The BitMap dataset: an open dataset on performance assessment of diffuse optics instruments

Open data is becoming increasingly popular in a wide range of service domains; however, most open datasets in Taiwan remain separate. The lack of linked open data (LOD) makes it difficult to locate and combine open datasets for the creation of innovative applications. In this study, we sought to facilitate the spread of open data in Taiwan using a novel approach referred to as define–produce–invoke (DPI). The proposed scheme employs a newly defined data query language, called LODQL (LOD query language), to allow the definition of rules for the generation of LOD by data experts. We also developed an LOD engine, which is able to produce linked open datasets and allow application developers to access LOD by invoking RESTful services. This scheme also allows data visualizations indicating the relevance of open datasets and the associations among open data items. Experiments demonstrate the feasibility and effectiveness of the proposed DPI approach.

Government institutions collect and produce an extraordinary number of datasets to conduct and execute their programs and agendas. Various types of datasets collected by the governments can increase transparency and accountability, improve citizen engagement, and create value-added services for the public. Through the Open Government Data (OGD) initiatives, Non-Government Organisations (NGOs), private agencies, business enablers, data analysts, researchers, civil societies, and other open data stakeholders can take advantage of disclosing the government datasets. Despite its significance, the decision-making process to disclose government datasets is given limited attention and encounters several challenges. Although numerous datasets have been published to the public, many datasets remain undisclosed. Government institutions face several challenges in deciding to open datasets. First, the governments have not systematically analysed datasets to identify the benefits and disadvantages of opening datasets. Decision-makers, policy-makers, civil servants, and administrative officers do not know how to balance the advantages and disadvantages of opening datasets. Second, various stakeholders’ backgrounds may have different objectives and interests to analyse and disclose datasets. Third, the easy understanding of possible disadvantages of opening datasets results in moving away from the potential benefits due to the risk-avoiding culture in government. Therefore, these results in keeping datasets undisclosed. Furthermore, the stakeholders’ involvement in the decision-making process to open data, such as politicians, executive boards, decision-makers, civil servants, data analysts, and societies, all play essential roles and have different objectives for opening and using the datasets. For example, some decision-makers might have the authority to publish or keep the dataset closed. Some public servants might be risk-averse, whereas others might open datasets without considering possible negative consequences. As a result, the decision-making process becomes fuzzy, and the objectives of disclosing data are not reached. The different roles and interests of the heterogonous actors in the internal government organisation might create uncertainty and delay the decision-making process. Although there are guidelines, there are no decision-making tools to help governments decide to open their datasets. On the governments’ side, the potential disadvantages might easily dominate over the advantages. It is much easier for the decision-makers to keep a dataset closed than take the disadvantages of releasing a dataset. The lack of insights and expertise in estimating the potential advantages and disadvantages of opening data can also lead to uncertainty, which might result in avoiding the disclosure of datasets. Therefore, this research aims to develop Decision-making Support for Opening Government Data (DSOD). This DSOD accommodates a systematic approach to decide to open datasets. To achieve the objective of this research, we followed the Design Science Research (DSR) approach. The DSR approach results in developing a prototype of the DSOD as a design artefact and demonstrate it to the stakeholders.

Biomedical research usually requires combining large volumes of data from multiple heterogeneous sources. Such heterogeneity makes difficult not only the generation of research-oriented dataset but also its exploitation. In recent years, the Open Data paradigm has proposed new ways for making data available in ways that sharing and integration are facilitated. Open Data approaches may pursue the generation of content readable only by humans and by both humans and machines, which are the ones of interest in our work. The Semantic Web provides a natural technological space for data integration and exploitation and offers a range of technologies for generating not only Open Datasets but also Linked Datasets, that is, open datasets linked to other open datasets. According to the Berners-Lee's classification, each open dataset can be given a rating between one and five stars attending to can be given to each dataset. In the last years, we have developed and applied our SWIT tool, which automates the generation of semantic datasets from heterogeneous data sources. SWIT produces four stars datasets, given that fifth one can be obtained by being the dataset linked from external ones. In this paper, we describe how we have applied the tool in two projects related to health care records and orthology data, as well as the major lessons learned from such efforts.

