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This research investigated the reuse of Video Records of Practice (VRPs) – i.e. a type of qualitative data documenting teaching and learning in educational settings. It studied how reusers' purposes and experience-level with VRP reuse influence the importance of various VRP selection criteria and how these differ depending on whether the main goal for reuse was research or teaching. It also examined whether two different dimensions of qualitative research – reflexivity and context – were factors in VRP reuse.

The study reports on surveys of reusers at four VRP repositories. Questions were based on the literature and interviews with VRP reusers. The response rate was 20.6% (180 of 872 distributed surveys). This paper focused on 126 respondents who affirmatively responded they reused VRPs from a repository.

Researchers using VRPs were primarily interested in examining a broad range of processes in education and studying/improving ways to measure differences and growth in education. Reusers with teaching goals were commonly interested in VRPs to engage learners in showing examples/exemplars of – and reflecting on – teaching and learning. These differences between research and teaching led to varied expectations about VRPs, such as the amount of content needed and necessary contextual information to support reuse.

While repositories focus on exposing content, understanding and communicating certain qualities of that content can help reusers identify VRPs and align goals with selection decisions.

Although qualitative data are increasingly reused, research has rarely focused on identifying how qualitative data reusers employ selection criteria. This study focused on VRPs as one type of qualitative data and identified the attributes of VRPs that reusers perceived to be important during selection. These will help VRP repositories determine which metadata and documentation meet reusers' goals.

The purpose of this paper is to provide new insights for managing knowledge reuse in terms of the duality of innovator personality. Continuously developing new products is crucial for firms to maintain and enhance their competitive advantages. However, the limited and highly specialized knowledge can cause innovators of firms to face difficulties in the process of new product development (NPD). In this setting, knowledge reuse becomes a solution that may benefit innovators to overcome the innovation dilemma. Given the fact that innovators with different personality are likely to form incongruent cognitions and affection on knowledge reuse, thus subsequently affecting the performance of NPD, there is an urgent need to investigate the effects of innovator personality in the entire process of knowledge reuse.

This paper exploits five-factor model (FFM) of personality to comprehensively investigate the dual effects of innovator personality in managing knowledge reuse based on the two distinct sets of knowledge reuse initiation and implementation.

By using the data from 981 innovators of knowledge-intensive firms in China, this study finds that the FFM traits of conscientiousness and agreeableness had opposing effects on initiation and implementation of knowledge reuse. While the FFM traits of emotional stability and openness to experience both positively affect the knowledge reuse initiation and implementation. Moreover, the FFM traits of extraversion benefit the shaping of knowledge reuse initiation whereas encumbering the implementation of knowledge reuse.

First, this study reveals the different roles of cognitive and affective traits of personality in shaping knowledge reuse. Second, this study exposes the role of innovator personality in determining the performance effects of knowledge reuse implementation. Third, this study highlights the dual effects of innovator personality in managing knowledge reuse. This study offers evidence for arranging the innovators with appropriate FFM traits in various stages of knowledge reuse.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

This study investigates differences and commonalities in data production, sharing and reuse across the widest range of disciplines yet and identifies types of improvements needed to promote data sharing and reuse.

The first authors of randomly selected publications from 2018 to 2019 in 20 Scopus disciplines were surveyed for their beliefs and experiences about data sharing and reuse.

From the 3,257 survey responses, data sharing and reuse are still increasing but not ubiquitous in any subject area and are more common among experienced researchers. Researchers with previous data reuse experience were more likely to share data than others. Types of data produced and systematic online data sharing varied substantially between subject areas. Although the use of institutional and journal-supported repositories for sharing data is increasing, personal websites are still frequently used. Combining multiple existing datasets to answer new research questions was the most common use. Proper documentation, openness and information on the usability of data continue to be important when searching for existing datasets. However, researchers in most disciplines struggled to find datasets to reuse. Researchers' feedback suggested 23 recommendations to promote data sharing and reuse, including improved data access and usability, formal data citations, new search features and cultural and policy-related disciplinary changes to increase awareness and acceptance.

