someone’s word for it’ to ‘caveat emptor’, i.e. it is possible to take any data and
understand it enough to be able to trust it, or not. ‘Poorer’ structures therefore need to
be supported by underlying data, and significant advances have been made with
structure factor deposition, but in certain cases underpinning with the raw data would
also be desirable. Given that a set of structure factors are extracted from diffraction
images, the user of a result still has to trust that the generator of it has done the best
possible job. In some cases, it may be possible to reprocess using different routines
or approaches and extract better structure factors. For particular types of problem,
e.g. where diffuse scattering is prominent, this would be highly beneficial. Indeed, in
many such cases, it would also enable the future potential to do better by supporting
and driving software and algorithm advances.
2.3.3 FAIR and Open Data
There is a moral requirement to make the outputs of funded research easily available
for the purpose of sharing and re-use. Recently there has been significant growth in a
strong global movement across all disciplines to make all research data/outputs
FAIR [76], that is, Findable, Accessible, Interoperable and Reusable. The FAIR
principles [77] are intended as a guide and detail for each of these aspects the key
factors that need to be considered and in place to realise them. Specifically, the
4 FAIR foundational principles can be explicitly and measurably described by
15 FAIR guiding principles, and any interpretation or implementation of these
principles may be chosen, so long as they lead to machine-actionable results. It is
important to note that these principles can readily be misinterpreted if they are not
embraced with the spirit by which they have been derived, e.g. FAIR is not a
standard, FAIR is not just about humans being able to find data, and FAIR is not
equal to Open [78]. Some examples from the 15 guiding principles that are relevant
to service crystallography and which the crystallographic community has been
working towards for some time include:
F1 (meta)data are assigned a globally unique and persistent identifier;
F2 data are described with rich metadata;
F4 (meta)data are registered or indexed in a searchable resource;
A2 metadata are accessible, even when the data are no longer available;
I1 (meta)data use a formal, accessible, shared, and broadly applicable language for
knowledge representation;
I2 (meta)data use vocabularies that follow FAIR principles;
R1.3 (meta)data meet domain-relevant community standards
At the discipline level, chemistry is not well prepared to implement these
principles [79]; however, crystallography is very well established in this respect.
For some time, the notion of ‘good data stewardship’ has been part of chemical
crystallography practice. The management of data in-house, that is, within one’s
own laboratory, is generally ad hoc but with some implicit principles, but at
centralised facilities and in some respects in the public domain, the community has
established data stewardship practices which are considered exemplary.
102
S. J. Coles et al.
understand it enough to be able to trust it, or not. ‘Poorer’ structures therefore need to
be supported by underlying data, and significant advances have been made with
structure factor deposition, but in certain cases underpinning with the raw data would
also be desirable. Given that a set of structure factors are extracted from diffraction
images, the user of a result still has to trust that the generator of it has done the best
possible job. In some cases, it may be possible to reprocess using different routines
or approaches and extract better structure factors. For particular types of problem,
e.g. where diffuse scattering is prominent, this would be highly beneficial. Indeed, in
many such cases, it would also enable the future potential to do better by supporting
and driving software and algorithm advances.
2.3.3 FAIR and Open Data
There is a moral requirement to make the outputs of funded research easily available
for the purpose of sharing and re-use. Recently there has been significant growth in a
strong global movement across all disciplines to make all research data/outputs
FAIR [76], that is, Findable, Accessible, Interoperable and Reusable. The FAIR
principles [77] are intended as a guide and detail for each of these aspects the key
factors that need to be considered and in place to realise them. Specifically, the
4 FAIR foundational principles can be explicitly and measurably described by
15 FAIR guiding principles, and any interpretation or implementation of these
principles may be chosen, so long as they lead to machine-actionable results. It is
important to note that these principles can readily be misinterpreted if they are not
embraced with the spirit by which they have been derived, e.g. FAIR is not a
standard, FAIR is not just about humans being able to find data, and FAIR is not
equal to Open [78]. Some examples from the 15 guiding principles that are relevant
to service crystallography and which the crystallographic community has been
working towards for some time include:
F1 (meta)data are assigned a globally unique and persistent identifier;
F2 data are described with rich metadata;
F4 (meta)data are registered or indexed in a searchable resource;
A2 metadata are accessible, even when the data are no longer available;
I1 (meta)data use a formal, accessible, shared, and broadly applicable language for
knowledge representation;
I2 (meta)data use vocabularies that follow FAIR principles;
R1.3 (meta)data meet domain-relevant community standards
At the discipline level, chemistry is not well prepared to implement these
principles [79]; however, crystallography is very well established in this respect.
For some time, the notion of ‘good data stewardship’ has been part of chemical
crystallography practice. The management of data in-house, that is, within one’s
own laboratory, is generally ad hoc but with some implicit principles, but at
centralised facilities and in some respects in the public domain, the community has
established data stewardship practices which are considered exemplary.
102
S. J. Coles et al.