Value Of Open Data Research Articles

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities.

Understanding the Motivation of Participants in Innovation Open Data Contests: A Task Presentation Affordance Perspective

ABSTRACTInnovation Open Data Contest (IODC) is an effective way to take advantage of public efforts to realize the great potential value of open data in the field of digital humanities. Previous literature focusses more on the challenge open data contest rather than innovation open data contest. Given this, understanding the underlying factors motivating participants to actively engage in the contest is necessary. Based on the task affordance theory and self‐determination theory (SDT), this study aims to identify and examine how task presentation affordances of IODC influence participants' motivations and thereby shape their level of effort. We employ partial least squares structural equation modeling (PLS‐SEM) techniques to analyze the responses from 215 individuals who have previously participated in the IODC. The findings indicate that participants' level of effort in the IODC is contingent upon their perception of relatedness and competence. Moreover, hedonic affordance and connective affordance positively influence participants' perceptions of relatedness and competence. Our findings contribute to the extant literature by proposing a theoretical model to understand the participants' motivation and have practical implications for IODC's organizers.

GOOGLE EARTH ENGINE AND THE USE OF OPEN BIG DATA FOR ENVIRONMENTAL AND CLIMATE-CHANGE ASSESSMENTS: A KOSOVO CASE STUDY

Abstract. This paper presents a framework for utilizing available open data resources, such as Google Earth Engine, to assess environmental conditions and climate change vulnerability in Kosovo. This paper lays the groundwork for academic, government, and private sector institutions to develop strategies for addressing climate change and environmental issues in Kosovo and for other developing nations to understand the extent of change occurring. The study demonstrates the value of open data for environmental sustainability and resilience building. The framework is based on the analysis of air pollution, groundwater monitoring, urban environments, and deforestation in Kosovo using publicly available models. The model is re-coded for Kosovo in JavaScript using open datasets to create an environmental assessment of the scopes and scales of the environmental issues that plague Kosovo. The results show that there is a lack of institutional frameworks for assessing climate change impacts in Kosovo, with limited capacity for conducting environmental assessments, and with limited data capacity for resource and data scarce environments with community managed frameworks. By leveraging extensive amounts of data and applying analytical frameworks, this paper contributes to the knowledge base of environmental conditions in Kosovo with emphasis on open data and technological advancements.

The Value of Open Data in HCI: A Case Report from Mobile Text Entry Research

For many years, HCI research has been known to suffer from a replication crisis, due to the lack of openly available datasets and accompanying code. Recent research has identified several barriers that prevent the wider sharing of primary research materials in HCI, but such material does, in fact, exist. Interested in the field of mobile text entry research, and largely hindered by the lack of access to participants due to the COVID-19 pandemic, the exploration of a recently published open gaze and touch dataset became an appealing prospect. This paper demonstrates the numerous problems and the extent of required effort related to understanding, sanitising and utilising open data in order to produce meaningful outcomes from it, through a detailed account of working with this dataset. Despite these issues, the paper demonstrates the value of open data as a means to produce novel contributions, without the need for additional new data (in this case, an unsupervised learning pipeline for the robust detection of gaze clusters in vertically distinct areas of interest). Framing the experience of this case study under a dataset lifecycle model intended for ML open data, a set of useful guidelines for researchers wishing to exploit open data is derived. A set of recommendations is also proposed, about the handling of papers accompanied by data, by conferences and journals in the future, Finally, the paper proposes a set of actions for the mobile text entry community, in order to facilitate data sharing across its members.

