Earth system data cubes unravel global multivariate dynamics

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Earth system data cubes unravel global multivariate dynamics

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

The Mind’s Eye on Personal Profiles: A Cognitive Perspective on Profile Elements that Inform Initial Trustworthiness Assessments in Virtual Project Teams

Rusman, E., Van Bruggen, J., Sloep, P., Valcke, M., & Koper, R. (2013). The Mind’s Eye on Personal Profiles: A Cognitive Perspective on Profile Elements that Inform Initial Trustworthiness Assessments and Social Awareness in Virtual Project Teams. Computer Supported Cooperative Work (CSCW), 22(2-3), 159-179.Collaboration in virtual project teams heavily relies on interpersonal trust, for which perceived trustworthiness is an important determinant. This study provides insight in the information that trustors value to assess a trustee’s professional trustworthiness in the initial phase of a virtual project team. We expect trustors in virtual teams to value those particular information elements that provide them with relevant cues of trust warranting properties of a trustee. We identified a list of commonly highly valued information elements to inform trustworthiness assessments (n=226). We then analysed explanations for preferences with the help of a theory-grounded coding scheme. Results show that respondents value those particular information elements that provide them with multiple cues to assess the trustworthiness of a trustee. This enables them to become aware of and assess the trustworthiness of another. Information elements that provide unique cues could not be identified. Insight in these information preferences can inform the design of artefacts, such as personal profile templates, to support acquaintanceships in the initial phase of a virtual project team

Children s Acceptance of a Collaborative Problem Solving Game Based on Physical Versus Digital Learning Spaces

[EN] Collaborative problem solving (CPS) is an essential soft skill that should be fostered from a young age. Research shows that a good way of teaching such skills is through video games; however, the success and viability of this method may be affected by the technological platform used. In this work we propose a gameful approach to train CPS skills in the form of the CPSbot framework and describe a study involving 80 primary school children on user experience and acceptance of a game, Quizbot, using three different technological platforms: two purely digital (tabletop and handheld tablets) and another based on tangible interfaces and physical spaces. The results show that physical spaces proved to be more effective than the screen-based platforms in several ways, as well as being considered more fun and easier to use by the children. Finally, we propose a set of design considerations for future gameful CPS systems based on the observations made during this study.Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (project TIN2014-60077-R); Spanish Ministry of Education, Culture and Sport (with fellowship FPU14/00136) and Conselleria d'Educacio, Cultura i Esport (Generalitat Valenciana, Spain) (grant ACIF/2014/214).Jurdi, S.; García Sanjuan, F.; Nácher-Soler, VE.; Jaén Martínez, FJ. (2018). Children s Acceptance of a Collaborative Problem Solving Game Based on Physical Versus Digital Learning Spaces. Interacting with Computers. 30(3):187-206. https://doi.org/10.1093/iwc/iwy006S18720630

Delexicalized Word Embeddings for Cross-lingual Dependency Parsing

International audienceThis paper presents a new approach to the problem of cross-lingual dependency parsing, aiming at leveraging training data from different source languages to learn a parser in a target language. Specifically , this approach first constructs word vector representations that exploit structural (i.e., dependency-based) contexts but only considering the morpho-syntactic information associated with each word and its contexts. These delexicalized word em-beddings, which can be trained on any set of languages and capture features shared across languages, are then used in combination with standard language-specific features to train a lexicalized parser in the target language. We evaluate our approach through experiments on a set of eight different languages that are part the Universal Dependencies Project. Our main results show that using such delexicalized embeddings, either trained in a monolin-gual or multilingual fashion, achieves significant improvements over monolingual baselines

Statistical Significance Testing at CHI PLAY: Challenges and Opportunities for More Transparency

Statistical Significance Testing -- or Null Hypothesis Significance Testing (NHST) -- is common to quantitative CHI PLAY research. Drawing from recent work in HCI and psychology promoting transparent statistics and the reduction of questionable research practices, we systematically review the reporting quality of 119 CHI PLAY papers using NHST (data and analysis plan at https://osf.io/4mcbn/. We find that over half of these papers employ NHST without specific statistical hypotheses or research questions, which may risk the proliferation of false positive findings. Moreover, we observe inconsistencies in the reporting of sample sizes and statistical tests. These issues reflect fundamental incompatibilities between NHST and the frequently exploratory work common to CHI PLAY. We discuss the complementary roles of exploratory and confirmatory research, and provide a template for more transparent research and reporting practices.Peer reviewe

Multiple wheat genomes reveal global variation in modern breeding

Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat (Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome 1, and the lack of genome-assembly data for multiple wheat lines 2,3. Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses 4,5. We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm1 6, a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars