Use Of Public Data Research Articles

A Study on the Current Status of After-School Programs in the Department of Beauty at Vocational High Schools in Daejeon

The purpose of this study was to provide basic data for the establishment of after-school programs in the beauty department by comparing and analyzing the operating conditions of after-school programs in the department of beauty at Vocational high schools. All five cosmetic departments of Daejeon Vocational high schools were selected and the status of each school's after-school program operation was sampled for a total of 7 semesters from the first semester of 2019 to the first semester of 2022. For data collection, public information (data for public use) of the school reminder system and the school website were referred to, and deficiencies were supplemented through interviews with teachers. The results are as follow. First, it can be seen that the name of the after-school program in the department of beauty is mainly determined similarly to the certificate and course name by using the national qualification for cosmetologist and the name of the cosmetology course in 4 schools. Second, as for the operating hours of the program, 40 sessions (hours) were the most common at 28, and the number of participants in the program ranged from 2 to 27, showing differences by school and program. It implies the need to unify and operate the classes in order to increase the equal educational opportunity and academic achievement of learners. Second, hair program accounted for the highest proportion of program opening because the hair field had a high rate of employment after high school graduation but more diverse education is needed to ensure well-rounded employment opportunity. Third, after-school program instructors were mostly taught by beauty department teachers. Since this is often conducted in connection with school classes, it can be inferred that the subject teacher alignment with after school programs were chosen over an external instructor to increase efficiency. Fourth, after-school tuition support varied by school, but tuition support increased in 2022 compared to 2019 showing each school’s growing efforts to relieve the financial pressure of after school programs. Therefore, it is necessary to expand the various majors of the after-school program, systematize the time period and the number of participants. In addition, the afterschool tuition support should be continued to reduce private education expenses and prevent class loss. Such efforts are expected to have a positive effect on the equal educational opportunity and academic achievement of learners. In the future, it is necessary to expand the scope of research to the whole country and continue research on the operation status of other majors in specialized high schools and after-school programs in non-specialized high schools.

How to Use Health Insurance Data Effectively for Healthcare Research

Objectives: The purpose of this study is to present an understanding of the structure and characteristics of health insurance data, which should be basically known in order to use this data more effectively to derive more accurate research results across health and medical care, and an effective use method based on it. Methods: The National Health Insurance Sharing Service and Healthcare Big data open system was reviewed to suggest the structure and characteristics of health insurance data and an efficient use method, and Google Scholar and the Korean Studies Information Service System were used to review the overall application method according to the characteristics of healthcare big data and related literature. Results: The types and characteristics of each research data for public use provided by National Health Insurance Service (NHIS), Health Insurance Review & Assessment Service (HIRA), and the differences between the data of the two institutions were compared and presented. In addition, for the efficient use of health insurance data in healthcare research, understanding the structure and characteristics of health insurance data based on the health insurance system, clear operational definition of analysis variables, adjust bias between comparison groups in case-control study, and the interpretation of analysis results suggest that both statistical and clinical significance should be considered. Conclusions: It is very important to understand the structure and characteristics of health insurance data based health insurance system. And if we consider in more depth ways to efficiently utilize the health insurance data presented in this study, it will be of great help to future researchers and to improve the quality of the research.

Comparative Study of Differentially Private Data Synthesis Methods

When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional data synthesis techniques often rely on strong assumptions of a data intruder’s behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for a strong and robust privacy guarantee in data release without having to model intruders’ behaviors. Efforts have been made aiming to incorporate the DP concept in the data synthesis process. In this paper, we examine current DIfferentially Private Data Synthesis (DIPS) techniques for releasing individual-level surrogate data for the original data, compare the techniques conceptually and evaluate the statistical utility and inferential properties of the synthetic data via each DIPS technique through extensive simulation studies. Our work sheds light on the practical feasibility and utility of the various DIPS approaches, and suggests future research directions for DIPS.

Using spatiotemporal models to generate synthetic data for public use

Summary. When agencies release public-use data, they must be cognizant of the potential risk of disclosure associated with making their data publicly available. This issue is particularly pertinent in disease mapping, where small counts pose both inferential challenges and potential disclosure risks. While the small area estimation, disease mapping, and statistical disclosure limitation literatures are individually robust, there have been few intersections between them. Here, we formally propose the use of spatiotemporal data analysis methods to generate synthetic data for public use. Specifically, we analyze ten years of county-level heart disease death counts for multiple age-groups using a Bayesian model that accounts for dependence spatially, temporally, and between age-groups; generating synthetic data from the resulting posterior predictive distribution will preserve these dependencies. After demonstrating the synthetic data’s privacy-preserving features, we illustrate their utility by comparing estimates of urban/rural disparities from the synthetic data to those from data with small counts suppressed.

