ORCID Profile
0000-0001-5911-6022
Current Organisation
Commonwealth Scientific and Industrial Research Organisation
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Interdisciplinary Engineering | Simulation and Modelling | Engineering Practice | Infrastructure Engineering and Asset Management
Productivity (excl. Public Sector) | Mining Machinery and Equipment | Expanding Knowledge in Engineering |
Publisher: SPE
Date: 19-10-2010
DOI: 10.2118/137667-MS
Abstract: Huge amounts of CH4 bound in natural gas hydrates lead to the idea of using hydrate bearing sediments as an energy resource. Natural gas hydrates remain stable as long as they are in mechanical, thermal and chemical equilibrium with their environments. Thus, for the production of gas from hydrate bearing sediments, at least one of these equilibrium states must be disturbed by depressurization, thermal stimulation or addition of chemicals such as CO2. In the framework of the German national gas hydrate research project SUGAR (Submarine Gas Hydrate Reservoirs), all three reaction routes - alone or in combination - are tested. The aim is to find the most flexible and efficient, but also environmentally friendly method for gas production from hydrates. One method in this context is the thermal stimulation using in situ combustion. Therefore, a heat exchange reactor was designed and tested for the catalytic oxidation of methane. Furthermore, a large scale reservoir simulator (Volume 425 l) was realized, to synthesize hydrates in sediments under conditions similar to nature and to test the efficiency of the reactor. Thermocouples placed in the reservoir simulator collect data regarding the expansion of the heat front, respectively. These data are used for numerical simulations for up scaling from laboratory to field conditions. However, thermal stimulation may be used alone or in combination with CO2 sequestration. Therefore, laboratory studies on the methane production from pure hydrate phases as well as hydrate bearing sediments by use of CO2 injection are investigated using several analytic tools such as Nuclear Magnetic Resonance spectroscopy, confocal Raman spectroscopy and X-ray diffraction. In this study we present the experimental set up of the large scale reservoir simulator and the reactor design. Preliminary results show that the catalytic oxidation of CH4 in a countercurrent heat exchange reactor operated as a temperature controlled, autothermal reaction outside of the flammability limits of CH4 is a safe and promising tool for the thermal stimulation of hydrates. In addition, preliminary results from the laboratory studies on the CO2-CH4 swapping process in pure and porefilling gas hydrates are presented focussing on the kinetics of this process.
Publisher: CSIRO
Date: 2018
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-10796
Abstract: & & The mapping of fracture networks from aerial photographs, tracing of fault scarps in digital elevation models, and digitisation of boundaries from potential field data is fundamental to many geological applications (e.g. resource management, natural hazard assessment, geotechnical stability etc.). However, conventional approaches to digitising geological features are labour intensive and do not scale.& & & & We describe how we designed an automated fracture detection workflow and implemented it in a cloud environment, using free and open-source software, as part of The Australian Scalable Drone Cloud (ASDC, asdc.io) national initiative. The ASDC aims to standardise and scale drone data, then analyse and translate it for users in academia, government, and industry.& & & & In this use case, we applied automatic ridge/edge detection techniques to generate trace maps of discontinuities (e.g. fractures or lineaments). The approach allows for internal classification based on statistical description and/or geometry and enhances the understanding of the internal structure of such networks. Further, photogrammetry and image analysis at scale can be limited by the available computing resources, but this issue was overcome through implementation in the cloud. The simple methods l serve as a basis for emerging techniques that utilise machine learning to fully automate the discontinuity identification and represents an important step in the cultural adoption of such tools in the Earth Science community.& & & & We deployed Open Drone Map (ODM) onto a cloud infrastructure to produce orthophoto mosaics from aerial images taken by UAV to implement this case study. We ported a fracture detection and mapping algorithm from Matlab to Python for the image analysis. The image analysis workflow is orchestrated through a Jupyter Notebook on a Jupyter Hub. The resulting prototype workflow will be used to better scope the services needed to manage the ASDC platform, like user management and data logistics.& &
Publisher: No publisher found
Date: 2018
DOI: 10.25919%2FY7HT-R568
Publisher: Zenodo
Date: 2022
Publisher: Ubiquity Press, Ltd.
Date: 10-11-2022
DOI: 10.5334/JCAA.96
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-4432
Abstract: & & As open source geospatial mapping toolkits and platforms continue to develop and mature, the developers of web portals using these solutions need to regularly review and revaluate their technology choices in order to stay up to date and provide the best possible experience and functionality to their users. We are currently undergoing such a refresh with our AuScope Discovery Portal, Virtual Geophysics Laboratory, and the AuScope 3D Geological Models Portal. The task of deciding which solutions to utilise as part of the upgrade process is not to be underestimated. Our main evaluation criteria include the ability to support commonly used map layer formats and web service protocols, support for 3D display capabilities, community size and activity, ease of adding custom display and scientific workflow / processing widgets, cost and benefits of integration with existing components and maintainability into the future. We are beginning a journey to update and integrate our portals& #8217 functionality and will outline the decision process and conclusions of our investigations as well as the detailed evaluation of web based geospatial solutions against our functional and operational criteria.& &
Publisher: Pensoft Publishers
Date: 12-10-2022
DOI: 10.3897/RIO.8.E94544
Abstract: With the advent of technological advances in research settings, scientific collections including s le material became on par with big data. Consequently there is a widespread need to highlight and recognise the inherent value of s les coupled with efforts in unlocking s le potential as resources for new scientific discovery. S les with informative metadata can be more easily discoverable, more readily shared and reused, allowing reanalysis of associated datasets, avoiding duplicate efforts, and providing metaanalysis yielding considerably enhanced insight. Metadata provides the framework for a consistent, systematic and standardized collection of s le information, enabling users to identify the availability of research output from the s les, relevancy to their intended use, and a way to conveniently identify s le material as well as access provenance information related to the physical s les. Researchers need this essential information aiding their decision making process on the quality, usability and accessibility of the s les and associated datasets. We propose to explore the practical implementation of FAIR Digital Objects (FDO) for biological life science physical s les and practically how to create an FDO framework centralized around biospecimen s les, linked datasets, s le information and PIDs (Persistent Identifiers)Klump et al. 2021. This effort is highly relevant to enhancing the portability of s le information between multiple repositories and other kinds of resources (e.g. e-infrastructures). In this session we would like to present our current work in order to mobilize the community to define the FAIR Digital Object Architecture for biospecimen in life science including all infrastructure components e.g. metadata, PIDs and their integration with technical solutions. To that end, in our community of practice we aim to: What : Identify the minimum set of attributes required for describing biospecimen in biological life science (Minimal Information About a Biological S le, MIABS) with ontological mapping for semantic unambiguity and machine actionability. Identify the required attributes for registering PIDs for biospecimens and how that will operate in an FDO ecosystem. This will pave the way for a framework of coupling the descriptive metadata to the digital object in a FAIR and comprehensive manner. How : Define a semantic FDO model for biospecimens. Define the role of biospecimen PIDs registration information and kernel attributes and how that translates to machine actionability and programmatic decisions. Define the implementation specifics for integration of biospecimen FDOs with operational infrastructure e.g. e-infrastructures, repositories and machines. What : Identify the minimum set of attributes required for describing biospecimen in biological life