Structural identifiability is an important property of parametric ODE models. When conducting an experiment and inferring the parameter value from the time-series data, we want to know if the value is globally, locally, or non-identifiable. Global identifiability of the parameter indicates that there exists only one possible solution to the inference problem, local identifiability suggests that there could be several (but finitely many) possibilities, while non-identifiability implies that there are infinitely many possibilities for the value. Having this information is useful since, one would, for example, only perform inferences for the parameters which are identifiable. Given the current significance and widespread research conducted in this area, we decided to create a database of linear compartment models and their identifiability results. This facilitates the process of checking theorems and conjectures and drawing conclusions on identifiability. By only storing models up to symmetries and isomorphisms, we optimize memory efficiency and reduce query time. We conclude by applying our database to real problems. We tested a conjecture about deleting one leak of the model states in the paper 'Linear compartmental models: Input-output equations and operations that preserve identifiability' by E. Gross et al., and managed to produce a counterexample. We also compute some interesting statistics related to the identifiability of linear compartment model parameters.

翻译：结构可辨识性是参数化常微分方程模型的重要属性。在进行实验并从时间序列数据推断参数值时，我们希望了解该值是全局可辨识、局部可辨识还是不可辨识。参数的全局可辨识性表明推断问题仅存在唯一解，局部可辨识性意味着可能存在有限多个解，而不可辨识性则说明该值存在无限多种可能性。掌握这些信息非常有用，例如研究者可以仅对可辨识参数进行推断。鉴于该领域当前的重要性和广泛开展的研究，我们决定创建线性隔室模型及其可辨识性结果的数据库。这有助于验证定理与猜想，并得出关于可辨识性的结论。通过仅存储对称性和同构性等价类下的模型，我们优化了内存效率并减少了查询时间。最后我们将数据库应用于实际问题：测试了E. Gross等人论文《线性隔室模型：输入输出方程与保持可辨识性的操作》中关于删除模型状态单泄漏的猜想，并成功构造出反例。同时我们还计算了与线性隔室模型参数可辨识性相关的若干统计量。