Record Re-Identification of Swapped Numerical Microdata

Government agencies, researchers, healthcare providers, and other organizations release data for public use. To protect the privacy of the data subjects, these organizations mask the data prior to ...

Data privacy preserving scheme using generalised linear models

When releasing data for public use, statistical agencies seek to reduce the risk of disclosure, while preserving the utility of the release data. Commonly used approaches (such as adding random noises, top coding variables and swapping data values) will distort the relationships in the original data. To preserve the utility and reduce the risk of disclosure for the released data, we consider the synthetic data approach in this paper where we release multiply imputed partially synthetic data sets comprising original data values, and with values at high disclosure risk being replaced by synthetic values. To generate such synthetic data, we introduce a new variant of factored regression model proposed by Lee and Mitra in 2016. In addition, we take a step forward to propose a new algorithm in identifying the original data that need to be replaced with synthetic data. More importantly, the algorithm that can identify the original data with high disclosure risk can be applied on other existing statistical disclosure control schemes. By using our proposed scheme, data privacy can be preserved since it is difficult to identify the individual under the scenario that the released synthetic data are not entirely similar with the original data. Besides, valid inference about the data can be made using simple combining rules, which take the uncertainty due to the presence of synthetic values. To evaluate the performance of our proposed scheme in terms of the risk of disclosure and the utility of the released synthetic data, we conduct an experiment on a data set taken from 1987 National Indonesia Contraceptive Prevalence. The results justify the applicability of our proposed data privacy preserving scheme in reducing the risk of disclosure while preserving the utility of the released data.

Comparing holistic and feature-based visual methods for estimating the relative pose of mobile robots

Feature-based and holistic methods present two fundamentally different approaches to relative-pose estimation from pairs of camera images. Until now, there has been a lack of direct comparisons between these methods in the literature. This makes it difficult to evaluate their relative merits for their many applications in mobile robotics. In this work, we compare a selection of such methods in the context of an autonomous domestic cleaning robot. We find that the holistic Min-Warping method gives good and fast results. Some of the feature-based methods can provide excellent and robust results, but at much slower speeds. Other such methods also achieve high speeds, but at reduced robustness to illumination changes. We also provide novel image databases and supporting data for public use.

Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data

ABSTRACTPopulation density is usually calculated from the census data, but it is dynamic over time and updating population data is often challenging because it is time-consuming and costly. Another problem is that population data for public use are often too coarse, such as at the county scale in China. Previous research on population estimation mainly focused on megacities due to their importance in socio-economic conditions, but has not paid much attention to the township or village scale because of the sparse population density and less importance in economic conditions. In reality, population density in townships and villages plays an important role in land-use/cover change and environmental conditions. It is an urgent task to timely update population density at the township and cell-size scales. Therefore, this article aims to develop an approach to estimate population density at the township scale and at a cell size of 1 km by 1 km through downscaling the population density from county to township and then to cell size. We estimated population density using Landsat Thematic Mapper (TM) and census data in Zhejiang Province, China. Landsat TM images in 2010 were used to map impervious surface area (ISA) distribution using a hybrid approach, in which a decision tree classifier was used to extract ISA data and cluster analysis was used to further modify the ISA results. A population density estimation model was developed at the county scale, and this model was then transferred to the township scale. The population density was finally redistributed to cell-size scale based on the assumption that population only occupied the sites having ISA. This research indicates that most townships have residuals within ±50 persons/km2 with a root mean squared error (RMSE) of 71.56 persons/km2, and a relative RMSE of 27.6%. The spatial patterns of population density distribution at the 1 km2 cell size are much improved compared to the township and county scales. This research indicates the importance of using the ISA for population density estimation, where ISA can be accurately extracted from remotely sensed data.

