Chapter 6 Big Data and FAIR Data for Data Science

Гвишиани, А. Д.; Dobrovolsky, M. N.; Rybkina, Alena

doi:10.1007/978-3-030-70370-7_6

Cited by 7 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further bolstering the argument for unified policies is the intrinsic link between FAIR data and Big Data . In broad terms, both concepts intersect across issues of data storage, access, and processing (Gvishiani et al, 2021). However, the former refers to solution-oriented guidelines directly related to data management and data sharing whereas the latter typically refers to rapid large-scale data processing and generation (Gvishiani et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…In broad terms, both concepts intersect across issues of data storage, access, and processing (Gvishiani et al, 2021). However, the former refers to solution-oriented guidelines directly related to data management and data sharing whereas the latter typically refers to rapid large-scale data processing and generation (Gvishiani et al, 2021). Data FAIRification undoubtedly plays a pivotal role in improving Big Data .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

FAIR data sharing: An international perspective on why medical researchers are lagging behind

Rainey¹,

Lutomski

Broeders

2023

Big Data & Society

View full text Add to dashboard Cite

FAIR data, that is, Findable, Accessible, Interoperable, and Reusable data, and Big Data intersect across issues related to data storage, access, and processing. The solution-oriented FAIR principles serve an integral role in improving Big Data; yet to date, the implementation of FAIR in multiple sectors has been fragmented. We conducted an exploratory analysis to identify incentives and barriers in creating FAIR data in the medical sector using digital concept mapping, a systematic mixed methods approach. Thirty-eight principal investigators (PIs) were recruited from North America, Europe, and Oceania. Our analysis revealed five clusters rated according to perceived relevance: ‘Efficiency and collaboration’ (rating 7.23), ‘Privacy and security’ (rating 7.18), ‘Data management standards’ (rating 7.16), ‘Organization of services’ (rating 6.98), and ‘Ownership’ (rating 6.28). All five clusters scored relatively high and within a narrow range (i.e., 6.28–7.69), implying that each cluster likely influences researchers’ decision-making processes. PIs harbor a positive view of FAIR data sharing, as exemplified by participants highly prioritizing ‘Efficiency and collaboration’. However, the other four clusters received only modestly lower ratings and largely contained barriers to FAIR data sharing. When viewed collectively, the benefits of efficiency and collaboration may not be sufficient in propelling FAIR data sharing. Arguably, until more of these reported barriers are addressed, widespread support of FAIR data will not translate into widespread practice. This research lays the preliminary foundation for conducting targeted large-scale research into FAIR data practices in the medical research community.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

FAIR data sharing: An international perspective on why medical researchers are lagging behind

Rainey¹,

Lutomski

Broeders

2023

Big Data & Society

View full text Add to dashboard Cite

show abstract

“…One example of suggested measures to aid in responsible data use to mitigate data privacy concerns is outlined in the Consultative Group on International Agricultural Research Platform for Big Data in Agriculture and Responsible Data Guidelines (CGIAR, 2020). The CGIAR Platform for Big Data in Agriculture suggests multiple benefits to society if open data practices are adopted including accelerated scientific advancement, economic growth, increased resource efficiency, and strengthened public support for research funding and public trust in research (Gvishiani et al., 2021). Beyond data privacy and data stewardship, there are also expectations that data comply with Findable, Accessible, Interoperable, Reusable (FAIR) principles (Prince Czarnecki & Jones, 2022; Wilkinson et al., 2016), which is a key tenet of the "Open Science" movement and were embodied during the construction of the Forage Data Hub .…”

Section: Introductionmentioning

confidence: 99%

Framework to develop an open‐source forage data network to improve primary productivity and enhance system resiliency

Ashworth,

Marshall,

Volenec

et al. 2023

Agronomy Journal

View full text Add to dashboard Cite

Data repositories using legacy data are needed to minimize research redundancy, enable knowledge synthesis, and optimize productivity and resiliency of agricultural systems. However, a framework is needed to develop an online searchable database to identify optimum systems for sustainable agricultural intensification and diversification, particularly for forages. Therefore, this paper outlines the development of a community‐driven forage database (Forage Data Hub) using legacy datasets encompassing multiple temporal and spatial scales and species to analyze system functionality and resiliency. Specific steps outlined included: i) developing minimum and preferred data requirements; ii) data standardization, structuring, and compilation; iii) creating a data thesaurus, data shapes, and data model; and iv) creating a web‐based system interface to facilitate database access. We demonstrate utility of curating these diverse datasets by quantifying forage system resiliency during extreme weather occurrences (relationship between standardized yields and yields during years receiving 25th percentile of the 30‐year normal precipitation) for three systems (monocrop annual, monocrop perennial, and mixed perennial) spanning 52,997 data entries (108 unique locations and 51 years). Overall, during low‐precipitation years, monocrop annual systems achieved 67% of their expected yield, while monocrop perennial systems achieved 93% and mixed perennial systems achieved 112% of potential yields. Therefore, diverse perennial systems were more resilient to climate‐related stresses compared to annual forage systems. Development of the US Forage Data Hub underscores the benefits of community‐driven data sharing and curation for a given commodity to provide systems‐level sustainability assessments for identifying practices that promote ecological intensification and resiliency to climatic stochasticity.This article is protected by copyright. All rights reserved

show abstract

“…It is in this context that findability, accessibility, interoperability, and reuse (FAIR) Data has been defined, i.e. data that is discoverable, accessible, interoperable and reusable but which, unlike open data, is not always available to everyone (Dunning et al , 2017; Gvishiani et al , 2021). FAIR data principles can be applied for achieving reusability (Hasnain and Rebholz-Schuhmann, 2018; Groth et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Open data-set identifier for open innovation and knowledge management

Ortiz‐de‐Urbina‐Criado

Abella²,

García-Luna

2023

JKM

View full text Add to dashboard Cite

Purpose This paper aims to highlight the importance of open data and the role that knowledge management and open innovation can play in its identification and use. Open data has great potential to create social and economic value, but its main problem is that it is often not easily reusable. The aim of this paper is to propose a unique identifier for open data-sets that would facilitate search and access to them and help to reduce heterogeneity in the publication of data in open data portals. Design/methodology/approach Considering a model of the impact process of open data reuse and based on the digital object identifier system, this paper develops a proposal of a unique identifier for open data-sets called Open Data-set Identifier (OpenDatId). Findings This paper presents some examples of the application and advantages of OpenDatId. For example, users can easily consult the available content catalogues, search the data in an automated way and examine the content for reuse. It is also possible to find out where this data comes from, solving the problems caused by the increasingly frequent federation of data in open data portals and enabling the creation of additional services based on open data. Originality/value From an integrated perspective of knowledge management and open innovation, this paper presents a new unique identifier for open data-sets (OpenDatId) and a new concept for data-set, the FAIR Open Data-sets.

show abstract

Chapter 6 Big Data and FAIR Data for Data Science

Cited by 7 publications

References 31 publications

FAIR data sharing: An international perspective on why medical researchers are lagging behind

FAIR data sharing: An international perspective on why medical researchers are lagging behind

Framework to develop an open‐source forage data network to improve primary productivity and enhance system resiliency

Open data-set identifier for open innovation and knowledge management

Contact Info

Product

Resources

About