This data set contains example data for exploration of the theory of regression based regionalization. The 90th percentile of annual maximum streamflow is provided as an example response variable for 293 streamgages in the conterminous United States. Several explanatory variables are drawn from the GAGES-II data base in order to demonstrate how multiple linear regression is applied. Example scripts demonstrate how to collect the original streamflow data provided and how to recreate the figures from the associated Techniques and Methods chapter.

Site-specific multiple linear regression models were developed for eight sites in Ohio—six in the Western Lake Erie Basin and two in northeast Ohio on inland reservoirs--to quickly predict action-level exceedances for a cyanotoxin, microcystin, in recreational and drinking waters used by the public. Real-time models include easily- or continuously-measured factors that do not require that a sample be collected. Real-time models are presented in two categories: (1) six models with continuous monitor data, and (2) three models with on-site measurements. Real-time models commonly included variables such as phycocyanin, pH, specific conductance, and streamflow or gage height. Many of the real-time factors were averages over time periods antecedent to the time the microcystin sample was collected, including water-quality data compiled from continuous monitors. Comprehensive models use a combination of discrete sample-based measurements and real-time factors. Comprehensive models were useful at some sites with lagged variables (< 2 weeks) for cyanobacterial toxin genes, dissolved nutrients, and (or) N to P ratios. Comprehensive models are presented in three categories: (1) three models with continuous monitor data and lagged comprehensive variables, (2) five models with no continuous monitor data and lagged comprehensive variables, and (3) one model with continuous monitor data and same-day comprehensive variables. Funding for this work was provided by the Ohio Water Development Authority and the U.S. Geological Survey Cooperative Water Program.

The Ice Cream Selling dataset is a simple and well-suited dataset for beginners in machine learning who are looking to practice polynomial regression. It consists of two columns: temperature and the corresponding number of units of ice cream sold.

The dataset captures the relationship between temperature and ice cream sales. It serves as a practical example for understanding and implementing polynomial regression, a powerful technique for modeling nonlinear relationships in data.

The dataset is designed to be straightforward and easy to work with, making it ideal for beginners. The simplicity of the data allows beginners to focus on the fundamental concepts and steps involved in polynomial regression without overwhelming complexity.

By using this dataset, beginners can gain hands-on experience in preprocessing the data, splitting it into training and testing sets, selecting an appropriate degree for the polynomial regression model, training the model, and evaluating its performance. They can also explore techniques to address potential challenges such as overfitting.

With this dataset, beginners can practice making predictions of ice cream sales based on temperature inputs and visualize the polynomial regression curve that represents the relationship between temperature and ice cream sales.

Overall, the Ice Cream Selling dataset provides an accessible and practical learning resource for beginners to grasp the concepts and techniques of polynomial regression in the context of analyzing ice cream sales data.

Summary : Fuel demand is shown to be influenced by fuel prices, people's income and motorization rates. We explore the effects of electric vehicle's rates in gasoline demand using this panel dataset.

Files : dataset.csv - Panel dimensions are the Brazilian state ( i ) and year ( t ). The other columns are: gasoline sales per capita (ln_Sg_pc), prices of gasoline (ln_Pg) and ethanol (ln_Pe) and their lags, motorization rates of combustion vehicles (ln_Mi_c) and electric vehicles (ln_Mi_e) and GDP per capita (ln_gdp_pc). All variables are all under the natural log function, since we use this to calculate demand elasticities in a regression model.

adjacency.csv - The adjacency matrix used in interaction with electric vehicles' motorization rates to calculate spatial effects. At first, it follows a binary adjacency formula: for each pair of states i and j, the cell (i, j) is 0 if the states are not adjacent and 1 if they are. Then, each row is normalized to have sum equal to one.

regression.do - Series of Stata commands used to estimate the regression models of our study. dataset.csv must be imported to work, see comment section.

dataset_predictions.xlsx - Based on the estimations from Stata, we use this excel file to make average predictions by year and by state. Also, by including years beyond the last panel sample, we also forecast the model into the future and evaluate the effects of different policies that influence gasoline prices (taxation) and EV motorization rates (electrification). This file is primarily used to create images, but can be used to further understand how the forecasting scenarios are set up.

