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Programming question and answer (Q&A) websites, such as Stack Overflow, leverage the knowledge and expertise of users to provide answers to technical questions. Over time, these websites turn into repositories of software engineering knowledge. Such knowledge repositories can be invaluable for gaining insight into the use of specific technologies and the trends of developer discussions. Previous work has focused on analyzing the user activities or the social interactions in Q&A websites. However, analyzing the actual textual content of these websites can help the software engineering community to better understand the thoughts and needs of developers. In the article, we present a methodology to analyze the textual content of Stack Overflow discussions. We use latent Dirichlet allocation (LDA), a statistical topic modeling technique, to automatically discover the main topics present in developer discussions. We analyze these discovered topics, as well as their relationships and trends over time, to gain insights into the development community. Our analysis allows us to make a number of interesting observations, including: the topics of interest to developers range widely from jobs to version control systems to C# syntax; questions in some topics lead to discussions in other topics; and the topics gaining the most popularity over time are web development (especially jQuery), mobile applications (especially Android), Git, and MySQL.

This article is the second part of the investigation of the determinants for the uptake of Open Access (OA). While the first part focusses on journal-based OA (hybrid and full OA) (Taubert et al. in Scientometrics 128(6):3601–3625, 2023), the article at hand investigates the determinants for the uptake of institutional and subject repository OA in the university landscape of Germany. Both articles consider three types of factors: the disciplinary profile of universities, their OA infrastructures and services and large transformative agreements The article also apply a conjoint methodological design: the uptake of OA as well as the determinants are measured by combining several data sources (incl. Web of Science, Unpaywall, an authority file of standardised German affiliation information, the ISSN-Gold-OA 4.0 list, and lists of publications covered by transformative agreements). For universities’ OA infrastructures and services , a structured data collection was created by harvesting different sources of information and by manual online search. To determine the explanatory power of the different factors, a series of regression analyses was performed for different periods and for both institutional as well as subject repository OA. Given that both articles derive from the same project, there is a thematical overlap in the methods and data section. As a result of the regression analyses, the most determining factor for the explanation of differences in the uptake of both repository OA-types turned out to be the disciplinary profile, whereas all variables that capture local infrastructural support and services for OA turned out to be non-significant. The outcome of the regression analyses is contextualised by an interview study conducted with 20 OA officers of German universities. The contextualisation provides hints that the original function of institutional repositories, offering a channel for secondary publishing is vanishing, while a new function of aggregation of metadata and full texts is becoming of increasing importance.

Background: Health sciences libraries in medical schools, academic health centers, health care networks, and hospitals have established institutional repositories (IRs) to showcase their research achievements, increase visibility, expand the reach of institutional scholarship, and disseminate unique content. Newer roles for IRs include publishing open access journals, tracking researcher productivity, and serving as repositories for data sharing. Many repository managers oversee their IR with limited assistance from others at their institution. Therefore, IR practitioners find it valuable to network and learn from colleagues at other institutions. Case Presentation: This case report describes the genesis and implementation of a new initiative specifically designed for a health sciences audience: the Medical Institutional Repositories in Libraries (MIRL) Symposium. Six medical librarians from hospitals and academic institutions in the U.S. organized the inaugural symposium held virtually in November 2021. The goal was to fill a perceived gap in conference programming for IR practitioners in health settings. Themes of the 2021 and subsequent 2022 symposium included IR management, increasing readership and engagement, and platform migration. Post-symposium surveys were completed by 73/238 attendees (31%) in 2021 and by 62/180 (34%) in 2022. Feedback was overwhelmingly positive. Discussion: Participant responses in post-symposium surveys rated MIRL highly. The MIRL planning group intends to continue the symposium and hopes MIRL will steadily evolve, build community among IR practitioners in the health sciences, and expand the conversation around best practices for digital archiving of institutional content. The implementation design of MIRL serves as a blueprint for collaboratively bringing together a professional community of practice.

Many AI researchers are publishing code, data and other resources that accompany their papers in GitHub repositories. In this paper, we refer to these repositories as academic AI repositories. Our preliminary study shows that highly cited papers are more likely to have popular academic AI repositories (and vice versa). Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers. We collect 1,149 academic AI repositories, in which we label the top 20% repositories that have the most number of stars as popular, and we label the bottom 70% repositories as unpopular. The remaining 10% repositories are set as a gap between popular and unpopular academic AI repositories. We propose 21 features to characterize the software engineering practices of academic AI repositories. Our experimental results show that popular and unpopular academic AI repositories are statistically significantly different in 11 of the studied features—indicating that the two groups of repositories have significantly different software engineering practices. Furthermore, we find that the number of links to other GitHub repositories in the README file, the number of images in the README file and the inclusion of a license are the most important features for differentiating the two groups of academic AI repositories. Our dataset and code are made publicly available to share with the community.

Model-based engineering (MBE) recognizes models as central in software construction with the possibility of their management in libraries and repositories with proper structuring of their spaces and operations. Due to this success, models (and metamodels) are becoming larger and larger and technics are needed in order to comprehend and exploit them, such as circumscribing sub(meta)models of interest, which is the subject of this paper. Following MBE, there are mainly two ways for circumscribing submodels: only at the model level (by selecting model elements of interest) or through the meta level (by selecting a submetamodeling dimension of interest). In a preceding paper, we deeply studied the first way. Here we concentrate on the second way. Model projection deeply relies on the concepts of submodels and submetamodels with their inclusion qualities for model space structuring and has to be systematically examined from this point of view. It is important to point out that model treatment has to deal with full models (as offered by “off the shelf” libraries) but also with not necessarily well-formed ones, such as unspecified model chunks, due, for example, to the storage in repositories of incomplete engineering choices or of intermediate results of operations. It is a difficulty to encompass all these forms of models, being well-formed or not, in a homogeneous manner through MBE operations. The operation for “Model projection relative to submetamodeling dimensions” presented here does take this difficulty into account.

CompOSE (CompStar Online Supernovae Equations of State) is an online repository of equations of state (EoS) for use in nuclear physics and astrophysics, e.g., in the description of compact stars or the simulation of core-collapse supernovae and neutron-star mergers, see https://compose.obspm.fr. The main services, offered via the website, are: a collection of data tables in a flexible and easily extendable data format for different EoS types and related physical quantities with extensive documentation and referencing; software for download to extract and to interpolate these data and to calculate additional quantities; webtools to generate EoS tables that are customized to the needs of the users and to illustrate dependencies of various EoS quantities in graphical form. This manual is an update of previous versions that are available on the CompOSE website, at arXiv:1307.5715 [astro-ph.SR], and that was originally published in the journal “Physics of Particles and Nuclei” with doi:10.1134/S1063779615040061. It contains a detailed description of the service, containing a general introduction as well as instructions for potential contributors and for users. Short versions of the manual for EoS users and providers will also be available as separate publications. Graphical Abstract

Introduction: Successfully managing an open-access repository requires constant attention to user community priorities in order to inform the development or selection of a platform that fulfills constantly evolving functional demands in an increasingly complex operational environment. This paper uses AgEcon Search (AES) as an example of the way that varying platforms address the metadata and other platform needs of a repository. AES is a successful subject repository with an international scope that has resided on several different platforms in its 25-year lifespan. Elements and Considerations: Critical among the technical requirements of a repository is interoperability with other information sources and the ability to accommodate and describe different types of objects, including data. Experienced in the use of easy and widely used Dublin Core (DC), as well as Machine-Readable Cataloging 21 (MARC 21)-based repository platforms, we discuss both metadata schemas from administrative and user perspectives. Reconsidering underlying metadata issues might positively impact both technical and administrative issues that are currently restricting the development of robust, interoperable systems. As managers of AES, we are uniquely placed to discuss both technical and sustainability issues. Conclusions: Although many institutional and subject repositories are on platforms that use DC for their metadata, other options are available. MARC, the well-established library standard, can provide the wide range of fields needed to fully and accurately describe the variety of document and data types that are included in repositories.

Research in Mining Software Repositories (MSR) is research involving human subjects, as the repositories usually contain data about developers’ and users’ interactions with the repositories and with each other. The ethics issues raised by such research therefore need to be considered before beginning. This paper presents a discussion of ethics issues that can arise in MSR research, using the mining challenges from the years 2006 to 2021 as a case study to identify the kinds of data used. On the basis of contemporary research ethics frameworks we discuss ethics challenges that may be encountered in creating and using repositories and associated datasets. We also report some results from a small community survey of approaches to ethics in MSR research. In addition, we present four case studies illustrating typical ethics issues one encounters in projects and how ethics considerations can shape projects before they commence. Based on our experience, we present some guidelines and practices that can help in considering potential ethics issues and reducing risks.

This study investigates the role of metadata quality in Electronic Theses and Dissertations (ETDs), focusing on its completeness and its impact on discoverability and user engagement within institutional repositories (IRs). Using DSpace@MIT as a case study, the current research analyzed 22,276 doctoral dissertations to assess metadata completeness and its correlation with the number of views and downloads. Various metadata fields and usage statistics were extracted for detailed analysis. The study identified a moderate positive correlation between the numbers of unique metadata fields and both the Department Views Ratio (DVR) and Department Download Ratio (DDR), suggesting that enriched metadata can improve the visibility and accessibility of dissertations. Additionally, the length of abstracts is positively correlated with engagement metrics (significance level for all reported results < 0.001). In contrast, title length does not significantly influence the visibility. These findings showed the importance of high-quality metadata in enhancing the discoverability of ETDs. Three limitations are discussed, including the focus on a single repository, the lack of control for other variables that may impact user engagement, and the massive upload of thousands of theses in 2005. This research not only emphasizes the necessity of high-quality metadata for enhancing discoverability but also positions it as a strategic asset that can significantly amplify the visibility and impact of scholarly work. As institutions strive to foster open access and maximize research dissemination, our study provides actionable insights that can guide repository managers in refining their metadata practices.