This data examines the two-month effect of vlogging on communication skills of first-year primary education students. It includes pre- and post-test scores from 75 students, assessed on politeness, comprehension, body language, content clarity, attitude, and creativity.

This dataset is a collection of CBT results from prospective students participating in the primary school teacher professional education program.

We include the course syllabus used to teach quantitative research design and analysis methods to graduate Linguistics students using a blended teaching and learning approach. The blended course took place over two weeks and builds on a face to face course presented over two days in 2019. Students worked through the topics in preparation for a live interactive video session each Friday to go through the activities. Additional communication took place on Slack for two hours each week. A survey was conducted at the start and end of the course to ascertain participants' perceptions of the usefulness of the course. The links to online elements and the evaluations have been removed from the uploaded course guide.Participants who complete this workshop will be able to:- outline the steps and decisions involved in quantitative data analysis of linguistic data- explain common statistical terminology (sample, mean, standard deviation, correlation, nominal, ordinal and scale data)- perform common statistical tests using jamovi (e.g. t-test, correlation, anova, regression)- interpret and report common statistical tests- describe and choose from the various graphing options used to display data- use jamovi to perform common statistical tests and graph resultsEvaluationParticipants who complete the course will use these skills and knowledge to complete the following activities for evaluation:- analyse the data for a project and/or assignment (in part or in whole)- plan the results section of an Honours research project (where applicable)Feedback and suggestions can be directed to M Schaefer schaemn@unisa.ac.za

Analysis of ‘Evolution of nominal wages, consumer prices and real wages’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/17524277-bundesamt-fur-statistik-bfs on 08 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset presents the annual figures for the indexes and variations of nominal and real wages on the base 1939=100 by sex and the variation of consumer prices, since 1942. Descriptions of the variables in the CSV file are available in the Appendix.

--- Original source retains full ownership of the source dataset ---

Key information about United States Nominal GDP

• United States Nominal GDP reached 6,621.6 USD bn in Mar 2023, compared with 6,534.5 USD bn in the previous quarter.
• Nominal GDP in US is updated quarterly, available from Mar 1947 to Mar 2023, with an average number of 1,057.5 USD bn.
• The data reached an all-time high of 6,621.6 USD bn in Mar 2023 and a record low of 60.8 USD bn in Mar 1947.

• In the latest reports, US GDP expanded 1.6 % YoY in Mar 2023.
• Its GDP deflator (implicit price deflator) increased 5.4 % in Mar 2023.
• US GDP Per Capita reached 59,484.0 USD in Dec 2017.
• Its Gross Savings Rate was measured at 15.9 % in Mar 2023.
• For Nominal GDP contributions, Investment accounted for 20.7 % in Mar 2023.
• Public Consumption accounted for 14.1 % in Mar 2023.
• Private Consumption accounted for 67.9 % in Mar 2023.

ObjectiveTo provide a practical guidance for the analysis of N-of-1 trials by comparing four commonly used models.MethodsThe four models, paired t-test, mixed effects model of difference, mixed effects model and meta-analysis of summary data were compared using a simulation study. The assumed 3-cycles and 4-cycles N-of-1 trials were set with sample sizes of 1, 3, 5, 10, 20 and 30 respectively under normally distributed assumption. The data were generated based on variance-covariance matrix under the assumption of (i) compound symmetry structure or first-order autoregressive structure, and (ii) no carryover effect or 20% carryover effect. Type I error, power, bias (mean error), and mean square error (MSE) of effect differences between two groups were used to evaluate the performance of the four models.ResultsThe results from the 3-cycles and 4-cycles N-of-1 trials were comparable with respect to type I error, power, bias and MSE. Paired t-test yielded type I error near to the nominal level, higher power, comparable bias and small MSE, whether there was carryover effect or not. Compared with paired t-test, mixed effects model produced similar size of type I error, smaller bias, but lower power and bigger MSE. Mixed effects model of difference and meta-analysis of summary data yielded type I error far from the nominal level, low power, and large bias and MSE irrespective of the presence or absence of carryover effect.ConclusionWe recommended paired t-test to be used for normally distributed data of N-of-1 trials because of its optimal statistical performance. In the presence of carryover effects, mixed effects model could be used as an alternative.

