scholarly journals Mapping Methods Metadata for Research Data

2015 ◽  
Vol 10 (1) ◽  
pp. 82-94 ◽  
Author(s):  
Tiffany Chao

Understanding the methods and processes implemented by data producers to generate research data is essential for fostering data reuse. Yet, producing the metadata that describes these methods remains a time-intensive activity that data producers do not readily undertake. In particular, researchers in the long tail of science often lack the financial support or tools for metadata generation, thereby limiting future access and reuse of data produced. The present study investigates research journal publications as a potential source for identifying descriptive metadata about methods for research data. Initial results indicate that journal articles provide rich descriptive content that can be sufficiently mapped to existing metadata standards with methods-related elements, resulting in a mapping of the data production process for a study. This research has implications for enhancing the generation of robust metadata to support the curation of research data for new inquiry and innovation.

2020 ◽  
Vol 25 (2) ◽  
pp. 38
Author(s):  
Konrad Lang ◽  
Sarah Stryeck ◽  
David Bodruzic ◽  
Manfred Stepponat ◽  
Slave Trajanoski ◽  
...  

Life sciences (LS) are advanced in research data management, since LS have established disciplinary tools for data archiving as well as metadata standards for data reuse. However, there is a lack of tools supporting the active research process in terms of data management and data analytics. This leads to tedious and demanding work to ensure that research data before and after publication are FAIR (findable, accessible, interoperable and reusable) and that analyses are reproducible. The initiative CyVerse US from the University of Arizona, US, supports all processes from data generation, management, sharing and collaboration to analytics. Within the presented project, we deployed an independent instance of CyVerse in Graz, Austria (CAT) in frame of the BioTechMed association. CAT helped to enhance and simplify collaborations between the three main universities in Graz. Presuming steps were (i) creating a distributed computational and data management architecture (iRODS-based), (ii) identifying and incorporating relevant data from researchers in LS and (iii) identifying and hosting relevant tools, including analytics software to ensure reproducible analytics using Docker technology for the researchers taking part in the initiative. This initiative supports research-related processes, including data management and analytics for LS researchers. It also holds the potential to serve other disciplines and provides potential for Austrian universities to integrate their infrastructure in the European Open Science Cloud.


2020 ◽  
Vol 6 (2) ◽  
pp. 191-220
Author(s):  
Lisa Börjesson ◽  
Olle Sköld ◽  
Isto Huvila

Abstract Digitalisation of research data and massive efforts to make it findable, accessible, interoperable, and reusable has revealed that in addition to an eventual lack of description of the data itself (metadata), data reuse is often obstructed by the lack of information about the datamaking and interpretation (i.e. paradata). In search of the extent and composition of categories for describing processes, this article reviews a selection of standards and recommendations frequently referred to as useful for documenting archaeological visualisations. It provides insight into 1) how current standards can be employed to document provenance and processing history (i.e. paradata), and 2) what aspects of the processing history can be made transparent using current standards and which aspects are pushed back or hidden. The findings show that processes are often either completely absent or only partially addressed in the standards. However, instead of criticising standards for bias and omissions as if a perfect description of everything would be attainable, the findings point to the need for a comprehensive consideration of the space a standard is operating in (e.g. national heritage administration or international harmonisation of data). When a standard is used in a specific space it makes particular processes, methods, or tools transparent. Given these premises, if the standard helps to document what needs to be documented (e.g. paradata), and if it provides a type of transparency required in a certain space, it is reasonable to deem the standard good enough for that purpose.


2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Sonia Elisa Caregnato ◽  
Samile Andrea de Souza Vanz ◽  
Caterina Groposo Pavão ◽  
Paula Caroline Jardim Schifino Passos ◽  
Eduardo Borges ◽  
...  

RESUMO O artigo apresenta análise exploratória das práticas e das percepções a respeito do acesso aberto a dados de pesquisa embasada em dados coletados por meio de survey, realizada com pesquisadores brasileiros. As 4.676 respostas obtidas demonstram que, apesar do grande interesse pelo tema, evidenciado pela prevalência de variáveis relacionadas ao compartilhamento e ao uso de dados e aos repositórios institucionais, não há clareza por parte dos sujeitos sobre os principais tópicos relacionados. Conclui-se que, apesar da maioria dos pesquisadores afirmar que compartilha dados de pesquisa, a disponibilização desses dados de forma aberta e irrestrita ainda não é amplamente aceita.Palavras-chave: Dados Abertos de Pesquisa; Compartilhamento de Dados; Reuso de Dados.ABSTRACT This article presents an exploratory analysis of the practices and perceptions regarding open access to research data based on information collected by a survey with Brazilian researchers. The 4,676 responses show that, despite the great interest in the topic, evidenced by the prevalence of variables related to data sharing and use and to institutional repositories, there is no clarity on the part of the subjects on the main related topics. We conclude that, although the majority of the researchers share research data, the availability of this data in an open and unrestricted way is not yet widely accepted.Keywords: Open Research Data; Data Sharing; Data Reuse.


