Open Data Platform Research Articles

Automated Estimation of Building Heights with ICESat-2 and GEDI LiDAR Altimeter and Building Footprints: The Case of New York City and Los Angeles

Accurate estimation of building height is crucial for urban aesthetics and urban planning as it enables an accurate calculation of the shadow period, the effective management of urban energy consumption, and thorough investigation of regional climatic patterns and human-environment interactions. Although three-dimensional (3D) cadastral data, ground measurements (total station, Global Positioning System (GPS), ground laser scanning) and air-based (such as Unmanned Aerial Vehicle—UAV) measurement methods are used to determine building heights, more comprehensive and advanced techniques need to be used in large-scale studies, such as in cities or countries. Although satellite-based altimetry data, such as Ice, Cloud and land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), provide important information on building heights due to their high vertical accuracy, it is often difficult to distinguish between building photons and other objects. To overcome this challenge, a self-adaptive method with minimal data is proposed. Using building photons from ICESat-2 and GEDI data and building footprints from the New York City (NYC) and Los Angeles (LA) open data platform, the heights of 50,654 buildings in NYC and 84,045 buildings in LA were estimated. As a result of the study, root mean square error (RMSE) 8.28 m and mean absolute error (MAE) 6.24 m were obtained for NYC. In addition, 46% of the buildings had an RMSE of less than 5 m and 7% less than 1 m. In LA data, the RMSE and MAE were 6.42 m and 4.66 m, respectively. It was less than 5 m in 67% of the buildings and less than 1 m in 7%. However, ICESat-2 data had a better RMSE than GEDI data. Nevertheless, combining the two data provided the advantage of detecting more building heights. This study highlights the importance of using minimum data for determining urban-scale building heights. Moreover, continuous monitoring of urban alterations using satellite altimetry data would provide more effective energy consumption assessment and management.

Competitive advantages of transparency: How anti-corruption initiatives can drive innovation and growth in the energy sector

This review examines the role of anti-corruption initiatives in driving innovation and growth within the energy sector, with a particular focus on emerging markets. As global demand for energy continues to rise, the imperative for ethical and transparent business practices grows stronger. This study synthesizes current literature on anti-corruption measures, transparency initiatives, and their impacts on competitive advantage in the energy industry. Through a comprehensive analysis of case studies and empirical research, we identify several key mechanisms through which transparency can foster innovation and drive growth. These include enhanced stakeholder trust, improved access to capital, attraction of top talent, and the creation of an ethical corporate culture that encourages innovation. Our findings suggest that successful anti-corruption initiatives in the energy sector require a nuanced understanding of local contexts, strong leadership commitment, and the strategic use of technology. The review also highlights the potential of open data platforms and blockchain technology in facilitating transparency and compliance. By identifying key factors contributing to successful anti-corruption efforts and their subsequent impacts on innovation and growth, this study provides valuable insights for policymakers, industry leaders, and researchers working towards a more transparent and innovative energy sector. Future research directions are proposed to further explore the long-term impacts and scalability of these initiatives across different market contexts.

Sex and gender reporting in UK emergency medicine trials from 2010 to 2023: a systematic review

BackgroundFemale participants are underrepresented in randomised control trials conducted in urgent care settings. Although sex and gender are frequently reported within demographic data, it is less common for primary outcomes...

Open data platforms: Design principles for embracing outlaw innovators

Open data platforms freely provide citizens with access to public data, thus enabling improved governance transparency, enhanced public services, and increased civic engagement. However, unlocking the potential of this digital transformation strategy requires that public institutions manage the tension between public and private interests. Furthermore, even when public institutions break down traditional barriers for citizens’ access to data, the potential users often lack the knowledge to leverage it in meaningful ways. Open data platforms therefore tend to fall short of expectations. Leveraging a 10-year action design research study (ADR) in the Swedish Transport Administration (STA), this paper develops design principles for creating value-generating open data platforms in the public domain. The ADR project was initiated to assist STA in its efforts to deal with outlaw innovators who scraped train data from different websites to develop travel apps. Through three iterative design cycles that eventually led to the formation of a new open data platform, the outlaw innovators increasingly became valued partners in the digital transformation process. Theorizing this development process, this paper offers three design principles that provide guidance to public institutions aspiring to digitally transform by making public data accessible. We also reflect upon how these institutions might mitigate the risks associated with partnering with outlaw innovators in the pursuit of an open data strategy.

