Part III: Collaboration

Failure and collaboration

Anna-Maria Sichani and Michael Donnay

While we often celebrate ground-breaking partnerships involving multiple stakeholders from various sectors, these collaborations are incredibly complex. More often than not, they are fraught with challenges that are not always visible or discussed. Tensions in collaborations and failed partnerships can often arise from the personalities of those involved; just as in life, not everyone will be an ideal match. However, it’s not always a clash of personalities that causes collaborations to struggle or fail. Often, failure results from poorly designed or inadequately considered frameworks, protocols, and working models. The despite the relative ubiquity of these issues, failed or challenging collaborations – and the reasons behind them – are seldom shared (Nowviskie 2012). On the contrary, the lessons learned from these struggles are often kept hidden and failures are repeated as new teams grapple with the same challenges.

Arran Rees, in his reflection, emphasises the importance of communicating these challenges faced in professional practice within a more structured framework of iterative and self- reflective practical experimentation, and not just in informal settings like ‘candid conversations over coffee’. He introduces the action research methodology as one way to deal with project complexities and collaboration struggles. In a similar vein, Jennifer Stertzer argues that a flexible, iterative mindset can enable a team to transform setbacks, challenges, and failures – arising from both institutional and disciplinary contexts – into valuable learning experiences.

Recent reflections from the UK-based Living with Machines project, however, point to many of the challenges of collaboration that still remain for complex, large-scale collaborations, from communication channels and meeting culture protocols to authorship attribution (Ahnert et al. 2023). Some have tried to ameliorate these challenges by drawing on project management approaches from other disciplines, but differences between communities of practice have meant these borrowings have seen mixed success (Neubert 2020).

Caio Mello argues that the pipeline model, for example – a concept and project approach borrowed from data science – is particularly vulnerable to failure in data-intensive, interdisciplinary, and collaborative projects. Jentery Sayers points to the failings of two other techniques, waterfall and agile, and advocates instead for a cooperative model that puts relationships at the heart of the process. This cooperative approach is also central to Lauren Tuckley’s reflection, in which she proposes a community- and values-based approach to funding applications as an antidote to the hyper individualistic, trophy-hunting mentality that can pervade those spaces. Each of these reflections recognises a core tenant of failure – it always happens in relationship with other people.

References

Ahnert, Ruth, Emma Griffin, Mia Ridge and Giorgia Tolfo. Collaborative Historical Research in the Age of Big Data: Lessons from an Interdisciplinary Project. Cambridge University Press, 2023.

Neubert, Anna Maria. ‘Navigating Disciplinary Differences in (Digital) Research Projects’. In Digital Methods in the Humanities: Challenges, Ideas, Perspectives, edited by Silke Schwandt, 1st ed. Vol. 1. Digital Humanities Research. Bielefeld University Press, 2020. https://doi.org/10.14361/9783839454190.

Nowviskie, Bethany. 2012. ‘Too Small to Fail’. Bethany Nowviskie (blog). 13 October 2012. https://nowviskie.org/2012/too-small-to-fail/. Accessed 25 November 2024.