Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Néstor A Nova
 -- 1407 2023-10-17 21:15:05 |
2 layout
Camila Xu
 Meta information modification 1407 2023-10-18 04:08:47 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Nova, N.A.; González, R.A. Features, Topics, and Barriers in Scientific Collaboration. Encyclopedia. Available online: https://encyclopedia.pub/entry/50414 (accessed on 16 November 2024).
Nova NA, González RA. Features, Topics, and Barriers in Scientific Collaboration. Encyclopedia. Available at: https://encyclopedia.pub/entry/50414. Accessed November 16, 2024.
Nova, Néstor Armando, Rafael Andrés González. "Features, Topics, and Barriers in Scientific Collaboration" Encyclopedia, https://encyclopedia.pub/entry/50414 (accessed November 16, 2024).
Nova, N.A., & González, R.A. (2023, October 17). Features, Topics, and Barriers in Scientific Collaboration. In Encyclopedia. https://encyclopedia.pub/entry/50414
Nova, Néstor Armando and Rafael Andrés González. "Features, Topics, and Barriers in Scientific Collaboration." Encyclopedia. Web. 17 October, 2023.
Features, Topics, and Barriers in Scientific Collaboration
Edit
This entry is adapted from the peer-reviewed paper 10.3390/systems11100503

Collaboration is defined as “a joint effort towards a common goal”. Collaboration drives complexity in organizational problems and is the key to innovation at the intra- and inter-organizational levels. Exploring scientific collaboration in large groups implies exploring human, social, and organizational factors.

research ecosystem research sustainability collaboration translational research

1. Introduction

Translational research bridges the gap between scientific discoveries and practical healthcare, comprising four phases: T1 (basic research), T2 (preclinical studies), T3 (clinical trials), and T4 (implementation and dissemination). The goal of translational research is to bridge the gap between basic science and clinical practice, with the ultimate aim of improving patient outcomes [1]. This has been particularly relevant in cancer research because of recent advances in the understanding of the molecular characterization of tumors and the tumor microenvironment, enabling the development of novel treatments [2]. According to Dolgos et al. (2016), by integrating basic science research with clinical development, translational medicine has helped to improve target understanding, patient subpopulation selection, and organizational learning. It has also led to advances in areas such as disease model validation, human cell- or tissue-derived models, and molecular characterization of retrospective human materials through biobanks, bioinformatics, and translational pharmacokinetics/pharmacodynamics [3]. The complexity and multifaceted nature of cancer demands a multidisciplinary and collaborative approach that brings together experts from a broader network of stakeholders, including researchers, clinicians, research centers, universities, government agencies, pharmaceutical partners, patient advocacy groups, and philanthropic organizations, all of whom play a vital role in supporting and advancing cancer research.
Therefore, collaboration is the cornerstone of successful large-scale translational research projects in cancer treatment. By fostering collaboration, these projects gain access to a diverse pool of knowledge, skills, and resources, which are essential for tackling the multifaceted challenges of cancer research [4]. Collaboration enhances the speed and efficiency of research, promotes knowledge exchange, and fosters innovation [5] by joining together advanced technologies, methodologies, extensive infrastructure, expertise, and resources, making it possible to undertake ambitious research endeavors that would be challenging for individual institutions to achieve. Collaborating with international partners allows for the exchange of insights and data from diverse patient populations, which can lead to more robust and generalizable findings.
The significant role of digital platforms in translational research has been crucial in providing support for the storage and integration of big data, an analysis context, and additional information from external sources. They were created as a solution to the growing amount of omics data and use informatics methods to link molecular and clinical data [6]. The benefits of these platforms include the ability to identify biomarkers, develop personalized medicine, and improve patient outcomes [7]. It is understood that such platforms enable collaboration by sharing research data and tools, such as in the case of Cancer Core Europe [8] or REDCap [9]. However, their actual impact on collaboration has not received attention in the literature in a systematic manner.
