Real-world Collaboration Research Articles

Convergence Research and Training in Computational Bioengineering: A Case Study on AI/ML-Driven Biofilm–Material Interaction Discovery

Historically, research disciplines have successfully operated independently. However, the emergence of transdisciplinary research has led to convergence methodologies, resulting in groundbreaking discoveries. Despite the benefits, graduate programs face challenges in implementing transdisciplinary research and preparing students for real-world collaboration across diverse disciplines and experience levels. We propose a convergence training framework integrating project-based learning, training modules, and collaborative teaming to address this. This approach, tested in a multi-institutional workshop, proved effective in bridging expertise gaps and fostering successful convergence learning experiences in computational biointerface (material–biology interface) research. Here, biointerface research focuses on control of biomolecular interactions with technologically relevant material surfaces, which is a critical component of biotechnology and engineering applications. Positive outcomes, including conference presentations and published models, endorse the framework's application in graduate curricula, particularly for students engaging in transdisciplinary collaboration.

Autoregressive graph Volterra models and applications

Graph-based learning and estimation are fundamental problems in various applications involving power, social, and brain networks, to name a few. While learning pair-wise interactions in network data is a well-studied problem, discovering higher-order interactions among subsets of nodes is still not yet fully explored. To this end, encompassing and leveraging (non)linear structural equation models as well as vector autoregressions, this paper proposes autoregressive graph Volterra models (AGVMs) that can capture not only the connectivity between nodes but also higher-order interactions presented in the networked data. The proposed overarching model inherits the identifiability and expressibility of the Volterra series. Furthermore, two tailored algorithms based on the proposed AGVM are put forth for topology identification and link prediction in distribution grids and social networks, respectively. Real-data experiments on different real-world collaboration networks highlight the impact of higher-order interactions in our approach, yielding discernible differences relative to existing methods.

Using early health economic modeling to inform medical innovation development: a soft robotic sock in poststroke patients in Singapore.

Based on a real-world collaboration with innovators in applying early health economic modeling, we aimed to offer practical steps that health technology assessment (HTA) researchers and innovators can follow and promote the usage of early HTA among research and development (R&D) communities. The HTA researcher was approached by the innovator to carry out an early HTA ahead of the first clinical trial of the technology, a soft robotic sock for poststroke patients. Early health economic modeling was selected to understand the potential value of the technology and to help uncover the information gap. Threshold analysis was used to identify the target product profiles. Value-of-information analysis was conducted to understand the uncertainties and the need for further research. Based on the expected price and clinical effectiveness by the innovator, the new technology was found to be cost-saving compared to the current practice. Risk reduction in deep vein thrombosis and ankle contracture, the incidence rate of ankle contracture, the compliance rate of the new technology, and utility scores were found to have high impacts on the value-for-money of the new technology. The value of information was low if the new technology can achieve the expected clinical effectiveness. A list of parameters was recommended for data collection in the impending clinical trial. This work, based on a real-world collaboration, has illustrated that early health economic modeling can inform medical innovation development. We provided practical steps in order to achieve more efficient R&D investment in medical innovation moving forward.

Restorative Pedagogy in the University Criminology Classroom: Learning about Restorative Justice with Restorative Practices and Values

This paper explores the use of restorative practices and values to facilitate teaching and learning regarding restorative justice in undergraduate criminology curricula in England and Wales. Applications of restorative practice, inside and outside of criminal justice contexts, continue to progress and strengthen in the UK and internationally. Similarly, the provision of undergraduate criminology programmes at universities in England and Wales, and globally, has increased substantially in recent decades. Yet, limited research has been conducted regarding the way restorative justice is taught at universities, particularly in a UK higher education context. This paper draws on research which set out to examine the extent and form of restorative justice knowledge production and exclusion, in undergraduate criminology programmes in England and Wales. In doing so, the innovative and effective use of restorative practice pedagogically was exposed. Evidence presented in this paper was collected via seven semi-structured interviews with criminology academics working at six different universities, and three focus groups with undergraduate criminology students, each at a different university. By exploring perspectives of academics and students regarding the use of restorative practices and values to support teaching and learning about restorative justice, this paper argues that real-world contextualisation, collaboration, and experiential learning are key elements of restorative pedagogy within undergraduate criminology.

