Public Open Data Research Articles

Open Data-Driven Decision Making: Observations from COVID-19 Minimal Essential Data, Canton Zürich, Switzerland.

This research delves into the relationship between open data and decision making, drawing insights from the utilization of Minimal Essential Data (MED) during the COVID-19 pandemic in Canton Zürich, Switzerland. It examines observations and lessons learned, emphasizing the pivotal role of open data in response to an emergency. Through a case study approach, this article sheds light on the practical implications and observed strategies in publishing open data from digital public health surveillance software. While it is arguable that open data has the potential to enhance data quality in the context of a public health emergency, our findings indicate that it is firstly essential that a Minimal Essential Dataset (MED) is defined and publicly published, an open data publication platform and procedure are secondly in place (ideally following FAIR data principles), and thirdly, that regular data quality reviews are conducted.

An improved digital soil mapping approach to predict total N by combining machine learning algorithms and open environmental data

Digital Soil Mapping (DSM) is fundamental for soil monitoring, as it is limited and strategic for human activities. The availability of high temporal and spatial resolution data and robust algorithms is essential to map and predict soil properties and characteristics with adequate accuracy, especially at a time when the scientific community, legislators and land managers are increasingly interested in the protection and rational management of soil.Proximity and remote sensing, efficient data sampling and open public environmental data allow the use of innovative tools to create spatial databases and digital soil maps with high spatial and temporal accuracy. Applying machine learning (ML) to soil data prediction can improve the accuracy of maps, especially at scales where geostatistics may be inefficient. The aim of this research was to map the nitrogen (N) levels in the soils of the Nurra sub-region (north-western Sardinia, Italy), testing the performance of the Ranger, Random Forest Regression (RFR) and Support Vector Regression (SVR) models, using only open source and open access data. According to the literature, the models include soil chemical-physical characteristics, environmental and topographic parameters as independent variables. Our results showed that predictive models are reliable tools for mapping N in soils, with an accuracy in line with the literature. The average accuracy of the models is high (R2 = 0.76) and the highest accuracy in predicting N content in surface horizons was obtained with RFR (R2 = 0.79; RMSE = 0.32; MAE = 0.18). Among the predictors, SOM has the highest importance. Our results show that predictive models are reliable tools in mapping N in soils, with an accuracy in line with the literature. The results obtained could encourage the integration of this type of approach in the policy and decision-making process carried out at regional scale for land management.

Integrating open education practices with data analysis of open science in an undergraduate course.

The open science movement produces vast quantities of openly published data connected to journal articles, creating an enormous resource for educators to engage students in current topics and analyses. However, educators face challenges using these materials to meet course objectives. I present a case study using open science (published articles and corresponding datasets) and open educational practices in a capstone course. While engaging in current topics of conservation, students trace connections in the research process, learn statistical analyses, and recreate analyses using the programming language R. I assessed the presence of best practices in open articles and datasets, examined student selection in the open grading policy, surveyed students on their perceived learning gains, and conducted a thematic analysis on student reflections. First, articles and datasets met just over half of the assessed fairness practices, which increased with the publication date. There was a marginal difference in how assessment categories were weighted by students, with reflections highlighting appreciation for student agency. In course content, students reported the greatest learning gains in describing variables, while collaborative activities (e.g., interacting with peers and instructor) were the most effective support. The most effective tasks to facilitate these learning gains included coding exercises and team-led assignments. Autocoding of student reflections identified 16 themes, and positive sentiments were written nearly 4x more often than negative sentiments. Students positively reflected on their growth in statistical analyses, and negative sentiments focused on how limited prior experience with statistics and coding made them feel nervous. As a group, we encountered several challenges and opportunities in using open science materials. I present key recommendations, based on student experiences, for scientists to consider when publishing open data to provide additional educational benefits to the open science community.