Sources: Fuel prices and sales: ANP (https://www.gov.br/anp/en/access-information/what-is-anp/what-is-anp) State population, GDP and vehicle fleet: IBGE (https://www.ibge.gov.br/en/home-eng.html?lang=en-GB) State EV fleet: Anfavea (https://anfavea.com.br/en/site/anuarios/)

The goal of this study was to adapt a recently proposed linear large-scale support vector machine to large-scale binary cheminformatics classification problems and to assess its performance on various benchmarks using virtual screening performance measures. We extended the large-scale linear support vector machine library LIBLINEAR with state-of-the-art virtual high-throughput screening metrics to train classifiers on whole large and unbalanced data sets. The formulation of this linear support machine has an excellent performance if applied to high-dimensional sparse feature vectors. An additional advantage is the average linear complexity in the number of non-zero features of a prediction. Nevertheless, the approach assumes that a problem is linearly separable. Therefore, we conducted an extensive benchmarking to evaluate the performance on large-scale problems up to a size of 175000 samples. To examine the virtual screening performance, we determined the chemotype clusters using Feature Trees and integrated this information to compute weighted AUC-based performance measures and a leave-cluster-out cross-validation. We also considered the BEDROC score, a metric that was suggested to tackle the early enrichment problem. The performance on each problem was evaluated by a nested cross-validation and a nested leave-cluster-out cross-validation. We compared LIBLINEAR against a Naïve Bayes classifier, a random decision forest classifier, and a maximum similarity ranking approach. These reference approaches were outperformed in a direct comparison by LIBLINEAR. A comparison to literature results showed that the LIBLINEAR performance is competitive but without achieving results as good as the top-ranked nonlinear machines on these benchmarks. However, considering the overall convincing performance and computation time of the large-scale support vector machine, the approach provides an excellent alternative to established large-scale classification approaches.

Full title: Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule Abstract: Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.

This dataset contains simulated datasets, empirical data, and R scripts described in the paper: “Li, Q. and Kou, X. (2021) WiBB: An integrated method for quantifying the relative importance of predictive variables. Ecography (DOI: 10.1111/ecog.05651)”.

A fundamental goal of scientific research is to identify the underlying variables that govern crucial processes of a system. Here we proposed a new index, WiBB, which integrates the merits of several existing methods: a model-weighting method from information theory (Wi), a standardized regression coefficient method measured by ß* (B), and bootstrap resampling technique (B). We applied the WiBB in simulated datasets with known correlation structures, for both linear models (LM) and generalized linear models (GLM), to evaluate its performance. We also applied two other methods, relative sum of wight (SWi), and standardized beta (ß*), to evaluate their performance in comparison with the WiBB method on ranking predictor importances under various scenarios. We also applied it to an empirical dataset in a plant genus Mimulus to select bioclimatic predictors of species’ presence across the landscape. Results in the simulated datasets showed that the WiBB method outperformed the ß* and SWi methods in scenarios with small and large sample sizes, respectively, and that the bootstrap resampling technique significantly improved the discriminant ability. When testing WiBB in the empirical dataset with GLM, it sensibly identified four important predictors with high credibility out of six candidates in modeling geographical distributions of 71 Mimulus species. This integrated index has great advantages in evaluating predictor importance and hence reducing the dimensionality of data, without losing interpretive power. The simplicity of calculation of the new metric over more sophisticated statistical procedures, makes it a handy method in the statistical toolbox.

Methods To simulate independent datasets (size = 1000), we adopted Galipaud et al.’s approach (2014) with custom modifications of the data.simulation function, which used the multiple normal distribution function rmvnorm in R package mvtnorm(v1.0-5, Genz et al. 2016). Each dataset was simulated with a preset correlation structure between a response variable (y) and four predictors(x1, x2, x3, x4). The first three (genuine) predictors were set to be strongly, moderately, and weakly correlated with the response variable, respectively (denoted by large, medium, small Pearson correlation coefficients, r), while the correlation between the response and the last (spurious) predictor was set to be zero. We simulated datasets with three levels of differences of correlation coefficients of consecutive predictors, where ∆r = 0.1, 0.2, 0.3, respectively. These three levels of ∆r resulted in three correlation structures between the response and four predictors: (0.3, 0.2, 0.1, 0.0), (0.6, 0.4, 0.2, 0.0), and (0.8, 0.6, 0.3, 0.0), respectively. We repeated the simulation procedure 200 times for each of three preset correlation structures (600 datasets in total), for LM fitting later. For GLM fitting, we modified the simulation procedures with additional steps, in which we converted the continuous response into binary data O (e.g., occurrence data having 0 for absence and 1 for presence). We tested the WiBB method, along with two other methods, relative sum of wight (SWi), and standardized beta (ß*), to evaluate the ability to correctly rank predictor importances under various scenarios. The empirical dataset of 71 Mimulus species was collected by their occurrence coordinates and correponding values extracted from climatic layers from WorldClim dataset (www.worldclim.org), and we applied the WiBB method to infer important predictors for their geographical distributions.

