Towards Interoperability for Observed Parameters: Position Statement of an Emerging Working Group

For decades, many communities have worked on the definitions of parameters, specifically scientific observation and measurement parameters. A well known example are the climate and forecast standard names (CF) [1]. Controlled vocabularies (e.g. EnvThes [2], Anaeethes [3], BODC Parameter Usage Vocabulary [4], ...) are often used for describing parameters in different domains. PANGAEA [5] as a multidisciplinary data publisher for environmental sciences holds around 375 thousand citable data sets which have to be described with consistent semantics; this can be really challenging when dealing with complex parameters. Inconsistencies among existing parameter definitions as well as syntactic and semantic heterogeneity in their representation in systems prevent the integration of data about parameters from different providers. For individual providers, the growing number and complexity of observation and measurement parameters referred to in published data urgently demands viable approaches for their representation and organization. To address these problems and find common approaches, a group of interested scientists involved in different national and international initiatives and research infrastructures (PANGAEA, LTER-Europe [6], GFBio [7], BODC [8], ENVO [9], LifeWatch Italy [10], ICOS [11], AnaEE [12], AquaDiva [13], ...) decided to organize themselves as an RDA Working Group (WG). Having met several times via conference calls to present each other’s related work, it became clear that the problem has been recognized and tackled in various ways, reflecting the specific needs of data and semantic infrastructures of varying maturity. In this talk, we will describe the process of defining a common strategy with a clear output that will be beneficial for all involved communities, and beyond. This entails a consistent terminology used within the group, thorough SWOT analysis of the different methodologies in use (within and outside the group) and a synopsis of the current state. The ultimate aim of this undertaking is to elaborate a common concept for the definition of parameters and develop best practices illustrated on a number of use cases. We will highlight the problem, present and discuss the findings of the current working group, and provide an outlook for the planned work, in particular also a possible work plan for the RDA WG. The talk is an opportunity for this working group to reach out to other potentially interested parties. KEYWORDS: Network, Baltimore, Ecology, Long-term REFERENCES: 1. Climate and Forecast Standard Name Table. http://cfconventions.org/Data/cf-standard-names/49/build/cf-standard-name-table.html (accessed 10 April 2018). 2. EnvThes. http://vocabs.ceh.ac.uk/evn/tbl/envthes.evn (accessed 10 April 2018)

The ENVRI reference model

Advances in automation, communication, sensing and computation enable experimental scientific processes to generate data at increasingly great speeds and volumes. Research infrastructures are devised to take advantage of these data, providing advanced capabilities for acquisition, sharing, processing, and analysis; enabling advanced research and playing an ever-increasing role in the environmental and Earth science research domain. The ENVRI community identified several recurring requirements in the development of environmental research infrastructures such as i) duplication of efforts to solve similar problems; ii) lack of standards to harmonise and accelerate development, and bring about interoperability; iii) a large number of data models and data information systems within the domain, and iv) a steep learning curve for integration complex research infrastructure systems. To address these challenges, the ENVRI community has developed and refined the Environmental Research Infrastructures Reference Model (ENVRI Reference Model or ENVRI RM), a modelling framework encoding this knowledge. The proposed modelling framework encompasses a language and a notation to describe the research domain, its systems and the requirements and challenges faced when implementing those systems. By adopting ENVRI RM as an integrative approach, the environmental research community can secure interoperability between infrastructures, enable reuse, share resources, experiences and common language, reduce unnecessary duplication of effort, and speed up the understanding of research infrastructure systems. This chapter provides a short introduction to the ENVRI RM

A common reference model for environmental science research infrastructures

Independent development of research infrastructures leads to unnecessary replication of technologies and solutions whilst the lack of standard definitions makes it difficult to relate experiences in one infrastructure with those of oth-ers. The ENVRI Reference Model, www.envri.eu/rm, uses the Open Distributed Processing standard framework in order to model the "archetypical" environmental research infrastructure. The use of the ENVRI-RM to illustrate common characteristics of European ESFRI environmental infrastructures from a number of different perspectives provides a common language for and understanding of environmental research infrastructures, promote technology and solution sharing between infrastructures, and improve interoperability between implemented services

InteroperAble Descriptions of Observable Property Terminologies (I-ADOPT) WG - outputs and recommendations