This study is the first to explore data sharing and reuse practices across the full range of academic discipline types. It expands and updates previous data sharing surveys and suggests new areas of improvement in terms of policy, guidance and training programs.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2021-0423.

Taking the researchers’ perspective, the purpose of this paper is to examine the types of context information needed to preserve data’s meaning in ways that support data reuse.

This paper is based on a qualitative study of 105 researchers from three disciplinary communities: quantitative social science, archaeology and zoology. The study focused on researchers’ most recent data reuse experience, particularly what they needed when deciding whether to reuse data.

Findings show that researchers mentioned 12 types of context information across three broad categories: data production information (data collection, specimen and artifact, data producer, data analysis, missing data, and research objectives); repository information (provenance, reputation and history, curation and digitization); and data reuse information (prior reuse, advice on reuse and terms of use).

This paper extends digital curation conversations to include the preservation of context as well as content to facilitate data reuse. When compared to prior research, findings show that there is some generalizability with respect to the types of context needed across different disciplines and data sharing and reuse environments. It also introduces several new context types. Relying on the perspective of researchers offers a more nuanced view that shows the importance of the different context types for each discipline and the ways disciplinary members thought about them. Both data producers and curators can benefit from knowing what to capture and manage during data collection and deposit into a repository.

Exploring diverse knowledge organization systems and metadata schemes in linked data, aiming to promote vocabulary usability and high-quality linked data creation within the LIS field.

We used content analysis to select 77 articles from 13 library and information science journals around our research theme. We identified four dimensions: vocabularies participation, reuse, functions, and naming variations in linked data.

The vocabulary comprises seven main categories and their corresponding 126 vocabularies, which participate in linked data in single, two, and multiple dimensions. These vocabularies are used in the eight LIS subfields. Reusing vocabularies has become integral to linked data publishing, with six categories and their corresponding 66 vocabularies being reused. Ontologies are the most engaged and widely reused category of vocabulary in linked data practice. The mutual support among the three major categories and seven subfunctions of vocabulary promotes the sustainable development of linked data. Under a combination of factors, the phenomenon of terminology name changes and cross-usage between “vocabulary” and “ontology.”

This study has limitations. Although 77 articles on the topic of vocabularies applied in linked data were analyzed and presented with quantitative statistics and visualizations, the exploration of the topic tends to be a practical activity, with limited presence in scholarly articles. Moreover, this study’s analysis of the practical applications of linked data is relatively limited, and the sample literature focused on articles published in English, which may have affected the diversity and inclusiveness of the research sample.

Practically, this study does not confine the application of content analysis solely to the traditional exploration of knowledge organization topics, development trends, or course content. Instead, it integrates the dual perspectives of linked data and vocabularies, employing content analysis to analyze and objectively reveal the application issues of vocabularies in linked data. The conclusions can provide specific guidelines for future applications of vocabularies in the LIS subfields and contribute to promoting interoperability of vocabularies.

This research explores the relationship between linked data and vocabularies, highlighting the diverse manifestations and challenges of vocabularies in linked data. It provides theoretical references for the construction and further development of vocabularies considering technologies such as linked data, drawing attention to the potential and existing issues associated with linked open data vocabularies.

This study extends the application of content analysis to exploring vocabularies, especially Knowledge Organization Systems and metadata schemes in the LIS field linked data, highlighting the mutually beneficial interactions between linked data and vocabularies. It provides guidance for future vocabularies applications in the LIS field and offers insights into vocabularies construction and the healthy development of linked data ecosystems in the era of information technology.

The purpose of this study is to offer a checklist that can be used for both creating and evaluating digital collections, which are also sometimes referred to as data sets as part of the collections as data movement, suitable for computational use.

The checklist was built by synthesising and analysing the results of relevant research literature, articles and studies and the issues and needs obtained in an observational study. The checklist was tested and applied both as a tool for assessing a selection of digital collections made available by galleries, libraries, archives and museums (GLAM) institutions as proof of concept and as a supporting tool for creating collections as data.