2015 ◽  
Author(s):  
Peter Weiland ◽  
Ina Dehnhard

See video of the presentation.The benefits of making research data permanently accessible through data archives is widely recognized: costs can be reduced by reusing existing data, research results can be compared and validated with results from archived studies, fraud can be more easily detected, and meta-analyses can be conducted. Apart from that, authors may gain recognition and reputation for producing the datasets. Since 2003, the accredited research data center PsychData (part of the Leibniz Institute for Psychology Information in Trier, Germany) documents and archives research data from all areas of psychology and related fields. In the beginning, the main focus was on datasets that provide a high potential for reuse, e.g. longitudinal studies, large-scale cross sectional studies, or studies that were conducted during historically unique conditions. Presently, more and more journal publishers and project funding agencies require researchers to archive their data and make them accessible for the scientific community. Therefore, PsychData also has to serve this need.In this presentation we report on our experiences in operating a discipline-specific research data archive in a domain where data sharing is met with considerable resistance. We will focus on the challenges for data sharing and data reuse in psychology, e.g.large amount of domain-specific knowledge necessary for data curationhigh costs for documenting the data because of a wide range on non-standardized measuressmall teams and little established infrastructures compared with the "big data" disciplinesstudies in psychology not designed for reuse (in contrast to the social sciences)data protectionresistance to sharing dataAt the end of the presentation, we will provide a brief outlook on DataWiz, a new project funded by the German Research Foundation (DFG). In this project, tools will be developed to support researchers in documenting their data during the research phase.


Author(s):  
Jadranka Stojanovski

>> See video of presentation (28 min.) The primary goal of scholarly communication is improving human knowledge and sharing is the key to achieve this goal: sharing ideas, sharing methodologies, sharing of results, sharing data, information and knowledge. Although the concept of sharing applies to all phases of scholarly communication, most often the only visible part is the final publication, with the journal article as a most common type. The traditional characteristics of the present journals allow only limited possibilities for sharing the knowledge. Basic functions, registration, dissemination, certification, and storage, are still present but they are no more effective in the network environment. Registration is too slow, there are various barriers to dissemination, certification system has many shortcomings, and used formats are not suitable for the long term preservation and storage. Although the journals today are digital and various powerful technologies are available, they are still focused on their unaltered printed versions. This presentation will discuss possible evolution of journal article to become more compliant with users' needs and to enable “the four R’s of openness” – reuse, redistribute, revise and remix (Hilton, Wiley, Stein, & Johnson, 2010).Several aspects of openness will be presented and discussed: open access, open data, open peer review, open authorship, and open formats. With digital technology which has become indispensable in the creation, collection, processing and storage of data in all scientific disciplines the way of conducting scientific research has changed and the concept of "data-driven science" has been introduced (Ware & Mabe, 2009). Sharing research data enhances the capabilities of reproducing the results, reuse maximizes the value of research, accelerating the advancement of science, ensuring transparency of scientific research, reducing the possibility of bias in the interpretation of results and increasing the credibility of published scientific knowledge. The open peer review can ensure full transparency of the entire process of assessment and help to solve many problems in the present scholarly publishing. Through the process of the open peer review each manuscript can be immediately accessible, reviewers can publicly demonstrate their expertise and could be rewarded, and readers can be encouraged to make comments and views and to become active part of the scholarly communication process. The trend to to describe the author's contribution is also present, which will certainly lead to a reduced number of “ghost”, "guest" and "honorary" authors, and will help to establish better standards for author’s identification.Various web technologies can be used also for the semantic enhancement of the article. One of the most important aspects of semantic publication is the inclusion of the research data, to make them available to the user as an active data that can be manipulated. It is possible to integrate data from external sources, or to merge the data from different resources (data fusion) (Shotton, 2012), so the reader can gain further understanding of the presented data. Additional options provide merging data from different articles, with the addition of the component of time. Other semantic enhancement can include enriched bibliography, interactive graphical presentations, hyperlinks to external resources, tagged text, etc.Instead of mostly static content, journals can offer readers dynamic content that includes multimedia, "living mathematics", “executable articles”, etc. Videos highlighting critical points in the research process, 3D representations of chemical compounds or art works, audio clips with the author's reflections and interviews, and animated simulations or models of ocean currents, tides, temperature and salinity structure, can became soon common part of every research article. The diversity of content and media, operating systems (GNU / Linux, Apple Mac OSX, Microsoft Windows), and software tools that are available to researchers, suggests the usage of the appropriate open formats. Different formats have their advantages and disadvantages and it would be necessary to make multiple formats available, some of which are suitable for "human" reading (including printing on paper), and some for machine reading that can be used by computers without human intervention. Characteristics and possibilities of several formats will be discussed, including XML as the most recommended format, which can enable granulate document structure as well as deliver semantics to the human reader or to the computer.Literature:Hilton, J. I., Wiley, D., Stein, J., & Johnson, A. (2010). The Four R’s of Openness and ALMS Analysis: Frameworks for Open Educational Resources. Open Learning: The Journal of Open, Distance and E-Learning, 25(1), 37–44. doi:10.1080/02680510903482132Shotton, D. (2012). The Five Stars of Online Journal Articles - a Framework for Article Evaluation. D-Lib Magazine, 18(1/2), 1–16. doi:10.1045/january2012-shottonWare, M., & Mabe, M. (2009). The stm report (p. 68).


