Data management challenges for artificial intelligence in plant and agricultural research

Williamson, Hugh F.; Brettschneider, Julia; Cáccamo, Mario; Davey, Robert P.; Goble, Carole; Kersey, Paul J.; May, Sean; Morris, Richard J.; Ostler, Richard; Pridmore, Tony; Rawlings, Christopher J.; Studholme, David J.; Tsaftaris, Sotirios A.; Leonelli, Sabina

doi:10.12688/f1000research.52204.2

Cited by 9 publications

(4 citation statements)

References 95 publications

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“…Numerous examples exist of where existing fields of endeavour have embraced the insights that data analytics bring. These fields include prescriptive and Data analytics for project delivery descriptive analytics in financial services research (Andriosopoulos et al, 2019); plant and precision agricultural (Olsen et al, 2019;Talaviya et al, 2020;Williamson et al, 2021); ecology (Fink et al, 2014); pandemic monitoring (Dutta et al, 2021;Fiore et al, 2019;Haycock et al, 2020); circadian disruption in shift workers (Zhang et al, 2022); urban analytics (Chohlas-Wood et al, 2015;Lee, 2022); post-disaster human displacement (Oxford, 2021); electricity demand forecasting (Farrokhabadi et al, 2022). Many of these achievements were enabled by collaborations including teams consisting of both domain specialists and data analytics experts.…”

Section: How Can Project Studies Adapt To Include Data Analytical Res...mentioning

confidence: 99%

Data analytics for project delivery: unlocking the potential of an emerging field

Shen,

Brookes,

Lattuf Flores

et al. 2024

IJMPB

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PurposeIn recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.Design/methodology/approachWe propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.FindingsThe literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.Originality/valueBased on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

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Section: How Can Project Studies Adapt To Include Data Analytical Res...mentioning

confidence: 99%

Data analytics for project delivery: unlocking the potential of an emerging field

Shen,

Brookes,

Lattuf Flores

et al. 2024

IJMPB

Self Cite

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“…Important aspects here include intelligent test selection and associated test data management. AI, for instance, can select the most relevant test cases and assist in the creation and management of test data by generating synthetic data or anonymizing and masking real data [8] [23] [24].…”

Section: The V-cycle and Its Phases In Detailmentioning

confidence: 99%

Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Nüßgen,

Degen,

Irmer

et al. 2024

IJIS

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Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.

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“…There has therefore been substantive investment in ways through which plant data may be linked and collectively mined, regardless of where they have been originally collected (Williamson and Leonelli 2022). Given the vast heterogeneity in the sources and materials from which data are extracted, and the difficulties in developing formats and infrastructures that can appropriately document such diversity, data linkage in this area remains an immense challenge (Williamson et al 2023). When focusing primarily on technical issues around data sharing, however, researchers often fail to address its broader contextincluding the political economy of data trading across local breeders, national governments and industries with a stake in farming, and the use of evidence from plant science to foster an understanding of agricultural development that is focused on technologically fueled solutions to increase plant yield through genetic selection (e.g.…”

Section: The Data Trade: Crop Data Linkage and Bioprospectingmentioning

confidence: 99%

Philosophy of Open Science

Leonelli

2023

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The Open Science [OS] movement aims to foster the wide dissemination, scrutiny and re-use of research components for the good of science and society. This Element examines the role played by OS principles and practices within contemporary research and how this relates to the epistemology of science. After reviewing some of the concerns that have prompted calls for more openness, it highlights how the interpretation of openness as the sharing of resources, so often encountered in OS initiatives and policies, may have the unwanted effect of constraining epistemic diversity and worsening epistemic injustice, resulting in unreliable and unethical scientific knowledge. By contrast, this Element proposes to frame openness as the effort to establish judicious connections among systems of practice, predicated on a process-oriented view of research as a tool for effective and responsible agency. This title is also available as Open Access on Cambridge Core.

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Data management challenges for artificial intelligence in plant and agricultural research

Cited by 9 publications

References 95 publications

Data analytics for project delivery: unlocking the potential of an emerging field

Data analytics for project delivery: unlocking the potential of an emerging field

Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Philosophy of Open Science

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