Simulation data generated from code in: https://github.com/TerrestrialEcology-ugent/simsem/blob/master/scripts/simSEM_simulations.R

This dataset provides comprehensive information about various Data Science and Analytics master's programs offered in the United States. It includes details such as the program name, university name, annual tuition fees, program duration, location of the university, and additional information about the programs.

Column Descriptions:

• Subject Name: The name or field of study of the master's program, such as Data Science, Data Analytics, or Applied Biostatistics.

• University Name: The name of the university offering the master's program.

• Per Year Fees: The tuition fees for the program, usually given in euros per year. For some programs, the fees may be listed as "full" or "full-time," indicating a lump sum for the entire program or for full-time enrollment, respectively.

• About Program: A brief description or overview of the master's program, providing insights into its curriculum, focus areas, and any unique features.

• Program Duration: The duration of the master's program, typically expressed in years or months.

• University Location: The location of the university where the program is offered, including the city and state.

• Program Name: The official name of the master's program, often indicating its degree type (e.g., M.Sc. for Master of Science) and format (e.g., full-time, part-time, online).

Explore the progression of average salaries for graduates in Master Of Science In Applied Statistics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Master Of Science In Applied Statistics relative to other fields. This data is essential for students assessing the return on investment of their education in Master Of Science In Applied Statistics, providing a clear picture of financial prospects post-graduation.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for applied mathematics statistics also recvd masters in applied mathematics statistics in the U.S.

Explore the progression of average salaries for graduates in Applied Statistics I.E., Masters Degree In Statistics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Applied Statistics I.E., Masters Degree In Statistics relative to other fields. This data is essential for students assessing the return on investment of their education in Applied Statistics I.E., Masters Degree In Statistics, providing a clear picture of financial prospects post-graduation.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Applied Statistics, Info Syst Stats Mgt Science. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Applied Statistics, Info Syst Stats Mgt Science. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

Result of the "Adding group-level predictors" in https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/multilevel_modeling.ipynb

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Statistical Science, Applied Statistics And Data Analytics. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Statistical Science, Applied Statistics And Data Analytics. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Master Of Science Applied Statistics. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Master Of Science Applied Statistics. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

The maximum recording duration at which decoding at 100 ms temporal resolution is possible with 95% decoding accuracy. An optimal DNAP with an elongation time of 1 ms and no pausing is used, along with 10000 DNA templates. The search for maximal achievable recording durations was performed at 25 second intervals.

Here we provide 3 spreadsheets with all the primary data (as received from the from the Laser Ablation ICP-MS facility at University of New Brunswick) LA-ICP-MS (Laser Ablation - Induced Coupled Plasma - Mass Spectrometry) performed on samples of stromatolitic dolostone from the Lower Transvaal Supergroup in Botswana.

These files contain the raw data of LA-ICP-MS as received from the laboratory at the University of New Brunswick (Canada). The analyses have been performed on Neoarchean stromatolitic dolostone from the Ramonnedi Formation (lower Transvaal Supergroup) of Botswana. Each file contains 2 spreadsheets, one with results reduced using NCS610 standard and the other one with results reduced using MACS standard.

Below is a description of the content of the spreadsheets. Columns A-B: reference material and source file name (internal laboratory code); Columns C-E: date and time of the execution of the analyses Column F: duration of the analyses Column G: name of the sample analyzed Column H: calcium content in Counts Per Second Column I: Internal Standard Error Columns J - BU: trace element concentrations and internal standard errors.

