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Communication
Published in Walter DeGrange, Lucia Darrow, Field Guide to Compelling Analytics, 2022
Some tips for conducting interviews: Plan out your questions in advance and ensure that they are relevant to the research question.Make sure you are familiar with the topic of the interview and can ask questions that probe further into the respondent's thoughts and experiences.Be respectful and attentive during the interview.Ensure that the interview is audio or video recorded. The recording will help to ensure accuracy and allow for later analysis.Make sure to thank the respondent for their time once the interview is completed.
Preparation for Experimental Biophysics
Published in Thomas M. Nordlund, Peter M. Hoffmann, Quantitative Understanding of Biosystems, 2019
Thomas M. Nordlund, Peter M. Hoffmann
In preparing for a course or career in the experimental biosciences, a critical skill is the collection and cultivation of experimental data. Virtually all modern experimental methods involve a measuring device that produces a digital file, which must be saved. Even more critical is the experimentalist’s skill at organizing and, when appropriate, archiving such data. Even in cases where the recording instrument autosaves the date and time, instrumental settings, operator’s name, and other information, the user must record a variety of information special to the particular experiment. This includes things like the date of sample preparation, sample source (e.g., a commercial company or a collaborator), a reference or link to a detailed description of the sample preparation, collaborators for the experiment, any problems or issues that you note in the sample preparation or data recording (e.g., the building temperature varied, possibly affecting the sample or a device’s stability; the water deionizer just had a filter change; a heavy truck drove by during data collection) locations for primary and backup data storage. As you can see, the data management problem can be daunting.
Force-System Resultants and Equilibrium
Published in Richard C. Dorf, The Engineering Handbook, 2018
The errors that occur in an experiment are usually categorized as mistakes or recording errors (blunders), systematic or fixed errors, and accidental or random errors. Mistakes or recording errors are usually the result of blunders (e.g., the observer reads 10.1 instead of 11.1 units on the scale of a meter). It is assumed that careful experimental practices will minimize the occurrence of these blunders. They are not a part of an objective uncertainty analysis.Systematic errors (formerly called bias errors) are errors that persist and cannot be considered due entirely to chance. Systematic errors may result from the residual errors in instrument calibration and relate to instrument performance (the ability of the instrument to indicate the true value). These errors do not change for the duration of a test or experiment.Random errors cause readings to take random values on either side of some mean value. They may be due to the observer or the instrument and are revealed by repeated observations. They are disordered in incidence and variable in magnitude.If an error source causes scatter in test data, it is a random source. If not, it is systematic.
Experiment and numerical investigation on optimal distribution of discrete ICs for different orientation of substrate board
Published in International Journal of Ambient Energy, 2022
V. K. Mathew, Tapano Kumar Hotta
Appropriate care is taken in recording the parameters and the instruments are well-calibrated. For derived quantities, it is calculated based on the recorded parameters (i.e measured value) as mentioned in Venkateshan (2008) and is given in Equation (2). α is the derived quantity, β is the recorded quantity, and Δβ is the error in the recorded quantity.