Data Requests Research Articles

A Gap Analysis Framework for an Open Data Portal Assessment Based on Data Provision and Consumption Activities

An Open Government Data (OGD) portal assessment is necessary to track and monitor the progress of the OGD initiative and to drive improvement. Although OGD benchmarks typically focus on assessing and ranking OGD portals, few have been developed specifically for internal process improvement within the portal. This paper proposes a gap analysis framework to support the Plan–Do–Check–Act (PDCA) cycle to guide OGD portal improvement. The framework adopted the Importance–Performance Analysis (IPA) to identify gaps in an OGD portal. The analysis measured the performance and importance of an OGD portal based on data provision and consumption activities. Several factors related to data provision and consumption activities are examined, including dataset creation, updates, views, searches, high-value datasets, resource formats, and user data requests. Gap analysis assessment results can help to identify the current situations of different areas on the portal and their gaps in achieving the objectives. A case study of the Data.go.th portal was conducted to exemplify and validate the framework’s adoption. The analysis results of the case study revealed existing patterns of relationships between data provision and consumption activities that can guide the improvement of similar OGD portals.

Using freedom of information requests to access novel data sources in health professions education research.

Educators and researchers are reliant upon access to data to drive teaching methods, curricular improvements, and progress in medical education research. However, data are not always accessible, due to resource constraints, institutional policies, and privacy concerns. Researchers have attempted to access novel data sources through surveys, semistructured interviews, and databases; however, these methodologies are limited. To improve access to data, Freedom of Information (FOI) Acts grant researchers the ability to formally request data that any public institute holds. Researchers have been reluctant to use this tool due to negative perceptions, despite its unique benefits. To increase awareness of this underutilized methodology, we summarize the process of FOI Act requests, its strengths and weaknesses, and the ways in which health professions education can leverage FOI requests within research. We provide examples of the use of FOI requests as a research method within adjacent fields and nascent use within the field of health professions research. In doing so, we hope to highlight how FOI requests can be a useful tool in health professions education researchers and its potential to increase access to unique data sources.

Designing a User-Friendly Data Request Management System for a Growing Health Data Network - A Case Study in the AKTIN Registry.

The AKTIN Emergency Department Registry, a German health data network, faces operational challenges due to rapid growth. Manual data request processes have become inefficient, hindering timely research and straining personnel. To address these challenges, we undertook a user-centered analysis utilizing Design Thinking principles to identify pain points and functional requirements in current data request creation and management processes. Future work will prioritize iterative implementation of the created concepts with continuous user engagement and rigorous software validation.

Nationwide Assessment of Solar PV Power Potential Using Geospatial Data and Collaborative Research with Higher Education Institutions: Case of the Philippines

Abstract. Geospatial technologies present a significant opportunity to assess solar photovoltaic (PV) power potential through remote sensing techniques and geospatial analysis. This aligns with the goal of ensuring access to affordable, reliable, and sustainable energy, one of the Sustainable Development Goals (SDG) that the Philippine National Economic and Development Authority (NEDA) aims to achieve. In support of the said SDG, a research project, called Project SINAG, has developed a method for estimating solar PV power resources utilizing high-temporal remotely-sensed data. The primary objectives of this study are twofold: first, to develop a webGIS platform for solar energy resource information access and decision support; second, to conduct information dissemination and capacitybuilding activities for various stakeholders in the solar energy sector. The localized assessment of solar PV power potential incorporates meteorological and geomorphological factors such as high temperature, dust deposition, and slope. Collaboration with academic institutions, government agencies, and the solar energy industry has been instrumental in providing capacity-building through training workshops, internships, and on-the-job training. Moreover, the project has produced information, education, and communication (IEC) materials, launched a webGIS portal, and supported data requests and student research. The webGIS platform serves as the primary information dissemination tool for the project, enabling rapid sharing of research outputs with end-users. To enhance the platform, future developments may include additional functionalities such as community forums, client feedback mechanisms, mobile application development, and the integration of real-time data for map layers and forecasting tools.

