scholarly journals Research and Implementation of TCM Knowledge Acquisition Based on Open Data Source

2017 ◽  
Author(s):  
Yonghong Xie ◽  
Yanxuan Qian ◽  
Shuang Ha ◽  
Dezheng Zhang
Keyword(s):  
Knowledge Acquisition ◽  
Open Data ◽  
Data Source

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

Using OpenStreetMap as a Source of Religious Mapping Data in an OER Human Geography Textbook

2020 ◽  
pp. 112-139
Author(s):  
David Dorrell
Keyword(s):  
Open Data ◽  
Global Scale ◽  
Mapping Data ◽  
Good Data ◽  
Data Production ◽  
Small Scales ◽  
Data Source ◽  
Open Datasets ◽  
Religious Landscape ◽  
Geography Textbook

OpenStreetMap (OSM) is a global scale geographic data source produced by individual volunteers and organizations. It is emblematic of two emerging themes in geographical technology: crowdsourcing and open data. One of the main problems with mapping religion at small scales is the lack of good data for representing the religious landscape. OSM provides data on the location of religious structures in the landscape. The dataset contains hundreds of thousands of religious structures that can be mapped, queried, and analyzed. Students often do not understand how mapping data are produced and how the data are refined and manipulated. In this study, students are asked to interact with maps made from the data as well as the data itself and provide their feedback. Open datasets can be used to make useful teaching maps, but they can also be used to help students see the inner workings of data production.

scholarly journals Reducing Consumer Uncertainty: Towards an Ontology for Geospatial User-Centric Metadata

2020 ◽  
Vol 9 (8) ◽  
pp. 488
Author(s):  
Hasti Ziaimatin ◽  
Alireza Nili ◽  
Alistair Barros
Keyword(s):  
Spatial Data ◽  
Open Data ◽  
Source Selection ◽  
Consumer Uncertainty ◽  
Selection Decisions ◽  
Metadata Standards ◽  
Geospatial Metadata ◽  
User Groups ◽  
Data Source ◽  
User Centric

With the increased use of geospatial datasets across heterogeneous user groups and domains, assessing fitness-for-use is emerging as an essential task. Users are presented with an increasing choice of data from various portals, repositories, and clearinghouses. Consequently, comparing the quality and evaluating fitness-for-use of different datasets presents major challenges for spatial data users. While standardization efforts have significantly improved metadata interoperability, the increasing choice of metadata standards and their focus on data production rather than potential data use and application, renders typical metadata documents insufficient for effectively communicating fitness-for-use. Thus, research has focused on the challenge of communicating fitness-for-use of geospatial data, proposing a more “user-centric” approach to geospatial metadata. We present the Geospatial User-Centric Metadata ontology (GUCM) for communicating fitness-for-use of spatial datasets to users in the spatial and other domains, to enable them to make informed data source selection decisions. GUCM enables metadata description for various components of a dataset in the context of different application domains. It captures producer-supplied and user-described metadata in structured format using concepts from domain-independent ontologies. This facilitates interoperability between spatial and nonspatial metadata on open data platforms and provides the means for searching/discovering spatial data based on user-specified quality and fitness-for-use criteria.

scholarly journals (SARS-CoV-2) COVID 19: Genomic surveillance and evaluation of the impact on the population speaker of indigenous language in Mexico

2021 ◽  
Author(s):  
Carlos Medel-Ramírez ◽  
Hilario Medel-López ◽  
Jennifer Lara Mérida
Keyword(s):  
National Level ◽  
Open Data ◽  
Case Fatality ◽  
Epidemiological Surveillance ◽  
Indigenous Language ◽  
Sources Of Information ◽  
Source Information ◽  
Data Source ◽  
The Government ◽  
The Impact

AbstractThe importance of the working document is that it allows the analysis of information and cases associated with (SARS-CoV-2) COVID-19, based on the daily information generated by the Government of Mexico through the Secretariat of Health, responsible for the Epidemiological Surveillance System for Viral Respiratory Diseases (SVEERV). The information in the SVEERV is disseminated as open data, and the level of information is displayed at the municipal, state and national levels. On the other hand, the monitoring of the genomic surveillance of (SARS-CoV-2) COVID-19, through the identification of variants and mutations, is registered in the database of the Information System of the Global Initiative on Sharing All Influenza Data (GISAID) based in Germany. These two sources of information SVEERV and GISAID provide the information for the analysis of the impact of (SARS-CoV-2) COVID-19 on the population in Mexico. The first data source identifies information, at the national level, on patients according to age, sex, comorbidities and COVID-19 presence (SARS-CoV-2), among other characteristics. The data analysis is carried out by means of the design of an algorithm applying data mining techniques and methodology, to estimate the case fatality rate, positivity index and identify a typology according to the severity of the infection identified in patients who present a positive result. for (SARS-CoV-2) COVID-19. From the second data source, information is obtained worldwide on the new variants and mutations of COVID-19 (SARS-CoV-2), providing valuable information for timely genomic surveillance. This study analyzes the impact of (SARS-CoV-2) COVID-19 on the indigenous language-speaking population, it allows us to provide information, quickly and in a timely manner, to support the design of public policy on health.

