Open Data Community Research Articles

OPEN PUBLIC DATA: HOW WE USE IT AND HOW IT USES US

Data policy aims to understand how governments, industry, academia, and civil society understand data availability and use. Data policy studies are broad in scope and include theory and practice of open sources of data from traditional sources (e.g., statistics and public health government agencies); representation and interpretation of data; investigations of the social relations and impacts of open public data; and attitudes surrounding data stewardship and public data availability and use. This study’s empirical research is based on two case studies of the Australian and New Zealand Open Data programs between 2010 and 2020. Based on current scholarship, some factors affecting open data supply were confirmed, and new insights were generated. Social psychology theorising concerning identity leadership was confirmed. When leaders promoted a sense of ‘us’ and ‘who we are’, data stewards were more likely to advance a shared vision for making data available for reuse. The concepts of _collateral peers_ and _collateral peer networks_ were introduced to data policy scholarship. Collateral peer networks engage in multistakeholder governance with no apparent leader-follower relationship. Instead, peers (in function but not necessary title) collaborate across the public service, at various levels of government, and with experts in research institutions (e.g., publicly funded scientific and applied research organisations). Future areas for public interest technology, specifically data policy research, include how researchers and practitioners can prioritise data stewardship, open data communities, and multistakeholder governance, especially during prolonged and compounding crises.

From Green Deal to Cultural Heritage: FAIR Digital Objects and European Common Data Spaces

From Green Deal to Cultural Heritage: FAIR Digital Objects and European Common Data Spaces

FAIR Forever? Accountabilities and Responsibilities in the Preservation of Research Data

Digital preservation is a fast-moving and growing community of practice of ubiquitous relevance, but in which capability is unevenly distributed. Within the open science and research data communities, digital preservation has a close alignment to the FAIR principles and is delivered through a complex specialist infrastructure comprising technology, staff and policy. However, capacity erodes quickly, establishing a need for ongoing examination and review to ensure that skills, technology, and policy remain fit for changing purpose. To address this challenge, the Digital Preservation Coalition (DPC) conducted the FAIR Forever study, commissioned by the European Open Science Cloud (EOSC) Sustainability Working Group and funded by the EOSC Secretariat Project in 2020, to assess the current strengths, weaknesses, opportunities and threats to the preservation of research data across EOSC, and the feasibility of establishing shared approaches, workflows and services that would benefit EOSC stakeholders.
 This paper draws from the FAIR Forever study to document and explore its key findings on the identified strengths, weaknesses, opportunities, and threats to the preservation of FAIR data in EOSC, and to the preservation of research data more broadly. It begins with background of the study and an overview of the methodology employed, which involved a desk-based assessment of the emerging EOSC vision, interviews with representatives of EOSC stakeholders, and focus groups with digital preservation specialists and data managers in research organizations. It summarizes key findings on the need for clarity on digital preservation in the EOSC vision and for elucidation of roles, responsibilities, and accountabilities to mitigate risks of data loss, reputation, and sustainability. It then outlines the recommendations provided in the final report presented to the EOSC Sustainability Working Group.
 To better ensure that research data can be FAIRer for longer, the recommendations of the study are presented with discussion on how they can be extended and applied to various research data stakeholders in and outside of EOSC, and suggest ways to bring together research data curation, management, and preservation communities to better ensure FAIRness now and in the long term.

House price valuation of environmental amenities: An application of GIS‐derived data

AbstractHedonic house price models are frequently used to improve our understanding of local housing markets. In recent years, rich registers containing details about home‐qualities and neighbourhood characteristics have successfully been coupled with spatial qualities such as job‐accessibility or distances to transport. Additional data sources provided by Open data communities, NGOs, data created by governmental agencies on regional national and international level has the potential of being very useful for analysing housing prices. However, the recent methodological advances in GIS and spatial analysis have not been extensively applied. We expand the hedonic price modelling toolbox with geo‐coded free data on environmental amenities. We specifically include local measures describing the view‐shed, and more varied specifications of access or dominance of green and blue amenities, in addition to urban public‐type service and sport facilities. The GIS‐derived data is used to study how the variables should be specified and to study their ability to improve even well specified hedonic price models. To our knowledge, this paper is the first to combine all the listed environmental properties in a hedonic model, and at the same time controlling for a large number of other important local neighbourhood characteristics.

Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings

Many new tools for improving the design and operation of buildings try to realize the potential of big data. In particular, data is an important element for occupant-centric design and operation as occupants’ presence and actions are affected by a high degree of uncertainty and, hence, are hard to model in general. For such research, data handling is an important challenge, and following an open science paradigm based on open data can increase efficiency and transparency of scientific work. This article reviews current practices and infrastructure for open data-driven research on occupant-centric design and operation of buildings. In particular, it covers related work on open data in general and for the built environment in particular, presents survey results for existing scientific practices, reviews technical solutions for handling data and metadata, discusses ethics and privacy protection and analyses principles for the sharing of open data. In summary, this study establishes the status quo and presents an outlook on future work for methods and infrastructures to support the open data community within the built environment.

Dataset search: a survey

Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta-released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems and discuss what makes dataset search a field in its own right, with unique challenges and open questions. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to tackle these questions as well as immediate next steps that will take the field forward.

Relation between facial fractures and socioeconomic deprivation in the north east of England

Patients with a low socioeconomic status suffer disproportionately from trauma, and have a high incidence of mandibular fractures. To explore how deprivation affects the incidence of facial fractures in the north east of England, we reviewed 1096 patients who were admitted to the oral and maxillofacial surgical (OMFS) unit at Sunderland Royal Hospital for treatment of a facial fracture between December 2013 and December 2017. Levels of socioeconomic deprivation, which were obtained from postcodes and the UK Government Open Data Communities database, were compared with a random sample of deprivation data from the catchment area of our hospital. Patients with nasal and mandibular fractures were more likely to be socioeconomically deprived than those in the catchment area of our hospital (p = 0.006 and p < 0.001, respectively), but this was not the case in those with malar/maxillary or orbital floor fractures (p = 0.184 and p = 0.641, respectively). The incidence of fractures that were caused by assault was not associated with increased socioeconomic deprivation (p = 0.241). Patients of low socioeconomic status were more likely to have been under the influence of a substance when the injury occurred (p = 0.014). There is a strong association between socioeconomic deprivation and facial fractures. OMFS departments should therefore be as accessible as possible to patients from more disadvantaged backgrounds, given their greater risk of injury.

Classifying the LOD cloud

Massive amounts of data from different contexts and producers are collected and connected relying often solely on statistical techniques. Problems to the acclaimed value of data lie in the precise definition of data and associated contexts as well as the problem that data are not always published in meaningful and open ways. The Linked Data paradigm offers a solution to the limitations of simple keywords by having unique, resolvable and shared identifiers instead of strings This paper reports on a three-year research project “Digging Into the Knowledge Graph,” funded as part of the 2016 Round Four Digging Into Data Challenge (https://diggingintodata.org/awards/2016/project/digging-knowledge-graph). Our project involves comparing terminology employed within the LOD cloud with terminology employed within two general but different KOSs – Universal Decimal Classification and Basic Concepts Classification. We are exploring whether these classifications can encourage greater consistency in LOD terminology and linking the largely distinct scholarly literatures that address LOD and KOSs. Our project is an attempt to connect the Linked Open Data community, which has tended to be centered in computer science, and the KO community, with members from linguistics, metaphysics, library and information science. We focus on the shared challenges related to Big Data between both communities.

Hidden concerns of sharing research data by low/middle-income country scientists

ABSTRACTThere has considerable interest in bringing low/middle-income countries (LMIC) scientists into discussions on Open Data – both as contributors and users. The establishment of in situ data sharing practices within LMIC research institutions is vital for the development of an Open Data landscape in the Global South. Nonetheless, many LMICs have significant challenges – resource provision, research support and extra-laboratory infrastructures. These low-resourced environments shape data sharing activities, but are rarely examined within Open Data discourse. In particular, little attention is given to how these research environments shape scientists’ perceptions of data sharing (dis)incentives. This paper expands on these issues of incentivizing data sharing, using data from a quantitative survey disseminated to life scientists in 13 countries in sub-Saharan Africa. This interrogated not only perceptions of data sharing amongst LMIC scientists, but also how these are connected to the research environments and daily challenges experienced by them. The paper offers a series of analysis around commonly cited (dis)incentives such as data sharing as a means of improving research visibility; sharing and funding; and online connectivity. It identifies key areas that the Open Data community need to consider if true openness in research is to be established in the Global South.

