a–c, Weighted moving average technique for data smoothing. The relationship between team size and disruption may be noisy owing to lack of data when we analyse WOS articles from the same journal. As shown in a, less than 1% of articles in ‘Artificial intelligence’ (a subfield of ‘Computer and Information Technology’) have more than six authors, but these articles contribute to substantial variance in the data. We use the moving average technique to limit noise in the data. More specifically, we define a parameter k, which provides the threshold value of mk for team size m such that P(m > mk) < k. For any data point with a team size greater than mk, its disruption percentile DPm is updated to be the average between its current value and the value of its left neighbour, DPm −1, weighted by corresponding sample sizes (the number of articles for a given team size). Panel a shows curves for the subfield ‘Artificial Intelligence’ before (blue dashed curve) and after (red curve) smoothing, in which the size of blue circles is proportional to sample size. Panels b and c show how smoothing depends on the value of k across ten randomly selected subfields. In d–l, each curve corresponds to a journal (only journals with more than three data points are shown) and each panel corresponds to a subfield. There are 15,146 journals, 258 subfields and 10 major fields represented in our WOS data. Owing to the limited figure size, only four subfields are shown for each field. Curves are smoothed by setting the smoothing parameter k = 0.2. The darkness of curves is equally proportional to sample size and the absolute value of the regression coefficient examining the impact of disruption percentile on team size, such that journals with more articles and that display stronger (both negative and positive) relationships are more distinguishable from the background.