Interpretable machine learning boosting the discovery of targeted organometallic compounds with optimal bandgap

Organometallic compounds (OMCs) have attracted tremendous attention in various fields, such as photovoltaic cell and high-k dielectric application, due to their beneficial properties. Despite their potential, the progression of OMCs into industrial applications is hindered by the limited databases available for their properties and the absence of efficient surrogate models. To address this, in this study, optimally selected feature-based surrogate models for predicting the electronic properties of OMCs are constructed via various multiscale features and extensive database. To this end, high-throughput calculation was performed to obtain electronic properties of more than 18k materials generally known as organometallics, augmenting around 12k organic materials obtained from the public open data set, OMDB-GAP1. For generating features closely related to OMCs, descriptors encapsulating the information ranging local to global, also other widely-used composition-, structure-based features (more than 3.5k in total) were employed. Among these descriptors, we identified 48 critical features that elucidates the physicochemical underpinnings of OMCs’ properties, suggesting their impact on the properties of OMCs. The light gradient boosting machine model achieved high-accuracy predictions across the entire database with just 1 % of the total descriptors, sufficiently compared to the entire sets (decreased of around 0.01 by R2 score and 0.01 eV by MAE). Furthermore, the efficacy of active learning process was demonstrated to find OMCs with optimal properties rapidly. As a result, expected improvement outperforms other methods by identifying 69 % of the target materials only searching 46 % of the total search space. Our constructed platform with a high-throughput calculated database can pave the way for the rapid screening of OMCs for the targeted industrial application, and suggest a comprehensive grasp of the intrinsic properties of OMCs and related compounds.

The Australian Park Life project: Development of a nationally standardised spatial layer and public participatory GIS for greenspace in Australian capital cities

This paper introduces the ‘Australian Park Life Project’ and describes a protocol to standardise the capture and collation of public open greenspace spatial data across Australian cities. This method will progress greenspace research allowing for unique coherent national analyses and comparative research across Australia. We also outline the development of the Park Life public participatory geographic information system (PPGIS) to spatially explore what, and how, public open greenspaces are being used by Australian communities. The combination of community crowdsourced spatial data providing location-specific information on the green public open spaces used, in combination with the national spatial layer of greenspace allows for unique analyses exploring the role of greenspace provision and design on use and represents a transformative strategy in shaping public open space policy and strategy.•A spatial layer of public open greenspace was created for the eight Australian State and Territory capital cities using a standardised data capture and collection method from local government planning schemes and land use data, and listings of managed and demarcated parks and reserves.•The Park Life public participatory geographic information system (PPGIS) was built to capture spatially referenced information on the use of greenspaces - specifically what spaces are used, and how they are used.

Are We Back to Normal? A Bike Sharing Systems Mobility Analysis in the Post-COVID-19 Era

In recent years, numerous cities around the world have adopted bike sharing systems (BSSs). The increasing popularity of these transportation means is directly related to their eco-friendly and health advantages. Several factors affect how citizens make use of BSS, for instance, the size and configuration of a city, where docking stations are located, the associated prices, and others. Moreover, these systems have different usage patterns that vary according to the month, day, or hour but remain constant when compared yearly. However, the onset of the COVID-19 pandemic modified mobility behaviors as various governments around the world implemented mobility restrictions to avoid the spread of the virus. The objective of this investigation is to determine if the usage patterns of BSSs have changed permanently or if we have recovered pre-pandemic levels and usage patterns. Given the special characteristics of each BSS, this study focuses on Barcelona’s BSS, called Bicing. To understand the impact of other BSSs, the further analysis of each system’s unique characteristics is necessary. The study employs bike usage information from the public open data service maintained by Barcelona City Hall, namely, the Open Data BCN website, from January 2019 to December 2023, and it covers mechanical and electrical bikes with more than 4 million records per month. The results show that usage patterns were similar before and after the pandemic; the usage increased in 2021 and 2022 and stabilized in 2023, registering a rise of 17.5% in Bicing usage from 2021 to 2023. However, bike type preferences have changed for reasons unrelated to the pandemic restrictions. The main limitation of this investigation is the lack of continuity in the data due to a change in the company that provides the service in Barcelona. For future research, data from other transportation means can be used to analyze all communication behavior in this city. Additionally, if data are available, a study by gender and age can be performed and used to improve the system for certain groups.

Semantic Representation of Low‐Cycle Fatigue Testing Data Using a Fatigue Test Ontology and ckan.kupferdigital Data Management System

Addressing a strategy for publishing open and digital research data, this paper presents the approach for streamlining and automating the process of storage and conversion of research data to those of semantically queryable data on the web. As the use case for demonstrating and evaluating the digitalization process, the primary datasets from Low‐Cycle Fatigue (LCF) testing of several copper alloys are prepared. The Fatigue Test Ontology (FTO) and ckan.kupferdigital data management system are developed as two main prerequisites of the data digitalization process. FTO has been modeled according to the content of the fatigue testing standard and by reusing the Basic Formal Ontology (BFO), Industrial Ontology Foundry (IOF) core ontology, and Material Science and Engineering Ontology (MSEO). The ckan.kupferdigital data management system was also constructed in such a way that enables the users to prepare the protocols for mapping the datasets into the knowledge graph, and automatically convert all the primary datasets to those machine‐readable data which are represented by the Web Ontology Language (OWL). The retrievability of the converted digital data was also evaluated by querying the example competency questions, confirming that ckan.kupferdigital enables publishing open data that can be highly reused in the semantic web.This article is protected by copyright. All rights reserved.

