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

Financial overview and grant giving statistics of University Of Pennsylvania Master Of Applied Positive Psychology Alu

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

That sounds like a valuable dataset for anyone interested in pursuing a master's degree in Data Science or Analytics in the United States! This dataset is very helpful to pursuing a master's degree in the US. - Are you looking to analyze this dataset for any specific insights or purposes? - Do you need assistance with anything related to it?

About .csv file

• 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 program's tuition fees are usually 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 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).

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.

Dataset used for the case study in the MSc thesis "Robust fleet planning under stochastic demand" and in the OMEGA journal article "Portfolio-based airline fleet planning under stochastic demand".

  The dataset contains:
  - historical passenger data extracted from the TranStats database of the Bureau of Transportation Statistics (BTS/US DOT). The underlying dataset that was used is the T-100 Domestic Market (U.S. Carriers) data table that contains monthly scheduled US domestic passenger data based on a 10 percent ticket sale information dataset, aggregated for all airlines for the period 1990-2014. 
  - estimated parameters, per market, for the the mean reverting Ornstein-Uhlenbeck process.
  - model parameters used in the optimisation model, including aircraft features.
  - computed aggregated transition probabilities used in the discrete-time Markov Chain in the scenario generation model.
  - BTS/US DOT fare data from 2014.

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

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 FMRIB�s 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 individual�s high-resolution space with a linear six-parameter rigid body transformation. We then registered the image to standard space (Montreal Neurological Institute, MNI) using FMRIB�s 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 participant�s 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 FMRIB�s 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 document contains additional information on the study and the full interview data of the 16 recently graduated master students. The students were asked, in the 11 questions lined out below, how they perceive research data management. The questions are grouped together and the numbers in the first column indicate the participant number. The original data has not been modified since the interview itself, so it contains the original notes as written down during the interviews. This means that some sentences do not read as good as after a language check, but it ensures that the reader can easily follow the original line of the interviews and the original notes that led to the report. Next to the original notes including the open and axial coding (table 4), this document also contains the participant’s study and university backgrounds (table 1), an extensive overview of the codes applied to the raw interview data (table 2), and a full list of quotes used in the paper (table 3).

The Light Vehicle Brake Master Cylinder market plays a crucial role in the automotive industry, serving as a key component in the hydraulic braking system of cars, SUVs, and light trucks. Acting as the heart of the braking system, the brake master cylinder converts the force applied by the driver on the brake pedal

Datasets included in our MS compendium.

Tests with postfix “base” were performed using in-sample test sets. For the others, the supervised set was split into 80% training and 20% testing.

The identification of differential patterns in data originating from combined measurement techniques such as LC/MS is pivotal to proteomics. Although “shotgun proteomics” has been employed successfully to this end, this method also has severe drawbacks, because of its dependence on largely untargeted MS/MS sequencing and databases for statistical analyses. Alternatively, several MS-signal-based (MS/MS-independent) methods have been published that are mainly based on (univariate) Student’s t-tests. Here, we present a more robust multivariate alternative employing linear discriminant analysis. Like the t-test-based methods, it is applied directly to LC/MS data, instead of using MS/MS measurements. We demonstrate the method on a number of simulated data sets, as well as on a spike-in LC/MS data set, and show its superior performance over t-tests.

MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information.

ABSTRACT Objective: To summarize the production of the Professional Master's Program in Nursing Care Management of the Federal University of Santa Catarina, between 2013 and 2016. Method: electronic documental research. After data collection, we analyzed the numbers of defenses in relation to what was predicted by the respective public notices; as well as sex, training time and professional area of the authors; scenario, context and research line; general objective, analysis support model, methodological approach, instruments/techniques of data collection, and technique of analysis; and, finally, technological productions. Results: 57 dissertations were found and subjected to analysis. The highest number of defenses took place in 2016, in the public scenario, in a care context, with a qualitative approach and having assistance protocols as a final product. Conclusion: Although the country has weaknesses in its educational system, results of the post-graduate level stand out through the technological productions of professional master's studies in nursing.