Modeling Users' Behavior for Testing the Performance of a Web Map Tile Service

AbstractWeb Map Tile Services (WMTS) are widely used in many fields to quickly and efficiently visualize geospatial data for public use. To ensure that a WMTS can successfully fulfill users' expectations and requirements, the performance of a service must be measured to track latencies and bottlenecks that may downgrade the overall quality of service (QoS). Traditional synthetic workloads used to evaluate WMTS applications are usually generated by repeated static URLs, through randomized requests, or by an access log replay. These three methods do not take request characteristics and users' behaviors into consideration, while access logs are not available for systems still under development. Thus, the evaluation outcomes obtained by these methods cannot represent the real performance of online WMTS applications.In this article a new workload model named HELP (Hotspot/think‐timE/Length/Path) is proposed to measure the performance of a prototype WMTS. This model describes how users browse a WMTS map and statistically characterizes complete map navigation behaviors. Then, the HELP model is implemented in HP LoadRunner and used to generate a synthetic workload to evaluate the target WMTS. Experimental results illustrate that the performance representation of the HELP workload is more accurate than that of the other two models, and how a bottleneck in the target system was identified. Additional statistical analysis of request logs and “hotspots” visualizations further validate the proposed HELP workload.

The Open Data Era in Health and Social Care

The United Kingdom has been a leader in the open data movement – a new movement by governments around the world to open up the vast repositories of data they hold across agencies and departments, and to collect new kinds of data for public use. Open data is publicly available data that can be universally and readily accessed, used, and redistributed free of charge. It is changing the way governments, nonprofits, and the private sector use data to understand public issues and solve problems in areas as diverse as financial regulation, energy, education, and more. Open data holds particular potential in the health sector. By releasing health data to patients and, when appropriate, on an anonymized basis to researchers and the public, governments and healthcare organizations are betting on the power of greater openness of data to improve the quality of care, lower healthcare costs, and facilitate patient choice. The NHS has made and continues to make significant investments in opening data. Over the past several years, it has launched a series of initiatives that have already had a positive impact on patient education, healthcare choice, healthcare costs, and patient outcomes. Now the NHS is planning a broader, more ambitious programme that has the potential to serve as a worldwide model for the opening of data in healthcare. The purpose of this report is to help design this programme, establishing priorities and ways of measuring impact to guide this and future efforts at data transparency

The Digital Atlas of Ordovician Life: digitizing and mobilizing data for paleontologists and the public; pp. 312–316

A new online resource, Digital Atlas of Ordovician Life: Exploring the Fauna of the Cincinnati Region (www.OrdovicianAtlas.org), provides a tool for exploring the diversity, systematics, biogeography and stratigraphy of Late Ordovician fossils of the Cincinnati, Ohio region. This atlas has been developed using georeferenced specimen-based data that have been newly digitized as part of a collaborative project to mobilize species occurrence data for public use. The goals of this project are to digitize specimens of Late Ordovician (Katian) fossils from the type Cincinnatian Series that crop out in the Cincinnati, Ohio, USA region, develop a comprehensive online atlas of the fossils and develop outreach materials for K-16 (grade school through college) education and avocational paleontologists.

정보공개 환경에서 개인정보 보호와 노출 위험의 측정에 대한 통계적 방법

최근 빅데이터의 등장과 정보 공개에 대한 급격한 수요 증가에 따라 자료를 일반에게 공개할 때 개인 정보를 보호해야 하는 필요성이 어느 때보다 절실하다. 본 논문에서는 마이크로 자료와 통계분석 서버를 중심으로 현재까지 제시된 개인정보 노출제한를 위한 통계적 방법, 정보 노출의 개념, 노출 위험을 측정하는 기준들을 개괄적으로 소개한다. Recently, along with emergence of big data, there are incresing demands for releasing information and micro data for public use so that protecting privacy and measuring risk of disclosure for released database become important issues in goverment and business sector as well as academic community. This paper reviews statistical methods for protecting privacy and measuring risk of disclosure when micro data or data analysis sever is released for public use.

A reflection on the structure and process of the Web of Data

The Web community has introduced a set of standards and technologies for representing, querying, and manipulating a globally distributed data structure known as the Web of Data. The proponents of the Web of Data envision much of the world's data being interrelated and openly accessible to the general public. This vision is analogous in many ways to the Web of Documents of common knowledge, but instead of making documents and media openly accessible, the focus is on making data openly accessible. In providing data for public use, there has been a stimulated interest in a movement dubbed Open Data. Open Data is analogous in many ways to the Open Source movement. However, instead of focusing on software, Open Data is focused on the legal and licensing issues around publicly exposed data. Together, various technological and legal tools are laying the groundwork for the future of global-scale data management on the Web. As of today, in its early form, the Web of Data hosts a variety of data sets that include encyclopedic facts, drug and protein data, metadata on music, books and scholarly articles, social network representations, geospatial information, and many other types of information. The size and diversity of the Web of Data is a demonstration of the flexibility of the underlying standards and the overall feasibility of the project as a whole. The purpose of this article is to provide a review of the technological underpinnings of the Web of Data as well as some of the hurdles that need to be overcome if the Web of Data is to emerge as the defacto medium for data representation, distribution, and ultimately, processing.

Commentary—How to Improve Federal Health Data for Coverage, Access, and State‐Specific Needs

Commentary—How to Improve Federal Health Data for Coverage, Access, and State‐Specific Needs

Modeling Reporting Delays and Reporting Corrections in Cancer Registry Data

The Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute is an authoritative source of cancer incidence statistics in the United States. The SEER program is a consortium of population-based cancer registries from different areas of the country. Each registry is charged with collecting data on all cancers that occur within its geographic area. As with any disease registry, there is a delay between the time that the disease (cancer) is first diagnosed and the time that it is reported to the registry. The SEER program has allowed for reporting delays of up to 19-months before releasing data for public use. Nevertheless, additional cases are discovered after the 19-month delay, and these cases are added in subsequent releases of the data. Further, any errors discovered are corrected in subsequent releases. Such reporting delays and corrections typically lead to underestimation of the cancer incidence rates in recent diagnosis years, making it difficult to monitor trends. In this article we study models that account for reporting delays and corrections in predicting eventual cancer counts for a diagnosis year from the preliminary counts. Previous models of this type have been studied, especially as applied to AIDS registries. We offer several additions to existing models. First, we explicitly model the reporting corrections. Second, we model the delay distribution with very general models, combining aspects of previous nonparametric-like models (i.e., models that have a separate parameter for each delay time) with more parametric models. Third, we allow random reporting-year effects in the model. Practical issues of model selection and how the data are classified are also discussed, particularly how the definition of a reporting correction may change depending on how subpopulations are defined. An example with SEER melanoma data is studied in detail.

Meteorological data acquisition in Ecuador, South America: problems and solutions

The acquisition of meterological data for public use is often taken for granted. A recent consultation trip to Ecuador, South America demonstrated that this is not always true. Meteorological data acquistion in Ecuador is hindered by: 1) lack of meteorological expertise, 2) lack of media presentation, 3) lack of cooperation between existing weather stations, 4) an unwillingness of weather facilities to share data with the public and 5) a lack of dedication to data collection responsibilities. These hinderances are further compounded by lack of power and communication lines, power failures, civil unrest, and a slow, unreliable postal service. This report discusses these problems and how two communities are solving them. Short-range solutions lie in establishing small weather stations where they are needed the most. More long-range solutions lie with the introduction of weather data to radio broadcasts, and the establishment of the first provincial climate data center in Ecuador.

Water Data Committee meets

The Interior Department's Advisory Committee on Water Data for Public Use met in Houston, Texas, from November 29 to December 1, 1977, to consider methods for obtaining information to solve current and future water problems. The committee represents the interests of the nonfederal sector, advising the department and the U.S. Geological Survey on plans and policies relating to water data programs and needs.The program included a review of the progress made over the past year in implementing Office of Management and Budget Circular A‐67, which provides guidelines for coordinating federal water data acquisition activities. Reports by working groups formed at the meeting recommended that the USGS move as rapidly as possible toward use of SI metric units in its reports, starting with data reports for international boundary stations; that a national groundwater quality monitoring program be immediately implemented and a standing nonfederal working group on groundwater quality assessment be organized; and that the USGS expand its water use data system, especially in developing guidelines for quality control of collected data, and inform the public about the system by cosponsoring with some appropriate professional organization(s) a symposium on the National Water Use Data Program.

SUMMARY OF 10TH MEETING ADVISORY COMMITTEE ON WATER DATA FOR PUBLIC USE

JAWRA Journal of the American Water Resources AssociationVolume 12, Issue 2 p. 428-429 SUMMARY OF 10TH MEETING ADVISORY COMMITTEE ON WATER DATA FOR PUBLIC USE First published: April 1976 https://doi.org/10.1111/j.1752-1688.1976.tb02694.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume12, Issue2April 1976Pages 428-429 RelatedInformation