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 Biomolecular systems were one of the first systems used in molecular dynamics (MD) simulations [@levitt1975computer]. As such biomolecular simulations build on a rich half a century history rich of methodological developments, embodied in a wide range of specialised software. The improvement in physical models dictating interatomic interactions coupled with an ever-increasing availability of computational power have enabled MD simulations to establish themselves as a technique complementary to experimental data [@hollingsworth2018molecular, @ciccotti2022molecular]. Starting from the simulation of small proteins for only a few nanoseconds [@levitt1975computer], nowadays large biomolecular complexes featuring millions of atoms can be simulated for timescales orders of magnitude longer [@lindorff-larsen2011howa]. The data produced by MD simulations is noisy and high-dimensional though, and its usefulness is directly dependent on how faithfully the molecular system simulated recapitulates the physiochemical conditions of its real-world counterpart. Since the mid-1970s, significant progress has been made in automating the preparation of biologically relevant atomistic models and the analysis of simulation data. Nonetheless, modern computational scientists must still make critical decisions on how to assemble and simulate the system, as well as which quantities to extract from the resulting data to accurately explain or predict experimental outcomes.
 
-The material presented in this course has been developed as training material for the CCPBioSim consortium. Since 2022, is has been delivered to three cohorts of 25-35 international postgraduates attending the UK-based CCP5 Summer School on Molecular simulation. A first key aspect of this course is that, under the same hood, it provides information on both the set-up and the analysis of MD simulations, typically presented separately. A second key aspect is that it demonstrates how machine learning techniques can be integrated in the analysis of MD simulations and used to extract relevant information from an MD simulation.
+The material presented in this course has been developed as training material for the CCPBioSim consortium. Since 2022, it has been delivered to three cohorts of 25-35 international postgraduates attending the UK-based CCP5 Summer School on Molecular simulation. A first key aspect of this course is that, under the same hood, it provides information on both the set-up and the analysis of MD simulations, typically presented separately. A second key aspect is that it demonstrates how machine learning techniques can be integrated in the analysis of MD simulations and used to extract relevant information from an MD simulation.
 
 
 # Overview, Content, and Structure