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As geochemical data enable understanding of the Earth system and help to address critical societal issues the organisation thereof is important. Questions asked about processes affecting our environment and geological past become more complex and interdisciplinary in nature as well as multidimensional. To help answer these questions within the geochemistry research capabilities and data compilations are required to be comprehensive and both human and machine readable. Various international organisations are building infrastructure to capture and distribute geochemical data in a consistent manner adhering to the FAIR principles. Since May 2021 the OneGeochemistry initiative has officially started efforts towards aligning these organisations’ data frameworks in order to standardise how geochemical data is reported around the globe. In November 2022 the OneGeochemistry initiative applied and was granted to become the OneGeochemistry CODATA Working Group as part of the International Science Councils Committee on Data. The initiative has now also been endorsed by the Geochemical Society, the European Association of Geochemistry and the Working Group has been endorsed by the IUGS Commission on Global Geochemical Baselines. Coordination of the OneGeochemistry initiative is funded through the WorldFAIR project where it is one of the work packages in the larger ‘WorldFAIR: Global cooperation on FAIR data policy and practice’ project. A FAIR Implementation Profile analyses of the geochemistry communities of Australia (AusGeochem), USA (EarthChem, AstroMat) and Europe (GEOROC-DIGIS, EPOS-MSL, NFDI4EARTH) resulted in recognition of the need for common vocabularies for geochemistry data reporting as one of the most important actions to undertake towards international geochemistry data interoperability. A task adopted by EarthChem-DIGIS(GEOROC)-GFZ(DataSystems) collaboration and Research Vocabularies Australia.Here we will present an overview of the current OneGeochemistry initiative and its preliminary outcomes with regards to FAIR Implementation Profiles and processes that will help enable geochemical data interoperability between various stakeholders.

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 individual research group. Modern research data processing pipelines/workflows can have quite complex lineages, and it is more likely that an individual 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 individual 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/processing and identify sources of funding (including instruments/field campaigns 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.

Persistent identifiers are applied to an ever-increasing diversity of research objects, including data, software, samples, 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 samples 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 samples could be shared at the scale of billions of samples 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.

‘WorldFAIR: Global cooperation on FAIR data policy and practice’ is a European Commission funded project composed of 11 discipline and cross-discipline case studies drawn together by CODATA, the Committee on DATA of the International Science Councils Committee on DATA, and is supported by the Research Data Alliance. WorldFAIR is a diverse, global community effort that currently has 19 partners located in Africa, Australasia, Europe, and North and South America, representing organisations from research, government and industry. The 11 individual case studies are drawn from Chemistry, Nanomaterials, Geochemistry, Social Surveys, Population Health, Urban Health, Biodiversity, Agriculture, Oceans, Disaster Risk Reduction and Cultural Heritage. The WorldFAIR project aims to focus on the interoperability and reusability of research data products from both within and across disciplines by creating a Cross-Domain Interoperability Framework (CDIF).The foundation of the CDIF will be a series of FAIR Implementation Profiles (FIPs) which will be used as a methodology for individual communities to express their FAIR practices and decisions for each of the 15 individual FAIR guiding principles. As an example of how this will work, the WorldFAIR’s Geochemistry case study is led by OneGeochemistry, an international network of national geochemical data infrastructure organisations. Initially an informal network with representatives from AuScope (Australia), GEOROC (Germany), EPOS Multi-scale Laboratories (Europe), EarthChem (US) and AstroMaterials (US). With the advent of WorldFAIR, OneGeochemistry has formalised it’s governance structure and is now a CODATA Work Group. Over the life of WorldFAIR, OneGeochemistry will work towards developing a community prototype FAIR Implementation Profile(s) for individual geochemical techniques, including the minimum defined variables, through workshops and consultations, and subsequently be responsible for their communication, publication and dissemination. The Geochemistry case study will work closely with the Chemistry case study and leverage relevant chemical standards and vocabularies wherever possible.Through the development of community lead FAIR Implementation Profile(s) for geochemistry within a global Cross-Domain Interoperability Framework (CDIF), WorldFAIR and OneGeochemistry are both advancing the adoption of the FAIR data principles within Geochemistry and simultaneously enabling interoperability of geochemical research data products across the other ten discipline case studies.

AuScope is Australia’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 – the AuScope Virtual Research Environment (AVRE) – provides a unifying technological platform for AuScope Programs’ 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 diverse 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 “language barrier” 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 – 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’ 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.