LiDB: Database of atomic radiative lifetimes for plasma processes

LiDB is a database of molecular radiative lifetimes (Owens et al., 2023), created to aid in the modelling of radiative effects in low-temperature plasmas. Here, we report the addition of atomic radiative lifetimes to LiDB. Datasets are generated for neutral and singly-charged atomic species based on energy levels, transitions, and transition probabilities extracted from the National Institute of Standards and Technology (NIST) Atomic Spectra Database (ASD). The main data output of LiDB is radiative lifetimes of atomic states defined uniquely by atomic term symbols and electronic configurations. The effects of the total angular momentum quantum number J on the lifetimes are averaged over using an atomic state lumping procedure. LiDB also provides partial lifetimes which yield information on the dominant radiative decay channels from each state. Datasets are available for 35 neutral and 19 singly-charged atoms with new species to be added in the future. LiDB is freely available at www.exomol.com/lidb where users can dynamically view atomic and molecular datasets or use an application programming interface (API) to request data.

Profit-driven distributed trading mechanism for IoT data

Data trading is a crucial means of unlocking the value of Internet of Things (IoT) data. However, IoT data differs from traditional material goods due to its intangible and replicable nature. This difference leads to ambiguous data rights, confusing pricing, and challenges in matching. Additionally, centralized IoT data trading platforms pose risks such as privacy leakage. To address these issues, we propose a profit-driven distributed trading mechanism for IoT data. First, a blockchain-based trading architecture for IoT data, leveraging the transparent and tamper-proof features of blockchain technology, is proposed to establish trust between data owners and data requesters. Second, an IoT data registration method that encompasses both rights confirmation and pricing is designed. The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data. For pricing, we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network. Finally, an IoT data matching method is designed based on the Stackelberg game. This establishes a Stackelberg game model involving multiple data owners and requesters, employing a hierarchical optimization method to determine the optimal purchase strategy. The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated. Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.

SMKA: Secure multi-key aggregation with verifiable search for IoMT

The Internet of Medical Things (IoMT) aggregates numerous smart medical devices and fully employs the collected health data to enhance patients’ experiences. In IoMT, patients generate various encryption keys and receive keyword information sent by data requesters to securely share selected health data, which increases the potential risk of key leakage. Besides, the cloud server may tamper with search results. The existing schemes did not consider that patients may inadvertently disclose the keyword information of data requesters. Additionally, these schemes entail a significant cost for verifying the search results. To deal with these challenges, we innovatively propose a secure multi-key aggregation (SMKA) scheme with verifiable search for IoMT. Firstly, the SMKA scheme is built upon key-aggregate searchable encryption, utilizing an oblivious search request and blockchain technology to achieve secure key aggregation. Secondly, a dual verifiable algorithm is integrated into the scheme to provide lightweight verification for the search results. The proposed scheme can achieve access control, requester privacy, accountability, and dual verification while ensuring secure search. Furthermore, the security analysis and proof have shown the effectiveness of the proposed protocol in achieving the intended security goals. Finally, the performance analysis indicates the significant feasibility and scalability of the proposed scheme.

Incidence, prevalence, and predictors of osteoporotic fracture in adult lung transplant recipients

As life expectancy following lung transplantation (LT) improves, vulnerability to glucocorticoid-induced osteoporotic fractures is increased. Our institution offers LT recipients protocolized anti-resorptive therapy, with zoledronic acid (ZA) used first line. Adults who underwent LT from January 2012 to December 2018 and survived at least 6 months were retrospectively studied. Co-primary outcomes were incidence, prevalence and predictors of osteoporotic fractures and major osteoporotic fractures post-LT. 405 LT recipients (41% female, median age 59 years) had median follow up of 4.9 years (Interquartile range (IQR) 3.4-6.7). Osteoporotic fracture prevalence was 12% (n=49) pre-LT and 15% (n=60) post-LT. Major osteoporotic fracture post-LT occurred in 11% (n=45). Antiresorptive therapy was received by 47% pre- and 89% post-LT. On multivariate analysis, risk factors for osteoporotic fracture were pre-LT osteoporotic fracture (Hazard ratio (HR) 2.32 (95% confidence interval (CI) 1.09-4.96)), female sex (HR 2.08 (95% CI 1.09-3.94)), glucocorticoid use pre-LT (HR 2.08 (95% CI 1.09-3.99)), and time (months) to first ZA infusion post-LT (HR 1.04 (95% CI 1.01-1.06)). Risk factors for major osteoporotic fracture were pre-LT osteoporotic fracture, female sex, age and time to first ZA infusion. LT recipients receiving protocolized anti-resorptive treatment post-LT had a low incidence of osteoporotic fracture. Data availability statementEthics approval for this project does not extend to sharing of data for this study. Specific data requests should be made to the corresponding author.

