Public Open Data Research Articles

Utilizing wavelet deep learning network to classify different states of task-fMRI for verifying activation regions

Purpose: We propose a convolutional neural network (CNN) based on wavelet for verifying the activation regions decided with statistical analysis. Because the functional magnetic resonance imaging (fMRI) data contains lots of noises, it is difficult to get the data of blood-oxygen-level dependent (BOLD) signal directly for intervention testing like animal studies. So it is difficult to effectively verify these activation regions. Based on the rapid development of deep learning technology. Materials and methods: We select the task fMRI data of presenting food and nonfood pictures to volunteer subjects from open public data, whose website is https://www.openfmri.org/dataset/ds000157/. Firstly, the brain activation regions are obtained by utilizing the method of statistical analysis. Then the spatial coordinates are acquired from the activation regions by checking the atlas table. The P-value of the activation regions are less 0.05. The activation regions are the most responsive to perceive the differences of BOLD in the brain between the two states, presenting food and nonfood pictures. We select the part task fMRI data of from the activation regions, for preparing the training and validation samples. Then we design a deep leaning network based on wavelet to classify the task fMRI data between food and nonfood.Results and conclusions: The classification accuracy is 80.23%. However, when we select the spatial coordinates of other inactivation regions, the classification accuracy is only 60%. The differences of classification accuracy between the activation regions and the inactivation regions prove that the activation regions selected with statistical analysis method are accurate and effective. The two methods of deep learning and statistical analysis can be cross-validated for the study of human being brain.

국내 동절기 일별 최대전력 예측에 관한 연구 : 개방형 공공 데이터 활용 사례

국내 동절기 일별 최대전력 예측에 관한 연구 : 개방형 공공 데이터 활용 사례

A method and analysis of aquifer thermal energy storage (ATES) system for district heating and cooling: A case study in Finland

Aquifer thermal energy storage (ATES) systems with groundwater heat pumps (GWHP) provide a promising and effective technology to match the renewable energy supply and demand between seasons. This paper analyses the integration of an ATES and GWHP system in both district heating (DH) and district cooling (DC) networks in terms of system's efficiency, techno-economic feasibility and impact on the surrounding groundwater areas. To that end, a novel method of holistic integration of groundwater modeling is proposed and demonstrated for retrieving and analyzing data from a variety of open Finnish public data sources. A case study is presented, where the ATES integration is examined within an existing district heating network in Southern Finland. It is concluded that combining heating and cooling, with seasonally reversible ATES operation and balanced pumping volumes during summer and winter periods, had low impact on the aquifer area and is economically feasible. Finally, the study concludes that even with limited data, obtained from open public data sources, it is possible to assess the ATES integration with an acceptable accuracy.

The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics.

The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) has standardized data submission and dissemination of mass spectrometry proteomics data worldwide since 2012. In this paper, we describe the main developments since the previous update manuscript was published in Nucleic Acids Research in 2017. Since then, in addition to the four PX existing members at the time (PRIDE, PeptideAtlas including the PASSEL resource, MassIVE and jPOST), two new resources have joined PX: iProX (China) and Panorama Public (USA). We first describe the updated submission guidelines, now expanded to include six members. Next, with current data submission statistics, we demonstrate that the proteomics field is now actively embracing public open data policies. At the end of June 2019, more than 14 100 datasets had been submitted to PX resources since 2012, and from those, more than 9 500 in just the last three years. In parallel, an unprecedented increase of data re-use activities in the field, including ‘big data’ approaches, is enabling novel research and new data resources. At last, we also outline some of our future plans for the coming years.

Tertiary building stock modeling: Area determination by fusion of different datasets

The building stock accounts for 43% of the total French final energy consumption in 2017. The French building stock mainly includes residential and tertiary buildings, which have very different consumption patterns. Today, building stock models are largely used in many countries for energy demand forecast and decision making. In this context, good knowledge of the building stock is essential for modeling. However, this type of model is lacking for the tertiary sector, as it is difficult to categorize buildings that have a wide variety of area and energy uses, different systems and complex dynamic trends. Existing stock models are mostly based on an aggregated representation of the total heated floor area of the building stock. Such a representation limits explicit studies of the factors affecting the energy demand and fails at taking into account heterogeneity. This paper exposes and discusses new methods aiming at modeling the tertiary building stock at the city scale in France. While mainly relying on open source public data, methods are designed to be applicable at any scale (from small cities to large region or even national scale).

