach to the selection of data sources, data source quality assessment, and design of a processing and ingestion system to produce analysis-ready data for input to the disease and water availability models

Position paper: Open web-distributed integrated geographic modelling and simulation to enable broader participation and applications

© 2020 The Authors Integrated geographic modelling and simulation is a computational means to improve understanding of the environment. With the development of Service Oriented Architecture (SOA) and web technologies, it is possible to conduct open, extensible integrated geographic modelling across a network in which resources can be accessed and integrated, and further distributed geographic simulations can be performed. This open web-distributed modelling and simulation approach is likely to enhance the use of existing resources and can attract diverse participants. With this approach, participants from different physical locations or domains of expertise can perform comprehensive modelling and simulation tasks collaboratively. This paper reviews past integrated modelling and simulation systems, highlighting the associated development challenges when moving to an open web-distributed system. A conceptual framework is proposed to introduce a roadmap from a system design perspective, with potential use cases provided. The four components of this conceptual framework - a set of standards, a resource sharing environment, a collaborative integrated modelling environment, and a distributed simulation environment - are also discussed in detail with the goal of advancing this emerging field

The European Marine Observation and Data Network (EMODnet): Visions and roles of the gateway to marine data in Europe

Marine data are needed for many purposes: for acquiring a better scientific understanding of the marine environment, but also, increasingly, as marine knowledge for decision making as well as developing products and services supporting economic growth. Data must be of sufficient quality to meet the specific users' needs. It must also be accessible in a timely manner. And yet, despite being critical, this timely access to known-quality data proves challenging. Europe's marine data have traditionally been collected by a myriad of entities with the result that much of our data are scattered throughout unconnected databases and repositories. Even when data are available, they are often not compatible, making the sharing of the information and data aggregation particularly challenging. In this paper, we present how the European Marine Observation and Data network (EMODnet) has developed over the last decade to tackle these issues. Today, EMODnet is comprised of more than 150 organizations which gather marine data, metadata, and data products and make them more easily accessible for a wider range of users. EMODnet currently consists of seven sub-portals: bathymetry, geology, physics, chemistry, biology, seabed habitats, and human activities. In addition, Sea-basin Checkpoints have been established to assess the observation capacity in the North Sea, Mediterranean, Atlantic, Baltic, Artic, and Black Sea. The Checkpoints identify whether the observation infrastructure in Europe meets the needs of users by undertaking a number of challenges. To complement this, a Data Ingestion Service has been set up to tackle the problem of the wealth of marine data that remain unavailable, by reaching out to data holders, explaining the benefits of sharing their data and offering a support service to assist them in releasing their data and making them available through EMODnet. The EMODnet Central Portal (www.emodnet.eu) provides a single point of access to these services, which are free to access and use. The strategic vision of EMODnet in the next decade is also presented, together with key focal areas toward a more user-oriented service, including EMODnet for business, internationalization for global users, and stakeholder engagement to connect the diverse communities across the marine knowledge value chain

Examining the “Urban Advantage” in Maternal Health Care in Developing Countries

Andrew Channon and colleagues outline the complexities of urban advantage in maternal health where the urban poor often have worse access to health care than women in rural areas

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance