Weight, temperature and humidity sensor data of honey bee colonies in Germany, 2019-2022

被引:0
|
作者
Senger, Diren [1 ]
Gruber, Clemens [2 ]
Kluss, Thorsten [1 ]
Johannsen, Carolin [1 ]
机构
[1] Univ Bremen, AG Cognit Neuroinformat, Enr Schmidt Str 5, D-28359 Bremen, Germany
[2] Hiveeyes, Berlin, Germany
来源
DATA IN BRIEF | 2024年 / 52卷
关键词
Insects; Internet of Things (IoT); Citizen science; Time series;
D O I
10.1016/j.dib.2023.110015
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Humans have kept honeybees as livestock to harvest honey, wax and other products for thousands of years and still continue doing so. Today however, beekeepers in many parts of the world report unprecedented high numbers of colony losses. Sensor data from honey bee colonies can contribute to new insights about development and health factors for honey bee colonies. The data can be incorporated in smart decision support systems and warning tools for beekeepers. In this paper, we present sensor data from 78 honey bee colonies in Germany collected as part of a citizen science project. Each honey bee hive was equipped with five temperature sensors within the hive, one temperature sensor for outside measurements, a combined sensor for temperature, ambient air pressure and humidity, and a scale to measure the weight. During the data acquisition period, beekeepers used a web app to report their observations and beekeeping activities. We provide the raw data with a measurement interval of up to 5 s as well as aggregated data, with per minute, hourly or daily average values. Furthermore, we performed several preprocessing steps, removing outliers with a threshold based approach, excluding changes in weight that were induced by beekeeping activities and combining the sensor data with the most important meta-data from the beekeepers' observations. The data is organised in directories based on the year of recording. Alternatively, we provide subsets of the data structured based on the occurrence or non-occurrence of a swarming event or the death of a colony. The data can be analysed using methods from time series analysis, time series classification or other data science approaches to form a better understanding of specifics in the development of honey bee colonies. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY -NC -ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
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页数:11
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