science (Minimal Information About a Biological S le, MIABS) with ontological mapping for semantic unambiguity and machine actionability. Identify the required attributes for registering PIDs for biospecimens and how that will operate in an FDO ecosystem. This will pave the way for a framework of coupling the descriptive metadata to the digital object in a FAIR and comprehensive manner. Identify the minimum set of attributes required for describing biospecimen in biological life science (Minimal Information About a Biological S le, MIABS) with ontological mapping for semantic unambiguity and machine actionability. Identify the required attributes for registering PIDs for biospecimens and how that will operate in an FDO ecosystem. This will pave the way for a framework of coupling the descriptive metadata to the digital object in a FAIR and comprehensive manner. How : Define a semantic FDO model for biospecimens. Define the role of biospecimen PIDs registration information and kernel attributes and how that translates to machine actionability and programmatic decisions. Define the implementation specifics for integration of biospecimen FDOs with operational infrastructure e.g. e-infrastructures, repositories and machines. Define a semantic FDO model for biospecimens. Define the role of biospecimen PIDs registration information and kernel attributes and how that translates to machine actionability and programmatic decisions. Define the implementation specifics for integration of biospecimen FDOs with operational infrastructure e.g. e-infrastructures, repositories and machines. Relevant technologies include: RO-Crate, Persistent identifiers, and metadata schemas Relevant technologies include: RO-Crate, Persistent identifiers, and metadata schemas The recent partnership between IGSN and DataCite described below is a catalyst in this call of action to the FDO community to build a Community of Practice (CoP) specifically focused on biospecimen s les. Community of practice: IGSN e.V. announced a partnership with DataCite, in which DataCite’s registration services and supporting technology for Digital Object Identifiers (another type of PID) are now being leveraged to register IGSN IDs, and thus ensure the ongoing sustainability of the IGSN ID infrastructure. Importantly, the two organizations are also focusing the community’s efforts on advocacy of PIDs for physical s les and expanding the global s le ecosystem. Assisted by the DataCite S les Community Manager, the IGSN e.V. is establishing working groups (Communities of Practice) within different research domains to support development and promotion of standardized methods for identifying, citing, and locating physical s les. In particular, the partnership wishes to work with the Bios les community to elaborate the necessary information (metadata) such that those within the community have a full understanding of a physical s le when its descriptive webpage is accessed via its PID, see this ex le.
Publisher: MDPI AG
Date: 06-07-2021
DOI: 10.3390/IJGI10070459
Abstract: Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. Geological processes at depth ultimately control and are linked to the resulting surface features. Large regions in Australia, West Africa, India, and China are blanketed by cover (intensely weathered surface material and/or later sediment deposition, both up to hundreds of metres thick). Mineral exploration through cover poses a significant technological challenge worldwide. Classifying and understanding landscape types and their variability is of key importance for mineral exploration in covered regions. Landscape variability expresses how near-surface geochemistry is linked to underlying lithologies. Therefore, landscape variability mapping should inform surface geochemical s ling strategies for mineral exploration. Advances in satellite imaging and computing power have enabled the creation of large geospatial data sets, the sheer size of which necessitates automated processing. In this study, we describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available digital elevation model, derived data, and s le landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve approximately 98% classification accuracy with a reasonable runtime of 48 minutes on a single Intel® Core™ i7-8550U CPU core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, whereas smaller tiles result in a more detailed and, at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region. Mapping landscape variability at a large scale can be used globally as a fundamental tool for guiding more efficient mineral exploration programs in regions under cover.
Publisher: Zenodo
Date: 2021
Publisher: Zenodo
Date: 2019
Publisher: Zenodo
Date: 2019
Publisher: CSIRO
Date: 2018
Publisher: MDPI AG
Date: 22-05-2019
DOI: 10.20944/PREPRINTS201905.0274.V1
Abstract: The use of small Unmanned Aircraft Systems (sUAS ) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on the outcomes of a four-year-long community-engagement-based investigation into what tools, practices, and challenges currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data & ndash as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases & ndash is characterised as both sharing many traits with traditional remote sensing data and also as exhibiting & ndash as common across the spectrum of disciplines and use cases & ndash novel characteristics that require novel data support infrastructure. And (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We then conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost.
Publisher: Research Data Alliance
Date: 2022
DOI: 10.15497/RDA00070
Publisher: No publisher found
Date: 2017
DOI: 10.25919%2FJTC2-1R88
Publisher: Open Science Framework
Date: 2022
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-13317
Abstract: & & When researchers collect or create physical s les they usually assign a user-generated number to each s le. Subsequently, that s le can be submitted to a laboratory for analysis of a variety of analytes. However, as geoanalytical laboratories are generating ever increasing volumes of data, most laboratories have automated workflows and it is no longer feasible for laboratories to use researcher-supplied s le numbers, particularly as it is not guaranteed that user-supplied numbers will be unique in comparison to numbers submitted by other users to the same laboratory. To address this issue new, laboratory-generated numbers may be assigned to that s le.& & & & Moreover, as a single laboratory rarely has the capability to offer all analytical techniques, in idual s les tend to move from laboratory to laboratory to acquire the desired suite of analytes.& Each laboratory may implement a different number to that s le. At the conclusion of their project, the researcher may submit the same s le to a museum or institutional repository, where the s le will be assigned yet another institution-generated number to ensure that all s les are uniquely identified in their repository.& & & & & Ultimately, by the time the researcher submits an article to a journal and wants to identify s les in the text or tables, they may have a multitude of locally-generated numbers to choose from. Not one of the locally assigned numbers to that s le can be guaranteed to be globally unique. It is also unlikely that any of these local numbers will be persistent over the longer term (decades), or be resolvable to enable the location of the identified resource or any information about it elsewhere on the web (metadata, landing page, services related to it, etc).& & & & Globally unique, persistent, resolvable identifiers such as the IGSN play a critical role in the unique identification of geoanalytical s les that pass between systems and organisations: they cannot be duplicated by another researcher, laboratory or s le repository. They persistently link to information about the origin of the s le to personas in the creation of the s le (collector, institution, funder) to the laboratory data and their creation (analyst, laboratory, institution, funder, data software) and to the s le curation phase (curator, repository, funder). They connect the phases of a s le& #8217 s path from collection in the field to lab analysis to the synthesis/research phase to the publication to the archive. Globally unique s le identifiers also enable cross linkages to any artefacts derived from that s le (images, analytical data, other articles). Further, identifiers like IGSN enable sub s les or s le splits to be linked back to their parent s le, creating a holistic picture of any information derived from the initial s le.& & & & & Hence, best practice is clearly to assign the globally unique resolvable identifier to the initial resource. Like a birth certificate, the identifier can be carried through the progressive stages of the research & #8216 life-cycle& #8217 including laboratory analysis, generation of further data, images, publication, and ultimately curation and preservation. Where any subs les are derived, they, and any data generated on them, can be linked back to the parent identifier.& &
Publisher: Elsevier BV
Date: 06-2015
Publisher: Ubiquity Press, Ltd.
Date: 2021
DOI: 10.5334/DSJ-2021-033
Publisher: Springer International Publishing
Date: 2023
Publisher: Springer International Publishing
Date: 2017
Publisher: Elsevier BV
Date: 11-2016
Publisher: American Geophysical Union (AGU)
Date: 16-01-2018
DOI: 10.1029/2018EO090337
Abstract: International Symposium on Linking Environmental Data and S les Canberra, Australia, 29 May to 2 June 2017
Publisher: Ubiquity Press, Ltd.
Date: 28-06-2017
DOI: 10.5334/DSJ-2017-034
Publisher: Springer International Publishing
Date: 2022
Publisher: PeerJ
Date: 09-04-2018
DOI: 10.7287/PEERJ.PREPRINTS.26836V1
Abstract: Acquiring data on the occurrences of many types of difficult to identify objects are often still made by human observation, e.g. in bio ersity and paleontologic research. Existing computer counting programs used to record such data have various limitations, including inflexibility and cost. We describe a pair of new open-source programs for this purpose - Raritas and RaritasVox, which share a similar graphical user interface for mouse based counting, and file output format. Raritas is written in Python and can be run as a standalone app for recent versions of either MacOS or Windows, or from the command line as easily customized source code. RaritasVox in addition supports voice based counting but is written in Java and is more complex to install or modify. Both programs explicitly support a rare category count mode which makes it easier to collect quantitative data on rare categories, e.g. rare species which are important in bio ersity surveys. Lastly, as to our knowledge no standards exist yet, we describe a new stratigraphic occurrence data (SOD) unitary file format which combines extensive metadata and a flexible structure for recording occurrence data of species or other categories in a series of s les.
Publisher: Springer Science and Business Media LLC
Date: 22-07-2016
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-10569
Abstract: & & The SciDataMover platform is a discipline- and scale-agnostic, lightweight, open source Data Movement Platform that transfers data, coupled with metadata from laboratories to shared workspaces then to repositories. The SciDataMover Platform leverages lightweight existing technologies that have a demonstrated capacity to be sustainably managed and can be affordably maintained.& & & & Despite significant investments in analytical instruments in Australian research laboratories relevant to earth sciences and particularly geochemistry, there has been underinvestment in storage and efficient, lossless transfer of data from & #8216 Private& #8217 lab instruments to & #8216 Collaboration& #8217 domains where researchers can analyse and share data, and then persist it to trusted & #8216 Publication& #8217 domains where researchers can persistently store the data that supports their scholarly publications.& & & & SciDataMover is a FAIR data movement platform that enables data from instruments to move in a scalable and sustainable manner and comprises:& & & & ) a data service to transfer data/metadata directly from instruments& br& ) collaboration areas to process, refine, standardise and share this data& br& ) a mechanism to transfer data supporting publications to a trusted repository (e.g., domain, institutional).& & & & The Platform, being built off existing components will enable researchers to have readily available access to laboratory data when and where they need it, along with the ability to collaborate with colleagues even during a pandemic where physical distancing is required. The benefits of SciDataMover are long term persistence of laboratory-generated data (at various stages from minimally processed to final published form), greater collaboration efficiency and enhanced scientific reproducibility.& &
Publisher: Elsevier BV
Date: 06-2001
Publisher: Elsevier BV
Date: 10-2000
Publisher: CNRI Acct
Date: 2011
Publisher: Informa UK Limited
Date: 11-11-2019
Publisher: Springer Science and Business Media LLC
Date: 10-07-2014
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-10897
Abstract: & & In October 2021, DataCite and the IGSN e.V. signed an agreement to form a partnership to support the global adoption, implementation, and use of physical s le identifiers. Both DataCite and IGSN currently offer the ability to provide Globally Unique Persistent, Resolvable Identifiers (GUPRIs) within the overall research ecosystem, and the proposed collaboration will bring together the strengths of each organization.& & & & DataCite is a community-led organisation that has been providing the means to create, find, cite, connect, and use research across 47 countries globally since 2009. DataCite provides persistent identifiers (DOIs) for research data and other research outputs, and supports the efforts of several identifier communities. DataCite also develops services that make it easier for researchers to connect and share their DOIs with the broader research ecosystem.& & br& IGSN e.V. is an international, non-profit organization with more than 20 members and has a narrower focus than DataCite. The core purpose of IGSN is to enable transparent and traceable connections between s les, instruments, grants, data, publications, people and organizations. Since 2011, IGSN has provided a central registration system that enables researchers to apply a globally unique and persistent identifier for physical s les.& & & & The proposed partnership will enable IGSN to leverage DataCite DOI registration while allowing IGSN to focus on community efforts such as promoting and expanding the global s les ecosystem and supporting new research and best practice in methods of identifying, citing, and locating physical s les. DataCite will provide the IGSN ID registration services and support to ensure the ongoing sustainability of the IGSN PID infrastructure and its integration with the global PID ecosystem.& & & & This partnership is an opportunity for IGSN to reenvision its governance and community engagement and to reassess how the IGSN can best serve the community in today& #8217 s open science ecosystem. This talk will focus on the developing changes to the IGSN governance and community efforts.& br& Different research communities have a wide range of requirements towards metadata and identification of s les. The IGSN plans to develop an international & #8216 Community of Communities& #8217 which will include members from the global s les community across multiple disciplines. The community will support varying levels of skills with PIDs and metadata. It will enable cohesion around the use of IGSN thus enabling greater research discovery, innovation and advancement for s les.& & & & The IGSN S les Community (IGSN SC) aspires to be a collaborative space for community development that promotes the use of s les and their connections to any derived observations, images, and analytical data.& &
Publisher: Department for Energy and Mining, South Australia / CSIRO
Date: 2018
Publisher: MIT Press - Journals
Date: 2020
DOI: 10.1162/DINT_A_00026
Abstract: Research data currently face a huge increase of data objects with an increasing variety of types (data types, formats) and variety of workflows by which objects need to be managed across their lifecycle by data infrastructures. Researchers desire to shorten the workflows from data generation to analysis and publication, and the full workflow needs to become transparent to multiple stakeholders, including research administrators and funders. This poses challenges for research infrastructures and user-oriented data services in terms of not only making data and workflows findable, accessible, interoperable and reusable, but also doing so in a way that leverages machine support for better efficiency. One primary need to be addressed is that of findability, and achieving better findability has benefits for other aspects of data and workflow management. In this article, we describe how machine capabilities can be extended to make workflows more findable, in particular by leveraging the Digital Object Architecture, common object operations and machine learning techniques.
Publisher: Zenodo
Date: 2022
Publisher: Zenodo
Date: 2022
Publisher: Elsevier BV
Date: 07-2014
Publisher: MDPI AG
Date: 19-01-2011
DOI: 10.3390/EN4010151
Publisher: Verlag Werner Hülsbusch, Glückstadt.; Universitätsverlag Göttingen
Date: 2013
DOI: 10.3249/WEBDOC-39006
Publisher: Ubiquity Press, Ltd.
Date: 2006
DOI: 10.2481/DSJ.5.79
Publisher: Ubiquity Press, Ltd.
Date: 2017
DOI: 10.5334/DSJ-2017-052
Publisher: American Geophysical Union (AGU)
Date: 2005
DOI: 10.1029/2005EO030004
Publisher: Springer Science and Business Media LLC
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 10-10-2007
Publisher: Center for Open Science
Date: 15-12-2020
Abstract: In less than one decade the open-access data journal Earth System Science Data (ESSD, a member of the Copernicus Open Access Publisher family) grew from a start-up venture into one of the highest-rated journals in global environmental science. Stimulated by data needs of the International Polar Year 2007-2008, ESSD now serves a very broad community of data providers and users, ensuring that users get free and easy access to quality data products and that providers gain full public credit for preparing, describing and sharing those products. Adopting technology and practices from research journals, ESSD moved data publication from an abstract concept to a working enterprise several publishers now support similar data-sharing journals. As it confronts increasing challenges and barriers, ESSD serves as a prominent voice for and an ex le of emphatic fully-free fully-open global data access. Data journals such as ESSD clearly meet a strong community need.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-16396
Abstract: AuScope is Australia& #8217 s premier research infrastructure provider to the national geoscience community, working on fundamental geoscience questions and grand challenges for the common good and into the future. The organisation is funded by the Australian Government via the National Collaborative Research Infrastructure Strategy (NCRIS). One of its programs & #8211 the AuScope Virtual Research Environment (AVRE) & #8211 provides a unifying technological platform for AuScope Programs& #8217 data and analytical needs and increases the uptake of data-driven research through various outreach activities. One such activity was the development of the AVRE Build Program that successfully ran over the past three years and aimed to improve the engagement of research teams and assist with translating scientific requirements into reusable solutions ranging from data management to numerical modelling and complex data visualisation.Developing scientific software solutions with the erse backgrounds of stakeholders involved is challenging in itself. In a multidisciplinary environment, we had to collaboratively develop and strengthen our design approach to break the & #8220 language barrier& #8221 between scientists and technologists to achieve greater user acceptance and ongoing adoption of developed solutions. Our approach stems from the Rapid Application Development and the Agile project management methodologies & #8211 both popular and widely applied in the realm of software engineering.We take a user-centred design approach and involve researchers with a vested interest in the project outcomes in all stages of the iterative development lifecycle. We pay particular attention to the definition of project success and a minimum viable project, requirements analysis, wireframing and prototyping through the project launch and handover phases. Organising projects into short, focused sprints with the direct involvement of researchers has allowed us to stay focused on our objectives, deliver projects in short timeframes, and maintain momentum. Through this process and the direct involvement of researchers in the design aspects of the product, we fostered a close collaborative relationship with our users, created a sense of ownership and, as a result, cemented the longevity of the project under the researchers& #8217 custodianship.Herein, we detail our approach to scientific software development, the social aspects of our experience of cross-institutional and cross-domain collaboration, the challenges we have experienced, and the successes we have achieved. Although still offering room for improvement, the methodologies we employ have proved successful over the last three years, producing low-maintenance tools that are freely accessible to researchers. They helped to engage a wider audience and improve the speed of science delivery, which inspired other projects within the CSIRO Mineral Resources Business Unit and external organisations to implement similar programs.
Publisher: MDPI AG
Date: 02-03-2016
DOI: 10.3390/IJGI5030025
Publisher: Zenodo
Date: 2020
Publisher: Cambridge University Press
Date: 19-05-2011
Publisher: Zenodo
Date: 2020
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-12001
Abstract: & & Like many research data infrastructures, the IGSN Global S le Number started as a research project. The rapid uptake of IGSN in the last five years as well as the expansion of ersity of use cases, in particular beyond the geosciences, mean that IGSN has outgrown its current structure as implemented in 2011, and the technology is in urgent need of a refresh. The expected exponential growth of the operation requires the IGSN Implementation Organization (IGSN e.V.) to better align the organisation and technical architecture.& & & & In 2018, the Alfred P. Sloan Foundation awarded a grant to redesign and improve the IGSN, to & #8220 achieve a trustworthy, stable, and adaptable architecture for the IGSN as a persistent unique identifier for material s les, both technically and organizationally, that attracts, facilitates, and satisfies participation within and beyond the Geosciences, that will be a reliable component of the evolving research data ecosystem, and that is recognized as a trusted partner by data infrastructure providers and the science community alike.& #8221 & & & & & IGSN is not the first PID service provider to make the transition from project to product and there are lessons to be learnt from other PID services. To this end, the project invited experts in the field of research data infrastructures and facilitated workshops to develop an organisational and technological strategy and roadmap towards long-term sustainability of the IGSN.& & & & & To be sustainable, a research data infrastructure like IGSN has to have a clearly defined service or product, underpinned by a scalable business model and technical system. We used the Lean Canvas to define the IGSN services. The resulting definition of service components helped us define IGSN user communities, cost structures and potential income streams. The workshop discussions had already highlighted the conflicting aims between offering a comprehensive service and keeping services lean to reduce their development and operational costs. Building on the Lean Canvas, the definition of a minimum viable product helped to define the role of the IGSN e.V. and the roles for actors offering value-added services based in IGSN outside of the core operation.& &
Publisher: Research Data Alliance
Date: 2020
DOI: 10.15497/RDA00042
Publisher: Research Data Alliance
Date: 2020
DOI: 10.15497/RDA00041
Publisher: European Association of Geochemistry
Date: 2022
Publisher: Wiley
Date: 02-2000
Publisher: MDPI AG
Date: 04-2014
DOI: 10.3390/EN7042148
Publisher: Zenodo
Date: 2022
Publisher: Zenodo
Date: 2022
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-3820
Abstract: & & Remotely Piloted Aircraft (RPA), commonly known as drones, provide sensing capabilities that address the critical scale-gap between ground- and satellite-based observations. Their versatility allows researchers to deliver near-real-time information for society.& & & & Key to delivering RPA information is the capacity to enable researchers to systematically collect, process, manage and share RPA-borne sensor data. Importantly, this should allow vertical integration across scales and horizontal integration across different RPA deployments. However, as an emerging technology, the best practice and standards are still developing and the large data volumes collected during RPA missions can be challenging.& & & & Australia& #8217 s Scalable Drone Cloud (ASDC) aims to coordinate and standardise how scientists from across earth, environmental and agricultural research manage, process and analyse data collected by RPA-borne sensors, by establishing best practices in managing 3D-geospatial data and aligned with the FAIR data principles.& & & & The ASDC is building a cloud-native platform for research drone data management and analytics, driven by exemplar data management practices, data-processing pipelines, and search and discovery of drone data. The aim of the platform is to integrate sensing capabilities with easy-to-use storage, processing, visualisation and data analysis tools (including computer vision / deep learning techniques) to establish a national ecosystem for drone data management.& & & & The ASDC is a partnership of the Monash Drone Discovery Platform, CSIRO and key National Collaborative Research Infrastructure (NCRIS) capabilities including the Australian Research Data Commons (ARDC), Australian Plant Phenomics Facility (APPF), Terrestrial Ecosystem Research Network (TERN), and AuScope.& & & & This presentation outlines the roadmap and first proof-of-concept implementation of the ASDC.& &
