Computational biology in the 21st century

Berger, Bonnie; Daniels, Noah M.; Yu, Yun William

doi:10.1145/2957324

Cited by 40 publications

(18 citation statements)

References 40 publications

(70 reference statements)

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“…All these fields are characterized by the mathematical, physical and computational modeling of complex systems and behaviors, and the validation of such models against experimental data which typically come from research articles. Indeed, similar challenges have been discussed recently in these fields, particularly those related to data integration, interpretation and large-scale analysis [1,6,21,35,47], highlighting several shared open issues. The framework investigated in this paper has been currently tested and optimized only for the scenario described in Section 2, but most of the design choices were taken trying to generalize its applicability.…”

Section: G Scalia Et Al / Towards a Scientific Data Framework To Sumentioning

confidence: 72%

“…Therefore, the solutions discussed in this paper have a general validity beyond the domain of combustion kinetics. Indeed, recent literature has highlighted "cases where datasets are reused in combination with other data, whether to make comparisons, build new models, or explore new questions altogether" [39], in fields such as computational biology [6], gene expression analysis [47], biomedical research [35], cell biology [21] or predictive structural materials science [1]. All these fields are characterized by the mathematical, physical and computational modeling of complex systems and behaviors, and the validation of such models against experimental data which typically come from research articles.…”

Section: G Scalia Et Al / Towards a Scientific Data Framework To Sumentioning

confidence: 99%

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Towards a scientific data framework to support scientific model development

Scalia

Pelucchi

Stagni

et al. 2019

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Editor: Silvio Peroni (https://orcid.org/0000-0003-0530-4305) Solicited reviews: HannaĆwiek-Kupczyńska (https://orcid.org/0000-0001-9113-567X); Aliaksandr Birukou (https://orcid.org/0000-0002-4925-9131); Stian Soiland-Reyes (https://orcid.org/0000-0001-9842-9718)Abstract. The sharing of scientific and scholarly data has been increasingly promoted over the last decade, leading to open repositories in many different scientific domains. However, data sharing and open data are not final goals in themselves, the real benefit is in data reuse, which allows leveraging investments in research and enables large-scale data-driven research progress.This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0). 2451-8484 © 2019 -IOS Press and the authors. G. Scalia et al. / Towards a scientific data framework to support scientific model developmentFocusing on reuse, this paper discusses the design of an integrated framework to automatically take advantage of large amounts of scientific data extracted from the literature to support research, and in particular scientific model development. Scientific models reproduce and predict complex phenomena and their development is a rather challenging task, within which scientific experiments have a key role in their continuous validation. Starting from the combustion kinetics domain, this paper discusses a set of use cases and a first prototype for such a framework which leads to a set of new requirements and an architecture that can be generalized to other domains. The paper analyzes the needs, the challenges and the research directions for such a framework, in particular those related to data management, automatic scientific model validation, data aggregation and data analysis, to leverage large amounts of published scientific data for new knowledge extraction.

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Section: G Scalia Et Al / Towards a Scientific Data Framework To Sumentioning

confidence: 72%

Section: G Scalia Et Al / Towards a Scientific Data Framework To Sumentioning

confidence: 99%

Towards a scientific data framework to support scientific model development

Scalia

Pelucchi

Stagni

et al. 2019

View full text Add to dashboard Cite

show abstract

“…For example, computational biologists rely on reference sequence data collected by the Human Genome Project (HGP) for mapping their own new data (Berger et al 2016). In "next-generation sequencing," DNA molecules are chopped into many small fragments (reads) that bioinformaticians will reassemble in the correct order (Berger et al 2016;Orelli 2016). Such data collections exist as initial sources of data to ask new questions, rather than assemblages of data collected for myriad purposes by individual researchers and teams.…”

Section: Use Vs Reuse Of Datamentioning

confidence: 99%

“…All the datasets involved might be from prior research of others, or available data might be integrated with new observations (Berger et al 2016;Rung and Brazma 2012).…”

Section: Independent Reuse Vs Data Integrationmentioning

confidence: 99%

On the Reuse of Scientific Data

Pasquetto¹,

Randles²,

Borgman³

2017

CODATA

153

174

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While science policy promotes data sharing and open data, these are not ends in themselves. Arguments for data sharing are to reproduce research, to make public assets available to the public, to leverage investments in research, and to advance research and innovation. To achieve these expected benefits of data sharing, data must actually be reused by others. Data sharing practices, especially motivations and incentives, have received far more study than has data reuse, perhaps because of the array of contested concepts on which reuse rests and the disparate contexts in which it occurs. Here we explicate concepts of data, sharing, and open data as a means to examine data reuse. We explore distinctions between use and reuse of data. Lastly we propose six research questions on data reuse worthy of pursuit by the community: How can uses of data be distinguished from reuses? When is reproducibility an essential goal? When is data integration an essential goal? What are the tradeoffs between collecting new data and reusing existing data? How do motivations for data collection influence the ability to reuse data? How do standards and formats for data release influence reuse opportunities? We conclude by summarizing the implications of these questions for science policy and for investments in data reuse.

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“…The situation in life science is growing increasingly complex as data are being used for more advanced modeling, which creates new datasets (Tenopir et al 2011). To illustrate, in computational biology, data reuse occurs in combination with the use of other data such as comparison studies (Berger, Daniels and Yu 2016).…”

Section: Metadata and Data Reuse In Biologymentioning

confidence: 99%

An examination of metadata practices for research data reuse: Characteristics and predictive probability of metadata elements

Park

2019

MJLIS

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This study explores metadata practices in the relation to data reuse in biology. Metadata has long been viewed as a major constituent in research data management and reuse. However, the topic of whether metadata is used in a way that encourages data reuse has been understudied. The current study examined metadata elements used to describe datasets and the predictive probability of those metadata elements for data reuse under the assumption that citation frequency reflects the frequency of research data reuse. A total of 34,491 cited records from the biology category of the Clarivate Analytics Data Citation Index were analyzed using descriptive comparison and multiple regression analysis to compare usage patterns of metadata elements between data records cited more than twice and those cited only once. Of the five types of metadata elements identified and examined, metadata elements that provided descriptions about datasets and author-related information dominantly appeared across datasets, whereas DOI and ORCID identifier were scarce. Metadata related to author and funding resources were found to be positive influential factors in predicting data reuse, whereas data descriptions and identifiers appeared to have negative influences. This study contributed to a better understanding of metadata needs for data reuse.

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Computational biology in the 21st century

Abstract: Algorithmic advances take advantage of the structure of massive biological data landscape.

Cited by 40 publications

References 40 publications

Towards a scientific data framework to support scientific model development

Towards a scientific data framework to support scientific model development

On the Reuse of Scientific Data

An examination of metadata practices for research data reuse: Characteristics and predictive probability of metadata elements

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