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The Evolution, Approval and Implementation of the U.S. Geological Survey Science Data Lifecycle Model

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Authors : John L. Faundeen, Vivian B. Hutchison

This paper details how the U.S. Geological Survey (USGS) Community for Data Integration (CDI) Data Management Working Group developed a Science Data Lifecycle Model, and the role the Model plays in shaping agency-wide policies and data management applications.

Starting with an extensive literature review of existing data lifecycle models, representatives from various backgrounds in USGS attended a two-day meeting where the basic elements for the Science Data Lifecycle Model were determined.

Refinements and reviews spanned two years, leading to finalization of the model and documentation in a formal agency publication1.

The Model serves as a critical framework for data management policy, instructional resources, and tools. The Model helps the USGS address both the Office of Science and Technology Policy (OSTP)2 for increased public access to federally funded research, and the Office of Management and Budget (OMB)3 2013 Open Data directives, as the foundation for a series of agency policies related to data management planning, metadata development, data release procedures, and the long-term preservation of data.

Additionally, the agency website devoted to data management instruction and best practices (www2.usgs.gov/datamanagement) is designed around the Model’s structure and concepts. This paper also illustrates how the Model is being used to develop tools for supporting USGS research and data management processes.

URL : http://escholarship.umassmed.edu/jeslib/vol6/iss2/4/

 

Does Peer Review Identify the Best Papers? A Simulation Study of Editors, Reviewers, and the Scientific Publication Process

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Author : Justin Esarey

How does the structure of the peer review process, which can vary among journals, influence the quality of papers published in a journal? This article studies multiple systems of peer review using computational simulation. I find that, under any of the systems I study, a majority of accepted papers are evaluated by an average reader as not meeting the standards of the journal.

Moreover, all systems allow random chance to play a strong role in the acceptance decision. Heterogeneous reviewer and reader standards for scientific quality drive both results. A peer review system with an active editor—that is, one who uses desk rejection before review and does not rely strictly on reviewer votes to make decisions—can mitigate some of these effects.

DOI : https://doi.org/10.1017/S1049096517001081

Make researchers revisit past publications to improve reproducibility

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Authors : Clare Fiala, Eleftherios P. Diamandis

Scientific irreproducibility is a major issue that has recently increased attention from publishers, authors, funders and other players in the scientific arena. Published literature suggests that 50-80% of all science performed is irreproducible. While various solutions to this problem have been proposed, none of them are quick and/or cheap.

Here, we propose one way of reducing scientific irreproducibility by asking authors to revisit their previous publications and provide a commentary after five years. We believe that this measure will alert authors not to over sell their results and will help with better planning and execution of their experiments.

We invite scientific journals to adapt this proposal immediately as a prerequisite for publishing.

URL : Make researchers revisit past publications to improve reproducibility

DOI : http://dx.doi.org/10.12688/f1000research.12715.1

 

Incidence of predatory journals in computer science literature

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Authors : Simona Ibba, Filippo Eros Pani, John Gregory Stockton, Giulio Barabino, Michele Marchesi, Danilo Tigano

Purpose

One of the main tasks of a researcher is to properly communicate the results he obtained. The choice of the journal in which to publish the work is therefore very important. However, not all journals have suitable characteristics for a correct dissemination of scientific knowledge.

Some publishers turn out to be unreliable and, against a payment, they publish whatever researchers propose. The authors call “predatory journals” these untrustworthy journals.

The purpose of this paper is to analyse the incidence of predatory journals in computer science literature and present a tool that was developed for this purpose.

Design/methodology/approach

The authors focused their attention on editors, universities and publishers that are involved in this kind of publishing process. The starting point of their research is the list of scholarly open-access publishers and open-access stand-alone journals created by Jeffrey Beall.

Specifically, they analysed the presence of predatory journals in the search results obtained from Google Scholar in the engineering and computer science fields. They also studied the change over time of such incidence in the articles published between 2011 and 2015.

Findings

The analysis shows that the phenomenon of predatory journals somehow decreased in 2015, probably due to a greater awareness of the risks related to the reputation of the authors.

Originality/value

We focused on computer science field, using a specific sample of queries. We developed a software to automatically make queries to the search engine, and to detect predatory journals, using Beall’s list.

