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Enregistrement W7033646563

Research information management (draft, to be published in the Digital Information Review #2 series, by Chandos Publishing.)

2013· other· en· W7033646563 sur OpenAlexaboutno aff

Notice bibliographique

RevueJISC Information Environment Repository (Jisc) · 2013
Typeother
Langueen
DomaineMaterials Science
ThématiqueDiatoms and Algae Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDebtContext (archaeology)Information technologyPie chartPublic sectorDigital economySubject (documents)Public policy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Academic research is increasingly subject to managerial practices. It is not the purpose of this chapter to debate the merits, or otherwise, of this trend. Instead, this chapter will chart some of the major developments in research information management in the UK, Europe, the USA, Canada and Australia, and their policy context and drivers. In doing so, it will identify some of the key challenges and points of discussion in 2013.
\n
\nThe main challenges faced by universities and other research institutes are related to changes in the wider economy and society, and these might be considered under three headings:
\n1. Debt: Many, but not all, western governments are saddled with very high levels of debt and are responding by restricitions in public spending. In some countries such as the UK, the research budget has been relatively protected. However, there remains a drive to reduce public spending as a proportion of GDP, and this is likely to impact on research funding sooner or later. As the pressure to delivery more with less increases, the opportunities to do so also increase because of developments in new digital and other techniologies. There are therefore both drivers and opportunities for greater efficiencies in the research process.
\n2. Growth: Whether growth is measured in purely financial terms or in more rounded metrics that take into account human and environmental well-being, many western countries are in need of more of it to both reduce debt and ensure social stability. There is renewed attention on the role of the public research base in contributing to (having an "impact" on) the wider economy and society. There is a complicated and necessarily ill-defined relationship between research and society, but it is clear that there are increasing calls from politicians (and therefore from public funders) for researchers to demonstrate the impact of their work, 3. Legitimacy. For various reasons, including the current "impact agenda", questions are raised as to the legitimacy of research, both as a recipient of public and charitable funds and, more basically, at an epistemological level. Certainly there are increasing calls for transparency in research, both with respect to how the money is spent (in common with wider moves in that direction), and with respect to research practices being open to scrutiny and demonstrably better than other alternative approaches that make knowledge claims.
\n
\nThere are therefore three strong agendas within research, which shape the ways in which research is managed, and therefore the information infrastructure that is needed. These are efficiency, impact and transparency.
\n
\nIt is possible to discern two broad responses by the research community to these agendas, These are moves to make research more open, and increasing managerialism in research.
\n
\nThere is a lot of talk about "open" at the moment; open access, open data, open source software, open science, and so on. While there are some commonalitiies between these (they all challenge existing business models, for example, and they all exploit the web), there are also important differences. From a research management perspective, they challenge universities and research institutes to provide their researchers with an environment in which they can be open, if they choose to be. This implies the provision of open access repositories, funds to pay open access publication charges, data repositories, IT services departments able to both provide reslient compute infrastructure and cooperate wih researchers writing or using open source tools, and information, IT, library, publication, knowledge transfer and IPR policies that support open approaches. UK universities such as Nottingham and Glasgow have been very active in addressing these challenges, as have universities elsewhere such as Arizona and Queensland.
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\nSometimes associated with open approaches, but sometimes in tension with them, there is a trend toward greater managerial accountability within public sector research. Many universities are implementing integrated research management systems, covering both pre- and post-award workflows, and linked to research information systems, repositories and so on. There are also international initiatives, such as ORCID for researcher identifiers, which are a response to widely felt inefficiencies in the system at the moment, from grant applications to journal manuscript submission. Furthermore, the HE sector is learning from large private sector corporations such as supermarkets and phone companies, that user data is key to business intelligence to guide successful organisations. Information about what its researchers are doing, with whom, to what effect, will be important to those charged with ensuring that public sector and charity-funded is successful, in the same way as it is important to private industry such as pharmaceutical companies. National research assessment exercises and the growth of commercial services such as Elsevier's SciVal are evidence of this.
\n
\nIn conclusion, research management, and the gathering, curation and exploitation of information about an increasingly open research process, are more and more central to the success of research. In the US, the phrase is "science of science policy", reflected in the STAR-Metrics initiative that covers some 90 top level US universities. This is intended to provide policy makers and research directors with data that can be treated with scientific methods to optimise the investments made in research. While this has taken things further than perhaps elsewhere, it represents a widespread trend; expect it to impact on your university soon.
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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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCommunication savante, Charge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,111
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0100,037
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,008

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 Opus0,012
Tête enseignante GPT0,241
Écart entre enseignants0,229 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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