Data Citation and the Citation Graph

The citation graph is a computational artifact that is widely used to represent the domain of published literature. It represents connections between published works, such as citations and authorship. Among other things, the graph supports the computation of bibliometric measures such as h-indexes and impact factors. There is now an increasing demand that we should treat the publication of data in the same way that we treat conventional publications. In particular, we should cite data for the same reasons that we cite other publications. In this paper we discuss what is needed for the citation graph to represent data citation. We identify two challenges: (i) to model the evolution of credit appropriately (through references) over time and (ii) to model data citation not only to a dataset treated as a single object but also to parts of it. We describe an extension of the current citation graph model that addresses these challenges. It is built on two central concepts: citable units and reference subsumption. We discuss how this extension would enable data citation to be represented within the citation graph and how it allows for improvements in current practices for bibliometric computations both for scientific publications and for data.

Track to the Past: Tracking Workflows, Versions, and Citations of Legacy Language Data

This paper discusses three issues encountered with legacy language data in archives: First, the provenance of an artefact containing the data may be unclear, as well as all procedures that shaped its form(at) or contents. Second, legacy language data are often orphan data with opaque links to other versions, or texts providing more information on them and their contents. Third, these data predate methods of data citation, thus requiring retroactive ways of citation tracking. With a few adjustments to their infrastructures, digital archives can be used as a platform to track workflows, versioning, and citations of legacy language data.

Challenges with organization, discoverability and access in Canadian open health data repositories

Introduction: Open health data provides healthcare professionals, biomedical researchers and the general public with access to health data which has the potential to improve healthcare delivery and policy. The challenge for data providers is to create and implement appropriate metadata, or structured data about the data, to ensure that data are easy to discover, access and re-use. The goal of this study is to identify, evaluate and compare Canadian open health data repositories for their searching, browsing and navigation functionalities, the richness of their metadata description practices, and their metadata-based filtering mechanisms.Methods: Metadata-based search and browsing was evaluated in addition to the number and nature of metadata elements. Canadian open health data repositories across national, provincial and institutional levels were evaluated. Data collected using verbatim text recording was evaluated using an analytical framework based on the 2019 Dataverse North Metadata Best Practices guide and 2019 Data Citation Implementation Project roadmap.Results: All six repositories required filtering to access “open health data”. All six repositories included subject facets for filtering, and title and description on the Results List. Inconsistencies suggest that improvements should address advanced search, health-specific search terms, records for all repositories and links to related publications.Discussion: Consistent use of title and description suggests that an interoperable interface is possible. Records indicate the need for explicit, easy to find mechanisms to access metadata in repositories. The analytical framework represents first draft guidelines for metadata creation and implementation to improve organization, discoverability and access to Canadian open health data.

Efficacy of praziquantel has been maintained over four decades (from 1977 to 2018): A systematic review and meta-analysis of factors influence its efficacy

Background The antihelminthic drug praziquantel has been used as the drug of choice for treating schistosome infection for more than 40 years. Although some epidemiological studies have reported low praziquantel efficacy in cure rate (CR) and/or egg reduction rate (ERR), there is no consistent robust evidence of the development of schistosome resistance to praziquantel (PZQ). There is need to determine factors that lead to variable treatment CR and/or ERR. Therefore, we conducted a systematic review and meta-analysis to review CR and ERR as well as identify their predictors. Methodology/Principal findings In this systematic review and meta-analysis, a literature review was conducted using Biosis Citation Index, Data Citation Index, MEDLINE, and Web of Science Core Collection all of which were provided through Web of Science. Alongside these, EMBASE, and CAB abstracts were searched to identify relevant articles. Random effect meta-regression models were used to identify the factors that influence CR and/or ERR by considering differences in host characteristics and drug dose. In total, 12,127 potential articles were screened and 146 eligible articles (published from 1979 to 2020) were identified and included for the meta-analysis. We found that there has been no significant reduction in CR or ERR over the study period. The results showed more variability in CR, compared with ERR which was more consistent and remained high. The results showed a positive effect of “PZQ treatment dose” with the current recommended dose of 40 mg/kg body weight achieving 57% to 88% CR depending on schistosome species, age of participants, and number of parasitological samples used for diagnosis, and ERR of 95%. Conclusions/Significance Based on a review of over 40 years of research there is no evidence to support concerns about schistosomes developing resistance to PZQ. These results indicate that PZQ remains effective in treating schistosomiasis.