Publisher: Pensoft Publishers
Date: 21-06-2019
DOI: 10.3897/BISS.3.37334
Abstract: There is growing recognition that unambiguous citation and tracking of physical s les allows previously impossible linking of s les to data and publications, linking and integration of s le-based observations across data systems, and paves the road towards advanced data mining of s le-based data. And in recent years, there has been an uptake in the use of Persistent Identifiers (PIDs) for physical s les to support such citation and tracking. The IGSN (International Geo S le Number) is a PID for physical s les. It was originally developed for the solid earth sciences, and has evolved into an international PID system with members in five continents and a network of active allocating agents. It has been adopted by a growing number and range of stakeholders worldwide, including national geological surveys, research infrastructure providers, collection curators, researchers, and data managers, and by other disciplines that need to refer to physical s les. Nearly 6.9 million s les have been registered with IGSNs so far. The IGSN system uses the Handle System (Kahn and Wilensky 1995 see also Handle.Net ® ) and has an international organization, IGSN e.V., to manage its governance structure and the technical architecture. The recent expansion of the IGSN beyond the geosciences into other domains such as bio ersity, archeology, and material sciences confirms the power of its concept and implementation, but imposes substantial pressures on the existing capacity and capabilities of the IGSN architecture and its governing organization. Modifications to the IGSN organizational and technical architecture are necessary at this point to keep pace with the growing demand and expectations. These changes are also necessary to ensure trustworthy and sustainable services for PID registration and resolution in a maturing research data ecosystem. The essential criteria for a trustworthy system include an organizational foundation that ensures longevity, sustainability, proper governance, and regular quality assessment of registration services. It also includes a reliable and secure technical platform, based on open standards, which is sufficiently scalable and flexible to accommodate the growing ersity of specimen types, use cases, and stakeholder requirements. In 2018, a major planning project for the IGSN was funded by the Alfred P. Sloan Foundation. An international group of experts participates in re-designing and improving the existing organization and technical architecture of the IGSN system, revising the current business model of the IGSN e.V. and professionalizing its operations. The goal is for the IGSN system to be able to respond to, and support in a sustainable manner, the rapidly growing demands of a global and increasingly multi-disciplinary user community, and to ensure that the IGSN will be a trustworthy, stable, and adaptable persistent identifier system for material s les, both technically and organizationally. The end result should also satisfy and facilitate participation across research domains, and will be a reliable component of the evolving research data ecosystem. Finally, it will ensure that the IGSN is recognized as a trusted partner by data infrastructure providers and the science community alike.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-12864
Abstract: Reproducible research necessitates full transparency and integrity in data collection (e.g. from observations) or generation of data, and further data processing and analysis to generate research products. However, Earth and environmental science data are growing in complexity, volume and variety and today, particularly for large-volume Earth observation and geophysics datasets, achieving this transparency is not easy. It is rare for a published data product to be created in a single processing event by a single author or in idual research group. Modern research data processing pipelines/workflows can have quite complex lineages, and it is more likely that an in idual research product is generated through multiple levels of processing, starting from raw instrument data at full resolution (L0) followed by successive levels of processing (L1-L4), which progressively convert raw instrument data into more useful parameters and formats. Each in idual level of processing can be undertaken by different research groups using a variety of funding sources: rarely are those involved in the early stages of processing/funding properly cited.The lower levels of processing are where observational data essentially remains at full resolution and is calibrated, georeferenced and processed to sensor units (L1) and then geophysical variables are derived (L2). Historically, particularly where the volumes of the L0-L2 datasets are measured in Terabytes to Petabytes, processing could only be undertaken by a minority of specialised scientific research groups and data providers, as few had the expertise/resources/infrastructures to process them on-premise. Wider availability of colocated data assets and HPC/cloud processing means that the full resolution, less processed forms of observational data can now be processed remotely in realistic timeframes by multiple researchers to their specific processing requirements, and also enables greater exploration of parameter space allowing multiple values for the same inputs to be trialled. The advantage is that better-targeted research products can now be rapidly produced. However, the downside is that far greater care needs to be taken to ensure that there is sufficient machine-readable metadata and provenance information to enable any user to determine what processing steps and input parameters were used in each part of the lineage of any released dataset/data product, as well as be able to reference exactly who undertook any part of the acquisition rocessing and identify sources of funding (including instruments/field c aigns that collected the data).The use of Persistent Identifiers (PIDs) for any component objects (observational data, synthetic data, software, model inputs, people, instruments, grants, organisations, etc.) will be critical. Global and interdisciplinary research teams of the future will be reliant on software engineers to develop community-driven software environments that aid and enhance the transparency and reproducibility of their scientific workflows and ensure recogniton. The advantage of the PID approach is that not only will reproducibility and transparency be enhanced, but through the use of Knowledge Graphs it will also be possible to trace the input of any researcher at any level of processing, whilst funders will be able to determine the impact of each stage from the raw data capture through to any derivative high-level data product.& &
Publisher: CSIRO
Date: 2018
Publisher: re3data.org
Date: 2012
DOI: 10.2312/RE3.001
Publisher: Pensoft Publishers
Date: 21-05-2018
DOI: 10.3897/BISS.2.26615
Abstract: Life sciences research, and even more specifically bio ersity sciences research, has yet to coalesece on a single system of identifiers for specimens (physical s les collected for research) or even a single set of standards for identifiers. Diverse identifier systems lead to duplication and ambiguity, which in turn lead to challenges in finding specimens, tracking and citing their usage, and linking them to data. Other research disciplines provide experience that bio ersity sciences could use to overcome these challenges. Earth sciences/geology may be the most advanced discipline in this regard, thanks to the use of the International GeoS le Number (IGSN) system, which was established to provide globally unique identifiers for geological s les. The original motivation of IGSN was to overcome duplication of s le numbers reported in the scientific literature and to support the correlation of observations on the same s les carried out by different laboratories and reported in different publications. The IGSN system is managed through a small set of 'allocating agents' who act on behalf of a national agency or community, under the overall coordination of the IGSN Organization - a volunteer group representing a mixture of research institutions and agencies. Similar to widely-recognized Digital Object Identifiers (DOIs), the primary requirement of an allocating agent is to maintain the mapping from an IGSN to a web 'landing page' corresponding to each s le. A standard (minimal) schema for describing s les registered with IGSN has been developed, but in idual IGSN allocating agents will often supplement the base metadata with additional information. Other efforts are working on cross-disciplinary s le metadata schemas, but no single core standard has been agreed upon yet. An important part of the development of the IGSN system has been an engagement with scholarly publishers, with a goal of making each mention of an IGSN within a report or paper be a hyperlink, and also for links to other observations relating to the same s le to be automatically highlighted by the publisher.
