Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research
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Notice bibliographique
Résumé
A pilot project worked with seven existing projects funded by the International Development Research Center of Canada (IDRC) to investigate the implementation of data management and sharing requirements within development research projects. The seven projects, which were selected to achieve a diversity of project types, locations, host institutions and subject areas, demonstrated a broad range of existing capacities to work with data and access to technical expertise and infrastructures. The pilot project provided an introduction to data management and sharing concepts, helped projects develop a Data Management Plan, and then observed the implementation of that plan. In examining the uptake of Data Management and Sharing practice amongst these seven groups the project came to question the underlying goals of funders in introducing data management and sharing requirements. It was established that the ultimate goal was a change in culture amongst grantees. The project therefore looked for evidence of how funder interventions might promote or hinder such cultural change. The project had two core findings. First that the shift from an aim of changing behaviour, to changing culture, has both subtle and profound implications for policy design and implementation. A particular finding is that the single point of contact that many data management and sharing policies create where a Data Management Plan is required at grant submission but then not further utilised is at best neutral and likely counter productive in supporting change in researcher culture. As expected, there are significant bottlenecks within research institutions and for grantees in effectively sharing data including a lack of resources and expertise. However, a core finding is that many of the bottlenecks for change relate to structural issues at the funder level. Specifically, the expectation that policy initiatives are implemented, monitored, and evaluated by Program Officers who are the main point of contact for projects. The single most productive act to enhance policy implementation may be to empower and support Program Officers. This could be achieved through training and support of individual POs, through the creation of a group of internal experts who can support others, or via provision of external support, for instance by expanding the services provided by the pilot project into an ongoing support mechanism for both internal staff and grantees. Other significant findings include: the importance of language barriers and the way in which assumptions of English language in materials, resources, services and systems permeate the entire system; that data infrastructures are poorly served by current funding arrangements and tools, particularly where they are obliged to seek continuing funding through project grants. There are also fundamental questions raised by the status of digital objects as "data". The concept of data is part of a western scientific discourse which may be both incompatible with other cultures, particularly indigenous knowledge systems. More importantly that discourse may be incompatible with values-based approaches that seek to respect indigenous knowledge through a commitment to retaining context. With the possible exception of the last finding, none of these issues are exclusive to development research. The Development Research context surfaces them more strongly through its greater diversity of goals and contexts. In many ways this project illustrates not that Development Research has particular special needs, but that it is a site that surfaces issues in policy design and implementation deserving of more consideration across the research enterprise.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,085 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,007 | 0,020 |
| Science ouverte | 0,018 | 0,061 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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