The InteroperAble Description of Observable Property Terminologies Working Group (I-ADOPT WG) was formed in June 2019 under the auspices of the Research Data Alliance’s Vocabulary and Semantic Services Interest Group. Its objective was to develop a framework to harmonise the way observable properties are named and conceptualised, in various communities within and across scientific domains. There was a realisation that the rapid demand for controlled vocabularies specialised in describing observed properties (i.e. measured, simulated, counted quantities, or qualitative observations) was presenting a risk of proliferation of semantic resources that were poorly aligned. This, in turn, was becoming a source of confusion for the end-users and a hindrance to data interoperability. The development of the I-ADOPT Framework proceeded in multiple phases. Following the initial phase dedicated to the collection of user stories, the identification of key requirements, and an in-depth analysis of existing semantic representations of scientific variables and of terminologies in use, the group focused on identifying the essential components of the conceptual framework, reusing as much as possible concepts that were common to existing operational resources. The proposed framework was then tested against a variety of examples to ensure that it could be used as a sound basis for the creation of new variable names as needed. The results were formalised into the I-ADOPT ontology and subsequently extended with usage guidelines to form the I-ADOPT Framework presented in this document. The output can now be used to facilitate interoperability between existing semantic resources and to support the provision of machine-readable variable descriptions whose components are mapped to FAIR vocabulary concepts. The group also issued the following six key recommendations: 1. Data creators, curators or publishers should describe the variable(s) held in datasets in both a human- and a machine-readable format. 2. The variable’s description should enable data reuse with minimum reliance on externally held free-text documentation. 3. The machine-readable description should make use of FAIR terminologies (e.g., controlled vocabularies, ontological relationships) adhering to Linked Data principles. 4. The translation from human readable to machine readable form should follow a decomposition approach that is compatible with the classes and relations defined in the I-ADOPT ontology (https://w3id.org/iadopt/). 5. Users should preferably reuse terminologies that are already aligned with the I-ADOPT Framework by either reusing existing concepts or extending collections, or by creating new concepts based on the I-ADOPT Framework. 6. For variables based on a different schema, a mapping to the I-ADOPT Framework should be provided. The group also set up public repositories to continue open collaboration and give access to resources that will be maintained and/or developed beyond the lifetime of the official RDA working group: 1) a catalogue of terminologies relevant to observable properties, 2) a repository of design patterns; 3) a step-by-step guide for minting new variables, 4) use-case specific guidelines on implementing the framework, 5) a repository of applications, and user implementation stories; 6) additional materials including a list of alignments with other ontological resources

Aligning Observable Property Terminologies using the I-ADOPT framework

During its lifetime, the RDA WG InteroperAble Descriptions of Observable Property Terminologies (I-ADOPT) developed a semantic framework to represent scientific variables and give a detailed account of what has been measured or otherwise been observed. The framework breaks down complex variable descriptions into essential atomic components, e.g., what quality or quantity kind of which object or phenomenon kind are the subject of the measurement or observation. While the ecological domain served as a starting point, we took into account other domains as part of the development. As a result, the I-ADOPT model is a generic framework to describe observational properties. The recommendations of the IADOPT WG have been published along with several outputs including the I-ADOPT ontology itself and a collection of terminologies to be used as atomic components. The I-ADOPT WG is now in maintenance mode but work is continuing on testing and supporting real-life implementation scenarios. Multiple terminology providers and data repositories have started aligning their variables to the I-ADOPT framework. This results in an increasing number of I-ADOPT-compliant variable descriptions from different stakeholders. We are now ready to test whether one of the main goals of the I-ADOPT WG has actually been achieved: does the I-ADOPT framework enable semantic interoperability of variable descriptions across datasets annotated using different controlled vocabularies? This poster highlights how the I-ADOPT model has been applied to existing terminologies of observational variables, providing detailed semantic context information. We present current efforts to exploit these details while aligning terminologies of different origins. We want to encourage other terminology providers and domains to explore the I-ADOPT framework to grow an increasing network of interoperable terminologies for observational variables

The ENVRI Reference Model (ENVRI RM) version 2.2, 30th October 2017

The ENVRI Reference Model (ENVRI RM, RM) exists to illustrate common characteristics of environmental science research infrastructures in order to provide a common language and understanding, promote technology and solution sharing and improve interoperability. Version 2.2 of the RM supersedes any earlier version

FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources

The FAIR principles articulate the behaviors expected from digital artifacts that are Findable, Accessible, Interoperable and Reusable by machines and by people. Although by now widely accepted, the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors. As different communities have their own, often well-established implementation preferences and priorities for data reuse, coordinating a broadly accepted, widely used FAIR implementation approach remains a global challenge. In an effort to accelerate broad community convergence on FAIR implementation options, the GO FAIR community has launched the development of the FAIR Convergence Matrix. The Matrix is a platform that compiles for any community of practice, an inventory of their self-declared FAIR implementation choices and challenges. The Convergence Matrix is itself a FAIR resource, openly available, and encourages voluntary participation by any self-identified community of practice (not only the GO FAIR Implementation Networks). Based on patterns of use and reuse of existing resources, the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services

D3.4 ENVRI Reference Model V1.1

It has been recognised that all ENVRI research infrastructures, although are very diverse, have some common characteristics, enabling them potentially to achieve a level of interoperability through the use of common standards for various functions. The objective of ENVRI Reference Model is to develop common ontological framework and standards for the description and characterisation of computational and storage infrastructures in order to achieve seamless interoperability between the heterogeneous resources of different infrastructures. The ENVRI Reference Model is a work-in-progress, hosted by the ENVRI project, intended for interested parties to directly comment on and contribute to