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

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

Measuring the Decarbonisation Progress of Buildings Based on European Open Big Data

The European Commission (EC) has established climate neutrality by 2050 as one of its objectives. To achieve this, decarbonising the building stock is crucial since buildings are responsible for 36% of European greenhouse gas emissions. Monitoring the progress of decarbonisation is crucial to understand where we stand in terms of achieving long-term decarbonisation goals, facilitate evidence-based decision-making, promote accountability among member states (MS), and engage the public in these initiatives. However, data collection for monitoring the decarbonisation of buildings represents a challenge for MS, for which they may not be adequately prepared.To promote the collection and publication of open data, the EC has established frameworks for developing open data infrastructures within MS through various directives, offering comprehensive and well-organized information. Directives such as the Infrastructure for Spatial Information in Europe (INSPIRE), which advocates for the collection and accessibility of data in diverse topics, and the Energy Performance of Buildings Directive (EPBD), enabling the analysis of extensive information on the energy efficiency of constructed assets through the open publication of Energy Performance Certificates (EPC) databases, exemplify these efforts. However, this information often remains dispersed and narrowly focused, necessitating the integration of other data sources to extract meaningful insights.The paper delves into the benefits of georeferencing and automated cross-referencing of open data on buildings, enabling the monitoring of decarbonisation progress. To achieve this, we have developed a national-scale Urban Building Energy Model (UBEM) for Spain. This model is founded on EPCs and other open data derived from public information sources established through European directives. Additionally, we have investigated the prospect of integrating data from Digital Building Logbooks (DBL), which are emerging data repositories on buildings promoted by the EC in the forthcoming EPBD, into the UBEM to enhance the model.The study showcases significant potential in developing information on various topics, including characterising the Spanish building stock — a framework that may be extrapolated to other MS — in terms of energy performance based on location, archetype, and age. The study also focuses on estimating energy consumption and carbon dioxide emissions, monitoring the progress of renovations, evaluating the achieved energy savings, and identifying and characterising the least energy-efficient segments of the building stock.

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.

Signal detection of adverse reactions for bendamustine based on FDA adverse event reporting system

ABSTRACT Background This study aimed to analyze the adverse events to bendamustine using data obtained from the Food and Drug Administration open public data project (openFDA) and to provide a reference for its use in clinical practice. Research design and methods Adverse events (AEs) due to bendamustine usage reported from 1 January 2008 to 31 March 2023 were collected from the FDA Adverse Event Reporting System (FAERS). The reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian plausible propagation neural network (BCPNN), and multinomial gamma-Poisson distribution shrinking (MGPS) algorithms were used to identify signs of adverse reactions caused by bendamustine. Results A total of 4214 AE reports where bendamustine was considered as the first suspected drug were obtained from FAERS. The analysis revealed 214 AE risk signals, among which 141 met the criteria but they were not listed as possible side effects on the drug information sheet provided in the package. Conclusion Our findings identified numerous common AEs with previously reported clinical observations. We also identified some signs of potential new AEs, indicating the need of careful clinical monitoring of patients treated with bendamustine and further risk identification research about this drug.

Metadata application profile as a mechanism for semantic interoperability in FAIR and open data publishing

