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\n\n \n \n LaZerte, S. E\n\n\n \n \n \n \n A tale of birds and data: How R saved a behavioural ecologist.\n \n \n \n\n\n \n\n\n\n 2017.\n
(SCO-SOC) Early Career Research Award Plenary\n\n
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@misc{lazerte_tale_2017,\n\taddress = {The joint meeting of the American Ornithological Society and the Society of Canadian Ornithologists/Société des ornithologistes du Canada (East Lansing, US)},\n\ttype = {Oral},\n\ttitle = {A tale of birds and data: {How} {R} saved a behavioural ecologist},\n\tabstract = {Through studies on chipmunks, chickadees, sparrows, and finches, I have investigated animal activity, communication, and movement, and even have had the occasional foray into geomorphology. Although seemingly having nothing in common, the thread that links these topics is the complexity of the underlying data. Some projects simply had too much data, some had 'hidden' data, which needed to be extracted. These types of projects are becoming ever more common; as researchers we have greater and more powerful techniques for data collection including physical tools (telemetry, GPS, Geo-Locators, RFID, Automated Recording Units, etc.) as well as social tools (citizen science). The conundrum that faces behavioural ecologists, accustomed to small sample sizes, is then how to manage and analyze these data: with great data comes the need for great management. While there are many tools for data management, R is one that has been growing in popularity. However, although R workshops and classes are cropping up everywhere, most users seem to focus on R for statistics, as opposed to R for data, and there often seems to be no middle ground between spreadsheet users and programming gurus. In this talk I will discuss my experiences with research in behavioural ecology and how using R software and programming language helped me address interesting questions that may otherwise have been out of my reach.},\n\tauthor = {LaZerte, Stefanie E},\n\tyear = {2017},\n\tnote = {(SCO-SOC) Early Career Research Award Plenary},\n}\n\n
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\n Through studies on chipmunks, chickadees, sparrows, and finches, I have investigated animal activity, communication, and movement, and even have had the occasional foray into geomorphology. Although seemingly having nothing in common, the thread that links these topics is the complexity of the underlying data. Some projects simply had too much data, some had 'hidden' data, which needed to be extracted. These types of projects are becoming ever more common; as researchers we have greater and more powerful techniques for data collection including physical tools (telemetry, GPS, Geo-Locators, RFID, Automated Recording Units, etc.) as well as social tools (citizen science). The conundrum that faces behavioural ecologists, accustomed to small sample sizes, is then how to manage and analyze these data: with great data comes the need for great management. While there are many tools for data management, R is one that has been growing in popularity. However, although R workshops and classes are cropping up everywhere, most users seem to focus on R for statistics, as opposed to R for data, and there often seems to be no middle ground between spreadsheet users and programming gurus. In this talk I will discuss my experiences with research in behavioural ecology and how using R software and programming language helped me address interesting questions that may otherwise have been out of my reach.\n
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\n\n \n \n LaZerte, S. E; and Albers, S.\n\n\n \n \n \n \n weathercan: An R package to access historical weather data from Environment and Climate Change Canada.\n \n \n \n\n\n \n\n\n\n 2017.\n
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@misc{lazerte_weathercan_2017,\n\taddress = {Meeting of the Prairie Division of the Canadian Association of Geographers (Morris, Canada)},\n\ttype = {Oral},\n\ttitle = {weathercan: {An} {R} package to access historical weather data from {Environment} and {Climate} {Change} {Canada}},\n\tauthor = {LaZerte, Stefanie E and Albers, SJ},\n\tyear = {2017},\n}\n\n
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