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This Research data consists of a cross-annotated dataset of 1154seconds and a novel set of evaluation measures, able to report thetype of errors made by the system. Both the dataset, and a Matlabtoolbox including the presented evaluation measures, are freely available. In order to show the utility of the work presented in thispaper, we have performed an detailed comparative study of threestate-of-the-art singing transcribers plus a baseline method, lead-ing to relevant information about the performance of each method., This work has been funded by the Ministerio de Economía y Competitividad of the Spanish Government under Project No.TIN2013-47276-C6-2-R and by the Junta de Andalucía underProject No. P11-TIC-7154. The work has been done at Universidadde Málaga. Campus de Excelencia Internacional Andalucía Tech.

Software and dataset, Companion files (code & dataset) - Matlab-Fieldtrip Implementation of Tools for Lessening the Influence of Artifacts in EEG Analysis This manuscript describes software tools implemented in Matlab to reduce the influence of artifacts, specifically focused on eye artifacts, in the acquisition and measurement of electroencephalogram signals (EEG). This process is of great importance for the analysis and study of long trial samples, when the appearance of eye artifacts can not be avoided. The implementations, which are provided to the readers, correspond to the methods proposed in \cite{b2}, which are aimed at the partial or complete elimination of EEG channels or independent components obtained after independent component analysis (ICA) of the EEG signals that most resemble the ocular signals (EOG) measured simultaneously. In addition to the documentation of each of the provided Matlab functions, examples of use and illustrative figures will be included to show the expected results and processing pipeline., Junta de Andalucía: Proyectos de I+D+i en el ámbito del Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020), under Project No. PY20_00237. La publicación es parte del proyecto PID2021-123207NB-I00, financiado por MCIN/AEI/10.13039/501100011033/FEDER, UE