Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

Why We Read Wikipedia

Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, we observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Our findings advance our understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.Comment: Published in WWW'17; v2 fixes caption of Table

Bulk Scheduling with the DIANA Scheduler

Results from the research and development of a Data Intensive and Network Aware (DIANA) scheduling engine, to be used primarily for data intensive sciences such as physics analysis, are described. In Grid analyses, tasks can involve thousands of computing, data handling, and network resources. The central problem in the scheduling of these resources is the coordinated management of computation and data at multiple locations and not just data replication or movement. However, this can prove to be a rather costly operation and efficient sing can be a challenge if compute and data resources are mapped without considering network costs. We have implemented an adaptive algorithm within the so-called DIANA Scheduler which takes into account data location and size, network performance and computation capability in order to enable efficient global scheduling. DIANA is a performance-aware and economy-guided Meta Scheduler. It iteratively allocates each job to the site that is most likely to produce the best performance as well as optimizing the global queue for any remaining jobs. Therefore it is equally suitable whether a single job is being submitted or bulk scheduling is being performed. Results indicate that considerable performance improvements can be gained by adopting the DIANA scheduling approach.Comment: 12 pages, 11 figures. To be published in the IEEE Transactions in Nuclear Science, IEEE Press. 200

Effects of immersion in inquiry-based learning on student teachers’ educational beliefs

Professional development on inquiry-based learning (IBL) generally draws heavily on the principle of providing instruction in line with what teachers are expected to do in their classroom. So far, however, relatively little is known about how this impacts teachers' educational beliefs, even though these beliefs ultimately determine their classroom behavior. The present study therefore investigates how immersion in inquiry-based learning affects student teachers' beliefs about knowledge goals, in addition to their self-efficacy for inquiry. In total, 302 student history teachers participated in a 4-h long inquiry activity designed within the WISE learning environment, and completed a pre- and posttest right before and after the intervention. Multilevel analyses suggest that the intervention had a significant positive effect on the value that student teachers attributed to procedural knowledge goals, or learning how historical knowledge is constructed, and on student teachers' self-efficacy for conducting inquiries. Despite these general positive results, however, the results also show that the impact of the intervention differed significantly across students. In particular, it appears that immersion in IBL had little effect on a subgroup of 25 student-teachers, who held largely content-oriented beliefs. Based on these findings, the present study discusses a number of implications for professional development on IBL

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

Learning entrepreneurship in a multicultural context

ABSTRACT Nowadays learning entrepreneurship in higher education became an important issue. International experiences promote the relationship between students from several countries in a multicultural context. In this sense it was developed an entrepreneurial game were tutors have the role to support students during the activities. The objective of entrepreneurial game objective is to create a business idea and develop a small business plan to present to the group. The general aim of this paper is to describe this international experience of Setúbal Business week. The specific goals are:  Understand how students learning in an international environment;  Understand how international multicultural groups function;  Evaluate how this kind of game improve a set of competencies, such as entrepreneurial spirit, capacity to work in an international team, oral communication, creativity, confidence and research skills;  Evaluate business week performance in order to improve future events. The study concludes with some recommendations and remarks about learning in an entrepreneurship in a multicultural environment.Entrepreneurial Game; Multiculturalism; International Environment

Innovative collaborative design in international interaction design summer schools

[About the book]: Design is changing, and to educate the next generation of designers, these changes need to be addressed. In light of the growing role research and interdisciplinary collaboration play in contemporary design performance, Design Integrations calls for an innovative shake up in design education. Poggenpohl asserts that design research is developed through a typology within academic and business contexts, and follows different research theories and strategies. Such issues in design collaboration are explored in-depth, with essays on an inter-institutional academic project, cross-cultural learning experiences, and a multi-national healthcare project, demonstrating the importance of shared values, interdisciplinary negotiated process and clear communication for tomorrow’s designers