URL : Incidence of predatory journals in computer science literature

DOI : https://doi.org/10.1108/LR-12-2016-0108

Building a Disciplinary, World‐Wide Data Infrastructure

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Authors: Françoise Genova, Christophe Arviset, Bridget M. Almas, Laura Bartolo, Daan Broeder, Emily Law, Brian McMahon

Sharing scientific data with the objective of making it discoverable, accessible, reusable, and interoperable requires work and presents challenges being faced at the disciplinary level to define in particular how the data should be formatted and described.

This paper represents the Proceedings of a session held at SciDataCon 2016 (Denver, 12–13 September 2016). It explores the way a range of disciplines, namely materials science, crystallography, astronomy, earth sciences, humanities and linguistics, get organized at the international level to address those challenges. T

he disciplinary culture with respect to data sharing, science drivers, organization, lessons learnt and the elements of the data infrastructure which are or could be shared with others are briefly described. Commonalities and differences are assessed.

Common key elements for success are identified: data sharing should be science driven; defining the disciplinary part of the interdisciplinary standards is mandatory but challenging; sharing of applications should accompany data sharing. Incentives such as journal and funding agency requirements are also similar.

For all, social aspects are more challenging than technological ones. Governance is more diverse, often specific to the discipline organization. Being problem‐driven is also a key factor of success for building bridges to enable interdisciplinary research.

Several international data organizations such as CODATA, RDA and WDS can facilitate the establishment of disciplinary interoperability frameworks. As a spin‐off of the session, a RDA Disciplinary Interoperability Interest Group is proposed to bring together representatives across disciplines to better organize and drive the discussion for prioritizing, harmonizing and efficiently articulating disciplinary needs.

URL : Building a Disciplinary, World‐Wide Data Infrastructure

DOI : http://doi.org/10.5334/dsj-2017-016

 

Betweenness and diversity in journal citation networks as measures of interdisciplinarity—A tribute to Eugene Garfield

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Authors : Loet Leydesdorff, Caroline S. Wagner, Lutz Bornmann

Journals were central to Eugene Garfield’s research interests. Among other things, journals are considered as units of analysis for bibliographic databases such as the Web of Science and Scopus. In addition to providing a basis for disciplinary classifications of journals, journal citation patterns span networks across boundaries to variable extents.

Using betweenness centrality (BC) and diversity, we elaborate on the question of how to distinguish and rank journals in terms of interdisciplinarity. Interdisciplinarity, however, is difficult to operationalize in the absence of an operational definition of disciplines; the diversity of a unit of analysis is sample-dependent. BC can be considered as a measure of multi-disciplinarity.

Diversity of co-citation in a citing document has been considered as an indicator of knowledge integration, but an author can also generate trans-disciplinary—that is, non-disciplined—variation by citing sources from other disciplines.

Diversity in the bibliographic coupling among citing documents can analogously be considered as diffusion  or differentiation of knowledge across disciplines. Because the citation networks in the cited direction reflect both structure and variation, diversity in this direction is perhaps the best available measure of interdisciplinarity at the journal level.

Furthermore, diversity is based on a summation and can therefore be decomposed; differences among (sub)sets can be tested for statistical significance. In the appendix, a general-purpose routine for measuring diversity in networks is provided.

URL : Betweenness and diversity in journal citation networks as measures of interdisciplinarity—A tribute to Eugene Garfield

DOI : https://doi.org/10.1007/s11192-017-2528-2

 

 

Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics

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Authors : Jian Qin, Kevin Crowston, Arden Kirkland

Objective

To support the assessment and improvement of research data management (RDM) practices to increase its reliability, this paper describes the development of a capability maturity model (CMM) for RDM. Improved RDM is now a critical need, but low awareness of – or lack of – data management is still common among research projects.

Methods

A CMM includes four key elements: key practices, key process areas, maturity levels, and generic processes. These elements were determined for RDM by a review and synthesis of the published literature on and best practices for RDM.

Results

The RDM CMM includes five chapters describing five key process areas for research data management: 1) data management in general; 2) data acquisition, processing, and quality assurance; 3) data description and representation; 4) data dissemination; and 5) repository services and preservation.

In each chapter, key data management practices are organized into four groups according to the CMM’s generic processes: commitment to perform, ability to perform, tasks performed, and process assessment (combining the original measurement and verification).

For each area of practice, the document provides a rubric to help projects or organizations assess their level of maturity in RDM.

Conclusions

By helping organizations identify areas of strength and weakness, the RDM CMM provides guidance on where effort is needed to improve the practice of RDM.

URL : Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics

DOI : https://doi.org/10.7191/jeslib.2017.1113