D3.1_Procurement Barriers Report
Notice bibliographique
Résumé
The acquisition of IT services is key to any public or private organisation and the advent of cloud computing requires innovation in the procurement of cloud services. Although cloud computing has become increasingly popular, it appears that potential customers, in the public sector in general and in the research community in particular, are facing barriers that inhibit the wider adoption of cloud services. This report presents a list of barriers to cloud services procurement identified through literature, in-depth interviews of IT managers surveyed over a period of 3 months, and input provided by the PICSE Task Force members as well as intergovernmental research organisations such as CERN and EMBL. The survey demonstrated that barriers to cloud service procurement mainly relate to the adoption of new technology (i.e. cloud computing) and the procurement process itself. The first category encompasses legal jurisdiction impediments, which Eurostat [1] highlighted as one of the main barriers to the procurement of cloud services. Indeed, services are often hosted in one country and consumed in another, hence cloud consumers’ uncertainty about data location and the applicable laws in case of dispute or in relation to compliance. Prerequisites such as expertise and knowledge of both contractual and operational aspects also impede the purchase of cloud computing services. The barriers posed by the procurement process vary depending on the type of process used. Restricted procurement processes suffer from a lack of competition and higher costs while open procurement processes are time consuming, require detailed specifications to be ready at the start of the procurement process and therefore lead to higher tendering and evaluation costs. Cloud marketplaces and brokerage services are seen as aiding the procurement process but suffering from under-investment and thus not sufficiently mature. In addition, the nature of cloud services and the pay-per-use method can complicate budget planning for research organisations. Therefore, framework procurement agreements are perceived as a good alternative for the procurement of cloud services; along with PCP (Pre-Commercial Procurement), PPI (Public Procurement of Innovative solutions), and JPA (Joint Procurement Actions), which could potentially fulfil the needs of the research community. To combat this situation and increase the uptake of cloud computing in the public research sector, cloud service providers (CSPs) are advised to increase transparency in their offers; particularly regarding security, privacy and data management, ensure the trustworthiness of their privacy policies and improve the Service Level Agreements (SLA) so that their offers stand out. Similarly users are advised to acknowledge that the commoditisation of IT services that cloud services represents has economic advantages and that customisation of those services will increase procurement costs and potentially increase service provider dependence.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,009 | 0,008 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,128 | 0,041 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».