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Register a new userData objects and documenting scientific processes: An analysis of data events in biodiversity data papers
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Added by
Kai Li
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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The data paper, an emerging scholarly genre, describes research datasets and is intended to bridge the gap between the publication of research data and scientific articles. Research examining how data papers report data events, such as data transactions and manipulations, is limited. The research reported on in this paper addresses this limitation and investigated how data events are inscribed in data papers. A content analysis was conducted examining the full texts of 82 data papers, drawn from the curated list of data papers connected to the Global Biodiversity Information Facility (GBIF). Data events recorded for each paper were organized into a set of 17 categories. Many of these categories are described together in the same sentence, which indicates the messiness of data events in the laboratory space. The findings challenge the degrees to which data papers are a distinct genre compared to research papers and they describe data-centric research processes in a through way. This paper also discusses how our results could inform a better data publication ecosystem in the future.
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Many altmetric studies analyze which papers were mentioned how often in specific altmetrics sources. In order to study the potential policy relevance of tweets from another perspective, we investigate which tweets were cited in papers. If many tweets were cited in publications, this might demonstrate that tweets have substantial and useful content. Overall, a rather low number of tweets (n=5506) were cited by less than 3000 papers. Most tweets do not seem to be cited because of any cognitive influence they might have had on studies; they rather were study objects. Most of the papers citing tweets are from the subject areas Social Sciences, Arts and Humanities, and Computer Sciences. Most of the papers cited only one tweet. Up to 55 tweets cited in a single paper were found. This research-in-progress does not support a high policy-relevance of tweets. However, a content analysis of the tweets and/or papers might lead to a more detailed conclusion.
Without sufficient information about researchers data sharing, there is a risk of mismatching FAIR data service efforts with the needs of researchers. This study describes a methodology where departmental publications are used to analyse the ways in which computer scientists share research data. All journal articles published by researchers in the computer science department of the case studys university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a coding framework was developed to capture the key elements of acquiring and sharing research data. Furthermore, a rudimentary classification of the main study types exhibited in the investigated articles was developed to accommodate the multidisciplinary nature of the case departments research agenda. Human interaction and intervention studies often collected original data, whereas research on novel computational methods and life sciences more frequently used openly available data. Articles that made data available for reuse were most often in life science studies, whereas data sharing was least frequent in human interaction studies. The use of open code was most frequent in life science studies and novel computational methods. The findings highlight that multidisciplinary research organisations may include diverse subfields that have their own cultures of data sharing, and suggest that research information system-based methods may be valuable additions to the questionnaire and interview methodologies eliciting insight into researchers data sharing. The collected data and coding framework are provided as open data to facilitate future research.
Increasing quantities of scientific data are becoming readily accessible via online repositories such as those provided by Figshare and Zenodo. Geoscientific simulations in particular generate large quantities of data, with several research groups studying many, often overlapping areas of the world. When studying a particular area, being able to keep track of ones own simulations as well as those of collaborators can be challenging. This paper describes the design, implementation, and evaluation of a new tool for visually cataloguing and retrieving data associated with a given geographical location through a web-based Google Maps interface. Each data repository is pin-pointed on the map with a marker based on the geographical location that the dataset corresponds to. By clicking on the markers, users can quickly inspect the metadata of the repositories and download the associated data files. The crux of the approach lies in the ability to easily query and retrieve data from multiple sources via a common interface. While many advances are being made in terms of scientific data repositories, the development of this new tool has uncovered several issues and limitations of the current state-of-the-art which are discussed herein, along with some ideas for the future.
To quantify the mechanism of a complex network growth we focus on the network of citations of scientific papers and use a combination of the theoretical and experimental tools to uncover microscopic details of this network growth. Namely, we develop a stochastic model of citation dynamics based on copying/redirection/triadic closure mechanism. In a complementary and coherent way, the model accounts both for statistics of references of scientific papers and for their citation dynamics. Originating in empirical measurements, the model is cast in such a way that it can be verified quantitatively in every aspect. Such verification is performed by measuring citation dynamics of Physics papers. The measurements revealed nonlinear citation dynamics, the nonlinearity being intricately related to network topology. The nonlinearity has far-reaching consequences including non-stationary citation distributions, diverging citation trajectory of similar papers, runaways or immortal papers with infinite citation lifetime etc. Thus, our most important finding is nonlinearity in complex network growth. In a more specific context, our results can be a basis for quantitative probabilistic prediction of citation dynamics of individual papers and of the journal impact factor.
This paper analyses the potential use of bibliometric data for mapping and applying network analysis to mobility flows. We show case mobility networks at three different levels of aggregation: at the country level, at the city level and at the institutional level. We reflect on the potential uses of bibliometric data to inform research policies with regard to scientific mobility.
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