Effectiveness in translational medicine projects rests on seamless collaboration between individuals, teams, and institutional and organizational stakeholders, ensuring that promising discoveries are efficiently tested, validated, and translated into clinical applications that ultimately save lives. However, collaboration is naturally “difficult to manage, and the likelihood of disappointing outputs is high” [10]. However, managing large collaborations proves challenging, often leading to less than satisfactory outcomes [11]. Therefore, assessing collaborative processes is a key activity to reach sustainable research ecosystems that go beyond the period of funding and transform activities in health solutions in the long term. Several evaluation models can be traced in the literature in three categories: indices, processes, and relatedness. Indices are related to bibliometric analysis [12][13][14], processes focus on the method of evaluation and the dimensions that must be analyzed [15][16][17], and relatedness is explored frequently through social network analysis (SNA) [18][19].
Assessing collaboration implies exploring several factors such as planning, infrastructure, relationships, complexity, boundaries, dialogue, and human behavior. Some evaluation models involving these collaboration principles can be traced in the literature, focused mainly on bibliometric indices [12][13][14], collaboration processes [15][16][20], and social network analysis [18][21]. Despite the efforts of these studies, researchers are cautioning that traditional approaches are difficult to operationalize in real contexts [15]. Nevertheless, more empirical research on actual translational projects is necessary, including the conditions and outcomes of collaborative initiatives [22].

2. Features, Topics, and Barriers in Scientific Collaboration

Collaboration is defined as “a joint effort towards a common goal” [23]. Collaboration drives complexity in organizational problems and is the key to innovation at the intra- and inter-organizational levels [24][25][26]. In collaborative settings, entities share information, resources, and responsibilities to jointly plan, implement, and evaluate a program of activities to achieve a common goal, thereby jointly generating value [27]. Collaboration leads to enhanced capabilities by sharing engagement, trust, time, effort, and dedication.
Exploring scientific collaboration in large groups implies exploring human, social, and organizational factors. A clinical and translational research project often involves the interaction and input of several stakeholders, such as “researchers, clinicians, pharmacists, statisticians, information technology staff, educators, institutional review board members, administrators, and others” [17].
Inter-organizational collaboration produces some benefits for participating organizations, including access to complementary assets, knowledge exchange, the creation of new knowledge, and the sharing of costs [28]. Collaboration can create value [29] and it also increases productivity [30]. Accordingly, collaborative research incorporates social interactions and a range of potential roles for those involved throughout the research process. Previous studies have outlined means of collaboration in the form of “linkage mechanisms” between researchers and users, such as the involvement of intermediaries (boundary spanners), formal and informal communication with users during the research, the participation of users in data collection, and the provision of interim feedback [22].
Interdisciplinary collaboration has gained importance in the last ten years for developing therapeutic discoveries more quickly than traditional methods and tackling more complicated biological challenges in translational projects. By analyzing 19.9 million papers and 2.1 million patents over 5 decades, Wuchty and colleagues found that “teams increasingly dominate solo authors in the production of knowledge” [31]. Building collaboration in biomedical research at individual and institutional levels leads to improved information sharing between researchers, increased publishing productivity, and new research resources and projects [32].
There are some important reasons for scientists’ collaboration in translational sciences. From a scientific perspective, motivational factors for collaboration are research data availability, co-authoring, synergy of the research, visibility of the research results, the possibility of solving complex research problems, and the legitimacy of one idea or solution. Regarding the financial dimension, research funding and financial incentives are the lead motivators. The social dimension is based on resource availability, such as data, equipment, materials, or technology [30][33][34].
Furthermore, research problems drawing scientists’ attention have changed because they are now more ill-defined, technically complex, and interdisciplinary, requiring highly specialized knowledge of a variety of disciplines [35]. For instance, a university research center can be an expert in developing molecules to reduce the effects of chemotherapy on the nervous system, and a pharmaceutical company can have the knowledge and technology to synthesize the molecules into a commercial product. Therefore, collaboration is now actively encouraged by universities and research institutions [30]. Industry–university collaborations conducting interdisciplinary research are required to solve social and human problems [36] such as COVID-19 [37]. Solutions derived from n-helix initiatives are often accepted as “close prototypes of complex evolving networks” [38]. Therefore, translational research supported by proper public policy can promote technology transfer mechanisms [39], or organizational initiatives such as new research institutes, research centers, and technology centers [15][40]. A comprehensive analysis of the literature about academy–industry–government relationships reveals recurring themes concerning the attributes of collaboration:
  • Planning and careful design are relevant in achieving the goals of the collaborative initiatives [41][42][43].
  • Collaboration requires a knowledge infrastructure for learning and sharing information [44][45][46].
  • Structure and relational elements determine social interaction and the quality of relationships [41][43][44][47].
  • Differences between individual and group interests often increase complexity in collaboration [42][48][49].
  • Collaboration embraces fuzzy boundaries at intra- and inter-organizational levels [16][50][51].
  • Dialogue, reflection, questioning, and clarification inform collaboration performance [52][53].
  • Collaboration is grounded in human behaviors within a workgroup setting [47][54].
Collaboration at the institutional level in translational research implies challenges related to differences in governance structures, funding mechanisms, protocols regarding human subjects’ protection, data integrity, authorship criteria, intellectual property laws, conflict resolution, institutional leadership, and support [55]. Moreover, differences in languages and time zones can present practical challenges to communication [56]. In addition, geographic distance and disciplinary disparities can also affect the success of partnerships [57].
Experience in collaboration networks has reduced the barriers of distance or interdisciplinarity [58]. In translational research, researchers prefer to collaborate with researchers with shared experiences and prefer those with whom they have interacted in previous projects [59]. However, collaboration between individuals and teams can also suffer in the absence of supportive and enabling group and institutional leadership [33].

References

  1. Rajan, A.; Caldas, C.; van Luenen, H.; Saghatchian, M.; Van Harten, W.H. Assessing Excellence in Translational Cancer Research: A Consensus Based Framework. J. Transl. Med. 2013, 11, 247.
  2. Lacombe, D.; Bogaerts, J.; Tombal, B.; Maignen, F.; Osipienko, L.; Sullivan, R.; Golfinopoulos, V. Late Translational Research: Putting Forward a New Model for Developing New Anti-Cancer Treatments That Addresses the Needs of Patients and Society. Mol. Oncol. 2019, 13, 558–566.
  3. Dolgos, H.; Trusheim, M.; Gross, D.; Halle, J.-P.; Ogden, J.; Osterwalder, B.; Sedman, E.; Rossetti, L. Translational Medicine Guide Transforms Drug Development Processes: The Recent Merck Experience. Drug Discov. Today 2016, 21, 517–526.
  4. Kanyengo, C.W.; Smith, J.G. Biomedical Research Collaboration Patterns of Academic Staff at the University of Zambia School of Medicine. J. Adv. Libr. Inf. Sci. 2022, 11, 146–155.
  5. Ma, H. The Dynamics of China’s Collaborative Innovation Network in Agricultural Biotechnology: A Spatial-Topological Perspective. Systems 2023, 11, 73.
  6. Skolariki, K.; Avramouli, A. The Use of Translational Research Platforms in Clinical and Biomedical Data Exploration. Adv. Exp. Med. Biol. 2017, 988, 301–311.
  7. Dunn, W.; Burgun, A.; Krebs, M.-O.; Rance, B. Exploring and Visualizing Multidimensional Data in Translational Research Platforms. Brief. Bioinform. 2017, 18, 1044–1056.
  8. Eggermont, A.M.M.; Apolone, G.; Baumann, M.; Caldas, C.; Celis, J.E.; De Lorenzo, F.; Ernberg, I.; Ringborg, U.; Rowell, J.; Tabernero, J.; et al. Cancer Core Europe: A Translational Research Infrastructure for a European Mission on Cancer. Mol. Oncol. 2019, 13, 521–527.
  9. Wright, A. REDCap: A Tool for the Electronic Capture of Research Data. J. Electron. Resour. Med. Libr. 2016, 13, 197–201.
  10. Vangen, S.; Huxham, C. Nurturing Collaborative Relations: Building Trust in Interorganizational Collaboration. J. Appl. Behav. Sci. 2003, 39, 5–31.
  11. Kerrissey, M.J.; Singer, S.J. Factors Affecting Collaboration between Clinical and Community Service Organizations. Health Care Manage. Rev. 2023, 48, 130–139.
  12. Abbasi, A.; Altmann, J.; Hwang, J. Evaluating Scholars Based on Their Academic Collaboration Activities: Two Indices, the RC-Index and the CC-Index, for Quantifying Collaboration Activities of Researchers and Scientific Communities. Scientometrics 2010, 83, 1–13.
  13. Liao, C.H.; Yen, H.R. Quantifying the Degree of Research Collaboration: A Comparative Study of Collaborative Measures. J. Informetr. 2012, 6, 27–33.
  14. Schneider, M.; Kane, C.M.; Rainwater, J.; Guerrero, L.; Tong, G.; Desai, S.R.; Trochim, W. Feasibility of Common Bibliometrics in Evaluating Translational Science. J. Clin. Transl. Sci. 2017, 1, 45–52.
  15. Gibson, E.; Daim, T.U.; Dabic, M. Evaluating University Industry Collaborative Research Centers. Technol. Forecast. Soc. Chang. 2019, 146, 181–202.
  16. Woodland, R.H.; Hutton, M.S. Evaluating Organizational Collaborations: Suggested Entry Points and Strategies. Am. J. Eval. 2012, 33, 366–383.
  17. Perloff, J.; Rushforth, A.; Welch, L.C.; Daudelin, D.; Suchman, A.L.; Hoffer Gittell, J.; Santos, H.; Beswick, J.; Moore, S.; Selker, H.P. Intervening to Enhance Collaboration in Translational Research: A Relational Coordination Approach. J. Clin. Transl. Sci. 2017, 1, 218–225.
  18. Dozier, A.M.; Martina, C.A.; O’Dell, N.L.; Fogg, T.T.; Lurie, S.J.; Rubinstein, E.P.; Pearson, T.A. Identifying Emerging Research Collaborations and Networks: Method Development. Eval. Health Prof. 2014, 37, 19–32.
  19. Luke, D.A.; Carothers, B.J.; Dhand, A.; Bell, R.A.; Moreland-Russell, S.; Sarli, C.C.; Evanoff, B.A. Breaking down Silos: Mapping Growth of Cross-Disciplinary Collaboration in a Translational Science Initiative. Clin. Transl. Sci. 2015, 8, 143–149.
  20. Ginexi, E.M.; Huang, G.; Steketee, M.; Tsakraklides, S.; MacAllum, K.; Bromberg, J.; Huffman, A.; Luke, D.A.; Leischow, S.J.; Okamoto, J.M.; et al. Social Network Analysis of a Scientist–Practitioner Research Initiative Established to Facilitate Science Dissemination and Implementation within States and Communities. Res. Eval. 2017, 26, 316–325.
  21. Luo, J.; Pelfrey, C.; Zhang, G.-Q. Visualizing and Evaluating the Growth of Multi-Institutional Collaboration Based on Research Network Analysis. AMIA Summits Transl. Sci. Proc. 2014, 2014, 60–66.
  22. Nyström, M.E.; Karltun, J.; Keller, C.; Andersson Gäre, B. Collaborative and Partnership Research for Improvement of Health and Social Services: Researcher’s Experiences from 20 Projects. Health Res. Policy Syst. 2018, 16, 46.
  23. De Vreede, G.J.; Briggs, R.O.; Massey, A.P. Collaboration Engineering: Foundations and Opportunities: Editorial to the Special Issue on the Journal of the Association of Information Systems. J. Assoc. Inf. Syst. 2009, 10, 7.
  24. Witherspoon, C.L.; Bergner, J.; Cockrell, C.; Stone, D.N. Antecedents of Organizational Knowledge Sharing: A Meta-analysis and Critique. J. Knowl. Manag. 2013, 17, 250–277.
  25. Hussein, M.; Erjavec, K.; Velikonja, N.K. Management Barriers to Inter-Organizational Collaboration in Preoperative Treatment of Patients with Hip or Knee Osteoarthritis. Healthcare 2023, 11, 1280.
  26. Nuhoff-Isakhanyan, G.; Wubben, E.F.M.; Omta, S.W.F. Sustainability Benefits and Challenges of Inter-Organizational Collaboration in Bio-Based Business: A Systematic Literature Review. Sustainability 2016, 8, 307.
  27. Camarinha-Matos, L.M.; Afsarmanesh, H.; Galeano, N.; Molina, A. Collaborative Networked Organizations—Concepts and Practice in Manufacturing Enterprises. Comput. Ind. Eng. 2009, 57, 46–60.
  28. Memon, A.B.; Meyer, M. Why We Need Dedicated Web-Based Collaboration Platforms for Inter-Organizational Connectivity? A Research Synthesis. IJ Inf. Technol. Comput. Sci. 2017, 11, 1–11.
  29. Fu, S.; Cheng, X.; Su, L.; Bilgihan, A.; Okumus, F. Designing Collaboration Process Facilitation in Hotel Management Teams to Improve Collaboration Performance. Int. J. Hosp. Manag. 2020, 88, 102527.
  30. Leahey, E. From Sole Investigator to Team Scientist: Trends in the Practice and Study of Research Collaboration. Annu. Rev. Sociol. 2016, 42, 81–100.
  31. Wuchty, S.; Jones, B.F.; Uzzi, B. The Increasing Dominance of Teams in Production of Knowledge. Science 2007, 316, 1036–1039.
  32. Ravid, K.; Seta, F.; Center, D.; Waters, G.; Coleman, D. Catalyzing Interdisciplinary Research and Training: Initial Outcomes and Evolution of the Affinity Research Collaboratives Model. Acad. Med. 2017, 92, 1399–1405.
  33. Chung, E.; Kwon, N.; Lee, J. Understanding Scientific Collaboration in the Research Life Cycle: Bio- and Nanoscientists’ Motivations, Information-Sharing and Communication Practices, and Barriers to Collaboration. J. Assoc. Inf. Sci. Technol. 2015, 67, 1836–1848.
  34. Shrum, W.; Genuth, J.; Chompalov, I. Structures of Scientific Collaboration; MIT Press: Cambridge, MA, USA, 2007.
  35. Bennett, L.M.; Gadlin, H. Collaboration and Team Science: From Theory to Practice. J. Investig. Med. 2012, 60, 768–775.
  36. Bozeman, B.; Boardman, C. Research Collaboration and Team Science: A State-of-the-Art Review and Agenda; Springer: New York, NY, USA, 2014; Volume 17.
  37. Bernardo, T.; Sobkowich, K.E.; Forrest, R.O.; Stewart, L.S.; D’Agostino, M.; Gutierrez, E.P.; Gillis, D. Collaborating in the Time of COVID-19: The Scope and Scale of Innovative Responses to a Global Pandemic. JMIR Public Health Surveill. 2021, 7, e25935.
  38. Çavuşoğlu, A.; Türker, İ. Scientific Collaboration Network of Turkey. Chaos Solitons Fract. 2013, 57, 9–18.
  39. Boardman, C.; Bozeman, B. Academic Faculty as Intellectual Property in University-Industry Research Alliances. Econ. Innov. New Technol. 2015, 24, 403–420.
  40. Llera, A.S.; Podhajcer, O.L.; Breitenbach, M.M.; Santini, L.; Muller, B.; Daneri Navarro, A.; Velázquez, C.A.; Artagaveytia, N.; Gómez, J.; Frech, M.S.; et al. Translational Cancer Research Comes of Age in Latin America. Sci. Transl. Med. 2015, 7, 319fs50.
  41. Ahmed, F.; Fattani, M.T.; Ali, S.R.; Enam, R.N. Strengthening the Bridge Between Academic and the Industry Through the Academia-Industry Collaboration Plan Design Model. Front. Psychol. 2022, 13, 875940.
  42. Cummings, J.N.; Kiesler, S. Organization Theory and the Changing Nature of Science; Social Science Research Network: Rochester, NY, USA, 2014.