Youth Climate Questions

AbstractAnthropogenic climate change and environmental crises are pressing challenges of our time, with changes to the climate system observed in every global region. Disastrous impacts on nature, including people, have already been observed in the form of drought, floods and extreme heat events across the planet. This study examines the role of an interdisciplinary approach to exploring different responses to climate-related disasters and potential disasters. The work we report takes place in the context of recent research on dialogue on climate interventions with youth. Climate interventions are large-scale interventions into the Earth’s climate system and include proposals such as solar radiation management, ocean liming, and carbon capture and storage technologies. Research and development of climate interventions as a response to one set of disasters (in the present and in-the-making), created as a result of anthropogenic climate change, are associated with much uncertainty and controversy. We explored these uncertainties and controversies with young people, scientists and policymakers, resulting in a set of illustrated climate conversation cards. In this paper, we analyse the questions produced by young people to find requests for data and knowledge from across disciplines, and for an exploration of ethics, feelings, positions and priorities. We also found problematisations of technofixes and desire for socio-political action. The implications for science and disaster education are discussed.

SERAV Deep-MAD: Deep Learning-Based Security–Reliability–Availability Aware Multiple D2D Environment

The tremendous growth of internet-connected devices can lead to network congestion, making it essential to incorporate new technologies to optimize performance. Thus, Device to Device(D2D) communication has been deemed as an emerging technology that can be used to communicate efficiently. However, establishing a secure and reliable mechanism for Device-to-Device (D2D) communication that ensures security, reliability, and availability poses a significant challenge. To tackle these issues a novel deep learning-based security-reliability-availability aware multiple D2D environment (SERAV Deep-MAD) has been proposed for Secure D-2-D Communication in a Fog environment. The proposed method utilizes a Fully Homomorphic Quantum Diffie Hellman Encryption (FHQDHE) to secure the data while transmitting. The proposed method utilizes the novel secure Quantum Diffie Hellman key exchange (QDHKE) technique for sharing the key to transform the plain data into cipher data, then applies FH operations on the encrypted data before transferring it to the Fog environment. Whenever a client requests data from the Fog, the Fog provider verifies the client's access rights. Moreover, the Attention-based Bi-GRU model is utilized to categorize whether the device is non-attack or attack, if the data is attacked then the proposed model classifies multiple attacks. The NS2 Simulator is used to verify and validate the proposed SERAV Deep-MAD model, and resiliency analysis is done to assess performance. The proposed techniques attain a higher accuracy of 98.18% which is 1.58%, 2.02%, and 2.41% better than MECC, MIMO, andAAKA-D2D respectively.

Research on Remote eSIM Provisioning Management Technology for 5G Terminal

The use of eSIM in mobile terminals such as the Internet of Vehicles has become the general trend, and more and more enterprises have put forward clear requirements for the remote provisioning of eSIM. This paper aims to combine 5G technology to analyze remote eSIM provisioning management technology for terminal, and combine algorithm improvement to improve remote eSIM information management and control effects. Moreover, this paper describes in detail the three-layer communication structure in the communication model, the design of edge gateway nodes, and the query mechanism for cloud center request names and data resources. In addition, this paper uses experiments to evaluate the connectivity of the eSIM three-layer communication model and the characteristics of the NDN network. Through experimental research, it can be seen that the remote eSIM provisioning management model proposed in this paper has certain advantages when applied to the Internet of Vehicles compared with the existing remote provisioning management technologies for the Internet of Vehicles. This article takes the Internet of Vehicles as an example to conduct research. At the same time, its support for mobility can be well applied to the Internet of Vehicles to meet the mobility of terminal vehicles. Therefore, it can provide some reference in the subsequent development of Internet of Vehicles technology.

Deep deterministic policy gradient caching method for privacy protection in Internet of Vehicles

To address the problem of low cache hit ratio in edge nodes for privacy-preserving in the Internet of Vehicles (IoV), a deep deterministic policy gradient caching (DDPGC) method was proposed. First, a taxi certified by a trusted authority acted as a second-level caching edge node to acquire hotspot data and store it in the local cache. It then broadcasted this information to the neighboring service requesting vehicles (SRVs). SRVs cached the broadcasted data locally and search for service requests in the order of priority of local cache, taxi, and cloud server when such requests arise. Secondly, a neural network was deployed in taxis and SRVs to maximize the caching benefit through deep reinforcement learning for decision replacement of their cached data. Finally, when SRVs were located in vehicle sparsity and could not obtain request data from neighboring vehicles, a combination of k-anonymity and random response perturbation mechanisms generated anonymity sets to send requests to cloud servers in an anonymous manner to obtain services while protecting user location privacy. Simulation experimental results show that DDPGC can effectively improve the vehicle cache hit ratio, reduce the frequency of SRV interaction with the cloud server, and effectively protect user privacy security.

A Description of the Dental Health and Treatment of Ukraine Military at U.S. Army Clinics in Germany.

Since the start of the Russo-Ukrainian war, most reports have focused on trauma and combat conditions. Trauma care is essential; however, disease and nonbattle injuries (DNBIs) also burden armies and reduce combat effectiveness. Dental emergencies (DEs) account for a substantial portion of DNBI, but there is limited information on Ukrainian military dental readiness. In September 2023, investigators were informed that Ukrainian military personnel were treated for DEs at 2 dental clinics in Germany. The investigators requested deidentified data from the Corporate Dental System (CDS), which is a repository for dental treatment records utilized by the U.S. Army. The data request captured DE treatment performed for Ukrainian military personnel in Germany from January 1, 2023, to September 20, 2023. The CDS report on DE encounters included the following: Gender, age, procedure codes, and provider narratives. This study was approved by the U.S. Army Institute of Surgical Research Institutional Review Board as research not involving human subjects and provided Log Number H-24-030nh. The CDS report captured 408 Ukrainian military DE encounters. A total of 360 Ukrainian military personnel were treated for DE during the 9-month reporting period in 2023. Oral surgery was the most common DE treatment category and accounted for 55.8% (N = 201) of DE procedures. Restorative dentistry procedures accounted for 18.1% of DE procedures. Of the 27 multiple category treatments, 25 had oral surgery procedures as one of the treatments. Restorative dentistry procedures were performed in 19 of the multiple category cases. The DEs in the Ukrainian military cohort suggest that chronic, untreated dental disease has progressed without routine care for years. The assertion that caries has progressed to a nonrestorable condition is supported by the distribution that 63% of all DE visits included oral surgery treatment. Based on the distribution of dental treatment, US dental officers must be proficient in oral surgery and prosthodontic care.

School-Level Data on COVID-19 Cases, Quarantines, and Student Absenteeism During the COVID-19 Pandemic: Understanding Missingness.

This study aims to understand availability of school-based infectious disease surveillance data (e.g., COVID-19 cases, student absences) based on experiences during the COVID-19 pandemic using a national sample of public K-12 schools (n = 1,602). Based on surveys administered to school administrators throughout the 2021-2022 school year, we found high levels of missingness data for school-level COVID-19 cases, quarantines, and student absenteeism, increasing missingness over time, and variations in missingness by school characteristics (e.g., school size) and protocols (e.g., having a school-based system to report at-home COVID-19 tests). For the same sample of schools, using data requests to health departments, we found similarly high levels of missingness of school-level COVID-19 case data and varying approaches in data collection. Developing nationally standardized case definitions-and systems to surveil or collect and monitor school-based infectious disease outcomes early in a public health emergency-may be helpful in producing actionable data.

The Regional Climate Center Program—Past, Present, and Future: Reflections from 40 Years of Regional Climate Services

Abstract Since its inception in 1983, NOAA’s Regional Climate Center (RCC) Program has been providing timely, customized climate services for decision making across all climate-sensitive sectors. Through this 40-yr period, the RCC Program has not only seen but also has played an active role in, the evolution of climate services from the days of climate data libraries—where books of data were consulted to fulfill simple data requests—to coproduced tools that can calculate sectoral-specific, on-the-fly climate analyses in a matter of seconds. With new technologies emerging, the RCC Program is poised to build on its reputation as a trusted climate service provider by incorporating advanced methods for climate service delivery to continue to meet the needs of the nation. This publication will provide a look back at the evolution of regional climate services over the past 40 years, along with a vision for the future.

Issues in the Spatial Analysis of Police Use of Force Data

This article discusses challenges encountered in the area of spatial analysis of policing practices. Some of the topics discussed include the overlapping of policing jurisdictions and its subsequent effect on the collection and analysis of spatial data, data anonymization techniques that protect individual’s privacy, relationships between use of force outcomes and socioeconomic measures, spatial data sources and collection, data request avenues, the ethics of proprietary spatial software, and the need for open-source spatial analysis software and oversight committees.

