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

Mondelli, Maria Luiza; Peterson, A. Townsend; Gadelha, Luiz M. R.

doi:10.1007/978-3-030-34146-6_3

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Reproducibility, a fundamental requirement of the scientific process, is related to the idea that a scientific experiment should be able to be reproduced to validate its results. 85 The reproducibility of a scientific study is often assessed by the extent to which it follows the FAIR (Findable, Accessible, Interoperable and Reusable) principles. 86 For our review, we analyzed if the authors have provided adequate details about the dataset used and the implementation to check if the FAIR principles were followed.…”

Section: Resultsmentioning

confidence: 99%

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

Kumar

Schindler

et al. 2021

JAMIA Open

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Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. Materials and Methods We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. Results There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). Discussion Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.

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Section: Resultsmentioning

confidence: 99%

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

Kumar

Schindler

et al. 2021

JAMIA Open

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“…The Whole Tale is an open source, web-based and multi-user platform for reproducible research enabling the creation, publication, and execution of tales-executable research objects that capture data, code, and the complete software environment used to produce research findings. Mondelli et al (2019) developed a conceptual model of the reproducibility of an experiment and a generic framework for findable, accessible, interoperable and reusable (FAIR) computational experiments. These four principles contains a set of requirements on how data, metadata, and infrastructure must be managed, allowing machines to retrieve them automatically, or at least with minimal human intervention (Wilkinson et al, 2016).…”

Section: Reproducibility In Data Sciencementioning

confidence: 99%

“…The functions comprise the activities of the experiment that consume input and produce output. Parameters can also be used as input to functions, and constitute an attribute of the consume relation (Mondelli et al, 2019).…”

Section: Reproducibility In Data Sciencementioning

confidence: 99%

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Applications of rough sets in big data analysis: An overview

Pięta¹,

Szmuc²

2021

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Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in current research and industrial applications. Processing massive amounts of data generated by the IoT and stored in distributed space is not a straightforward task and may cause many problems. During the last few decades, scientists have proposed many interesting approaches to extract information and discover knowledge from data collected in database systems or other sources. We observe a permanent development of machine learning algorithms that support each phase of the data mining process, ensuring achievement of better results than before. Rough set theory (RST) delivers a formal insight into information, knowledge, data reduction, uncertainty, and missing values. This formalism, formulated in the 1980s and developed by several researches, can serve as a theoretical basis and practical background for dealing with ambiguities, data reduction, building ontologies, etc. Moreover, as a mature theory, it has evolved into numerous extensions and has been transformed through various incarnations, which have enriched expressiveness and applicability of the related tools. The main aim of this article is to present an overview of selected applications of RST in big data analysis and processing. Thousands of publications on rough sets have been contributed; therefore, we focus on papers published in the last few years. The applications of RST are considered from two main perspectives: direct use of the RST concepts and tools, and jointly with other approaches, i.e., fuzzy sets, probabilistic concepts, and deep learning. The latter hybrid idea seems to be very promising for developing new methods and related tools as well as extensions of the application area.

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“…Feng et al (2019) present a checklist for maximizing the reproducibility of ENM, describing in detail each step of the process, and what should be preserved in each of them to enable better reproducibility. Mondelli, Townsend Peterson, and Gadelha (2019) presented a conceptual model and framework for supporting reproducibility and FAIR principles in computational experiments. The framework is evaluated with an ENM case study.…”

Section: Biodiversity Workflows and Reproducibilitymentioning

confidence: 99%

A survey of biodiversity informatics: Concepts, practices, and challenges

Gadelha

Siracusa

Dalcin

et al. 2020

WIREs Data Min & Knowl

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The unprecedented size of the human population, along with its associated economic activities, has an ever‐increasing impact on global environments. Across the world, countries are concerned about the growing resource consumption and the capacity of ecosystems to provide resources. To effectively conserve biodiversity, it is essential to make indicators and knowledge openly available to decision‐makers in ways that they can effectively use them. The development and deployment of tools and techniques to generate these indicators require having access to trustworthy data from biological collections, field surveys and automated sensors, molecular data, and historic academic literature. The transformation of these raw data into synthesized information that is fit for use requires going through many refinement steps. The methodologies and techniques applied to manage and analyze these data constitute an area usually called biodiversity informatics. Biodiversity data follow a life cycle consisting of planning, collection, certification, description, preservation, discovery, integration, and analysis. Researchers, whether producers or consumers of biodiversity data, will likely perform activities related to at least one of these steps. This article explores each stage of the life cycle of biodiversity data, discussing its methodologies, tools, and challenges. This article is categorized under: Algorithmic Development > Biological Data Mining

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

Cited by 5 publications

References 17 publications

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

Applications of rough sets in big data analysis: An overview

A survey of biodiversity informatics: Concepts, practices, and challenges

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