Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models

The approaches taken to describe and develop spatial discretisations of the domains required for geophysical simulation models are commonly ad hoc, model or application specific and under-documented. This is particularly acute for simulation models that are flexible in their use of multi-scale, anisotropic, fully unstructured meshes where a relatively large number of heterogeneous parameters are required to constrain their full description. As a consequence, it can be difficult to reproduce simulations, ensure a provenance in model data handling and initialisation, and a challenge to conduct model intercomparisons rigorously. This paper takes a novel approach to spatial discretisation, considering it much like a numerical simulation model problem of its own. It introduces a generalised, extensible, self-documenting approach to carefully describe, and necessarily fully, the constraints over the heterogeneous parameter space that determine how a domain is spatially discretised. This additionally provides a method to accurately record these constraints, using high-level natural language based abstractions, that enables full accounts of provenance, sharing and distribution. Together with this description, a generalised consistent approach to unstructured mesh generation for geophysical models is developed, that is automated, robust and repeatable, quick-to-draft, rigorously verified and consistent to the source data throughout. This interprets the description above to execute a self-consistent spatial discretisation process, which is automatically validated to expected discrete characteristics and metrics.Comment: 18 pages, 10 figures, 1 table. Submitted for publication and under revie

Towards Exascale Scientific Metadata Management

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions

A linked data approach to publishing complex scientific workflows

Past data management practices in many fields of natural science, including climate research, have focused primarily on the final research output - the research publication - with less attention paid to the chain of intermediate data results and their associated metadata, including provenance. Data were often regarded merely as an adjunct to the publication, rather than a scientific resource in their own right. In this paper, we attempt to address the issues of capturing and publishing detailed workflows associated with the climate/research datasets held by the Climatic Research Unit (CRU) at the University of East Anglia. To this end, we present a customisable approach to exposing climate research workflows for the effective re-use of the associated data, through the adoption of linked-data principles, existing widely adopted citation techniques (Digital Object Identifier) and data exchange mechanisms (Open Archives Initiative Object Reuse and Exchange)

Seeing the trees as well as the forest: the importance of managing forest genetic resources

Reliable data on the status and trends of forest genetic resources are essential for their sustainable management. The reviews presented in this special edition of Forest Ecology and Management on forest genetic resources complement the first ever synthesis of the State of the World’s Forest Genetic Resources (SOW-FGR) that has just been published by the Food and Agriculture Organization. In this editorial, we present some of the key findings of the SOW-FGR and introduce the seven reviews presented in this special edition on: (1) tree genetic resources and livelihoods; (2) the benefits and dangers of international germplasm transfers; (3) genetic indicators for monitoring threats to populations and the effectiveness of ameliorative actions; (4) the genetic impacts of timber management practices; (5) genetic considerations in forest ecosystem restoration projects using native trees; (6) genetic-level responses to climate change; and (7) ex situ conservation approaches and their integration with in situ methods. Recommendations for action arising from the SOW-FGR, which are captured in the first Global Plan of Action for the Conservation, Sustainable Use and Development of Forest Genetic Resources, and the above articles are discussed. These include: increasing the awareness of the importance of and threats to forest genetic resources and the mainstreaming of genetic considerations into forest management and restoration; establishing common garden provenance trials to support restoration and climate change initiatives that extend to currently little-researched tree species; streamlining processes for germplasm exchange internationally for research and development; and the intelligent use of modern molecular marker methods as genetic indicators in management and for improvement purposes

Sustainable Development Report: Blockchain, the Web3 & the SDGs

This is an output paper of the applied research that was conducted between July 2018 - October 2019 funded by the Austrian Development Agency (ADA) and conducted by the Research Institute for Cryptoeconomics at the Vienna University of Economics and Business and RCE Vienna (Regional Centre of Expertise on Education for Sustainable Development).Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc

Sustainable Development Report: Blockchain, the Web3 & the SDGs

This is an output paper of the applied research that was conducted between July 2018 - October 2019 funded by the Austrian Development Agency (ADA) and conducted by the Research Institute for Cryptoeconomics at the Vienna University of Economics and Business and RCE Vienna (Regional Centre of Expertise on Education for Sustainable Development).Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc

Global to local genetic diversity indicators of evolutionary potential in tree species within and outside forests

There is a general trend of biodiversity loss at global, regional, national and local levels. To monitor this trend, international policy processes have created a wealth of indicators over the last two decades. However, genetic diversity indicators are regrettably absent from comprehensive bio-monitoring schemes. Here, we provide a review and an assessment of the different attempts made to provide such indicators for tree genetic diversity from the global level down to the level of the management unit. So far, no generally accepted indicators have been provided as international standards, nor tested for their possible use in practice. We suggest that indicators for monitoring genetic diversity and dynamics should be based on ecological and demographic surrogates of adaptive diversity as well as genetic markers capable of identifying genetic erosion and gene flow. A comparison of past and present genecological distributions (patterns of genetic variation of key adaptive traits in the ecological space) of selected species is a realistic way of assessing the trend of intra-specific variation, and thus provides a state indicator of tree genetic diversity also able to reflect possible pressures threatening genetic diversity. Revealing benefits of genetic diversity related to ecosystem services is complex, but current trends in plantation performance offer the possibility of an indicator of benefit. Response indicators are generally much easier to define, because recognition and even quantification of, e.g., research, education, breeding, conservation, and regulation actions and programs are relatively straightforward. Only state indicators can reveal genetic patterns and processes, which are fundamental for maintaining genetic diversity. Indirect indicators of pressure, benefit, or response should therefore not be used independently of state indicators. A coherent set of indicators covering diversity–productivity–knowledge–management based on the genecological approach is proposed for application on appropriate groups of tree species in the wild and in cultivation worldwide. These indicators realistically reflect the state, trends and potentials of the world’s tree genetic resources to support sustainable growth. The state of the genetic diversity will be based on trends in population distributions and diversity patterns for selected species. The productivity of the genetic resource of trees in current use will reflect the possible potential of mobilizing the resource further. Trends in knowledge will underpin the potential capacity for development of the resource and current management of the genetic resource itself will reveal how well we are actually doing and where improvements are required

Formal Provenance Representation of the Data and Information Supporting the National Climate Assessment

The Global Change Information System (GCIS) provides a framework for the formal representation of structured metadata about data and information about global change. The pilot deployment of the system supports the National Climate Assessment (NCA), a major report of the U.S. Global Change Research Program (USGCRP). A consumer of that report can use the system to browse and explore that supporting information. Additionally, capturing that information into a structured data model and presenting it in standard formats through well defined open inter- faces, including query interfaces suitable for data mining and linking with other databases, the information becomes valuable for other analytic uses as well