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Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

Full title: Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule Abstract: Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.

Context

A fictional dataset for exploratory data analysis (EDA) and to test simple prediction models.

This toy dataset features 150000 rows and 6 columns.

Columns

Note: All data is fictional. The data has been generated so that their distributions are convenient for statistical analysis.

Number: A simple index number for each row

City: The location of a person (Dallas, New York City, Los Angeles, Mountain View, Boston, Washington D.C., San Diego and Austin)

Gender: Gender of a person (Male or Female)

Age: The age of a person (Ranging from 25 to 65 years)

Income: Annual income of a person (Ranging from -674 to 177175)

Illness: Is the person Ill? (Yes or No)

Acknowledgements

Stock photo by Mika Baumeister on Unsplash.

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Description:

The advertising dataset captures the sales revenue generated with respect to advertisement costs across multiple channels like radio, tv, and newspapers.

It is required to understand the impact of ad budgets on the overall sales.

Acknowledgement:

The dataset is taken from Kaggle

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

Author: Isabelle Guyon
Source: UCI
Please cite: Isabelle Guyon, Steve R. Gunn, Asa Ben-Hur, Gideon Dror, 2004. Result analysis of the NIPS 2003 feature selection challenge.

Abstract:

MADELON is an artificial dataset, which was part of the NIPS 2003 feature selection challenge. This is a two-class classification problem with continuous input variables. The difficulty is that the problem is multivariate and highly non-linear.

Source:

Isabelle Guyon Clopinet 955 Creston Road Berkeley, CA 90708 isabelle '@' clopinet.com

Data Set Information:

MADELON is an artificial dataset containing data points grouped in 32 clusters placed on the vertices of a five-dimensional hypercube and randomly labeled +1 or -1. The five dimensions constitute 5 informative features. 15 linear combinations of those features were added to form a set of 20 (redundant) informative features. Based on those 20 features one must separate the examples into the 2 classes (corresponding to the +-1 labels). It was added a number of distractor feature called 'probes' having no predictive power. The order of the features and patterns were randomized.

This dataset is one of five datasets used in the NIPS 2003 feature selection challenge. The original data was split into training, validation and test set. Target values are provided only for two first sets (not for the test set). So, this dataset version contains all the examples from training and validation partitions.

There is no attribute information provided to avoid biasing the feature selection process.

Relevant Papers:

The best challenge entrants wrote papers collected in the book: Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lofti Zadeh (Eds.), Feature Extraction, Foundations and Applications. Studies in Fuzziness and Soft Computing. Physica-Verlag, Springer.

Isabelle Guyon, et al, 2007. Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark. Pattern Recognition Letters 28 (2007) 1438–1444.

Isabelle Guyon, et al. 2006. Feature selection with the CLOP package. Technical Report.

In this assignment, we will be implementing linear and logistic regression on a given dataset. In addition, we will experiment with design and feature choices.

We will be using the SGEMM GPU kernel performance Data Set available for download at https://archive.ics.uci.edu/ml/datasets/SGEMM+GPU+kernel+performance

Goal: Implement a linear regression model on the dataset to predict the GPU run time. Use the average of four runs as the target variable. You are not allowed to use any available implementation of the regression model. You should implement the gradient descent algorithm with batch update (all training examples used at once). Use the sum of squared error normalized by 2*number of samples [J(β0, β1) = (1/2m)[Σ(yᶺ(i) – y(i))2] as your cost and error measures, where m is number of samples. You should use all 14 features.

Also implement a logistic regression model as described in Part 4. Again, you are not allowed to use any available implementation of the logistic regression model. You should implement the gradient descent algorithm with batch update (all training examples used at once). You should use the logistic regression cost/error function from the class. In addition you can also use accuracy/ROC/etc.

