[et_pb_section fb_built=”1″ _builder_version=”4.10.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.10.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.10.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.10.7″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
During our training events, we often discuss with the audience, consisting of data stewards and other research supporters, what sort of information researchers need. Specifically, during a 2023 training course on FAIR organised by the <\/span>DCC-PO<\/span><\/a>, we gathered frequently asked questions about FAIR data. Below, you can find a selection of these questions, accompanied by answers and sources for further information. If you have a question regarding research data management, FAIR data, or open science, please contact<\/a> us <\/span>– perhaps your question will be the next to appear below!<\/span><\/p>\n If you have questions that are more specifically related to the DANS Data Stations, have a look here<\/a><\/span>. <\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.10.7″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_toggle title=”What is FAIR and why should I implement it?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n The FAIR guiding principles for scientific data management and stewardship are a set of guidelines that you can follow to ultimately make your data better reusable by others.<\/span><\/p>\n Being able to find and reuse data on a large scale benefits science as a whole ecosystem, and can prevent costly duplicate efforts. Making your data FAIR can help it reach a broader audience and increase its impact.<\/span><\/p>\n Further information:<\/span><\/p>\n FAIR is not exclusive to data, there are also FAIR principles for research software: Barker, M., Chue Hong, N.P., Katz, D.S. et al. Introducing the FAIR Principles for research software. Sci Data 9, 622 (2022). <\/span>https:\/\/doi.org\/10.1038\/s41597-022-01710-x<\/span><\/a><\/p>\n [\/et_pb_toggle][et_pb_toggle title=”Can my data be FAIR?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n FAIR is not an all-or-nothing principle, so your data can be incrementally more or less FAIR based on different practices that you implement. From the original FAIR principles, various different interpretations of metrics and practices have been defined that can help you figure out what steps you can take to increase the FAIRness of your data. Many elements of FAIR are discussed in the other FAQs. You can also use a tool to assess the FAIRness of your data, as a starting point to identifying ways to improve it. See <\/span>https:\/\/fairassist.org\/#!\/<\/span><\/a> for an overview of tools you can use to assess the FAIRness of your data.<\/span><\/p>\n <\/p>\n [\/et_pb_toggle][et_pb_toggle title=”What is the difference between FAIR data and Open data?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n Data being FAIR is not the same as data being open. Your data can be restricted or closed access, while still being considerably FAIR. The \u2018A\u2019 in FAIR stands for accessible, but the principle does not dictate that data should be openly accessible, only that it should be clearly communicated what the level of accessibility is. If your data is not openly accessible, it is important that you clearly state what the conditions of access and reuse are (for example, you may provide access upon request, after confirming the purpose of the reuse or the role of the requester). You can do this by providing a data availability statement or data access protocol, and specifying a licence for the data.<\/span><\/p>\n [\/et_pb_toggle][et_pb_text _builder_version=”4.10.7″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_toggle title=”Why do I have to have a DMP?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n Most funders and many institutions now require a DMP for each research project. The idea behind this is to make research transparent, replicable, and as open as possible. By writing a DMP before the start of a project, and by keeping this up-to-date throughout, you are thinking ahead about how to manage and share your research data. By putting effort into the DMP, less effort is subsequently required to manage the data effectively throughout the project. A good DMP can also reduce the risk of data loss or other threats that can negatively impact your data (e.g. obsolescence of software).<\/span>\u00a0<\/p>\n [\/et_pb_toggle][et_pb_toggle title=”Which DMP template should I use?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n This depends on what it is for: funders and institutions often have their own templates or guidelines that they want you to adhere to, so the funder and university library website are good first points to check. In Europe, more and more research funders work along <\/span>requirements and guidance developed by Science Europe<\/span><\/a>. Their document is a good place to start, and while templates and guidelines vary, the main themes identified by Science Europe are part of every template. There are also various DMP tools that you can use and these often have inbuilt templates. Good and free-to-use examples are <\/span>Argos<\/span><\/a> and <\/span>DMPonline<\/span><\/a>. Added advantages are that they support joint writing and that the DMP produced can also be machine-readable.<\/span><\/p>\n Further information:<\/span><\/p>\n Horizon Europe DMP template: <\/span>https:\/\/enspire.science\/wp-content\/uploads\/2021\/09\/Horizon-Europe-Data-Management-Plan-Template.pdf<\/span><\/a><\/p>\n [\/et_pb_toggle][et_pb_toggle title=”Are example DMPs available?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n Because the information in a DMP differs per research project, institution, discipline, and so on, it is most useful to check out examples of similar projects. You can find public DMPs in Argos and DMPonline, on Zenodo, and, for example, on the DMP Use Case Project website. Your institution may have their own template in which certain aspects are already filled in for you, like how data is shared or backed-up in your institution.<\/span><\/p>\n <\/p>\n [\/et_pb_toggle][et_pb_text _builder_version=”4.10.7″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_toggle title=”What is metadata?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n Metadata is \u201cdata about data\u201d, so data that gives information about other data. It is important in order to discover, find, and reuse data. You can think of <\/span>project-level<\/span><\/i> metadata, like what is the title of the project, what is it about (e.g., keywords), who is involved (e.g., who are the authors of a dataset), who is the funder, and so on. Other important metadata relate to what the dataset contains (e.g., what sort of files), how it was collected, how it was processed, and how it can be accessed and reused (e.g., licences, PIDs such as DOI). There is also metadata on the <\/span>data-level<\/span><\/i>, such as information about the file (data type, format, etc.) and about variables used in the file. Structured metadata can also be machine-readable, and therefore more easily found during catalogue or internet searches.