Mendeley Data for journals
Upload research data directly to Mendeley Data while submitting your manuscript
Mendeley Data(打開新的分頁／視窗) is an open, free-to-use research data repository, which enables researchers to make their research data publicly available. Benefits of sharing research data include complying with funder mandates, enabling reuse by other researchers, and increasing the reproducibility, transparency and trust of the original research.
Making data available for an article has also been shown by several studies to increase article citations. Several Elsevier journals collaborate with Mendeley Data to make underlying research data available. Datasets are linked with the article in question, making it accessible to readers with one click access. View an article featuring Mendeley data(打開新的分頁／視窗) (just select the "Research Data" link in the left-hand bar or scroll down the page).
Mendeley Data at a glance
Enables authors to comply with the increasing number of funder mandates requiring data to be made publicly available.
Links datasets to the associated article on ScienceDirect and vice versa.
Allows fellow researchers to cite research data independently, as datasets posted to Mendeley Data receive a DOI.
Lets readers preview data files directly within the browser without having to download large files to their computer.
How data and articles are linked
Database linking is available for researchers and data repositories as one method to ensure that data can be easily discovered and accessed. This is done by creating bidirectional links between data repositories and articles on ScienceDirect. Readers on ScienceDirect enjoy one-click access to relevant, trusted data that may help to validate research or drive further investigations.
Examples
Reproducible experiments on dynamic resource allocation in cloud data centers(打開新的分頁／視窗) on Mendeley Data is associated with the peer reviewed publication SoNeR: Social Network Ranker.
SoNeR FOAF dataset(打開新的分頁／視窗) on Mendeley Data is associated with the peer reviewed publication Reproducible experiments on dynamic resource allocation in cloud data centers.
Hydatigera(打開新的分頁／視窗) on Mendeley Data is associated with the peer reviewed publication Reappraisal of Hydatigera taeniaeformis (Batsch, 1786) (Cestoda: Taeniidae) sensu lato with description of Hydatigera kamiyai n. sp.
Get started
Before you start
Gather the research data files you wish to share.
Ensure your data doesn't contain any data points about individuals which could identify them, or any patient-sensitive information - and that you have permission to share the data.
As far as possible try and ensure the data files and contents are clearly labelled (e.g. clear column headings), and that the labels are defined and described in the dataset description.
Submitting data with your manuscript
There are currently different methods for authors to submit data with their article. A number of journals support direct submission of data during the article submission process. To find out if this option is supported by the journal of your choice, please view the guide for authors.
Cite underlying or relevant datasets in your manuscript by including a data reference in the reference list. For more information about data citation, visit the references section “Reference Styles” in the guide for authors.
To make data available separately to your article, you can upload your dataset directly(打開新的分頁／視窗) to Mendeley Data.
Publishing a research elements article
These brief, peer-reviewed articles complement full research papers and are an easy way to receive proper credit and recognition for the work you have done. Research elements are research outputs that have come about as a result of following the research cycle – this includes things like data, methods and protocols, software, hardware and more.
You can publish research elements articles in several different Elsevier journals, including our suite of dedicated Research Elements journals. They are easy to submit, are subject to a peer review process, receive a DOI and are fully citable. They also make your work more sharable, discoverable, comprehensible, reusable and reproducible.
The accompanying raw data can still be placed in a repository of your choice (see below).