Visualizing and animating large-scale spatiotemporal data with ELBAR explorer

Visual exploration of data enables users and analysts observe interesting patterns that can trigger new research for further investigation. With the increasing availability of Linked Data, facilitating support for making sense of the data via visual exploration tools for hypothesis generation is critical. Time and space play important roles in this because of their ability to illustrate dynamicity, from a spatial context. Yet, Linked Data visualization approaches typically have not made efficient use of time and space together, apart from typical rather static multivisualization approaches and mashups. In this paper we demonstrate ELBAR explorer that visualizes a vast amount of scientific observational data about the Brazilian Amazon Rainforest. Our core contribution is a novel mechanism for animating between the di↵erent observed values, thus illustrating the observed changes themselves

Learning in the Era of Online Videos: How to Improve Teachers’ Competencies of Producing Educational Videos

[EN] Online videos have gained huge popularity among people seeking for entertainment, and increasingly also among learners. People seek for tips in videos, ranging widely from improving language skills to bike reparation, or from solving mathematical equations to indoor decoration. At the same time teachers are facing challenges of producing professional videos that can support learners to learn the variety of topics. Teachers at the higher education institutes are often professionals in presentation skills in classrooms, but can face challenges when presenting in videos. In this paper we present a process for improving skills needed to produce educational videos. The process starts from the assumption of often explicit question by the teacher: “How can I produce professional educational videos?” The process includes identifying main challenges teachers face, and continues with a series of hands-on workshops targeted to develop each of the skills followed by the video production itself. We report also an evaluation of the process with teachers from the Aalto University, discuss the implications and resulting categorization of production models.http://ocs.editorial.upv.es/index.php/HEAD/HEAD18Guseva, Y.; Kauppinen, T. (2018). Learning in the Era of Online Videos: How to Improve Teachers’ Competencies of Producing Educational Videos. Editorial Universitat Politècnica de València. 847-854. https://doi.org/10.4995/HEAD18.2018.8096OCS84785

Methods for creating and using geospatio-temporal semantic web

This dissertation discusses the problems and the methods of creating and using ontologies in the area of digital cultural heritage. One of the problems is that content annotations in semantic cultural heritage portals commonly make spatiotemporal references to historical regions and places using names whose meanings are different in different times. For example, historical administrational regions such as countries, municipalities, and cities have been renamed, merged together, split into parts, and annexed or moved to and from other regions. The contribution of this dissertation to this problem is to develop methods which can be used to model, produce and utilize geospatio-temporal ontologies. The resources in geospatio-temporal ontologies can be used as annotation terms for describing content, and also for seeking information. The main point of this dissertation is to describe schemas, models and methods that produce and utilize a geospatio-temporal ontology. The schemas and the models are used as inputs for the methods. These methods generate identifiers for spatio-temporal instances, and also relationships between them. In this work, historical Finnish municipalities were modeled and geospatio-temporal descriptions for them created from a filled-up schema. Methods enriched the models by creating geospatio-temporal relationships between these temporal municipalities. The resulting collection of models are referred to as the Finnish Spatio-temporal Ontology (Suomen ajallinen paikkaontologia, SAPO). Specific relationships of the geo-spatiotemporal instances provided the basis for novel recommendation, data mining and visualization schemes. The results of the experiments were promising. For example, with the help of the ontology a user has the ability to retrieve also the content annotated to a historic region even if she searches using a contemporary name of the same or partially overlapping region. The work contributes also to modeling and reasoning about imprecise temporal intervals. A set of different measures based on analyzing two fuzzy temporal intervals are presented and evaluated in the work. The use of a combination of different measures for calculating relevance between temporal intervals was found out to perform best

VGI Edit History Reveals Data Trustworthiness and User Reputation

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Volunteered Geographic Information (VGI) is an approach to crowdsource information about geospatial features around us. People around the world are engaged with typing in their observations about the world (like locations of shops, cafeterias), or to semi-automatically gather them with mobile devices (like hiking paths or roads). In this process people might make mistakes, for instance assign misleading tags to features or provide over simplistic boundaries for features. In this paper we study what kinds of things might contribute to assess trustworthiness of data, and reputation of contributors for VGI. We present a model for analysing the different factors, and a method for automatically creating the trust and reputation scores

Youth Centres as Foodscapes and Informal Learning Environments in Finland

Although not adequately studied in the research on youth work, food is and has been an integral element of youth centres. This article examines what characterizes youth centres as foodscapes and explores which aspects of youth centres support the food-related learning of young people. We merge the traditions of youth work research, food education and learning. The data of the study consist of two rounds of focus group interviews (N = 14) conducted with young people aged 13 to 17. The data are qualitatively analysed using the five aspects meal model, which was developed to analyse foodscapes. The results of this study suggest that when youth centres, as foodscapes, are based on the active participation of young people and offer them opportunities to have an influence on the activities and work with peers, it is easier for young people to be inspired and learn.Peer reviewe

Physical characteristics of collimators for dual-isotope imaging with Tc-99m and I-123

The purpose of this study was to compare the physical characteristics of Low Energy High Resolution (LEHR), Low Energy Ultra High Resolution (LEUHR) and Medium Energy Low Penetration (MELP) collimators for simultaneous Tc-99m and I-123 imaging. MELP collimator performed well with I-123 high-energy gamma photons, but low resolution makes it unsuitable to use for acquisition of small structures such as parathyroid adenomas. LEUHR collimators optimized for Tc-99m have highest resolution, but the differences in septal penetration and sensitivity in favor of LEHR collimator needs to be tested with specific parathyroid phantoms.Peer reviewe

What Can Be Learnt from Experienced Data Scientists? A Case Study

Data science has the potential to create value and deep customer insight for service and software engineering. Companies are increasingly applying data science to support their service and software development practices. The goal of our research was to investigate how data science can be applied in software development organisations. We conducted a qualitative case study with an industrial partner. We collected data through a workshop, focus group interview and feedback session. This paper presents the data science process recommended by experienced data scientists and describes the key characteristics of the process, i.e., agility and continuous learning. We also report the challenges experienced while applying the data science process in customer projects. For example, the data scientists highlighted that it is challenging to identify an essential problem and ensure that the results will be utilised. Our findings indicate that it is important to put in place an agile, iterative data science process that supports continuous learning while focusing on a real business problem to be solved. In addition, the application of data science can be demanding and requires skills for addressing human and organisational issues.Peer reviewe