Open data sets that contain personal information are susceptible to adversarial attacks even when anonymized. By performing low-cost joins on multiple datasets with shared attributes, malicious users of open data portals might get access to information that violates individuals' privacy. However, open data sets are primarily published using a release-and-forget model, whereby data owners and custodians have little to no cognizance of these privacy risks. We address this critical gap by developing a visual analytic solution that enables data defenders to gain awareness about the disclosure risks in local, joinable data neighborhoods. The solution is derived through a design study with data privacy researchers, where we initially play the role of a red team and engage in an ethical data hacking exercise based on privacy attack scenarios. We use this problem and domain characterization to develop a set of visual analytic interventions as a defense mechanism and realize them in PRIVEE, a visual risk inspection workflow that acts as a proactive monitor for data defenders. PRIVEE uses a combination of risk scores and associated interactive visualizations to let data defenders explore vulnerable joins and interpret risks at multiple levels of data granularity. We demonstrate how PRIVEE can help emulate the attack strategies and diagnose disclosure risks through two case studies with data privacy experts.

While open data concepts become more important in our society, education about its benefits and technical issues is still behind the practice. Students of STEM disciplines should be introduced to open data during their education. The Open Computing course, completely redesigned in the new Computing curriculum, introduces open data concepts, providing both the basics and advanced topics, from technical to social and legal viewpoints. Among the several educational activities, one was particularly useful for understanding the needs and implications of using open data: a synchronous group activity where students had to choose a societal issue, find and analyze two open datasets that would help gaining insight into this issue, assess interdisciplinarity approaches and stakeholders, and finally propose the added value emerging from the solution. In a short amount of time needed, this activity - which tackled multiple aspects of the problem - brought a clearer insight into the topic, building upon the conventional lectures. Students highly graded such an approach to their education, where they had to construct their knowledge by the group experience. A similar group activity appeared to be useful in the context of open data PhD training and might also be used in other disciplines and domains.

PurposeThis paper aims to examine the use and acceptance of open government data (OGD) of different stakeholder groups in India.Design/methodology/approachFollowing an empirical investigation among 244 respondents in India, the paper deploys path analysis via LISREL. The conceptual construction of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework is used to assess the behavioral intention to use and accept OGD. Multiple regression analysis informs the relationship of demographic variables with the behavioral intention to use and accept OGD.FindingsPath analysis showed that there is an increased use and acceptance of OGD among the respondents. Multiple regression analysis shows that men are more likely to use open data sets than women. Individual differences are found among the respondents with regard to OGD usage. Thus, while men may tap open data sets in line with their purposes and professional backgrounds, women are likely to tap the data sets even for non-professional purposes. Furthermore, the respondents who are younger in age are more behaviorally inclined to accepting and using OGD than their older counterparts.Practical implicationsThe Indian government needs to popularize and familiarize OGD initiatives among the people to a greater extent. The utilitarian value of OGD may be provided when data sets are more user-friendly, frequently updated and accurate. This would facilitate in better provision of public services besides appreciating the public sentiment. Further, with increased interaction between citizens and the government, public accountability and transparency may be better realized.Social implicationsThe study shows that different groups of people are engaging in tapping information through government websites and related portals. Societal influence was found to be an important factor which predicts the acceptance and usage of OGD. An infrastructure can help to enable the use of OGD.Originality/valueHitherto, studies have focused on OGD initiatives in the developed countries, but extant literature is scarce on developing countries. Therefore, this study seeks to fill the literature gap by probing OGD use and acceptance among different stakeholder groups in India.

Near-infrared (NIR) frequency-domain Diffuse Optical Spectroscopy (DOS) is an emerging technology with a growing number of potential clinical applications. In an effort to reduce DOS system complexity and improve portability, we recently demonstrated a direct digital sampling method that utilizes digital signal generation and detection as a replacement for more traditional analog methods. In our technique, a fast analog-to-digital converter (ADC) samples the detected time-domain radio frequency (RF) waveforms at each modulation frequency in a broad-bandwidth sweep (50- 300MHz). While we have shown this method provides comparable results to other DOS technologies, the process is data intensive as digital samples must be stored and processed for each modulation frequency and wavelength. We explore here the effect of reducing the modulation frequency bandwidth on the accuracy and precision of extracted optical properties. To accomplish this, the performance of the digital DOS (dDOS) system was compared to a gold standard network analyzer based DOS system. With a starting frequency of 50MHz, the input signal of the dDOS system was swept to 100, 150, 250, or 300MHz in 4MHz increments and results were compared to full 50-300MHz networkanalyzer DOS measurements. The average errors in extracted μ a and μ s ' with dDOS were lowest for the full 50-300MHz sweep (less than 3%) and were within 3.8% for frequency bandwidths as narrow as 50-150MHz. The errors increased to as much as 9.0% when a bandwidth of 50-100MHz was tested. These results demonstrate the possibility for reduced data collection with dDOS without critical compensation of optical property extraction.

