How to share research data ****************************************************************************************** * ****************************************************************************************** There are multiple ways to share your research data. A common, yet not particularly suitab sending the data via e-mail upon request. This approach not only complicates access to dat also burdens the authors who has to manage the requests individually. Additionally, there the researcher might lose their data due to hardware failure or human error, or if a longe passed since the publication of the article, the researcher might not remember which file data analysis. Sharing research data via e-mail also does not allow the user to properly c Therefore, it is recommended to use a different method for sharing data. This section takes a closer look at three approaches to sharing data: 1.Supplementary Materials to your research article  2.Data Repository  3.Data journal  ****************************************************************************************** * Supplementary Materials to a research article ****************************************************************************************** Some publishers offer the option of attaching Supplementary Materials to an article. In th can add, for example, raw data or code to their article, and the publisher then publishes online and adds a link to the materials to your published article. If you choose this opti that unless the publication is Open Access, the copyrights might be transferred to the pub this option would not allow readers to reuse the data, or cite it independently of the mai This method of sharing might be suitable, for instance, if you would like to share a large measured values that would normally be a part of the article but would not fit onto a jour publishing other types of data, it would be more suitable to deposit the data in a data re a link to the dataset to your paper. ****************************************************************************************** * Data repositories ****************************************************************************************** The best way to preserve your data - whether you decide to share them or not - is to depos repository.  To increase the impact of your data, you should deposit your data in a subject specific re advantages of subject specific repositories are that they bring together researchers from scientific discipline who share their data with one another, and they are also usually bet to meet the needs of the community, for example, when it comes to the type of data the res An example of a subject specific repository is LINDAT/CLARIAH-CZ [ URL "https://lindat.mff repository/xmlui/"]  for linguistic data and tools, which is developed at the Institute of Applied Linguistics at MFF UK. When choosing a suitable subject specific repository, it is a good idea to use a repositor already established for your research domain - you can ask your colleagues where they depo or use the international registry of data repositories re3data.org [ URL "https://www.re3d If you cannot find a suitable subject specific repository, you can deposit your data in a purpose repository, which store data of all scientific disciplines. The most commonly used purpose repositories are Zenodo [ URL "https://zenodo.org/"] , Figshare [ URL "https://fig or Dryad [ URL "https://datadryad.org/stash/"] . The Generalist Repository Comparison Char doi.org/10.5281/zenodo.3946720"]  can help you select a general repository.  When choosing a suitable repository, check the following as well:  • Does the repository assign a persistent and unique identifier (e.g., DOI)? Thanks to a p identifier, your data are more easily findable and citable.  • Is the repository certified as a ‘trusted data repository’? If the repository is certifi likely that your data will be well looked after.  • Does the repository enable open access to your data? If you decide to share your data, t information.  • Does the repository license your data? Does it offer clear terms and conditions for data important that others know what they can and cannot do with your data.  • Does the repository provide a landing page for your dataset with metadata? Metadata will find your data, tell what they are and how to cite them.  • Does the repository enable versioning? If you update your dataset, you can upload it as the original dataset. The new dataset is given its own identifier and users can easily f the latest version or which version was used in a particular study.  You can easily check some of these information at re3data.org [ URL "http://re3data.org/"] in the upper right corner, includes a series of pictograms  which tell you, for example, w repository uses persistent identifiers, whether it is open or certified. You can find more the detailed entry or on the website of the repository.  This method of sharing is suitable for both standalone datasets and for underlying data fo articles. If you are publishing underlying data for published articles, remember to includ dataset in your publication, and a link to the publication in the metadata of your data (p form of a persistent identifier, such as DOI). ****************************************************************************************** * Data journals ****************************************************************************************** Data journals mirror the traditional model of scientific publication through articles and sharing in repositories with publishing a data paper (also known as a data descriptor or a data paper is analogous to traditional research papers, it can be cited and can be reporte current research information system used at Charles University). The structure of data papers may vary depending on the requirements of individual journals fundamental characteristic is that they describe a specific, publicly accessible dataset, conducted on it. A data paper should provide information on what the data are, how and whe created and so on, and the paper should also contain a link back to the dataset (ideally v identifier such as DOI). Generally, the publishers should not host the data; instead, the  deposited in a trusted open access repository so that even if the paper might have restric dataset would still be available.  Just like traditional publications, data journals follow a standard peer review process, h be differences in terms of what the reviewers are requested to assess and whether the peer is open or not. Listed below are some examples of data journals and you can find further examples here [ U zenodo.org/doi/10.5281/zenodo.7082125"] .  • Scientific data [ URL "https://www.nature.com/sdata/"]  - mainly natural science discipl • Earth System Science Data [ URL "https://www.earth-system-science-data.net/"]  - geoscie • Journal of Open Archaeology Data [ URL "https://openarchaeologydata.metajnl.com/"]  - ar • Biodiversity Data Journal [ URL "https://bdj.pensoft.net/"]  - biodiversity  • GigaScience [ URL "https://academic.oup.com/gigascience"]  - life and biomedical science • Journal of Open Research Software [ URL "https://openresearchsoftware.metajnl.com/about/ software  ****************************************************************************************** * Useful Resources ****************************************************************************************** Chavan, Vishwas & Lyubomir Penev. 2011. The data paper: a mechanism to incentivize data pu in biodiversity science. BMC Bioinformatics 12, S2. https://doi.org/10.1186/1471-2105-12-S "https://doi.org/10.1186/1471-2105-12-S15-S2"]    Gould, Julie. 2014. How to publish your data in a data journal. Naturejobs Blog.  http://b naturejobs/2014/12/04/how-to-publish-your-data-in-a-data-journal/ [ URL "http://blogs.natu naturejobs/2014/12/04/how-to-publish-your-data-in-a-data-journal/"]    Stall, Shelley, Maryann E. Martone, et al. 2020. Generalist Repository Comparison Chart [ doi.org/10.5281/zenodo.3946720"] . Zenodo. http://doi.org/10.5281/zenodo.3946720 [ URL "ht doi.org/10.5281/zenodo.3946720"]