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.

Exploratory: Migration studies

Scientific migration has aroused growing interest in recent years due to its impact on governments' policies and institutions. We analyse the traits of highly-skilled people who emigrate to understand the movements' factors. Also, we focus on the evolution of researchers' collaborative network by measuring the tendency to collaborate with researchers belonging to institutions of the same country of affiliation or not and relating it to changes in affiliation to find out the factors related to scientific mobility.

Modern high-performance computing facilities (HPCs) generate a colossal amount of data. Recent studies have shown that a significant percentage (up to 3.41% of total storage capacity) of the data HPCs produce might never be used again — for reasons as mundane as improper labelling. Given that it’s often the case that large amounts of public money go into the production of that data, this is obviously hugely problematic.

Exploratory: Demography, Economy and Finance 2.0

Could data revolution help the measurement of peacefulness? Data Scientists for Social Good and Social Good organisations, such as the United Nations (UN), highlight the importance of harnessing the data revolution [1] to put the best available tools and methods to work for the well-being and its dimensions [2] or in achieving the Sustainable Development Goals [3], such as peacefulness.

Iknoor Singh, UIET, Panjab University, India | iknoor.ai@gmail.com

TransNational Access @ GATE - University of Sheffield