Over the past few years, there has been a growing interest in making available digital collections published by GLAM organisations for computational use. Based on previous work, the authors defined a methodology to build a checklist for the publication of Collections as data. The authors’ evaluation showed several examples of applications that can be useful to encourage other institutions to publish their digital collections for computational use.

While some work on making available digital collections suitable for computational use exists, giving particular attention to data quality, planning and experimentation, to the best of the authors’ knowledge, none of the work to date provides an easy-to-follow and robust checklist to publish collection data sets in GLAM institutions. This checklist intends to encourage small- and medium-sized institutions to adopt the collection as data principles in daily workflows following best practices and guidelines.

As the last link of product remanufacturing, reassembly process is of great importance in increasing the utilization of remanufactured parts as well as decreasing the production cost for remanufacturing enterprises. It is a common problem that a large amount of remanufactured part/reused part which past the dimension standard have been scrapped, which have increased the production cost of remanufacturing enterprises to a large extent. With the aim to improve the utilization of remanufacturing parts with qualified quality attributes but exceed dimension, the purpose of this paper is to put forward a reassembly classification selection method based on the Markov Chain.

To begin with, a classification standard of reassembly parts is proposed. With the thinking of traditional ABC analysis, a classification management method of reassembly parts for remanufactured engine is proposed. Then, a homogeneous Markov Chain of reassembly process is built after grading the matching dimension of reassembly parts with different variety. And the reassembly parts selection model is constructed based on the Markov Chain. Besides, the reassembly classification selection model and its flow chart are proposed by combining the researches above. Finally, the assembly process of remanufactured crankshaft is adopted as a representative example for illustrating the feasibility and the effectiveness of the method proposed.

The reassembly classification selection method based on the Markov Chain is an effective method in improving the utilization of remanufacturing parts/reused parts. The average utilization of remanufactured crankcase has increased from 35.7 to 80.1 per cent and the average utilization of reused crankcase has increased from 4.2 to 14 per cent as shown in the representative example.

The reassembly classification selection method based on the Markov Chain is of great importance in enhancing the economic benefit for remanufacturing enterprises by improving the utilization of remanufactured parts/reused parts.

The purpose of this paper is to present a case‐based system for offline automatic programming in robotic assembly production. This system can reuse past learned robot programs to generate programs for new assembly tasks.

The approach used in this paper is case‐based reasoning. The assembly knowledge acquired from the robot program for an assembly task is retained in a case, which is composed of the primitive task description and the corresponding robot program schema. The retained cases are retrieved by matching features of their primitive task descriptions, and are reused to automatically program for new tasks by instantiating their robot program schemata.

A case not only can be reused as a whole, but also can be reused partly by synthesizing different parts of several cases to generate a program for a new task in a variant environment.

The teaching time of robots can be greatly reduced. This helps to introduce robots into small and medium enterprises.

This paper proposes a novel system that can automatically program for assembly tasks in various environments by flexibly reusing past robot programs.

This paper aims to share the contribution of records managers to the Open Government in the City Council of Girona (Catalonia), based on the strategy of participation in the Open Data project.

The contribution of the Records Management Department is to facilitate locating data sets and to be responsible for the data selection processes. Records Management allows a first global identification of the functions, activities and producers, using the Records Classification Scheme and the General Register of Case Files. In addition, it can obtain a greater detail of information using the Application Catalogue and the Register of Personal Data Files.

Records Management can contribute to data selection in the Open Data projects by the appraisal of data, also taking into consideration functions and records of the organization. At the same time, Open Data projects could reinforce the strategies of records preservation in databases, because the same extraction processes could be shared. In addition, Open Data projects could mean an opportunity for Historical Archives if they take charge of the maintenance of the Open Data Archive. This could be relevant in the future for the transformation of data from information systems into documentary heritage.

The opening of the city’s Open Government portal is scheduled for the beginning of 2014. Thus, it is necessary to consider this work as a work in progress.

This work is a specific case study of the contribution of Records Management to the Open Data projects; its proposals and reflections have been made from a very practical point of view.