Design and specifications of a repository for real-time open data

2014 ◽  
Author(s):  
Sudesh Lutchman ◽  
Patrick Hosein
Keyword(s):  
Real Time ◽  
Open Data

Towards guaranteed data-integrity: A method of preventing Questionable Research Practices

2018 ◽  
Author(s):  
Dick Bierman ◽  
Jacob Jolij
Keyword(s):  
Real Time ◽  
Open Data ◽  
Data Integrity ◽  
Drop Out ◽  
Research Practices ◽  
Major Aspect ◽  
Questionable Research Practices ◽  
Blockchain Technology ◽  
Integrity System ◽  
Secure Server

We have tested the feasibility of a method to prevent the occurrence of so-called Questionable Research Practices (QRP). A part from embedded pre-registration the major aspect of the system is real-time uploading of data on a secure server. We outline the method, discuss the drop-out treatment and compare it to the Born-open data method, and report on our preliminary experiences. We also discuss the extension of the data-integrity system from secure server to use of blockchain technology.

scholarly journals Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico

2021 ◽  
Author(s):  
O. Mendoza-Cano ◽  
R. Aquino-Santos ◽  
J. López-de la Cruz ◽  
R. M. Edwards ◽  
A. Khouakhi ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Web Application ◽  
Critical Infrastructure ◽  
Open Data ◽  
Wireless Sensor ◽  
Tropical Storms ◽  
Essential Information ◽  
Flood Monitoring

Abstract Urban flooding is one of the major issues in many parts of the world, and its management is often challenging. One of the challenges highlighted by the hydrology and related communities is the need for more open data and monitoring of floods in space and time. In this paper, we present the development phases and experiments of an Internet of Things (IoT)-based wireless sensor network for hydrometeorological data collection and flood monitoring for the urban area of Colima-Villa de Álvarez in Mexico. The network is designed to collect fluvial water level, soil moisture and weather parameters that are transferred to the server and to a web application in real-time using IoT Message Queuing Telemetry Transport protocol over 3G and Wi-Fi networks. The network is tested during three different events of tropical storms that occurred over the area of Colima during the 2019 tropical cyclones season. The results show the ability of the smart water network to collect real-time hydrometeorological information during extreme events associated with tropical storms. The technology used for data transmission and acquisition made it possible to collect information at critical times for the city. Additionally, the data collected provided essential information for implementing and calibrating hydrological models and hydraulic models to generate flood inundation maps and identify critical infrastructure.

Efficient Medical Image Adaption via Axis-Aligned Mesh Deformation

2021 ◽  
Vol 11 (8) ◽  
pp. 2048-2054
Author(s):  
Qing-Fang Liu ◽  
Bao-Sheng Kang ◽  
Hai-Yun Liu ◽  
Hong-An Li ◽  
Rui Chen ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Image ◽  
High Efficiency ◽  
Open Data ◽  
Strong Stability ◽  
Saliency Map ◽  
Mesh Deformation ◽  
Visual Effect ◽  
Real Time Image ◽  
Time Image

Content-aware medical image adaptation can make medical images be well presented on different display devices. The existing adaption algorithms mainly consider the visual effect of salient regions, such as specific organ areas of the patient body, but either ignore the quality of unimportant areas or execute more slowly. In order to enhance the effect of adaption and accelerate the speed of adaptation, we propose an efficient medical image adaptation method via axis-aligned mesh deformation. With this method, importance map is firstly produced by combing the weighted edge map and saliency map. Then, integer programming is used to initialize and deform the axis-aligned mesh based on importance map. Finally, image adaptation is operated rapidly by bi-linear interpolation. With the proposed method, the real-time image adaptation can be realized, and not only the visual effect of the significant areas but also the contour integrity and continuity of the non significant areas can be maintained. Experiments on open data-sets show that the proposed method has high efficiency, better effect and strong stability, and is suitable for real-time image adaptation.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

scholarly journals Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6230 ◽  
Author(s):  
Jonas S. Almeida ◽  
Janos Hajagos ◽  
Joel Saltz ◽  
Mary Saltz
Keyword(s):  
Real Time ◽  
Web Application ◽  
Open Data ◽  
Patient Records ◽  
Public Health Agencies ◽  
P4 Medicine ◽  
Population Science ◽  
Data Layer ◽  
Client Side ◽  
The Web

In a previous report, we explored the serverless OpenHealth approach to the Web as a Global Compute space. That approach relies on the modern browser full stack, and, in particular, its configuration for application assembly by code injection. The opportunity, and need, to expand this approach has since increased markedly, reflecting a wider adoption of Open Data policies by Public Health Agencies. Here, we describe how the serverless scaling challenge can be achieved by the isomorphic mapping between the remote data layer API and a local (client-side, in-browser) operator. This solution is validated with an accompanying interactive web application (bit.ly/loadsparcs) capable of real-time traversal of New York’s 20 million patient records of the Statewide Planning and Research Cooperative System (SPARCS), and is compared with alternative approaches. The results obtained strengthen the argument that the FAIR reproducibility needed for Population Science applications in the age of P4 Medicine is particularly well served by the Web platform.

scholarly journals Serverless OpenHealth at data commons scale - traversing the 20 million patient records of New York's SPARCS dataset in real-time

2018 ◽  
Author(s):  
Jonas S Almeida ◽  
Janos Hajagos ◽  
Joel Saltz ◽  
Mary Saltz
Keyword(s):  
Real Time ◽  
Web Application ◽  
Open Data ◽  
Patient Records ◽  
Public Health Agencies ◽  
P4 Medicine ◽  
Population Science ◽  
Data Layer ◽  
Client Side ◽  
The Web

In a previous report, we explored the serverless OpenHealth approach to the Web as a Global Compute space. That approach relies on the modern browser full stack, and, in particular, its configuration for application assembly by code injection. The opportunity, and need, to expand this approach has since increased markedly, reflecting a wider adoption of Open Data policies by Public Health Agencies. Here, we describe how the serverless scaling challenge can be achieved by the isomorphic mapping between the remote data layer API and a local (client-side, in-browser) operator. This solution is validated with an accompanying interactive web application (bit.ly/loadsparcs) capable of real-time traversal of New York’s 20 million patient records of the Statewide Planning and Research Cooperative System (SPARCS), and is compared with alternative approaches. The results obtained strengthen the argument that the FAIR reproducibility needed for Population Science applications in the age of P4 Medicine is particularly well served by the Web platform.

scholarly journals ROAD NETWORK COMPARISON AND MATCHING TECHNIQUES. A WORKFLOW PROPOSAL FOR THE INTEGRATION OF TRAFFIC MESSAGE CHANNEL AND OPEN SOURCE NETWORK DATASETS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1503-1509
Author(s):  
A. Ajmar ◽  
E. Arco ◽  
P. Boccardo
Keyword(s):  
Open Source ◽  
Real Time ◽  
Road Network ◽  
Open Data ◽  
Added Value ◽  
Heterogeneous Databases ◽  
Geographic Datasets ◽  
Wide Availability ◽  
Set Up ◽  
Matching Techniques

<p><strong>Abstract.</strong> The rapid growth of methods and techniques to acquire geospatial data has led to a wide availability of overlapping geographic datasets with different characteristics. Road network data sources are today a significant number, with high differences in level of detail and modelling schemas, depending on the main purpose. In addition, continuous information about people and freight movement is today available also in real-time. This type of data is today exchanged between traffic operators using referencing standards as Traffic Message Channel. Integrating these heterogeneous databases, in order to build an added value product, is a serious task in geographical data management. The paper is focus on techniques to conflate the Traffic message Channel logical network on Open Source road network dataset, in order to allow the precise visualisation of traffic data also in real-time.</p><p>A first step of the research was the quality assessment of available Open Source (OS) road network dataset, then, a specific procedure to conflate data was set up, using an iterative process in order to reduce at every step the number of possible matching features. A first application of the enhanced OTM dataset is shown for the city of Turin: real-time open data of traffic flows recorded by road network fixed sensors, made available by the metropolitan Traffic Operation Centre (5T) and based on the TMC location referencing, are matched on the OTM road network, allowing a detailed real-time visualisation of traffic state.</p>

scholarly journals PrAna: An R package to calculate and visualize England NHS primary care prescribing data

2021 ◽  
Author(s):  
Kishore Kumar Jagadeesan ◽  
James Grant ◽  
Sue Griffin ◽  
Ruth Barden ◽  
Barbara Kasprzyk-Hordern
Keyword(s):  
Real Time ◽  
National Level ◽  
Open Data ◽  
R Package ◽  
Public Healthcare ◽  
Prescription Data ◽  
Support Tool ◽  
North East ◽  
Long Term Trends

Abstract Background The objective of this work to calculate prescribed quantity of an active pharmaceutical ingredient (API) in prescription medications for human use, to facilitate research on the prediction of amount of API released to the environment and create an open-data tool to facilitate spatiotemporal and long-term prescription trends for wider usage. Design We have developed an R package, PrAna to calculate the prescribed quantity (in kg) of an APIs by postcode using England’s national level prescription data provided by National Health Service, for the years 2015–2018. Datasets generated using Prana can be visualized in a real-time interactive web-based tool, PrAnaViz to explore spatiotemporal and long-term trends. The visualisations can be customised by selecting month, year, API, and region. Results PrAnaViz’s targeted API approach is demonstrated with the visualisation of prescribed quantities of 14 APIs in the Bath and North East Somerset (BANES) region during 2018. Once the APIs list is loaded, the back end retrieves relevant data and populates the graphs based on user-defined data features in real-time. These plots include the prescribed quantity of APIs over a year, by month, and individual API by month, general practice, postcode, and medicinal form. The non-targeted API approach is demonstrated with the visualisation of clarithromycin prescribed quantities at different postcodes in the BANES region. Conclusion PrAna and PrAnaViz enables the analysis of spatio-temporal and long-term trends with prescribed quantities of different APIs by postcode. This can be used as a support tool for policymakers, academics and researchers in public healthcare, and environmental scientist to monitor different group of pharmaceuticals emitted to the environment and for prospective risk assessment of pharmaceuticals in the environment.

scholarly journals A Smart Decision System for Digital Farming

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 216 ◽  
Author(s):  
Carlos Cambra Baseca ◽  
Sandra Sendra ◽  
Jaime Lloret ◽  
Jesus Tomas
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Information Exchange ◽  
New Technologies ◽  
Open Data ◽  
Weather Conditions ◽  
Added Value ◽  
Aerial Images ◽  
Sampled Data ◽  
Application Development

New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT)-based application development platforms have the potential to run farm management tools capable of monitoring real-time events when integrated into interactive innovation models for fertirrigation. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision-making systems based on data-analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real-time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images), and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision-making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management.

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