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Information Technology Infrastructure for Agriculture Genotyping Studies
Pardamean, Bens,Baurley, James W.,Perbangsa, Anzaludin S.,Utami, Dwinita,Rijzaani, Habib,Satyawan, Dani Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.3
In efforts to increase its agricultural productivity, the Indonesian Center for Agricultural Biotechnology and Genetic Resources Research and Development has conducted a variety of genomic studies using high-throughput DNA genotyping and sequencing. The large quantity of data (big data) produced by these biotechnologies require high performance data management system to store, backup, and secure data. Additionally, these genetic studies are computationally demanding, requiring high performance processors and memory for data processing and analysis. Reliable network connectivity with large bandwidth to transfer data is essential as well as database applications and statistical tools that include cleaning, quality control, querying based on specific criteria, and exporting to various formats that are important for generating high yield varieties of crops and improving future agricultural strategies. This manuscript presents a reliable, secure, and scalable information technology infrastructure tailored to Indonesian agriculture genotyping studies.
Information Technology Infrastructure for Agriculture Genotyping Studies
Bens Pardamean,James W. Baurley,Anzaludin S. Perbangsa,Dwinita Utami,Habib Rijzaani,Dani Satyawan 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.3
In efforts to increase its agricultural productivity, the Indonesian Center for Agricultural Biotechnology andGenetic Resources Research and Development has conducted a variety of genomic studies using highthroughputDNA genotyping and sequencing. The large quantity of data (big data) produced by thesebiotechnologies require high performance data management system to store, backup, and secure data. Additionally, these genetic studies are computationally demanding, requiring high performance processorsand memory for data processing and analysis. Reliable network connectivity with large bandwidth to transferdata is essential as well as database applications and statistical tools that include cleaning, quality control,querying based on specific criteria, and exporting to various formats that are important for generating highyield varieties of crops and improving future agricultural strategies. This manuscript presents a reliable, secure,and scalable information technology infrastructure tailored to Indonesian agriculture genotyping studies.
Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health
Bens Pardamean,Haryono Soeparno,Arif Budiarto,Bharuno Mahesworo,James Baurley 대한의료정보학회 2020 Healthcare Informatics Research Vol.26 No.2
Objectives: Recently, wearable device technology has gained more popularity in supporting a healthy lifestyle. Hence, researchers have begun to put significant efforts into studying the direct and indirect benefits of wearable devices for health and wellbeing. This paper summarizes recent studies on the use of consumer wearable devices to improve physical activity, mental health, and health consciousness. Methods: A thorough literature search was performed from several reputable databases, such as PubMed, Scopus, ScienceDirect, arXiv, and bioRxiv mainly using “wearable device research” as a keyword, no earlier than 2018. As a result, 25 of the most recent and relevant papers included in this review cover several topics, such as previous literature reviews (9 papers), wearable device accuracy (3 papers), self-reported data collection tools (3 papers), and wearable device intervention (10 papers). Results: All the chosen studies are discussed based on the wearable device used, complementary data, study design, and data processing method. All these previous studies indicate that wearable devices are used either to validate their benefits for general wellbeing or for more serious medical contexts, such as cardiovascular disorders and post-stroke treatment. Conclusions: Despite their huge potential for adoption in clinical settings, wearable device accuracy and validity remain the key challenge to be met. Some lessons learned and future projections, such as combining traditional study design with statistical and machine learning methods, are highlighted in this paper to provide a useful overview for other researchers carrying out similar research.