Structural Inequality in Collaboration Networks

AbstractRecent models of scientific collaboration show that minorities can end up at a disadvantage in bargaining scenarios. However, these models presuppose the existence of social categories. Here, I present a model of scientific collaboration in which inequality arises in the absence of social categories. I assume that all agents are identical except for the position that they occupy in the collaboration network. I show that inequality arises in the absence of social categories. I also show that this is due to the structure of the collaboration network and that similar patterns arise in two real-world collaboration networks.

Evolutionary dynamics of higher-order interactions in social networks.

We live and cooperate in networks. However, links in networks only allow for pairwise interactions, thus making the framework suitable for dyadic games, but not for games that are played in larger groups. Here, we study the evolutionary dynamics of a public goods game in social systems with higher-order interactions. First, we show that the game on uniform hypergraphs corresponds to the replicator dynamics in the well-mixed limit, providing a formal theoretical foundation to study cooperation in networked groups. Second, we unveil how the presence of hubs and the coexistence of interactions in groups of different sizes affects the evolution of cooperation. Finally, we apply the proposed framework to extract the actual dependence of the synergy factor on the size of a group from real-world collaboration data in science and technology. Our work provides a way to implement informed actions to boost cooperation in social groups.

INITIAL COURSE DESIGN PHASE OF GROUP WORK EDUCATION USING AUTHENTIC E-LEARNING

Group work education develops student competences for praxis. The Authentic eLearning framework has the potential to strengthen course design, develop critical thinking, multiple perspectives, articulation and reflection. This qualitative study conducted using educational design research explored the pedagogical practices of South African social workers in group work education. Ethics clearance for this study was received and data were analysed using content analysis. Findings identified educator strategies including the use of real-world context, reflection and group collaboration, but the study noted the potential for incorporating technology-enhanced learning. Extrapolating from this study, opportunities for educators in this field during the Covid-19 pandemic are indicated.

Network-theoretic modeling of complex activity using UK online sex advertisements

Online sex has become a fast-growing business in both developing and developed network, with advertisements of (not necessarily unique) individuals numbering in the hundreds of millions across different Web portals. One such major hub of sex advertisement activity, before it was shut down by US federal agencies, was backpage.com. The backpage.com website was a classifieds-advertising portal that had become the largest marketplace for buying and selling sex by the time that federal law enforcement agencies seized it in April 2018. Since then, investigations have been actively underway. However, the data (which has recently been made available to us for research on UK Backpage) also offers valuable insights into the nature of the online sex business, including complex properties that can be best studied using network science. One of the challenges, however, is a rigorous modeling of the data as a network, since the primary data are web advertisements and metadata (backend database) on accounts that posted that ad. In this article, we conduct an empirical study of an important sample of the online sex marketplace using UK backpage, including presenting a methodology for constructing simple ‘activity networks’ that define some notion of real-world collaboration or connection between two entities (in our case, at the level of ad-posting accounts) and then studying the properties of these networks. We gather a set of insights into a domain that has not been studied at scale, let alone a national level, but that is continuing to be a growing social problem for many countries.

Augmented Virtual Teleportation for High-Fidelity Telecollaboration.

Telecollaboration involves the teleportation of a remote collaborator to another real-world environment where their partner is located. The fidelity of the environment plays an important role for allowing corresponding spatial references in remote collaboration. We present a novel asymmetric platform, Augmented Virtual Teleportation (AVT), which provides high-fidelity telepresence of a remote VR user (VR-Traveler) into a real-world collaboration space to interact with a local AR user (AR-Host). AVT uses a 360° video camera (360-camera) that captures and live-streams the omni-directional scenes over a network. The remote VR-Traveler watching the video in a VR headset experiences live presence and co-presence in the real-world collaboration space. The VR-Traveler's movements are captured and transmitted to a 3D avatar overlaid onto the 360-camera which can be seen in the AR-Host's display. The visual and audio cues for each collaborator are synchronized in the Mixed Reality Collaboration space (MRC-space), where they can interactively edit virtual objects and collaborate in the real environment using the real objects as a reference. High fidelity, real-time rendering of virtual objects and seamless blending into the real scene allows for unique mixed reality use-case scenarios. Our working prototype has been tested with a user study to evaluate spatial presence, co-presence, and user satisfaction during telecollaboration. Possible applications of AVT are identified and proposed to guide future usage.

