Application of blockchain technology in libraries and information centers services
Notice bibliographique
Résumé
Purpose As information providers, libraries select, acquire, process, store, preserve and distribute information resources to customers. Innovative technologies like the blockchain have posed major challenges to education and library services. The main goal of the application of blockchain technology (BT) in libraries and information centers is to give all possible users in participating library systems unrestricted access to digital content and print collections while minimizing risk to protect each user’s privacy and identification. A fresh and dependable technology called blockchain aids in the security, reliability and preservation of information. The 21st century’s change agents’ libraries are implementing new technologies to deliver the most knowledge possible in the shortest amount of time. It cannot be overstated how this technology has the potential to transform library services, operations and functions. Design/methodology/approach An extensive review of literature on “smart libraries” was carried to ascertain the emerging technologies in the smart library domain. Literature was searched against various keywords like smart libraries, smart technologies, Internet of Things, electronic resource management, data mining, artificial intelligence, ambient intelligence, BT and augmented reality. Later on, the works citing the literature on smart libraries were also explored to visualize a broad spectrum of emerging concepts about this growing trend in libraries. Findings Blockchain is one of the rapidly expanding and evolving technology. Librarians and library need to understand the opportunities, benefits and risks of blockchain. Perhaps in libraries, BT will become a useful tool for storing, storing and sharing information. This technology also aids in the acquisition of library materials that can improve collection maintenance. Blockchain can protect user and patron records and enhance the privacy of user and research data. This technology also improves collaboration among library patrons and staff. BT is increasingly being adopted by libraries in various ways. It creates an improved metadata system to protect digital-first selling rights, peer-to-peer sharing and more. Apart from what have been already discussed, this technology is perfect for academia and perfect for achieving potential pace in libraries. It can be used to safeguard library user records, retrieve libraries document and improve collection management. A special bulk request enables identification and discovery of unique holdings. Scientific writing is another good use case for blockchain, where researchers can record and time-stamp ideas and spread information. BT presents a significant opportunity for libraries to enhance user privacy, enhance collaboration and transform the manner in which libraries and communities collaborate. The University of San Jose in Toronto, the Reference Library, the Suffolk Collaborative Library System and many other libraries are exploring this technology, and various other libraries are set to follow suit. Originality/value The paper tries to highlight the usage of blockchain technologies in the libraries and information center setups for the efficient working of library setups. Blockchain is one of the emerging technologies that have the potential to bring the technology revolution.
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 | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,007 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».