News from the GGOS DOI Working Group

<p>Data publications with digital object identifiers (DOI) are best practice for FAIR sharing data. Originally developed with the purpose of providing permanent access to (static) datasets described in scholarly literature, DOI today are more and more assigned to dynamic data. These DOIs are providing a citable and traceable reference of various types of sources (data, software, samples, equipment) and means of rewarding the originators and institutions. As a result of international groups, like the Coalition on Publishing Data in the Earth, Space and Environmental Sciences (COPDESS) and the Enabling FAIR Data project, data with assigned DOIs are fully citable in scholarly literature and many journals require the data underlying a publication to be available – even before accepting an article. Initial metrics for data citation allows data providers to demonstrate the value of the data collected by institutes and individual scientists.</p><p>This is especially relevant for the geodesy, because, geodesy researchers are often much more involved in operational aspects and data provision than researchers in other fields might be. Therefore, compared to other scientific disciplines, geodesy researchers appear to be producing less “countable scientific” output. Consequently, geodesy data and equipment require a structured and well-documented mechanism which will enable citability, scientific recognition and reward that can be provided by assigning DOI to data and data products.</p><p>To address these challenges and to identify opportunities for improved coordination and advocacy within the geodetic community, the International Association of Geodesy’s (IAG) Global Geodetic Observing System (GGOS) has established a Working Group on “Digital Object Identifiers (DOIs) for Geodetic Data Sets” in 2019. This Working Group is designated to establish best practices and advocate for the consistent implementation of DOIs across all IAG Services and in the greater geodetic community.</p><p>The main objectives and activities of this working group are:</p><ul><li>(1) to identify what the community needs from consistent usage of DOIs for data in terms of being able to discover data, permanently cite data, and acknowledge the data providers</li> <li>(2) to develop recommendations for DOI minting strategies for different geodetic data types and granularity across IAG Services (static, dynamic, observational data, data products, combination products, networks)</li> <li>(3) to develop recommendations for a consistent method for data citation across all IAG Services, to support data providers, and to provide quantitative support detailing the use of geodetic datasets and other resources.</li> <li>(4) to develop recommendations for connecting metadata standards for data discovery (e.g. DataCite, ISO19115) with community metadata standards (GeodesyML, Station Logs)</li> </ul><p>This presentation will provide an update on recent topics and first recommendations from the GGOS DOI Working Group.</p>

Principles of open, transparent and reproducible science in author guidelines of sleep research and chronobiology journals

Background: "Open science" is an umbrella term describing various aspects of transparent and open science practices. The adoption of practices at different levels of the scientific process (e.g., individual researchers, laboratories, institutions) has been rapidly changing the scientific research landscape in the past years, but their uptake differs from discipline to discipline. Here, we asked to what extent journals in the field of sleep research and chronobiology encourage or even require following transparent and open science principles in their author guidelines. Methods: We scored the author guidelines of a comprehensive set of 27 sleep and chronobiology journals, including the major outlets in the field, using the standardised Transparency and Openness (TOP) Factor. The TOP Factor is a quantitative summary of the extent to which journals encourage or require following various aspects of open science, including data citation, data transparency, analysis code transparency, materials transparency, design and analysis guidelines, study pre-registration, analysis plan pre-registration, replication, registered reports, and the use of open science badges. Results: Across the 27 journals, we find low values on the TOP Factor (median [25 th, 75 th percentile] 3 [1, 3], min. 0, max. 9, out of a total possible score of 29) in sleep research and chronobiology journals. Conclusions: Our findings suggest an opportunity for sleep research and chronobiology journals to further support recent developments in transparent and open science by implementing transparency and openness principles in their author guidelines.

Data Documents

This article presents and discusses different kinds of data documents, including data sets, data studies, data papers and data journals. It provides descriptive and bibliometric data on different kinds of data documents and discusses the theoretical and philosophical problems by classifying documents according to the DIKW model (data documents, information documents, knowl­edge documents and wisdom documents). Data documents are, on the one hand, an established category today, even with its own data citation index (DCI). On the other hand, data documents have blurred boundaries in relation to other kinds of documents and seem sometimes to be understood from the problematic philosophical assumption that a datum can be understood as “a single, fixed truth, valid for everyone, everywhere, at all times”