BackgroundGovernments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient’s length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper.MethodsWe investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns.ResultsThe study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns.ConclusionOur study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.

IntroductionCurrently, there are no reliable non-invasive methods of monitoring the severity of in vivo cyanide (CN) toxicity and the resulting physiological changes. A non-invasive technique to monitor CN toxicity would...

Cerebral hemodynamic changes during vaso-occlusive episodes in pediatric sickle cell disease

Предметом вивчення у статті є процес тестування методів побудови рекомендаційних систем на основі відкритих наборів даних у мережі Інтернет. Метою є дослідження відкритих наборів даних веб-ресурсів у контексті застосування їх для тестування різних методів побудови рекомендаційних систем. Завдання: дослідити сучасні вебплатформи з відкритими наборами даних та можливість застосування їх даних для тестування якості роботи різних рекомендаційних систем. Отримані такі результати: Розглянуто найбільш популярні веб-платформи з відкритими наборами різнотипних мережевих даних. Здійснено порівняльний аналіз цих платформ з точки зору наявності вільного доступу до завантаження даних, їх функціональності та територіальної приналежності, формату даних та зручності для подальшого використання для машинного навчання, а також можливості застосування для тестування рекомендаційних систем. Також проведено оцінку актуальності даних, що зберігаються у репозиторіях з вільним доступом та наявності їх оновлення з часом. Висновки. Досліджено веб-платформи, що містять відкриті набори даних, які можна використати для тестування рекомендаційних систем. Основними перевагами більшості платформ є підтримка сучасних форматів даних та умовно вільний або вільний доступ. Серед недоліків розглянутих платформ слід зазначити недостатню структурованість деяких наборів даних, зокрема текстових, що значно обмежує їх застосування для тестування методів контентної фільтрації. Окрім того, одним з факторів, що обмежує використання відкритих наборів даних є їхня актуальність, тому що деякі набори, що зберігаються на платформах, є застарілими та не оновлюються. Усі розглянуті набори даних можуть бути застосовані для дослідницьких цілей та тестування роботи рекомендаційних систем.

New areas of research and discovery benefit from a space in which a community can discuss key questions, interact and be allowed to grow. For the open education data community these questions are still crude and fundamental. For example: what exactly is open education data, why is open data relevant to education and how can we use open datasets to meet educational needs? The Open Education Working Group inaugurated by Open Knowledge, a community-based not-for-profit organisation that promotes open knowledge in the digital age, is beginning to explore the relationship between data and education by exposing projects and bringing together people working on related activities. This chapter will consider initial discussions and explorations related to open data in education that have taken place through recent online activity and workshops. It will also look at the creation of a working group established with the intention of taking these discussions further.

Intracranial hemorrhage (ICH) is considered an emergency that requires rapid medical or surgical management. Previous studies have used artificial intelligence to attempt to expedite the diagnosis of this pathology on neuroimaging. However, these studies have used local, institution-specific data for training of networks that limit deployment of across broader hospital networks or regions because of data biases. To demonstrate the creation of a neural network based on an openly available imaging data tested on data from our institution demonstrating a high-efficacy, institution-agnostic network. A data set was created from publicly available noncontrast computed tomography images of known ICH. These data were used to train a neural network using distinct windowing and augmentation. This network was then validated in 2 phases using cohort-based (phase 1) and longitudinal (phase 2) approaches. Our convolutional neural network was trained on 752 807 openly available slices, which included 112 762 slices containing intracranial hemorrhage. In phase 1, the final network performance for intracranial hemorrhage showed a receiver operating characteristic curve (AUC) of 0.99. At the inflection point, our model showed a sensitivity of 98% at a threshold specificity of 99%. In phase 2, we obtained an AUC of 0.98 after analysis of 726 scans with a negative predictive value of 99.70% (n = 726). We demonstrate an effective neural network trained on completely open data for screening ICH at an unrelated institution. This study demonstrates a proof of concept for screening networks for multiple sites while maintaining high efficacy.