Tasks: *Part 1: *Download the dataset and partition it randomly into train and test set using a good train/test split percentage. Part 2: Design a linear regression model to model the average GPU run time. Include your regression model equation in the report. Part 3: Implement the gradient descent algorithm with batch update rule. Use the same cost function as in the class (sum of squared error). Report your initial parameter values. Part 4: Convert this problem into a binary classification problem. The target variable should have two categories. Implement logistic regression to carry out classification on this data set. Report accuracy/error metrics for train and test sets.

Experimentation: *1. *Experiment with various parameters for linear and logistic regression (e.g. learning rate ∝) and report on your findings as how the error/accuracy varies for train and test sets with varying these parameters. Plot the results. Report the best values of the parameters. 2. Experiment with various thresholds for convergence for linear and logistic regression. Plot error results for train and test sets as a function of threshold and describe how varying the threshold affects error. Pick your best threshold and plot train and test error (in one figure) as a function of number of gradient descent iterations. 3. Pick eight features randomly and retrain your models only on these ten features. Compare train and test error results for the case of using your original set of features (14) and eight random features. Report the ten randomly selected features. 4. Now pick eight features that you think are best suited to predict the output, and retrain your models using these ten features. Compare to the case of using your original set of features and to the random features case. Did your choice of features provide better results than picking random features? Why? Did your choice of features provide better results than using all features? Why?

Source:

Enrique G. Paredes (egparedes '@' ifi.uzh.ch). Visualization and MultiMedia Lab, Department of Informatics, University of Zurich. Zurich, 8050. Switzerland Rafael Ballester-Ripoll (rballester '@' ifi.uzh.ch). Visualization and MultiMedia Lab, Department of Informatics, University of Zurich. Zurich, 8050. Switzerland

Citation:

If you use this data set, please cite one or both of these:

• Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola. Sobol Tensor Trains for Global Sensitivity Analysis. In arXiv Computer Science / Numerical Analysis e-prints, 2017

• Cedric Nugteren and Valeriu Codreanu. CLTune: A Generic Auto-Tuner for OpenCL Kernels. In: MCSoC: 9th International Symposium on Embedded Multicore/Many-core Systems-on-Chip. IEEE, 2015

Multiple linear regression models were developed using data collected in 2016 and 2017 from three recurring bloom sites in Kabetogama Lake in northern Minnesota. These models were developed to predict concentrations of cyanotoxins (anatoxin-a, microcystin, and saxitoxin) that occur within the blooms. Virtual Beach software (version 3.0.6) was used to develop four models: two cyanotoxin mixture (MIX) models and two microcystin (MC) models. Models include those using readily available environmental variables (for example, wind speed and specific conductance) and those using additional comprehensive variables (based on laboratory analyses). Many of the independent variables were averages over a certain time period prior to a sample date, whereas other independent variables were lagged between 4 and 8 days. Funding for this work was provided by the U.S Geological Survey – National Park Service Partnership and the U.S. Geological Survey Environmental Health Program (Toxic Substance Hydrology and Contaminant Biology). The resulting model equations and final datasets are included in this data release while an associated child item model archive includes all the files needed to run and develop these VB models.