Spatiotemporal and Environmental Completeness of eBird Data in Taiwan

Recent developments in citizen science have significantly enhanced the public availability of biodiversity data (Chandler et al. 2017, Kelling et al. 2019). Associated open data platforms, such as eBird, have become the major data source supporting scientific research and biodiversity conservation (Tsai et al. 2021, Meehan et al. 2022, Walker and Taylor 2017). However, the increasing availabililty of data does not resolve, but rather exacerbates, data bias issues (Escribano et al. 2019, La Sorte and Somveille 2020, Troia and McManamay 2016). While over 11.2 million bird observation records from Taiwan, including historical data spanning 40 years, are currently available on the eBird platform, their usefulness for characterizing the spatiotemporal patterns of bird communities remains unclear. To address this gap, we evaluated the data’s completeness in representing species richness across spatial and temporal scales and examined the representativeness of the data along several environmental gradients. Using the iNEXT package in R, we found an increase in data completeness with increasing spatial scales Fig. 1, with the mean proportion of bird species represented being increased from 14.0% at the 1 km scale to 68.0 and 82.9% at the 5 and 10 km scales, respectively, across Taiwan. We also found an exponential improvement of data completeness over time. At the 5 km scale, the represented species proportion increased from 6.78 and 32.14% in the 1980s and 1990s to 36.86 and 63.56% in the 2000s and 2010s Fig. 2, with the data in the last decade alone achieving similar completeness to the entire four-decade data. We also found that the areas with good data completeness (i.e., over 80%) adequately represented the full gradient of annual precipitation across Taiwan, but showed a bias toward areas at lower elevations and with lower forest coverage. These results indicate overall good data completeness of the eBird dataset for representing bird species richness in Taiwan. However, this study also highlights some data gaps, especially at finer scales, which need attention when using the dataset. This information is crucial for shaping future research designs and survey plans, aiming to enhance the applications of biodiversity data.

Exploring the Relationship Between E-Government, Transparency, and Citizen Trust in Government Services

This study investigates the relationship between e-government initiatives, transparency, and citizen trust in government services. By synthesizing existing literature and analyzing case studies from various countries, the research aims to understand how digital governance and transparency can influence public trust in governmental operations and services. E-government, which encompasses the use of digital platforms and technologies to deliver government services, is found to play a significant role in enhancing transparency and accessibility. The study reveals that e-government initiatives facilitate greater openness by providing citizens with easy access to information and services, thereby reducing opportunities for corruption and enhancing accountability. This increased transparency is shown to be crucial in fostering a more informed and engaged citizenry. Transparency in government is identified as a key factor in building citizen trust. The research highlights that when governments are open about their processes, decisions, and use of resources, citizens are more likely to perceive them as reliable and trustworthy. Transparency initiatives, such as open data platforms and online public consultations, allow for greater scrutiny and participation, which in turn can increase public confidence in government actions. Citizen trust in government services is essential for the effective functioning of a democratic society. The study indicates that trust is significantly influenced by the perceived transparency and accessibility of government services. When citizens trust that their government is transparent and accountable, they are more likely to engage with and utilize government services, leading to greater societal cohesion and cooperation. In conclusion, the interplay between e-government, transparency, and citizen trust is critical in shaping the effectiveness and credibility of government services. The study suggests that enhancing digital governance and transparency can lead to increased public trust and more effective government operations. Future research should explore the specific mechanisms through which e-government initiatives can further enhance transparency and trust in different socio-political contexts.

An Open Data Platform to Advance Gender Equality in STEM in Latin America

An Open Data Platform to Advance Gender Equality in STEM in Latin America

Open government data initiatives as agents of digital transformation in the public sector: Exploring the extent of use among early adopters

Open government data initiatives are important agents of public sector digital transformation and understanding how agencies design, implement, and evaluate strategies for these initiatives is paramount to their ongoing success. However, a challenging and little understood aspect of open government data initiatives is precisely how open data users engage with and use these vital resources. This study examines the extent of open dataset use among early adopters using a cross-sectional analysis of web analytic behavioral data. We quantify early users' extent of use of open datasets from Health Data NY, the first state-level open health data platform. Results of a PLS-SEM analysis provide new empirical evidence that prior, or initial levels of open dataset use positively influence the later extent of open dataset use. Further, prior open government data use influences not only the later variety of open data activities, but also their depth of sophistication. Implications for practice include open data strategies to facilitate deeper, more extensive use of open dataset resources that lead to increased value creation, and ultimately, more effective digital transformation. Theoretically, this study contributes to the body of research on the Unified Theory of Acceptance and Use of Technology (UTAUT) by empirically testing its Use Behavior construct in light of early open data users' extent of use of open datasets, thus providing a more refined understanding of open data use behavior.

METB-12. GIFT FROM A CHILD POSTMORTEM SPECIMEN DATASET EXTENSION OF THE PEDIATRIC BRAIN TUMOR ATLAS EMPOWERS PEDIATRIC BRAIN TUMOR RESEARCH

Abstract BACKGROUND Pediatric brain and spinal cord tumors are the leading cause of cancer-related mortality in children. An incomplete understanding of brain tumor biology and associated limited access to high-quality biological samples for research are the main factors driving the lack of clinical therapeutic development for pediatric brain tumors that recur or progress. Post-mortem tissue donation provides an unprecedented resource for addressing some of these limitations. METHODS The Gift from a Child (GFAC) program by the Swifty Foundation has a unique mission to increase post-mortem pediatric brain tissue donations through advocacy as well as the education of clinicians and families. Through GFAC’s strategic collaboration with the Children’s Brain Tumor Network (CBTN), CBTN has leveraged postmortem tissue to expand the Pediatric Brain Tumor Atlas (PBTA), a cross-histology multi-omics atlas resource. As part of the effort CBTN has sequenced and released data for over 350 post-mortem pediatric brain tumor specimens including multiple brain region sampling cases with specimen and sequencing quality metrics. RESULTS Here we present an assessment of postmortem samples and available multi-omic data on postmortem samples within the PBTA dataset. Data have been harmonized and released with no publication embargo. To access data, researchers can utilize existing open source data resources and platforms including PedCbioPortal and OpenPedCan to: (1) Identify tumor spatial and temporal specific alterations (2) Establish tumor evolution trajectory leading to therapeutic resistance and tumor progression; (3) Understand tumor heterogeneity longitudinally across multiple ‘omics layers; and (4) Identify and request specimens and derived tumor models. CONCLUSIONS Together, we present the largest deeply characterized cohort of postmortem pediatric brain tumor samples as powerful expansion of the PBTA cohort of >3,000 pediatric brain tumors. CBTN’s open-science model supported by the GFAC mission highlights the value and utility of autopsy-based specimen collection on behalf of improving outcomes for children with brain tumors.

