This blog was written by Iain Hrynaszkiewicz, Head of Data Publishing.
With the publication of the Concordat on Open Research Data last week, the UK further cemented its leadership position in promoting access to tax payer-funded research data.
The Concordat sets out 10 principles that promote access to and reuse of research data as an enabler of high quality research, while recognising the costs that can be involved. Amongst other principles, the concordat promotes: Read more
Manuscripts published at Scientific Data contain a ‘Data Citations’ section that helps authors formally acknowledge any datasets mentioned in their manuscript. We know that this section is unfamiliar to many of our authors, so here we provide some background on the purpose of data citations, and advice on completing this section when submitting to Scientific Data. Read more
This blog was written by Iain Hrynaszkiewicz, Head of Data Publishing.
Guidelines on clinical data sharing developed by the Scientific Data team have been published in Research Integrity and Peer Review, and several published Data Descriptors at Scientific Data demonstrate the guidelines in practice. Our aim is to implement the best features of journals, data repositories and secure data request services to enable effective sharing of experimental clinical research data. Read more
We have a galaxy of open research stars giving talks at this year’s edition of Publishing Better Science through Better Data (#scidata16). And if you have a great example of research data sharing or reuse, you could be joining them.
Tickets for the last two events, in 2014 and 2015, went within 24 hours of their announcement and gained wide attention online. This year Springer Nature have partnered with the Wellcome Trust to provide a bigger and better event exploring issues in research data, focused on the needs of early career researchers. The day will include advice on publishing and advancing careers, as well as good practice for data management and presentation. It will also feature tools and resources available to researchers to help them, and society, derive maximum benefit from research data. Prior knowledge of open science, open data and open access are not needed to attend – the event is for anyone interested in carrying out and publishing better research. Read more
This blog was written by Mathias Astell & Iain Hrynaszkiewicz and was originally published on the DNAdigest blog.
The benefits of sharing data generated by researchers have long been understood to be of great value to science (as exemplified by this British Medical Journal piece from 1994). And over recent years there has been a rapid increase in the ability to share and access research data – as can be seen in the rise of data journals (such as Scientific Data and Gigascience), the increase in research data repositories (both general and subject-specific), and the establishment of data sharing policies around the world. Read more
Ruedi Aebersold & George Rosenberger
Guest post by Ruedi Aebersold, Professor of Systems Biology with a joint appointment at ETH Zurich and the University of Zurich, & George Rosenberger, PhD student in the Aebersold group at the Institute of Molecular Systems Biology, ETH Zurich.
Mass spectrometry-based proteomics is a data-intense research discipline that primarily aims at identifying and quantifying the proteins that constitute the proteome1. This is achieved by generating large numbers (104 to 106) of fragment ion spectra that represent peptides generated by proteolysis of the respective proteome. Mass spectrometers can operate in different data acquisition modes, referred to as data-dependent acquisition (DDA), targeted acquisition exemplified by selected reaction monitoring (SRM) or data-independent acquisition (DIA)2 exemplified by SWATH-MS3,4. Specific software tools then generate from these raw data processed mass spectra – from which sets of identified peptides, proteins and their abundance are inferred and annotated with metadata. Both, the generation and the processing of such raw data sets are resource and time intensive. Further, if unique, irreplaceable samples are being analyzed, as is often the case with clinical cohorts the data cannot be re-generated. Therefore, the proteomics community has started to embrace data sharing by the means of different specialized public repositories, for example GPMDB5, PRIDE6, PeptideAtlas7 or ProteomicsDB8. For the last few years, the ProteomeXchange9 consortium has provided centralized deposition of raw data and their meta-annotation. Read more
Last month Scientific Data was all about Japan, with the Research Data Alliance (RDA) Seventh Plenary Meeting in Tokyo, and the Scientific Data Japan Roadshow stopping off in Yokohama, Mishima, Fukuoka, Tokyo, Kyoto, Kobe and Tsukuba.
Photo by Chizuko Kobayashi
Mark Viant
Guest post by Mark Viant, Professor of Metabolomics in the School of Biosciences at the University of Birmingham, UK, and Director of both the national NERC Biomolecular Analysis Facility – Metabolomics and the Phenome Centre Birmingham.
In 2014, my research team published the first Scientific Data Data Descriptor for metabolomics measurements, Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control. This article described in great detail the many steps that are critical for ensuring the production of high quality (direct infusion) mass spectrometry (DIMS) data. It was our intention that this publication would help to establish the benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment. The data was also made freely available in the MetaboLights public database for metabolomics data (dataset MTBLS79).1
Scientific Data is expanding the kinds of content it publishes, providing a richer forum for advances in open, reproducible science. Authors may now submit manuscripts under the following new formats:
The Analysis format can now be used to submit reports on new analyses or meta-analyses of existing data. Analyses should present particularly innovative examples of data reuse, and may be used to report compelling new findings and conclusions derived from published data. Analysis submissions are not required to use data previously published at Scientific Data, although submissions of this kind are encouraged. Analysis submissions should exemplify reproducible research by clearly describing all steps in the analysis, providing supporting source code, and explaining how and where others may access all underlying data.
The Article is a flexible format for presenting original reports on systems or techniques that clearly advance data sharing and reuse. This includes research on sharing, managing and processing scientific research data, as well as articles describing data repositories, standards and ontologies. Articles may also present sociological research on data sharing habits or perceptions, or the effectiveness of sharing policies. Read more
Organizers:
Scientific Data is inviting submissions releasing and describing datasets generated in the process of attempting to replicate one or more previous experimental studies. Submissions may be from a wide range of research disciplines including, but not limited to, the domains of psychology, biology and Earth science. Submitted articles may be considered for inclusion in a special article collection. Read more