Using Data Lake Stack in Animal Sciences

Big Data is a theme that receives a lot of attention, and is often characterised as managing and analysing large datasets to reveal new valuable patterns. In the livestock domain, big data is also becoming more common and is being anchored into the mind-set of researchers, due to, for example, sensors generating ..

Contourite depositional system after the exit of a strait: Case study from the late Miocene South Rifian Corridor, Morocco

Idealized facies of bottom current deposits (contourites) have been established for fine-grained contourite drifts in modern deep-marine sedimentary environments. Their equivalent facies in the ancient record however are only scarcely recognized due to the weathered nature of most fine-grained deposits in outcrop. Facies related to the erosional elements (i.e. contourite channels) of contourite depositional systems have not yet been properly established and related deposits in outcrop appear non-existent. To better understand the sedimentary facies and facies sequences of contourites, the upper Miocene contourite depositional systems of the South Rifian Corridor (Morocco) is investigated. This contourite depositional system formed by the dense palaeo-Mediterranean Outflow Water. Foraminifera assemblages were used for age-constraints (7.51 to 7.35 Ma) and to determine the continental slope depositional domains. Nine sedimentary facies have been recognized based on lithology, grain-size, sedimentary structures and biogenic structures. These facies were subsequently grouped into five facies associations related to the main interpreted depositional processes (hemipelagic settling, contour currents and gravity flows). The vertical sedimentary facies succession records the tectonically induced, southward migration of the contourite depositional systems and the intermittent behaviour of the palaeo-Mediterranean Outflow Water, which is mainly driven by precession and millennial-scale climate variations. Tides substantially modulated the palaeo-Mediterranean Outflow Water on a sub-annual scale. This work shows exceptional examples of muddy and sandy contourite deposits in outcrop by which a facies distribution model from the proximal continental slope, the contourite channel to its adjacent contourite drift, is proposed. This model serves as a reference for contourite recognition both in modern environments and the ancient record. Furthermore, by establishing the hydrodynamics of overflow behaviour a framework is provided that improves process-based interpretation of deep-water bottom current deposits

Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd

Granulating wound after a holiday in Peru

Immunogenetics and cellular immunology of bacterial infectious disease

Storing, combining and analysing turkey experimental data in the Big Data era

With the increasing availability of large amounts of data in the livestock domain, we face the challenge to store, combine and analyse these data efficiently. With this study, we explored the use of a data lake for storing and analysing data to improve scalability and interoperability. Data originated from a 2-day animal experiment in which the gait score of approximately 200 turkeys was determined through visual inspection by an expert. Additionally, inertial measurement units (IMUs), a 3D-video camera and a force plate (FP) were installed to explore the effectiveness of these sensors in automating the visual gait scoring. We deployed a data lake using the IMU and FP data of a single day of that animal experiment. This encompasses data from 84 turkeys for which we preprocessed by performing an ‘extract, transform and load’ (ETL-) procedure. To test scalability of the ETL-procedure, we simulated increasing volumes of the available data from this animal experiment and computed the ‘wall time’ (elapsed real time) for converting FP data into comma-separated files and storing these files. With a simulated data set of 30 000 turkeys, the wall time reduced from 1 h to less than 15 min, when 12 cores were used compared to 1 core. This demonstrated the ETL-procedure to be scalable. Subsequently, a machine learning (ML) pipeline was developed to test the potential of a data lake to automatically distinguish between two classses, that is, very bad gait scores v. other scores. In conclusion, we have set up a dedicated customized data lake, loaded data and developed a prediction model via the creation of an ML pipeline. A data lake appears to be a useful tool to face the challenge of storing, combining and analysing increasing volumes of data of varying nature in an effective manner

Using a data lake in animal sciences

In the livestock domain, Big Data is becoming more common and is being anchored into the mind-set of researchers. With the increasing availability of large amounts of data of varying nature, there is the challenge of how to store, combine, and analyse these data efficiently. With this study, we explored the possibility of using a data lake for storing and analysing sensor data, using an animal experiment as the use case, to improve scalability and interoperability. The use case was an experiment within Breed4Food (a public-private partnership), in which the gait score of 200 turkeys was determined. In the experiment, a gait score was traditionally assigned to each animal by a highly-skilled person who visually inspected them walking. Next to it, a set of sensor data streams was recorded for each animal, specifically inertial measurement units (IMUs), a 3D-video camera, and a force plate, with the ambition to explore the effectiveness of these data streams as predictors for estimating the gait score. The resulting sensor output, i.e. raw data, were successfully stored in its original format in the data lake. Subsequently, for each sensor output we performed extract, transform, and load activities, by executing custom-made scripts to generate tab or comma separated files. Lastly, by using Apache Spark it was possible to easily perform parallel processing of the data, allowing for fast computing. In conclusion, we managed to set up a data lake, load animal experimental data and run preliminary analyses. The data lake allowed for easy scale up of both data loading and analyses, which is desired for dynamic analyses pipelines, especially when more data are collected in the future.</p

Using a data lake in animal sciences

In the livestock domain, Big Data is becoming more common and is being anchored into the mind-set of researchers. With the increasing availability of large amounts of data of varying nature, there is the challenge of how to store, combine, and analyse these data efficiently. With this study, we explored the possibility of using a data lake for storing and analysing sensor data, using an animal experiment as the use case, to improve scalability and interoperability. The use case was an experiment within Breed4Food (a public-private partnership), in which the gait score of 200 turkeys was determined. In the experiment, a gait score was traditionally assigned to each animal by a highly-skilled person who visually inspected them walking. Next to it, a set of sensor data streams was recorded for each animal, specifically inertial measurement units (IMUs), a 3D-video camera, and a force plate, with the ambition to explore the effectiveness of these data streams as predictors for estimating the gait score. The resulting sensor output, i.e. raw data, were successfully stored in its original format in the data lake. Subsequently, for each sensor output we performed extract, transform, and load activities, by executing custom-made scripts to generate tab or comma separated files. Lastly, by using Apache Spark it was possible to easily perform parallel processing of the data, allowing for fast computing. In conclusion, we managed to set up a data lake, load animal experimental data and run preliminary analyses. The data lake allowed for easy scale up of both data loading and analyses, which is desired for dynamic analyses pipelines, especially when more data are collected in the future.</p