Rationale for Data
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Data (ISSN 2306-5729) is a unique international, scientific open access journal on ʻdata in scienceʼ. It provides a forum for data scientists as well as for scientists working with data. The journal publishes two sections:
Methods: the Methods section publishes research articles, review papers and technical notes on methods for collecting, processing (treating), managing, storing and analyzing scientific and scholarly data. Related source code, if available, can be deposited as supplementary material.
Data Descriptors: the Data Descriptors section publishes descriptions of scientific and scholarly datasets (one dataset per paper). Described datasets need to be publicly deposited prior to publication, preferably under an open license, thus allowing others to re-use the dataset. Small datasets might also be published as supplementary material to the dataset paper in the journal Data. The link to the publicly hosted version of the dataset must be given in the paper. Data descriptors therefore provide easy citability, traceability and accountability of datasets used in scientific research. Research articles published elsewhere based on the data can link back to the data descriptors via a standard reference and DOI number. Data descriptors are published under a CC BY license, thus allowing the reuse of the descriptions in other research papers without copyright infringement.
Datasets and descriptions:
- description of data and the methodologies used in the collection and treating of scholarly or scientific research data and experimental data
Data in applications:
- data in natural sciences
- data in healthcare and medicine
- data in finance, business and economics
- research data, experimental data
- data collection and data acquisition
- data processing
- data analysis
- data maintenance and data integrity
- data curation
- data management systems
- data compression
MDPI Publication Ethics Statement
Data is a member of the Committee on Publication Ethics (COPE). MDPI takes the responsibility to enforce a rigorous peer-review together with strict ethical policies and standards to ensure to add high quality scientific works to the field of scholarly publication. Unfortunately, cases of plagiarism, data falsification, inappropriate authorship credit, and the like, do arise. MDPI takes such publishing ethics issues very seriously and our editors are trained to proceed in such cases with a zero tolerance policy. To verify the originality of content submitted to our journals, we use iThenticate to check submissions against previous publications. MDPI works with Publons to provide reviewers with credit for their work.
Authors and publishers are encouraged to send review copies of their recent related books to the following address. Received books will be listed as Books Received within the journal's News & Announcements section.
Copyright / Open Access
Articles published in Data will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution License (CC BY). The copyright is retained by the author(s). MDPI will insert the following note at the end of the published text: