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Organizations constantly evaluate their activities to ensure that they are attaining their management goals. Maturity assessment enables organizations to examine their capabilities, support innovation and evaluate development. The aim of this paper is to evaluate the maturity statuses of a selection of Nigerian university libraries in a study to investigate their quality management (QM) approaches. The study provides recommendations for means to attain the required statuses in academic library development.

The study involved a multisite case study in which interviews were conducted with 15 university librarians (or their representatives) and ten focus groups were conducted with non-management library staff. The resulting qualitative data were analyzed using an aspect of framework analysis – charting, while a maturity model from the field of project management (Prince 2 Maturity Model, P2MM) was used to assess maturity in QM of the libraries.

The results of the maturity assessment indicate a basic knowledge of the concept of QM implementation among the libraries. The scores obtained on the P2MM capability scale placed the libraries studied mainly on Level 1 (awareness level) of the model.

This paper demonstrates that the culture of QM in academic libraries in Nigeria is at a low level with considerable potential for development. It is suggested that future adoption of quality maturity models to assess performance and organizational effectiveness would aid improvements for value-added services.

This is the first study to attempt the assessment of quality maturity levels in Nigerian academic libraries for identification of the organization’s positioning in QM and strategy.

The purpose of this paper is to provide an overview of one of the most important and controversial areas of scholarly communication: Open Access publishing and dissemination of research outputs. It identifies and discusses recent trends and future challenges for various stakeholders in delivering Open Access (OA) to the scholarly literature.

The study is based on a number of interrelated strands of evidence which make up the current discourse on OA, comprising the peer-reviewed literature, grey literature and other forms of communication (including blogs and e-mail discussion lists). It uses a large-scale textual analysis of the peer-reviewed literature since 2010 (carried out using the VOSviewer tool) as a basis for discussion of issues raised in the OA discourse.

A number of key themes are identified, including the relationship between “Green” OA (deposit in repositories) and “Gold” OA (OA journal publication), the developing evidence base associated with OA, researcher attitudes and behaviours, policy directions, management of repositories, development of journals, institutional responses and issues around impact and scholarly communication futures. It suggests that current challenges now focus on how OA can be made to work in practice, having moved on from the discussion of whether it should happen at all.

The paper provides a structured evidence-based review of major issues in the OA field, and suggests key areas for future research and policy development.

In computational chemistry, the chemical bond energy (pKa) is essential, but most pKa-related data are submerged in scientific papers, with only a few data that have been extracted by domain experts manually. The loss of scientific data does not contribute to in-depth and innovative scientific data analysis. To address this problem, this study aims to utilize natural language processing methods to extract pKa-related scientific data in chemical papers.

Based on the previous Bert-CRF model combined with dictionaries and rules to resolve the problem of a large number of unknown words of professional vocabulary, in this paper, the authors proposed an end-to-end Bert-CRF model with inputting constructed domain wordpiece tokens using text mining methods. The authors use standard high-frequency string extraction techniques to construct domain wordpiece tokens for specific domains. And in the subsequent deep learning work, domain features are added to the input.

The experiments show that the end-to-end Bert-CRF model could have a relatively good result and can be easily transferred to other domains because it reduces the requirements for experts by using automatic high-frequency wordpiece tokens extraction techniques to construct the domain wordpiece tokenization rules and then input domain features to the Bert model.

By decomposing lots of unknown words with domain feature-based wordpiece tokens, the authors manage to resolve the problem of a large amount of professional vocabulary and achieve a relatively ideal extraction result compared to the baseline model. The end-to-end model explores low-cost migration for entity and relation extraction in professional fields, reducing the requirements for experts.