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<p>This Data Descriptor presents an openly available movement dataset collected from 16 finishing pigs during the final 20 days before slaughter using a custom RFID and LoRaWAN monitoring system. The released CSV file (MOVEMENT_Final.csv) contains 1,048,573 repeated within-hour movement increment records rather than one pre-calculated hourly total per pig and hour. Each record includes pig identifier, study day, hourly block start time, and incremental movement in cm. In complete pig-day-hour blocks, the file typically contains about 180 records, consistent with an approximately 20-second processed output interval. Hourly totals can be reconstructed by summing distance values within each pig × day × hour block, yielding 6,026 valid hourly observations out of 7,680 theoretically possible blocks. The dataset supports research on activity rhythms, behavioural variability, preprocessing workflows for livestock sensor data, and precision livestock farming. Complete data gaps occurred at the study boundaries because of technical interruptions and should be treated as missing rather than zero activity.</p>

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Within-hour incremental movement data of finishing pigs over 20 days using RFID–LoRaWAN technology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

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  • Marko Ocepek

摘要

This Data Descriptor presents an openly available movement dataset collected from 16 finishing pigs during the final 20 days before slaughter using a custom RFID and LoRaWAN monitoring system. The released CSV file (MOVEMENT_Final.csv) contains 1,048,573 repeated within-hour movement increment records rather than one pre-calculated hourly total per pig and hour. Each record includes pig identifier, study day, hourly block start time, and incremental movement in cm. In complete pig-day-hour blocks, the file typically contains about 180 records, consistent with an approximately 20-second processed output interval. Hourly totals can be reconstructed by summing distance values within each pig × day × hour block, yielding 6,026 valid hourly observations out of 7,680 theoretically possible blocks. The dataset supports research on activity rhythms, behavioural variability, preprocessing workflows for livestock sensor data, and precision livestock farming. Complete data gaps occurred at the study boundaries because of technical interruptions and should be treated as missing rather than zero activity.