Consequences of Remote Access More generally, remote access not only eliminates the need for users to travel to the synchrotron with the glass-ware containing
their samples, which carries safety considerations, but it also offers them the
potential to organise their beamtime into short, and more regularly spaced, intervals
rather than whole day blocks separated by weeks. This more regular access provides
a more timely turnaround for urgent, high-priority, samples. Remote access also
allows greater cooperation between groups in a BAG (Block Allocation Group) as
the GDA baton can be passed freely between geographically separated institutions in
the same BAG so that each end user can run their own set of samples at a convenient
period during the allocated beamtime. This greater flexibility is a compelling means
of introducing new users.
Automation
Chemical crystallography facilities are now available for use by a wide range of
researchers of varying experience working across a large range of scientific disciplines and often geographically distributed across the campus/region/country/world.
Therefore, the ability to track, process and deliver a high volume of datasets is
paramount and is now a critical capability for most service crystallography laboratories and a key component of being an efficient facility. Larger-scale facilities are
developing information management systems to address this; for example, see
ISPyB [48] described below. However, to a certain extent, every chemical crystallography facility requires systems that can support application, access, sample
submission, sample tracking and reporting, data accessibility and data management.
For a number of facilities, some of these may still be largely ‘manual’ processes;
however, as rates and volumes of data production increase exponentially, these are
beginning to become untenable. While crystallographic data is well structured and
understood and only of medium size in terms of modern digital storage, over time the
volume and heterogeneous nature of the files, ranging from binary images to small
text (e.g. CIF) files, becomes an issue for data management at the facility scale. For
example, as a high-throughput facility, the NCS diffraction laboratory at Southampton generates approximately 2,500–3,000 data collections per annum – this rate
provides an indication of the issues that all laboratories will need to address in the
future.
The recent development of highly automated methodologies for the production
and purification of proteins, along with techniques for optimising crystallisation, has
led to an ever-greater demand for the collection of X-ray diffraction data at MX
beamlines at synchrotron sources. This greater demand has driven the automation of
the beamlines with emphasis placed on intuitive and user-friendly software and the
use of robotics for sample handling and exchange, with samples mounted in a variety
of formats – including loops and X-ray diffraction-compatible crystallisation trays.
With ever-brighter sources, coupled with high-precision goniometry and fast pixel
array photon counting detectors, there has been a step change in data collection
88
S. J. Coles et al.
their samples, which carries safety considerations, but it also offers them the
potential to organise their beamtime into short, and more regularly spaced, intervals
rather than whole day blocks separated by weeks. This more regular access provides
a more timely turnaround for urgent, high-priority, samples. Remote access also
allows greater cooperation between groups in a BAG (Block Allocation Group) as
the GDA baton can be passed freely between geographically separated institutions in
the same BAG so that each end user can run their own set of samples at a convenient
period during the allocated beamtime. This greater flexibility is a compelling means
of introducing new users.
Automation
Chemical crystallography facilities are now available for use by a wide range of
researchers of varying experience working across a large range of scientific disciplines and often geographically distributed across the campus/region/country/world.
Therefore, the ability to track, process and deliver a high volume of datasets is
paramount and is now a critical capability for most service crystallography laboratories and a key component of being an efficient facility. Larger-scale facilities are
developing information management systems to address this; for example, see
ISPyB [48] described below. However, to a certain extent, every chemical crystallography facility requires systems that can support application, access, sample
submission, sample tracking and reporting, data accessibility and data management.
For a number of facilities, some of these may still be largely ‘manual’ processes;
however, as rates and volumes of data production increase exponentially, these are
beginning to become untenable. While crystallographic data is well structured and
understood and only of medium size in terms of modern digital storage, over time the
volume and heterogeneous nature of the files, ranging from binary images to small
text (e.g. CIF) files, becomes an issue for data management at the facility scale. For
example, as a high-throughput facility, the NCS diffraction laboratory at Southampton generates approximately 2,500–3,000 data collections per annum – this rate
provides an indication of the issues that all laboratories will need to address in the
future.
The recent development of highly automated methodologies for the production
and purification of proteins, along with techniques for optimising crystallisation, has
led to an ever-greater demand for the collection of X-ray diffraction data at MX
beamlines at synchrotron sources. This greater demand has driven the automation of
the beamlines with emphasis placed on intuitive and user-friendly software and the
use of robotics for sample handling and exchange, with samples mounted in a variety
of formats – including loops and X-ray diffraction-compatible crystallisation trays.
With ever-brighter sources, coupled with high-precision goniometry and fast pixel
array photon counting detectors, there has been a step change in data collection
88
S. J. Coles et al.