Presenting an open data management (ecosystem) model with developing innovative information flow approach in Iranian knowledge-based companies

PurposeThis study aimed to present a model for open-data management for developing innovative information flow in Iranian knowledge-based companies (businesses).Design/methodology/approachThe method was mixed (qualitative-quantitative) and data collection tools were interview and questionnaire. The qualitative part was done to identify the influential components in open data management (ecosystem) using the grounded theory method. A questionnaire was developed based on the results of the qualitative section and the theoretical foundations, and the quantitative section was conducted by analytical survey method and the model was extracted using factor analysis and the integration of the qualitative section.FindingsSeven categories of entrepreneurial incentives, sustainable value, innovative features, challenges and barriers, actors, business model and requirements are the main categories that should be considered in open data management (ecosystem) with all categories of research have a significant relationship with open data management.Originality/valueThe study focused on open data management from an innovation paradigm perspective and its role in developing innovative information flow. The study aimed to identify the key components of the open data ecosystem, open-data value creation, and the need to use the “open data” approach to develop data-driven and knowledge-based businesses in Iran–an emerging approach largely ignored.

The Open Data Canvas–Analyzing Value Creation from Open Data

Expectations to derive value from open data are high. However, how value is created from open data is still largely unknown. Open data value is usually generated in constellation of actors in which each player has different capabilities and roles. To understand the open data value creation process, the business model canvas is introduced in this article. The typical components of the business model canvas and open data value creation are derived from the literature. By combining these two research streams, the open data value model canvas is created. The case of Coronavirus disease 2019 (COVID-19) worldwide dashboard developed by the Johns Hopkins University is used to evaluate the model's utility. Key components of the open data value model are creating an overview of various data sources from public and private organizations, having capabilities to combine heterogeneous data, and connecting data and needs. In this way, the open data canvas helps to grasp the value creation logic.

Improving Opportunities for New Value of Open Data: Assessing and Certifying Research Data Repositories

Investments in research that produce scientific and scholarly data can be leveraged by enabling the resulting research data products and services to be used by broader communities and for new purposes, extending reuse beyond the initial users and purposes for which the data were originally collected. Submitting research data to a data repository offers opportunities for the data to be used in the future, providing ways for new benefits to be realized from data reuse. Improvements to data repositories that facilitate new uses of data increase the potential for data reuse and for gains in the value of open data products and services that are associated with such reuse. Assessing and certifying the capabilities and services offered by data repositories provides opportunities for improving the repositories and for realizing the value to be attained from new uses of data. The evolution of data repository certification instruments is described and discussed in terms of the implications for the curation and continuing use of research data.

Knowledge discovery in open data for epidemic disease prediction

ObjectiveThe research reveals the determinants associated with the epidemic diseases (dengue, influenza, and enterovirus) in Taiwan. It demonstrates the value of open data in prediction model development to support policymaking in the domain of public health care. MethodA knowledge discovery technique was employed to extract determinants from open data on epidemic diseases. The open dataset collected and integrated from Taiwan's Center for Disease Control, the Center Weather Bureau, and Google Trends includes 70,915 dengue, 34,062 enterovirus, and 52,908 influenza cases. A prediction model using the classification-oriented extraction mechanism was applied to open epidemic data, climate data, and Google Trends data. Prediction models that either included or did not include Google Trends data were compared. Prediction accuracy and simplicity of the decision rules are presented. ResultsPrediction accuracy and simplicity of three diseases is acceptable when Google Trends is excluded but is slightly different when Google Trends is considered. Location (county) holds the main predictor of the three epidemic diseases. Time (month) presents the second-highest determinant for dengue, and age shows remarkable determinant for enterovirus and influenza. Mean temperature exhibits the highest entropy for dengue, time for enterovirus, and humidity for influenza. ConclusionsThe number of confirmed cases for all three epidemic diseases cannot be predicted by a single variable. Knowledge extraction using the classification-oriented technique can be successfully applied in prediction model development. Google Trends data reveal a remarkable but inconsistent role in predicting three epidemic diseases with respect to prediction accuracy and simplicity of the generated decision tree.

