Comprehensive Five-Year Dataset: Internal, External and In-Wall Climate Monitoring of a Retrofitted Historic Building in South Tyrol

The "Comprehensive Five-Year Dataset: Internal, External and In-Wall Climate Monitoring of a Retrofitted Historic Building in South Tyrol" represents an extensive and meticulous data collection stemming from a five-year long monitoring effort in a retrofitted 17th-century building located in Settequerce, South Tyrol, Italy.

This dataset includes comprehensive and continuous hygrothermal data both from within the retrofit wall stratigraphy (at three different depths) as well as from the surrounding indoor and outdoor climatic conditions. These conditions were recorded every five minutes over the course of the monitoring period. This rich dataset provides a profound understanding of the building's performance in the specific climatic context of South Tyrol's zone E or Köppen's Dfb class.

This dataset could prove particularly valuable for studies seeking to understand the impact of local climate on building performance, refine numerical models, and inform future renovation and preservation projects of similar historic buildings.

The recorded data was subjected to meticulous post-processing using Python scripts to generate hourly averages. This procedure has been undertaken to facilitate better understanding and analysis of the data. Moreover, in scenarios of equipment malfunction, signal non-reception, or inconsistent data, specific values are denoted as "NaN" (Not a Number) in the dataset. This practice allows researchers to quickly identify and exclude potential outliers or erroneous entries during analysis, maintaining the integrity and reliability of the data.

《南蒂罗尔地区一座改造历史建筑的内部、外部及墙内气候综合五年监测数据集》源自对意大利南蒂罗尔州塞特凯切（Settequerce）一座经改造的17世纪历史建筑开展的为期五年的精细化监测工作，所积累的数据集规模庞大且处理严谨。本数据集包含全面连续的湿热数据，涵盖改造后墙体三个不同深度的层内热湿环境，以及建筑周边的室内外气候条件。监测周期内，所有数据均以5分钟为采样间隔持续记录。该丰富数据集可助力研究者深入理解该建筑在南蒂罗尔E类气候区（即柯本气候分类的Dfb类）的特定气候背景下的运行表现。本数据集对于旨在探究局地气候对建筑性能的影响、优化建筑数值模型，以及为同类历史建筑的未来修缮与保护项目提供决策参考的研究，具有极高的应用价值。记录所得的原始数据已通过Python脚本完成精细化后处理，生成小时均值数据，此举旨在提升数据的可解读性与分析效率。此外，当出现设备故障、信号丢失或数据不一致的情况时，数据集中将以“NaN（Not a Number，非数值）”标记对应异常或缺失值。该处理规则可帮助研究者在分析阶段快速识别并剔除无效条目，保障数据集的完整性与可靠性。