Data collection

Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture evidence that allows data analysis to lead to the formulation of credible answers to the questions that have been posed. Regardless of the field of or preference for defining data (quantitative or qualitative), accurate data collection is essential to maintain research integrity. The selection of appropriate data collection instruments (existing, modified, or newly developed) and delineated instructions for their correct use reduce the likelihood of errors. Methodology scientific method D

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CS-411: Digital education

This course addresses the relationship between specific technological features and the learners' cognitive processes. It also covers the methods and results of empirical studies on this topic: do student actually learn due to technologies?

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This course provides the bases to understand material and energy production and consumption processes. Students learn how to develop a material flow analysis and apply it to cases of resource management. They analyze the implications of their models on resource use, economic activities and policy.

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JAMAP: a Web-Based Management Platform for IP Networks

Jean-Philippe Martin-Flatin

In this paper, we describe JAMAP, a prototype of a Web-based management platform for IP networks. It is entirely written in Java. It implements the push model to perform regular management (permanent network monitoring and data collection) and ad hoc management (temporary network monitoring and troubleshooting). The communication between agents and managers relies on HTTP transfers between Java applets and servlets over persistent TCP connections. The SNMP MIB data is encapsulated in serialized Java objects that are transmitted as MIME parts via HTTP. The manager consists of two parts: the management server, a static machine that runs the servlets, and the management station, which can be any desktop running a Web browser. The MIB data is transparently compressed with gzip, which saves network bandwidth without increasing latency too significantly.

1999

A Grammar of Muqarnas: Drawings of the Alhambra by Jones and Goury (1834-1845)

Ignacio Ferrer Perez-Blanco

The muqarnas of the Nasrid Alhambra stand out as one of the most singular architectural episodes o f Medieval Islamic art due to their sophisticated three-dimensional construction, whose layout remains little known. In 1834 and 1837, the architects Owen Jones and Jules Gouty visited the monument in Granada and accomplished surprising drawings thereof later published towards 1842-1845 as chromolithograph prints, which represented its architecture, ornaments, and muqarnas in a systematic way for the first time. In order to value their pioneering drawings, the historical data is briefly reviewed and several previous drawings of the Alhambra are cited. After consideration of a few questions regarding the process of data collection in Granada, a comparative analysis is presented of a cornice, a capital, an arch and a pendentive, with photos and computer-aided drawings. Thus, the principles or elementary grammar of muqarnas groupings described by these architects are highlighted, as well as the precision of their images, which resulted crucial to integrate and disseminate the architectural legacy of the Alhambra in the contemporary Western culture.

2019

State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data

Michael Ingram, Mario Paolone, Panagiotis Papadopoulos

Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of such technologies has unlocked new opportunities to introduce new data driven algorithms for improving the stability assessment and control of the system. Motivated by these challenges, a group of experts have worked together to highlight and establish a baseline set of these common concerns, which can be used as motivation to propose innovative analytics and data-driven solutions. In this document, the results of a survey on 10 transmission system operators around the world are presented and it aims to understand the current practices of the participating companies, in terms of data acquisition, handling, storage, modelling and analytics. The overall objective of this document is to capture the actual needs from the interviewed utilities, thereby laying the groundwork for setting valid assumptions for the development of advanced algorithms in this field.

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