scholarly journals Book Metadata for an Open Access World

2021 ◽  
Author(s):  
Nina Tscheke ◽  
Stephanie Dawson
Keyword(s):  
Open Access ◽  
Research Environment ◽  
Electronic Environment ◽  
Bibliographic Data

Most book and monograph publications still lack visibility within an electronic environment due to missing digitalized and persistent bibliographic data and/or fragmentary portability or interoperability based on the variety of formats, resulting in a more error-prone, non-standardized communication between various indexing systems and platforms. But books that cannot be discovered will not be read. OA metadata for books is essential for the transformation of the whole scholarly landscape, and one of its greatest advantages is full, immediate accessibility. 1 The point of origin for the OA META project was the realization that available book metadata (i.e. ONIX/MARC records) was not primarily created for the usage of indexing within an electronic research environment. Consequentially, both detail and focus of elements tagged in datasets are not necessarily selected to output solidified and persistent book records. The question was how to enrich or edit available metadata so that it can unfold its greatest impact in an e-context.

scholarly journals Evaluating institutional open access performance: Sensitivity analysis

2020 ◽  
Author(s):  
Chun-Kai Huang ◽  
Cameron Neylon ◽  
Richard Hosking ◽  
Lucy Montgomery ◽  
Katie Wilson ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Open Access ◽  
Selection Bias ◽  
Web Of Science ◽  
Data Sources ◽  
Institutional Level ◽  
Margin Of Error ◽  
Bibliographic Data ◽  
Data Source ◽  
Major Data

AbstractIn the article “Evaluating institutional open access performance: Methodology, challenges and assessment” we develop the first comprehensive and reproducible workflow that integrates multiple bibliographic data sources for evaluating institutional open access (OA) performance. The major data sources include Web of Science, Scopus, Microsoft Academic, and Unpaywall. However, each of these databases continues to update, both actively and retrospectively. This implies the results produced by the proposed process are potentially sensitive to both the choice of data source and the versions of them used. In addition, there remain the issue relating to selection bias in sample size and margin of error. The current work shows that the levels of sensitivity relating to the above issues can be significant at the institutional level. Hence, the transparency and clear documentation of the choices made on data sources (and their versions) and cut-off boundaries are vital for reproducibility and verifiability.

scholarly journals CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries

2020 ◽  
Vol 3 ◽  
Author(s):  
Patricia L. Mabry ◽  
Xiaoran Yan ◽  
Valentin Pentchev ◽  
Robert Van Rennes ◽  
Stephanie Hernandez McGavin ◽  
...  
Keyword(s):  
Academic Libraries ◽  
Big Data Analytics ◽  
Reproducible Research ◽  
Scientific Enterprise ◽  
Research Environment ◽  
Data Services ◽  
Bibliographic Data ◽  
The Many ◽  
Diverse Data ◽  
State Of The Science

Big bibliographic datasets hold promise for revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Yet, hosting proprietary and open big bibliographic datasets poses significant difficulties for libraries, both large and small. Libraries face significant barriers to hosting such assets, including cost and expertise, which has limited their ability to provide stewardship for big datasets, and thus has hampered researchers' access to them. What is needed is a solution to address the libraries' and researchers’ joint needs. This article outlines the theoretical framework that underpins the Collaborative Archive and Data Research Environment project. We recommend a shared cloud-based infrastructure to address this need built on five pillars: 1) Community–a community of libraries and industry partners who support and maintain the platform and a community of researchers who use it; 2) Access–the sharing platform should be accessible and affordable to both proprietary data customers and the general public; 3) Data-Centric–the platform is optimized for efficient and high-quality bibliographic data services, satisfying diverse data needs; 4) Reproducibility–the platform should be designed to foster and encourage reproducible research; 5) Empowerment—the platform should empower researchers to perform big data analytics on the hosted datasets. In this article, we describe the many facets of the problem faced by American academic libraries and researchers wanting to work with big datasets. We propose a practical solution based on the five pillars: The Collaborative Archive and Data Research Environment. Finally, we address potential barriers to implementing this solution and strategies for overcoming them.

scholarly journals Open Science meets Food Modelling: Introducing the Food Modelling Journal (FMJ)

2019 ◽  
Vol 1 ◽  
Author(s):  
Matthias Filter ◽  
Leonardo Candela ◽  
Laurent Guillier ◽  
Maarten Nauta ◽  
Teodor Georgiev ◽  
...  
Keyword(s):  
Open Access ◽  
Peer Review ◽  
Open Science ◽  
Research Environment ◽  
Research Article ◽  
Research Cycle ◽  
Virtual Research Environment

This Editorial describes the rationale, focus, scope and technology behind the newly launched, open access, innovative Food Modelling Journal (FMJ). The Journal is designed to publish those outputs of the research cycle that usually precede the publication of the research article, but have their own value and re-usability potential. Such outputs are methods, models, software and data. The Food Modelling Journal is launched by the AGINFRA+ community and is integrated with the AGINFRA+ Virtual Research Environment (VRE) to facilitate and streamline the authoring, peer review and publication of the manuscripts via the ARPHA Publishing Platform.

Open Access – Sie haben die Wahl mit OpenMind

2009 ◽  
Vol 20 (2) ◽  
pp. 47-48
Author(s):  
G.-Jürgen Hogrefe
Keyword(s):  
Open Access

Strukturierte klinische Interviews zur Erfassung psychischer Störungen über die Lebensspanne

2017 ◽  
Vol 46 (3) ◽  
pp. 176-186 ◽  
Author(s):  
Jürgen Margraf ◽  
Jan Christopher Cwik ◽  
Verena Pflug ◽  
Silvia Schneider
Keyword(s):  
Open Access ◽  
Psychische Störungen ◽  
Dsm 5

Zusammenfassung. Psychische Störungen können über die ganze Lebensspanne auftreten. Strukturierte klinische Interviews sind zentrale Hilfsmittel für ihre rasche, zuverlässige und umfassende Diagnostik. Im deutschsprachigen Raum stehen mit den Verfahren der DIPS-Familie Interviews zur Diagnostik psychischer Störungen über die gesamte Lebensspanne zur Verfügung, die seit den 90er Jahren regelmäßig aktualisiert wurden. Ihre Reliabilität, Validität und Akzeptanz wurde wiederholt in großen Stichproben aus ambulanten, stationären und Forschungssettings überprüft. Die Einführung des DSM-5 erforderte eine umfassende Überarbeitung der DIPS-Interviews, deren wesentliche Merkmale dargestellt werden. Um die breitere Verwendung von strukturierten klinischen Interviews zu fördern, werden die Verfahren der DIPS-Familie neu als „Open Access-Dokumente“ zur Verfügung gestellt. Abschließend werden weitere Entwicklungen zu Training, Dissemination und Computerisierung im Ausblick angesprochen.

Hogrefe OpenMind – Your Road to Open Access

2009 ◽  
Vol 25 (3) ◽  
pp. 131-132
Author(s):  
Karl Schweizer ◽  
Eric E.J. De Bruyn ◽  
G.-Jürgen Hogrefe
Keyword(s):  
Open Access

Hogrefe OpenMind – Your Road to Open Access

2009 ◽  
Vol 40 (3) ◽  
pp. 176-177
Author(s):  
G.-Jürgen Hogrefe
Keyword(s):  
Open Access
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