Evaluating the implications of Brexit for research collaboration and policy: a network analysis and simulation study

ObjectiveTo evaluate the role of the European Union (EU) as a research collaborator in the UK’s success as a global leader in healthcare research and innovation and quantify the impact...

TMNVis: Visual analysis of evolution in temporal multivariate network at multiple granularities

Temporal (Dynamic) multivariate networks consist of objects and relationships with a variety of attributes, and the networks change over time. Exploring such kind of networks in visualization is of great significance and full of challenges as its time-varying and multivariate nature. Most of the existing dynamic network visualization techniques focus on the topological structure evolution lacking of exploration on the multivariate data (multiple attributes) thoroughly, and do not cover comprehensive analyses on multiple granularities. In this paper, we propose TMNVis, an interactive visualization system to explore the evolution of temporal multivariate network. Firstly we list a series of tasks on three granularities: global level, subgroup level and individual level. Secondly three main views, which rely mainly on timeline-based method while animation subsidiary, are designed to resolve the analysis tasks. Thirdly we design a series of flexible interactions and develop a prototype system. At last we verify the effectiveness and usefulness of TMNVis using a real-world academic collaboration data.

Collaboration Process Pattern Approach to Improving Teamwork Performance: A Data Mining-Based Methodology

It is well documented in management literature that characteristics of collaboration processes strongly influence team performance in a business environment. However, little work has been done on how specific collaboration process patterns affect teamwork performance, leading to an open issue in collaboration management. To address this research gap, we develop a Collaboration Process Pattern (CPP) approach that analyzes teamwork performance by mining collaboration system logs from open source software development. Our research is novel in three ways. First, our research is fact-driven, as the result is based on teamwork tracking logs. Second, we develop a pattern mining approach based on sequence mining and graph mining. Third, using time-dependent Cox regression, our approach derives business insights from real-world collaboration data that are directly applicable to managerial actions. Our empirical study identifies collaboration patterns that can lead to more efficient teamwork. It also shows that the effects of collaboration patterns vary depending on the types of tasks. These findings are of significant business value since they suggest that managers should carefully prioritize their limited attention on certain types of tasks for intervention. Data and the online supplement are available at https://doi.org/10.1287/ijoc.2016.0739 .

Games for Social Change

Building on the use of the internet and social media as sites for activism, this paper highlights the emergence of political activism and collective protest in the online gaming environment. Referencing social movement theory and the rapidly evolving capacity of multiplayer online games to facilitate the development of strong group identities and real-time, real-world collaboration, the paper explores the potential of such games to create a space and a mechanism for enabling the emergence of movements for social change. Highlighting the growing number of social activist games designers, building values of equality and social justice into their gameplay, the paper draws an epistemological link between the work of these “conscientious designers” and the process of Conscientization within youth and community work inspired by the critical analysis of political activists such as Paulo Freire and Augusto Boal.

A Collaboration Network Model with Multiple Evolving Factors**Supported by the National Natural Science Foundation of China under Grant Nos 11305139 and 11147178.

To describe the empirical data of collaboration networks, several evolving mechanisms have been proposed, which usually introduce different dynamics factors controlling the network growth. These models can reasonably reproduce the empirical degree distributions for a number of well-studied real-world collaboration networks. On the basis of the previous studies, in this work we propose a collaboration network model in which the network growth is simultaneously controlled by three factors, including partial preferential attachment, partial random attachment and network growth speed. By using a rate equation method, we obtain an analytical formula for the act degree distribution. We discuss the dependence of the act degree distribution on these different dynamics factors. By fitting to the empirical data of two typical collaboration networks, we can extract the respective contributions of these dynamics factors to the evolution of each networks.

A fractal and scale-free model of complex networks with hub attraction behaviors

It is widely believed that fractality of complex networks originate from hub repulsion behaviors (anticorrelation or disassortativity), which means that large degree nodes tend to connect with small degree nodes. This hypothesis was demonstrated by a dynamical growth model, which evolves as the inverse renormalization procedure, proposed by Song et al. Now we find that the dynamical growth model is based on the assumption that all the cross-box links have the same probability e to link to the most connected nodes inside each box. Therefore, we modify the growth model by adopting the flexible probability e, which makes hubs to have higher probability to connect with hubs than non-hubs. With this model, we find that some fractal and scale-free networks have hub attraction behaviors (correlation or assortativity). The results are the counter-examples of former beliefs. Actually, the real-world collaboration network of movie actors also is fractal and shows assortative mixing.