  43. Sjögren Forss, K.; Kottorp, A.; Rämgård, M. Collaborating in a Penta-Helix Structure within a Community Based Participatory Research Programme: ‘Wrestling with Hierarchies and Getting Caught in Isolated Downpipes’. Arch. Public Health 2021, 79, 27.
  44. Hasche, N.; Höglund, L.; Linton, G. Quadruple Helix as a Network of Relationships: Creating Value within a Swedish Regional Innovation System. J. Small Bus. Entrep. 2020, 32, 523–544.
  45. Yoda, N.; Kuwashima, K. Triple Helix of University–Industry–Government Relations in Japan: Transitions of Collaborations and Interactions. J. Knowl. Econ. 2020, 11, 1120–1144.
  46. Ye, W.; Wang, Y. Exploring the Triple Helix Synergy in Chinese National System of Innovation. Sustainability 2019, 11, 6678.
  47. Fernandes, G.; Santos, J.M.; Ribeiro, P.; Ferreira, L.M.D.; O’Sullivan, D.; Barroso, D.; Pinto, E.B. Critical Success Factors of University-Industry R&D Collaborations. Procedia Comput. Sci. 2023, 219, 1650–1659.
  48. Cummings, J.N.; Kiesler, S.; Bosagh Zadeh, R.; Balakrishnan, A.D. Group Heterogeneity Increases the Risks of Large Group Size: A Longitudinal Study of Productivity in Research Groups. Psychol. Sci. 2013, 24, 880–890.
  49. Dankbaar, B. Design Rules for ‘Triple Helix’ Organizations. Technol. Innov. Manag. Rev. 2019, 9, 53–61.
  50. Ferrigno, G.; Crupi, A.; Di Minin, A.; Ritala, P. 50+ Years of R&D Management: A Retrospective Synthesis and New Research Trajectories. RD Manag. 2023, 1–27.
  51. Ma, J. Developing Joint R&D Institutes between Chinese Universities and International Enterprises in China’s Innovation System: A Case at Tsinghua University. Sustainability 2019, 11, 7133.
  52. Roberts, M.D. Exploring Practices for Effective Collaboration. In Proceedings of the 28th Annual Conference of the Southern African Institute of Management Scientists, University of Pretoria, Pretoria, South Africa, 4–7 September 2016.
  53. Zhang, Y.; Chen, K.; Fu, X. Scientific Effects of Triple Helix Interactions among Research Institutes, Industries and Universities. Technovation 2019, 86–87, 33–47.
  54. López-Mendoza, X.; Mauricio, D. Critical Success Factors in the Stages of Technological Transfer from University to Industry: Study in the Andean Countries. Int. J. Bus. Syst. Res. 2020, 14, 95–124.
  55. Kolars, J.C.; Fang, W.; Zheng, K.; Huang, A.Y.; Sun, Q.; Wang, Y.; Woolliscroft, J.O.; Ke, Y. Collaboration Platforms in China for Translational and Clinical Research: The Partnership Between Peking University Health Science Center and the University of Michigan Medical School. Acad. Med. J. Assoc. Am. Med. Coll. 2017, 92, 370–373.
  56. Espinosa, J.A.; Cummings, J.N.; Pickering, C. Time Separation, Coordination, and Performance in Technical Teams. IEEE Trans. Eng. Manag. 2012, 59, 91–103.
  57. Malhotra, A.; Majchrzak, A. Enhancing Performance of Geographically Distributed Teams through Targeted Use of Information and Communication Technologies. Hum. Relat. 2014, 67, 389–411.
  58. Cummings, J.N.; Kiesler, S. Who Collaborates Successfully?—Prior Experience Reduces Collaboration Barriers in Distributed Interdisciplinary Research. In Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work, ACM, San Diego, CA, USA, 8–12 November 2008; pp. 437–446.
  59. Long, J.C.; Cunningham, F.C.; Carswell, P.; Braithwaite, J. Patterns of Collaboration in Complex Networks: The Example of a Translational Research Network. BMC Health Serv. Res. 2014, 14, 225.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Social Issues
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Néstor Armando Nova
,
Rafael Andrés González
View Times: 304
Revisions: 2 times (View History)
Update Date: 18 Oct 2023
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback