Open Data in Scientific Settings

Pasquetto, Irene V.; Sands, Ashley E.; Darch, Peter T.; Borgman, Christine L.

doi:10.1145/2858036.2858543

Cited by 19 publications

(18 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, we summarize and depict the role of both HCI practitioners and researchers through design recommendations and emerging research challenges. Defining and establishing those roles is particularly important as we recognize that today's availability of online technologies enables institutes, libraries, and service providers to develop platforms that support scientists in preserving and sharing their research [146,203]. We argue that HCI methods are valuable assets in the systematic study and design of interactive tools for reproducible science.…”

Section: Reproducible Sciencementioning

confidence: 99%

See 1 more Smart Citation

Open is not enough

et al. 2018

View full text Add to dashboard Cite

show abstract

Section: Reproducible Sciencementioning

confidence: 99%

“…It is also expected to enable systematic sharing and reuse in science [99,103]. It is mainly through today's availability and access to online technologies that we see new opportunities for the development of tools that support scientists in their Research Data Management (RDM) [146,203].…”

Section: Introductionmentioning

confidence: 99%

Open is not enough

et al. 2018

View full text Add to dashboard Cite

show abstract

“…"Open data" is perhaps the most problematic term of all, given the array of concepts and conditions to which it may refer (Pasquetto et al 2016). Baseline conditions for open data usually refer to "fewest restrictions" and "lowest possible costs."…”

Section: What Is Open Data?mentioning

confidence: 99%

On the Reuse of Scientific Data

Pasquetto¹,

Randles²,

Borgman³

2017

CODATA

Self Cite

154

174

View full text Add to dashboard Cite

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.

show abstract

“…The goals of this work stem from the increasing availability of open and structured scientific data and the resulting needs of new ways to effectively leverage and reuse them. In general, the concept of "open data" is not easily definable and baseline conditions are "fewest restrictions" and "lowest possible costs" [40]. It has been noted how data sharing practices have been investigated more than data reuse [39].…”

Section: Related Workmentioning

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

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.

show abstract

Open Data in Scientific Settings

Cited by 19 publications

References 36 publications

Open is not enough

Open is not enough

On the Reuse of Scientific Data

Towards a scientific data framework to support scientific model development

Contact Info

Product

Resources

About