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To meet the criteria for trustworthiness during data collection, we used multiple approaches as suggested by Lincoln and Guba (1985) and detailed below.
For data collection, we used the Sony Xperia Active smartphone which is dust and water-resistant, has a 3-inch capacitive touchscreen and a built-in ANT-radio (http://www.thisisant.com).
For the data collection, we used interviews, as well as information provided by members of our expert panel, in addition to information collected from websites and provided at a bioeconomy event.
For the data collection, we used interviews, as well as information provided by members of our expert panel, in addition to information collected from websites and provided at a bioeconomy event.1 To guide the analysis, we used the conceptual work of Wüstenhagen et al., as discussed in the Background section.
To minimize delays during data collection, we used event-recording software (ETHOM) to record the time of each observation [23].
For automated data collection we used a Resusci-Anne™ Skill Meter (Laerdal Medical AS, Stavanger, Norway).
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As the method for data collection, we use an online questionnaire.
Since these were not available in coded form in the large data collection we are using, we sought to obtain estimates for these covariates from the case notes and discharge summary text corpus available for each patient, using NLP tools.
For data collection we will use a questionnaire containing items from the Epa cardio abstraction tool [ 32].
Given the different scales of measure (frequencies versus counts) used for above- and belowground data collection, we chose not to use sample rarefaction methods (Simberloff 1972).
For data collection, we selected three frequently used beaches (English Bay, Kitsilano Beach and Second Beach in Stanley Park) and parks (Oppenheimer Park, Victoria Park and Victory Square) in Vancouver.
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