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At the domains of agriculture and livestock farming a large amount of data are produced through numerous heterogeneous sources including sensor data, weather/climate data, statistical and government data, drone/satellite imagery, video, and maps. This plethora of data can be used at precision agriculture and precision livestock farming in order to provide predictive insights in farming operations, drive real-time operational decisions, redesign business processes and support policy-making. The predictive power of the data can be further boosted if data from diverse sources are integrated and processed together, thus providing more unexplored insights. However, the exploitation and integration of data used in precision agriculture is not straightforward since they: i) cannot be easily discovered across the numerous heterogeneous sources and ii) use different structural and naming conventions hindering their interoperability. The aim of this paper is to: i) study the characteristics of data used in precision agriculture & livestock farming and ii) study the user requirements related to data modeling and processing from nine real cases at the agriculture, livestock farming and aquaculture domains and iii) propose a semantic meta-model that is based on W3C standards (DCAT, PROV-O and QB vocabulary) in order to enable the definition of metadata that facilitate the discovery, exploration, integration and accessing of data in the domain.

In the distributed heterogeneous environmental data ecosystems, the number of data sources, volume and variances of derivatives, purposes, formats, and replicas are increasingly growing. In theory, this can enrich the information system as a whole, enabling new data value to be revealed via the combination and fusion of several data sources and data types, searching for further relevant information hidden behind the variety of expressions, formats, replicas, and unknown reliability. It is now visible how complex data alignment is, and even more, it is not always justified due to capacity and business issues. One of the challenging, but also most rewarding approaches is semantic alignment, which promises to fill the information gap of data discovery and joins. To formalise one, an inevitable enabler is an aligned, linked, and machine readable data model enabling the specification of relations between data elements generated information. The Iliad - digital twins of the ocean are cases of this kind, where in-situ data and citizen science observations are mixed with multidimensional environmental data to enable data science and what-if models implementation and to be integrated into even broader ecosystems like the European Digital Twin Ocean (EDITO) and European Data Spaces. An Ocean Information Model (OIM) that will enable traversals and profiles is the semantic backbone of the ecosystem. Defined as the multi-level ontology, it will explain data using well known generic (Darwin Core, WoT), spatio-temporal (SOSA/SSN, OGC Geo, W3C Time, QUDT, W3C RDF Data Cube, WoT) and domain (WORMS, AGROVOC) ontologies. Machine readability and unambiguity allow for both automated validation and some translations.On the other hand, efficient use of this requires yet another skill in data management and development besides GIS, ICT and domain expertise. In addition, as the semantics used in the data and metadata have not yet been stabilised on the implementation level, it introduces a few more flexibilities of data expression. Following the GEO data sharing and data management principles along with FAIR, CARE and TRUST, the environmental data is prepared for harmonisation. Furthermore, to ease the entry and to harmonise conventions, the authors introduce a multi-touchpoint data value chain API suite with an aligned approach to semantically enrich, entail and validate data sets such as observations streams in JSON or JSON-LD based on OIM, through storage and scientific data in NetCDF to exposing this semantically aligned data via the newly endorsed and already successful OGC Environmental Data Retrieval API. The practical approach is supported by a ready-to-use toolbox of components that presents portable tools to build and validate multi-source geospatial data integrations keeping track of the information added during mesh-up and predictions and what-if implementations.

