When a digital collection has been processed by OCR, the usability expectationsof patrons and researchers are high. While the former expect full textsearch to return all instances of terms in historical collections correctly, the latterare more familiar with the impacts of OCR errors but would still like toapply big data analysis or machine-learning methods. All of these use casesdepend on high quality textual transcriptions of the scans. This is why theNational Library of Luxembourg (BnL) has developed a pipeline to improveOCR for existing digitised documents. Enhancing OCR in a digital library notonly demands improved machine learning models, but also requires a coherentreprocessing strategy in order to apply them efficiently in production systems. The newly developed software tool, Nautilus, fulfils these requirements usingMETS/ALTO as a pivot format. The BnL has open-sourced it so that otherlibraries can re-use it on their own collections. This paper covers the creationof the ground truth, the details of the reprocessing pipeline, its production useon the entirety of the BnL collection, along with the estimated results. Basedon a quality prediction measure, developed during the project, approximately28 million additional text lines now exceed the quality threshold.
The field of Open Science is subject to constant change and expansion. In order to stay up to date, exchange is paramount – not only within universities, but also within larger spheres, such as academic alliances. This paper analyses the development of a format of international Open Science-related exchange called “ERUA Open Science Meet-Ups” within the Re:ERUA project (research trajectory of the European Reform University Alliance). We will look at in which contexts the format came to be, how we got the format started and which measures of promotion and dissemination we took. Moreover, we will discuss the individual sessions’ scope and provide insight into which lessons we learned when performing the Open Science Meet-Ups. Specific recommendations for everyone wanting to set up a similar format as well as an outlook on future sessions will conclude the paper.
Subject indexing, i.e., the enrichment of metadata records for textual resources with descriptors from a controlled vocabulary, is one of the core activities of libraries. Due to the proliferation of digital documents, it is no longer possible to annotate every single document intellectually, which is why we need to explore the potentials of automation on every level. At ZBW the efforts to partially or completely automate the subject indexing process started as early as 2000 with experiments involving external partners and commercial software. The conclusion of that first exploratory period was that commercial, supposedly shelf-ready solutions would not suffice to cover the requirements of the library. In 2014 the decision was made to start doing the necessary applied research in-house which was successfully implemented by establishing a PhD position. However, the prototypical machine learning solutions that they developed over the following years were yet to be integrated into productive operations at the library. Therefore in 2020 an additional position for a software engineer was established and a pilot phase was initiated (planned to last until 2024) with the goal to complete the transfer of our solutions into practice by building a suitable software architecture that allows for real-time subject indexing with our trained models and the integration thereof into the other metadata workflows at ZBW. In this paper we address the question of how to transfer results from applied research into a productive service, and we report on the milestones we have reached so far and on those that are yet to be reached on an operational level. We also discuss the challenges we were facing on a strategic level, the measures and resources (computing power, software, personnel) that were needed in order to be able to affect the transfer, and those that will be necessary in order to subsequently ensure the continued availability of the architecture and to enable a continuous development during running operations. We conclude that there are still no shelf-ready open source systems for the automation of subject indexing – existing software has to be adapted and maintained continuously which requires various forms of expertise. However, the task of automation is here to stay, and librarians are witnessing the dawn of a new era where subject indexing is done at least in part by machines, and the respective roles of machines and human experts may shift even further and more rapidly in a not-so-distant future. We argue that in general, the format of “project” and the mindset that goes with it may not suffice to secure the commitment that an institution and its decision-makers and the library community as a whole will have to bring to the table in order to face the monumental task of the digital transformation and automation in the long run. We also highlight the importance of all parties – applied researchers, software engineers, stakeholders – staying involved and continuously communicating requirements and issues back and forth in order to successfully create and establish a productive service that is suitable and equipped for operation.
The goal of this research was to investigate the communication channels that enhance data sharing among agriculture researchers in Tanzania. Specifically, the study aimed to identify communication channels that are used by agricultural researchers in Tanzania, examine the extent to which such channels were used to enhance data sharing among agricultural researchers, and examine the factors that influence the choices of channels used in data sharing. A descriptive cross-sectional design, alongside quantitative, and qualitative approaches, was employed to collect data from 204 respondents. The Concentric Layered Model for the channel choices was used to guide this study. Results indicate that both mediated, and non-mediated channels existed and were used as data-sharing avenues and channels. The majority of the researchers (77.9%) preferred to use non-mediated channels. Also, the findings indicate that more than 50% of respondents agreed that timely delivery, the cost of the channel, and convenience of a channel were among the factors influencing researchers in their channel selection. It can be concluded therefore that research institutions should invest in mediated channels that have been underutilized to strengthen data-sharing practices among researchers.
The commercial control of academic publishing and research infrastructure by a few oligopolistic companies has crippled the development of open access movement and interfered with the ethical principles of information access and privacy. In recent years, vertical integration of publishers and other service providers throughout the research cycle has led to platformisation, characterized by datafication and commodification similar to practices on social media platforms. Scholarly publications are treated as user-generated contents for data tracking and surveillance, resulting in profitable data products and services for research assessment, benchmarking and reporting. Meanwhile, the bibliodiversity and equal open access are denied by the dominant gold open access model and the privacy of researchers is being compromised by spyware embedded in research infrastructure. This article proposes four actions to fight the platformisation of scholarly information after a brief overview of the market of academic journals and research assessments and their implications for bibliodiversity, information access, and privacy: (1) Educate researchers about commercial publishers and APCs; (2) Allocate library budget to support scholar-led and library publishing; (3) Engage in the development of public research infrastructures and copyright reform; and (4) Advocate for research assessment reforms.