The National Health and Nutrition Examination Survey (NHANES) provides data and have considerable potential to study the health and environmental exposure of the non-institutionalized US population. However, as NHANES data are plagued with multiple inconsistencies, processing these data is required before deriving new insights through large-scale analyses. Thus, we developed a set of curated and unified datasets by merging 614 separate files and harmonizing unrestricted data across NHANES III (1988-1994) and Continuous (1999-2018), totaling 135,310 participants and 5,078 variables. The variables conveydemographics (281 variables),dietary consumption (324 variables),physiological functions (1,040 variables),occupation (61 variables),questionnaires (1444 variables, e.g., physical activity, medical conditions, diabetes, reproductive health, blood pressure and cholesterol, early childhood),medications (29 variables),mortality information linked from the National Death Index (15 variables),survey weights (857 variables),environmental exposure biomarker measurements (598 variables), andchemical comments indicating which measurements are below or above the lower limit of detection (505 variables).csv Data Record: The curated NHANES datasets and the data dictionaries includes 23 .csv files and 1 excel file.The curated NHANES datasets involves 20 .csv formatted files, two for each module with one as the uncleaned version and the other as the cleaned version. The modules are labeled as the following: 1) mortality, 2) dietary, 3) demographics, 4) response, 5) medications, 6) questionnaire, 7) chemicals, 8) occupation, 9) weights, and 10) comments."dictionary_nhanes.csv" is a dictionary that lists the variable name, description, module, category, units, CAS Number, comment use, chemical family, chemical family shortened, number of measurements, and cycles available for all 5,078 variables in NHANES."dictionary_harmonized_categories.csv" contains the harmonized categories for the categorical variables.“dictionary_drug_codes.csv” contains the dictionary for descriptors on the drugs codes.“nhanes_inconsistencies_documentation.xlsx” is an excel file that contains the cleaning documentation, which records all the inconsistencies for all affected variables to help curate each of the NHANES modules.R Data Record: For researchers who want to conduct their analysis in the R programming language, only cleaned NHANES modules and the data dictionaries can be downloaded as a .zip file which include an .RData file and an .R file.“w - nhanes_1988_2018.RData” contains all the aforementioned datasets as R data objects. We make available all R scripts on customized functions that were written to curate the data.“m - nhanes_1988_2018.R” shows how we used the customized functions (i.e. our pipeline) to curate the original NHANES data.Example starter codes: The set of starter code to help users conduct exposome analysis consists of four R markdown files (.Rmd). We recommend going through the tutorials in order.“example_0 - merge_datasets_together.Rmd” demonstrates how to merge the curated NHANES datasets together.“example_1 - account_for_nhanes_design.Rmd” demonstrates how to conduct a linear regression model, a survey-weighted regression model, a Cox proportional hazard model, and a survey-weighted Cox proportional hazard model.“example_2 - calculate_summary_statistics.Rmd” demonstrates how to calculate summary statistics for one variable and multiple variables with and without accounting for the NHANES sampling design.“example_3 - run_multiple_regressions.Rmd” demonstrates how run multiple regression models with and without adjusting for the sampling design.

The U.S. Geological Survey (USGS) and the Minnesota Pollution Control Agency (MPCA) conducted a cooperative study to develop linear regression models that quantify relations among 173 hydrologic explanatory metrics in five categories (duration, frequency, magnitude, rate-of-change, and timing) computed from streamgage records and 132 biological response metrics in six categories (composition, habitat, life history, reproductive, tolerance, trophic) computed from fish community samples collected from the 1996-2015 water years (WYs). In addition, linear relations were quantified between hydrologic metrics, fish-based indices of biotic integrity (FIBI) scores, and FIBI scores normalized to the impairment threshold of the corresponding stream class (FIBI_BCG4), resulting in a total of 134 regression equations per hydrologic dataset. Three hydrologic datasets were used to examine relations between altered hydrology and fish community responses at different temporal scales. First, the period-of-record (POR) dataset was created by computing hydrologic metrics using all complete WYs of streamflow record (1945WY and later) and ending with the WY of corresponding biological sample collection. Next, datasets representing long-term changes (LTC) and short-term changes (STC) in hydrology were created using ratios of hydrologic metrics computed for different time periods. The LTC ratios were obtained by dividing hydrologic metrics computed from available streamflow records from the 1981WY through the WY of biological sample collection by hydrologic metrics computed from available streamflow records during the 1945-79WYs. The STC ratios were obtained by dividing hydrologic metrics computed from the last 10 water years up to the WY of biological sample collection by hydrologic metrics computed from the POR for each streamgage. The POR, LTC, and STC datasets included 54, 39, and 48 hydrologic and biological site pairs, respectively. Results of regression analyses are described in a companion publication (https://doi.org/TBD). A subset of the best regression models based on pseudo-R2 values is published in the companion publication, but all 134 final regression models for each of the three datasets are published in this data release. Model archives of best subset and left-censored linear regression models are provided and include readme files, raw data files, R scripts used to compute regression analyses, and model outputs. Daily streamflow data were retrieved from the National Water Information System (NWIS; at https://waterdata.usgs.gov/nwis). A minimum of 10 years of complete daily streamflow record was required for computing hydrologic metrics to pair with biological metrics. RStudio (version 3.5.0) and the EflowStats (version 5.0.0) and NWCCompare (version 5.0) packages were used to compute hydrologic metrics. Biological metrics used in described datasets were computed by and obtained from the MPCA (MPCA, 2016). Similar hydrologic statistics were computed using the EflowStats package and published in a previous data release (https://doi.org/10.5066/P9ND1NPT).

This dataset contains 2D quasistatic non-linear structural mechanics solutions, under geometrical variations.

A Description is provided in the MMGP paper Sections 4.1 and A.2.