In May 2007, the INSPIRE directive established the path towards creating the European Spatial Data Infrastructure (ESDI). While the Joint Research Centre (JRC) defined a set of detailed implementation guidelines, the European member states determined the agencies responsible for delivering the different topics specified in the directive’s annexes. INSPIRE’s goal was - and still is - to organize and share Europe’s data supporting environmental policies and actions. However, the way that INSPIRE was defined limited contributions to the public sector, and limited topics to those specifically listed in its annexes. Technical challenges and a lack of appropriate tools have impeded INSPIRE from implementing its own guidelines, and even after 15 years, the dream of a continuous, consistent description of Europe’s environment has still not completely materialized. We should apply the lessons learnt in INSPIRE when we build the Green Deal Data Space (GDDS). To create the GDDS, we should start with ESDI (the European Spatial Data Infrastructure), but also engage and align with the ongoing preparatory actions for data spaces (e.g., for green deal and agriculture) as well as include actors and networks that have emerged or been organized in the recent years. These include: networks of in situ observations (e.g. the Environmental Research Infrastructures (ENVRI) community); Citizen Science initiatives (such as the biodiversity observations integrated in the Global Biodiversity Information Facility (GBIF), or sensor communities for e.g. air quality); predictive algorithms and machine learning models and simulations based on artificial intelligence (such as the ones deployed in the European Open Science Cloud, International Data Space Association and Gaia-X; services driven both by the scientific community and the private sector); remote sensing derived products developed by the Copernicus Services. Most of these data providers have already embraced the FAIR principles and open data, providing many examples of best practice which can assist newer adopters on the path to open science. In the Horizon Europe project AD4GD (AllData4GreenDeal), we believe that, instead of trying to force data producers to adopt cumbersome new protocols, we should take advantage of the latest developments in geospatial standards and APIs. These allow loosely coupled but well documented and interlinked data sources and models in the GDDS while achieving scientifically robust integration and easy access to data in the resulting workflows. Another fundamental element will be the adoption of a common and extensible information model enabling the representation and exchange of Green Deal related data in an unambiguous manner, including vocabularies for Essential Variables to organize the observable measurements and increase the level of semantic interoperability. This will allow systems and components from different technology providers to seamless interoperate and exchange data, and to have an integrated view and access to exploit the full value of the available data. The project will validate the approach in three pilot cases: water quality and availability of Berlin lakes, biodiversity corridors in the metropolitan area of Barcelona and low cost air quality sensors in Europe. The AD4GD project is funded by the European Union under the Horizon Europe program.

CAMS SURFACE RESIDENTIAL ELEMENARY CARBON

This deliverable presents the second version of the tools and services for Research Objects (ROs) management, resulting from the WP5 tasks. In particular, such tools and services include the latest release of ROHub platform, the services for FAIR assessment and quality assessment of Research Objects and their integration in ROHub, the extended Research Objects evolution mechanism adapted to EOSC scenarios and their implemented in ROHub, the integration of services from WP3 (text mining) and WP4 (data cubes) in ROHub, the integration of other external EOSC services in ROHub, and the latest user interfaces of ROHub. An important input for this deliverable and for the work carried out in the implementation of WP5 services is the vocabulary specification that is used in RELIANCE for the representation of the Research Objects, which has been implemented as an RO-crate profile, and which is described in D5.1. A key characteristic of the proposed RO specification is regarding the data-cube centric Research Object, as a fundamental differentiating principle of the Earth Science communities that are supported by RELIANCE. As described in D4.1, Data Cubes are becoming a reference service to provide a data access infrastructure and a reference set of tools for geospatial data management, mostly related to the access and exploitation of large collections of multi-temporal, multi-resolution satellite imagery at national/international scale. RELIANCE leverages data cubes to provide earth scientists with an efficient and scalable structured data access and discovery. Scientists are able to aggregate and describe data cubes in their Research Objects, and connect them with related resources (e.g., products generated, methods used for processing, publications generated, infrastructures used, etc.), in line with FAIR and open science principles. This document is the second version describing the design, implementation and deployment of Research Object services. This is an updated version of deliverable D5.2, therefore it includes content from D5.2 regarding previous results, plus the new content describing the results during this second period., This is the draft version of the deliverable not yet approved by the European Commission.

This report documents the activities undertaken in the past 12 months to offer the project services through the EOSC delivery channels, including the technical and service provisioning integration activities and their status, as well as a report of the helpdesk activities., This is the draft version of the deliverable not yet approved by the European Commission.

Data-intensive science communities are progressively adopting FAIR practices that enhance the visibility of scientific breakthroughs and enable reuse. At the core of this movement, research objects contain and describe scientific information and resources in a way compliant with the FAIR principles and sustain the development of key infrastructure and tools. This paper provides an account of the challenges, experiences and solutions involved in the adoption of FAIR around research objects over several Earth Science disciplines. During this journey, our work has been comprehensive, with outcomes including: an extended research object model adapted to the needs of earth scientists; the provisioning of digital object identifiers (DOI) to enable persistent identification and to give due credit to authors; the generation of content-based, semantically rich, research object metadata through natural language processing, enhancing visibility and reuse through recommendation systems and third-party search engines; and various types of checklists that provide a compact representation of research object quality as a key enabler of scientific reuse. All these results have been integrated in ROHub, a platform that provides research object management functionality to a wealth of applications and interfaces across different scientific communities. To monitor and quantify the community uptake of research objects, we have defined indicators and obtained measures via ROHub that are also discussed herein.