The Integration of Internet of Things (IoT) in Smart Cities A Systemic Approach to Urban Management

The use of the Internet of Things (IoT) has become an integral part of efforts to develop efficient and sustainable smart cities. This research aims to investigate the integration of IoT in the context of systemic urban management. The research methods employed include in-depth literature studies, comprehensive policy document analysis, and field research to understand IoT implementation in several smart cities. The results indicate that a systemic approach to integrating IoT in urban management is crucial to achieving efficiency and sustainability. This integration involves various aspects, including physical infrastructure, information systems, policies, and community participation. In this context, the research highlights the importance of developing integrated and interoperable IoT infrastructure, open data platforms, and adequate regulatory frameworks. Additionally, community participation in designing and utilizing IoT solutions is a key factor for successful implementation. By analyzing case studies in several smart cities, this research also provides insights into the challenges faced and strategies employed in integrating IoT into urban management. Based on these findings, the research concludes that a systemic approach encompassing cross-sector collaboration, proven technology use, and active community participation is necessary for successful IoT integration in urban management. This research contributes to expanding the understanding of IoT implementation in a broader urban context and provides practical guidance for policymakers, practitioners, and academics involved in smart city development.

Vibration-based water leakage detection system for public open data platforms

Vibration-based water leakage detection system for public open data platforms

Synchronization and Standardization of Open Data Platforms: A Systematic Literature Review

This study is a systematic review that intends to improve our knowledge of the ideas of open data synchronization and standardization by reviewing the methodology and instruments utilized in previous studies through a careful examination of accessible literature. The methodology of this study was developed in several key phases. Firstly, a search was conducted in electronic databases to identify relevant studies in the field for Open Government Data (OGD). After identifying the studies, they were carefully reviewed through an initial abstract-based screening process. In the next phase, the research design and sample used in each identified study were analyzed to assess their quality and reliability. Subsequently, data from the studies were analyzed and categorized into various categories, including the usability of open data, synchronization, scalability, and reliability. The gained results highlight the importance of open data in our information-driven society. Open data positively impact transparency, anti-corruption efforts, and free access to information. To achieve these benefits, it is crucial to standardize and license open data, giving priority to their usability. The study underscores the importance of open data for transparency, innovation, and citizen engagement. Despite the disadvantages, this technology has the potential to change how governments and society use data.

Sense (and) the city: From Internet of Things sensors and open data platforms to urban observatories

AbstractDigitalisation and the Internet of Things (IoT) help city councils improve services, increase productivity and reduce costs. City‐scale monitoring of traffic and pollution enables the development of insights into low‐air quality areas and the introduction of improvements. IoT provides a platform for the intelligent interconnection of everyday objects and has become an integral part of a citizen's life. Anyone can monitor from their fitness to the air quality of their immediate environment using everyday technologies. With caveats around privacy and accuracy, such data could even complement those collected by authorities at city‐scale, for validating or improving policies. The authors explore the hierarchies of urban sensing from citizen‐to city‐scale, how sensing at different levels may be interlinked, and the challenges of managing the urban IoT. The authors provide examples from the UK, map the data generation processes across levels of urban hierarchies and discuss the role of emerging sociotechnical urban sensing infrastructures, that is, independent, open, and transparent capabilities that facilitate stakeholder engagement and collection and curation of grassroots data. The authors discuss how such capabilities can become a conduit for the alignment of community‐ and city‐level action via an example of tracking the use of shared electric bicycles in Bristol, UK.

Beyond built environment: Unveiling the interplay of streetscape perceptions and cycling behavior

As an important means of shared micro-mobility, shared bicycles have become a crucial component of urban transportation in China. The impact of the built environment on bicycling has been widely acknowledged. However, can streetscape perceptions influence bicycle-sharing volume (BSV) and supplement the built environment? We first obtained millions of pieces of shared-cycling data from the Shenzhen Open Data Platform and carried out geographical quantification of BSV. As for streetscape, we improved the classification of subjective streetscape perception based on street view images using the k-means clustering algorithm and conducted predictions using XGBoost. Through the application of different regression models, we unveiled the nonlinear spatial interdependencies between BSV and streetscape perceptions as a complement to the built environment. Our findings indicate that greenery, vivid street-front facades, and diverse street facilities can promote BSV. Targeted strategies are proposed for different districts. For instance, urban planners can provide incentives for high-income groups in central urban areas to adopt active travel, and increase the supply of shared bicycles in suburban areas with high building density, particularly in industrial urban villages. As a supplement to the long-term planning recommendations derived from the macro-built environment analysis, an in-depth spatial perception quantitative assessment proffers a human-centric, flexible blueprint for urban street design.

How to restore ecological impacts from wind energy? An assessment of Zhongying Wind Farm through MSPA-MCR model and circuit theory

The wind energy industry is developing rapidly in China with a maximum capacity worldwide. Meanwhile, the stability of the ecological network is challenged by extensive renewable energy expansion. There are still research deficiencies in wind farm ecological impacts at project scale from an ecological restoration perspective. It is of great necessity to monitor the ecological impacts with time going and explore ecological restoration strategies for wind power development. The research case of Zhongying Wind Farm is selected based on investigation and interviews regarding its ecological impact in the prior study. It employs a comparative methodology by comparing the ecological quality before and after the construction of Zhongying Wind Farm. Raw data from Landsat 8, the Digital Elevation Model (DEM), the Normalized Difference Vegetation Index (NDVI), and wind turbine statistics originating from open data platforms are analyzed and visually represented with ArcGIS. This paper establishes an ecological network based on morphological spatial pattern analysis (MSPA) and the Minimum Cumulative Resistance (MCR) model to quantify wind farms’ impacts on ecological corridors. The circuit theory and Pajek software are used to identify and categorize the ecological nodes into weak and obstacle points. The results reveal that ecological impacts on ecological corridors decay with distance, reflecting changes in length, connection strength and cumulative values of corridors. While the impacts on ecological nodes depend on nodes’ attributes: the number of weak points increased by 18.5 %, and obstacle points increased by 350 %, with their locations shifting from the edges of urban construction sites to the edges of wind farm. Differentiated strategies are proposed according to the impact magnitude of each ecological corridor and node: such as dynamic inspection mechanism, construction prohibition for protection; corridor structure and function recovering, stepping stones addition for restoration; buffer belts, warning radar, and monetary schemes for compensation. This research fills the gap of ecological impact assessment under the challenges of wind energy expansion. It helps to balance wind energy development and ecological restoration at the regional level.

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning

The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This study aims to develop machine learning models to detect potential (re)hemorrhage in sporadic CCM patients. This study was based on a dataset of 731 sporadic CCM patients in open data platform Dryad. Sporadic CCM patients were followed up 5 years from January 2003 to December 2018. Support vector machine (SVM), stacked generalization, and extreme gradient boosting (XGBoost) were used to construct models. The performance of models was evaluated by area under receiver operating characteristic curves (AUROC), area under the precision-recall curve (PR-AUC) and other metrics. A total of 517 patients with sporadic CCM were included (330 female [63.8%], mean [SD] age at diagnosis, 42.1 [15.5] years). 76 (re)hemorrhage (14.7%) occurred during follow-up. Among 3 machine learning models, XGBoost model yielded the highest mean (SD) AUROC (0.87 [0.06]) in cross-validation. The top 4 features of XGBoost model were ranked with SHAP (SHapley Additive exPlanations). All-Elements XGBoost model achieved an AUROCs of 0.84 and PR-AUC of 0.49 in testing set, with a sensitivity of 0.86 and a specificity of 0.76. Importantly, 4-Elements XGBoost model developed using top 4 features got a AUROCs of 0.83 and PR-AUC of 0.40, a sensitivity of 0.79, and a specificity of 0.72 in testing set. Two machine learning-based models achieved accurate performance in identifying potential (re)hemorrhages within 5 years in sporadic CCM patients. These models may provide insights for clinical decision-making.

How can open public data promote efficient and equitable green production? Evidence from eco-efficiency in China.

