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The purpose of this paper is to evaluate the impact of scientific data in order to assess the reliability of data to support data curation, to establish trust between researchers to support reuse of digital data and encourage researchers to share more data.

The authors compared the correlations between usage counts of associated data in Dryad and citation counts of articles in Web of Science in different subject areas in order to assess the possibility of using altmetric indicators to evaluate scientific data.

There are high positive correlations between usage counts of data and citation counts of associated articles. The citation counts of article’s shared data are higher than the average citation counts in most of the subject areas examined by the authors.

The paper suggests that usage counts of data could be potentially used to evaluate scholarly impact of scientific data, especially for those subject areas without special data repositories.

The study examines the possibility to use usage counts to evaluate the impact of scientific data in a generic repository Dryad by different subject categories.

Data sharing is widely thought to help research quality and efficiency. Data sharing mandates are increasingly being adopted by journals and the purpose of this paper is to assess whether they work.

This study examines two evolutionary biology journals, Evolution and Heredity, that have data sharing mandates and make extensive use of Dryad. It uses a quantitative analysis of presence in Dryad, downloads and citations.

Within both journals, data sharing seems to be complete, showing that the mandates work on a technical level. Low correlations (0.15-0.18) between data downloads and article citation counts for articles published in 2012 within these journals indicate a weak relationship between data sharing and research impact. An average of 40-55 data downloads per article after a few years suggests that some use is found for shared life sciences data.

The value of shared data uses is unclear.

Data sharing mandates should be encouraged as an effective strategy.

This is the first analysis of the effectiveness of data sharing mandates.

The purpose of this paper is to examine the effect of the Helping Interdisciplinary Vocabulary Engineering (HIVE) system on the inter-indexer consistency of information professionals when assigning keywords to a scientific abstract. This study examined first, the inter-indexer consistency of potential HIVE users; second, the impact HIVE had on consistency; and third, challenges associated with using HIVE.

A within-subjects quasi-experimental research design was used for this study. Data were collected using a task-scenario based questionnaire. Analysis was performed on consistency results using Hooper's and Rolling's inter-indexer consistency measures. A series of t-tests was used to judge the significance between consistency measure results.

Results suggest that HIVE improves inter-indexing consistency. Working with HIVE increased consistency rates by 22 percent (Rolling's) and 25 percent (Hooper's) when selecting relevant terms from all vocabularies. A statistically significant difference exists between the assignment of free-text keywords and machine-aided keywords. Issues with homographs, disambiguation, vocabulary choice, and document structure were all identified as potential challenges.

Research limitations for this study can be found in the small number of vocabularies used for the study. Future research will include implementing HIVE into the Dryad Repository and studying its application in a repository system.

This paper showcases several features used in HIVE system. By using traditional consistency measures to evaluate a semantic web technology, this paper emphasizes the link between traditional indexing and next generation machine-aided indexing (MAI) tools.

In recent years, a large number of data repositories have been built and used. However, the extent to which scientific data are re-used in academic publications is still unknown. The purpose of this paper is to explore the functions of re-used scientific data in scholarly publication in different fields.

To address these questions, the authors identified 827 publications citing resources in the Dryad Digital Repository indexed by Scopus from 2010 to 2015.

The results show that: the number of citations to scientific data increases sharply over the years, but mainly from data-intensive disciplines, such as agricultural, biology science, environment science and medicine; the majority of citations are from the originating articles; and researchers tend to reuse data produced by their own research groups.

Dryad data may be re-used without being formally cited.

The conservatism in data sharing suggests that more should be done to encourage researchers to re-use other’s data.

The study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania.

A survey research design was employed to collect data from postgraduate students at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. The data were collected and analysed quantitatively and qualitatively. A census sampling technique was employed to select the sample size for this study. The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS), whilst the qualitative data were analysed thematically.

Less than half of the respondents were aware of and were using open RDRs, including Zenodo, DataVerse, Dryad, OMERO, GitHub and Mendeley data repositories. More than half of the respondents were not willing to share research data and cited a lack of ownership after storing their research data in most of the open RDRs and data security. HILs need to conduct training on using trusted repositories and motivate postgraduate students to utilise open repositories (ORs). The challenges for underutilisation of open RDRs were a lack of policies governing the storage and sharing of research data and grant constraints.

Research data storage and sharing are of great interest to researchers in HILs to inform them to implement open RDRs to support these researchers. Open RDRs increase visibility within HILs and reduce research data loss, and research works will be cited and used publicly. This paper identifies the potential for additional studies focussed on this area.

Research data management (RDM) is gaining a lot of momentum in the present day and rightly so. Research data are the core of any research study. The findings and conclusions of a study are entirely dependent on the research data. Traditional publishing did not focus on the presentation of data, along with the publications such as research monographs and especially journal articles, probably because of the difficulties involved in managing the research data sets. The current day technology, however, has helped in making this task easier. The purpose of this paper is to present a conceptual framework for managing research data at the institutional level.