LA-ICP-MS analysis has been carried out on thin sections at the Laser Ablation ICP-MS facility at University of New Brunswick (UNB), Fredericton (Canada), using an Agilent 7700x ICP-MS coupled with a Coherent CompexPro 110 (193 nm Excimer laser) and a Resonetics M-50-LR laser ablation system. Carbonate analyses were performed using a 33µm spot size with a repetition rate of 3Hz and an on-sample energy of 5J/cm2, with a 30s ablation and a 30s gas blank between each ablation. Carrier gasses were ultra-pure helium (300 ml/min), ultra-pure nitrogen (2 ml/min), and standard Argon (930 ml/min). The second rotary pump was also used which almost doubles the sensitivities of heavy isotopes. A full suite of elements was monitored during tuning to ensure maximum sensitivity over the range of masses we were analyzing, while keeping doubly charged ions and oxides at a maintainable level (below 0.3% for each). Standards used were NIST610 and MACS-3. Calcium was used as an internal standard for data reduction of carbonate samples. The dwell times for most isotopes were kept at 0.01 sec per isotope, allowing us the lowest possible sweep time for each method.

We also provide a zip file with the R codes for the clustering, MANOVA, and discriminant analysis and the table with the primary data in the right format for running the codes.

Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both undergraduate and graduate statistics and data science curricula due to their wide range of applications. In this article, we present a one-week course module for students in advanced undergraduate and applied graduate courses on variational inference, a popular optimization-based approach for approximate inference with probabilistic models. Our proposed module is guided by active learning principles: In addition to lecture materials on variational inference, we provide an accompanying class activity, an R shiny app, and guided labs based on real data applications of logistic regression and clustering documents using Latent Dirichlet Allocation with R code. The main goal of our module is to expose students to a method that facilitates statistical modeling and inference with large datasets. Using our proposed module as a foundation, instructors can adopt and adapt it to introduce more realistic case studies and applications in data science, Bayesian statistics, multivariate analysis, and statistical machine learning courses.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Master Of Science In Applied Statistics. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Master Of Science In Applied Statistics. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

More information about the context and the methodology can be found in the README.md file and online at this link: https://github.com/sdgis-edu-tud/fair-data-publication-groupf.

Along with the Elbe river, Dresden comprises a dense network of streams, which are spread out across its fabric. Presently, the streams are secluded from being a valuable part of the city. The problems are characterised by ecological issues, inappropriate land use by residents, and artificial channeling. They, along with the Elbe river hold potential to become elements of integrating the ecological and social functions of the city by reclaiming the historical identity of waterfronts and restoring natural habitats. Therefore, there arises a need to understand how to integrate these streams into the network of protected green areas and public spaces, while maximising their contribution to biodiversity while adapting to the risk of flooding within and around the city.

These concerns and identified potentials beg the question that, how can urban streams be restored and integrated in Dresden's fabric, such that there is a synergy between human activities and the natural environment?

This is investigated by adopting an integrated approach for biodiversity, climate adaptation and quality of life.

Based on the three criteria that we decided to tackle, we came up with numerical indicators that we could use to evaluate them. These numerical indicators are called attributes and have to be normalised—in our case between 0 and 1—so that they can be compared, weighted and thereafter clustered properly depending on their relevance and similarities.

The spatial units used in this study are hexagons with a dimension of 250 meters. The study area of Dresden is divided using a complete surface of a hexagonal pattern. Then it is overlaid with the water stream network and river body from OpenStreetMap to keep only the hexagons that intersect with at least one stream. Finally, the isolated hexagons were removed.

Two data-driven methods were used to conduct the analysis:

• S-MCDA (Spatial Multi-Criteria Decision Analysis) — S-MCDA was used to weigh the different attributes against each other. The method supports decision-making by evaluating and ranking alternatives (the attributes) within the three objectives of biodiversity, climate adaptation and quality of life.
• Typology Construction — Typology construction is used to group attributes into homogenous types based on similarities. This was used to identify patterns in data and make clusters of attributes that show similarity, which can thereafter be used to understand the type of interventions which would be impactful.

This dataset contains both the values computed for the attributes in each spatial unit and the final results of the two methods.