This is the preprint of the accepted article: Klöcking, M., et. al. (2023). Community recommendations for geochemical data, services and analytical capabilities in the 21st century. In Geochimica et Cosmochimica Acta. Elsevier BV. https://doi.org/10.1016/j.gca.2023.04.024 Abstract The majority of geochemical and cosmochemical research is based upon observations and, in particular, upon the acquisition, processing and interpretation of analytical data from physical samples. The exponential increase in volumes and rates of data acquisition over the last century, combined with advances in instruments, analytical methods and an increasing variety of data types analysed, has necessitated the development of new ways of data curation, access and sharing. Together with novel data processing methods, these changes have enabled new scientific insights and are driving innovation in Earth and Planetary Science research. Yet, as approaches to data-intensive research develop and evolve, new challenges emerge. As large and often global data compilations increasingly form the basis for new research studies, institutional and methodological differences in data reporting are proving to be significant hurdles in synthesising data from multiple sources. Consistent data formats and descriptions as well as appropriate information on data quality are becoming crucial to enabling reproducibility and integration of results and fostering confidence for data reuse. Here, we explore the key challenges faced by the geo- and cosmochemistry community and, by drawing comparisons from other communities, recommend possible approaches to overcome them. The first challenge is bringing together the numerous sub-disciplines within our community. One key factor for this convergence will be gaining endorsement from the international geochemical, cosmochemical and analytical societies and associations, journals and institutions. Increased education and outreach, spearheaded by ambassadors recruited from leading scientists across disciplines, will further contribute to raising awareness, and to uniting and mobilising the community. Appropriate incentives, recognition and credit for good data management as well as an improved, user-oriented technical infrastructure will be essential for achieving a cultural change towards an environment in which the effective use and real-time interchange of large datasets is common-place. Finally, the development of best practices for standardised data reporting and exchange, driven by expert working groups, will be a crucial step towards making geo- and cosmochemical data more Findable, Accessible, Interoperable and Reusable by both humans and machines (FAIR).

The Australian 2030 Geophysics Collections Project seeks to make accessible online a selection of rawer, high-resolution versions of geophysics datasets that comply with the FAIR and CARE principles, and ensure they are suitable for programmatic access in HPC environments by future 2030 next-generation scalable, data-intensive computation (including AI and ML). The 2030 project is not about building systems for the infrastructures and stakeholder requirements of today, rather it is about positioning geophysical data collections to be capable of taking advantage of next generation technologies and computational infrastructures by 2030.There are already many known knowns of 2030 computing: high end computational power will be at exascale and today’s emerging collaborative platforms will continue to evolve as a mix of HPC and cloud. Data volumes will be measured in Zettabytes (1021 bytes), which is about 10 times more than today. It will be mandatory for data access to be fully machine-to-machine as envisaged by the FAIR principles in 2016. Whereas we currently discuss Big Data Vs (volume, variety, value, velocity, veracity, etc), by 2030 the focus will be on Big Data Cs (community, capacity, confidence, consistency, clarity, crumbs, etc).So often today’s research is undertaken on pre-canned, analysis-ready datasets (ARD) that are tuned towards the highest common denominator as determined by the data owner. However, increased computational power colocated with fast-access storage systems will mean that geophysicists will be able to work on less processed data levels and then transparently develop their own derivative products that are more tuned to the parameters of their particular use case. By 2030, as research teams analyse larger volumes of high-resolution data they will be able to see the quality of their algorithms quickly and there will be multiple versions of open software being used as researchers fine tune individual algorithms to suit their specific requirements. We will be capable of more precise solutions and in hazards space and other relevant areas, analytics will be done in faster-than-real-time. The known unknowns emerging are how we will preserve and make transparent any result from this diversity and flexibility with regards to the exact software used, the precise version of the data accessed, and the platforms utilised, etc. When we obtain a scientific ‘product’, how will we vouch for its fidelity and ensure it can be consistently replicated to establish trust? How do we preserve who funded what so that sponsors can see which investments have had the greatest impact and uptake? To have any confidence in any data product, we will need to have transparency throughout the whole scientific process. We need to start working now on more automated systems that capture provenance through successive levels of processing, including how it was produced and which dataset/dataset extract was used. But how do we do this in a scaleable, machine readable way?And then there will be the unknown unknowns of 2030 computing. Time will progressively expose these to us in the next decade as the scale and speed at which collaborative research is undertaken increases.