Tie strength distribution in scientific collaboration networks.

Science is increasingly dominated by teams. Understanding patterns of scientific collaboration and their impacts on the productivity and evolution of disciplines is crucial to understand scientific processes. Electronic bibliography offers a unique opportunity to map and investigate the nature of scientific collaboration. Recent studies have demonstrated a counterintuitive organizational pattern of scientific collaboration networks: densely interconnected local clusters consist of weak ties, whereas strong ties play the role of connecting different clusters. This pattern contrasts itself from many other types of networks where strong ties form communities while weak ties connect different communities. Although there are many models for collaboration networks, no model reproduces this pattern. In this paper, we present an evolution model of collaboration networks, which reproduces many properties of real-world collaboration networks, including the organization of tie strengths, skewed degree and weight distribution, high clustering, and assortative mixing.

Collaboration in Virtual Worlds: The Role of the Facilitator

Virtual worlds (VWs) are becoming a popular medium for meetings and collaborative problem solving efforts. However, complex VW communication tools and challenges in managing online social interactions are likely to complicate VW collaboration efforts. Therefore, the purpose of our study was to investigate the role of the facilitator when collaboration is conducted in a virtual environment. In order to conduct our study, we developed a questionnaire based on major issues in real world collaboration and interviewed 14 subject-matter experts. Participants were asked to identify what key differences facilitators perceive between virtual and real world collaboration. In response, participants provided many insights, such as the new interpersonal management challenges that arise from the absence of face-to-face communication. Participants also warned of the challenges associated with the introduction of more technology to the collaboration process. Further, they identified credibility and trust issues that arise due to facilitators’ avatar manipulation skills and avatar appearance. Suggestions for avoiding pitfalls and optimizing collaboration are provided.

Developing Software-Dependent Artwork: Artist and Software Developers' Collaboration

Software-dependent artwork is on the increase as software use expands into every part of our lives. Sometimes artists develop this style of artwork by themselves, but more commonly they seek help from technologists, creating an opportunity for artists and software developers to collaborate. This paper presents the experience of a real-world collaboration through a case study of the development of a software-dependent artwork where students were the software developers.

A Web 2.0 vision, Web 2.0 project management and real-world student learning in a website redevelopment project

The Arts Libraries Society of Australia and New Zealand (Arlis/ANZ) recently implemented a new web presence. More than just a website, it was envisaged as a web ‘identity’, a virtual clubhouse where the Society could conduct its ‘virtual business’ and where members could ‘meet’ and contribute to the activities of their Society, free from physical and technical barriers. This paper concentrates on the initial stage in the project: the real-world learning collaboration with three student design teams; the Web 2.0 technologies and approach which enabled us to manage the project effectively; and how the project modeled the vision for the Web 2.0 look, feel and attitude of the final site. The site is newly implemented, and is still in its infancy. Evaluation of the success of the Web 2.0 approach will be the focus of the next stages of the project. So while this paper does not provide an evaluation per se, we reflect upon the next phase of actively engaging members, and measuring the success of the site against our vision of an ‘Arlis/ANZ 2.0’.

Assortativity and act degree distribution of some collaboration networks

Empirical investigation results on weighted and un-weighted assortativity, act degree distribution, degree distribution and node strength distribution of nine real world collaboration networks have been presented. The investigations propose that act degree distribution, degree distribution and node strength distribution usually show so-called “shifted power law” (SPL) function forms, which can continuously vary from an ideal power law to an ideal exponential decay. Two parameters, α and η , can be used for description of the distribution functions. Another conclusion is that assortativity coefficient and the parameter, α or η , is monotonously dependent on each other. By the collaboration network evolution model introduced in a reference [P. Zhang et al., Physica A 360 (2006) 599], we analytically derived the SPL distributions, which typically appeared in general situations where nodes are selected partially randomly, with a probability p , and partially by linear preferential principle, with the probability 1 - p . The analytic discussion gives an explicit expression on the relationship between the random selection proportion p and the parameters α and η . The numerical simulation results by the model show a monotonic dependence of assortativity on the random selection proportion p . The empirically obtained assortativity versus α or η curve for the four collaboration networks with small maximal act size, T max , shows a good agreement with the model prediction. According to the curves, one can qualitatively judge the random selection proportion of the real world network in its evolution process.