“…Chromatographic data remains locked in vendors’ hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC–DAD (photodiode array detector) raw data. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Dictionaries have historically presented selective information about words and their meanings within a language, or translation equivalents between languages, in idiosyncratic, incommensurable formats with little basis in data science. The Kamusi Project introduces a new approach, conceiving of language as a matrix of interrelated data elements. …”
Enlace del recurso

“…Lizhen Lin, PhD, is Assistant Professor in the Department of Statistics and Data Science at the University of Texas at Austin. She received a PhD in Mathematics from the University of Arizona and was a Postdoctoral Associate at Duke University. …”
Enlace del recurso
Enlace del recurso

“…This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. …”
Enlace del recurso
Enlace del recurso

“…This dissertation presents the first study of data preservation and research reproducibility in data science at the Large Hadron Collider at CERN. In particular, provenance capture of the experimental data and the reproducibility of physics analyses at the LHCb experiment were studied. …”
Enlace del recurso

“…These data contain valuable information for system reliability optimization, which can be extracted and processed with data-science methods and algorithms. However, many existing data-driven reliability optimization methods fail to exploit these data, because they make too simplistic assumptions of the system behavior, do not consider organizational contexts for cost-effectiveness, and build on specific monitoring data, which are too expensive to record. …”
Enlace del recurso

“…</p><h3>Speaker bio:&nbsp;</h3><p>Raja Appuswamy is an Assistant Professor in the Data Science department at EURECOM--a Grandes Écoles located in the sunny Sophia Antipolis tech-valley of southern France. …”
Enlace del recurso

“…Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. …”
Enlace del recurso
Enlace del recurso

“…Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The conventional modeling methods and severity risk scores lack multiple laboratories, paraclinical and imaging data. Data science and machine learning (ML) based prediction models may help better predict outcomes. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

Enlace del recurso
Enlace del recurso

“…Applied sciences have increased focus on omics studies which merge data science with analytical tools. These studies often result in large amounts of data produced and the objective is to generate meaningful interpretations from them. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Here, we present a principled veridical data science methodology for quantitative histology that shifts focus from image-level investigations towards neuron-level representations of cortical regions, with the neurons in the image as a subject of study, rather than pixel-wise image content. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…We present a new knowledgebase and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…A continued investment in this area should fill remaining gaps and seize opportunities, including i) supporting genebanks to play a more active role in linking with farmers using data-driven approaches; ii) designing low-cost, appropriate technologies for phenotyping; iii) generating more and better gender and socioeconomic data; iv) designing information products to facilitate decision-making; and v) building more capacity in data science. Broad, well-coordinated policies and investments are needed to avoid fragmentation of such capacities and achieve coherence between domains and disciplines so that crop diversity management systems can become more effective in delivering benefits to farmers, consumers, and other users of crop diversity.…”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…We believe that easy-to-use and multi-modal data science techniques, such as the one proposed in this study, could give rise to significant improvements in policy-making for successfully contrasting climate change.…”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The integrating theme of the Utah center focused on leveraging big data‐science approaches to improve the quality of care and outcomes for children with congenital heart defects, within the context of the patient and their family. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…We argue that this fuller understanding of the functionality and tractability of pathways must go beyond a focus on the mechanistic details of protein and drug structure to encompass their physiological history as well as their embedding within higher levels of organization in the organism, with numerous implications for data science addressing health and disease. Exploiting tools and concepts from behavioral and cognitive sciences to explore a proto-cognitive metaphor for the pathways underlying health and disease is more than a philosophical stance on biochemical processes; at stake is a new roadmap for overcoming the limitations of today’s pharmacological strategies and for inferring future therapeutic interventions for a wide range of disease states.…”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso