Git4Voc: Git-based Versioning for Collaborative Vocabulary Development

Collaborative vocabulary development in the context of data integration is the process of finding consensus between the experts of the different systems and domains. The complexity of this process is increased with the number of involved people, the variety of the systems to be integrated and the dynamics of their domain. In this paper we advocate that the realization of a powerful version control system is the heart of the problem. Driven by this idea and the success of Git in the context of software development, we investigate the applicability of Git for collaborative vocabulary development. Even though vocabulary development and software development have much more similarities than differences there are still important differences. These need to be considered within the development of a successful versioning and collaboration system for vocabulary development. Therefore, this paper starts by presenting the challenges we were faced with during the creation of vocabularies collaboratively and discusses its distinction to software development. Based on these insights we propose Git4Voc which comprises guidelines how Git can be adopted to vocabulary development. Finally, we demonstrate how Git hooks can be implemented to go beyond the plain functionality of Git by realizing vocabulary-specific features like syntactic validation and semantic diffs

A Global Data Ecosystem for Agriculture and Food

Agriculture would benefit hugely from a common data ecosystem. Produced and used by diverse stakeholders, from smallholders to multinational conglomerates, a shared global data space would help build the infrastructures that will propel the industry forward. In light of growing concern that there was no single entity that could make the industry-wide change needed to acquire and manage the necessary data, this paper was commissioned by Syngenta with GODAN’s assistance to catalyse consensus around what form a global data ecosystem might take, how it could bring value to key players, what cultural changes might be needed to make it a reality and finally what technology might be needed to support it. This paper looks at the challenges and principles that must be addressed in in building a global data ecosystem for agriculture. These begin with building incentives and trust: amongst both data providers and consumers: in sharing, opening and using data. Key to achieving this will be developing a broad awareness of, and making efforts to improve, data quality, provenance, timeliness and accessibility. We set out the key global standards and data publishing principles that can be followed in supporting this, including the ‘Five stars of open data’ and the ‘FAIR principles’ and offer several recommendations for stakeholders in the industry to follow

The Digital Classicist 2013

This edited volume collects together peer-reviewed papers that initially emanated from presentations at Digital Classicist seminars and conference panels. This wide-ranging volume showcases exemplary applications of digital scholarship to the ancient world and critically examines the many challenges and opportunities afforded by such research. The chapters included here demonstrate innovative approaches that drive forward the research interests of both humanists and technologists while showing that rigorous scholarship is as central to digital research as it is to mainstream classical studies. As with the earlier Digital Classicist publications, our aim is not to give a broad overview of the field of digital classics; rather, we present here a snapshot of some of the varied research of our members in order to engage with and contribute to the development of scholarship both in the fields of classical antiquity and Digital Humanities more broadly

The Digital Classicist 2013

This edited volume collects together peer-reviewed papers that initially emanated from presentations at Digital Classicist seminars and conference panels. This wide-ranging volume showcases exemplary applications of digital scholarship to the ancient world and critically examines the many challenges and opportunities afforded by such research. The chapters included here demonstrate innovative approaches that drive forward the research interests of both humanists and technologists while showing that rigorous scholarship is as central to digital research as it is to mainstream classical studies. As with the earlier Digital Classicist publications, our aim is not to give a broad overview of the field of digital classics; rather, we present here a snapshot of some of the varied research of our members in order to engage with and contribute to the development of scholarship both in the fields of classical antiquity and Digital Humanities more broadly

A provenance-based semantic approach to support understandability, reproducibility, and reuse of scientific experiments

Understandability and reproducibility of scientific results are vital in every field of science. Several reproducibility measures are being taken to make the data used in the publications findable and accessible. However, there are many challenges faced by scientists from the beginning of an experiment to the end in particular for data management. The explosive growth of heterogeneous research data and understanding how this data has been derived is one of the research problems faced in this context. Interlinking the data, the steps and the results from the computational and non-computational processes of a scientific experiment is important for the reproducibility. We introduce the notion of end-to-end provenance management'' of scientific experiments to help scientists understand and reproduce the experimental results. The main contributions of this thesis are: (1) We propose a provenance modelREPRODUCE-ME'' to describe the scientific experiments using semantic web technologies by extending existing standards. (2) We study computational reproducibility and important aspects required to achieve it. (3) Taking into account the REPRODUCE-ME provenance model and the study on computational reproducibility, we introduce our tool, ProvBook, which is designed and developed to demonstrate computational reproducibility. It provides features to capture and store provenance of Jupyter notebooks and helps scientists to compare and track their results of different executions. (4) We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility) for the end-to-end provenance management. This collaborative framework allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational steps in an interoperable way. We apply our contributions to a set of scientific experiments in microscopy research projects

Semantics in Support of Biodiversity Knowledge Discovery: An Introduction to the Biological Collections Ontology and Related Ontologies

The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers