Individual differences in knowledge network navigation

With the rapid accumulation of online information, efficient web navigation has grown vital yet challenging. To create an easily navigable cyberspace catering to diverse demographics, understanding how people navigate differently is paramount. While previous research has unveiled individual differences in spatial navigation, such differences in knowledge space navigation remain sparse. To bridge this gap, we conducted an online experiment where participants played a navigation game on Wikipedia and completed personal information questionnaires. Our analysis shows that age negatively affects knowledge space navigation performance, while multilingualism enhances it. Under time pressure, participants' performance improves across trials and males outperform females, an effect not observed in games without time pressure. In our experiment, successful route-finding is usually not related to abilities of innovative exploration of routes. Our results underline the importance of age, multilingualism and time constraint in the knowledge space navigation.

Who Am I? The Influence of Knowledge Networks on PhD Students' Formation of a Researcher Role Identity

Higher education institutes both foster the advancement of knowledge and address society's socioeconomic and environmental challenges. To fulfil these multiple missions requires significant changes to how the role of a researcher is perceived e.g. a researcher identity that is congruent with the objective of contributing to fundamental knowledge while also engaging with non-academic actors, broadly, and entrepreneurship, in particular. We argue that the early stages of an academic career-namely the PhD training trajectory-and the knowledge networks formed during this period have a major influence on the scientist's future capacity to develop an appropriate researcher role identity. We draw on knowledge network and identity theories to investigate how the knowledge networks (i.e. business, scientific and career knowledge networks) of PhD students promote changes to, reinforce or conflict with the perception of a researcher role identity. Our longitudinal qualitative network study includes PhD students and their supervisors funded by the H2020 FINESSE project. At the network level, we show that scientific knowledge is distributed equally throughout young academics' networks but that entrepreneurial (business) and career knowledge tend to be concentrated around certain individuals in these networks. On the PhD student level, we observe different pronunciations of the researcher role identity linked to students' interactions with their knowledge networks. We distinguish identity conflicts due to misalignment between ego and alters which leads to withdrawal from the network. Our findings have practical implications and suggest that universities and PhD student supervisors should support PhD students to develop a researcher identity which is in line with the individual PhD student's expectations.

Global bibliometric mapping of the frontier of knowledge in the field of artificial intelligence for the period 1990-2019

Artificial Intelligence (AI) has emerged as a field of knowledge that is displacing and disrupting technologies, leading to changes in human life. Therefore, the purpose of this study is to scientifically map this topic and its ramifications, in order to analyze its growth. The study was developed under the bibliometric approach and considered the period 1990-2019. The steps followed were (i) Identification and selection of keyword terms in three methodological layers by a panel of experts. (ii) Design and application of an algorithm to identify these selected keywords in titles, abstracts, and keywords using terms in Web of Science to contrast them. (iii) Performing data processing based on the Journals of the Journal Citation Report during 2020. Knowing the evolution of a field of knowledge such as AI from a bibliometric study and subsequently establishing the ramifications of new research streams is in itself a relevant finding. Addressing a broad field of knowledge as AI from a multidisciplinary approach given the convergence it generates with other disciplines and specialties is of high strategic value for decision makers such as governments, academics, scientists, and entrepreneurs.

Recommendations for coastal planning and beach management in Caribbean insular states during and after the COVID-19 pandemic

The COVID-19 pandemic has implications for coastal planning and management. Rules for isolation and physical distancing, among other measures for human life protection, have led to the closure of most beaches around the world. The present critical situation has raised the following question: How can some recommendations be designed in sun, sea, and sand tourism-dependent-insular countries to face "the COVID-19 new normality?" We used the content analysis technique to analyze representative publications on a global level to ascertain information on best management practices. A survey of 58 experts provided additional information. We used inferential statistics for sample selection and produced a list of 43 practices and beach planning and management actions to face the COVID-19 pandemic. This led to 27 new recommendations designed for beach planning and management within insular contexts, some of which were tested in the Republic of Cuba. Recommendations aim to guarantee a culture of safety and improvement within the field of beach or coastal planning and management. These recommendations should prove useful for other insular countries, during the COVID-19 period, in the new normality that follows, and in other post-pandemic scenarios.

Extracting knowledge networks from plant scientific literature: potato tuber flesh color as an exemplary trait

BACKGROUND

Scientific literature carries a wealth of information crucial for research, but only a fraction of it is present as structured information in databases and therefore can be analyzed using traditional data analysis tools. Natural language processing (NLP) is often and successfully employed to support humans by distilling relevant information from large corpora of free text and structuring it in a way that lends itself to further computational analyses. For this pilot, we developed a pipeline that uses NLP on biological literature to produce knowledge networks. We focused on the flesh color of potato, a well-studied trait with known associations, and we investigated whether these knowledge networks can assist us in formulating new hypotheses on the underlying biological processes.

RESULTS

We trained an NLP model based on a manually annotated corpus of 34 full-text potato articles, to recognize relevant biological entities and relationships between them in text (genes, proteins, metabolites and traits). This model detected the number of biological entities with a precision of 97.65% and a recall of 88.91% on the training set. We conducted a time series analysis on 4023 PubMed abstract of plant genetics-based articles which focus on 4 major Solanaceous crops (tomato, potato, eggplant and capsicum), to determine that the networks contained both previously known and contemporaneously unknown leads to subsequently discovered biological phenomena relating to flesh color. A novel time-based analysis of these networks indicates a connection between our trait and a candidate gene (zeaxanthin epoxidase) already two years prior to explicit statements of that connection in the literature.

