Public Open Data Research Articles

Advances in conservation biogeography: towards protected area effectiveness under anthropogenic threats

This study endorses the main findings of a PhD thesis (Hoffmann 2020) and the manuscripts included intend to advance the success of protected areas in biodiversity conservation mediated through effective and efficient protected area management. The manuscripts provide missing scientific evaluations that modern conservation planning over large geographical extents requires: the comprehensive quantification of species diversity within and between protected areas; the development and application of efficient and effective in-situ monitoring and remote sensing of species diversity; and the assessment of anthropogenic climate change threats to protected areas. Moreover, the manuscripts aim at spreading conservation-minded data and knowledge by means of publishing open-access papers, open-source software and open data. This thesis synopsis is to stimulate a growing scientific and public debate on the effectiveness of protected areas and nature conservation under anthropogenic threats, which is necessary to stop nature’s decline and thus guarantee a sustainable future for the welfare of generations to come.

Relationship between plan for the advancement of public and private sector data utilization and open data promotion

Relationship between plan for the advancement of public and private sector data utilization and open data promotion

A streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data papers.

BackgroundData papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer review and increases the impact of datasets by enhancing their visibility, accessibility, and reusability.ObjectiveWe aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivize researchers to publish well-described datasets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript.MethodsAn omics data paper template was designed by defining key article sections that encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress, and BioSamples databases.FindingsThe template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing.ConclusionThe omics data paper structure and workflow for import of genomics metadata will help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata reuse for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.

Darkness, Datafication, and Provenance as an Illuminating Methodology

Darkness, Datafication, and Provenance as an Illuminating Methodology

Balancing the needs of consumers and producers for scientific data collections

Recent emphasis and requirements for open data publication have led to significant increases in data availability in the Earth sciences, which is critical to long-tail data integration. Currently, data are often published in a repository with an identifier and citation, similar to those for papers. Subsequent publications that use the data are expected to provide a citation in the reference section of the paper. However, the format of the data citation is still evolving, particularly with regards to citing dynamic data, subsets, and collections of data. Considering the motivations of both data producers and consumers, the most pressing need is to create user-friendly solutions that provide credit for data producers and enable accurate citation of data, particularly integrated data. Providing easy-to-use data citations is a critical foundation that is required to address the socio-technical challenges around data integration. Studies that integrate data from dozens or hundreds of datasets must often include data citations in supplementary material due to page limits. However, citations in the supplementary material are not indexed, making it difficult to track citations and thus giving credit to the data producer. In this paper, we discuss our experiences and the challenges we have encountered with current citation guidance. We also review the relative merits of the currently available mechanisms designed to enable compact citation of collections of data, such as data collections, data papers, and dynamic data citations. We consider these options for three data producer scenarios: a domain-specific data collection, a data repository, and a large-scale, multidisciplinary project. We posit that a new mechanism is also needed to enable citation of multiple datasets and credit to data producers.

Looking for datasets to open: An exploration of government officials' information behaviors in open data policy implementation

In recent years, more and more government administrations around the world are participating in open data policy implementation. Government officials who are charged with implementing this new policy usually need to look for related information for deciding what datasets to open to the public. Accordingly, a research gap exists in understanding government officials' information behaviors in implementing open data policy. By using a qualitative approach to conduct semi-structured interviews with government officials who have extensive experience in implementing open data policy, this study explores government officials' information behaviors in searching and selecting datasets for open data publication. Specifically, this research investigates government officials' information needs, the employed information sources, and the intervening variables that influence their information behaviors. While open data is an ongoing policy implementation, government officials' information behaviors form an iterative cycle where information needs continually occur, information seeking activities are conducted by using a variety of information sources, and the obtained information is used to satisfy information needs. The findings and discussions of this study are expected to provide insights of government officials' data search and selection to practitioners and to enrich the current literature of open government data.

Public Sector Information and Open Data: A Focus on Sweden

Good governance and inclusive development require governments to make their information flows accessible to all citizens. The United Nations Sustainable Development Goal 16 advocates the promotion of peaceful and inclusive societies, with the emphasis being placed on access to government information. Apart from this Sustainable Development Goal, globally, several open-data initiatives and advocates of freedom of information are pushing governments to open up their data flows. Open data are seen as a driver of public service innovation that generates new knowledge and enables the creation of new electronic services. The narrative about open data assumes that the data are within reach of the citizens to explore and to develop new electronic services based on, for example, statistical, mobility, meteorological, judicial and court data to boost national innovation. However, research shows the contrary that open data are still concentrated in the hands of a few people such as politicians, journalists, system developers and data miners. This raises questions about the openness of the data. In Sweden, most of the interactions that citizens have are with the municipalities because they offer a broad range of public services. This is why the researcher argues that to promote awareness and open data usage by ordinary citizens, the Swedish government should work with the municipalities to create platforms for the exploitation of the data and to facilitate the development of technical expertise. If open data are to be inclusive, the data will require the involvement of citizens in the current open-data developments.

Exploring the relationship between bike-sharing and public transport in Poznań, Poland

Abstract It is widely believed that bike-sharing has the potential to encourage sustainable travel by combining the flexibility of cycling with the reliability of public transport. However, there is actually little empirical evidence concerning the scale of that effect. While many models of bike-sharing travel patterns include station locations, only a few have accounted for heterogeneity in service levels. This paper aims to fill this gap by examining the case of the bike-sharing system in the city of Poznan (536,000 inhabitants). We hypothesise that a higher number of bike-sharing trips could be found in places with a higher frequency of public transport. A model based on trips data mined through a web application programming interface (over 19,240,000 GPS recorded bicycle positions), and open public transport frequency data from the general transit feed specification is used. Regression results show that while including control variables and spatial effects, the frequency of public transport was significantly associated with the number of bike-sharing trips. A positive effect existed for short and medium trips, whereas no relationship was found for long trips. Findings support the view that public transport frequency is a relevant factor for bike-sharing which should be taken into account in planning.

Value from health data: European opportunity to catalyse progress in digital health

Value from health data: European opportunity to catalyse progress in digital health

Distributed L-diversity using spark-based algorithm for large resource description frameworks data

Privacy protection issues for resource description frameworks (RDFs) have emerged over the use of public government open data and the healthcare data of individuals. As these data may include personal information, they must undergo a de-identification process that deletes or replaces parts of the original data. To enable these protections, a method has been developed to apply k-anonymization to RDF data. However, sensitive RDF information anonymized using k-anonymization is not completely secure and is vulnerable to attacks. In this paper, we propose an l-diversity anatomy de-identification method that can overcome the limitations of k-anonymity and guarantee stronger privacy protection than k-anonymization. Further, as this data anonymization process is computationally time-intensive, we use Spark distributed computing to provide rapid de-identification to enhance its utility. We also propose l-diversity preservation for dynamically evolving RDF datasets. Experimental results show that our proposed distributed l-diversity algorithm processes the data more efficiently than conventional approaches.

PUBLIC VALUE THROUGH TECHNOLOGIES DEVELOPED WITH OPEN GOVERNMENT DATA: THE LOVE SERENADE OPERATION CASE

ABSTRACT Purpose: To understand the public value that the website Operação Serenata de Amor [Love Serenade Operation], based on open government data, can generate for society. Originality/value: Researchers are investigating public value generation through digitally transformed public services, i.e., public value directly generated by the government. However, the literature needs a better understanding of this phenomenon through digital technologies, based on open public data, developed by non-governmental actors, such as the Operação Serenata de Amor website. Design/methodology/approach: The study adopted a qualitative approach to understand and describe the public value generated by Operação Serenata de Amor. The data were collected through citizen comments about the website’s initiatives on social media and in articles published on the internet. The data were analyzed through content analysis. Findings: The Operação Serenata de Amor generated public value by doing, facilitating, and stimulating social control. It was also identified that the site added value to society by increasing citizens’ ability to exercise social control. However, this work was limited to analyzing data collected on the internet. In future studies, researchers may collect data through interviews with citizens to understand, with more details, the public value generated by the website.