Kadaster Knowledge Graph: Beyond the Fifth Star of Open Data

After more than a decade, the supply-driven approach to publishing public (open) data has resulted in an ever-growing number of data silos. Hundreds of thousands of datasets have been catalogued and can be accessed at data portals at different administrative levels. However, usually, users do not think in terms of datasets when they search for information. Instead, they are interested in information that is most likely scattered across several datasets. In the world of proprietary in-company data, organizations invest heavily in connecting data in knowledge graphs and/or store data in data lakes with the intention of having an integrated view of the data for analysis. With the rise of machine learning, it is a common belief that governments can improve their services, for example, by allowing citizens to get answers related to government information from virtual assistants like Alexa or Siri. To provide high-quality answers, these systems need to be fed with knowledge graphs. In this paper, we share our experience of constructing and using the first open government knowledge graph in the Netherlands. Based on the developed demonstrators, we elaborate on the value of having such a graph and demonstrate its use in the context of improved data browsing, multicriteria analysis for urban planning, and the development of location-aware chat bots.

Centralne Repozytorium Informacji Publicznej jako tryb ponownego wykorzystywania informacji

The Central Public Information Repository (CRIP) was introduced into the legal system by the act of 16 September 2011 amending the act on access to public information. The main goal behind the introduction of CRIP was to support the economic exploitation of information. CRIP’s functions are implemented by the Ministry of Administration and Digitization launching an open public data portal (danepubliczne.gov.pl) in May 2014. So far, CRIP has appeared in the literature in the context of access to public information. However, there is a lack of works on CRIP as a national open data portal to distribute information for re-use. This issue in particular has become increasingly important in view of the adoption by the Council of Ministers of the Public Data Access Program, a government strategy dedicated to public policy in the area of open data. Furthermore, due to the entry into force of the Act of 25 February 2016 on the re-use of public sector information, there is an interesting issue about the scope of the CRIP. The article analyzes CRIP regulations in terms of their effectiveness for the implementation of the right to reuse and the policy of open data. The article concludes the findings of the analysis and attempts to propose de lege ferenda conclusion.

Suitability Analysis for the Emergency Shelters Allocation after an Earthquake in Japan

Japan is exposed to several natural phenomena; the damages caused by earthquakes were enormous in particular. An emergency shelter is a place for people to temporarily live when they cannot remain in their previous homes, and it is necessary for each community to, respectively, allocate such facilities in Japan. There are the districts that are short of such facilities, especially in rural and suburban areas, because emergency shelters mainly concentrate near large-scale stations and city centres in Japan. Against these backdrops, using geographic information systems (GIS), an applied statistical method and public open data related to population and emergency shelters, the present research aims to quantitatively conduct a suitability analysis for the emergency shelters allocation after an earthquake in Japan. Based on the results, the present research grasps the districts that are short of emergency shelters, and visually shows the places where such facilities should be newly established on the digital map of GIS. Additionally, the assessment method is reproducible in the spatial and temporal dimension. It is necessary to create an original data related to emergency shelters to raise the reliability of the results, as the present research has the limitation of data availability.

Attitudes Toward Open Science and Public Data Sharing

Abstract. Central values of science are, among others, transparency, verifiability, replicability, and openness. The currently very prominent Open Science (OS) movement supports these values. Among its most important principles are open methodology (comprehensive and useful documentation of methods and materials used), open access to published research output, and open data (making collected data available for re-analyses). We here present a survey conducted among members of the German Psychological Society ( N = 337), in which we applied a mixed-methods approach (quantitative and qualitative data) to assess attitudes toward OS in general and toward data sharing more specifically. Attitudes toward OS were distinguished into positive expectations (“hopes”) and negative expectations (“fears”). These were un-correlated. There were generally more hopes associated with OS and data sharing than fears. Both hopes and fears were highest among early career researchers and lowest among professors. The analysis of the open answers revealed that generally positive attitudes toward data sharing (especially sharing of data related to a published article) are somewhat diminished by cost/benefit considerations. The results are discussed with respect to individual researchers’ behavior and with respect to structural changes in the research system.

The Freshwater Information Platform: An online network supporting freshwater biodiversity research and data publishing

Species distribution data are crucial for understanding biodiversity dynamics and the underlying drivers. For freshwaters, which cover only a small proportion of the world's surface, but host a large variety of species, knowledge on species occurrences is extremely important as they are among the most endangered ecosystems globally. However, a huge body of data gathered by scientists and water managers is currently difficult to access: systematic data publishing practices have not been fully adopted yet and data embedded in scientific papers and research project websites are often challenging to extract. At the same time, data and knowledge generated through publically-funded research or monitoring programmes are considered a common good. The Freshwater Information Platform (FIP) aims at pooling freshwater related research information from multiple projects and initiatives to make it easily accessible for scientists, water managers, conservationists and the interested public. The FIP consists of several major components, three of which form its “data publication unit”: The Freshwater Metadatabase (1) is an online tool where data characterising and documenting actual datasets can be entered in a simple way. With one more mouseclick these metadata can then be published as open access articles in the connected Freshwater Metadata Journal. The second part of the unit is the Freshwater Biodiversity Data Portal (2), where we aim to mobilise and publish the connected freshwater biodiversity data (occurrence records) through GBIF (Global Biodiversity Information Facility). The use of collected datasets for large-scale analyses and models is demonstrated in the Global Freshwater Biodiversity Atlas (3) that publishes interactive online maps featuring research results on freshwater biodiversity, threats and conservation priorities. Here, we focus on introducing these components as tools to streamline open access freshwater data publication, arguing it will improve the capacity to protect and manage freshwater biodiversity in the face of global change. We further present linkages to and cooperations with other key initiatives in the field, namely the "Alliance for Freshwater Life" as well as "FreshwaterBON".

Identifying commonly used and potentially unsafe transit transfers with crowdsourcing

Public transit is an important contributor to sustainable transportation as well as a public service that makes necessary travel possible for many. Poor transit transfers can lead to both a real and perceived reduction in convenience and safety, especially for people with disabilities. Poor transfers can expose riders to inclement weather and crime, and they can reduce transit ridership by motivating riders who have the option of driving or using paratransit to elect a more expensive and inefficient travel mode. Unfortunately, knowledge about inconvenient, missed, and unsafe transit transfers is sparse and incomplete.We show that crowdsourced public transit ridership data, which is more scalable than conducting traditional surveys, can be used to analyze transit transfers. The Tiramisu Transit app merges open transit data with information contributed by users about which trips they take. We use Tiramisu data to do origin-destination analysis and identify connecting trips to create a better understanding of where and when poor transfers are occurring in the Pittsburgh region. We merge the results with data from other open public data sources, including crime data, to create a data resource that can be used for planning and identification of locations where bus shelters and other infrastructure improvements may facilitate safer and more comfortable waits and more accessible transfers. We use generalizable methods to ensure broader value to both science and practitioners.We present a case study of the Pittsburgh region, in which we identified and characterized 338 transfers from 142 users. We found that 66.6% of transfers were within 0.4 km (0.25 mi.) and 44.1% of transfers were less than 10 min. We identified the geographical distribution of transfers and found several highly-utilized transfer locations that were not identified by the Port Authority of Allegheny County as recommended transfer points, and so might need more planning attention. We cross-referenced transfer location and wait time data with crime levels to provide additional planning insight.

Critically studying openness: A way forward

Critically studying openness: A way forward

Ridesharing car detection by transfer learning

Ridesharing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesharing activities taking advantages of the sharing economy can greatly impair the healthy development of this emerging industry. As the first step to regulate on-demand ride services and eliminate black market, we design a method to detect ridesharing cars from a pool of cars based on their trajectories. Since licensed ridesharing car traces are not openly available and may be completely missing in some cities due to legal issues, we turn to transferring knowledge from public transport open data, i.e., taxis and buses, to ridesharing detection among ordinary vehicles. We propose a novel two-stage transfer learning framework, called CoTrans. In Stage 1, we take taxi and bus data as input to learn a random forest (RF) classifier using trajectory features shared by taxis/buses and ridesharing/other cars. Then, we use the RF to label all the candidate cars. In Stage 2, leveraging the subset of high confident labels from the previous stage as input, we further learn a convolutional neural network (CNN) classifier for ridesharing detection, and iteratively refine the RF and CNN, as well as the feature set, via a co-training process. Finally, we use the resulting ensemble of the RF and CNN to identify the ridesharing cars in the candidate pool. Experiments on real car, taxi and bus traces show that CoTrans, with no need of a pre-labeled ridesharing dataset, can outperform state-of-the-art transfer learning methods with an accuracy comparable to human labeling.

