We analyse the globular cluster (GC) systems of a sample of 15 massive, compact early-type galaxies (ETGs), 13 of which have already been identified as good relic galaxy candidates on the basis of their compact morphologies, old stellar populations and stellar kinematics. These relic galaxy candidates are likely the nearby counterparts of high

Aims: We aim to demonstrate the scientific potential of the Gaia Early Data Release 3 (EDR3) for the study of different aspects of the Milky Way structure and evolution and we provide, at the same time, a description of several practical aspects of the data and examples of their usage. Methods: We used astrometric positions, proper motions

Aims: We produce a clean and well-characterised catalogue of objects within 100 pc of the Sun from the Gaia Early Data Release 3. We characterise the catalogue through comparisons to the full data release, external catalogues, and simulations. We carry out a first analysis of the science that is possible with this sample to demonstrate its

Context. We present the early installment of the third Gaia data release, Gaia EDR3, consisting of astrometry and photometry for 1.8 billion sources brighter than magnitude 21, complemented with the list of radial velocities from Gaia DR2. Aims: A summary of the contents of Gaia EDR3 is presented, accompanied by a discussion on the differences with

Context. This work is part of the Gaia Data Processing and Analysis Consortium papers published with the Gaia Early Data Release 3 (EDR3). It is one of the demonstration papers aiming to highlight the improvements and quality of the newly published data by applying them to a scientific case. Aims: We use the Gaia EDR3 data to study the structure

Context. Gaia Early Data Release 3 (Gaia EDR3) contains astrometry and photometry results for about 1.8 billion sources based on observations collected by the European Space Agency Gaia satellite during the first 34 months of its operational phase. Aims: In this paper, we focus on the photometric content, describing the input data, the algorithms