Reproducible big data science: A case study in continuous FAIRness

Madduri, Ravi; Chard, Kyle; D’Arcy, Mike; Jung, Su-Young; Rodriguez, Alexis; Sulakhe, Dinanath; Deutsch, Eric W.; Funk, Cory C.; Heavner, Ben; Richards, Matthew A.; Shannon, Paul; Dinov, Ivo D.; Glusman, Gustavo; Price, Nathan D.; Horn, John D. Van; Kesselman, Carl; Toga, Arthur W.; Foster, Ian

doi:10.1101/268755

Cited by 7 publications

(6 citation statements)

References 47 publications

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“…Addressing the FAIR principles, Madduri et al [10] presented a set of tools that can be used to support the implementation of computational experiments, and ensure the aspects related to each of the principles. Madduri et al [10] used a case study in biomedicine in which data and computation are often distributed. As in this paper, the need to apply FAIR principles in the experiment as a whole is discussed.…”

Section: Related Workmentioning

confidence: 99%

Exploring Reproducibility and FAIR Principles in Data Science Using Ecological Niche Modeling as a Case Study

Mondelli

Peterson

Gadelha

2019

Lecture Notes in Computer Science

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Reproducibility is a fundamental requirement of the scientific process since it enables outcomes to be replicated and verified. Computational scientific experiments can benefit from improved reproducibility for many reasons, including validation of results and reuse by other scientists. However, designing reproducible experiments remains a challenge and hence the need for developing methodologies and tools that can support this process. Here, we propose a conceptual model for reproducibility to specify its main attributes and properties, along with a framework that allows for computational experiments to be findable, accessible, interoperable, and reusable. We present a case study in ecological niche modeling to demonstrate and evaluate the implementation of this framework.

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Section: Related Workmentioning

confidence: 99%

Exploring Reproducibility and FAIR Principles in Data Science Using Ecological Niche Modeling as a Case Study

Mondelli

Peterson

Gadelha

2019

Lecture Notes in Computer Science

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“…A review of big data concerning health research was presented in [15]. Yet another case study for big data science to replicate complex analysis was designed in [16]. A framework for parallelization on big data using Apache Hadoop with its Map Reduce function was investigated in [17].…”

Section: Related Workmentioning

confidence: 99%

Tobit Regressive Based Gaussian Independence Bayes Map Reduce Classifier on Data Warehouse for Predictive Analytics

Sivakkolundu¹,

Kavitha²

2019

IJITEE

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Data warehouse comprises of data collected from different probable heterogeneous resources at different time intervals with the objective of responding to user analytic queries. Big data is a field that helps in analysing and extracting information from large datasets. The unfolding Big Data incorporation inflicts multiple confronts, compromising the feasible business research practice. Heterogeneous resources, high dimensionality and massive volumes that confront Big Data prototype may prevent the effectual data and system integration processes. In this work, we plan to develop a Tobit Regressive based Gaussian Independence Bayes Map Reduce Classifier (TR-GIBMRC) method for categorizing the collected and stored data which helps the users in making decision with minimum time consumption. The TR-GIBMRC method consists of two processes. They are, Tobit Regressive Feature Selection and Gaussian Independence Bayes Map Reduce Classification. Tobit Regressive Feature Selection process is used to select relevant features from collected and stored data. Tobit statistical model, used to describe the relationship between non-negative dependent variable and an independent variable for selecting relevant features. Next, Gaussian Independence Bayes Map Reduce Classifier is used to classify the selected relevant features for decision making with lesser time consumption. Gaussian Independence Bayes Map Reduce Classifier, a probabilistic classifier segments the data by class by measuring the mean and variance of data in each class. The data point gets allocated to the class with minimal variance. This in turn helps to perform efficient data classification for accurate decision making. Experimental evaluation is carried out on the factors such as feature selection rate, classification accuracy, classification time and error rate with respect to number of features and number of data points.

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“…The demand for large volumes of multimodal biomedical data has grown drastically, partially due to active research in personalized medicine, and further understanding diseases [1][2][3] . This shift has made reproducing research findings much more challenging because of the need to ensure the use of adequate data handling methods, resulting in the validity and relevance of studies to be questioned 4,5 .…”

Section: Introductionmentioning

confidence: 99%

“…Even though sharing of data immensely helps in reproducing study results 6 , current sharing practices are inadequate with respect to the size of data and corresponding infrastructure requirements for transfer and storage 2,7 . As computational processing required to process biomedical data is becoming increasingly complex 3 , expertise is now needed for building the tools and workflows for this large-scale handling 1,2 . There have been multiple community efforts in creating standardized workflow languages, such as the Common Workflow Language (CWL) and the Workflow Definition Language (WDL), along with associated workflow management systems such as Snakemake 8 and Nextflow 9 , in order to promote reproducibility 10,11 .…”

Section: Introductionmentioning

confidence: 99%

Orchestrating and sharing large multimodal data for transparent and reproducible research

Mammoliti

Смирнов

Nakano

et al. 2020

Preprint

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Reproducibility is essential to Open Science, as there is limited relevance for finding that cannot be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, challenged, and built upon. However, due to recent advances in the biological and computational sciences, it has become difficult to process, analyze, and share data with the community in a manner that is transparent. This has made reproducing research findings more challenging, with some researchers going as far as suggesting that the biomedical sciences are experiencing a “reproducibility crisis”. To overcome these issues, we created a cloud-based platform called ORCESTRA (www.orcestra.ca), which provides a flexible framework for the reproducible processing of multimodal biomedical data. The platform enables processing of genomic and pharmacological profiles of cancer samples through the use of automated processing pipelines that are user-customizable, which are executed through Pachyderm, a data versioning and orchestration tool. ORCESTRA creates an integrated and fully documented data object known as a PharmacoSet (PSet), with a persistent identifier (DOI), that can be used and shared for future analyses using the Bioconductor PharmacoGx package.

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Reproducible big data science: A case study in continuous FAIRness

Cited by 7 publications

References 47 publications

Exploring Reproducibility and FAIR Principles in Data Science Using Ecological Niche Modeling as a Case Study

Exploring Reproducibility and FAIR Principles in Data Science Using Ecological Niche Modeling as a Case Study

Tobit Regressive Based Gaussian Independence Bayes Map Reduce Classifier on Data Warehouse for Predictive Analytics

Orchestrating and sharing large multimodal data for transparent and reproducible research

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