Agriculture as a whole consists of many sectors reliant on each other for proper functioning. The need for cross sector cooperation is especially important between apiary owners and farmers. Indeed, it provides benefits for both: increasing and diversifying access to appropriate flowering plants on one hand and providing means of increasing pollination of plants and in turn yield from fields on the other. On the contrary, lack of information may result in poisoning beehive families operating near regions where specific plant protection products have been used. However, even although IT and IoT solutions are increasingly prevalent in agri-food sectors, there is still a significant barrier in connecting various systems, exchanging data and helping users in facilitating modern solutions. In this paper we present a viable solution to this problem by leveraging DEMETER platform and show it’s usage to enable interoperability between commercial IoT beekeeper solution and emerging national farm management (eDWIN) and advisory tools.

RELIANCE, short for Research Lifecycle Management for Earth Science Communities and Copernicus users in EOSC, aims to realize the vision of FAIR research in EOSC by adopting a holistic research management approach based on three key and complementary technologies: i) Research objects (RO) as the overarching mechanism to manage scientific research activities, which relies upon ROHub platform as the reference service; ii) data cubes as the mechanisms enabling an efficient and scalable Earth Observation data access and discovery, which relies upon the Advanced geospatial Data Management (ADAM) platform as the reference service; iii) text mining and semantic enrichment services allowing to extract machine-readable metadata from RO resources, enabling researchers to discover scientific information at scale and to structure their own research, and which rely on the AI-based platform COGITO as base system. As part of the integration in EOSC, RELIANCE services will leverage and integrate with some of the EOSC core-cross cutting and added value services, playing a complementary role to what is already available and bridging between various EOSC services. RELIANCE will pilot the services in three different Earth Science communities, fostering the use of Copernicus data and demonstrating their efficacy in real-life vertical and multi-disciplinary scenarios, and will launch an Open Call to engage other communities. This document comprises the project management handbook. It contains all related information for running the RELIANCE project, including an introduction to the project and an overview of the rationale and the objectives. The main part of the document summarizes the information regarding the project management, structures and procedures, including some useful information from the Grant Agreement and its annexes, the Consortium Agreement and further decisions within the Consortium. RELIANCE partners will use this handbook as a reference manual during the implementation of their actions. The handbook will facilitate the management of the project, the monitoring of the overall progress and the communication between project partners and the Commission. Accordingly, it also provides the relevant information about the collaboration and communication tools, the organization and management of the meetings, and the timely and quality production of the planned deliverables.

Web-version: https://w3id.org/ro/crate/1.1 This document specifies a method, known as RO-Crate (Research Object Crate), of aggregating and describing research data with associated metadata. RO-Crates can aggregate and describe any resource including files, URI-addressable resources, or use other addressing schemes to locate digital or physical data. RO-Crates can describe data in aggregate and at the individual resource level, with metadata to aid in discovery, re-use and long term management of data. Metadata includes the ability to describe the context of data and the entities involved in its production, use and reuse. For example: who created it, using which equipment, software and workflows, under what licenses can it be re-used, where was it collected, and/or where is it about. RO-Crate uses JSON-LD to to express this metadata using linked data, describing data resources as well as contextual entities such as people, organizations, software and equipment as a series of linked JSON-LD objects - using common published vocabularies, chiefly schema.org. The core of RO-Crate is a JSON-LD file, the RO-Crate Metadata File, named ro-crate-metadata.json. This file contains structured metadata about the dataset as a whole (the Root Data Entity) and, optionally, about some or all of its files. This provides a simple way to, for example, assert the authors (e.g. people, organizations) of the RO-Crate or one its files, or to capture more complex provenance for files, such as how they were created using software and equipment. While providing the formal specification for RO-Crate, this document also aims to be a practical guide for software authors to create tools for generating and consuming research data packages, with explanation by examples.