Ownership of Open Data: Governance Options for Agriculture and Nutrition

Ownership rights are a major factor in access and use of open data, distinct from yet as important as the availability of education, skills, technology, infrastructure, and finances. There are real deficits in law, understanding, and frameworks for governing open data ownership. These challenges must be addressed to achieve meaningful and equitable open data as default. The chief policy lesson from this paper is that moving to a model where data is open as default requires change in legal, social and technological norms, which all influence ownership of agriculture and nutrition data. Copyrights are not the only, nor even most important, legal rights establishing ownership of data. Relevant legal rights that facilitate access to and use of data at the international, national and subnational level include copyrights, database rights, technical protection measures, trade secrets, and patents and plant breeders’ rights, privacy and even tangible property rights. The open data community must broaden its engagement in all these areas to address emerging challenges. Open data is often achieved by persuading data owners to voluntarily licence their rights on open terms. Open licensing contracts are important to transfer legal rights between parties, but licences do not create new rights that would bind all stakeholders or change overarching data governance structures. Most legal rights to data are owned by intermediaries that invest in the selection of data, arrangement of databases, safeguarding of confidential information, or similar activities. The lack of enforceable data rights owned by certain communities, particularly smallholder farmers, contributes to inequality and marginalization. The line between data and traditional knowledge is blurred in indigenous knowledge systems. Ethically, informed consent to use such data should be sought, although legally, rights are just starting to be recognized in international law. The current contract-based model for ownership of open data leaves many stakeholders vulnerable to the whims of entities that own data, without addressing more systemic challenges and opportunities for open data governance. Meanwhile, expanding ownership rights to protect individual or community data providers could cause significant complications for the intermediaries that practice and promote open data. Several real-world case stories demonstrate the challenges of open data ownership for farmers and grassroots community groups, non/inter-governmental organizations, and multinational commercial entities. There are four possible governance strategies for ownership of open agricultural and nutrition data. First, interinstitutional cooperation can help build understanding and consensus about the terms and conditions of ownership of open data. Second, model frameworks adopted at the local, national or regional level can offer a governance example other jurisdictions might emulate. Third, a social certification scheme can leverage the power of ethical consumerism and market pressures to promote best open data practices. Fourth, stakeholders can work toward an international agreement on ownership of open data. The task for GODAN and others in the open data community is to engage with the challenges outlined in this paper, in order to facilitate more inclusive sharing of the benefits of open data.

Pathways, parallels and pitfalls: the Scholarly Web, the ESRC and Linked Open Data

This paper highlights key principles that the eScholarship Research Centre (ESRC) shares with the Linked Open Data (LOD) community, primarily relationship-centric contextualisation of information resources, and a commitment to producing and publishing sustainable, standards-based data outputs suitable for machine-based interchange. This paper illustrates how these principles have enabled the ESRC, without pursuing LOD as a specific end, to hold a path close to the Linked Data road. We also note that, by extension, this has positioned the ESRC ready to translate many of its resources into LOD formats in exchanges where complementary technologies and ontology mappings are available.

Integration Electronic Patients’ Records with Open Life Sciences Datasets Using Semantic Web Tools

Semantic Web and its related technologies such as linked data, provide a powerful infrastructure for integrating and publishing heterogeneous data as a Resource Description Framework (RDF). In particular, Linked Open Data (LOD) community project from the Wide Web Consortium (W3C) has published many life sciences open datasets such as drugs, genes, pathways, proteins and diseases. This is a powerful platform for life sciences, and we will obtain more benefits if we link it with real patient records from standardized Electronic Health Record Systems (EHR). In this paper, we present our approach to applying linked data principles for representing electronic patients’ records. Particularly, we focus on Malaria patient records, and link it to various LOD ontologies, in order to enable clinicians and researchers to make federated queries. Finally, we present opportunities and challenges for our approach.

One way traffic: The open data initiative project and the need for an effective demand side initiative in Ghana

In recent years the necessity for governments to develop new public values of openness and transparency, and thereby increase their citizenries' sense of inclusiveness, and their trust in and confidence about their governments, has risen to the point of urgency. The decline of trust in governments, especially in developing countries, has been unprecedented and continuous. A new paradigm that signifies a shift to citizen-driven initiatives over and above state- and market-centric ones calls for innovative thinking that requires openness in government. The need for this new synergy notwithstanding, Open Government cannot be considered truly open unless it also enhances citizen participation and engagement. The Ghana Open Data Initiative (GODI) project strives to create an open data community that will enable government (supply side) and civil society in general (demand side) to exchange data and information. We argue that the GODI is too narrowly focused on the supply side of the project, and suggest that it should generate an even platform to improve interaction between government and citizens to ensure a balance in knowledge sharing with and among all constituencies.

