Dataset Information

A dataset for internet of things based fish farm monitoring and notification system.

ABSTRACT: Water quality depends on many factors. Some of them are essential for maintaining the minimum sustainability of water. Because of the great dependence of fishes on the condition of the aquatic environment, the water quality can directly affect their activity. Therefore monitoring water quality is a very important issue to consider, especially in the fish farming industry. In this paper a digital fish farm monitoring system is introduced and a collection of experimental data of water quality monitoring was presented, which were directly collected from a fish pond. As the quality factor of water affects its aquatic life form sustainability, therefore the quality factors of the water were measured using digital sensors. Temperature, pH factor and Turbidity were selected as the basic quality factors to measure. The dataset contains data recorded from two different water levels to analyze the aquatic environment more efficiently. Each level has 9623 sets of data of the selected parameters. Collection was continued all day long for several days. Later collected sensor data were analyzed as short period time series to find its properties. Machine Learning regression method was used to predict near future conditions. Moreover data were processed to find any repetitive patterns in its properties. This dataset represents the exact condition of the environment of the fish pond. Therefore it can be used to develop a system to monitor fish farms digitally. Using these data in machine learning, predicting the future is possible for advance monitoring of a fish farm. The dataset is available in Mendeley Data[1].

SUBMITTER: Arafat AI

PROVIDER: S-EPMC7644873 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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A dataset for internet of things based fish farm monitoring and notification system.

Arafat Arif Istiaq AI Akter Tasmima T Ahammed Md Ferdous MF Ali Md Younus MY Nahid Abdullah-Al AA

Data in brief 20201024

Water quality depends on many factors. Some of them are essential for maintaining the minimum sustainability of water. Because of the great dependence of fishes on the condition of the aquatic environment, the water quality can directly affect their activity. Therefore monitoring water quality is a very important issue to consider, especially in the fish farming industry. In this paper a digital fish farm monitoring system is introduced and a collection of experimental data of water quality moni ...[more]

PMID: 33195775

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Project description:BackgroundDuring intrahospital transport, adverse events are inevitable. Real-time monitoring can be helpful for preventing these events during intrahospital transport.ObjectiveWe attempted to determine the viability of risk signal detection using wearable devices and mobile apps during intrahospital transport. An alarm was sent to clinicians in the event of oxygen saturation below 90%, heart rate above 140 or below 60 beats per minute (bpm), and network errors. We validated the reliability of the risk signal transmitted over the network.MethodsWe used two wearable devices to monitor oxygen saturation and heart rate for 23 patients during intrahospital transport for diagnostic workup or rehabilitation. To determine the agreement between the devices, records collected every 4 seconds were matched and imputation was performed if no records were collected at the same time by both devices. We used intraclass correlation coefficients (ICC) to evaluate the relationships between the two devices.ResultsData for 21 patients were delivered to the cloud over LTE, and data for two patients were delivered over Wi-Fi. Monitoring devices were used for 20 patients during intrahospital transport for diagnostic work up and for three patients during rehabilitation. Three patients using supplemental oxygen before the study were included. In our study, the ICC for the heart rate between the two devices was 0.940 (95% CI 0.939-0.942) and that of oxygen saturation was 0.719 (95% CI 0.711-0.727). Systemic error analyzed with Bland-Altman analysis was 0.428 for heart rate and -1.404 for oxygen saturation. During the study, 14 patients had 20 risk signals: nine signals for eight patients with less than 90% oxygen saturation, four for four patients with a heart rate of 60 bpm or less, and seven for five patients due to network error.ConclusionsWe developed a system that notifies the health care provider of the risk level of a patient during transportation using a wearable device and a mobile app. Although there were some problems such as missing values and network errors, this paper is meaningful in that the previously mentioned risk detection system was validated with actual patients.

Project description:The data presented in this data article were collected at three points in the 2012-2013 academic year; Fall 2012, Spring 2013, and Summer 2013 from undergraduate students enrolled in an introductory health education course at a large university in the United States. The data regarding undergraduate students' perceptions of and experiences with the campus emergency notification system were ascertained using a self-administered online-delivered survey instrument. The data included in the Mendeley Data repository affiliated with this data article encompass closed- and open-ended responses from 746 undergraduate students. Closed-ended questions included items based on central constructs from Technology Acceptance Model research-perceived usefulness, perceived ease of use, attitudes toward use, and behavioral intention. Survey questions also assessed students' actual use of emergency notification messages, students' perceived self-efficacy to respond to future potential emergency notifications, and demographics and technology use characteristics. This research team asked open-ended questions to collect students' ideas for systematic improvement in their own words. Descriptive statistics for demographic variables, participant characteristic variables, and scale variables were conducted in SPSS 27 and are provided in tables. The open-ended question response frequencies were also calculated in SPSS 27 and are provided within a supplemental PDF. To date, no data pertaining to an institution of higher education's emergency notification system are published for open access use. This article provides open access data and surveys that campus emergency planners, researchers, and health education specialists can use to inform emergency communication plans, improve the content of critical campus alert messages, structure future emergency notification studies, and frame future emergency notification system evaluations. This research team anticipates these data will help campus emergency personnel craft more effective messages and optimize their channel mixture to make emergency notifications reach and resonate with students in situations when minutes matter. The data for this article are hosted in a .csv file for widespread access in the following Mendeley Data repository: https://data.mendeley.com/datasets/6jdwfbwzk5/1.

Dataset Information

A dataset for internet of things based fish farm monitoring and notification system.

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A dataset for internet of things based fish farm monitoring and notification system.

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