Publisher: CSIRO
Date: 2020
DOI: 10.25919/5DQE-6X48
Publisher: re3data.org
Date: 2013
DOI: 10.2312/RE3.004
Publisher: Public Library of Science (PLoS)
Date: 04-11-2013
Publisher: Zenodo
Date: 2019
Publisher: CSIRO
Date: 2017
DOI: 10.25919/JTC2-1R88
Publisher: IEEE
Date: 04-2013
Publisher: Edinburgh University Library
Date: 11-09-2019
Abstract: The Data Curation Continuum was developed as a way of thinking about data repository infrastructure. Since its original development over a decade ago, a number of things have changed in the data infrastructure domain. This paper revisits the thinking behind the original data curation continuum and updates it to respond to changes in research objects, storage models, and the repository landscape in general.
Publisher: Elsevier BV
Date: 03-2004
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-4169
Abstract: & & Envisioned as one solution to data challenges of the International Polar Year (2007-2008), the Copernicus data journal Earth System Science Data (ESSD) has developed into a useful rewarding data-sharing option for an unprecedented array of researchers. ESSD has published peer-reviewed descriptions of more than 500 easily- and freely-accessible data products, from more than 4000 data providers archiving their products at more than 100 data centres. ESSD processes and products provide a useful step toward Findable, Accessible, Interoperable, Reusable (FAIR) expectations but also a caution about implementation.& & & & For ESSD, findable and accessible derive from the journal& #8217 s consistent mandate for open access coupled with useful title, author, abstract and full-text search functions on the publisher& #8217 s website (which lead users quickly to data sources) and excellent (but varied) topical, geographic, textual and chronologic search functions of host data centres. Due to an intense focus on data reliability and reusability during peer review of data descriptions, ESSD-referenced data products achieve very high standards of accessibility and reusability. ESSD experience over an amazing variety of data products suggests that & #8216 interoperability& #8217 depends on the intended use of the data and experience of users. Many ESSD-published products adopt a shared grid format compatible with climate models. Other ESSD products, for ex le in ocean biogeochemistry or land agricultural cultivation, adopt or even declare interoperable terminologies and new standards for expression of uncertainty. Very often an ESSD publication explicitly describes data collections intended to enhance interoperability within a specific user community, through a new database for ex le. For a journal that prides itself on ersity and quality of its products published in service to a very broad array of oceanographic, terrestrial, atmospheric, cryospheric and global research communities, the concept of interoperability remains elusive.& & & & Implementing open access to data has proven difficult. FAIR principles give us guidelines on the technical implementation of open data. However, ESSD& #8217 s experience (involving publisher, data providers, reviewers and data centres) in achieving very high impact factors (we consider these metrics as indicators of use and reuse of via ESSD published data products) can serve as a guide to the pursuit of the FAIR principles. For most researchers, data handling remains confusing and unrewarding. Data centres vary widely in capability, resources and approaches even the & #8216 best& #8217 (busiest) may change policies or practices according to internal needs independent of external standards or may - unexpectedly - go out of service. Software and computation resources grow and change rapidly, with simultaneous advances in open and proprietary tools. National mandates often conflict with international standards. Although we contend that ESSD represents one sterling ex le of promoting findable, accessible, interoperable and reusable data of high quality, we caution that those objectives remain a nebulous goal for any institution - in our case a data journal - whose measure of success remains a useful service to a broad research community.& &
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-12020
Abstract: & & One of the challenges for students of geosciences is learning to read geological maps, interpret structural geology, and understand the link between geology and geophysical properties. Augmented Reality (AR) sandboxes are interactive visualization tools that are becoming increasingly popular to demonstrate various earth processes.& & & & & An AR sandbox consists of a box filled with white sand and uses a Kinect 3D camera to continuously scan the topography of the sand surface. The topographic view of the structures sculpted by the user is then blended with digital information and a computed image is projected back onto the sand surface. Due to their intuitive operation, AR Sandboxes serve as a powerful science outreach and communication tool by making abstract concepts easy to see through the leveraging of playful learning and visualization, offering huge potential for teaching geological and geophysical principles.& & & & Several versions of AR Sandboxes have been developed for a whole range of scenarios, spanning a wide variety of Earth Science topics and learning environments. The most common scenarios are from physical geography, hydrology and ecology. Their underlying data models stay at or close to the surface, making it hard to incorporate geological models.& & & & & Recently, an Open-AR-Sandbox software was published by researchers at the Institute for Computational Geoscience and Reservoir Engineering (CGRE), RWTH Aachen University, Germany. With this AR Sandbox, geological models can be projected onto real sand and the relations of subsurface structures, topography and outcrop can be explored in an AR environment.& & & & & We tested the Open-AR-Sandbox software after successfully installing and running a conventional AR sandbox software. The combination of the Sandbox and GemPy geomodelling tool offers unique 3D interactive modelling solutions to explore geoscientific data and processes, with linkages to other software tools. We can use the AR sandbox to project a variety of geophysical measurement data onto the sand surface, offering an interactive experience that integrates geological and geophysical data. The Open-AR-Sandbox is, therefore, an innovative tool in geoscience education for the public as well as the classroom because of its benefits for teaching geological mapping, structural geology and geophysics.& &
Publisher: Elsevier BV
Date: 04-2009
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-13071
Abstract: & & & #8220 & em& We kill people based on metadata.& /em& & #8221 (Gen. Michael V. Hayden, 2014) [1]& & & & Over the past fifteen years, a number of persistent identifier (PID) systems have been built to help identify the stakeholders and their outputs in the research process and scholarly communication. Transparency is a fundamental principle of science, but this principle of transparency can be in conflict with the principles of the right to privacy. The development of Knowledge Graphs (KG), however, introduces completely new, and possibly unintended uses of publication metadata that require critical discussion. In particular, when personal data, as is linked with ORCID identifiers, are used and linked with research artefacts and personal information, KGs allow identifying personal as well as collaborative networks of in iduals. This ability to analyse KGs may be used in a harmful way. It is a sad fact that in some countries, personal relationships or research in certain subject areas can lead to discrimination, persecution or prison. We must, therefore, become aware of the risks and responsibilities that come with networked data in KGs.& & & & & The trustworthiness of PID systems and KGs has so far been discussed in technical and organisational terms. The inclusion of personal data requires a new definition of & #8216 trust& #8217 in the context of PID systems and Knowledge Graphs which should also include ethical aspects and consider the principles of the General Data Protection Regulation.& & & & New, trustworthy technological approaches are required to ensure proper maintenance of privacy. As a prerequisite, the level of interoperability between PID needs to be enhanced. Further, new methods and protocols need to be defined which enable secure and prompt cascading update or delete actions of personal data between PID systems as well as knowledge graphs.& & & & & Finally, new trustworthiness criteria must be defined which allow the identification of trusted clients for the exchange of personal data instead of the currently practised open data policy which can be in conflict with legislation protecting privacy and personal data.& & & & [1] aily/2014/05/10/we-kill-people-based-metadata/& &
Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
Date: 2000
Publisher: MDPI AG
Date: 31-07-2019
DOI: 10.3390/RS11151797
Abstract: The use of small Unmanned Aircraft Systems (sUAS) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on a four-year, community-based investigation into the tools, data practices, and challenges that currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data—as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases—is characterized as both sharing many traits with traditional remote sensing data and also as exhibiting—as common across the spectrum of disciplines and use cases—novel characteristics that require novel data support infrastructure and (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost.
Publisher: Springer International Publishing
Date: 2014
Publisher: The Open Journal
Date: 09-06-2020
DOI: 10.21105/JOSS.02314
Publisher: CSIRO
Date: 2018
DOI: 10.25919/Y7HT-R568
Publisher: Zenodo
Date: 2018
Publisher: Zenodo
Date: 2018
Publisher: Zenodo
Date: 2021
Publisher: No publisher found
Date: 2013
Publisher: No publisher found
Date: 2013
Publisher: Zenodo
Date: 2020
Publisher: Elsevier BV
Date: 08-2003
Publisher: Deutsches GeoForschungsZentrum GFZ
Date: 2014
Publisher: Elsevier BV
Date: 03-2021
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-10855
Abstract: & & The AuScope Virtual Research Environment (AVRE) program& #8217 s Engage activity was devised as a vehicle to promote low-barrier collaboration projects with Australian universities and publicly-funded research agencies and to provide an avenue for exploring new applications and technologies that could become part of the broader AuScope AVRE portfolio. In its second year, we developed two projects with another cohort of collaborative projects proponents from two Australian research institutions. Both projects have leveraged and extended upon previously developed open-source projects while tailoring them to clients& #8217 specific needs.& & & & The latest projects developed under the AuScope AVRE Engage program were the AuScope Geochemistry Network (AGN) Lab Finder Application and the Magnetic Component Symmetry (MCS) Analysis application. The Lab Finder application fits within a broader ecosystem of AGN projects and is an online tool that provides an overview of participating laboratories, their equipment, techniques, contact information with a catalogue that sums up the possibilities of each analytical technique, and a user-friendly search and browsing interface. The MCS Analysis application implements the CSIRO Orthogonal Magnetic Component (OMC) analysis method for the detection of l variations in the magnetic field (i.e., anomalies) that are the result of subsurface magnetizations. Both applications were developed using free and open-source software (FOSS) and leveraged prior work and further expand on it. The AGN Lab Finder is an adaptation of the Technique Finder originally developed by Intersect for Microscopy Australia, which was redesigned to accommodate geochemistry-specific equipment and describe its analytical capabilities It provides an indexing mechanism and a search functionality allowing researchers to efficiently locate and identify laboratories with the equipment necessary to their research needs and that satisfies their analytical capability requirements. The MCS Analysis application is a derivative product based on Geophysical Processing Toolkit (GPT) that implements a user-centred approach to visual data analytics and modelling. It significantly improves user experience by integrating with open data services, adding complex interactivity and data visualisation functionality, and improving overall exploratory data analysis capability.& & & & The Engage approach to running collaborative projects has proved successful over the last two years and produced low-maintenance tools that are made freely accessible to researchers. The approach to engage a wider audience and improve the speed of science delivery has influenced other projects within the CSIRO Mineral Resources business unit to implement similar programs.& & & & This case study will demonstrate the social aspects of our experience in cross-institutional collaboration, showcase our learnings during the development of pilot projects, and outline our vision for future work.& &
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10223
Abstract: Persistent identifiers are applied to an ever-increasing ersity of research objects, including data, software, s les, models, people, instruments, grants, and projects. There is a growing need to apply identifiers at a finer and finer granularity. The systems developed over two decades ago to manage identifiers and the metadata describing the identified objects struggle with this increase in scale. Communities working with physical s les have grappled with these challenges of the increasing volume, variety, and variability of identified objects for many years. To address this dual challenge, the IGSN 2040 project explored how metadata and catalogues for physical s les could be shared at the scale of billions of s les across an ever-growing variety of users and disciplines. This presentation outlines how identifiers and their describing metadata can be scaled to billions of objects. In addition, it analyses who the actors involved with this system are and what their requirements are. This analysis resulted in the definition of a minimum viable product and the design of an architecture that addresses the challenges of increasing volume and variety. The system is also easy to implement because it reuses commonly used Web components. Our solution is based on a Web architectural model that utilises Schema.org, JSON-LD and sitemaps. Applying these commonly used architectural patterns on the internet allows us not only to handle increasing volume, variety and variability but also enable better compliance with the FAIR Guiding Principles.
Publisher: IEEE
Date: 04-2013
Publisher: BMJ
Date: 07-2017
Publisher: CSIRO
Date: 2020
Publisher: PeerJ
Date: 09-10-2018
DOI: 10.7717/PEERJ.5453
Abstract: Acquiring data on the occurrences of many types of difficult to identify objects are often still made by human observation, for ex le, in bio ersity and paleontologic research. Existing computer counting programs used to record such data have various limitations, including inflexibility and cost. We describe a new open-source program for this purpose—Raritas. Raritas is written in Python and can be run as a standalone app for recent versions of either MacOS or Windows, or from the command line as easily customized source code. The program explicitly supports a rare category count mode which makes it easier to collect quantitative data on rare categories, for ex le, rare species which are important in bio ersity surveys. Lastly, we describe the file format used by Raritas and propose it as a standard for storing geologic bio ersity data. ‘Stratigraphic occurrence data’ file format combines extensive s le metadata and a flexible structure for recording occurrence data of species or other categories in a series of s les.
Publisher: MDPI AG
Date: 26-04-2021
DOI: 10.20944/PREPRINTS202104.0678.V1
Abstract: Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. These processes link geological processes at depth to surface features. Consequently, the study of landscapes can reveal essential information about the geochemical footprint of ore deposits at depth. Advances in satellite imaging and computing power have enabled the creation of large geospatial datasets, the sheer size of which necessitates automated processing. We describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available Digital Elevation Model, derived data, and s le landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve about 98\\% classification accuracy with reasonable runtime of 48 minutes on a single core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, while smaller tiles result in a more detailed, and at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region.
Publisher: Ubiquity Press, Ltd.