Data is the basis for creating information and knowledge. Having data in a structured and machine-readable format facilitates the processing and analysis of the data. Moreover, metadata—data about the data, can help discovering data based on features as, e.g., by whom they were created, when, or for which purpose. These associated features make the data more interpretable and assist in turning it into useful information. This chapter briefly introduces the concepts of metadata and Linked Data—highly structured and interlinked data, their standards and their usages, with some elaboration on the role of Linked Data in bioeconomy.

Musical genre classification by ensembles of audio and lyrics features

One of the main goals of DataBio was the provision of solutions for big data management enabling, among others, the harmonisation and integration of a large variety of data generated and collected through various applications, services and devices. The DataBio approach to deliver such capabilities was based on the use of Linked Data as a federated layer to provide an integrated view over (initially) disconnected and heterogeneous datasets. The large amount of data sources, ranging from mostly static to highly dynamic, led to the design and implementation of Linked Data Pipelines. The goal of these pipelines is to automate as much as possible the process to transform and publish different input datasets as Linked Data. In this chapter, we describe these pipelines and how they were applied to support different uses cases in the project, including the tools and methods used to implement them.

Web-version: https://w3id.org/ro/crate/1.1 This document specifies a method, known as RO-Crate (Research Object Crate), of organizing file-based data with associated metadata, using linked data principles, in both human and machine readable formats, with the ability to include additional domain-specific metadata. The core of RO-Crate is a JSON-LD file, the RO-Crate Metadata File, named ro-crate-metadata.json. This file contains structured metadata about the dataset as a whole (the Root Data Entity) and, optionally, about some or all of its files. This provides a simple way to, for example, assert the authors (e.g. people, organizations) of the RO-Crate or one its files, or to capture more complex provenance for files, such as how they were created using software and equipment. While providing the formal specification for RO-Crate, this document also aims to be a practical guide for software authors to create tools for generating and consuming research data packages, with explanation by examples.

Web-version: https://w3id.org/ro/crate/1.0 This document specifies a method, known as RO-Crate (Research Object Crate), of organizing file-based data with associated metadata, using linked data principles, in both human and machine readable formats, with the ability to include additional domain-specific metadata. The core of RO-Crate is a JSON-LD file, the RO-Crate Metadata File, named ro-crate-metadata.jsonld. This file contains structured metadata about the dataset as a whole (the Root Data Entity) and, optionally, about some or all of its files. This provides a simple way to, for example, assert the authors (e.g. people, organizations) of the RO-Crate or one its files, or to capture more complex provenance for files, such as how they were created using software and equipment. While providing the formal specification for RO-Crate, this document also aims to be a practical guide for software authors to create tools for generating and consuming research data packages, with explanation by examples.

Web-version: https://w3id.org/ro/crate/0.2 This document specifies a method, known as RO-Crate (Research Object Crate), of organizing file-based data with associated metadata, using linked data principles, in both human and machine readable formats, with the ability to include additional domain-specific metadata. The core of RO-Crate is a JSON-LD file, the RO-Crate Metadata File, named ro-crate-metadata.jsonld. This file contains structured metadata about the dataset as a whole (the Root Data Entity) and, optionally, about some or all of its files. This provides a simple way to, for example, assert the authors (e.g. people, organizations) of the RO-Crate or one its files, or to capture more complex provenance for files, such as how they were created using software and equipment. While providing the formal specification for RO-Crate, this document also aims to be a practical guide for software authors to create tools for generating and consuming research data packages, with explanation by examples.

Data-intensive science disciplines, like Earth Science, are increasingly producing and consuming a variety of digital resources during the course of a scientific investigation. Instead of having these resources in isolated repositories, scientists are seeking ways for managing and making these resources available from a single place, and at the same time they are also increasingly interested in the adoption of FAIR principles to enhance the visibility and reusability of scientific results. This has called for new methods to improve the access and communication of results. Research Objects are a key building block towards realising this vision. They provide a structured way (a model) to describe scientific resources related to an investigation, along with the context in which they were used and the people involved. But research objects are as useful in practice as the availability of tools supporting their adoption. In this paper, we present a toolkit, tailored for Earth Sciences, comprising a set of services and applications around research objects that support scientists throughout the research lifecycle to manage, share, find and reuse scientific results, and we discuss initial insights into the community adoption