Application profiles, also known as metadata application profiles, are customised collections of vocabularies adapted from various namespaces and tailored for specific local applications. These profiles act as constrainers and explainers for the (meta)data. Semantic interoperability is the ability of computer systems to exchange data in a mutually understandable manner, facilitating data sharing across diverse platforms and applications without compromising its meaning. As a critical component of semantic interoperability, application profiles enforce semantics to (meta)data, enhancing its openness, interoperability, and reusability. This study assesses the feasibility of representing a comprehensive application profile in a format aligned with the semantic web, ensuring interoperability between profiles and datasets. Dublin Core Description Set Profiles (DSP) is adapted as the modeling framework for metadata application profiles, steering the associated datasets toward RDF compliance. The research outcomes include “Yet Another Metadata Application Profiles” (YAMA) as a preprocessor grounded in the DSP framework for developing and managing metadata application profiles. YAMA facilitates the generation of various standard formats of application profiles, ensuring they are represented in human-readable documentation, machine-actionable forms, and even data validation languages. A data mapping extension to YAMA is proposed to ensure the semantic interoperability of open data, bridging non-RDF data structures to RDF, thus enabling the publication of 5-star open data. This ensures smooth dataset integration and the creation of linkable, semantically rich open datasets. The work emphasizes the pivotal role of application profiles in fortifying the semantic interoperability of (meta)data, thereby elevating dataset openness.

ИНФОРМАЦИОННОЕ СОСТОЯНИЕ ДЕНЕЖНОЙ СИСТЕМЫ

The article examines the information state of the monetary system. It is shown that one of the fundamental difficulties in the development of the modern economy is high-quality and timely financial information. The idea of obtaining open government data is formulated. Regulatory legal acts are provided for the publication of open data in the Russian Federation. Certain areas of fintech have been identified, which already play an important role in the information state of the global monetary system. Embedded finance or seamless services (Embedded Finance), salary on call (Payon Demand), autonomous Finance, and Open Banking have been studied. The main tasks of obtaining open government data are given. It is shown that the syntactic and semantic quality of data should be taken into account when managing the monetary system. It is proposed to use open financial information in the ontology format to build a single semantic financial information portal.

COMPARISON OF DEEP LEARNING ARCHITECTURES FOR THE SEMANTIC SEGMENTATION OF SLUM AREAS FROM SATELLITE IMAGES

Abstract. The mapping of slum areas is an important task when considering the necessity for an inclusive, safe and resilient cities. While many methods exist in this regard, the use of machine learning and more specifically deep learning has gained traction in recent years. In this paper, we present a systematic comparison of existing deep learning architectures and backbones. The experiments in the paper investigate the question of which architecture and backbone combination and which configuration of dataset preparation is best for use in slum mapping. In another experiment we implemented the trained model to predict slums in existing open data. The experiments in the paper used public open data provided by Helber et al. (2018). Results show that FPN with vgg16 backbone showed the most potential in this particular application. The results of the semantic segmentation also shows promise, although the discrepancy in slum characteristic still hinders a proper generalization of its use.

Assessing the readability of open data portals: a case study of Open Data Pakistan

Open data portals have been implemented in numerous countries to provide accessible, usable, and reproducible data. This study aims to assess the readability of datasets from Open Data Pakistan. Using the URL (https://opendata.com.pk/) in an online readability checking tool, six well-known readability formulas were employed, including Flesch Kincaid Reading Ease, Flesch Kincaid Grade Level, Gunning Fog Score, Smog Index, Coleman Liau Index, and Automated Readability Index. The average score of each formula was calculated to analyze the standard readability of the open data portal. Additionally, the website was explored to evaluate the overall status of open datasets, showcases, and features like "Connect" on Open Data Pakistan. The findings indicated that the Open Data Pakistan website has a reading difficulty, scoring an average of 47.4 out of 100, making it challenging for the general public to read. However, the overall analysis suggested that the readability status of open data in Pakistan is satisfactory. These results offer insights into improving the readability of open data for enhanced citizen participation and informed decision-making. Moreover, the study has implications for policymakers and stakeholders regarding the readability of Open Data Pakistan, emphasizing the importance of considering readability in open data publication policies and guidelines, with a recommendation to decrease the percentage of open data portals categorized as hard to read.

Investigating Academic Graph‐Based Factors behind Funding Success in National Institutes of Health

ABSTRACTWhile major funding agencies are striving for diversity and fairness, the mechanisms behind funding success have yet to be fully elucidated. Existing studies reveal valuable evidences about the effect of the applicant's individual attributes, e.g., gender and age, on the funding success. However, the relationship between funding success and academic activities, e.g., collaborator's characteristics, remains underexplored. This work collects massive scholarly data from open academic graphs and public data about National Institutes of Health awards to investigate the effect of various academic graph‐based factors on the “K to R” success. Leveraging a heterogeneous graph model for predicting the “K to R” success, we regard the gain in the model performance of a factor as a proxy variable for the magnitude of its effect on the “K to R” success. Our preliminary results suggest that interest by peers in the applicant's research and the timing of the interest are strongly correlated with the outcome. Meanwhile, the applicant's social connections, e.g., their collaborators, can also contribute to the outcome.