Morocco MA: Tariff Rate: Applied: Weighted Mean: Primary Products data was reported at 6.950 % in 2016. This records a decrease from the previous number of 7.070 % for 2015. Morocco MA: Tariff Rate: Applied: Weighted Mean: Primary Products data is updated yearly, averaging 11.990 % from Dec 1993 (Median) to 2016, with 17 observations. The data reached an all-time high of 30.190 % in 1993 and a record low of 4.250 % in 2014. Morocco MA: Tariff Rate: Applied: Weighted Mean: Primary Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank: Trade Tariffs. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;

The average age of German first degree university graduates has gone down in recent years, which means that students are both starting their studies and finishing them earlier, without prolonging. Currently the average age stands at **** years old. After their first degree many graduates might also decide to pursue a second one, for example a Masters. Over half a million first-years In the most recent winter semester, there were ******* first-year students. At German universities, the academic year is divided into the winter and summer semesters. Start and end dates may vary depending on the type of university and course of study. On average, first-degree students studied for around eight semesters. State universities still attracted the ******* student numbers, followed by universities of applied sciences. What do they study? German universities offer a wide variety of courses and degrees. In terms of subject groups, the ******* number of students were enrolled in law, economics and sciences, followed by engineering. These numbers might be related to thoughts about the future, when looking at average starting salaries for university graduates by field.

Context

This dataset is originally from Dhaka Stock Exchange Ltd. The objective of the dataset is to assign analytical report writing tasks to Summer 2020 students enrolled in ASDS18: Data Mining course in proceedings of the partial fulfillment of the requirements for the Professional Masters in Applied Statistics and Data Science (PMASDS) degree. This data set was collected using the Dhaka Stock Exchange API.

Content

The datasets consist of several stock company predictor (independent) variables and one target (dependent) variable, Outcome. Independent variables include the last price, net asset value (NAV) of the stock, Earnings Per Share (EPS), price-to-earnings (P/E) ratio of the stock, paid-up capital per share, and so on.

It contains information on 374 listed companies from Dhaka Stock Exchange - DSE, Bangladesh. The outcome tested was Category, 258 tested positive and 500 tested negative. Therefore, there is one target (dependent) variable and 8 attributes.

Acknowledgements

Dr. Md. Rezaul Karim, Associate Professor, Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh (2021) provided us with this dataset. Using the Dhaka Stock Exchange API this data set was collected to assign analytical report writing tasks to Summer 2020 students in proceedings of the partial fulfillment of the requirements for the Professional Masters in Applied Statistics and Data Science (PMASDS) degree.

DSE Listed Companies Database

Inspiration

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Applied Statistics Data Analytics. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Applied Statistics Data Analytics. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

EEG_data files contain EEG preprocessed data for each subject,session. EEG_events contain two cells of relevant events for the two sessions of each subject

EEG_data Y : [number of electrodes x number_of_times double] => EEG activity for all electrodes and all times

EEG_events fields for each cell

respt: [1xnumber_of_trials double] => response onset times (in ms)

rt: [1xnumber_of_trials double] => reaction times (ms)

tstim: [1xnumber_of_trials double] => stimulus onset times (in ms)

resptexcl: [1x13 double] => excluded response times

tstimexcl: 5.8806e+05 => excluded stimulus onset times

diffV: [1xnumber_of_trials double]=>item rating difference (absolute stimulus difficulty)

corr: [1xnumber_of_trials logical] =>accuracy (1:correct, 0:error)

ratL: [1xnumber_of_trials double]=>rating of item on the left of fixation cross

ratR: [1xnumber_of_trials double]=>rating of item on the right of fixation cross

chooseL: [1xnumber_of_trials logical]=>choosing left? (1:yes, 0:no)

chooseR: [1xnumber_of_trials logical]=>choosing right? (1:yes, 0:no)

t0: constant time to shift fMRI events to align to EEG onset times (see below)