The file format is PLAID, see the plaid documentation.

The variablity in the samples are 6 input scalars and the geometry (mesh). Outputs of interest are 4 scalars and 6 fields.

Seven nested training sets of sizes 8 to 500 are provided, with complete input-output data. A testing set of size 200, as well as two out-of-distribution sample, are provided, for which outputs are not provided.

Tips to access the data:

After decompressing the downloaded file:

dataset = Dataset()
problem = ProblemDefinition()

problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))
dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)

print("problem =", problem)
print("dataset =", dataset)

sample = dataset[0]
print("sample =", sample)

for fn in sample.get_field_names():
print(f"{fn} =", sample.get_field(fn))
for sn in sample.get_scalar_names():
print(f"{sn} =", sample.get_scalar(sn))

print("nodes =", sample.get_nodes())
print("elements =", sample.get_elements())
print("nodal_tags =", sample.get_nodal_tags())

R2 values were calculated using the test set with hold-out samples.

Verbal and Quantitative Reasoning GRE scores and percentiles were collected by querying the student database for the appropriate information. Any student records that were missing data such as GRE scores or grade point average were removed from the study before the data were analyzed. The GRE Scores of entering doctoral students from 2007-2012 were collected and analyzed. A total of 528 student records were reviewed. Ninety-six records were removed from the data because of a lack of GRE scores. Thirty-nine of these records belonged to MD/PhD applicants who were not required to take the GRE to be reviewed for admission. Fifty-seven more records were removed because they did not have an admissions committee score in the database. After 2011, the GRE’s scoring system was changed from a scale of 200-800 points per section to 130-170 points per section. As a result, 12 more records were removed because their scores were representative of the new scoring system and therefore were not able to be compared to the older scores based on raw score. After removal of these 96 records from our analyses, a total of 420 student records remained which included students that were currently enrolled, left the doctoral program without a degree, or left the doctoral program with an MS degree. To maintain consistency in the participants, we removed 100 additional records so that our analyses only considered students that had graduated with a doctoral degree. In addition, thirty-nine admissions scores were identified as outliers by statistical analysis software and removed for a final data set of 286 (see Outliers below). Outliers We used the automated ROUT method included in the PRISM software to test the data for the presence of outliers which could skew our data. The false discovery rate for outlier detection (Q) was set to 1%. After removing the 96 students without a GRE score, 432 students were reviewed for the presence of outliers. ROUT detected 39 outliers that were removed before statistical analysis was performed. Sample See detailed description in the Participants section. Linear regression analysis was used to examine potential trends between GRE scores, GRE percentiles, normalized admissions scores or GPA and outcomes between selected student groups. The D’Agostino & Pearson omnibus and Shapiro-Wilk normality tests were used to test for normality regarding outcomes in the sample. The Pearson correlation coefficient was calculated to determine the relationship between GRE scores, GRE percentiles, admissions scores or GPA (undergraduate and graduate) and time to degree. Candidacy exam results were divided into students who either passed or failed the exam. A Mann-Whitney test was then used to test for statistically significant differences between mean GRE scores, percentiles, and undergraduate GPA and candidacy exam results. Other variables were also observed such as gender, race, ethnicity, and citizenship status within the samples. Predictive Metrics. The input variables used in this study were GPA and scores and percentiles of applicants on both the Quantitative and Verbal Reasoning GRE sections. GRE scores and percentiles were examined to normalize variances that could occur between tests. Performance Metrics. The output variables used in the statistical analyses of each data set were either the amount of time it took for each student to earn their doctoral degree, or the student’s candidacy examination result.

This dataset contains 2D quasistatic non-linear structural mechanics solutions, with finite elasticity and topology variations.

The file format is PLAID, see the plaid documentation.

The variablity in the samples are 3 input scalars and the geometry (mesh). Outputs of interest are 1 scalar and 7 fields. Sample feature variable topology, in the form of variable number of holes in the meshes.

Various training and testing sets are provided (for all topologies together and for each topology), and outputs are not provided on the testing sets.

Tips to access the data:

After decompressing the downloaded file:

from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinition

dataset = Dataset()problem = ProblemDefinition()

problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)

print("problem =", problem)print("dataset =", dataset)

sample = dataset[0]print("sample =", sample)

for fn in sample.get_field_names(): print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names(): print(f"{sn} =", sample.get_scalar(sn))

print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())