Public open space exposure measures in Australian health research: a critical review of the literature

AbstractNumerous studies have shown associations between public open space and a variety of health outcomes. Yet the extent to which firm conclusions and planning policy recommendations can be drawn from this body of work depends on how public open space availability has been measured and reported. Other researchers have highlighted potential issues with the way that public open space has been measured but have not systematically assessed the extent of this problem. This paper provides a comprehensive critical review of studies of public open space and health conducted in Australia to identify and compare public open space measurement and data treatment. Our analysis showed wide variation in how public open space was measured, as well as a lack of consistency in reporting public open space exposure measures and under‐reporting of measurement methods. We find that such tendencies limit how much these studies can be compared and contrasted with each other. The corollary of that finding is that without more detailed reporting of exposure measures, it will be difficult to establish an evidence base that informs planning for healthy, liveable environments. In response, we develop and present a checklist for reporting public open space exposure to address this challenge.

Privacy risk assessment in context: A meta-model based on contextual integrity

Publishing data in open format is a growing trend, particularly for public bodies who have a legal obligation to make data available as open data. We look at the privacy implications of publishing open data and, in particular, how organisations can make informed decisions around privacy risks in relation to open data publishing before publication occurs.Using a well established theoretical privacy assessment framework, Contextual Integrity, we illustrate how this can be translated into a practical meta-model that can assist public bodies in assessing what privacy implications or risks might be associated with making a particular dataset available as open data.We validate the meta-model by providing a worked example and illustrate the effectiveness of this by reference to a case study application where the meta-model was successfully applied in practice.

「標準的なバス情報フォーマット」に基づいた 公共交通オープンデータ可視化システムの開発

「標準的なバス情報フォーマット」に基づいた 公共交通オープンデータ可視化システムの開発

Open Data Portal Prototype for the Conservation of Biodiversity of the ‘Universidad Nacional de Loja’

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The process of open data publication and reuse

Open data movement advocates support to public authorities by making available to society the public information they manage. The data released are identified as open government data and the creation of open data portals supports their commitment through open government policies. The worldwide increase of the open data publication is making more necessary the modelling of its impact on society. This research analyses the process of open data publication starting in the internal systems of the organization and reaching the actual reuse of data in reuser's ecosystem surrounding the open data portals. Different reuser's profiles are identified and described within the reuser's ecosystem. Some key elements of the publication process are presented in order to guarantee sustainability of open data initiatives and to further analyse the social and economic impact.

Web Semântica: fluxo para publicação de dados abertos e ligados

Publicar dados em formato aberto e semântico tem se tornado um grande desafio as organizações ao redor do mundo. Há uma grande variedade de ações que devem ser executadas para que um projeto de publicação de dados possa ser concluído. Esta pesquisa tem como objetivo principal apresentar uma proposta de fluxo organizacional, segmentado em fases, que descreva as atividades que devem ser desenvolvidas no processo de publicação de dados em formato aberto e semântico seguindo as melhores práticas de dados ligados. A metodologia utilizada é baseada em pesquisa descritiva e analítica, baseada em análise documental. Como resultado espera-se que o fluxo apresentado possa contribuir com o desenvolvimento de novos projetos de publicação de dados em formato aberto e semântico.

Publicación de datos abiertos en instituciones de patrimonios culturales

A partir del creciente movimiento de datos abiertos y de los debates acerca de la publicación de datos, se cuestiona: ¿cómo los centros de información pueden incitar a la publicación de estos datos en un formato abierto para facilitar la difusión, el acceso, uso y reutilización? El objetivo es, entonces, presentar la propuesta de publicación de datos abiertos en las instituciones de patrimonios culturales, proveniente de los resultados de un estudio comparativo entre los aspectos de metadatos relacionados con la descripción de patrimonios culturales y las buenas prácticas de publicación de datos que son recomendadas por el Consorcio World Wide Web (W3C). Los métodos y procedimientos incluyeron la investigación exploratoria en la literatura científica y documental, nacional e internacional, acerca del movimiento de publicación de datos y sobre el uso de metadatos en la representación de patrimonios culturales. Se presenta un estudio que compara los aspectos de metadatos relacionados con la descripción de patrimonios culturales y las buenas prácticas de publicación de datos recomendados en documento por el Consorcio World Wide Web (W3C). Los resultados mostraron una confluencia entre los objetivos del consorcio W3C, los procesos de representación practicados por la Ciencia de la Información y el alcance general de las Humanidades digitales con respecto a la sostenibilidad de los datos publicados y de la gestión de datos en el ambiente digital. Se propone un procedimiento de publicación de datos abiertos para ser aplicado por las instituciones culturales, como un incentivo para el entendimiento y uso de las herramientas del movimiento por las instituciones culturales nacionales.