Project Management Processes in a Large Humanities Research Project: Lessons from INKE
Bibliographic record
Abstract
Collaboration is becoming more common in the humanities, especially within the digital humanities.Research questions are becoming increasingly complex and larger-scale which means that a project needs different skills and expertise, more than a single individual often possesses (Hara et al.).These projects then become team efforts, something that is contrary to the lone scholar model that is often found in the humanities (Ruecker and Radzikowska).Granting agencies are supporting this trend with funding programs that require team-based approaches (McGinn and Niemczyk; Newell and Swan; van Rijnsoever and Hessels).Examples of these programs include the Digging into Data Challenge, the Social Sciences and Humanities Research Council's (SSHRC) Partnership Grants, and the National Endowment for the Humanities Digital Humanities Start-up Grants (National Endowment for the Humanities).Consequently, upon successful grant awarding, principalinvestigators often find themselves instantly responsible for collaborations made up of different team members, sizable budgets, and project complexity.As "accidental managers" (Revels) or "instant managers" (Greer; Kinkus), they are often not sure of the best way to manage a project and associated tasks, budget, and people.As they find, management becomes more complex as the project grows in the number of researchers, sites, budgets, and tasks.Ultimately, these project leads need tools and processes that can coordinate the project and its parts (Gold and Gold; Hara et al.; Newell and Swan; Northcraft and Neale; Saxberg and Newell).Without these, a team may not meet their research objectives with potential outcomes being research that remains uncompleted, disrupted personal relationships, and loss of reputation and research money (Newell and Swan).While it is primarily used in business (Barnes et al.; Eriksson et al.; Koster; Winston and Hoffman), project management is a set of processes, tools, methods, and techniques that can be applied to academic research projects by directing and coordinating people and resources to attain objectives to various stakeholder satisfaction (Kinkus; Riol and Thuillier).These mechanisms allow a project to maximize the benefits of collaboration while minimizing the challenges surrounding communication and coordination (Amabile et al.;Cuneo).These challenges can create misunderstandings and mismatched expectations, especially within multidisciplinary teams (Dewulf et al.).To be successful, a project needs a research plan that outlines the way that research will be conducted so that goals, objectives, deliverables, schedules, and budgets are met (Philbin).A project needs this documentation to avoid problems such as a lack of results, time or cost overruns, or dissatisfaction with results (Muszyńska and Marx).Already funding agencies and others are requiring, even demanding, detailed and realistic plans, in response to a growing need for public accountability (Dowling and Turner; Fowler et al.; Riol and Thuillier).And it is recognized that management practices are needed to "hold research institutes accountable for meeting their obligations, maintaining their reputation and remaining competitive in terms of their productivity" (Riol and Thuillier 2).Finally, project management is a way to "gain time-, resource-, and funding efficiencies" (Atkinson Alpert and Hartshorne 543).However, there are several gaps in knowledge regarding the application of project management to research projects in the academy.First, there is a lack of wide-spread opportunities to develop skills in project management in the humanities and digital humanities.There are workshops at training institutes such as the Digital Humanities Summer Institute (Siemens, "DHSI Project Planning Course Pack"), websites such as DevDH (Appleford and Guiliano), and other one-off offerings.There are also a growing number of books on the topic (Katz; Koster).And of course, many project leaders learn through the school of hard knocks (Dowling and Turner; Leon) which can be an effective but not necessarily an efficient way to learn about project management.Because researchers have not received training, they might not even be aware of project management processes, tools, methods, and techniques and their application within projects that enable effective teams.Second, few studies look at project management and its use by professors for research (Atkinson Alpert; Atkinson Alpert and Hartshorne; Philbin).It is not a set of tools and processes that can be applied easily to university research, which is about creating new knowledge (Riol and Thuillier).In many cases, the research cycle is uncertain and not straightforward.Research goals may be well understood, but the means to reach them are not.Even the feasibility of the methodology may not be known in advance (Burress and Rowell; Dowling and Turner; Riol and Thuillier; Zhang).This is further complicated by the fact that faculty members often resist the use of such tools (Ermolaev et al.), seeing them as "emblematic of corporatization" (Burress and Rowell 3) or the application of "rigid management approaches" (Philbin 1).Some studies in the sciences have been undertaken (Riol and Thuillier).However, concrete examples of humanities scholars implementing these skills and knowledge are lacking.This raises questions about the best ways to apply project management and its associated processes, tools, techniques, and methods to university research projects in general and digital humanities (DH) projects specifically.How can they be adapted for use in academic projects?What processes, tools, techniques, and methods might be most effective in managing people, tasks, timelines, and resources?What can be learned from successful DH projects and applied to other ones?This article contributes to this discussion with an exploration of the application of two project management processes within a large-scale collaboration in the digital humanities.In particular, the case study will examine the use of governance documents and an implementation of a yearly project planning and reporting cycle.The paper concludes with implications for practice for project managers and their projects. Case StudyImplementing New Knowledge Environments (INKE) was a large seven-year project, operating from 2009-2016.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".