Most scientific research generates data. Analysis of the data from scientific research helps create new knowledge or a deep understanding of natural phenomena. Statistical software is used mainly in data analysis. SPSS and Minitab appear to be most popular, especially for those that could neither code nor mathematical inclined to handle advanced software such as R, MATLAB, Maple, etc. Trends and usage pattern of SPSS and Minitab Software in Scientific research was studied in this paper with the data obtained from the Scopus database. In their abstracts or keywords, documents that have mentioned SPSS were extracted for the years 2010 to 2019. Frequency analysis showed that the trend of using SPSS and Minitab is steadily increasing, although the use of Minitab is a fraction of SPSS. Minitab is mostly used in engineering, materials science, and computer science, while SPSS is mainly used in medicine, social science, and engineering. Analysis of the document type showed that SPSS and Minitab are mostly stated in abstracts or keywords of research articles, conference papers, review papers, and books indexed in Scopus.

<p class="0abstract"><strong>Abstract—</strong> Social media and YouTube, in particular, has become an avenue for quick dissemination of information. Patients now search the YouTube website for information on diseases, treatment options, surgery, and general health information. This paper reviews the different reliability methods, results, conclusions and recommendations of contributions on the medical videos on YouTube. A keyword search was done on different databases such as PubMed, Scopus, Web of Science and Google Scholar to generate articles related to the subject matter. No eligibility criteria were defined because the research is partly systematic. Descriptive statistics were used to present the information obtained from the analysis of the previously published papers in this context.                                                                                                                                              �                        � The results are as follows: (i). DISCERN, JAMAS and GQS are the most frequent assessment tools used by authors in the determination of the reliability of medical videos on YouTube. (ii). 60% of the independent reviewers that assessed the reliability of the YouTube videos are often two in number. (iii). 65% of the articles concluded that medical videos on YouTube contain misleading information. (iv). User engagements for low and high-quality videos are 58% and 42% respectively. (v). 36.3 % of the total videos were uploaded by trusted sources such as medical and health professionals from recognized or prestigious hospitals, while 63.7% were uploaded by other sources whose affiliations cannot be independently verified. (vi). Out of the total 2675 medical videos assessed, 1589 (59%) are categorized as having useful contents that can influence positively on patient education while 1086 (41%) are categorized as misleading and (vii). Only 35% of the papers strongly recommended that medical videos on YouTube are useful and can be a good source of patient education. Awareness is needed to educate patients on the benefits and dangers of assessing medical videos on YouTube. Videos uploaded by authentic medical personnel or organizations are strongly recommended. </p>

This data article contains the statistical analysis of Igbo personal names and a sample of randomly selected of such names. This was presented as the following: 1). A simple random sampling of some Igbo personal names and their respective gender associated with each name. 2). The distribution of the vowels, consonants and letters of alphabets of the personal names. 3). The distribution of name length. 4). The distribution of initial and terminal letters of Igbo personal names. The significance of the data was discussed.

Impact factor (Web of Science, Clarivate Analytics) and CiteScore (Scopus, Elsevier) are the two leading metrics for journal evaluation, assessment and ranking. The relationship between the two is now established, using their respective percentile in this paper for 105 journal in the Computer science, theory and methods (CSTM) subject category. The available studies did not consider the quartile comparison of the journal percentiles of the two database (Scopus and Science Citation Index expanded). The mean impact factor and CiteScore are 2.08 and 2.67 respectively. Pearson correlation coefficient between the impact factor and CiteScore is (0.919, p = 0.000) and between their respective journal percentiles is (r = 0.804, p = 0.000). Analysis of variance revealed that the means of the impact factor and CiteScore of the 105 CSTM journals are the same (F = 3.64, P = 0.058) but different (F = 38.94, P = 0.00) for their respective percentiles. The median test contradicts the ANOVA as the medians of impact factor and CiteScore are different at 0.05 level of significance. The median journal percentiles are the same for only 2 journal titles. The median journal percentile (SCIE) is greater than the median journal percentile (Scopus) for 5 journal titles and less than the median journal percentile (Scopus) for 98 journal titles. The same result was obtained when the percentiles were converted to quartiles, but in this case, the median journal quartiles are the same for 37 journal titles. The median journal quartile (SCIE) is greater than the median journal quartile (Scopus) for 67 journal titles and less than the median journal quartile (Scopus) in only one journal title. Only 37 (35 %) journals are in the same quartile of the two metrics. Caution is recommended in journal evaluation as conflicting different results can be obtained using the same metric.

<p class="0abstract">The impact factor  and CiteScore of journals are known to be positively correlated with journal percentile but the use of the later to predict the formers are scarcely discussed, especially for journals in a specific subject classifications based on the web of science. This paper proposed different curve estimation models for predicting the impact factor and CiteScore of 89 telecommunication journals using their corresponding percentiles. Out of the 11 models, only Logistic, exponential, Growth and Compound models are the best models for predicting the impact factor and CiteScore using their corresponding journal percentiles. The models were chosen because of their high values of R Square and Adjusted R Square and low values of the standard error of the estimates. In addition, strong significant positive correlations were obtained between impact factor and the CiteScore of the journals. The findings will help authors and editors in decision making as regards to manuscript submission and planning.</p>