Date: 22-03-2017
DOI: 10.5334/DSJ-2017-009
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-10877
Abstract: & & AuScope is Australia& #8217 s National Geoscience Research Infrastructure Program. As outlined in is 2020-2030 10-year Strategy& sup& & /sup& , AuScope seeks to provide a world-class research physical and digital infrastructure to help tackle Australia's key geoscience challenges, in particular, food and water sustainability, minerals and energy security, and mitigating impact from geohazards. These challenges tie in directly with the following United Nations (UN) Sustainable Development Goals (SDGs): SDG#6 (Clean Water and Sanitation) SDG#7 (Affordable and Clean Energy) SDG#8 (Decent Work and Economic Growth) SDG#9 (Industry, Innovation and Infrastructure) SDG#13 (Climate Action) and SDG#15 (Life on Land).& & & & & & & & & & The SDGs were set in 2015 by the UN General Assembly to be achieved by the year 2030. If the global research sector is to support achieving them, is a rethink required? Current practices tend to focus on building infrastructures in domain and/or national/regional and/or sector (research, government, private) and/or institutional/network silos. These are not necessarily enabling global interoperability, reuse and open sharing of data. For ex le, AuScope is building high-quality geoscience research data and software infrastructures that are at the heart of positioning Australia to meet these SDG challenges. Equivalent geoscience research infrastructures are also being built internationally (EPOS (Europe) EarthScope, EarthCube (USA)) and AuScope is looking for ways to interoperate more effectively with these.& & & & & & & & & Within the international geoscience community some interoperable networks are in place to enable global collaborations that share data and software (e.g., Earth System Grid Federation (ESGF), which develops software infrastructure for the management, dissemination, and analysis of model output and observational climate data the Federation of Digital Seismograph Networks (FDSN) enables members to coordinate station siting and provide free and open data). However, these are the exceptions rather than the rule.& & & & & & br& None of the SDGs depend exclusively on geoscience data: all require integration with data from other domains, particularly from the social sciences and humanities. Some initiatives trying to assist data combination between the social sciences and the physical or environmental sciences are emerging (e.g., the Data Documentation Initiative - Cross Domain Integration (DDI-CDI)& sup& & /sup& the CODATA/ISC Decadal programme on & #8220 Making data work for cross-domain grand challenges& #8221 & sup& & /sup& ) , but traditional organizational and funding arrangements do not usually facilitate this. While there are exemplars of how to achieve integration of global domain and cross-domain research infrastructures and data sharing frameworks, we urgently need to leverage these to develop a roadmap that enables global integration of data and research infrastructures, both within the geosciences and beyond, to ensure sustainable production of data, products and services that support the realisation of the UN SDGs by 2030. In doing so, potentially the main tension will be to ensure that in enabling the broader, global transdisciplinary goals of the SDGs that deeper domain science is not compromised, scarce expertise is not misdirected, and that infrastructure developments within the domains are not unduly h ered.& & & & & sup& & /sup& www.auscope.org.au/news-features/strategy-and-investment-plan-launch& & & & & & sup& & /sup& iki/spaces/DDI4 ages/860815393/DDI+Cross+Domain+Integration+DDI-CDI+Review& & & & & & sup& & /sup& nitiatives/strategic-programme/decadal-programme/ & &
Publisher: Elsevier BV
Date: 08-2012
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-3261
Abstract: & & AuScope, founded in 2006, is the provider of research infrastructure to Australia& #8217 s& Earth and geospatial science community. Its unifying strategic goals include building the Downward Looking Telescope (DLT) (a metaphor for an integrated system of Earth and geospatial instruments, services, data and analytics to enable scientists to understand Earth& #8217 s evolution through time) and exploring how Earth resources may support growing human demands. The AuScope Virtual Research Environment (AVRE) program is responsible for enabling the DLT through providing persistent access to required data and tools from a erse range of Australian research organisations, government geological surveys and the international community.& & & & In 2009 AuScope released a portal to provide online access to evolved data products to specific groups of users. Subsequently, this portal was combined with online tools to create the AVRE platform of specialised Virtual Laboratories that enabled the execution of explicit workflows. By 2021 it was recognised that AVRE should modernise and take advantage of new technologies that could empower researchers to access higher storage capacities and wider varieties of computational processing options. AVRE also needed to leverage notebooks, containerisation and mobile solutions and facilitate a greater emphasis on ML and AI techniques. Increased storage meant researchers could access less processed, rawer forms of data, which they could then prepare for their own specific requirements, whilst the growth in Open Source software meant easy access to tools that could meet or efficiently be adapted to their needs.& & & & & Recognising that AuScope researchers now required new mechanisms to help them find and reuse multiple resources from globally distributed sites and be able to integrate these with their own data types and tools, the AVRE informatics and technology experts began assessing the requirements for modernising the AVRE platform. The technologists reviewed other virtual research environments, research data portals, and e-commerce platforms for ex les of well-designed interfaces and services that help users get the best use out of a platform.& & & & & We then undertook a series of interactive consultations across a broad range of AuScope researchers (geophysics, geochemistry, geospatial, geology, etc). We accepted there were multiple requirements, from simple data processing on small volume data sets through to complex data modelling and assimilation at petascale, and openly acknowledged that there were numerous ways of processing: one size would not fit all.& & & & In the consultations, we focussed on the context that AVRE was about enabling researchers to use a ersity of resources to realise the AuScope strategic goal of the DLT. We recognised that this would require an ability to meet the specialised requirements of a broad range of the current in idual AuScope geoscience programs, but at the same time, there was a need to allow for future integration with global transdisciplinary challenges that explore how Earth resources may support growing human demands.& & & & In this presentation, we will discuss the outcomes from our consultations with various AuScope Programs and will present initial plans for a co-designed, re-engineered AVRE platform to meet the expressed needs of a erse range of DLT developers and users.& &
Publisher: ACM
Date: 14-11-2019
Publisher: Ubiquity Press, Ltd.
Date: 2021
DOI: 10.5334/DSJ-2021-012
Publisher: PeerJ
Date: 28-05-2013
DOI: 10.7287/PEERJ.PREPRINTS.21V1
Abstract: Researchers require infrastructures that ensure a maximum of accessibility, stability and reliability to facilitate working with and sharing of research data. Such infrastructures are being increasingly summarized under the term Research Data Repositories (RDR). The project re3data.org – Registry of Research Rata Repositories has begun to index research data repositories in 2012 and offers researchers, funding organizations, libraries and publishers an overview of the heterogeneous research data repository landscape. Information icons help researchers to easily identify an adequate repository for the storage and reuse of their data. This article describes the RDR landscape, outlines the practicality of re3data.org as a service, and shows how this service helps to find research data.
Publisher: Ubiquity Press, Ltd.
Date: 2023
DOI: 10.5334/DSJ-2023-005
Publisher: American Geophysical Union (AGU)
Date: 2008
DOI: 10.1029/2008EO490007
Publisher: Ubiquity Press, Ltd.
Date: 04-04-2017
DOI: 10.5334/DSJ-2017-013
Publisher: Ubiquity Press, Ltd.
Date: 04-04-2017
DOI: 10.5334/DSJ-2017-014
Publisher: Ubiquity Press, Ltd.
Date: 04-04-2017
DOI: 10.5334/DSJ-2017-012
Publisher: DE GRUYTER SAUR
Date: 14-03-2013
Location: Australia
Location: No location found
Start Date: 2010
End Date: 2013
Funder: Bundesministerium für Bildung und Forschung
View Funded ActivityStart Date: 2012
End Date: 2015
Funder: Deutsche Forschungsgemeinschaft
View Funded ActivityStart Date: 2009
End Date: 2010
Funder: European Space Agency
View Funded ActivityStart Date: 2011
End Date: 2014
Funder: Deutsche Forschungsgemeinschaft
View Funded ActivityStart Date: 2008
End Date: 2014
Funder: Bundesministerium fur Wirtschaft und Technologie
View Funded ActivityStart Date: 2022
End Date: 2023
Funder: Alfred P. Sloan Foundation
View Funded ActivityStart Date: 2011
End Date: 2013
Funder: Deutsche Forschungsgemeinschaft
View Funded ActivityStart Date: 2018
End Date: 2022
Funder: Australian Research Council
View Funded ActivityStart Date: 2020
End Date: 2023
Funder: Australian Research Data Commons
View Funded ActivityStart Date: 02-2019
End Date: 02-2025
Amount: $3,925,357.00
Funder: Australian Research Council
View Funded Activity