There is an increasing tension in agriculture between the requirements to assure full safety on the one hand and keep costs under control on the other hand, both with respect to (inter) national strategies. Farmers need to measure and understand the impact of huge amount and variety of data which drive overall quality and yield in their fields. Among others, those are local weather data, Global Navigation System of Systems data, orthophotos and satellite imagery, data on soil specifics etc. A strong need to secure Big Data arises due to various repositories and heterogeneous sources. Data storage and visualisation requirements are in some cases competing as they are a common interest as well as a threat that helps one part of a value chain to gain a higher profit. As demonstrated in this paper, handling (Big) data is therefore a sensitive topic, where trust of producers on data security is essential.

A research object is a single information unit encapsulating all the knowledge relevant to a particular scientific investigation, their associated metadata and the context where such resources were produced and came into play. Aimed at enhancing the preservation, reuse and scholarly communication of data-intensive science, research objects are both technical and social artifacts that represent a partnership between scientific communities and the computational support required in nowadays science. In this paper, we explore such partnership, identifying the lack of appropriate machine-readable metadata as one of its main inhibitors, and address the semantic enrichment of research objects as one key aspect towards its establishment. Focused on the specific needs of Earth Science communities, we propose extensions to research object representation models and present novel methods and tools to enrich research object metadata through automatic means. Finally, we validate the approach through the implementation of a recommender system that exploits the resulting metadata to facilitate research object discovery and reuse, enabling humans and machines to work together and accelerate the research life cycle.

Research objects act as a semantically rich container of all the information that lead to a scientific result, and have the potential to change how data intensive science shares and reuses methods, datasets and results. Despite a comprehensive set of vocabularies to describe semantically the aggregated resources, users often limit themselves to provide metadata at the container level, ignoring the valuable information that the aggregated resources contain. In this poster we explore how the combination of semantic technologies and natural language processing can be used to enrich research objects with structured metadata aiming at enhancing their findability as a crucial aspect towards their reuse.

Earth science disciplines are increasingly producing large amounts of heterogeneous data that needs to be accessed, integrated and processed during the course of a research or operational process. In this setting, there is a growing need from earth scientists to collect and manage effectively these resources, including the data used, the methods applied, and the results produced. This is even more challenging if we consider i) the dynamic and collaborative nature of the earth science processes; ii) the interfaces typically used by scientists where they should be able to accomplish core management tasks with a minimal overhead. In this poster, we present our approach to address these challenges, which is based on research objects and on the associated technological support provided by ROHub platform. We show how ROHub is used as the underlying technology for supporting earth scientists work through different interfaces tailored to their communities needs and usage scenario.

Research objects were designed in data-intensive science under the premises of interoperability and machine-readability to describe scientific processes and findings including all the resources that were used in the research endeavour. In this poster we present our work with Earth Science communities, that have embraced the research object model for long-term preservation and reuse of knowledge, to design checklists, the main tool available to assess the quality of research objects and upon which quality metrics such completeness, stability and reliability are calculated.

FOODIE project aims at building an open and interoperable agricultural specialized platform on the cloud for the management, discovery and large-scale integration of data relevant for farming production. In particular, the integration focuses on existing open datasets as well as their publication in Linked data format in order to maximize their reusability and enable the exploitation of the extra knowledge derived from the generated links. Based on such data, for instance, FOODIE platform aims at providing high-value applications and services supporting the planning and decision-making processes of different stakeholders related to the agricultural domain. The keystone for data integration is FOODIE data model, which has been defined by reusing and extending current standards and best practices, including data specifications from the INSPIRE directive which are in turn based on the ISO/OGC standards for geographical information. However, as these data specifications are available as XML documents, the first step to publish Linked Data required transforming or lifting FOODIE data model into semantic format. In this paper, we describe this process, which was conducted semi-automatically by reusing existing tools, and adhering to the mapping rules for transforming geographic information UML models to OWL ontologies defined by the ISO 19150-2 standard. We describe the challenges associated to this transformation, and finally, we describe the generated ontology, providing an INSPIRE-based vocabulary for the publication of Agricultural Linked Data.

This paper describes our methodological and technological approach for collaborative ontology development in inter-organizational settings. It is based on the formalization of the collaborative ontology development process by means of an explicit editorial workflow, which coordinates proposals for changes among ontology editors in a flexible manner. This approach is supported by new models, methods and strategies for ontology change management in distributed environments: we propose a new form of ontology change representation, organized in layers so as to provide as much independence as possible from the underlying ontology languages, together with methods and strategies for their manipulation, version management, capture, storage and maintenance, some of which are based on existing proposals in the state of the art. Moreover, we propose a set of change propagation strategies that allow keeping distributed copies of the same ontology synchronized. Finally, we illustrate and evaluate our approach with a test case in the fishery domain from the United Nations Food and Agriculture Organisation (FAO). The preliminary results obtained from our evaluation suggest positive indication on the practical value and usability of the work here presented.

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner.

Research Objects (ROs) are semantic aggregations of related scientific resources, their annotations and research context. They are meant to help scientists to refer to all the materials supporting their investigation. ROHub is a digital library system for ROs that supports their storage, lifecycle management and preservation. It provides a Web interface and a set of RESTful APIs enabling the sharing of scientific findings via ROs. Additionally, ROHub includes different features that help scientists throughout the research lifecycle to create and maintain high-quality ROs that can be interpreted and reproduced in the future. For instance, scientists can assess the conformance of an RO to a set of predefined requirements and create RO Snapshots, at any moment, to share, cite or submit to review the current state of research outcomes. ROHub can also generate nested ROs for workflow runs, exposing their content and annotations, and includes monitoring features that generate notifications when changes are detected.

New digital artifacts are emerging in data-intensive science. For example, scientific workflows are executable descriptions of scientific procedures that define the sequence of computational steps in an automated data analysis, supporting reproducible research and the sharing and replication of best-practice and know-how through reuse. Workflows are specified at design time and interpreted through their execution in a variety of situations, environments, and domains. Hence it is essential to preserve both their static and dynamic aspects, along with the research context in which they are used. To achieve this, we propose the use of multidimensional digital objects (Research Objects) that aggregate the resources used and/or produced in scientific investigations, including workflow models, provenance of their executions, and links to the relevant associated resources, along with the provision of technological support for their preservation and efficient retrieval and reuse. In this direction, we specified a software architecture for the design and implementation of a Research Object preservation system, and realized this architecture with a set of services and clients, drawing together practices in digital libraries, preservation systems, workflow management, social networking and Semantic Web technologies. In this paper, we describe the backbone system of this realization, a digital library system built on top of dLibra.

In the domain of eScience, investigations are increasingly collaborative. Most scientific and engineering domains benefit from building on top of the outputs of other research: By sharing information to reason over and data to incorporate in the modelling task at hand.This raises the need to provide means for preserving and sharing entire eScience workflows and processes for later reuse. It is required to define which information is to be collected, create means to preserve it and approaches to enable and validate the re-execution of a preserved process. This includes and goes beyond preserving the data used in the experiments, as the process underlying its creation and use is essential.This tutorial thus provides an introduction to the problem domain and discusses solutions for the curation of eScience processes.

Interoperability on multiple levels, concerning both the ontologies themselves and their engineering activities, is a key requirement for ontology networks to be efficient, with minimal redundancy and high reuse. This requirement has a strict binding for software tools that can support some interoperability levels, yet they can be hindered by a lack of shared models and vocabularies describing the resources to be handled, as well as the ways of handling them. Here, three examples of metalevel vocabularies are proposed, each covering at least one peculiar interoperability aspect: OMV for modeling the artifacts themselves, LIR for managing a multilingual layer on top of them, and C-ODO Light for modeling collaboration-supportive life cycle management tasks and processes. All of these models lend themselves to handling by dedicated software tools and are all being employed within NeOn products.

Interoperability on multiple levels, concerning both the ontologies themselves and their engineering activities, is a key requirement for ontology networks to be efficient, with minimal redundancy and high reuse. This requirement has a strict binding for software tools that can support some interoperability levels, yet they can be hindered by a lack of shared models and vocabularies describing the resources to be handled, as well as the ways of handling them. Here, three examples of metalevel vocabularies are proposed, each covering at least one peculiar interoperability aspect: OMV for modeling the artifacts themselves, LIR for managing a multilingual layer on top of them, and C-ODO Light for modeling collaboration-supportive life cycle management tasks and processes. All of these models lend themselves to handling by dedicated software tools and are all being employed within NeOn products.

The widespread use of ontologies in the last years has raised new challenges for their development and maintenance. Ontology development has transformed from a process normally performed by one ontology engineer into a process performed collaboratively by a team of ontology engineers, who may be geographically distributed and play different roles. For example, editors may propose changes, while authoritative users approve or reject them following a well defined process. This process, however, has only been partially addressed by existing ontology development methods, methodologies, and tool support. Furthermore, in a distributed environment where ontology editors may be working on local copies of the same ontology, strategies should be in place to ensure that changes in one copy are reflected in all of them. In this paper, we propose a workflow-based model for the collaborative development of ontologies in distributed environments and describe the components required to support them. We illustrate our model with a test case in the fishery domain from the United Nations Food and Agriculture Organisation (FAO).

In this paper, we present Oyster, a Peer-to-Peer system for exchanging ontology metadata among communities in the Semantic Web. Oyster exploits semantic web techniques in data representation, query formulation and query result presentation to provide an online solution for sharing ontologies, thus assisting researchers in re-using existing ontologies.

Efficient knowledge sharing and reuse—a pre-requisite for the realization of the Semantic Web vision—is currently impeded by the lack of standards for documenting and annotating ontologies with metadata information. We argue that the availability of metadata is a fundamental dimension of ontology reusability. Metadata information provides a basis for ontology developers to evaluate and adapt existing Semantic Web ontologies in new application settings, and fosters the development of support tools such as ontology repositories. However, in order for the metadata information to represent real added value to ontology users, it is equally important to achieve a common agreement on the terms used to describe ontologies, and to provide an appropriate technology infrastructure in form of tools being able to create, manage and distribute this information. In this paper we present DEMO, a framework for the development and deployment of ontology metadata. Besides OMV , the proposed core vocabulary for ontology metadata, the framework comprises an inventory of methods to collaboratively extend OMV in accordance to the requirements of an emerging community of industrial and academia users, and tools for metadata management.

Ontologies have seen quite an enormous development and application in many domains within the last years, especially in the context of the next web generation, the Semantic Web. Besides the work of countless researchers across the world, industry starts developing ontologies to support their daily operative business. Currently, most ontologies exist in pure form without any additional information, e.g. authorship information, such as provided by Dublin Core for text documents. This burden makes it difficult for academia and industry e.g. to identify, find and apply – basically meaning to reuse – ontologies effectively and efficiently. Our contribution consists of (i) a proposal for a metadata standard, so called Ontology Metadata Vocabulary (OMV) which is based on discussions in the EU IST thematic network of excellence Knowledge Web1 and (ii) two complementary reference implementations which show the benefit of such a standard in decentralized and centralized scenarios, i.e. the Oyster P2P system and the Onthology metadata portal.

This paper presents Oyster, a Peer-to-Peer system for exchanging ontology metadata among communities in the Semantic Web. We describe how Oyster assists researchers in re-using existing ontologies, and how Oyster exploits semantic web techniques in data representation, query formulation, query result presentation to provide an online solution to share ontologies.

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption., 20 pages

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the conclusions that were derived, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.

The agriculture sector is a unique sector due to its strategic importance for both citizens (consumers) and economy (regional and global), which ideally should make the whole sector a network of interacting organizations. Rural areas are of particular importance with respect to the agro-food sector and should be specifically addressed within this scope. The FOODIE project aims at building an open and interoperable agricultural specialized platform hub on the cloud for the management of spatial and non-spatial data relevant for farming production; for discovery of spatial and non-spatial agriculture related data from heterogeneous sources; integration of existing and valuable European open datasets related to agriculture; data publication and data linking of external agriculture data sources contributed by different public and private stakeholders allowing to provide specific and high-value applications and services for the support in the planning and decision-making processes of different stakeholders groups related to the agricultural and environmental domains.