Hence, governments, businesses, and communities around the world are grappling with urgent questions concerning how to manage social life, trade, education, communications, travel, and social services beyond the immediate response to a new threat. While progress on developing vaccines is swift, it is doubtful that those will provide long-lasting immunity. The first wave of lockdowns in East Asia, Europe, and North America also transpired, and a second (worse) wave hit Europe at the time of this article going to press. We are well past hopes of containment, with infections sprouting even in countries that successfully avoided the brunt of the initial contagion, such as Australia, Serbia, and New Zealand. Over the coming years the human race will need to learn to live with the SARS-CoV-2 coronavirus-a biological entity that is now irrevocably entangled with our species, an invisible yet decisive part of our ecology and our social life. Introduction: Learning to Live With SARS-CoV-2 Keywords: COVID-19, predictive modeling, public health, surveillance, engagement, research planningġ. Placing more emphasis on the latter three imaginaries, which include (3) causal explanation, (4) evaluation of logistical decisions, and (5) identification of social and environmental need, I argue, would provide a more balanced, sustainable, and responsible avenue toward using data science to support human coexistence with coronavirus. The first two of these imaginaries, which consist of (1) population surveillance and (2) predictive modeling, have dominated the first wave of governmental and scientific responses, with potentially problematic implications for both research and society. In this article I provide a scaffold for such considerations by identifying five ways in which the data science contributions to the pandemic response are imagined and projected into the future, and reflecting on how such imaginaries inform current allocations of investment and priorities within and beyond the scientific research landscape. A portable version of the program can be found at the project's GitHub page.What are the priorities for data science in tackling COVID-19, and in which ways can big data analysis inform and support responses to the outbreak? It is imperative for data scientists to spend time and resources scoping, scrutinizing, and questioning the possible scenarios of use of their work- particularly given the fast-paced knowledge production required by an emergency situation such as the coronavirus pandemic. The only way to correct an error is to exit the program, or switch to a different book and discard the changes, that is assuming you didn't already save the book after the error was made.ĮPUB Metadata Editor is an open source application. The biggest problem with EPUB Metadata Editor is that it doesn't have an undo option. Click on the arrow button on the right edge of the screen for advanced options including batch file processing. You can save an ebook's cover to an image file should you decide to use it later.ĮPUB Metadata Editor cannot be used to read e-books, you'll need to click on the Set External Viewer button to choose a handler for viewing the file, e.g. The image will be loaded as the EPUB's cover. Or, use the right-click menu's "Use existing image or Change image" options and select a JPG, JPEG,PNG from your computer. Copy an image to the clipboard and switch to the EPUB editor, right-click on the cover and select paste image. Look up the book's name and you are likely to find several pictures that can be used as the cover image. The cover of the e-book is displayed on the right-hand side of the EPUB Metadata Editor interface.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |