Search

Ecological and social processes of the Spermonde Archipelago, South Sulawesi, Indonesia, have been intensively studied during the Science for the Protection of Indonesian Coastal Ecosystems (SPICE) program. The archipelago is of specific interest to better understand how intensive exploitation of marine resources results in the degradation of reef systems. The projects specifically targeted (1) ecological processes in coral reefs, (2) genetic structure of populations, and (3) socialeecological dynamics relating to resource use, social networks, and governance structures. A modeling compo- nent emphasized (4) the integration of different ecological, social, and environmental components. Results indicated that reef resources in the Spermonde Archipelago are intensively exploited and further stressed by pollution effluents from hinterland processes. The lack of alternative livelihoods perpetuates dependencies within the patroneclient system of the artisanal fisheries and supports high exploitation and also destructive resource uses. Greater inclusion of local stakeholders in the governance may result in better conservation practices, sustainable resource use, and improved livelihoods for the people.

Pressing environmental and societal challenges demand the reuse of data on a much larger scale. Central to improvements on this front are approaches that support structured and detailed data descriptions of published data. In general, the reusability of scientific datasets such as measurements generated by instruments, observations collected in the field, and model simulation outputs, require information about the contexts through which they were produced. These contexts include the instrumentation, methods, and analysis software used. In current data curation practice, data providers often put a significant effort in capturing descriptive metadata about datasets. Nonetheless, metadata about instruments and methods provided by data authors are limited, and in most cases are unstructured.The ‘Interoperability’ principle of FAIR emphasizes the importance of using formal vocabularies to enable machine-understandability of data and metadata, and establishing links between data and related research entities to provide their contextual information (e.g., devices and methods). To support FAIR data, PANGAEA is currently elaborating workflows to enrich instrument information of scientific datasets utilizing internal as well as third party services and ontologies and their identifiers. This abstract presents our ongoing development within the projects FREYA and FAIRsFAIR as follows:Integrating the AWI O2A (Observations to Archives) framework and associated suite of tools within PANGAEA’s curatorial workflow as well as semi-automatized ingestion of observatory data. Linking data with their observation sources (devices) by recording the persistent identifiers (PID) from the O2A sensor registry system (sensor.awi.de) as part of the PANGAEA instrumentation database. Enriching device and method descriptions of scientific data by annotating them with appropriate vocabularies such as the NERC device type and device vocabularies or scientific methodology classifications. In our contribution we will also outline the challenges to be addressed in enabling FAIR vocabularies of instruments and methods. This includes questions regarding reliability and trustworthiness of third party ontologies and services. Further, challenges in content synchronisation across linked resources and implications on FAIRness levels of data sets such as dependencies on interlinked data sources and vocabularies.We will show in how far adapting, harmonizing and controlling the used vocabularies, as well as identifier systems between data provider and data publisher, improves the findability and re-usability of datasets , while keeping the curational overhead a slow as possible. This use case is a valuable example of how improving interoperability through harmonization efforts, though initially problematic and labor intensive, can benefits to a multitude of stakeholders in the long run: data users, publishers, research institutes, and funders.

This deliverable demonstrates how the various partners in FREYA are taking up persistent identifiers for organizations as part of the Work Package 4 work on integrating emerging PID types into disciplinary contexts. A range of organization IDs are discussed with special focus on the ROR ID as a community-led initiative with open infrastructure and data that is well suited for use in an open science environment.&nbsp

A comprehensive survey of the landscape of persistent identifiers across many disciplines is presented, with assessments of maturity of different PID types and conclusions for the future. [Revised version]

The main focus of this deliverable is the different approaches to provenance as understood, expressed, and implemented by the FREYA disciplinary partners in their various pilot applications. The presentations here outline general approaches to provenance in the particular research context of each organisation, current and future implementations, and describe provenance activities that are supported by persistent identifiers. The FREYA project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777523.&nbsp

During the webinar we looked back at the progress of the first part of the project and discussed the PID Graph and the growth of a PID community.&nbsp

A comprehensive analysis of user stories relating to a range of entities needing persistent identifiers, with conclusions for further work in the FREYA project. The deliverable was updated after the FREYA midterm review and uploaded again in November 2019.

ROBEX Sensor Workshop; Vienna, Austria, 27 April 2017