The Data Conservancy Instance: Infrastructure and Organizational Services for Research Data Curation

Mayernik, Matthew S.; Choudhury, Sayeed; DiLauro, Tim; Metsger, Elliot; Pralle, Barbara; Rippin, Mike; Duerr, Ruth

doi:10.1045/september2012-mayernik

Cited by 19 publications

(16 citation statements)

References 9 publications

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“…Second, many organizations support research across the disciplinary board, and thus need to develop support mechanisms that span disciplinary categories. Data curation infrastructures, for example, developed in the context of cross-disciplinary organizations should either be discipline agnostic, or highly customizable (Mayernik, Choudhury et al, 2012;Witt, 2012). Third, studies of data and metadata practices and standards suggest that many relevant issues are independent of discipline (Mayernik, 2011;Willis, Greenberg, & White, 2012), and that intradisciplinary data practices can be as variable as interdisciplinary practices, as illustrated in studies of astronomy (Norris et al, 2006;Wynholds, Fearon, Borgman, & Traweek, 2011), ecology , and the quantitative social sciences (Pienta, Alter, & Lyle, 2010).…”

Section: Background-analyzing Research Data Practicesmentioning

confidence: 97%

Research data and metadata curation as institutional issues

Mayernik

2015

Asso for Info Science & Tech

Self Cite

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Research data curation initiatives must support heterogeneous kinds of projects, data, and metadata. This article examines variability in data and metadata practices using "institutions" as the key theoretical concept. Institutions, in the sense used here, are stable patterns of human behavior that structure, legitimize, or delegitimize actions, relationships, and understandings within particular situations. Based on prior conceptualizations of institutions, a theoretical framework is presented that outlines 5 categories of "institutional carriers" for data practices: (a) norms and symbols, (b) intermediaries, (c) routines, (d) standards, and (e) material objects. These institutional carriers are central to understanding how scientific data and metadata practices originate, stabilize, evolve, and transfer. This institutional framework is applied to 3 case studies: the Center for Embedded Networked Sensing (CENS), the Long Term Ecological Research (LTER) network, and the University Corporation for Atmospheric Research (UCAR). These cases are used to illustrate how institutional support for data and metadata management are not uniform within a single organization or academic discipline. Instead, broad spectra of institutional configurations for managing data and metadata exist within and across disciplines and organizations.

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Section: Background-analyzing Research Data Practicesmentioning

confidence: 97%

Research data and metadata curation as institutional issues

Mayernik

2015

Asso for Info Science & Tech

Self Cite

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“…Many proposals from the literature focus on data archival and curation, such as the Data Conservancy Instance [8], Médici [9], and the SEAD Project [5]. They are not suited to our situation because they do not cover aspects related to the execution of processing tasks.…”

Section: Requirements and Related Workmentioning

confidence: 99%

Instrumental Data Management and Scientific Workflow Execution: the CEA Case Study

Boito

Mehaut

Deutsch

et al. 2019

2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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In this paper, we study a typical scenario in research facilities. Instrumental data is generated by lab equipment such as microscopes, collected by researchers into USB devices, and analyzed in their own computers. In this scenario, an instrumental data management framework could store data in a institutionlevel storage infrastructure and allow to execute tasks to analyze this data in some available processing nodes. This setup has the advantages of promoting reproducible research and the efficient usage of the expensive lab equipment (in addition to increasing researchers productivity). We detail the requirements for such a framework regarding the needs of our case study of the CEA, review existing solutions and recommend the choice of Galaxy. We then analyze the performance limitations of the proposed architecture, and point to the connection between centralized storage and the processing nodes as the critical point. We also conduct a performance evaluation over an experimental platform to observe the limitations encountered in practice. We finish by pointing issues that are not addressed by existing solutions, and are therefore future work perspectives for the research field.

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“…But sharing data in arXiv's "LaTeX to PDF" paradigm is not possible. A pilot to support data deposit alongside papers which was run at the arXiv from 2010 to 2013 (Mayernik et al, 2012), failed to gain traction. While the project had to face an unexpected cut in government support, we believe that part of its failure can be associated with the fact that the papers and the data were deposited as separate entities.…”

Section: Datamentioning

confidence: 99%

The arXiv of the future will not look like the arXiv

Pepe¹,

Nicholson³

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The arXiv is the most popular preprint repository in the world. Since its inception in 1991, the arXiv has allowed researchers to freely share publication-ready articles prior to formal peer review. The growth and the popularity of the arXiv emerged as a result of new technologies that made document creation and dissemination easy, and cultural practices where collaboration and data sharing were dominant. The arXiv represents a unique place in the history of research communication and the Web itself, however it has arguably changed very little since its creation. Here we look at the strengths and weaknesses of arXiv in an effort to identify what possible improvements can be made based on new technologies not previously available. Based on this, we argue that a modern arXiv might in fact not look at all like the arXiv of today.Disclaimer: This article has originally been written and posted on Authorea, a collaborative online platform for technical and data-driven documents. Authorea is being developed to respond to some of the concerns with current methodology raised in this very piece, and as such is suggested as a possible future alternative to existing preprint servers.

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The Data Conservancy Instance: Infrastructure and Organizational Services for Research Data Curation

Cited by 19 publications

References 9 publications

Research data and metadata curation as institutional issues

Research data and metadata curation as institutional issues

Instrumental Data Management and Scientific Workflow Execution: the CEA Case Study

The arXiv of the future will not look like the arXiv

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