The three datasets contain the spectral data acquired on waste wood samples using a handheld spectrophotometer (MicroNIR™ OnSite instrument). The waste wood samples have been collected in a panel board company located in the Northern part of Italy during two days of sampling (February 18-19, 2020). In detail, 24 randomly distributed increments have been collected from 16 static lots, resulting in a total of 384 samples (we note these DT-SamTot). All the samples have been analyzed by Near-Infrared (NIR) spectrophotometer directly on site. In addition, four of the 24 increments for each lot - resulting in a total of 64 samples - have been sent to the lab for further analysis (DT-Lab). Additionally, another dataset has been created based on a reduced DT-SamTot dataset, where we only consider the four of 24 increments for each lot that were sent to the lab (DT-SamRed). It is important for having more accurate indications about the differences in variability between DT-Lab and DT-SamTot samples. We provide three CSV files: DT-Sam_Tot_270521_v01.csv: spectral data and information of DT-SamTot.; DT-Sam_Red_270521_v01.csv: spectral data and information of DT-SamRed. DT-Lab_270521_v01.csv: spectral data and information of DT-Lab. The three CSV files contain similar information in the columns: Sample code: it is reporting the sample code where S1 is the number of lot, the successive number is the number of sample (from 1 to 24) and the last number the NIR replicate. E.g. S4-13-1.sam: lot number 4, sample number 13, NIR replicate number 1. Please note that for DT-Lab dataset we have a different coding where labA and labB are the two sample replicates for the moisture content analysis. Rep: number indicating the NIR replicates for each sample. Please note that for DT-Lab dataset we have also rep2 column reporting the sample replicates for the moisture content analysis. Lot: number of lot to which the sample belongs (from 1 to 16). Day: day in which the sample has been collected (1 = 18/02/2020; 2 = 19/02/2020). Mois: moisture content of the sample (%). PCN: net calorific value of the sample (J/g). Spectral data: absorbance values for each sample from 908.1 nm to 1676.2 nm. The aim behind this dataset is to investigate the variability of the waste wood (WP1 of WoodSpec project) and this information is essential for increasing the reuse of the material and guarantee an accurate and successful use of a NIR sensor into real industrial applications. A second aim is the development of regression models for predicting the moisture content and net calorific value of the samples (WP3 of WoodSpec project). First indications about the variability and the chemical-physical characteristics of the material are essential for determining the suitability in energy applications. If you would like know more about the data, or to use these data, please refer to our article in Renewable Energy, doi: https://doi.org/10.1016/j.renene.2021.05.137 Funding: The project leading to this application has received funding from theEuropean Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 838560. Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.