Open data in public libraries: Gauging activities and supporting ambitions

AbstractAs the open data movement grows, public libraries must assess if and how to invest resources in this new service area. This paper reports on a recent survey on open data in public libraries across Washington state, conducted by the Open Data Literacy project (ODL) in collaboration with the Washington State Library. Results document interests and activity in open data across small, medium, and large libraries in relation to traditional library services and priorities. Libraries are particularly active in open data through reference services and are beginning to release their own library data to the public. While capacity and resource challenges hinder progress for some, many libraries, large and small, are making progress on new initiatives, including strategic collaborations with local government agencies. Overall, the level and range of activity suggest that Washington state public libraries of all sizes recognize the value of open data for their communities, with a groundswell of libraries moving beyond ambition to action as they develop new services through evolution and innovation.

Open weather and climate science in the digital era

Abstract. The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a session on “Weather and Climate Science in the Digital Era” at the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Roughly 80 % of the studies presented in the conference session showed the added value of open data and software. These studies included open datasets from disparate sources in their analyses or developed tools and approaches that were made openly available to the research community. Furthermore, shared software is a prerequisite for the studies which presented systems like a model coupling framework or digital collaboration platform. Although these studies showed that sharing code and data is important, the consensus among the participants was that this is not sufficient to achieve open weather and climate science and that there are important issues to address. At the level of technology, the application of the findable, accessible, interoperable, and reusable (FAIR) principles to many datasets used in weather and climate science remains a challenge. This may be due to scalability (in the case of high-resolution climate model data, for example), legal barriers such as those encountered in using weather forecast data, or issues with heterogeneity (for example, when trying to make use of citizen data). In addition, the complexity of current software platforms often limits collaboration between researchers and the optimal use of open science tools and methods. The main challenges we observed, however, were non-technical and impact the practice of science as a whole. There is a need for new roles and responsibilities in the scientific process. People working at the interface of science and digital technology – e.g., data stewards and research software engineers – should collaborate with domain researchers to ensure the optimal use of open science tools and methods. In order to remove legal boundaries on sharing data, non-academic parties such as meteorological institutes should be allowed to act as trusted agents. Besides the creation of these new roles, novel policies regarding open weather and climate science should be developed in an inclusive way in order to engage all stakeholders. Although there is an ongoing debate on open science in the community, the individual aspects are usually discussed in isolation. Our approach in this paper takes the discourse further by focusing on “open science in weather and climate research” as a whole. We consider all aspects of open science and discuss the challenges and opportunities of recent open science developments in data, software, and hardware. We have compiled these into a list of concrete recommendations that could bring us closer to open weather and climate science. We acknowledge that the development of open weather and climate science requires effort to change, but the benefits are large. We have observed these benefits directly in the studies presented in the conference and believe that it leads to much faster progress in understanding our complex world.

ПУБЛІЧНА ІНФОРМАЦІЯ У ФОРМІ ВІДКРИТИХ ДАНИХ ТА ПРОБЛЕМИ ЇЇ КЛАСИФІКАЦІЇ

The concept of public information in the form of open data and its main features are studied. It is determined that due to the possible wide application of open data, the definition of the main grounds for the classification of open data is an insufficiently covered issue. The purpose of this study is to determine the types and criteria for the classification of public information in the form of open data. This is necessary for their widespread use in order to solve socially important tasks and to use their full potential in unusual directions. The author has analyzed international reports on open data and identified the problems of the quality of such data and the possibility of using them to solve socially important tasks. The classification of open data is formed on the following grounds: 1. by data type; 2. by data format; 3. by subject. The article determines the value of open data to society and the possibility of its wide use in the example of other countries. The problems that complicate the implementation of the government data discovery initiative in Ukraine include the underdeveloped culture of open government; insufficient level of training of public authorities to work with open data; insufficient funding for the public data discovery initiative; low level of public awareness and interest in public data and the benefits of using it. Unfortunately, most citizens are still satisfying their curiosity by using open data. The author notes that using data that describes the patterns we live in can help us solve problems in ways we may not have anticipated. As a rule, public sector systems do not respond too quickly on changes. With open data, they could track, predict and respond to real-time changes. This would allow the public sector to streamline its processes and services and it would be possible to clearly identify areas for improving and increasing productivity, to develop specialized solutions based on various demographic indicators and other factors. This would be a huge transformational leap in attracting open data to the public sector, as it opens up a number of areas for innovation. The author substantiates the importance of open data for public sector transformation, economic benefits and their use as an instrument for creating an information society.

