scholarly journals Dataset of tau neutrino interactions recorded by the OPERA experiment

2020 ◽  
Vol 245 ◽  
pp. 08013
Author(s):  
Giovanni De Lellis ◽  
Sergey Dmitrievsky ◽  
Giuliana Galati ◽  
Artemis Lavasa ◽  
Tibor Šimko ◽  
...  
Keyword(s):  
Neutrino Oscillations ◽  
Rare Events ◽  
Open Data ◽  
Muon Neutrinos ◽  
Tau Neutrino ◽  
Neutrino Interactions ◽  
Visualisation Tool ◽  
Data Release ◽  
Data Portal

We describe the dataset of very rare events recorded by the OPERA experiment. The events represent tracks of particles associated with tau neutrino interactions coming from the transformation of muon neutrinos due to a process known as neutrino oscillations. The events have been published on the CERN Open Data Portal. We describe the dataset semantics and the interactive event display visualisation tool accompanying the data release.

scholarly journals OPERA tau neutrino charged current interactions

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
N. Agafonova ◽  
A. Alexandrov ◽  
A. Anokhina ◽  
S. Aoki ◽  
A. Ariga ◽  
...  
Keyword(s):  
Neutrino Oscillations ◽  
Statistical Significance ◽  
Open Data ◽  
Photographic Emulsion ◽  
Charged Current ◽  
Lead Target ◽  
Tau Neutrino ◽  
Data Portal

AbstractThe OPERA experiment was designed to discover the vτ appearance in a vμ beam, due to neutrino oscillations. The detector, located in the underground Gran Sasso Laboratory, consisted of a nuclear photographic emulsion/lead target with a mass of about 1.25 kt, complemented by electronic detectors. It was exposed from 2008 to 2012 to the CNGS beam: an almost pure vμ beam with a baseline of 730 km, collecting a total of 1.8·1020 protons on target. The OPERA Collaboration eventually assessed the discovery of vμ→vτ oscillations with a statistical significance of 6.1 σ by observing ten vτ CC interaction candidates. These events have been published on the Open Data Portal at CERN. This paper provides a detailed description of the vτ data sample to make it usable by the whole community.

scholarly journals MUNICIPAL OPEN DATA PORTALS: THEIR IMPLEMENTATION IN THE FACE OF A MUNICIPALITY’S POLITICAL AND TERRITORIAL REALITY

2021 ◽  
Vol XLVI-4/W1-2021 ◽  
pp. 11-19
Author(s):  
J. Diaz ◽  
S. Breux
Keyword(s):  
Economic Development ◽  
Citizen Participation ◽  
Public Service ◽  
Open Data ◽  
Link Open Data ◽  
Data Release ◽  
The Face ◽  
Data Portal ◽  
The City

Abstract. Municipal open data portals have been criticized for their inability to fulfill the promises of transparency, citizen participation and economic development that are supposed to accompany data release. Based on an analysis of certain aspects of the City of Montréal’s open data portal and interviews with reusers of these data, we show that the limitations observed stem – at least in part – from an absence of consideration of the municipality’s political and territorial reality. Three facts contribute to this absence: 1) the Montreal open data portal was designed as a public service; 2) it was created upstream, and not based on the identification of possible needs of the population or the territory; and 3) the relevance of the published datasets raises questions with respect to the promises made. These elements invite us to better link open data portals to objectives and needs that are first and foremost local, while inserting them into a broader framework for achieving the initial democratic and economic promises.

Promote Good Governance in Public Financial: The Practice of Local Budget (APBD) Transparency Through Open Data Jakarta in Jakarta Provincial Government

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Dodi Faedlulloh ◽  
Fetty Wiyani
Keyword(s):  
Public Finance ◽  
Good Governance ◽  
Open Data ◽  
Public Trust ◽  
Provincial Government ◽  
Financial Governance ◽  
One Stop ◽  
Data Portal ◽  
Data Transparency ◽  
Local Budget

This paper aimed to explain public financial governance based on good governance implementation in Jakarta Provincial Government. This paper specifically discussed towards transparancy implementation of local budget (APBD) through open data portal that publishes budget data to public. In general, financial transparency through open data has met Transparency 2.0 standards, namely the existence of encompassing, one-stop, one-click budget accountability and accessibility. But there are indeed some shortcomings that are still a concern in order to continue to maintain commitment to the principle of transparency, namely by updating data through consistent data visualization.Transparency of public finance needs to continue to be developed and improved through various innovations to maintain public trust in the government.Keywords: Public Finance, Open Data, Transparency

scholarly journals DPUK Cohort Explorer: An Interactive Visualisation Tool

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Laura North
Keyword(s):  
Real World ◽  
Data Transfer ◽  
Real World Data ◽  
World Data ◽  
Visualisation Tool ◽  
Field Level ◽  
Level Information ◽  
Data Portal ◽  
Portal Website ◽  
Interactive Visualisation

IntroductionThe Dementias Platform UK (DPUK) Cohort Explorer is an interactive, online visualisation tool that allows users to explore data for a number of DPUK cohorts. Over 30 variables across cohorts have been harmonised, including information on demographics, lifestyle, cognition, health, and genetic biomarkers. Objectives and ApproachThe tool has been developed to complement existing DPUK cohort metadata to provide a visual representation of participant numbers and field-level information for a selection of cohorts. This enables users to determine a cohort’s eligibility before applying for access to a cohort’s data, and aid in shaping potential hypotheses. Developed using Microsoft PowerBI, the Explorer hosts a subset of the cohort’s baseline, harmonised data, allowing a user to interrogate the visualisations of the uploaded data in a secure manner on the DPUK Data Portal website. Visualisations are linked so that participant numbers and distributions can be explored interactively. ResultsThis approach allows the user to explore the harmonised data across a number of cohorts simultaneously whilst setting and adjusting filters that are of interest to the user’s search criteria. This provides a better understanding of the real-world data and enables the user to determine the feasibility of each cohort for potential studies, whilst facilitating meaningful comparisons across cohorts. The tool currently visualises five DPUK cohorts with a total of 82,391 participants, however it is being incrementally developed with more cohorts being added continually. Conclusion / ImplicationsBy combing an easy-to-use, interactive dashboard with harmonised sets of real-world data, the tool allows the user to explore, interrogate and better understand field-level information in a secure manner with zero data transfer. This provides more insight for the user when applying for access to a cohort dataset using the DPUK Data Portal and may help the user to make more informed decisions and/or hypotheses.

scholarly journals RESOURCES PROVIDING DATA FOR MACHINE LEARNING AND TESTING ARTIFICIAL INTELLIGENCE TECHNOLOGIES

2021 ◽  
pp. 39-52
Author(s):  
Денис Валерьевич Сикулер
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
World Bank ◽  
Open Data ◽  
Data Access ◽  
Free Access ◽  
Internet Resources ◽  
Data Registry ◽  
Data Portal ◽  
Access To Data

В статье выполнен обзор 10 ресурсов сети Интернет, позволяющих подобрать данные для разнообразных задач, связанных с машинным обучением и искусственным интеллектом. Рассмотрены как широко известные сайты (например, Kaggle, Registry of Open Data on AWS), так и менее популярные или узкоспециализированные ресурсы (к примеру, The Big Bad NLP Database, Common Crawl). Все ресурсы предоставляют бесплатный доступ к данным, в большинстве случаев для этого даже не требуется регистрация. Для каждого ресурса указаны характеристики и особенности, касающиеся поиска и получения наборов данных. В работе представлены следующие сайты: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Портал открытых данных Российской Федерации, World Bank, The Big Bad NLP Database, Common Crawl. The work presents review of 10 Internet resources that can be used to find data for different tasks related to machine learning and artificial intelligence. There were examined some popular sites (like Kaggle, Registry of Open Data on AWS) and some less known and specific ones (like The Big Bad NLP Database, Common Crawl). All included resources provide free access to data. Moreover in most cases registration is not needed for data access. Main features are specified for every examined resource, including regarding data search and access. The following sites are included in the review: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Open Data portal of the Russian Federation, World Bank, The Big Bad NLP Database, Common Crawl.

The User Engagement of Open Data Portal

2021 ◽  
Author(s):  
Dewi Krismawati ◽  
Achmad Nizar Hidayanto
Keyword(s):  
Open Data ◽  
User Engagement ◽  
Data Portal

LigADOS: Interlinking Datasets in Open Data Portal Platforms on the Semantic Web

2021 ◽  
pp. 73-84
Author(s):  
Glaucia Botelho de Figueiredo ◽  
Kelli de Faria Cordeiro ◽  
Maria Luiza Machado Campos
Keyword(s):  
Semantic Web ◽  
Open Data ◽  
Data Portal

scholarly journals Unitarity of neutrino mixing matrix

2019 ◽  
Vol 206 ◽  
pp. 09009
Author(s):  
Ha Nguyen Thi Kim ◽  
Van Nguyen Thi Hong ◽  
Son Cao Van
Keyword(s):  
Cp Violation ◽  
Neutrino Oscillations ◽  
Graphical Form ◽  
Muon Neutrinos ◽  
Neutrino Mixing ◽  
Electron Neutrinos ◽  
Tau Neutrinos ◽  
The Matrix ◽  
Mixing Matrix ◽  
Unitary Triangle

Neutrinos are neutral leptons and there exist three types of neutrinos (electron neutrinos νe, muon neutrinos νµ and tau neutrinos ντ). These classifications are referred to as neutrinos’s “flavors”. Oscillations between the different flavors are known as neutrino oscillations, which occurs when neutrinos have mass and non-zero mixing. Neutrino mixing is governed by the PMNS mixing matrix. The PMNS mixing matrix is constructed as the product of three independent rotations. With that, we can describe the numerical parameters of the matrix in a graphical form called the unitary triangle, giving rise to CP violation. We can calculate the four parameters of the mixing matrix to draw the unitary triangle. The area of the triangle is a measure of the amount of CP violation.

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