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

Citizen science Insects Internet of Things (IoT) Time series

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Feb 2024
Historique:
received: 23 10 2023
revised: 20 12 2023
accepted: 22 12 2023
medline: 26 1 2024
pubmed: 26 1 2024
entrez: 26 1 2024
Statut: epublish

Résumé

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.

Identifiants

pubmed: 38274156
doi: 10.1016/j.dib.2023.110015
pii: S2352-3409(23)01042-9
pmc: PMC10809063
doi:

Types de publication

Journal Article

Langues

eng

Pagination

110015

Informations de copyright

© 2023 The Author(s).

Auteurs

Diren Senger (D)

AG Cognitive Neuroinformatics, University of Bremen, Enrique-Schmidt-Str. 5, 28359 Breme.

Clemens Gruber (C)

Hiveeyes, Berlin.

Thorsten Kluss (T)

AG Cognitive Neuroinformatics, University of Bremen, Enrique-Schmidt-Str. 5, 28359 Breme.

Carolin Johannsen (C)

AG Cognitive Neuroinformatics, University of Bremen, Enrique-Schmidt-Str. 5, 28359 Breme.

Classifications MeSH