Direct-To-Consumer (DTC) genetic testing is still a rather recent but growing phenomenon, with some companies now having as many as 850k paying customers (1). DTC genetic tests are still largely based on the analysis of Single Nucleotide Polymorphisms (SNPs) with micro arrays. The analysis of such SNP data sets in general, using Genome-Wide Association Studies (GWAS), has lead to the discovery of many genotype-phenotype associations for a wide array of phenotypic traits and disesases (2,3). The growing number of customers also enabled providers of DTC genetic testing to perform their own GWAS (4). By and large the data generated in the field of DTC genetic testing is not openly available to third parties, like academic researchers, citizen scientists, and hobbyist genealogists. This is partially due to privacy and ethical concerns (5,6), but also due to financial interests of the DTC providers (7,8). With openSNP we created an easily accessible platform to enable customers of DTC to dedicate their own DTC genetic testing data along with their phenotypes into the public domain using Creative Commons Zero. Additionally the platform offers annotations for those SNPs generated by mining different public and open data sets, such as the Public Library of Science, the SNPedia (9), Mendeley and more. Since openSNP started at the end of 2011 people have used it to release over 1700 genotyping files into the public domain and included information for over 289 phenotypes, thus creating a free and growing resource for scientists and citizen scientists alike. With this the project actively contributes to the discussion on open human genetic data, such as bioethical implications and privacy research (10,11), genealogy, teaching, pharmacogenomics (12) and even art (13). The video of this presentation is available on the BOSC YouTube channel: view video . REFERENCES (1 ) http://mediacenter.23andme.com/blog/2015/02/19/fdabloomupdate/ (2) Johnson A, O’Donnell C. BMC Med. Genet. (2009); 10: 6. (3) Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, et al. Proc Nat Acad Sci USA. (2009); 106: 9362-9367. (4) Do CB, Tung JY, Dorfman E, Kiefer AK, et al. PLoS Genet. (2011); 7(6): e1002141. (5) Caulfield T, McGuire AL. Annu. Rev. Med. (2011); 63: 1.1-1.11. (6) Hogarth S, Javitt G, Melzer D. Annu. Rev. Genomics Hum. Genet. (2008); 9: 161–82. (7) http://mediacenter.23andme.com/blog/2015/01/06/23andme-genentech-pd/ (8) http://mediacenter.23andme.com/blog/2015/01/12/23andme-pfizer-research-platform/ (9) Cariaso M and Lennon G, Nucl. Acids Res. (2012) 40 (D1):D1308-D1312. (10) Angrist M, PLoS ONE (2014) 9(3): e92060. (11) http://seclab.soic.indiana.edu/GenomePrivacy/papers/Genome%20Privacy-paper4.pdf (12) Samwald et al. BMC Medical Informatics and Decision Making (2015) 15:12 (13) https://soundcloud.com/thesoundofpeople/the-sound-of-bastian-greshake-6-channels-remixed

Not only men suffer from sexual dysfunction, but the number of women who have sexual dysfunction rises. Therefore, it is necessary to develop an objective diagnostic technique to examine the sexual dysfunction of female patients, who are afflicted with the disorders. For this purpose, we developed a diffuse optical spectroscopy (DOS) probe to measure the change of oxy-, deoxy-, and total hemoglobin concentration along with blood flow from vaginal wall of female rats. A cylindrical stainless steel DOS probe with a diameter of 3 mm was designed for the vaginal wall of rats which consisted of two lasers (785 and 850nm) and two spectrometers with a separation of 2 mm. A thermistor was placed on the top of the probe to measure the temperature change from vaginal wall during experiments. A modified Beer-Lambert's law is utilized to acquire the changes of oxy-, deoxy-, and total hemoglobin, and blood flow information is obtained by diffuse speckle contrast analysis technique. For the experiments, Sprague Dawley (~400 g) female rats were divided into two groups (control and vaginal dryness model). Vaginal oxygenation, blood flow and temperature were continuously monitored before and after sexual around induced by apomorphine. After the measurement, histologic examination was performed to support the results from DOS probe in the vaginal wall. The hemodynamic information acquired by the DOS probe can be utilized to establish an objective and accurate standard of the female sexual disorders.