CONCLUSIONS

Our time-based analysis indicates that network-assisted hypothesis generation shows promise for knowledge discovery, data integration and hypothesis generation in scientific research.

It's a match! Simulating compatibility-based learning in a network of networks

In this article, we develop a new way to capture knowledge diffusion and assimilation in innovation networks by means of an agent-based simulation model. The model incorporates three essential characteristics of knowledge that have not been covered entirely by previous diffusion models: the network character of knowledge, compatibility of new knowledge with already existing knowledge, and the fact that transmission of knowledge requires some form of attention. We employ a network-of- networks approach, where agents are located within an innovation network and each agent itself contains another network composed of knowledge units (KUs). Since social learning is a path-dependent process, in our model, KUs are exchanged among agents and integrated into their respective knowledge networks depending on the received KUs' compatibility with the currently focused ones. Thereby, we are also able to endogenize attributes such as absorptive capacity that have been treated as an exogenous parameter in some of the previous diffusion models. We use our model to simulate and analyze various scenarios, including cases for different degrees of knowledge diversity and cognitive distance among agents as well as knowledge exploitation vs. exploration strategies. Here, the model is able to distinguish between two levels of knowledge diversity: heterogeneity within and between agents. Additionally, our simulation results give fresh impetus to debates about the interplay of innovation network structure and knowledge diffusion. In summary, our article proposes a novel way of modeling knowledge diffusion, thereby contributing to an advancement of the economics of innovation and knowledge.

The nearly universal link between the age of past knowledge and tomorrow's breakthroughs in science and technology: The hotspot

Scientists and inventors can draw on an ever-expanding literature for the building blocks of tomorrow's ideas, yet little is known about how combinations of past work are related to future discoveries. Our analysis parameterizes the age distribution of a work's references and revealed three links between the age of prior knowledge and hit papers and patents. First, works that cite literature with a low mean age and high age variance are in a citation "hotspot"; these works double their likelihood of being in the top 5% or better of citations. Second, the hotspot is nearly universal in all branches of science and technology and is increasingly predictive of a work's future citation impact. Third, a scientist or inventor is significantly more likely to write a paper in the hotspot when they are coauthoring than whey they are working alone. Our findings are based on all 28,426,345 scientific papers in the Web of Science, 1945-2013, and all 5,382,833 U.S. patents, 1950-2010, and reveal new antecedents of high-impact science and the link between prior literature and tomorrow's breakthrough ideas.

Assessing the success of invasive species prevention efforts at changing the behaviors of recreational boaters

Aquatic invasive species (AIS) pose major conservation challenges in freshwater ecosystems. In response, conservation organizations invest considerable resources in outreach to encourage AIS prevention behaviors among recreational boaters. Despite this, remarkably little is known about whether these efforts catalyze significant changes in boaters' perceptions, or whether they cause changes in behaviors that reduce AIS risk. We interviewed managers at the 14 Illinois organizations active in AIS outreach to determine regional priorities for, and investment in, AIS outreach. The results show a network of collaboration that reinforces a limited set of conservation messages. Next, we surveyed 515 recreational boaters to evaluate access to outreach, knowledge of AIS, and consistency of prevention behavior. Boater recognition of prevention slogans and knowledge of AIS and AIS prevention behavior was similar across Illinois regions despite large regional differences in investment in outreach. Most boaters (94%) report never intentionally moving organisms among waterbodies. Fewer reported that they Always perform recommended actions to reduce risk of AIS spread on their boat interior (68%), boat exterior (63%), or fishing tackle (47%). Recognition of prevention slogans and the number of AIS recognized were significantly, positively, associated with Always performing AIS prevention behavior on the vectors of the boat exterior, and fishing tackle, respectively. Our results suggest that increasing knowledge may be a necessary condition for higher adoption of AIS prevention behaviors, but that this alone may not be sufficient. Instead, efforts targeted at boaters who do not currently practice the recommended actions are likely to be necessary.

The feeling of understanding: an exploration with neural models

There exists a dynamic interaction between the world of information and the world of concepts, which is seen as a quintessential byproduct of the cultural evolution of individuals as well as of human communities. The feeling of understanding (FU) is that subjective experience that encompasses all the emotional and intellectual processes we undergo in the process of gathering evidence to achieve an understanding of an event. This experience is part of every person that has dedicated substantial efforts in scientific areas under constant research progress. The FU may have an initial growth followed by a quasi-stable regime and a possible decay when accumulated data exceeds the capacity of an individual to integrate them into an appropriate conceptual scheme. We propose a neural representation of FU based on the postulate that all cognitive activities are mapped onto dynamic neural vectors. Two models are presented that incorporate the mutual interactions among data and concepts. The first one shows how in the short time scale, FU can rise, reach a temporary steady state and subsequently decline. The second model, operating over longer scales of time, shows how a reorganization and compactification of data into global categories initiated by conceptual syntheses can yield random cycles of growth, decline and recovery of FU.