This manual focuses on the initial phase of a (digital) publishing process. It offers methods to critically examine the narrative structures of content and explore alternative conceptions of a publication. By raising the question of how modular publishing can be used as a way to create, edit and structure content it tries to resist a monolithic story line, and embraces multiple perspectives.
This method paper presents a template solution for text mining of scientific literature using the R tm package. Literature to be analyzed can be collected manually or automatically using the code provided with this paper. Once the literature is collected, the three steps for conducting text mining can be performed as outlined below:• loading and cleaning of text from articles,• processing, statistical analysis, and clustering, and• presentation of results using generalized and tailor-made visualizations.The text mining steps can be applied to a single, multiple, or time series groups of documents.References are provided to three published peer reviewed articles that use the presented text mining methodology. The main advantages of our method are: (1) Its suitability for both research and educational purposes, (2) Compliance with the Findable Accessible Interoperable and Reproducible (FAIR) principles, and (3) code and example data are made available on GitHub under the open-source Apache V2 license.
An overview of innovations in a particular area, for example retail developments in the fashion sector (Van Vliet, 2014), and a subsequent discussion about the probability as to whether these innovations will realise a ‘breakthrough’, has to be supplemented with the question of what the added value is for the customer of such a new service or product. The added value for the customer must not only be clear as to its direct (instrumental or hedonic) incentives but it must also be tested on its merits from a business point of view. This requires a methodology. Working with business models is a method for describing the added value of products/services for customers in a systematic and structured manner. The fact that this is not always simple is evident from the discussions about retail developments, which do not excel in well-grounded business models. If there is talk about business models at all, it is more likely to concern strategic positioning in the market or value chain, or the discussion is about specifics like earning- and distribution-models (see Molenaar, 2011; Shopping 2020, 2014). Here we shall deal with two aspects of business models. First of all we shall look at the different perspectives in the use of business models, ultimately arriving at four distinctive perspectives or methods of use. Secondly, we shall outline the context within which business models operate. As a conclusion we shall distil a research framework from these discussions by presenting an integrated model as the basis for further research into new services and product.
The scientific publishing industry is rapidly transitioning towards information analytics. This shift is disproportionately benefiting large companies. These can afford to deploy digital technologies like knowledge graphs that can index their contents and create advanced search engines. Small and medium publishing enterprises, instead, often lack the resources to fully embrace such digital transformations. This divide is acutely felt in the arts, humanities and social sciences. Scholars from these disciplines are largely unable to benefit from modern scientific search engines, because their publishing ecosystem is made of many specialized businesses which cannot, individually, develop comparable services. We propose to start bridging this gap by democratizing access to knowledge graphs – the technology underpinning modern scientific search engines – for small and medium publishers in the arts, humanities and social sciences. Their contents, largely made of books, already contain rich, structured information – such as references and indexes – which can be automatically mined and interlinked. We plan to develop a framework for extracting structured information and create knowledge graphs from it. We will as much as possible consolidate existing proven technologies into a single codebase, instead of reinventing the wheel. Our consortium is a collaboration of researchers in scientific information mining, Odoma, an AI consulting company, and the publisher Brill, sharing its data and expertise. Brill will be able to immediately put to use the project results to improve its internal processes and services. Furthermore, our results will be published in open source with a commercial-friendly license, in order to foster the adoption and future development of the framework by other publishers. Ultimately, our proposal is an example of industry innovation where, instead of scaling-up, we scale wide by creating a common resource which many small players can then use and expand upon.
