On serendipity in science: discovery at the intersection of chance and wisdom

Copeland, Samantha

doi:10.1007/s11229-017-1544-3

Cited by 86 publications

(73 citation statements)

References 42 publications

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“…Makri et al [17] regarded serendipity as a new connection made by integrating unexpectedness and insight. Copeland [18] summarized three interrelated features of serendipity in science. First, serendipity is volatile.…”

Section: Concepts Of Serendipity In Sciencementioning

confidence: 99%

To Your Surprise: Identifying Serendipitous Collaborators

Wan

Yuan

Xia

et al. 2021

IEEE Trans. Big Data

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Scientific collaboration has become a universal phenomenon in recent years. Meanwhile, scholars tend to hunt for surprising collaborators for broadening their horizons. Serendipity initially denotes the fortunate discovery. Although a lot of literature is available on the topic of serendipity, little research has investigated serendipity in scientific collaborations. The objective of this paper is to identify serendipitous scientific collaborators of target scholars based on their collaboration data. First, we induce the definition of serendipitous scientific collaborators by three components, which are relevance, unexpectedness, and value, respectively. They are quantified as three intuitive indices corresponding to the network proximity, topic diversity, and collaborator influence, respectively. Second, we propose a classification model, called RUVMod, to classify all collaborators based on the analysis of three indices in definition. The serendipitous collaborator has lower network proximity, higher topic diversity and higher influence than his/her target scholar relatively. Finally, we cluster all collaborators via Self Organizing Maps and identify the serendipitous collaborator class according to the classes divided in our RUVMod. We apply our definition to the scientific collaborators extracted from DBLP dataset. The evaluation from the serendipity-based metrics suggests that RUVMod is effective in identifying serendipitous scientific collaborators.

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Section: Concepts Of Serendipity In Sciencementioning

confidence: 99%

To Your Surprise: Identifying Serendipitous Collaborators

Wan

Yuan

Xia

et al. 2021

IEEE Trans. Big Data

View full text Add to dashboard Cite

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“…Throughout these years, he constantly trained his vision to detect relevant anomalies, which made him "the perfect eyewitness for this unlikely microbiological contingency […] a receptive observer." (Bennett and Chung 2001, p. 168) What it means to be a skilled observer varies with the context: In this case, it presumes knowledge of the state of the art of the scientific discipline and an idea of what counts as relevant and novel from this perspective (Copeland 2017). McFarlane summarizes Fleming's scientific merits in a similar manner: "If [Fleming] cannot be given the credit for the accidents, he should be granted the keen eye that observed them and the prepared mind that appreciated their interest."…”

Section: The Case Of Alexander Flemingmentioning

confidence: 99%

“…68-69). 6 In this manner, it is suggested that developing certain epistemic skills increases the likelihood of becoming scientifically lucky, such as training certain virtues increases the likelihood of becoming morally lucky (Copeland 2017;McKinnon 2014). In so far as it is up to the agent to deliberately train such skills or accept certain risks when driving negligently, these freely adopted volitions constitute the basis of someone's moral standing (Concepcion 2002, p. 459).…”

Section: Moral Luck and Common Objectionsmentioning

confidence: 99%

“…An inherited scientific curiosity does not help one telling apart a dirty petri dish from one that contains an important antibacterial substance. These skills have to be learned and continuously trained, which requires diligence and effort (Copeland 2017;McKinnon 2014).…”

Section: Different Types Of Luck and A Defence Of The Control Principlementioning

confidence: 99%

“…It has been argued that serendipitous discoveries are emergent properties of such scientific networks(Copeland 2017). 9 A list of the different types of contingencies that affected the rise of modern science is presented byTrout (2016Trout ( , 2019.…”

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confidence: 99%

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Did Alexander Fleming Deserve the Nobel Prize?

Sand

2019

Sci Eng Ethics

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Penicillin is a serendipitous discovery par excellence. But, what does this say about Alexander Fleming's praiseworthiness? Clearly, Fleming would not have received the Nobel Prize, had not a mould accidently entered his laboratory. This seems paradoxical, since it was beyond his control. The present article will first discuss Fleming's discovery of Penicillin as an example of moral luck in science and technology and critically assess some common responses to this problem. Second, the Control Principle that says that people are not responsible for things beyond their control will be defended. An implication of this principle is that Alexander Fleming's desert, which is based on his epistemic skills, remains untouched by luck. Third, by distinguishing different notions of praiseworthiness, a way to resolve the paradox of moral luck will be elaborated. Desert provides only a pro tanto reason to determine whether someone is an appropriate addressee of reward. Here, luck can make a difference. Forth, it will be argued that stimulating the quest for socially beneficial science provides a compelling reason to treat scientists with equal desert differently. Penicillin provides striking evidence for the importance of this quest and showcasing it incentivizes the making of socially beneficial science. Ultimately, it will be justified why Fleming deserved the Nobel Prize in at least one sense of the concept.

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Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Bhowmik¹,

Berecibar

Casas‐Cabanas

et al. 2021

Advanced Energy Materials

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BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high-performance batteries.Here, a description is given of how the AI-assisted toolkits and methodologies developed in BATTERY 2030+ can be transferred and applied to representative examples of future battery chemistries, materials, and concepts. This perspective highlights some of the main scientific and technological challenges facing emerging low-technology readiness level (TRL) battery chemistries and concepts, and specifically how the AI-assisted toolkit developed within BIG-MAP and other BATTERY 2030+ projects can be applied to resolve these. The methodological perspectives and challenges in areas like predictive long time-and length-scale simulations of multi-species systems, dynamic processes at battery interfaces, deep learned multi-scaling and explainable AI, as well as AI-assisted materials characterization, self-driving labs, closed-loop optimization, and AI for advanced sensing and self-healing are introduced. A description is given of tools and modules can be transferred to be applied to a select set of emerging low-TRL battery chemistries and concepts covering multivalent anodes, metalsulfur/oxygen systems, non-crystalline, nano-structured and disordered systems, organic battery materials, and bulk vs. interface-limited batteries.

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On serendipity in science: discovery at the intersection of chance and wisdom

Abstract: (226 words):

Cited by 86 publications

References 42 publications

To Your Surprise: Identifying Serendipitous Collaborators

To Your Surprise: Identifying Serendipitous Collaborators

Did Alexander Fleming Deserve the Nobel Prize?

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

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