Amplifying the Power of eDNA by Making it FAIR

Environmental DNA (eDNA) is a fast-growing biomonitoring approach to detect species and map their distributions, with the number of eDNA publications exponentially increasing in the past decade. While millions of DNA sequences are often generated and assigned to taxa in each publication, these records are stored in numerous locations (e.g., supplementary materials at journals’ servers, open data publishing platforms such as Dryad) and in various formats, which makes it difficult to find, access, re-use and integrate datasets. Making eDNA data FAIR (findable, accessible, interoperable, re-usable) has vast potential to improve how the biological environment is measured and how change is detected and understood. For instance, it would allow biomonitoring and species distribution modelling studies across extended space and time scales, which is logistically difficult or impossible for individual projects. It would also shed light on “dark” (unassigned) DNA sequences by facilitating their storage and re-analyses with updated ever-growing DNA reference databases. Several challenges are associated with making eDNA FAIR, including how to standardise data formats and bioinformatics workflows, and simplifying the process of post-publication data archiving so that it is acceptable for eDNA practitioners to adopt. Over the next three years, we plan to work closely with biodiversity data platforms such as the Global Biodiversity Information Facility (GBIF) and Atlas of Living Austrlia (ALA), eDNA science journals, and eDNA practitioners, to solve these challenges and enable eDNA to achieve its revolutionary potential as a unified source of information that supports environmental management.

Tests on reinforced concrete U-shaped walls subjected to torsion and flexure

This data paper describes an experimental campaign on two reinforced concrete (RC) U-shaped walls, including details of the test units, materials, test setup, loading protocol, instrumentation, primary observations and response of each unit during testing, organization of the test data in the public open source research data repository (DOI: 10.14428/DVN/FDJ4EU), and examples of derived data. The two wall units represent the lower stories of a half-scale 6-story prototype core wall that was designed for medium-to-high ductility. The tests conducted aimed at assessing the flexural and torsional response and capacity of RC U-shaped walls. The first wall unit, UW1, was tested for axial-flexure and subjected to quasi-static reverse-cyclic bending about its weak axis until failure. Furthermore, UW1 was subjected to an additional overturning moment to increase the shear span. The second wall unit, UW2, was tested for axial-torsion and subjected to a quasi-static reverse-cyclic rotation about its vertical axis until failure. Both wall units were subjected to a constant axial load ratio of 5%. A range of conventional instrumentation was used to capture salient global and local measurements of the wall, including three-dimensional (3D) digital image correlation on multiple faces. High-definition fiber-optic sensors were also utilized to capture the entire strain profile of several longitudinal reinforcing bars in each wall unit.

Retracted: A Model-Driven Analysis of the Relationship between Innovation and Growth in a Green Low-Carbon Economy Based on Open Public Data

Retracted: A Model-Driven Analysis of the Relationship between Innovation and Growth in a Green Low-Carbon Economy Based on Open Public Data

Tutorial on time series prediction using 1D-CNN and BiLSTM: A case example of peak electricity demand and system marginal price prediction

Although research on time series prediction based on deep learning is being actively carried out in various industries, deep learning technology still has a high entry barrier for researchers who have not majored in computer science. This paper presents a tutorial on time series prediction using a deep learning-based model. The entire process of time series data prediction is presented—from data collection to evaluation of prediction results. The details of each step are shown through a case example of predicting peak electricity demand and system marginal price of Jeju Island in Korea using the 1D-CNN and BiLSTM model. In Jeju Island, the proportion of renewable energy in the total power generation capacity is increased to 67% in 2021, requiring more accurate electricity demand forecasts. Therefore, using 808 days of training data from February 2018, electricity demand and SMP for the next 21 days were predicted. To make it easier for readers to follow, the example uses only open public data, and the entire Python source code is shared via a GitHub repository. The prediction error calculated by WRMSSE showed 0.42 in electricity demand and 0.63 in SMP max. A WRMSSE value less than one means that the forecast was relatively good, that is, better than naïve forecasting. This tutorial is not limited to the energy industry but can be utilized for any application requiring time-series data prediction. This article is expected to be of great help to researchers who need to understand the process of time series prediction using deep learning and use it for application in their industry.