When researchers collect or create physical samples they usually assign a user-generated number to each sample. Subsequently, that sample 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 sample 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 sample.Moreover, as a single laboratory rarely has the capability to offer all analytical techniques, individual samples tend to move from laboratory to laboratory to acquire the desired suite of analytes. Each laboratory may implement a different number to that sample. At the conclusion of their project, the researcher may submit the same sample to a museum or institutional repository, where the sample will be assigned yet another institution-generated number to ensure that all samples are uniquely identified in their repository. Ultimately, by the time the researcher submits an article to a journal and wants to identify samples 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 sample 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 samples that pass between systems and organisations: they cannot be duplicated by another researcher, laboratory or sample repository. They persistently link to information about the origin of the sample; to personas in the creation of the sample (collector, institution, funder); to the laboratory data and their creation (analyst, laboratory, institution, funder, data software); and to the sample curation phase (curator, repository, funder). They connect the phases of a sample’s path from collection in the field to lab analysis to the synthesis/research phase to the publication to the archive. Globally unique sample identifiers also enable cross linkages to any artefacts derived from that sample (images, analytical data, other articles). Further, identifiers like IGSN enable sub samples or sample splits to be linked back to their parent sample, creating a holistic picture of any information derived from the initial sample. 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 ‘life-cycle’ including laboratory analysis, generation of further data, images, publication, and ultimately curation and preservation. Where any subsamples are derived, they, and any data generated on them, can be linked back to the parent identifier.

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 sample 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. 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 samples, 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 samples.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 samples ecosystem and supporting new research and best practice in methods of identifying, citing, and locating physical samples. 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’s open science ecosystem. This talk will focus on the developing changes to the IGSN governance and community efforts.Different research communities have a wide range of requirements towards metadata and identification of samples. The IGSN plans to develop an international ‘Community of Communities’ which will include members from the global samples 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 samples.The IGSN Samples Community (IGSN SC) aspires to be a collaborative space for community development that promotes the use of samples and their connections to any derived observations, images, and analytical data.

AuScope, founded in 2006, is the provider of research infrastructure to Australia’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’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 diverse 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 examples 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 diversity 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 individual 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 diverse range of DLT developers and users.

The geoscience and chemistry communities have numerous common practices and dependency on data standards. Recent efforts from the International Union on Pure and Applied Chemistry (IUPAC) and the American Geophysical Union (AGU) are to explore and collaborate on approaches and sharing lessons learned on efforts to implement the FAIR Guiding Principles as they apply to data in their respective communities. This paper summarizes their efforts-to-date highlighting the importance of existing communities, Scientific Unions, standards bodies and societies in taking deliberate steps to move and encourage researcher adoption of the FAIR tenets.

In recent years, magnetotelluric (MT) processing has become computationally intensive as the scale and size of MT surveys being run increases. Consequently, High Performance Computing (HPC) is now ...

This article is composed of three independent commentaries about the state of ICON principles (Goldman et al., 2021) in Earth and Space Science Informatics (ESSI) and includes discussion on the opp...

A correspondence submitted to Nature as a response to an article authored by Dr. Mark Musen (https://doi.org/10.1038/d41586-022-02820-7).

Science gateways, virtual laboratories and virtual research environments are all terms used to refer to community-developed digital environments that are designed to meet a set of needs for a research community. Specifically, they refer to integrated access to research community resources including software, data, collaboration tools, workflows, instrumentation and high-performance computing, usually via Web and mobile applications. Science gateways, virtual laboratories and virtual research environments are enabling significant contributions to many research domains, facilitating more efficient, open, reproducible research in bold new ways. This paper explores the global impact achieved by the sum effects of these programs in increasing research impact, demonstrates their value in the broader digital landscape and discusses future opportunities. This is evidenced through examination of national and international programs in this field.

