DArk Matter SPIkes (DAMSPI) is a fully Python-based software for the analysis of dark matter spikes around Intermediate Mass Black Holes (IMBHs) in the Milky Way. It allows to extract an IMBH catalogue and their corresponding dark matter spike parameters from the EAGLE simulations in order to probe a potential gamma-ray signal from dark matter self-annihilation. The dataset contains the IMBH catalogue including, among others, the coordinates, mass, formation redshift and spike parameters for each individual IMBH. Each column of the catalogue is described in detail in J. Aschersleben et al. (2024). We also provide separate files for which we calculated the gamma-ray fluxes for different dark matter masses and annihilation cross sections. The columns of these files are also described in J. Aschersleben et al. (2024). The source code to extract this dataset is publicy available here: https://doi.org/10.5281/zenodo.10566372 Description of the data files The imbh_catalogue/imbh/ directory contains the following files: catalogue_nfw.h5 catalogue_cored_gamma_0p0.h5 catalogue_cored_gamma_0p4.h5 catalogue_cored_gamma_free.h5 They contain the IMBH catalogues, including the coordinates and dark matter spike parameters, calculated assuming the 1.) NFW profile, 2.) cored profile with a fixed core index of 0.0, 3.) cored profile with a fixed core index of 0.4 and 4.) cored profile with the core index as a free fitting parameter. The imbh_catalogue/flux/// directory contains the gamma-ray fluxes of the IMBHs for a given annihilation channel, energy threshold, and dark matter mass. E.g. the imbh_catalogue/flux/b_channel/e_th_0.1GeV/m_dm_10.0GeV.h5 file contains the IMBH fluxes assuming the b-channel, an energy threshold of 0.1 GeV and a dark matter mass of 10 GeV. The IMBH fluxes are calculated for a variety of velocity weighted annihilation cross sections. The HDF files can be opened in Python with: import pandas as pd file_path = ".h5" df = pd.read_hdf(file_path, key="table") # Printing the first few rows of the DataFrame print(df.head())