scholarly journals DEEP LEARNING (CNN) MODEL FOR COVID-19 DETECTION FROM CHEST XRAY IMAGES

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Ahmed Mohamed ◽  
Ahmed Abdelhady
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Open Data ◽  
Disease Outbreak ◽  
Imaging Features ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Data Source

The Coronavirus disease outbreak result in many people to have severe respira- tory problems and it was recognized as a global health threat. Since the virus is targeting the lungs in the human body initially, chest x-ray imaging features were considered to be useful for the detection of the infection in the early stage. In this study, the chest x-ray data of 130 infected patients from an open data source that referenced Cohen J. Morrison P. Dao L., 2020 was used to build a CNN( Convolutional Neural-Network) model for the early detection of the disease. The model was trained with both infected and not-infected peoples’ chest x-ray images with 100 epochs which led to 0.98 accuracy finally. In order to use this model as a professional diagnosis element, it is highly recommended it be improved with more images and the model can be restructured to get a better accuracy.

Stakeholder Management in Collaborative Research Projects

2017 ◽  
Vol 6 (4) ◽  
pp. 27-35
Author(s):  
Анна Яковлева ◽  
Anna Yakovleva
Keyword(s):  
Innovation Policy ◽  
Stakeholder Management ◽  
System Development ◽  
Open Data ◽  
Energy System ◽  
Collaborative Project ◽  
System A ◽  
European Experience ◽  
Data Source ◽  
Technological Platform

Mechanisms of collaborative project and programme stakeholder management by the example of technological platform “Intellectual energy system” (a tool of state innovation policy) are analyzed. During the research the aspects of collaborative projects stakeholder management, including stakeholder’s diversity, contradictory expectations, geographic fragmentation are considered. Using the open data source, the lack of collaborative project stakeholder management system in Project/Programme of technological platform “Intellectual energy system” development was diagnosed. There is the necessity of revealing to European experience in technological platform collaborative projects implementation, as far as this phenomenon is successfully adopted abroad and facilitates creation of benefits to the diverse number of its stakeholders.

scholarly journals From containers to content to context

2014 ◽  
Vol 70 (2) ◽  
pp. 241-260 ◽  
Author(s):  
Stefan Gradmann
Keyword(s):  
Open Data ◽  
Information Architecture ◽  
Literature Study ◽  
Cultural Changes ◽  
Content Type ◽  
Research Libraries ◽  
Research Environments ◽  
Data Source ◽  
New Perspective ◽  
Practical Implications

Purpose – The aim of this paper is to reposition the research library in the context of the changing information and knowledge architecture at the end of the “Gutenberg Parenthesis” and as part of the rapidly emerging “semantic” environment of the Linked Open Data paradigm. Understanding this process requires a good understanding of the evolution of the “document” notion in the passage from print based culture to the distributed hypertextual and RDF based information architecture of the WWW. Design/methodology/approach – These objectives are reached using literature study and a descriptive historical approach as well as text mining techniques using Google nGrams as a data source. Findings – The paper presents a proposal for effectively repositioning research libraries in the context of eScience and eScholarship as well as clear indications of the proposed repositioning already taking place. Furthermore, a new perspective of the “document” notion is provided. Practical implications – The evolution described in the contribution creates opportunities for libraries to reposition themselves as aggregators and selectors of content and as contextualising agents as part of future Linked Data based scholarly research environments provided they are able and ready to operate the related cultural changes. Originality/value – The paper will be useful for practitioners in search of strategic guidance for repositioning their librarian institutions in a context of ever increasing competition for scarce funding resources.

scholarly journals Spanish Public Procurement: legislation, open data source and extracting valuable information of procurement announcements

2019 ◽  
Vol 164 ◽  
pp. 441-448 ◽  
Author(s):  
Manuel J. García Rodríguez ◽  
Vicente Rodríguez Montequín ◽  
Francisco Ortega Fernández ◽  
Joaquín Villanueva Balsera
Keyword(s):  
Public Procurement ◽  
Open Data ◽  
Data Source
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