Mobility is an important aspect of a researcher's life, affecting the career of the scientist in many ways: through the change of host institutions, new career opportunities are chased, positions with higher prestige acquired, stronger collaborations can be created, and novel projects are started. As a result, the collaboration network, the productivity, and the research impact of the studies of the researcher are possibly affected every time a researcher changes their host institution.
From 6th to 9th October 2020, the 12th International Conference on Social Informatics (SocInfo 2020), took place in a digital format.
The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. This month we address the problem of building a data-driven model for predicting car drivers’ risk of experiencing a crash in the mid-term future.
Monitoring training load is a fundamental process to maximize the physical capacity of athletes and to manage their fatigue throughout the season. An athlete’s training load can be quantified by external (e.g., global position system and video analysis) and internal parameters (e.g., rate of perceived exertion, heart rate, and lactate). The external training load represents the dose performed, while the internal training load reflects the psycho-physiological response of the athlete.
Exploratory: Sustainable Cities for Citizens
Exploratory: Sports Data Science
A new work developed within the Sports Data Science exploratory of SobigData++ has been presented at the 19th edition of the European Conference of Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), one of the prominent European conferences dedicated to data mining and machine learning.
Exploratory: Social Impact of AI and Explainable ML
Exploratory: Network Medicine
Genes disease associations have been identified by genome-wide association studies (GWAS). Unluckily, our knowledge of the mechanisms underlying these associations that are responsible for the diseases remains largely undefined. There is increasing evidence that a set of proteins associated with a disease do not work in an isolated way, but they interact with each other to form a distinct networkmodule representing perturbed and dysfunctional pathways.