Many decades ago Patrick Suppes argued rather convincingly that theoretical hypotheses are not confronted with the direct, raw results of an experiment, rather, they are typically compared with models of data. What exactly is a data model however? And how do the interactions of particles at the subatomic scale give rise to the huge volumes of data that are then moulded into a polished data model? The aim of this paper is to answer these questions by presenting a detailed case study of the construction of data models at the LHCb for testing Lepton Flavour Universality in rare decays of B-mesons. The close examination of the scientific practice at the LHCb leads to the following four main conclusions: (i) raw data in their pure form are practically useless for the comparison of experimental results with theory, and processed data are in some cases epistemically more reliable, (ii) real and simulated data are involved in the co-production of the final data model and cannot be easily distinguished, (iii) theory-ladenness emerges at three different levels depending on the scope and the purpose for which background theory guides the overall experimental process and (iv) the overall process of acquiring and analysing data in high energy physics is too complicated to be fully captured by a generic methodological description of the experimental practice.

几十年前，帕特里克·苏佩斯颇有说服力地指出，理论假设并不面对实验的直接、原始结果，而是通常与数据模型进行比较。然而，究竟什么是数据模型？亚原子尺度的粒子相互作用如何产生大量数据，然后将这些数据塑造成一个完善的数据模型？本文的目的是通过提供一个详细的案例研究来回答这些问题，该案例研究在 LHCb 上构建数据模型以测试 B 介子罕见衰变中的轻子风味普遍性。对 LHCb 科学实践的仔细检查得出以下四个主要结论：(i) 纯形式的原始数据对于将实验结果与理论进行比较实际上毫无用处，