<\/span><\/p>\n More information:<\/span><\/p>\n [\/et_pb_toggle][et_pb_toggle title=”What is a metadata standard?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n As mentioned briefly under \u2018What is metadata?\u2019, there are different types of metadata, and these contain different metadata elements (e.g., \u2018title\u2019, \u2018author\u2019, etc.). For different purposes or in different disciplines, different combinations of these elements are required. A metadata standard is a subject-specific agreed-on or recommended (\u2018standard\u2019) group of metadata elements. The metadata standard includes which metadata elements should be used, and can also include rules on the syntax and which controlled vocabularies should be used (per metadata element).\u00a0<\/span><\/p>\n Internationally recognized, and widely used, metadata standards are:<\/span><\/p>\n If you deposit your dataset in a trustworthy repository, the repository will already have metadata standards in place. DANS uses metadata from Dublin Core, DDI, and DataCite, following OpenAIRE guidelines and other community recommendations when deciding which metadata elements to include.<\/span><\/p>\n More information:\u00a0<\/span><\/p>\n [\/et_pb_toggle][et_pb_toggle title=”How do I document metadata?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n As a researcher, it is good to be aware of the importance and types of metadata, as it does not cost much time to document metadata if you start from the beginning of a project (see \u2018What is metadata\u2019 and \u2018What is a metadata standard?\u2019). There are metadata templates that help to check if you are collecting all relevant metadata (metadata fields), like <\/span>dublincoregenerator<\/span><\/a>. The most important thing to consider is whether you are adding all information that would be necessary to find, access, and reuse your data. You can collect your metadata simply in a text file and embed data-level information in the data files (think for example about EXIF information in an image file). To generate machine-readable metadata, you can submit your dataset to a trustworthy data repository (see also \u2018Where should I store my data?\u2019) – so there is no need to do this yourself. If you want to, however, it is possible to generate a machine-readable .xml file yourself, for example using the <\/span>dublincoregenerator<\/span><\/a> mentioned above.<\/span><\/p>\n More information:<\/span><\/p>\n [\/et_pb_toggle][et_pb_toggle title=”Where and how do I register metadata?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n If you deposit your dataset in a repository, like one of the <\/span>DANS Data Stations<\/span><\/a>, you will be asked to fill out metadata fields. In this way, the metadata elements are connected to your dataset. Always make sure to deposit your data in a repository where you have the option to add metadata, ideally as rich (extensive) as possible. This increases the chance that others will find and reuse the data.<\/span><\/p>\n Depending on the repository, the metadata is also indexed (i.e. findable through search engines) or harvested by metadata catalogues. Metadata in the DANS Data Station Archaeology, for example, are also visible in the <\/span>ARIADNE Portal<\/span><\/a>, while metadata of the DANS Data Station Social Sciences and Humanities can be seen in the <\/span>ODISSEI Portal<\/span><\/a>.<\/span><\/p>\n In some cases you may want to increase the visibility of your output through adding its metadata manually to a register or catalogue. For example, if you deposited training or teaching materials (e.g. slides or exercise templates) into Zenodo, you could then decide that to increase their visibility you want to add the metadata to the <\/span>TeSS platform<\/span><\/a>, a platform for finding training materials. (NB: while you can add metadata referring to your data in multiple places, you should never deposit your actual data or other materials twice.)<\/span><\/p>\n More information:<\/span><\/p>\n [\/et_pb_toggle][et_pb_text _builder_version=”4.10.7″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_toggle title=”What is a file format?” icon_color=”#00a7d4″ _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n Files are saved in different formats. File formats are standard ways to encode information for storage on a computer. You can see what the file format is based on the file extension (the suffix), like .jpg or .docx.\u00a0<\/span><\/p>\n There are normally multiple options of formats for the same type of file. For example, you can save an image as a JPG, TIFF, PNG, and more. There is a basic distinction between proprietary formats, which are owned by a company or organisation, and non-proprietary or open formats, which are publicly accessible.<\/span><\/p>\n Reference and more information:<\/span><\/p>\n [\/et_pb_toggle][et_pb_toggle title=”What is meant with \u2018preferred (file) formats\u2019?” icon_color=”#00a7d4″ module_id=”preferredformats” _builder_version=”4.10.7″ _module_preset=”default” title_text_color=”#003A49″ title_font=”Open Sans||||||||” title_line_height=”1.5em” closed_title_font=”Open Sans||||||||” closed_title_line_height=”1.5em” body_font=”Open Sans||||||||” body_text_color=”#003A49″ body_line_height=”1.5em” body_ol_text_color=”#003A49″ background_color=”#E4F9FF” border_color_all=”#00a7d4″ global_colors_info=”{}”]<\/p>\n Preferred formats are file formats of which an organisation or repository, like DANS, based on international agreements, is confident that they offer the best long-term guarantee in terms of usability, accessibility and sustainability. As a general guideline, these include file formats which are:<\/span><\/p>\n It is good to know that in practice it will not always be possible to use a format which adheres to all three of the criteria. For example, a proprietary format may be the one that is most used for a specific purpose in your field, like SPSS .sav files or ESRI shapefiles. In this case, it is best to store your data both in the proprietary, often used format and in a preferred, open format which has better chances of long-term preservation and accessibility.<\/span><\/p>\n More information:<\/span><\/p>\nFAIR data<\/h3>\n
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<\/p>\nData Management Plans (DMPs)<\/h3>\n
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Metadata<\/h3>\n
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File formats<\/h3>\n
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