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@YvonneKallberg
YvonneKallberg committed Dec 15, 2023
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<div class="card-header"><h5>F1: (meta)data are assigned a globally unique and persistent identifier</h5></div>
<div class="card-header"><h4><b>F1: (meta)data are assigned a globally unique and persistent identifier</b></h4></div>
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<b>Explanation:</b> Each dataset is assigned a globally unique and persistent identifier (PID), for example a <a href="https://www.doi.org/">DOI</a>. These identifiers allow to find, cite and track (meta)data. <br><br>
<b>Action:</b> Ensure that each dataset is assigned a globally unique and persistent identifier. Certain repositories automatically assign identifiers to datasets as a service. If not, researchers must obtain a PID via a PID registration service.
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<div class="card-header"><h5>F2: data are described with rich metadata</h5></div>
<div class="card-header"><h4><b>F2: data are described with rich metadata</b></h4></div>
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<b>Explanation:</b> Each dataset is thoroughly (see below, in R1) described: these metadata document how the data was generated, under what term (license) and how it can be (re)used, and provide the necessary context for proper interpretation. This information needs to be machine-readable. <br><br>
<b>Action:</b> Fully document each dataset in the metadata, which may include descriptive information about the context, quality and condition, or characteristics of the data. Another researcher in any field, or their computer, should be able to properly understand the nature of your dataset. Be as generous as possible with your metadata (see R1).
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<div class="card-header"><h5>F3: metadata clearly and explicitly include the identifier of the data it describes</h5></div>
<div class="card-header"><h4><b>F3: metadata clearly and explicitly include the identifier of the data it describes</b></h4></div>
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<b>Explanation:</b>The metadata and the dataset they describe are separate files. The association between a metadata file and the dataset is obvious thanks to the mention of the dataset’s PID in the metadata. <br><br>
<b>Action:</b> Make sure that the metadata contains the dataset’s PID.
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<div class="card-header"><h5>F4: (meta)data are registered or indexed in a searchable resource</h5></div>
<div class="card-header"><h4><b>F4: (meta)data are registered or indexed in a searchable resource</b></h4></div>
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<b>Explanation:</b> Metadata are used to build easily searchable indexes of datasets. These resources will allow to search for existing datasets similarly to searching for a book in a library. <br><br>
Expand All @@ -84,14 +87,15 @@ Data and metadata should be stored for the long term such that they can be easil
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<div class="card-header"><h5>A1: (meta)data are retrievable by their identifier using a standardized communications protocol</h5></div>
<div class="card-header"><h4><b>A1: (meta)data are retrievable by their identifier using a standardized communications protocol</b></h4></div>
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<b>Explanation:</b> If one knows a dataset’s identifier and the location where it is archived, one can access at least the metadata. Furthermore, the user knows how to proceed to get access to the data. <br><br>
<b>Action:</b> Clearly define who can access the actual data, and specify how. It is possible that data will actually not be downloaded, but rather reused in situ. If so, the metadata must specify the conditions under which this is allowed (sometimes versus the conditions needed to fulfill for external usage/“download”).
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<div class="card-header"><h5>A1.1: the protocol is open, free, and universally implementable</h5></div>
<div class="card-header"><h4><b>A1.1: the protocol is open, free, and universally implementable</b></h4></div>
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<b>Explanation:</b> Anyone with a computer and an internet connection can access at least the metadata.
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<div class="card-header"><h5>A1.2: the protocol allows for an authentication and authorization procedure, where necessary</h5></div>
<div class="card-header"><h4><b>A1.2: the protocol allows for an authentication and authorization procedure, where necessary</b></h4></div>
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<b>Explanation:</b> It often makes sense to request users to create a user account on a repository. This allows to authenticate the owner (or contributor) of each dataset, and to potentially set user specific rights.
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<div class="card-header"><h5>A2: metadata are accessible, even when the data are no longer available</h5></div>
<div class="card-header"><h4><b>A2: metadata are accessible, even when the data are no longer available</b></h4></div>
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<b>Explanation:</b> Maintaining all datasets in a readily usable state eternally would require an enormous amount of curation work (adapting to new standards for formats, converting to different format if specifically needed software is discontinued, etc.). Keeping the metadata describing each dataset accessible, however, can be done with much less resources. This allows to build comprehensive data indexes including all current, past and potentially arising datasets. <br><br>
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<div class="card-header"><h5>I1: (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation</h5></div>
<div class="card-header"><h4><b>I1: (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation</b></h4></div>
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<b>Explanation:</b> Interoperability typically means that each computer system has at least knowledge of the other system’s formats in which data is exchanged. If (meta)data are to be searchable and if compatible data sources should be combinable in a (semi)automatic way, computer systems need to be able to decide if the content of datasets are comparable. Obvious issues arise when different languages are used to describe the data or when spelling errors make the comparison of descriptions and variable names more difficult. It is critical to use controlled vocabularies and a well-defined framework to describe and structure (meta)data in order to ensure findability and interoperability of datasets. <br><br>
<b>Action:</b> Provide machine readable data and metadata in an accessible language, using a well-established formalism. In particular, data and metadata are annotated with resolvable vocabularies/ontologies/thesauri that are commonly used in the field. The <a href="https://www.w3.org/RDF/">RDF</a> extensible knowledge representation model is a way to describe and structure datasets. You can refer to the <a href="https://dublincore.org/schemas/">Dublin Core Schema</a> as an example.
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<div class="card-header"><h5>I2: (meta)data use vocabularies that follow FAIR principles</h5></div>
<div class="card-header"><h4><b>I2: (meta)data use vocabularies that follow FAIR principles</b></h4></div>
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<b>Explanation:</b> The controlled vocabulary used to describe datasets needs to be documented. This documentation needs to be easily findable and accessible by anyone who uses the dataset. <br><br>
<b>Action:</b> The vocabularies/ontologies/thesauri are themselves findable, accessible, interoperable and thoroughly documented, hence FAIR. Researchers can refer to metrics assessing the FAIRness of a digital resource (if available).
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<div class="card-header"><h5>I3: (meta)data include qualified references to other (meta)data</h5></div>
<div class="card-header"><h4><b>I3: (meta)data include qualified references to other (meta)data</b></h4></div>
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<b>Explanation:</b> If the dataset builds on another dataset, if additional datasets are needed to complete the data, or if complementary information is stored in a different dataset, this needs to be specified. In particular, the scientific link between the datasets needs to be described. Furthermore, all datasets need to be properly cited (i.e. including their persistent identifiers). <br><br>
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<div class="card-header"><h5>R1: meta(data) are richly described with a plurality of accurate and relevant attributes</h5></div>
<div class="card-header"><h4><b>R1: meta(data) are richly described with a plurality of accurate and relevant attributes</b></h4></div>
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<b>Explanation:</b> Description of a dataset is required at two different levels:
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<div class="card-header"><h5>R1.1: (meta)data are released with a clear and accessible data usage license</h5></div>
<div class="card-header"><h4><b>R1.1: (meta)data are released with a clear and accessible data usage license</b></h4></div>
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<b>Explanation:</b> The conditions under which the data can be used should be clear to machines and humans. This has to be specified in the metadata describing a dataset. <br><br>
<b>Action:</b> Include information about the license in the metadata. If a particular license is needed, you have to provide it along with the dataset. Where possible it is suggested to use common licenses, such as CC 0, CC BY, etc., which can be referred to by URL.
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<div class="card-header"><h5>R1.2. (meta)data are associated with detailed provenance</h5></div>
<div class="card-header"><h4><b>R1.2. (meta)data are associated with detailed provenance</b></h4></div>
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<b>Explanation:</b> Detailed information about the provenance of data is necessary for reuse: this will, for example, allow researchers to understand how the data was generated, in which context it can be reused, and how reliable it is. Provenance is a central issue in scientific databases to validate data. <br><br>
<b>Action:</b> The metadata to thoroughly describe the workflow that led to your data: Who generated or collected it? How has it been processed? Has it been published before? Does it contain data from someone else, potentially transformed or completed? Ideally the workflow is described in a machine-readable format. Criterion I3 is closely linked to this issue when reusing published datasets.
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<div class="card-header"><h5>R1.3: (meta)data meet domain relevant community standards</h5></div>
<div class="card-header"><h4><b>R1.3: (meta)data meet domain relevant community standards</b></h4></div>
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<b>Explanation:</b> It is easier to reuse datasets if they are similar: same type of data, data organized in a standardized way, well-established and sustainable file formats, documentation (metadata) following a common template and using common vocabulary. If community standards or best practices for data archiving and sharing exist, they should be followed. Note that quality issues are not addressed by the FAIR principles. How reliable data is lies in the eye of the beholder and depends on the foreseen application. <br><br>
<b>Action:</b> Prepare your (meta)data according to community standards and best practices for data archiving and sharing in your research field. There might be situations where good practice exist for the type of data to be submitted but the submitter has valid and specified reasons to divert from the standard practice. This needs to be addressed in the metadata.
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