test_dataset.csv

## 1 FILE CONTENTS ############################################# 751 x 40003 values: - 1 header row, which corresponds to the 40002 resolved wavelengths and 1 target identifier. - 750 spectra (rows) of the targets whose content is to be predicted: 50 spectra of 15 distinct targets. <br> Note that the dataset occupies significantly less RAM when loaded than the file's disk sizes (cca. 50 %). <br> Note that the column names may be modified by the software/library used for processing the spectra. Hence, we advise increased caution when extracting the wavelengths. Alternatively, you can use the wavelength values provided separately in the wavelengths.csv file. <br> Note that the target identifier column is a character vector of the format "target_i", i = {1,2,...,15}. Thus, we recommended loading in the dataset as a dataframe instead of a matrix (a dataframe can contain both numeric and character values). <br> ## 2 DATA ACQUISITION ############################################# <br> The dataset was collected from metallic targets. Each target's surface was homogenized using a 800 grid sandpaper and cleaned with isopropyl alcohol using paper towels. The targets were sampled at distinct spots in single-shot mode, collecting a single spectrum from each individual spot. There are a total of 50 spectra available for each target. <br> A 1064 nm Nd:YAG laser was used for ablation with a pulse energy of 95 mJ, 10 ns pulse width. The laser pulse was focused into a spot with a diameter of 0.2 mm. <br> The collected emission was resolved with an echelle spectrograph in the 240--1000 nm spectral range (40002 resolved wavelength values), resolving power {\lambda} / {\Delta\lambda} = 6000. <br> The resolved emission was recorded using an EMCCD camera with a 1.5 us delay and 50 us gate width (exposition time). <br> The spectra were not intensity calibrated! This, in combination with the long gate width is part of the challenge.