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This paper aims to show how an illegal repository of literature, the Z-library, relates to and influences its users and how this relation is unique due to the illegal nature of the platform. The paper utilizes the idea of gamification to exemplify how to motivate users to contribute to a large shadow library in order to create the “world's largest e-book library,” sans “librarians.”

The study makes use of an ethnographic approach. It interrogates the functions of the website through intensive use—a close reading of sorts. The data provide a foundation for illustrating how illegal text repositories function at a surface level and how their design appeals to their user-base.

The paper provides a thorough and non-biased overview of how a “black open access” or “shadow library” site provides its users with pirated literature. It suggests that the lynchpin sustaining their functionality is a gamification of piracy designed to motivate a fragmented collective of individuals who work primarily for personal reward, rather than altruistic goals.

Due to the design of the study, the findings are not universal or applicable to all illegal repositories of text. Readers and researchers are encouraged to apply the concept introduced here to other cases.

This paper includes implication on the perception of literature piracy, how pirated literature is distributed and who performs the labor required to sustain illicit text repositories.

This paper provides a novel conceptual basis to study literature piracy.

In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.

The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.

The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.

In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.

The purpose of this paper is to evaluate innovations in intellectual property rights (IPR) databases, techniques and software tools, with an emphasis on selected new developments and their contribution towards achieving advantages for IPR management (IPRM) and wider social benefits. Several industry buzzwords are addressed, such as IPR-linked open data (IPR LOD) databases, blockchain and IPR-related techniques, acknowledged for their contribution in moving towards artificial intelligence (AI) in IPRM.

The evaluation, following an original framework developed by the authors, is based on a literature review, web analysis and interviews carried out with some of the top experts from IPR-savvy multinational companies.

The paper presents the patent databases landscape, classifying patent offices according to the format of data provided and depicting the state-of-art in the IPR LOD. An examination of existing IPR tools shows that they are not yet fully developed, with limited usability for IPRM. After reviewing the techniques, it is clear that the current state-of-the-art is insufficient to fully address AI in IPR. Uses of blockchain in IPR show that they are yet to be fully exploited on a larger scale.

A critical analysis of IPR tools, techniques and blockchain allows for the state-of-art to be assessed, and for their current and potential value with regard to the development of the economy and wider society to be considered. The paper also provides a novel classification of patent offices and an original IPR-linked open data landscape.

The study is aimed at analyzing the perceptions of students and teachers regarding the effectiveness of massive open online courses (MOOCs) on learning efficiency of students and also evaluating MOOCs as an ideal tool for designing a blended model for education.

The analysis was carried out by using the data gathered from the students as well as teachers of University of Mysore, Karnataka, India. Two separate sets of questionnaires were developed for both the categories of respondents. Also, the respondents were required to have prior experience in MOOCs. Further, the collected data was analyzed using statistical package for social sciences (SPSS).

The study showed that MOOCs have a more positive influence on learning efficiency, as opined by both teachers and students. Negative views such as cheating during the assessment, lack of individual attention to students and low teacher-student ratio were also observed.

Many educational institutions view that the MOOCs do not influence learning efficiency and also do not support in achieving their vision. However, this study provides evidence that MOOCs are positively influencing the learning efficiency and also can be employed in a blended model of education so as to promote collaborative learning.

Technology is playing a pivotal role in all fields of life and the education sector is not an exception. It can be rightly said that the technology-based education models such as MOOCs are the need of the hour. This study may help higher education institutions to adopt MOOCs as part of their blended model of education, and, if already adopted, the outcome of the present study will help them to improve the effectiveness of the MOOCs they are offering.

The purpose of this paper is to present data that begin to detail the deficiencies of log file analytics reporting methods that are commonly built into institutional repository (IR) platforms. The authors propose a new method for collecting and reporting IR item download metrics. This paper introduces a web service prototype that captures activity that current analytics methods are likely to either miss or over-report.

Data were extracted from DSpace Solr logs of an IR and were cross-referenced with Google Analytics and Google Search Console data to directly compare Citable Content Downloads recorded by each method.

This study provides evidence that log file analytics data appear to grossly over-report due to traffic from robots that are difficult to identify and screen. The study also introduces a proof-of-concept prototype that makes the research method easily accessible to IR managers who seek accurate counts of Citable Content Downloads.

The method described in this paper does not account for direct access to Citable Content Downloads that originate outside Google Search properties.

This paper proposes that IR managers adopt a new reporting framework that classifies IR page views and download activity into three categories that communicate metrics about user activity related to the research process. It also proposes that IR managers rely on a hybrid of existing Google Services to improve reporting of Citable Content Downloads and offers a prototype web service where IR managers can test results for their repositories.

The purpose of this study is to survey the landscape of online collections of digital games.

First, the study identifies existing sites hosting collections and criteria that make a collection valuable for research, then it reports on sites that fit the criteria and analyzes trends.

Most sites provide simple binary downloads, but some choose encapsulation. Common metadata terms consistently include genre, year of release and publisher. Most sites claim the right to provide their collections as “abandonware,” but remove games if they are asked to.

This study was conducted using a very limited subcategory of digital games, which could be expanded in other studies. Future research may require a multilingual team to account for collections based in non–English-speaking countries. Direct communication with sites’ management may be valuable in the future as well, but was not conducted in this study.

The study identifies practices that have developed organically in this field without any guiding standards. Understanding these may aid in Humanities research into digital games, as well as potential collection development in the future.

Digital games are increasingly important as cultural artifacts, and there is a growing effort to preserve them for the future, but there are no standards for collecting and providing them. Understanding how this is currently done can help in providing access into the future for both casual and analytical use.

While game preservation is a growing and active field of research, no study has been published in recent years on this particular subject. It will be valuable for the development of future collections and for research using current ones.