METHODS OF EEG PREPROCESSING We performed EEG pre-processing offline using MATLAB (Mathworks, Natick, MA). EEG signals recorded inside an MR scanner are contaminated with gradient and ballistocardiogram (BCG) artifacts due to magnetic induction on the EEG leads. We first removed the gradient artifacts. Specifically, from each functional volume acquisition we subtracted the average artifact template constructed using the 80 volumes centred on the volume-ofinterest using in-house MATLAB software. We repeated this process for as many times as there were functional volumes in our data sets. We subsequently applied a 10-ms median filter to remove any residual spike artifacts. Next, we band-pass filtered the data by applying a 0.5-Hz high-pass filter to remove direct current (DC) drifts and a 40Hz low-pass filter to remove high frequency artifacts not associated with neurophysiological processes of interest. These filters were applied together, non-causally to avoid distortions caused by phase delays. BCG artifacts share frequency content with the EEG and as such are more challenging to remove. To avoid loss of signal power in the underlying EEG we adopted a conservative approach and removed a small number of BCG components using principal component analysis in two steps. Firstly, four BCG principal components were extracted from data that were initially low-pass filtered at 4Hz to extract the signal within the frequency range where BCG artifacts are observed. Secondly, the sensor weightings corresponding to those components were projected onto the broadband (original) data and subtracted out.

fMRI_data files contain fMRI preprocessed data for each subject,session.

METHODS FOR fMRI PREPROCESSING We discarded the first ten volumes from each fMRI run to ensure a steady-state MR signal, and we used the remaining 307 volumes for the statistical analysis presented in this study. Pre-processing of our data was performed using the FMRIBĺs Software Library (Functional MRI of the Brain, Oxford, UK) and included: head-related motion correction, slice-timing correction, high-pass filtering (4100 s), and spatial smoothing (with a Gaussian kernel of 8mm full-width at half maximum). To register our EPI image to standard space, we first transformed the EPI images into each individualĺs high-resolution space with a linear six-parameter rigid body transformation. We then registered the image to standard space (Montreal Neurological Institute, MNI) using FMRIBĺs Non-linear Image Registration Tool with a resolution warp of 10 mm. Finally, B0 unwarping was applied to correct for signal loss and geometric distortions due to B0 field inhomogeneities in the EPI images.

METHODS TO CREATE fMRI REGRESSORS We performed whole-brain statistical analyses of functional data using a multilevel approach within the generalized linear model (GLM) framework, as implemented in FSL through the FEAT module: Y= Xb + E = b1X1+ b2X2 + b3X3 +b4X4 + E where Y is the times series of a given voxel comprising T time samples and X is a Tx4 design matrix with columns representing four different regressors (see below) convolved with a canonical hemodynamic response function (double-g function). The regressors times are shifted by the fMRI t0 (the EEG time at which the scanner started) which is saved in the EEG events files.

b is a 4x1 column vector of regression coefficients and e a Tx1 column vector of residual error terms. We performed a first-level analysis to analyse each participantĺs individual runs, which were then combined using a second-level analysis (fixed effects). Finally, we used a third-level, mixed-effects model (FLAME 1) to combine data across subjects, treating participants as a random effect. Time-series statistical analysis was carried out using FMRIBĺs improved linear model with local autocorrelation correction.

Our GLM model included an EEG-informed regressor capturing the trial-by-trial dynamics of the process of EA. Specifically, for each trial we used the raw EEG time-series (from the subject-specific sensor that was most predictive of the model-derived EA profile) to parametrically modulate the regressor amplitudes. We considered the entire trial duration (that is, RT) minus the subject-specific nDT estimated by the model, which accounted for stimulus processing and motor execution. More specifically, we split this nDT in two intervals by fixing the motor preparation to 100 ms prior to the response (when a sudden increase in corticospinal excitability occurs) and setting the average duration of the stimulus encoding to nDT-100 ms . To absorb the variance associated with other task-related processes we included three additional regressors: (1) an unmodulated stick function regressor at the onset of the stimuli, (2) a stick function regressor at the onset of stimuli that was parametrically modulated by the VD between the decision alternatives and (3) a stick function regressor aligned at the time of response and modulated by RT . As a control analysis we also removed the RT and VD regressors from the GLM design to test if our EEG-informed regressor absorbed additional activations. The only activation we found in the EEG-informed regressor was the one capturing accumulation dynamics as in the main analysis (that is, pMFC) with a marginal improvement in the statistical significance of the area.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Applied Statistics (Related To Statistics And Mathematics). It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Applied Statistics (Related To Statistics And Mathematics). This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.