Suchergebnisse

Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models and more.

"This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes."

The proper preservation of both current and historical scientific data will underpin a multitude of ecological, economic and political decisions in the future of our society. The SCIDIP-ES project addresses the long-term persistent storage, access and management needs of scientific data by providing preservation infrastructure services. Taking exemplars from the Earth Science domain we highlight the key preservation challenges and barriers to be overcome by the SCIDIP-ES infrastructure. SCIDIP-ES augments existing science data e-infrastructures by adding specific services and toolkits, which implement core preservation concepts, thus guaranteeing the long-term access to data assets across and beyond their designated communities. ; European Space Agency ESA-ESRIN, Italy, Alliance for Permanent Access, The Netherlands, Science and Technology Facility Council, United Kingdom.

Scientific Data and Social Science Data Libraries

AbstractThis essay explores how to broaden the scope of what constitutes anthropological and ethnographic research by cross‐fertilizing with data science. I discuss four types of relationships anthropologists have sought to foster with data science: anthropology of data science, anthropology over data science, anthropology with data science, and, the least developed of the four, anthropology by data science. Data science as a field has cultivated abductive, bottom‐up forms of quantitative research, which provide useful quantitative parallels to similarly abductive, bottom‐up qualitative techniques in ethnographic research. Anthropologists should adopt an anthropology by data science perspective through incorporating machine‐learning and other data science techniques into anthropological research. Nick Seaver's concept of bastard disciplines and methodologies provides a helpful framework for such work. [data science, machine learning, ethnography, anthropology]

Cell imaging has entered the 'Big Data' era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the 'omics' fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools – democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.

The paper examines Facebook's ambition to extend into the entire web by focusing on social buttons and developing a medium-specific platform critique. It contextualises the rise of buttons and counters as metrics for user engagement and links them to different web economies. Facebook's Like buttons enable multiple data flows between various actors, contributing to a simultaneous de- and re-centralisation of the web. They allow the instant transformation of user engagement into numbers on button counters, which can be traded and multiplied but also function as tracking devices. The increasing presence of buttons and associated social plugins on the web creates new forms of connectivity between websites, introducing an alternative fabric of the web. Contrary to Facebook's claim to promote a more social experience of the web, this paper explores the implementation and technical infrastructure of such buttons to conceptualise them as part of a so-called 'Like economy'.