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Publicly funded data-driven innovation programmes frequently involve partnerships between small and medium enterprises (SMEs) and municipal authorities utilizing citizen data. The intention of these projects is to benefit citizens. However, few such projects achieve success or impact within the project timeframe. This may result in benefit accruing mainly to the SME partner, who gains both learning and data, engendering questions of data justice around whether citizen data are being exploited without sufficient benefit returning to citizens. Through case studies composed of interviews and document analysis, we examine how benefits for citizens are conceived and achieved in the publicly funded data-driven air quality projects Data Pitch and Smart Cities Innovation Framework Implementation. We find the differences between the programme funders’ policies had a clear influence on the citizen engagement elements. There are also a number of ways in which the desired citizen engagement and benefit becomes diluted, including through misalignment of incentives and focus, a lack of prioritization and ownership, and power imbalances between citizens and the other actors in the quadruple helix model. To retain the focus on ensuring citizens benefit from data-driven innovation programmes using citizen data, we propose the use of data Justice plans. More work is required to specify the content and mechanisms of such plans for application in such programmes.

Citizen science (CS) is a way to open up the scientific process, to make it more accessible and inclusive, and to bring professional scientists and the public together in shared endeavours to advance knowledge. Many initiatives engage citizens in the collection or curation of data, but do not state what happens with such data. Making data open is increasingly common and compulsory in professional science. To conduct transparent, open science with citizens, citizens need to be able to understand what happens with the data they contribute. Data management documentation (DMD) can increase understanding of and trust in citizen science data, improve data quality and accessibility, and increase the reproducibility of experiments. However, such documentation is often designed for specialists rather than amateurs. This paper analyses the use of DMD in CS projects. We present analysis of a qualitative survey and assessment of projects’ DMD, and four vignettes of data management practices. Since most projects in our sample did not have DMD, we further analyse their reasons for not doing so. We discuss the benefits and challenges of different forms of DMD, and barriers to having it, which include a lack of resources, a lack of awareness of tools to support DMD development, and the inaccessibility of existing tools to citizen scientists without formal scientific education. We conclude that, to maximise the inclusivity of citizen science, tools and templates need to be made more accessible for non-experts in data management.

One of the current goals of the European Commission is to stimulate the development and uptake of data and AI technologies in the economy. Earlier foundations of this work included initiatives to promote the publication of open data in the public domain. More recently, the Commission’s attention has been shifting to open innovation programmes that help startups and small-medium enterprises (SMEs) to develop the capacity to engage with the latest technical and regulatory trends, and to share their data innovations with other organisations. In order to assess the efficacy and impact of such initiatives, each programme’s specific social and economic objectives must be taken into consideration. As an example of how this can be done in practice, our paper presents the motivating objectives and methodological approaches that were used to assess the impact of the Data Market Services Accelerator (DMS), an EU-funded initiative for data-centric companies. We evaluated the performance of the programme in terms of its effect on the market, fundraising capabilities of companies, innovation, and socio-economic aspects. In addition to assessing how DMS was able to meet its intended objectives, our examination also underscored current challenges related to specific outcomes that are meaningful to the European Commission, including data standardisation and long-term legal strategy. We conclude the paper with a series of recommendations to support the impact assessment efforts of other similar innovation programmes.

The objective of this paper is to classify and analyse all research on open data performed in the scientific community from a technological viewpoint, providing a detailed exploration based on six key facets: publication venue, impact, subject, domain, life-cycle phases and type of research. This paper therefore provides a consolidated overview of the open data arena that allows readers to identify well-established topics, trends, and open research issues. Additionally, we provide an extensive qualitative discussion of the most interesting findings to pave the way for future research. Our first identification phase resulted in 893 relevant peer-reviewed articles, published between 2006 and 2019 in a wide variety of venues. Analysis of the results shows that open data research grew slowly from 2006 but increased significantly as from 2009. In 2019, research interest in open data from a technological perspective overall decreased. This fact could indicate that research is beginning to stabilise, i.e., the open data research hype is over, and the research field is reaching maturity. Main findings are (i) increasing effort in researching on Semantic Web technologies as a mechanism to publish and reuse linked open data, (ii) software systems are proposed to solve open data technical problems; and (iii) considering technological aspects of legislation and standardization is needed to widely introduce open data in society. Finally, we provide complementary insights regarding open data innovation projects, with special emphasis on publication (e.g., open data portals) and consumption (e.g., open data as business enabler) of open data.

Open innovation programmes related to data and artificial intelligence have interested European policy-makers as a means of supporting startups and small and medium-sized enterprises to succeed in the digital economy. We discuss the objectives behind the typical service offerings of such programmes and propose a case for exploring how they align with the motivations of individual companies who are targeted by these initiatives. Using a qualitative analysis of 50 startup applications from the Data Market Services Accelerator programme, we find that applicants wrote most frequently about fundraising, acceleration and data skills. A smaller number of startups expressed interest in services related to standardization or legal guidance on General Data Protection Regulation and intellectual property rights, which are some of the ongoing priority areas for the European Commission. We discuss how the value propositions of these less desired offerings can be amplified by appealing the existing business motivations of data-driven startups.

Purpose – Ensuring quality is one of the most significant challenges in microtask crowdsourcing tasks. Aggregation of the collected data from the crowd is one of the important steps to infer the correct answer, but the existing study seems to be limited to the single-step task. This study aims to look at multiple-step classification tasks and understand aggregation in such cases; hence, it is useful for assessing the classification quality. Design/methodology/approach – The authors present a model to capture the information of the workflow, questions and answers for both single- and multiple-question classification tasks. They propose an adapted approach on top of the classic approach so that the model can handle tasks with several multiple-choice questions in general instead of a specific domain or any specific hierarchical classifications. They evaluate their approach with three representative tasks from existing citizen science projects in which they have the gold standard created by experts. Findings – The results show that the approach can provide significant improvements to the overall classification accuracy. The authors’ analysis also demonstrates that all algorithms can achieve higher accuracy for the volunteer- versus paid-generated data sets for the same task. Furthermore, the authors observed interesting patterns in the relationship between the performance of different algorithms and workflow-specific factors including the number of steps and the number of available options in each step. Originality/value – Due to the nature of crowdsourcing, aggregating the collected data is an important process to understand the quality of crowdsourcing results. Different inference algorithms have been studied for simple microtasks consisting of single questions with two or more answers. However, as classification tasks typically contain many questions, the proposed method can be applied to a wide range of tasks including both single- and multiple-question classification tasks.

Crowdsourcing has become the mot du jour when it comes to resolving any types of problems, online or offline, that require sustained human involvement. We see it applied in order to motivate employees to engage with less rewarding daily routines, to attract the best possible ideas and approaches to boost innovation, or to complete data processing tasks that computing technology has yet to master quickly and accurately. In this paper we look at its various forms and flavors, from gamification to human computation and grand challenges, and discuss how it could be used to turn conventional content management applications into social machines in which tasks are performed as optimal combinations of human and computational intelligence. We introduce a framework for the analysis of the most important building blocks of such systems, as well as design and participation best practices that should guide their development.

Summary: The web provides access to millions of datasets that can have additional impact when used beyond their original context. We have little empirical insight into what makes a dataset more reusable than others and which of the existing guidelines and frameworks, if any, make a difference. In this paper, we explore potential reuse features through a literature review and present a case study on datasets on GitHub, a popular open platform for sharing code and data. We describe a corpus of more than 1.4 million data files, from over 65,000 repositories. Using GitHub's engagement metrics as proxies for dataset reuse, we relate them to reuse features from the literature and devise an initial model, using deep neural networks, to predict a dataset's reusability. This demonstrates the practical gap between principles and actionable insights that allow data publishers and tools designers to implement functionalities that provably facilitate reuse. The Bigger Picture: The web provides access to millions of datasets. These data can have additional impact when it is used beyond the context for which it was originally created. We have little empirical insight into what makes a dataset more reusable than others, and which of the existing guidelines and frameworks, if any, make a difference. In this paper, we explore potential reuse features through a literature review and present a case study on datasets on GitHub, a popular open platform for sharing code and data. We describe a corpus of more than 1.4 million data files, from over 65,000 repositories. Using GitHub's engagement metrics as proxies for dataset reuse, we relate them to reuse features from the literature and devise an initial model, using deep neural networks, to predict a dataset's reusability. This work demonstrates the practical gap between principles and actionable insights that allow data publishers and tools designers to implement functionalities that provably facilitate reuse.

This work analyses blockchain-mediated decentralization based on a systematic review of the scholarly understanding of the term ‘decentralization’ across multiple disciplines from computer to political sciences, examining how its various meanings are reflected in popular discourse on blockchains and distributed ledgers. The paper aims to capture the rigorous cross-domain understanding of decentralization and its most important features, and to map the commonalities and differences between it and some closely related concepts such as distribution, disintermediation and peer-to-peer (P2P). Across all domains, decentralization appears to be used as a solution to problems requiring non-trivial coordination across heterogeneous stakeholders. Blockchain-mediated decentralization appears to have unique characteristics reflecting an idiosyncratic set of authority-related values prevalent in so-called “crypto” online communities. Within blockchain space, the article argues against the binary positioning of “decentralization” and “centralization,” proposing a dialectical approach and arguing that a system’s authority allocation is a quality positioned on a spectrum between purely decentralized and completely centralized, noting how a blockchain set-up could simultaneously both have facets that are significantly centralized and others that are not. The authors document their systematic review findings and propose a framework for understanding blockchain-mediated decentralization, suggesting a definition, and outlining new directions for further human-centric research into distributed ledger technologies and for designing decentralized ecosystems.

Data trusts have been proposed as a mechanism through which data can be more readily exploited for a variety of aims, including economic development and social-benefit goals such as medical research or policy-making. Data trusts, and similar data governance mechanisms such as data co-ops, aim to facilitate the use and re-use of datasets across organizational boundaries and, in the process, to protect the interests of stakeholders such as data subjects. However, the current discourse on data trusts does not acknowledge another common stakeholder in the data value chain—the crowd workers who are employed to collect, validate, curate, and transform data. In this paper, we report on a preliminary qualitative investigation into how crowd data workers themselves feel datasets should be used and governed. We find that while overall remuneration is important to those workers, they also value public-benefit data use but have reservations about delayed remuneration and the trustworthiness of both administrative processes and the crowd itself. We discuss the implications of our findings for how data trusts could be designed, and how data trusts could be used to give crowd workers a more enduring stake in the product of their work.

Data trusts have been conceived as a mechanism to enable the sharing of data across entities where other formats, such as open data or commercial agreements, are not appropriate, and make data sharing both easier and more scalable. By our definition, a data trust is a legal, technical, and organizational structure for enabling the sharing of data for a variety of purposes. The concept of the “data trust” requires further disambiguation from other facilitating structures such as data collaboratives. Irrespective of the terminology used, attempting to create trust in order to facilitate data sharing, and create benefit to individuals, groups of individuals, or society at large, requires at a minimum a process-based mechanism, that is, a workflow that should have a trustworthiness-by-design approach at its core. Data protection by design should be a key component of such an approach.

Governments need to be accountable and transparent for their public spending decisions in order to prevent losses through fraud and corruption as well as to build healthy and sustainable economies. Open data act as a major instrument in this respect by enabling public administrations, service providers, data journalists, transparency activists, and regular citizens to identify fraud or uncompetitive markets through connecting related, heterogeneous, and originally unconnected data sources. To this end, in this article, we present our experience in the case of Slovenia, where we successfully applied a number of anomaly detection techniques over a set of open disparate data sets integrated into a Knowledge Graph, including procurement, company, and spending data, through a linked data-based platform called TheyBuyForYou. We then report a set of guidelines for publishing high quality procurement data for better procurement analytics, since our experience has shown us that there are significant shortcomings in the quality of data being published. This article contributes to enhanced policy making by guiding public administrations at local, regional, and national levels on how to improve the way they publish and use procurement-related data; developing technologies and solutions that buyers in the public and private sectors can use and adapt to become more transparent, make markets more competitive, and reduce waste and fraud; and providing a Knowledge Graph, which is a data resource that is designed to facilitate integration across multiple data silos by showing how it adds context and domain knowledge to machine-learning-based procurement analytics.