Ontological data set consisting of indoor environment parameters collected in the laboratories
Laboratories, which are an indispensable part of modern life, have become indispensable elements of both public areas such as schools and hospitals and R&D departments of private companies. However, especially in the analyzes and studies carried out in chemistry, biology and environmental laboratories are prone to accidents because the environmental parameters cannot be monitored instantly, cannot be kept under control or necessary preventive measures cannot be taken on time. For this reason, there have been numerous serious accidents with death and injury until today in laboratory environments. As a result of not taking the necessary precautions, there is a possibility of many accidents in the future. All of this shows the fact that monitor and control environmental parameters are vital in order to reduce accident rates in laboratory environments. In this study, a data set was created in order to prevent possible security vulnerabilities in laboratory environments and to ensure that the necessary action plans are put into action on time. This data set contains instantaneous values of some laboratory indoor parameters. These indoor parameters can reach dangerous levels from time to time, depending on the number of analyze done, the number of people in the environment, and the temperature and amount of various gases that emerge as a result of the analyses. These parameters include some physical, chemical, and biological values in the environment such as temperature, humidity, carbon dioxide, total volatile organic substances, carbon monoxide, particulate matter 2.5, particulate matter 10, light level. These data were collected in the most frequently used MaldiTof, AoxMercury, and Chromotography laboratories in Bolu Abant İzzet Baysal University Scientific Industrial Technological Application and Research Center. Data collection started on 28.08.2019 and ended on 12.10.2019. The hourly average of each parameter was recorded in the database in order to avoid unnecessary repetitions in the data set. A total of 8 sensor nodes and 20 sensors were used in the creation of this data set.