The CEDA vocabulary editor: a new tool for managing controlled vocabularies

A poster to informing how to manage controlled metadata vocabularie

Climate and forecast metadata conventions: a community driven metadata standard

A poster to show the climate and forecast metadata convention

Whats in a name? Managing a controlled vocabulary for climate and forecast data

A poster to describe how to manage a controlled vocabulary for climate and forecast dat

The CMIP6 Data Request (DREQ, version 01.00.31)

The data request of the Coupled Model Intercomparison Project Phase 6 (CMIP6) defines all the quantities from CMIP6 simulations that should be archived. This includes both quantities of general interest needed from most of the CMIP6-endorsed model intercomparison projects (MIPs) and quantities that are more specialized and only of interest to a single endorsed MIP. The complexity of the data request has increased from the early days of model intercomparisons, as has the data volume. In contrast with CMIP5, CMIP6 requires distinct sets of highly tailored variables to be saved from each of the more than 200 experiments. This places new demands on the data request information base and leads to a new requirement for development of software that facilitates automated interrogation of the request and retrieval of its technical specifications. The building blocks and structure of the CMIP6 Data Request (DREQ), which have been constructed to meet these challenges, are described in this paper

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

Climate and Forecast (CF) Metadata Conventions: A Community Driven Metadata Standard

This poster provides a brief introduction to the purpose and scope of CF Metadata. It describes some of the main features of the metadata conventions and explains the process by which the conventions evolve

The I-ADOPT Interoperability Framework: a proposal for FAIRer observable property descriptions

Global environmental challenges like climate change, pollution, and biodiversity loss are complex. To understand environmental patterns and processes and address these challenges, scientists require the observations of natural phenomena at various temporal and spatial scales and across many domains. The research infrastructures and scientific communities involved in these activities are often following their own data management practices which inevitably leads to a high degree of variability and incompatibility of approaches. Consequently, a variety of metadata standards and vocabularies have been proposed to describe observations and are actively used in different communities. However, this diversity in approaches now causes severe issues regarding the interoperability across datasets and hampers their exploitation as a common data source. Projects like ENVRI-FAIR, FAIRsFAIR, FAIRplus are addressing this difficulty by working on the full integration of services across research infrastructures based on FAIR Guiding Principles supporting the EOSC vision towards an open research culture. Beyond these projects, we need collaboration and community consensus across domains to build a common framework for representing observable properties. The Research Data Alliance InteroperAble Descriptions of Observable Property Terminology Working Group (RDA I-ADOPT WG) was formed in October 2019 to address this need. Its membership covers an international representation of terminology users and terminology providers, including terminology developers, scientists, and data centre managers. The group’s overall objective is to deliver a common interoperability framework for observable property variables within its 18-month work plan. Starting with the collection of user stories from research scientists, terminology managers, and data managers or aggregators, we drafted a set of technical and content-related requirements. A survey of terminology resources and annotation practices provided us with information about almost one hundred terminologies, a subset of which was then analysed to identify existing conceptualisation practices, commonalities, gaps, and overlaps. This was then used to derive a conceptual framework to support their alignment. In this presentation, we will introduce the I-ADOPT Interoperability Framework highlighting its semantic components. These represent the building blocks for specific ontology design patterns addressing different use cases and varying degrees of complexity in describing observed properties. We will demonstrate the proposed design patterns using a number of essential climate and essential biodiversity variables. We will also show examples of how the I-ADOPT framework will support interoperability between existing representations. This work will provide the semantic foundation for the development of more user-friendly data annotation tools capable of suggesting appropriate FAIR terminologies for observable properties