Nowadays, concurrent attention to economic development and ecological issues is becoming an important trend. In this paper, we measure the eco-efficiency of 285 Chinese cities from 2003 to 2019 using a non-radial directional distance function and the data envelopment analysis method, based on which we analyze the club convergence of cities' eco-efficiency using the logt test; we estimate the impact of open public data platforms on eco-efficiency and its convergence using a multi-period difference in difference model and panel-ordered logit model, respectively. We find that, first, open public data platforms improve cities' eco-efficiency by about 6.5%, and the impact mechanisms include scale efficiency, technical efficiency, and total factor productivity, or, at the micro level, increasing the economic agglomeration degree, boosting the amount of foreign investment used, and increasing green innovation level. Second, there are three convergence clubs of eco-efficiency in China's cities, whose average eco-efficiency trends are above, close to, and below average, respectively. Third, public data platforms significantly increase the probability of cities belonging to the convergence clubs of high and medium eco-efficiency (Clubs 1 and 2) and decrease the probability of belonging to the low one (Club 3). However, the mechanisms only include technical efficiency and total factor productivity, or the amount of foreign investment used and the green innovation level at the micro level.

Cybersecurity and Efficacity of Open Data Platforms

Cybersecurity is critical for protecting open data. Transparency and innovation are facilitated by open data platforms; however, concerns about cybersecurity and privacy persist. This study examines the role of cybersecurity in public institutions in the Republic of Kosovo to determine methods of safeguarding data integrity. The main aim of this study was to examine the role of cybersecurity in securing open data in public organizations in the Republic of Kosovo. The study aimed to identify optimal cybersecurity practices in the context of open data and provide a comprehensive overview of the implementation of cybersecurity measures. This study employed a structured and methodical approach to assess cybersecurity and the effectiveness of open data platforms in public organizations in the Republic of Kosovo. Results: The study provides an overview of the status of open data platforms in the Republic of Kosovo and highlights the importance of cybersecurity, data privacy, and data integrity. Despite the stated concerns, such as enhancing security measures and increasing user knowledge, it is evident that public institutions have made significant progress in securing and enhancing their open data platforms. It is suggested that institutions in the Republic of Kosovo continue to invest in cybersecurity, promote privacy protection measures, and focus on enhancing the quality of open data to develop in this sector. Furthermore, collaboration and coordination across institutions and government agencies are required to enhance the efficiency and effectiveness of these platforms.

Does digital transformation benefit innovation in manufacturing firms? Empirical evidence based on data mining and textual analysis methods

With the increasing spread of the digital economy and the application of digital technologies, more and more manufacturing enterprises have begun to improve the efficiency of digital transformation with the help of big data and other digital technologies to promote the high-quality development of enterprises. Through the open big data platform, manufacturing enterprises can collect data and information reflecting the needs of users in a timely manner to achieve real-time optimization of the production process and timely detection of problems to a certain extent, thereby improving the ability to research and innovation. Through the establishment of econometric models and empirical tests, we also find that the education level of managers affects the process of digital transformation and influences the impact of digital transformation on innovation.

The Pandemic Response Commons.

A data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons (PRC) is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic. The PRC was developed using the open source Gen3 data platform and is based upon consortium, data, and platform agreements developed by the not-for-profit Open Commons Consortium. A formal consortium of Chicagoland area organizations was formed to develop and operate the PRC. The consortium developed a general PRC and an instance of it for the Chicagoland region called the Chicagoland COVID-19 Commons. A Gen3 data platform was set up and operated with policies, procedures, and controls for a NIST SP 800-53 revision 4 Moderate system. A consensus data model for the commons was developed, and a variety of datasets were curated, harmonized and ingested, including statistical summary data about COVID cases, patient level clinical data, and SARS-CoV-2 viral variant data. Given the various legal and data agreements required to operate a data commons, a PRC is designed to be in place and operating at a low level prior to the occurrence of an epidemic, with the activities increasing as required during an epidemic. A regional instance of a PRC can also be part of a broader data ecosystem or data mesh consisting of multiple regional commons supporting pandemic response through sharing regional data.