To find any favored zodiac sign for celebrity births, a sample of 100 celebrities were randomly selected from people of different walks of life. The sample contained politicians, natural scientists, social scientists, authors of literary works, social workers, humanitarian workers, business personnel, sports icons, singers, actors, actresses, etc. etc. from history and from the current time. The zodiac signs for the celebrities were found from their known dates of births. In the analysis of data, zodiac signs and the number of celebrities were represented as the independent x and the dependent y variables, respectively. For academic interests for the 9th grade high school juniors (at the time of the project performance), the co-authors of this article, as well as for the potentially illustrative uses in high school mathematics textbooks, bar and scatter plots were made, the line of best-fit and the equation of the line were found, probabilities of occurrences of celebrities for each of the zodiac signs were calculated and the correlation coefficients between the variables were determined for the sample. It was found that the zodiac Aquarius has the largest number of celebrities in the sample and that the two variables are moderately correlated. The sample sizes which were increased to 200 and then 300. By including another 100 more celebrities to find if the trend remains unchanged. In all the three cases, Aquarius turned out to be the zodiac when most of the celebrities are born.
As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand their data sets’ contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a data set. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search-engine indexing to reach a broader audience of interested parties. This Tutorial first explains terminology and standards relevant to data dictionaries and codebooks. Accompanying information on OSF presents a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared data set accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we discuss freely available Web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable.
As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand its contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a dataset. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search engine indexing to reach a broader audience of interested parties. This tutorial first explains the terminology and standards surrounding data dictionaries and codebooks. We then present a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared dataset accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we explain how to use freely available web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable (FAIR; Wilkinson et al., 2016).
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