scholarly journals Modelo baseado em Frictionless Data aplicado aos dados abertos governamentais

Author(s):  
Melissa Figueira Fagundes ◽  
Divino Ignácio Ribeiro Junior

O presente trabalho propõe um modelo baseado em Fricitionless Data (FD) para auxiliar na publicação de dados abertos governamentais (DAGs). FD é uma iniciativa da Open Knowledge Foundation, que pretende remover o "atrito" no trabalho com os dados, ou seja, quando se perde muito tempo e recursos para entender e trabalhar com o dado. No âmbito das instituições públicas, a ausência de padrões para publicação e processamento dos DAGs é problema comum quando se trata de abertura dos DAGs. O estudo procurou contribuir ao aplicar o modelo ao conjunto de dados com informações sobre as boas práticas executadas pelo Judiciário relacionadas aos Objetivos de Desenvolvimento Sustentável (ODS). Segundo a Portaria nº 133, a integração dos ODS com as boas práticas do Judiciário pode trazer diversos benefícios como: aperfeiçoamento dos mecanismos de busca nos Portais de Transparência dos Tribunais, de forma associada aos ODS; auxiliar na medição da eficiência do Poder Judiciário em atingir os ODS, entre outros. O modelo se baseou no Data Publication Workflow, um fluxo para publicação de dados disponível no site da iniciativa Frictionless Data e que aborda etapas como empacotamento, tratamento e publicação dos dados. A aplicação do modelo também utilizou ferramentas de código aberto baseados em Frictionless Data e os resultados mostraram a sua viabilidade para a abertura de um conjunto de dados abertos governamentais.

Author(s):  
Joana Rodrigues ◽  
Nelson Pereira ◽  
Joao Rocha Da Silva ◽  
Yulia Karimova ◽  
Joao Aguiar Castro ◽  
...  

Author(s):  
Yulia Karimova ◽  
Joao Aguiar Castro; ◽  
Joao Rocha Da Silva ◽  
Nelson Pereira ◽  
Joana Rodrigues ◽  
...  

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michael Denker ◽  
Sonja Grün ◽  
Thomas Wachtler ◽  
Hansjörg Scherberger

Abstract Preparing a neurophysiological data set with the aim of sharing and publishing is hard. Many of the available tools and services to provide a smooth workflow for data publication are still in their maturing stages and not well integrated. Also, best practices and concrete examples of how to create a rigorous and complete package of an electrophysiology experiment are still lacking. Given the heterogeneity of the field, such unifying guidelines and processes can only be formulated together as a community effort. One of the goals of the NFDI-Neuro consortium initiative is to build such a community for systems and behavioral neuroscience. NFDI-Neuro aims to address the needs of the community to make data management easier and to tackle these challenges in collaboration with various international initiatives (e.g., INCF, EBRAINS). This will give scientists the opportunity to spend more time analyzing the wealth of electrophysiological data they leverage, rather than dealing with data formats and data integrity.


2021 ◽  
Author(s):  
G. Agoua ◽  
P. Cauchois ◽  
O. Chaouy ◽  
I. Gazeau ◽  
B. Grossin

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Li Kuang ◽  
Yujia Zhu ◽  
Shuqi Li ◽  
Xuejin Yan ◽  
Han Yan ◽  
...  

With the rapid development of sensor acquisition technology, more and more data are collected, analyzed, and encapsulated into application services. However, most of applications are developed by untrusted third parties. Therefore, it has become an urgent problem to protect users’ privacy in data publication. Since the attacker may identify the user based on the combination of user’s quasi-identifiers and the fewer quasi-identifier fields result in a lower probability of privacy leaks, therefore, in this paper, we aim to investigate an optimal number of quasi-identifier fields under the constraint of trade-offs between service quality and privacy protection. We first propose modelling the service development process as a cooperative game between the data owner and consumers and employing the Stackelberg game model to determine the number of quasi-identifiers that are published to the data development organization. We then propose a way to identify when the new data should be learned, as well, a way to update the parameters involved in the model, so that the new strategy on quasi-identifier fields can be delivered. The experiment first analyses the validity of our proposed model and then compares it with the traditional privacy protection approach, and the experiment shows that the data loss of our model is less than that of the traditional k-anonymity especially when strong privacy protection is applied.


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