We are now facing profound changes (biodiversity, climate, pandemic, etc.). Human impacts and their mitigation will depend on our ability to mobilize research at the global level. The sustainable development of the society will largely depend on the sustainable development of global science and scientific research tools, outputs, and research ecosystems. This globalization of research requires interoperating our observation and experimentation systems in order to better understand these changes, to better simulate their effects. The Covid-19 pandemic is now raging around the world. The reproducibility of research and results across regions in different contexts should accelerate human responses. Data sharing and the development of Synthesis Research with data aggregation at large scale is critical to enable such processes. The use of common knowledge, vocabularies, standards and procedures at a large scale is necessary. The objective of this poster is to report on the challenges met while building data dictionaries in three global projects related to biodiversity and/or disease research: PARSEC, Kakila, ERINHA-Advance. The Kakila database centralizes and harmonizes marine mammal observation data for the AGOA sanctuary around the French archipelago of Guadeloupe, French Antilles. The PARSEC Project is building new tools for data sharing and reuse through a transnational investigation of the socioeconomic impact of protected areas. The ERINHA-Advance project aims to support the operations of the ERINHA research infrastructure which is designed to generate data from transnational access research activities on highly pathogenic agents. In these 3 global case-studies, similar challenges have arisen: to aggregate and interoperate pre-existing heterogeneous data at the global scale, and to share common tools to monitor, maintain quality, scan scale and cope with uncertainty. This poster proposes a draft common methodology, a data dictionary cookbook, which will provide a roadmap towards the building of large scale - data dictionaries. Topics proposed to be covered in such a cookbook include: how to search for existing and appropriate data dictionaries, controlled vocabularies or other semantic resources (before building a new one), the first steps for data dictionary building, data dictionary literacy (and why it is a mandatory work), how to define all scientific objects, aspects (or use existing one) and agree on the definitions with the whole community, building / proposing variables / indicators with ontology models, schemas, variables naming rules and context awareness, and finally addressing dimension issues considering each context. The common experience of our three projects showed that we need to proceed step by step as simply as possible and to ensure that each step is understandable for the whole community. It is necessary to improve access and re-use of all existing semantic materials and not trying to build a cathedral with a little spoon.

Under the auspices of the Earth Science Information Partners (ESIP) and with collaboration among members of the ESIP Information Quality Cluster (IQC), the Barcelona Supercomputing Center (BSC) Evaluation and Quality Control (EQC) team, and the Australia/New Zealand Data Quality Interest Group (AU/NZ DQIG), a community effort has been undertaken by international Earth Science domain experts. The objective of this effort is to develop global community guidelines with practical recommendations to promote the representation, sharing and reuse of quality information at the dataset level, leveraging the experiences and expertise of a team of interdisciplinary domain experts and community best practices. The community guidelines are inspired by the guiding principles of findability, accessibility, interoperability, and reusability (FAIR) and aim to help stakeholders such as science data centers, repositories, data producers and publishers, data managers and stewards, etc., i) to capture, describe, and represent quality information of their datasets in a way that is in line with the FAIR guiding principles; ii) to allow for the maximum discovery, trust, sharing, reuse and value of their datasets; and iii) to enable global access to and integration of dataset quality information. The vision of developing these guidelines is to promote the creation and use of freely and openly shared dataset quality information that is consistently described, readily available in community standardized formats, and capable of being integrated across commonly-used Earth science systems and tools for search and access with explicitly expressed usage licenses.

AuScope is Australia’s National Geoscience Research Infrastructure Program. As outlined in is 2020-2030 10-year Strategy1, 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 example, 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. 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)2; the CODATA/ISC Decadal programme on “Making data work for cross-domain grand challenges”3) , 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 hampered.1https://www.auscope.org.au/news-features/strategy-and-investment-plan-launch 2https://ddi-alliance.atlassian.net/wiki/spaces/DDI4/pages/860815393/DDI+Cross+Domain+Integration+DDI-CDI+Review 3https://codata.org/initiatives/strategic-programme/decadal-programme/

The Earth, space, and environmental sciences have made significant progress in awareness and implementation of policy and practice around the sharing of data, software, and samples. In specific, the Coalition for Publishing Data in the Earth and Space Sciences (https://copdess.org/) brings together data repositories and journals to discuss and address common challenges in support of more transparent and discoverable research and the supporting data. Since the inception of COPDESS in 2014 and the completion of the Enabling FAIR Data Project in 2019, work has continued on the improvement of availability statements for data and software as well as corresponding citations. As the broad research community continues to make progress around data and software management and sharing, COPDESS is focused on several key efforts. These include 1) supporting authors in identifying the most appropriate data repository for preservation, 2) validating that all manuscripts have data and software availability statements, 3) ensuring data and software citations are properly included and linked to the publication to support credit, 4) encouraging adoption of best practices. We will review the status of these current efforts around data and software sharing, the important role that repositories and researchers have to ensure that automated credit and attribution elements are in place, and the recent publications on software citation guidance from the FORCE11 Software Implementation Working Group.