IMS: Inductive Monitoring System The Inductive Monitoring System (IMS) is a tool that uses a data mining technique called clustering to extract models of normal system operation from archived data. IMS works with vectors of data values. IMS analyzes data collected during periods of normal system operation to build a system model. It characterizes how the parameters relate to one another during normal operation by finding areas in the vector space where nominal data tends to fall. These areas are called nominal operating regions and correspond to clusters of similar points found by the IMS clustering algorithm. These nominal operating regions are stored in a knowledge base that IMS uses for real-time telemetry monitoring or archived data analysis. During the monitoring operation, IMS reads real-time or archived data values, formats them into the predefined vector structure, and searches the knowledge base of nominal operating regions to see how well the new data fits the nominal system characterization. For each input vector, IMS returns the distance that vector falls from the nearest nominal operating region. Data that matches the normal training data well will have a deviation distance of zero. If one or more of the data parameters is slightly outside of expected values, a small non-zero result is returned. As incoming data deviates further from the normal system data, indicating a possible malfunction, IMS will return a higher deviation value to alert users of the anomaly. IMS also calculates the contribution of each individual parameter to the overall deviation, which can help isolate the cause of the anomaly.

Analysis of ‘Energiepreise Verkehr, nominal, brutto Wien’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/f282acfb-d4ed-4f22-80b1-1530c4bb1765 on 11 January 2022.

--- Dataset description provided by original source is as follows ---

Energiepreise Verkehr, nominal, brutto

--- Original source retains full ownership of the source dataset ---

Patient categorical and nominal attributes.

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

Descriptive statistics for the nominal-scaled predictors and dependent variables.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over was 1252.00000 $ in January of 2023, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over reached a record high of 1252.00000 in January of 2023 and a record low of 893.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over - last updated from the United States Federal Reserve on June of 2025.

Czech Republic Real Services Sales Index: NACE 2: AE: Technical Testing and Analysis data was reported at 190.547 2010=100 in Dec 2017. This records an increase from the previous number of 154.676 2010=100 for Nov 2017. Czech Republic Real Services Sales Index: NACE 2: AE: Technical Testing and Analysis data is updated monthly, averaging 100.363 2010=100 from Jan 2000 (Median) to Dec 2017, with 216 observations. The data reached an all-time high of 197.513 2010=100 in Dec 2008 and a record low of 53.852 2010=100 in Aug 2000. Czech Republic Real Services Sales Index: NACE 2: AE: Technical Testing and Analysis data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.H012: Services Sales Index: Nominal and Real: 2010=100. Rebased from 2010=100 to 2015=100 Replacement series ID: 401334827

Czech Republic Services Sales Index: NACE 2: AE: Technical Testing and Analysis data was reported at 193.718 2010=100 in Dec 2017. This records an increase from the previous number of 157.409 2010=100 for Nov 2017. Czech Republic Services Sales Index: NACE 2: AE: Technical Testing and Analysis data is updated monthly, averaging 95.807 2010=100 from Jan 2000 (Median) to Dec 2017, with 216 observations. The data reached an all-time high of 201.385 2010=100 in Dec 2008 and a record low of 39.512 2010=100 in Aug 2000. Czech Republic Services Sales Index: NACE 2: AE: Technical Testing and Analysis data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.H012: Services Sales Index: Nominal and Real: 2010=100. Rebased from 2010=100 to 2015=100 Replacement series ID: 401334557

FAST5/FASTQ data used for accuracy characterization of decoding techniques applied to the HEDGEs DNA-information storage code. FASTQ data is used to evaluate the hard-decoding algorithm as explained by Press et al. in (https://doi.org/10.1073/pnas.2004821117). FAST5 data is used in evaluation for both our novel Alignment Matrix soft decoder (https://doi.org/10.5281/zenodo.11454877), and the soft decoder developed by Chandak et al. in the publication (10.1109/ICASSP40776.2020.9053441). Our code repository at https://doi.org/10.5281/zenodo.11454877 includes a GPU accelerated adaptation of Chandak et al.’s algorithm in order to scale analysis on the submitted FAST5 data, and this is the version of code used to evaluate the algorithm’s accuracy and runtime overhead.

Within the archive there are several sub-archives. Explanations for each sub-archive can be found for the corresponding archive name within the README.md file.

Analysis of ‘Energiepreise privater Haushalte, nominal, brutto Wien’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/63a4d51b-4b5c-4c4d-8d1e-74945c143036 on 12 January 2022.

--- Dataset description provided by original source is as follows ---

Energiepreise privater Haushalte, nominal, brutto

--- Original source retains full ownership of the source dataset ---

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over (LEU0257856500A) from 2011 to 2023 about second quartile, occupation, jobs, full-time, compensation, benefits, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.