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

Talking Open Data

Enticing users into exploring Open Data remains an important challenge for the whole Open Data paradigm. Standard stock interfaces often used by Open Data portals are anything but inspiring even for tech-savvy users, let alone those without an articulated interest in data science. To address a broader range of citizens, we designed an open data search interface supporting natural language interactions via popular platforms like Facebook and Skype. Our data-aware chatbot answers search requests and suggests relevant open datasets, bringing fun factor and a potential of viral dissemination into Open Data exploration. The current system prototype is available for Facebook (https://m.me/OpenDataAssistant) and Skype (https://join.skype.com/bot/6db830ca-b365-44c4-9f4d-d423f728e741) users.Comment: Accepted at ESWC2017 demo trac

Open budget data: mapping the landscape

This report offers analysis of the emerging issue of open budget data, which has begun to gain traction amongst advocates and practitioners of financial transparency. Issues and initiatives associated with the emerging issue of open budget data are charted in different forms of digital media. The objective is to enable practitioners – in particular civil society organisations, intergovernmental organisations, governments, multilaterals and funders – to navigate this developing field and to identify trends, gaps and opportunities for supporting it. How public money is collected and distributed is one of the most pressing political questions of our time, influencing the health, well-being and prospects of billions of people. Decisions about fiscal policy affect everyone - determining everything from the resourcing of essential public services, to the capacity of public institutions to take action on global challenges such as poverty, inequality or climate change. Digital technologies have the potential to transform the way that information about public money is organised, circulated and utilised in society, which in turn could shape the character of public debate, democratic engagement, governmental accountability and public participation in decision-making about public funds. Data could play a vital role in tackling the democratic deficit in fiscal policy and in supporting better outcomes for citizens

Chem# - Semantically Enriched Linked Open Chemical Data

The problem: Vast quantities of chemical data (e.g. crystal structures, NMR spectra, experimental reports) are generated every day. The majority of this data is never published, and the data that is published is fragmented,trapped in legacy formats and difficult to discover. The solution: Semantically Enriched Linked Open Chemical Data: browsable, searchable, discoverable and interpretable by humans and machines alike, using standardized extensible data formats (Chemical Markup Language) and technologies (HTTP, RDF).Funded by JISC

Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of 'grey data' about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them. The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection. This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201

Dimensional enrichment of statistical linked open data

On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process.Peer ReviewedPostprint (author's final draft