The knowledge of data quality and the quality of the associated information, including metadata, is critical for data use and reuse. Assessment of data and metadata quality is key for ensuring credible available information, establishing a foundation of trust between the data provider and various downstream users, and demonstrating compliance with requirements established by funders and federal policies.Data quality information should be consistently curated, traceable, and adequately documented to provide sufficient evidence to guide users to address their specific needs. The quality information is especially important for data used to support decisions and policies, and for enabling data to be truly findable, accessible, interoperable, and reusable (FAIR).Clear documentation of the quality assessment protocols used can promote the reuse of quality assurance practices and thus support the generation of more easily-comparable datasets and quality metrics. To enable interoperability across systems and tools, the data quality information should be machine-actionable. Guidance on the curation of dataset quality information can help to improve the practices of various stakeholders who contribute to the collection, curation, and dissemination of data.This presentation outlines a global community effort to develop international guidelines to curate data quality information that is consistent with the FAIR principles throughout the entire data life cycle and inheritable by any derivative product.

Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science. The virtual pre-ESIP workshop held on July 13, 2020 was sponsored by ESIP and co-organized by the ESIP IQC and the BSC EQC team, in collaboration with the ARDC AU/NZ DQIG. An additional community engagement event was carried out by the AU/NZ DQIG prior to the pre-ESIP workshop. ESIP is primarily supported by the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA) and the United States Geological Survey (USGS). The technological and infrastructural support during the preparation and conduct of the workshop was invaluable. In particular, we thank Megan Carter, ESIP Community Director, for supporting us throughout the workshop and providing helpful advice during the planning stage of the virtual workshop, and ESIP Community Fellow, Alexis Garretson, for supporting the ESIP SM20 report-out session. We thank all participants for attending the pre-ESIP workshop and the ESIP SM20 session and contributing to productive discussions during the live sessions and the two weeks of the ESIP SM20 period. Portions of this work have been extracted from Peng et al. (2020a), which reported on the workshop and the ESIP SM20 report-out session. The Australian participants acknowledge the support of the ARDC. The constructive suggestions from two anonymous reviewers of Data Science Journal have helped improve the quality of the paper. Peer Reviewed "Article signat per 33 autors/es: Ge Peng , Robert R. Downs, Carlo Lacagnina, Hampapuram Ramapriyan, Ivana Ivánová, David Moroni, Yaxing Wei, Gilles Larnicol, Lesley Wyborn, Mitch Goldberg, Jörg Schulz, Irina Bastrakova, Anette Ganske, Lucy Bastin, Siri Jodha S. Khalsa, Mingfang Wu, Chung-Lin Shie, Nancy Ritchey, Dave Jones, Ted Habermann, Christina Lief, Iolanda Maggio, Mirko Albani, Shelley Stall, Lihang Zhou, Marie Drévillon, Sarah Champion, C. Sophie Hou, Francisco Doblas-Reyes, Kerstin Lehnert, Erin Robinson, Kaylin Bugbee"

A dataset, small or big, is often changed to correct errors, apply new algorithms, or add new data (e.g., as part of a time series), etc. In addition, datasets might be bundled into collections, distributed in different encodings or mirrored onto different platforms. All these differences between versions of datasets need to be understood by researchers who want to cite the exact version of the dataset that was used to underpin their research. Failing to do so reduces the reproducibility of research results. Ambiguous identification of datasets also impacts researchers and data centres who are unable to gain recognition and credit for their contributions to the collection, creation, curation and publication of individual datasets. Although the means to identify datasets using persistent identifiers have been in place for more than a decade, systematic data versioning practices are currently not available. In this work, we analysed 39 use cases and current practices of data versioning across 33 organisations. We noticed that the term ‘version’ was used in a very general sense, extending beyond the more common understanding of ‘version’ to refer primarily to revisions and replacements. Using concepts developed in software versioning and the Functional Requirements for Bibliographic Records (FRBR) as a conceptual framework, we developed six foundational principles for versioning of datasets: Revision, Release, Granularity, Manifestation, Provenance and Citation. These six principles provide a high-level framework for guiding the consistent practice of data versioning and can also serve as guidance for data centres or data providers when setting up their own data revision and version protocols and procedures.