Ravnetrykk ◽  
2020 ◽  
Author(s):  
Philipp Conzett

Research data repositories play a crucial role in the FAIR (Findable, Accessible, Interoperable, Reusable) ecosystem of digital objects. DataverseNO is a national, generic repository for open research data, primarily from researchers affiliated with Norwegian research organizations. The repository runs on the open-source software Dataverse. This article presents the organization and operation of DataverseNO, and investigates how the repository contributes to the increased FAIRness of small and medium sized research data. Sections 1 to 3 present background information about the FAIR Data Principles (section 1), how FAIR may be turned into reality (section 2), and what these principles and recommendations imply for data from the so-called long tail of research, i.e. small and medium-sized datasets that are often heterogenous in nature and hard to standardize (section 3). Section 4 gives an overview of the key organizational features of DataverseNO, followed by an evaluation of how well DataverseNO and the repository application Dataverse as such support the FAIR Data Principles (section 5). Section 6 discusses how sustainable and trustworthy the repository is. The article is rounded up in section 7 by a brief summary including a look into the future of the repository.


2019 ◽  
Vol 39 (06) ◽  
pp. 280-289 ◽  
Author(s):  
Raj Kumar Bhardwaj

The study aims to trace the development of Indian research data repositories (RDRs) and explore their content with the view of identifying prospects and possibilities. Further, it analyses the distribution of data repositories on the basis of content coverage, types of content, author identification system followed, software and the application programming interface used, subject wise number of repositories etc. The study is based on data repositories listed on the registry of data repositories accessible at http://www.re3data.org.The dataset was exported in Microsoft Excel format for analysis. A simple percentage method was followed in data analyses and results are presented through Tables and Figures. The study found a total of 2829 data repositories in existence worldwide. Further, it was seen that 1526 (53.9 %) are open and 924 (32.4 %) are restricted data repositories. Also, there are embargoed data repositories numbering 225 (8.0 %) and closed ones numbering 154 (5.4 %). There are 2829 RDRs covering 72 countries in the world. The study found that out of total 45 Indian RDRs, only 30 (67 %) are open, followed by restricted 12 (27 %) and 3 (6 %) that are closed. Majority of Indian RDRs (20) were developed in the year 2014. The study found that the majority of Indian RDRs (17) are‘disciplinary’. Further, the study also revealed that statistical data formats are available in a maximum of 31 (68.9 %) Indian RDRs. It was also seen that the majority of Indian RDRs (28) has datasets relating to ‘Life Sciences’. It was identified that only 20% of data repositories have been using metadata standards in metadata; the remaining 80% do not use any standards in metadata entry. This study covered only the research data repositories in India registered on the registry of data repositories. RDRs not listed in the registry of data repositories are left out.


2017 ◽  
Vol 87 (3) ◽  
pp. 583-618 ◽  
Author(s):  
Juanjuan Zhao ◽  
Gulbahar H. Beckett ◽  
Lihshing Leigh Wang

There has been a rapid growth of academic research and publishing in non-Western countries. However, academic journal articles in these peripheral countries suffer from low citation impact and limited global recognition. This critical review systematically analyzed 1,096 education research journal articles that were published in China in a 10-year span using a multistage stratified cluster and random sampling method and a validated rubric for assessing research quality. Our findings reveal that the vast majority of the articles lacked rigor, with insufficient or nonsystematic literature reviews, incomplete descriptions of research design, and inadequately grounded recommendations for translating research into practice. Acknowledging the differences in publishing cultures in the center-periphery divide, we argue that education research publications in non-Western countries should try to meet Western publishing standards in order to participate in global knowledge production and research vitality. Implications for emerging countries that strive to transform their research scholarship are discussed.


2020 ◽  
Vol 50 (1) ◽  
pp. 99-119
Author(s):  
Christine Neubert ◽  
Ronja Trischler

We analyze the relations between ethnographic data and theory through an examination of materiality in research practices, arguing that data production is a form of material theorizing. This entails reviewing and (re-)applying practice-theoretical discussions on materiality to questions of ethnography, and moving from understanding theory primarily as ideas to observing theorizing in all steps of research practice. We introduce “pocketing” as a heuristic concept to analyze how and when ethnographic data materializes: the concept defines data’s materiality relationally, through the affective and temporal dimensions of practice. It is discussed using two examples: in a study on everyday architectural experience where ethnographic data materialized as bodies affected by architecture; and in a study on digital cooperation where research data’s materialization was distributed over time according to the use of a company database. By conceptualizing data’s materiality as practice-bound, “pocketing” facilitates understanding the links between data and theory in ethnographic data production.


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