FAIRness status monitoring

Each laboratory belonging to NFFA-DI has to fill the following form in order to collect necessary information to monitor the FAIRness state of the art of each node. This form has been created by merging “The FAIR principles” (Wilkinson et al.) with “Explanation of the FAIR data principles“. Answers has to be motivated as much as possible, there are no correct answers! All the information collected will be used to write down a plan of actions inside WP3.

In the following questions “Data Collection Phase” refers to all the data and metadata that are produced BEFORE any result publications, that is those produced in the laboratory, including sample informations, data generated in the experiments as well as those obtained by processing raw data or performing intermediate analysis. “Data Published Phase” refers to all the data and metadata published, that is datasets related to any published result.

Laboratory info

Name *Head of laboratory

Email Address *

Select *Please select the NFFA-DI node you belong to.

Laboratory name *Please enter the name of the laboratory you work in.

Findability

F1. (Meta)data are assigned a globally unique and persistent identifierEach dataset is assigned a globally unique and persistent identifier (PID), for example a DOI, ARK, RRID… These identifiers allow to find, cite and track (meta)data when published.

Data Collection Phase: during your experiment do you collect (meta)data by using a well defined convention that does not change along the experiment and does not change among experiments/projects?

Data Published Phase: do you publish data and metadata with a PID?

F2. Data are described with rich metadata (defined by R1 below)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.

Data Collection Phase: when you collect data, do you collect also all metadata associated in the different experimetal phases? Do you use a laboratory notebook? Is it open? Do you collect sample's metadata? What about instruments metadata?

Data Published Phase: do you 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.

F3. Metadata clearly and explicitly include the identifier of the data they describeThe 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.

Data Collection Phase: on your server, do you use a unique key to link metadata with their corresponding data?

Data Published Phase: when you publish metadata, do they include the PID of the corresponding data?

F4. (Meta)data are registered or indexed in a searchable resourceMetadata are used to build easily searchable indexes of datasets. These resources will allow to search for existing data sets similarly to searching for a book in a library.

Data Published Phase: do you provide detailed and complete metadata for each dataset ?

Accessibility

A1. (Meta)data are retrievable by their identifier using a standardised communications protocolIf 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.

Data Published Phase: do you clearly define who can access the actual data? Do you 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”).

A1.1 The protocol is open, free, and universally implementable

Data Published Phase: may anyone with a computer and an internet connection access at least the metadata ?

A1.2 The protocol allows for an authentication and authorisation procedure, where necessary

Data Published Phase: how may external users have access to your data? 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.

A2. Metadata are accessible, even when the data are no longer availableMaintaining 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.

Data Published Phase: do you preserve metadata ?

Interoperability

I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.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.

Data Collection Phase: when you collect metadata, do you use authomatic pipeline to save them in a standard format that can be understood both by humans and machines?

Data Published Phase: do you publish metadata in a standard format that can be understood both by humans and machines?

I2. (Meta)data use vocabularies that follow FAIR principlesThe 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.

Data Collection Phase: are the vocabularies/ontologies/thesauri you use findable, accessible, interoperable and thoroughly documented, hence FAIR?

Data Published Phase: are the vocabularies/ontologies/thesauri you use findable, accessible, interoperable and thoroughly documented, hence FAIR? Researchers can refer to metrics assessing the FAIRness of a digital resource (if available).

I3. (Meta)data include qualified references to other (meta)dataIf 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).

Data Collection Phase: do you properly cite relevant/associated datasets, in particular by providing their name convention, in the metadata, and describe the scientific link/relation to your dataset ?

Data Published Phase: when you publish your data&metadata, do you properly cite relevant/associated datasets, in particular by providing their persistent identifiers, in the metadata, and describe the scientific link/relation to your dataset?

Reusability

R1. (Meta)data are richly described with a plurality of accurate and relevant attributesDescription of a dataset is required at two different levels: (1) metadata describing the dataset (intrinsic): what does the dataset contain, how was the data generated, how has it been processed, how can it be reused; (2) metadata describing the data (submitterdefined): any needed information to properly use the data, such as definitions of the variable names.

Data Collection Phase: do you provide complete metadata for each data file? Some points to take into consideration (non-exhaustive list): a) Scope of your data: for what purpose was it generated/collected? b) Particularities or limitations about the data that other users should be aware of. c) Date of the dataset generation, lab conditions, who prepared the data, parameter settings, name and version of the software used. d) Is it raw or processed data? e) Variable names are explained or self-explanatory (i.e. defined in the research field’s controlled vocabulary).

Data Published Phase: do you provide complete metadata for each data file? Some points to take into consideration (non-exhaustive list): a) Scope of your data: for what purpose was it generated/collected? b) Particularities or limitations about the data that other users should be aware of. c) Date of the dataset generation, lab conditions, who prepared the data, parameter settings, name and version of the software used. d) Is it raw or processed data? e) Variable names are explained or self-explanatory (i.e. defined in the research field’s controlled vocabulary) f) Version of the archived and/or reused data is clearly specified and documented.

R1.1. (Meta)data are released with a clear and accessible data usage licenseThe 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.

Data Published Phase: do you 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 creative common licenses, such as CC0, CC BY, etc., which can be referred to by URL.

R1.2. (Meta)data are associated with detailed provenanceDetailed 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.

Data Collection Phase: along the data collection phase, do you save all the metadata that describe the workflow that led to your data?

Data Published Phase: do your metadata 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 machinereadable format. Criterion I3 is closely linked to this issue when reusing published datasets.

R1.3. (Meta)data meet domain-relevant community standardsIt 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.

Data Collection Phase: do you prepare your (meta)data according to community standards and best practices for data archiving and sharing in your research field?

Data Published Phase: do you publish metadata 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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