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Enregistrement W4366204519 · doi:10.21428/f1f23564.01e5c97e

Project Management Processes in a Large Humanities Research Project: Lessons from INKE

2023· article· en· W4366204519 sur OpenAlexafffund
Lynne Siemens

Notice bibliographique

RevueIDEAH · 2023
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueDigital Humanities and Scholarship
Établissements canadiensUniversity of Victoria
Organismes subventionnairesSocial Sciences and Humanities Research Council of CanadaNational Endowment for the Humanities
Mots-clésDigital humanitiesProject managementProcess managementComputer scienceEngineering managementHumanitiesLibrary scienceEngineeringSystems engineeringPhilosophy

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,060
score de la tête « metaresearch » (Gemma)0,130
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,940
Score d'incertitude au seuil0,318

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0600,130
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,004
Études des sciences et des technologies0,0190,010
Communication savante0,0190,018
Science ouverte0,0050,017
Intégrité de la recherche0,0050,006
Charge utile insuffisante (le modèle a refusé de juger)0,0070,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,319
Tête enseignante GPT0,392
Écart entre enseignants0,073 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeQualitatif
DomaineMéthodes
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission2
Résumé présentoui

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