Étiquette : Daniel Mietchen

Publié le

Making Mathematical Research Data FAIR: A Technology Overview

Authors : Tim Conrad, Eloi Ferrer, Daniel Mietchen, Larissa Pusch, Johannes Stegmuller, Moritz Schubotz

The sharing and citation of research data is becoming increasingly recognized as an essential building block in scientific research across various fields and disciplines. Sharing research data allows other researchers to reproduce results, replicate findings, and build on them. Ultimately, this will foster faster cycles in knowledge generation.

Some disciplines, such as astronomy or bioinformatics, already have a long history of sharing data; many others do not. The current landscape of so-called research data repositories is diverse. This review aims to perform a technology review on existing data repositories/portals with a focus on mathematical research data.

URL : Making Mathematical Research Data FAIR: A Technology Overview

Original location: https://arxiv.org/abs/2309.11829

Publié le

Ten principles for machine-actionable data management plans

Authors : Tomasz Miksa, Stephanie Simms, Daniel Mietchen, Sarah Jones

Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice.

There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others.

The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows.

This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP.

This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves.

We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved.

URL : Ten principles for machine-actionable data management plans

DOI : https://doi.org/10.1371/journal.pcbi.1006750

Publié le

Wikipedia as a gateway to biomedical research: The relative distribution and use of citations in the English Wikipedia

Authors : Lauren A. Maggio, John M. Willinsky, Ryan M. Steinberg, Daniel Mietchen, Joseph L. Wass, Ting Dong

Wikipedia is a gateway to knowledge. However, the extent to which this gateway ends at Wikipedia or continues via supporting citations is unknown. Wikipedia’s gateway functionality has implications for information design and education, notably in medicine.

This study aims to establish benchmarks for the relative distribution and referral (click) rate of citations, as indicated by presence of a Digital Object Identifier (DOI), from Wikipedia, with a focus on medical citations.

DOIs referred from the English Wikipedia in August 2016 were obtained from Crossref.org. Next, based on a DOI presence on a WikiProject Medicine page, all DOIs in Wikipedia were categorized as medical (WP:MED) or non-medical (non-WP:MED).

Using this categorization, referred DOIs were classified as WP:MED, non-WP:MED, or BOTH, meaning the DOI may have been referred from either category. Data were analyzed using descriptive and inferential statistics.

Out of 5.2 million Wikipedia pages, 4.42% (n=229,857) included at least one DOI. 68,870 were identified as WP:MED, with 22.14% (n=15,250) featuring one or more DOIs. WP:MED pages featured on average 8.88 DOI citations per page, whereas non-WP:MED pages had on average 4.28 DOI citations.

For DOIs only on WP:MED pages, a DOI was referred every 2,283 pageviews and for non-WP-MED pages every 2,467 pageviews. DOIs from both pages accounted for 12% (n=58,475) of referrals, making determining a referral rate for both impossible.

While these results cannot provide evidence of greater citation referral from WP:MED than non-WP:MED, they do provide benchmarks to assess strategies for changing referral patterns.

These changes might include editors adopting new methods for designing and presenting citations or the introduction of teaching strategies that address the value of consulting citations as a tool for extending learning.

URL : Wikipedia as a gateway to biomedical research: The relative distribution and use of citations in the English Wikipedia

DOI : https://doi.org/10.1101/165159

Publié le

A multi-disciplinary perspective on emergent and future innovations in peer review

Authors : Jonathan P. Tennant, Jonathan M. Dugan, Daniel Graziotin, Damien C. Jacques, François Waldner, Daniel Mietchen, Yehia Elkhatib, Lauren B. Collister, Christina K. Pikas, Tom Crick, Paola Masuzzo, Anthony Caravaggi, Devin R. Berg, Kyle E. Niemeyer, Tony Ross-Hellauer, Sara Mannheimer, Lillian Rigling, Daniel S. Kat, Bastian Greshake Tzovaras, Josmel Pacheco-Mendoza, Nazeefa Fatima, Marta Poblet, Marios Isaakidis, Dasapta Erwin Irawan, Sébastien Renaut, Christopher R. Madan, Lisa Matthias, Jesper Nørgaard Kjær, Daniel Paul O’Donnell, Cameron Neylon, Sarah Kearns, Manojkumar Selvaraju, Julien Colomb

Peer review of research articles is a core part of our scholarly communication system. In spite of its importance, the status and purpose of peer review is often contested. What is its role in our modern digital research and communications infrastructure?

Does it perform to the high standards with which it is generally regarded? Studies of peer review have shown that it is prone to bias and abuse in numerous dimensions, frequently unreliable, and can fail to detect even fraudulent research.

With the advent of Web technologies, we are now witnessing a phase of innovation and experimentation in our approaches to peer review. These developments prompted us to examine emerging models of peer review from a range of disciplines and venues, and to ask how they might address some of the issues with our current systems of peer review.

We examine the functionality of a range of social Web platforms, and compare these with the traits underlying a viable peer review system: quality control, quantified performance metrics as engagement incentives, and certification and reputation.

Ideally, any new systems will demonstrate that they out-perform current models while avoiding as many of the biases of existing systems as possible. We conclude that there is considerable scope for new peer review initiatives to be developed, each with their own potential issues and advantages.

We also propose a novel hybrid platform model that, at least partially, resolves many of the technical and social issues associated with peer review, and can potentially disrupt the entire scholarly communication system.

Success for any such development relies on reaching a critical threshold of research community engagement with both the process and the platform, and therefore cannot be achieved without a significant change of incentives in research environments.

URL : A multi-disciplinary perspective on emergent and future innovations in peer review

DOI : http://dx.doi.org/10.12688/f1000research.12037.1