The meaningfulness of open data in Public Health and Healthcare

Abstract Background The growing challenges and opportunities of Big Data for Public Health have revealed the potential to improve the efficiency and cost-effectiveness of public policy, for example through better targeting of resources with regard to General Practice (GP) prescribing. Open data has an important role due to its easy access and potential to complement proprietary data sources from, e.g., regional hospitals, and also itself be complemented with social data acquired by specialized approaches. Methods MIDAS pipeline of open source tools aiming integrating, analysing and visualising Open Data enabling health professionals and decision-makers to: (i) improve the usability of open data in combination with proprietary data through combining multiple visualisation tools in an integrated dashboard (ii) to explore the meaning of data in a global/local context based on new information using tone analysis and natural language techniques; and (iii) to have better informed decision-making based on evidence from trusted knowledge-bases. Specific data sources used have included information extracted from the biomedical database MEDLINE, worldwide news and government open data. Social media sources have also been used to gather information from the general public. Results Results include a strong correlation between antidepressant prescribing and economic deprivation, and a wide variation in how individual GP practices respond to demographic conditions. Automated anomaly detection based on the Local Outlier Probability has also been shown to be an easily understood and controllable approach to identifying prescribing outliers. Conclusions MIDAS demonstrates the significant value of open data from heterogeneous sources as basis decision-making in public health and healthcare, particularly when it is combined with proprietary or closed datasets. A key challenge in this regard is the ability to integrate and utilize data from diverse sources in a variety of formats and standards. Key messages MIDAS is exemplar on tackling the need for improved standards of open data, and new software architectures, tools and platforms addressing a complex ecosystem of heterogenous data sources and formats. MIDAS demonstrates the significant value of open data from heterogeneous sources as basis decision-making in public health and healthcare, particularly when combined with proprietary datasets.

La percepción del docente universitario ante el uso y valor de los datos abiertos

El avance de la conciencia democrática en la sociedad junto con el aumento de población, incrementa exponencialmente la necesidad de ofrecer a la ciudadanía datos que los propios gobiernos se obligan legalmente y que los ciudadanos necesitan cada vez más. Muchos de estos datos son ofrecidos en modo abierto; son los denominados datos abiertos, Open Data en inglés (en adelante OD), y que son actualidad, futuro, apertura, transparencia, conocimiento, eficiencia, ahorro de tiempo, profundidad en la investigación, desarrollo en la educación, precisión en los análisis y por tanto en las patologías y diagnósticos de cualquier ámbito de nuestra vida que interese estudiar. No obstante, de poco o nada sirve conocer todo sobre los mismos si no sabemos qué hacer con ellos ni tenemos la capacidad para explotarlos. En este contexto, y ante las escasas iniciativas en el entorno educativo, se presenta el siguiente trabajo que tiene como objeto de estudio los datos abiertos dentro del ámbito de las facultades de educación españolas, intentando dar respuesta a si se pueden aprovechar en la enseñanza superior como recurso docente. Como objetivo principal se plantea conocer qué tipo de percepción poseen los docentes universitarios ante el uso y el valor de los datos abiertos para el proceso de enseñanza-aprendizaje. La muestra ha sido de 192 docentes, con un margen de error máximo aplicable a datos globales no segmentados con un nivel de confianza del 95,5%, que en el caso más desfavorable (p=q=50%), es de ±7,2%. Para ello, se ha elaborado un diseño de investigación no experimental, de tipo exploratorio, utilizando el instrumento del cuestionario para la recogida de información. Los resultados demuestran que el uso de datos abiertos en Educación Superior obliga a una mejora de las políticas educativas que impulse la alfabetización digital, fomentando con ello el empoderamiento educativo.

