Scholarly Communication

Big Data is a collection of dataset which is large and complex so that existing data processing tools can not handle it. Big Data is collected and processed using different sources and tools, which leads to privacy issues. Privacy preserving data publishing techniques such as, k-anonymity, l-diversity, t-closeness, etc., are used to de-identify data, but the chance of re-identification is still present as data is collected from multiple sources. Big data is having a characteristic of 3Vs (Volume, Velocity, and Variety), which makes the de-identification task difficult.

We have collected research papers and articles from various journals related to privacy issues in big data, existing privacy preserving data publishing techniques, and privacy preserving big data publishing techniques.The main objective of this collection is to help reader to learn basics of privacy preserving techniques and its application to big data analytics or big data publication.

What are the factors controlling research citation behaviour and practice? What are the conscious and unconscious biases that influence these?

A collection on Scientific literature on the use of digital tools for science

This is a literature review on the role of metadata in scholarly publishing developed out of the Metadata2020 initiative.

A collection of articles about the journal impact factor, metrics, altmetrics, and their role in research assessment.

A selection of articles on Nanopublications

Data publishing is a practice where data are made public so that they can be reused by others, in accordance with associated policies, while guaranteeing that data provenance and attribution are tracked.

This is a collection of articles related to open access and it's impact on scholarly communication, mostly focused on library science. 

Following the rise of Open Access, making datasets available is increasingly becoming a new standard. Open data can ensure validity, and increase the pace of research. Some potential issues include ownership, reuse and storage of open research data. This collection encompases research done on the open data movement. 

A series of peer reviewed articles and preprints relating in some way to Open Science, Open Research, and Open Scholarship.

Authors in this collection discuss how scholarly publishing and communication in science will develop in the 21st century.

A selection of research article and other resources relating to the issue of "Predatory Publishing. See: https://osf.io/8xvpm/

For a definition of "Predatory Publishers": https://www.nature.com/articles/d41586-019-03759-y

The Paper of the Future foresees an executable and reproducible research compendium: a FAIR+E contribution to the record of knowledge that is Findable, Accessible, Inspectable/Interoperable, Reuseable/Reproducible and Extensible. The 'Paper' metaphor is reconsidered as a different knowledge representation than today's static article, chapter, conference paper, or thesis. A #futurepaper comprises multiple research objects -- text, code, data, materials, and methods-- that, in combination, tell the full story of the research claim and make that story accessible and interactive, for humans + machines to harvest, reuse, and build upon. Some see"computational narratives" that expose the entire data-to-claim process. Others see micropublications -- discrete claims founded on shared data and clearly disclosed methods and materials.

The papers in this Collection discuss the features and functions of a #FuturePaper, or model this emergent genre.

A series of research articles documenting the way in which the impact factor is both calculated, and statistically manipulated.

A collection of studies that have investigated the potential Open Access citation advantage. The majority, to date, have concluded that there is a significant citation advantage for Open Access articles. Much of the data here is sourced from The Open Access Citation Advantage Service, SPARC Europe (accessed August 2017)