This paper discusses the significance and advantages of sharing research data. In the spirit of open access to publications, freeing research data and making it available openly, with minimal restrictions, will help in not only furthering research and development but also avoiding duplication of efforts. The issues and challenges involved in RDM at the institutional level are discussed.

A conceptual framework for RDM at the institutional level is presented. A model for a National Repository of Open Research Data (NRORD) is also proposed, and the workflow of the functioning of NRORD is also presented.

The framework clearly presents the workflow of the data life-cycle in its various phases right from its creation, storage, organization and sharing. It also attempts to address crucial issues in RDM such as data privacy, data security, copyright and licensing. The framework may help the institutions in managing the research data life-cycle in a more efficient and effective manner.

The purpose of this paper is to examine a lacuna in Heidegger's “The Origin of the Work of Art”, that is, that it does not consider that such “origin” might be gendered. Originally published in a collection entitled Holzwege, which in the Cambridge (2002) edition is translated as Off the Beaten Track, Heidegger explains, “Wood” is an old name for the forest. In the wood there are paths, mostly overgrown, that come to an abrupt stop where the wood is untrodden. They are “Holzwege”.

The paper is written within a philosophical tradition and seeks to examine Derrida's lengthy deconstruction of Heidegger's essay and Irigaray's elegant gender critique, which are among works attesting to the importance and controversy The Origin has provoked among post‐structuralist thinkers.

The paper considers The Origin as an auto‐deconstructive text, through which other voices (one, or more, not Heidegger's) are heard; voices calling him, through him, as him; which he cannot identify or silence. They disrupt and confound his thinking and writing; he tries (unsuccessfully) to mend the discrepancies, assuage the violence, but ultimately leaves the essay in limbo, intuiting something even he cannot comprehend, an alien writing, that its internal contradictions be reserved for others to puzzle over.

The paper offers a reading of The Origin against the Classical male hegemony excluding women from their proper participation in architecture, art in general, aesthetic hermeneutics and philosophy of art. These circuits of exclusion being integral to the phallocentric orthodoxy inherited from the Ancient Greeks.

Industry 4.0 is a promising concept that allows industries to meet customers’ demands with flexible and resilient processes, and highly personalised products. This concept is made up of different dimensions. For a long time, innovative digital technology has been thought of as the only dimension to succeed in digital transformation projects. Other dimensions have been identified such as organisation, strategy, and human resources as key while rolling out digital technology in factories. From these findings, researchers have designed industry 4.0 theoretical models and then built readiness models that allow for analysing the gap between the company initial situation and the theoretical model. Nevertheless, this purely deductive approach does not take into consideration a company’s background and context, and eventually favours one single digital transformation model. This article aims at analysing four actual digital transformation projects and demonstrating that the digital transformation’s success or failure depends on the combination of two variables related to a company’s background and context. This research is based on a double approach: deductive and inductive. First, a literature review has been carried out to define industry 4.0 concept and its main dimensions and digital transformation success factors, as well as barriers, have been investigated. Second, a qualitative survey has been designed to study in-depth four actual industry digital transformation projects, their genesis as well as their execution, to analyse the key variables in succeeding or failing. 46 semi-structured interviews were carried out with projects’ members; interviews have been analysed with thematic content analysis. Then, each digital transformation project has been modelled regarding the key variables and analysed with regards to succeeding or failing. Investigated projects have consolidated the models of digital transformation. Finally, nine digital transformation types have been identified.

Background: Fear has been a common response to the coronavirus disease 2019 (COVID-19) pandemic throughout the world. In Mauritius, the outbreak of COVID-19 has been an exceptional occurrence requiring stringent confinement of the population. In this study, we have explored people’s reactions to COVID-19 during confinement, with emphasis on fear and the impact of news on the level of fear.

Methods: An anonymized online survey was carried out during confinement. Participation was voluntary. Participants reported fear level on a scale from 1-10, where no fear scored 1 and maximum fear scored 10. Participants reported the impact of news on their fear level on a scale of 1-10, where 1 represented no impact and 10 represented maximum impact. Participants reported the status of information received about COVID-19 on a scale of 1-10 where 1 represented no information on COVID-19 and 10 represented maximum information on COVID-19.

Results: The self-rated level of fear during confinement had a mean of 5.09 with 95%CI [4.70, 5.47]. This increased to a mean of 6.39 with 95%CI [6.00-6.78] at the prospect of confinement being lifted. The difference was statistically significant (paired-sample T-test, p<0.001). With regard to the impact of news on fear of COVID-19, the mean for local news was 5.97 with 95%CI [5.59, 6.34] whereas that of worldwide news was 6.86 with 95%CI [6.50, 7.23]. Worldwide news had a more significant impact (paired-sample T-test, p<0.001). The information score about COVID-19 had a mean of 5.12 with 95%CI [4.71, 5.53].

Conclusions: Participants experienced a moderate level of fear of COVID-19 during confinement which increased at the prospect of confinement being lifted, implying that people felt safer during confinement. Their fear was influenced more by international news than by local news. Overall participants reported that they were moderately well informed about the COVID-19 pandemic.