Geothematic open data in Umbria region

10.7287/peerj.preprints.2096v2 ◽

2016 ◽

Author(s):

Andrea Motti ◽

Norman Natali

Keyword(s):

Open Data ◽

Weighted Average ◽

Geological Survey ◽

Geological Model ◽

Data Format ◽

Characteristic Index ◽

Geological Maps ◽

Umbria Region ◽

Key Word ◽

Data Portal

Detailed information about geology, hydrogeology and seismic hazard issues for Umbria region are contained in a spatial database available as open data format (shapefile or KMZ) and distributed under the regional open data portal called Open Data Umbria (http://dati.umbria.it) where 297 datasets have been produced by Umbria Region until now and most of them are made by Geological Survey. Geological Survey of Regione Umbria carried out a 20 years program to produce 276 geological maps at 1:10.000 reference scale with an accurate geological model of the regional surface and providing millions of geological data. The key word is the characteristic index of the single geologic unit. Characteristic index , shown in percentage, calculates the ratio between the surface of the geologic units compared to their thickness. Thickness value for each geologic unit is intended to be based on rank level and calculated as weighted average of the thickness for each geologic unit.

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Geothematic open data in Umbria region

10.7287/peerj.preprints.2096v1 ◽

2016 ◽

Author(s):

Andrea Motti ◽

Norman Natali

Keyword(s):

Seismic Hazard ◽

Open Data ◽

Weighted Average ◽

Geological Survey ◽

Land Area ◽

Data Format ◽

Local Conditions ◽

Characteristic Index ◽

Italian Regions ◽

Umbria Region

Detailed information about geology, hydrogeology and seismic hazard issues for Umbria region are contained in a spatial database available as open data format (shape file or KMZ) and distributed under the regional open data portal called Open Data Umbria ( http://dati.umbria.it ) where 297 datasets have been produced by Umbria Region until now and most of them are made by Geological Survey. Development of standardized regional geologic database (BDG from now on) took about 20 years since 2010 to manage the huge set of information contained in the 276 geologic maps. As a result of migration to BDG, 231 distinct geologic units were found for Umbria Region territory represented by about 47,000 polygon features. The total land area of Umbria 8,475 km 2 wide is divided in the BDG into 46,982 different geological areas. Analysis of the information contained in the BDG is preliminary to the creation of more geothematic layers and custom maps. The key word is the characteristic index of the single geologic unit. Characteristic index, shown in percentage, calculates the ratio between the surface of the geologic units compared to their thickness. Thickness value for each geologic unit is intended to be based on rank level and calculated as weighted average of the thickness for each geologic unit. Calculations in terms of land area percentage show many differences between portions of the territory capable of storing water and the characteristic index of the geologic units capable of storing water. The situation changes if instead we analyze aquifers within individual geological domains and their characteristic index of the single geologic unit whose charts show significant differences. Moreover, after accurate analysis by the Geological Survey, regional seismic hazard maps were derived from the BDG and available as open data format. Umbria has been divided in thirteen zones where local conditions, i.e. presence of artificial fills or particular surface topography, may affect the shaking levels and amplify the effects of the earthquake. The total land area of Umbria is 8,475 square kilometers, and it has been classified in 69,675 unique zones each one characterized by particular seismic hazard. Statistics also show (in percent) that 48 of Umbria land area is characterized by morphological and stratigraphic conditions affecting the shake while 52 is not subject to amplification. Population living in area with no amplification is 322,987 accounting for 36.5 % of the total while 561,281 accounting for 63.5 % of the total live in area where amplification of the shake is likely to happen. Currently four italian regions, Emilia-Romagna, Marche, Tuscany and Umbria, have planned to cooperate starting from their own BDG and develop, after data generalization and analysis, a shared GIS based geologic database of Northern Appennines, following the European Standard database structure and format.

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

Jurnal Good Governance ◽

10.32834/gg.v15i1.41 ◽

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

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Open Data Portal - A Technical Enabler to Drive Innovation in Namibia

2016 2nd International Conference on Open and Big Data (OBD) ◽

10.1109/obd.2016.19 ◽

2016 ◽

Cited By ~ 2

Author(s):

Lameck M. Amugongo ◽

Shawulu H. Nggada ◽

Jurgen Sieck

Keyword(s):

Open Data ◽

Data Portal

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RESOURCES PROVIDING DATA FOR MACHINE LEARNING AND TESTING ARTIFICIAL INTELLIGENCE TECHNOLOGIES

Информационные и математические технологии в науке и управлении ◽

10.38028/esi.2021.22.2.004 ◽

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.

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