Definition of important variables.

Problem Statement:

A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system.

A US bike-sharing provider BoomBikes has recently suffered considerable dip in their revenue due to the Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue.

In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits.

They have contracted a consulting company to understand the factors on which the demand for these shared bikes depends. Specifically, they want to understand the factors affecting the demand for these shared bikes in the American market. The company wants to know:

• Which variables are significant in predicting the demand for shared bikes.
• How well those variables describe the bike demands

Based on various meteorological surveys and people's styles, the service provider firm has gathered a large dataset on daily bike demands across the American market based on some factors.

Business Goal:

You are required to model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market.

Data Preparation:

1. You can observe in the dataset that some of the variables like 'weathersit' and 'season' have values as 1, 2, 3, 4 which have specific labels associated with them (as can be seen in the data dictionary). These numeric values associated with the labels may indicate that there is some order to them - which is actually not the case (Check the data dictionary and think why). So, it is advisable to convert such feature values into categorical string values before proceeding with model building. Please refer the data dictionary to get a better understanding of all the independent variables.
2. You might notice the column 'yr' with two values 0 and 1 indicating the years 2018 and 2019 respectively. At the first instinct, you might think it is a good idea to drop this column as it only has two values so it might not be a value-add to the model. But in reality, since these bike-sharing systems are slowly gaining popularity, the demand for these bikes is increasing every year proving that the column 'yr' might be a good variable for prediction. So think twice before dropping it.

Model Building:

In the dataset provided, you will notice that there are three columns named 'casual', 'registered', and 'cnt'. The variable 'casual' indicates the number casual users who have made a rental. The variable 'registered' on the other hand shows the total number of registered users who have made a booking on a given day. Finally, the 'cnt' variable indicates the total number of bike rentals, including both casual and registered. The model should be built taking this 'cnt' as the target variable.

Model Evaluation:

When you're done with model building and residual analysis and have made predictions on the test set, just make sure you use the following two lines of code to calculate the R-squared score on the test set. python from sklearn.metrics import r2_score r2_score(y_test, y_pred) - where y_test is the test data set for the target variable, and y_pred is the variable containing the predicted values of the target variable on the test set. - Please perform this step as the R-squared score on the test set holds as a benchmark for your model.

These are the data and code to go with "Rapid evolution of thermal tolerance and phenotypic plasticity in variable environments"

Figure 01 takes the following data/scripts:

20210610_Thally02_Figure01_plot_and_stats.R with track_keeper.csv and corr_Response growth .csv . These files contain growth rates per transfers for all selection environments throughout the experiment and growth rates in correlated environments during reciprocal transplants, respectively.

Figure 02 takes the following data/scripts:

20210610_Thally02_Figure02_plot_and_stats.R with 20161120_res_logis_t000.csv and 20161123_resloglint300.csv . These files contain the output of the shapes of the growth curves (i.e. information on lag time , growth at µmax, K etc) for all samples in all selection environments at t0 and t300, respectively

The remaining figures - position not clear at time of submission - take the following data/script.

For plasticity in FRRF data, the script 20181204_FRRF_plasticity.R takes the FRRF raw data contained in allfvfmdata_thally_t300_t000.csv . Extracted parameters are in files CvaluesThally02.csv, psiPSI_slope_intercept.csv, and rP_extracted_values.csv and can be analysed using the R script extracted parameter plots.R . R script FRRF visualisation only .R is for visualisation only, as the title suggests.

For comparing plasticity/growth , the data are in 20170327_giantbigtable.csv , and can be visualised/analysed in plast vs growth.R

In order to recreate the AMOVAS based on SNVs, use all_variants_fixed-only_using_5x_depth_threshold.cvs with amova thally02.R

For additional information, please contact elisa.schaum@uni-hamburg.de

See text in Content.txt Prospective gating and automatic reacquisition of data corrupted by respiration motion were implemented in variable flip angle (VFA) and actual flip angle imaging (AFI) MRI scans to enable cardio-respiratory synchronised T1 mapping of the whole mouse. Stability tests of cardio-respiratory gating (CR-gating) and respiratory gating (R-gating) with and without reacquisition were compared with un-gated scans in 4 mice. The automatic and immediate reacquisition of data corrupted by respiration motion is observed to properly eliminate respiration motion artefact. CR-gated VFA scans with 16 flip angles and 32 k-lines per cardiac R-wave were acquired with R-gated AFI scans in a total scan time of less than 14 minutes. The VFA data were acquired with a voxel size of 0.075 mm3. T1 was calculated in the whole mouse with a robust and efficient nonlinear least squares fit of data. The standard deviation in the T1 measurement is conservatively estimated to be less than 6.2%. The T1 values measured from VFA scans with 32 k-lines per R-wave are in very good agreement with those measured from VFA scans with 8 k-lines per R-wave, even for myocardium. As such, it is demonstrated that prospective gating and reacquisition enables fast and accurate T1 mapping of small animals.