Isolating the effect of cycling on local business environments in London.

We investigate whether increasing cycling activity affects the emergence of new local businesses. Historical amenity data from OpenStreetMap is used to quantify change in shop and sustenance amenity counts. We apply an instrumental variable framework to investigate a causal relationship and to account for endogeneity in the model. Measures of cycling infrastructure serve as instruments. The impact is evaluated on the level of 4835 Lower Super Output Areas in Greater London. Our results indicate that an increase in cycling trips significantly contributes to the emergence of new local shops and businesses. Limitations regarding data quality, zero-inflation and residual spatial autocorrelation are discussed. While our findings correspond to previous investigations stating positive economic effects of cycling, we advance research in the field by providing a new dataset of unprecedented high granularity and size. Furthermore, this is the first study in cycling research looking at business amenities as a measure of economic activity. The insights from our analysis can enhance understandings of how cycling affects the development of local urban economies and may thus be used to assess and evaluate transport policies and investments. Beyond this, our study highlights the value of open data in city research.

Motivation Perspectives on Opening up Municipality Data: Does Municipality Size Matter?

National governments often expect municipalities to develop toward open cities and be equally motivated to open up municipal data, yet municipalities have different characteristics influencing their motivations. This paper aims to reveal how municipality size influences municipalities’ motivation perspectives on opening up municipality data. To this end, Q-methodology is used, which is a method that is suited to objectify people’s frames of mind on a particular topic. By applying this method to 37 municipalities in the Netherlands, we elicited the motivation perspectives of three main groups of municipalities: (1) advocating municipalities, (2) careful municipalities, and (3) conservative municipalities. We found that advocating municipalities are mainly large-sized municipalities (>65,000 inhabitants) and a few small-sized municipalities (<35,000 inhabitants). Careful municipalities concern municipalities of all sizes (small, medium, and large). The conservative municipality perspective is more common among smaller-sized municipalities. Our findings do not support the statement “the smaller the municipality, the less motivated it is to open up its data”. However, the type and amount of municipality resources do influence motivations to share data or not. We provide recommendations for how open data policy makers on the national level need to support the three groups of municipalities and municipalities of different sizes in different ways to stimulate the provision of municipal data to the public as much as possible. Moreover, if national governments can identify which municipalities adhere to which motivation perspective, they can then develop more targeted open data policies that meet the requirements of the municipalities that adhere to each perspective. This should result in more open data value creation.

Open Data as Public Archaeology: The Monumental Archive Project

The value of open data is transforming archaeological practice while also introducing new concerns relating to the ethics of studying the dead. This paper uses the Monumental Archive Project, recently launched as a public database of cemetery records from Barbados, as a case study to critically examine the realities of platforms created to bring together academic and general audiences in open mortuary archaeology. Digital literacy and support structures are significant barriers to digital data within the discipline, while the impact of open data on the public(s) that archaeologists seek to engage and collaborate with is rarely considered let alone measured. Is it possible to serve diverse audiences with a single platform? What are the implications (social, ethical, emotional) for sharing cemetery data? When digitizing the dead, strategies in platform design, marketing and communication for public interest and use becomes even more complex and necessitates further attention.

Webcrawling and machine learning as a new approach for the spatial distribution of atmospheric emissions.