From CSV to Arrow: Creating a Unified Data Set for Efficient Cross-Platform Analysis

Handling open data, like the vast repository of New York City (NYC) 311 service requests, often starts with the ubiquitous CSV (comma-separated value) file format. However, CSV files are notoriously inefficient for curation, bogged down by redundancy and potential misinterpretations. Enter Apache Arrow, a game-changing approach that not only slashes storage requirements but also primes data for seamless analysis across popular platforms like R, Python, and Julia. Using the NYC 311 service request data, we demonstrate the conversion of a CSV file to the Arrow IPC (Inter-Process Communication) format. An Arrow file stores the table schema with the data in a binary format that can be memorymapped for reading, enabling instantaneous access to potentially large datasets. The Arrow IPC data serves as a universal starting point for analysis across various environments. In our example, this conversion is done in Julia, which has powerful packages for reading and writing CSV or Arrow files and calling functions in other popular environments such as R and Python.

Magyarországi helyzetkép az alternatív gondoskodás lehetőségeiről, különös tekintettel a gyermekotthoni, nevelőszülői elhelyezésre

In this study, we compare institutional and foster care. The method chosen is a secondary analysis of statistical data. In the study, we seek to answer the question of how the number of places, the number of children, the age composition of children, the duration of placement and the type of placement vary in Hungary and in the Northern Great Plain region. We analyse data for the years 2014, 2021 and 2023 in terms of deinstitutionalisation and present the current situation of child protection care alongside the trends in institutional restructuring in recent years. A further aim of our comparative analysis is to review the number of cases of runaways from care in recent years, assuming that they occur less frequently in foster care. For the analysis, we have primarily used data for the years 2014, 2021 and 2023. In some places, the year 2020 was analysed due to data availability. We have used publicly available data series from the KSH, as well as data recorded on the basis of individual data requests according to the KSH OSAP Form 1208, which contains data provided by child protection centres based in the respective counties.

Secure Framework Optimizes QAES Technique Used for Computing in the Cloud

Several consumers are concerned about the security of their data kept in the cloud, and many see local servers as a safer choice due to perceived control. However, cloud service companies frequently promise greater security procedures and employ dedicated security specialists, making data stored in the cloud potentially more secure. When the data holder expires, new files are saved in the cloud and encrypted with the QAES technology. The data owner then receives the encrypted data request. The sender and user both analyze data before sending encoded documents to the cloud. The key will be used to remove encryption from subsequent data. If users are unable to access the desired data, they should submit a fresh request. The most recent innovation focuses on combining an enhanced form of Quantum Key Distribution (QKD) with AES. This integration resulted in Quantum-AES (QAES), a novel quantum symmetric encryption scheme. QAES is based on the development of a quantum encryption key using dynamic quantum S-Boxes (DQS-Boxes), as opposed to the frequently utilized static S-Boxes. This strategy enhances security. Comparably, time is required to build files faster than they do now. This approach prevents brute force attacks since it uses the QAES algorithm, which provides additional security.

Conceptualizing community data governance for race-related, population data: a scoping review and key informant interviews

ObjectiveThere is growing recognition of the importance of community data governance to build accountability of research institutions to communities. Our organization, a steward of health and administrative population-level data, has previously implemented community governance structures for Indigenous data. This scoping initiative explores development and implementation of an additional community governance structure for race-related data. ApproachWe conducted a scoping review of peer-reviewed and grey literature to identify existing practices of community data governance. We also conducted key informant interviews with thirteen racialized community stakeholders, who addressed open-ended questions on potential co-design processes as well as governance mandates, scopes, barriers, and facilitators. ResultsThe scoping review identified eight community data governance examples. Two of these pertained to race-related data, while the remaining six pertained to other data that identified “community” geographically, by disease condition, life stage, and/or economic circumstance. Governance structures were diverse, ranging from one-time crowd-design of a data-sharing agreement to quarterly meetings of a governing board to review project-level data requests. Key informant interviews provided four themes to guide implementation in the context of our organization: exploring organizational readiness, considering who should be involved, defining the scope and mandate, and drafting an approach and process. ConclusionWe are committed to implementing a community governance structure for race-related, population-level data. However, there are limited examples of similar structures in the existing literature. ImplicationsThe identified examples and the advice of community stakeholders will guide co-design of a preliminary structure, scope, and mandate for community governance of race-related, population-level data.