Opening Government Data Through Mediation: Exploring the Roles, Practices and Strategies of Data Intermediary Organisations in India

The study explores the existing practices of accessing and using government data in India to understand what challenges the recently approved National Data Sharing and Accessibility Policy and its implementation measures must overcome to actualise the open data agenda. It surveys selected non­governmental and non­commercial “data intermediary organisations,” across sectors, that enable other organisations to better work with (access, use, and share) government and non­government data. Interviewing representatives of these organisations and understanding their actual data practices, the study foregrounds three key topics of discussion: (1) the nature of “open data community” in India, (2) the many challenges of effectively opening up government data in India, and (3) the applications of and communities around Right to Information Act and National Data Sharing and Accessibility Policy. The study analyses the first two issues, but a detailed discussion of the last one remains pending.

Open Data Day Hackathon 2014 at Edmonton Public Library

Edmonton Public Library (EPL) hosted its first hackathon for International Open Data Day 2014. International Open Data Day promotes open data policies in local, regional, and national governments worldwide, in the spirit of transparency and civic innovation. The open data movement, like public libraries, values access to information and civic engagement, and it offers opportunities for public libraries to improve their efficiency, transparency, and programming. Celebrating the event provided the Library with the additional benefit of strengthening our relationship to local government. This case study provides a practical introduction to hosting an open data hackathon as a first step to engaging the open data movement. Two follow-up surveys, one immediately after the hackathon and another five months later, were used to assess the event and determine how the Library could better support the open data community in the future. The majority of hackathon participants labelled themselves beginner programmers, were not regular library users, and appreciated the opportunity to meet city employees and other hackers who shared their interests. The Library was encouraged to increase our output of open data and to host more hackathons. Results also suggested room for improvement in the areas of developing a more formal structure to the event, connecting participants with similar interests, and providing long term support for app development. By hosting a hackathon for International Open Data Day, EPL gained both the information and the relationships necessary to release meaningful datasets and put itself in an excellent position to understand and respond to the interests and needs of the open data community.

A Platform for Closing the Open Data Feedback Loop Based on Web2.0 Functionality

One essential element of open data ecosystems concerns their development through feedback loops, discussions and dynamic supplier and user interactions. These user-centric features communicate the users’ needs to the open data community as well to the public sector bodies responsible for data publication. Addressing these needs by the corresponding public sector bodies or even by utilising the power of the community as ENGAGE supports will actually accelerate innovation. However, these elements appear barely to be part of existing open data practices. We conducted a survey which showed that most professional open data users did not know at least one open data infrastructure that enabled five specific types of discussion and feedback mechanisms. The survey showed that much can still be done to improve feedback and discussion on open data infrastructures. In this paper we discuss an open data platform which has started to contribute to filling this gap and present a usage scenario explaining the sequence of the underlined functionality. The discussed ENGAGE open data infrastructure combines functionalities to close the feedback loop and to return information to public authorities for better open data use and publication as well as establishing communication channels between stakeholders. This may effectively lead to the stimulation and facilitation of value generation from open data, as such functionality position the user at the centre of the open data publication process.

Natural Language Generation from Graphs

The Resource Description Framework (RDF) is the primary language to describe information on the Semantic Web. The deployment of semantic web search from Google and Microsoft, the Linked Open Data Community project along with the announcement of schema.org by Yahoo, Bing and Google have significantly fostered the generation of data available in RDF format. Yet the RDF is a computer representation of data and thus is hard for the non-expert user to understand. We propose a Natural Language Generation (NLG) engine to generate English text from a small RDF graph. The Natural Language Generation from Graphs (NLGG) system uses an ontology skeleton, which contains hierarchies of concepts, relationships and attributes, along with handcrafted template information as the knowledge base. We performed two experiments to evaluate NLGG. First, NLGG is tested with RDF graphs extracted from four ontologies in different domains. A Simple Verbalizer is used to compare the results. NLGG consistently outperforms the Simple Verbalizer in all the test cases. In the second experiment, we compare the effort spent to make NLGG and NaturalOWL work with the M-PIRO ontology. Results show that NLGG generates acceptable text with much smaller effort.

Apps For Amsterdam

Recently an app contest was organized, much in the vain of the "Apps for Democracy" initiative in Washington DC, called "Apps For Amsterdam". This was a partnership between the City of Amsterdam, Waag Society (Media Lab), and Hack de Overheid (a coders' open data community initiative). In this field note we will explore the process, the enthusiasm, the difficulties and results of this initiative.