SeSAM: semi-automated semantic analysis method of urban areas’ events with extreme levels of popularity based on public open data

This study describes the semi-automated pipeline created for the comprehensive analysis of the urban areas with the extremely low and extremely high popularity levels. It includes the geo-frequency analysis of the Russian-language Instagram publications for the St. Petersburg area and selection of areas with the extreme values of the popularity level according to the number of publications in them. Semantic analysis of the urban areas with an extremely low number of publications includes comparing of algorithms for descriptions extraction and classification for these areas and results of such descriptions extraction and classification using TF-IDF vectorization technique and most valuable words extraction. Semantic analysis of areas with an extremely high number of publications includes the structure description of such areas, comparing of algorithms for advertisement publications extraction, results of the advertisement extraction using BigARTM model and further development and implementation of the algorithm for extracting events related to the the points of attraction in extremely popular urban areas, which is based on the strong time binding hypothesis and the idea of similarity queries using combination of LDA models for revealing semantic structure and algorithm based on frequency analysis. Developed algorithm was tested to extract events in the urban area of St. Petersburg where Ice Palace is placed and showed interpretable results and allow us to correctly extract 89 events out of 102 which occurred in this area in 2019. Finally, SeSAM pipeline for comprehensive urban analysis was created that combined the described algorithms.

ТЕСТИРОВАНИЕ ЗАРУБЕЖНЫХ И ОТЕЧЕСТВЕННЫХ МОДЕЛЕЙ ПРОГНОЗИРОВАНИЯ БАНКРОТСТВ НА РОССИЙСКИХ ПРЕДПРИЯТИЯХ

Recently, the topic of forecasting the financial insolvency of an organization based on public open data has become quite popular in domestic analytical science and practice. In general, all models are built according to the same principle: a sample of organizations from the bankruptcy register reveals certain dependencies between individual coefficients that must satisfy certain statistical tests. In fact, the researcher makes extensive use only of the mathematical (statistical) apparatus, not attaching importance to the logic and economic meaning of the represented ratios. Without at all belittling the role of mathematical tools in research of this kind, we note that any model should have a certain economic core that describes the logic and the nature of the influence of the proposed factors on the final result. The article is devoted to testing the most popular forecasting models at enterprises of the real sector in order to study the possibility of using these models in economic practice.

Systematic Mapping of Open Data Studies: Classification and Trends From a Technological Perspective

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.

SuDaMa: Sustainable Open Government Data Management Framework for Long-Term Publishing and Consumption

Sustainable open data management systems are key elements to overcome long-term publishing and consumption challenges on open data platforms. However, most existing solutions have not been envisaged as sustainable approaches to cope with key data attributes such as timeliness, accessibility and usability. In this paper, we present a framework that addresses these challenges and ensures the efficient publication of dynamic data on open data platforms, as well as improving the consumption experience of end-users. The framework describes the architecture of a sustainable system that can operate continuously in an automated way. It involves: 1) introducing an evolvable and scalable ecosystem to guarantee the access to dynamic data when they are made available; 2) tackling governance using an autonomous agent to provide the capacity of dynamically publishing/unpublishing data resources on open data platforms. It ensures easier accessibility and greater efficiency for data developers, facilitating the development of data-enabled products, thus helping developers to seize the opportunities of the data economy; and 3) improving the consumption experience by introducing conversational bots, which enhances the usability of open data. We demonstrate the results and validity of the solution in practice through its implementation, evaluation and exploitation as a practical system in the Parliament of the Canary Islands, Spain. The framework developed can be considered a generic solution to manage open data publishing and consumption challenges in other domains.

Framework for Prioritization of Open Data Publication: An Application to Smart Cities

Public Sector Information is considered to play a fundamental role in the growth of the knowledge economy and improvements in society. Given the difficulty in publishing and maintaining all available data, due to budget constraints, institutions need to select which data to publish, giving priority to data most likely to generate social and economic impact. Priority of publication could become an even more significant problem in Smart Cities: as huge amounts of information are generated from different domains, the way data is prioritized and thus reused, could be a determining factor in promoting, among others, new and sustainable business opportunities for local entrepreneurs, and to improve citizen quality of life. However, people in charge of prioritizing which data to publish through open data portals (such as Chief Data Officers, or CDOs) do not have available any specific support in their decision-making process. In this work, a proposal of a framework for prioritization of open data publication as well as its application to Smart Cities is presented. This specific application of the framework relies on OSS (Open Source Software) indicators to help making decisions on the most relevant data to publish focused on developers and businesses operating within the Smart City context.