In this study we apply two methods for data collection that are relatively new in the field of atmospheric science. The two developed methods are designed to collect essential geo-localized information to be used as input data for a high resolution emission inventory for residential wood combustion (RWC). The first method is a webcrawler that extracts openly online available real estate data in a systematic way, and thereafter structures them for analysis. The webcrawler reads online Norwegian real estate advertisements and it collects the geo-position of the dwellings. Dwellings are classified according to the type (e.g., apartment, detached house) they belong to and the heating systems they are equipped with. The second method is a model trained for image recognition and classification based on machine learning techniques. The images from the real estate advertisements are collected and processed to identify wood burning installations, which are automatically classified according to the three classes used in official statistics, i.e., open fireplaces, stoves produced before 1998 and stoves produced after 1998. The model recognizes and classifies the wood appliances with a precision of 81%, 85% and 91% for open fireplaces, old stoves and new stoves, respectively. Emission factors are heavily dependent on technology and this information is therefore essential for determining accurate emissions. The collected data are compared with existing information from the statistical register at county and national level in Norway. The comparison shows good agreement for the proportion of residential heating systems between the webcrawled data and the official statistics. The high resolution and level of detail of the extracted data show the value of open data to improve emission inventories. With the increased amount and availability of data, the techniques presented here add significant value to emission accuracy and potential applications should also be considered across all emission sectors.

Enterprise Business Model Innovation Under Open Data

In the big data era, enterprise competition environment has changed. Business model innovation has a multiplier effect on the development of enterprises. The key to successful business model is to use data as an asset and as the basis for business model innovation. The open data of web community can realize innovation application, inspire mass entrepreneurship and bring economic value to enterprise. This article first introduces the status quo and economic value of open data. Then it analyzes enterprise business model innovation approaches under open data through the typical enterprise application cases at home and abroad, so as to provide reference for enterprises to carry out business model innovation. The innovation approaches include "take the customer demand as the guidance", "sharing co-operation", “enterprise releases open data”, “incubator creates emerging enterprises using open data”.

Making Open Data more Personal Through a Social Value Perspective: a Methodological Approach

In this paper, we address the problem of assessing the social value of open data. While the number of open data initiatives increases and many data sets are currently available to lay people, common citizens and end users, a still limited number of studies specifically address how to improve open data understandability, their usability by common users and the measurability of their value in terms of concrete outcomes and benefits for the intended communities and the individual who appropriate those data, to make them more personal and hence more valuable. Our goal is to contribute to the success of open data initiatives by defining a methodology by which to assess their perceived social value. In this paper, we present the conceptual content of the methodology, that is its main concepts and logic structure, and discuss it by means of an empirical user study in which we applied it to real-life open data sets and involving a large sample of prospective consumers of those open data. In particular, we focus on the health care domain in order to improve the welfare of the citizens that need health care services and use the Web to look for relevant information to address those situated needs. Among our main findings, we discovered a clear preference for visual information formats by women with respect to men, and a clear preference for hospitals ranking by disease for senior people with respect to younger.

The wicked problem of commercial value creation in open data ecosystems: Policy guidelines for governments

Creating commercial value through open data use in open data ecosystems is a wicked problem. This problem is characterized by a lack of specific formulation, complex interactions amongst a variety of stakeholders, a lack of criteria determining a satisfactory solution, including what constitutes `value', and uncertain outcomes. Wicked problems cannot be solved by only considering part of the problem. Yet, open data efforts all too often focus mainly on open data publication and neglect the use of the data; while it is the use of data (not just publication) that generates value. This paper addresses the question: Which policy guidelines can support commercial value creation from open data? This study first elicits characteristics of wicked problems for studying the wicked problem of commercial open data value creation. Subsequently, we use the wicked problem characteristics to study open data innovation in two countries and consult experts. This work results in eliciting four policy guidelines that can help to derive greater commercial value from open data ecosystems. The four policy guidelines show that governmental open data policies for commercial value should pay attention to: 1) increasing the knowledge of open data ecosystem stakeholders about contextual factors influencing open data use and commercial value creation, 2) ensuring the availability and quality of five types of resources: open data, open Information Technology (IT), internal IT, knowledge and governance, 3) cooperating between businesses and citizens to create alliances, and 4) reducing the negative effects of value creation by companies in the open data ecosystem. These policy guidelines are expected to stimulate commercial value creation with open data use.