Model-based Generation of Web Application Programming Interfaces to Access Open Data

In order to facilitate the reusing of open data from open data platforms’ catalogs, Web Application Programming Interfaces (APIs) are an important mechanism for reusers. However, there is a lack of suitable Web APIs to access data from open data platforms. Moreover, in most cases, the currently available APIs only allow to access catalog’s metadata or to download entire data resources (i.e. coarse-grain access to data), hampering the reuse of data. Therefore, we propose a model-based approach to automatically generate Web APIs from open data. Our generated Web APIs facilitate the access and reuse of specific data (i.e., providing fine-grain or query-level access to data), which will result in many societal and economic benefits such as transparency and innovation. With this approach we address open data publishers which will be able to include a Web API within their data, but also open data reusers in case of missing APIs. This APIfication process, which means the creation of APIs for every available dataset, is based on automatic, generic and standardised generation mechanisms. The performance and functioning of this approach is validated with different datasets, which successfully generates Web APIs that facilitate the reuse of data.

Frictionless Data-based model applied to open government data

O presente trabalho propõe um modelo baseado em Fricitionless Data (FD) para auxiliar na publicação de dados abertos governamentais (DAGs). FD é uma iniciativa da Open Knowledge Foundation, que pretende remover o "atrito" no trabalho com os dados, ou seja, quando se perde muito tempo e recursos para entender e trabalhar com o dado. No âmbito das instituições públicas, a ausência de padrões para publicação e processamento dos DAGs é problema comum quando se trata de abertura dos DAGs. O estudo procurou contribuir ao aplicar o modelo ao conjunto de dados com informações sobre as boas práticas executadas pelo Judiciário relacionadas aos Objetivos de Desenvolvimento Sustentável (ODS). Segundo a Portaria nº 133, a integração dos ODS com as boas práticas do Judiciário pode trazer diversos benefícios como: aperfeiçoamento dos mecanismos de busca nos Portais de Transparência dos Tribunais, de forma associada aos ODS; auxiliar na medição da eficiência do Poder Judiciário em atingir os ODS, entre outros. O modelo se baseou no Data Publication Workflow, um fluxo para publicação de dados disponível no site da iniciativa Frictionless Data e que aborda etapas como empacotamento, tratamento e publicação dos dados. A aplicação do modelo também utilizou ferramentas de código aberto baseados em Frictionless Data e os resultados mostraram a sua viabilidade para a abertura de um conjunto de dados abertos governamentais.

Indigenous Data Sovereignty in the Era of Big Data and Open Data

AbstractIndigenous Data Sovereignty, in its proclamation of the right of Indigenous peoples to govern the collection, ownership, and application of data, recognises data as a cultural and economic asset. The impact of data is magnified by the emergence of Big Data and the associated impetus to open publicly held data (Open Data). Aboriginal and Torres Strait Islander peoples, families and communities, heavily overrepresented in social disadvantage–related data will also be overrepresented in the application of these new technologies, but in a data landscape, Indigenous peoples remain largely alienated from the use of data and its utilization within the channels of policy power. Existing data infrastructure, and the emerging Open Data infrastructure, neither recognise Indigenous agency and worldviews nor consider Indigenous data needs. This is demonstrated in the absence of any consideration of Indigenous data issues in Open Data discussions and publication. Thus, while the potential benefits of this data revolution are trumpeted, our marginalised social, cultural and political location suggests we will not share equally in these benefits. This paper discusses the unforeseen (and likely unseen) consequences of the influence of Open Data and Big Data and discusses how Indigenous Data Sovereignty can mediate risks while providing pathways to collective benefits.

Creating value using public big data: comparison of driving factors from the provider's perspective

PurposeThe revitalization of big data has gained attention in the public sector. However, such open government data (OGD) is facing major challenges with respect to data quality and limited use. To solve this problem, this study analyzes the factors driving the use of OGD from the perspective of data providers in the public sector.Design/methodology/approachUsing the analytic hierarchy process and analytic network process methodologies, the importance of the factors driving the use of big data in the public sector was ranked. In addition, the different characteristics of tasks among the departments in a public agency were compared based on expert interviews.FindingsThe factors driving OGD use are not only political environment or the technological environment. The importance of the institutional culture within the organization increases with the motivation of the data provider. The priorities of the OGD factors also depend on the objectives of the department involved.Originality/valueThis study provides implications for improving the publication of open data by analyzing the priorities of the factors driving its use from the perspective of big data providers. It focuses on different perceptions of the factors valued by public officials in charge of data in institutions. The results suggest the need to explore officials' perceptions of value creation in big data fields.