Jungfraujoch/docs/ACKNOWLEDGEMENT.md

# Acknowledgements

Citation: F. Leonarski, M. Bruckner, C. Lopez-Cuenca, A. Mozzanica, H.-C. Stadler, Z. Matej, A. Castellane, B. Mesnet, J. Wojdyla, B. Schmitt and M. Wang "Jungfraujoch: hardware-accelerated data-acquisition system for kilohertz pixel-array X-ray detectors" (2023), J. Synchrotron Rad., 30, 227-234 [doi:10.1107/S1600577522010268](https://doi.org/10.1107/S1600577522010268).

The project is supported by :
* Innosuisse via Innovation Project "NextGenDCU high data rate acquisition system for X-ray detectors in structural biology applications" (101.535.1 IP-ENG; Apr 2023 - Sep 2025).
* ETH Domain via Open Research Data Contribute project (Jan - Dec 2023)
* AMD University Program with donation of licenses of Ethernet IP cores and Vivado software

Decoding bitshuffle+LZ4 images on the GPU, rather than decompressing them on the host and uploading
the result, follows Jon Wright (ESRF): "Experiences with GPU decompression for bitshuffle + LZ4
data", HDF5 User Group meeting (2021), and [bslz4decoders](https://github.com/jonwright/bslz4decoders).
The CUDA kernels in Jungfraujoch are its own, but the approach is his.

Spot extraction groups strong pixels into spots with the sparse connected-component labelling of the
ACTS traccc project: P. Gessinger, H. M. Gray, A. Krasznahorkay, C. Leggett, J. Niermann,
A. Salzburger, S. N. Swatman and B. Yeo, "traccc: GPU track reconstruction library for HEP
experiments" (2025), [arXiv:2505.22822](https://arxiv.org/abs/2505.22822);
[traccc](https://github.com/acts-project/traccc). The CPU spot extractor adapts its SparseCCL source,
and the CUDA spot extractor follows the design of its GPU counterpart - a backward-neighbour graph
over a sorted hit list, resolved by a parallel union-find. traccc is MPL-2.0; see
[THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md). The SparseCCL algorithm itself is A. Hennequin,
B. Couturier, V. V. Gligorov and L. Lacassagne, "SparseCCL: Connected Components Labeling and
Analysis for sparse images" (2019), DASIP 2019, 65-70
[doi:10.1109/DASIP48288.2019.9049184](https://doi.org/10.1109/DASIP48288.2019.9049184).

This software uses Viridis, Magma and Inferno colormaps from Matplotlib under its BSD-compatible license

## Public diffraction data used for testing

In addition to in-house datasets collected at SLS 2.0, Jungfraujoch is tested against public
diffraction data collected on other people's beamlines, on detectors and in file formats we do not
produce ourselves - most of it at other facilities, a few sets at the Swiss Light Source but not by
this system. That data was collected and published by other people. Every dataset used, the DOI to
cite for it, and the deposition it belongs to are listed in
[EXTERNAL_TEST_DATA](EXTERNAL_TEST_DATA.md); we thank the depositors, and the repositories that make
the data findable and citable.

**[IRRMC](https://proteindiffraction.org/)**, the Integrated Resource for Reproducibility in
Macromolecular Crystallography (Minor lab, University of Virginia), is the source of most of them.
IRRMC releases its data under CC0 and asks that the DOI of the dataset be cited; those DOIs are in
the table. M. Grabowski, K. M. Langner, M. Cymborowski, P. J. Porebski, P. Sroka, H. Zheng,
D. R. Cooper, M. D. Zimmerman, M.-A. Elsliger, S. K. Burley and W. Minor, "A public database of
macromolecular diffraction experiments" (2016), Acta Cryst. D72, 1181-1193
[doi:10.1107/S2059798316014716](https://doi.org/10.1107/S2059798316014716); M. Grabowski,
M. Cymborowski, P. J. Porebski, T. Osinski, I. G. Shabalin, D. R. Cooper and W. Minor, "The
Integrated Resource for Reproducibility in Macromolecular Crystallography: Experiences of the first
four years" (2019), Struct. Dyn. 6, 064301
[doi:10.1063/1.5128672](https://doi.org/10.1063/1.5128672).

**[SBGrid Data Bank](https://data.sbgrid.org/)** supplied nine of the datasets. P. A. Meyer,
S. Socias, J. Key, E. Ransey, E. C. Tjon, A. Buschiazzo et al., "Data publication with the
structural biology data grid supports live analysis" (2016), Nat. Commun. 7, 10882
[doi:10.1038/ncomms10882](https://doi.org/10.1038/ncomms10882).

**[Zenodo](https://zenodo.org/)** hosts eleven, deposited there directly by the groups that
collected them. European Organization for Nuclear Research and OpenAIRE, "Zenodo" (2013), CERN
[doi:10.25495/7GXK-RD71](https://doi.org/10.25495/7GXK-RD71). Three of those datasets were
published as IUCrData Raw Data Letters; the letters are cited on the
[EXTERNAL_TEST_DATA](EXTERNAL_TEST_DATA.md) page, beside the datasets they describe.

The beamline, resolution, space group and unit cell quoted for each dataset are the values
deposited with the corresponding PDB entry, read from the RCSB PDB data API. H. M. Berman,
J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov and P. E. Bourne,
"The Protein Data Bank" (2000), Nucleic Acids Res. 28, 235-242
[doi:10.1093/nar/28.1.235](https://doi.org/10.1093/nar/28.1.235).

## File formats read from a published specification

**CBF / imgCIF** - the native miniCBF reader implements the `x-CBF_BYTE_OFFSET` compression scheme
and reads the imgCIF `_axis` table (the laboratory directions of the image's fast and slow pixel
directions, of the goniometer axes and of a 2theta arm) from the specification alone; no CBFlib or
other CBF code is used, so there is no licence obligation, only this credit.
H. J. Bernstein and A. P. Hammersley, "Specification of the Crystallographic Binary File
(CBF/imgCIF)" (2006), International Tables for Crystallography Vol. G, 37-43
[doi:10.1107/97809553602060000729](https://doi.org/10.1107/97809553602060000729);
A. P. Hammersley, H. J. Bernstein and J. D. Westbrook, "Image dictionary (imgCIF)" (2006),
International Tables for Crystallography Vol. G, 444-458
[doi:10.1107/97809553602060000746](https://doi.org/10.1107/97809553602060000746).

## Crystallographic methods adopted from other packages

The analysis pipeline reimplements methods first published, and in most cases first implemented, by
other crystallographic software. The code below is Jungfraujoch's own; the methods are theirs, and
are acknowledged here. Where a package's source was consulted this is said explicitly. None of these
packages is linked or vendored, with the single exception of GEMMI (see
[THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md)).

**[XDS](https://xds.mr.mpg.de/)** — rotation geometry and notation, the reciprocal Lorentz and
partiality treatment, the maximum-likelihood mosaicity estimate, the `MINPK` criterion for rejecting
a reflection whose predicted profile is not cleanly its own, the intensity-based test for a
centred lattice, and the scaling correction surfaces indexed by image number and detector region. W. Kabsch, "XDS" (2010), Acta Cryst. D66, 125-132
[doi:10.1107/S0907444909047337](https://doi.org/10.1107/S0907444909047337); W. Kabsch, "Integration,
scaling, space-group assignment and post-refinement" (2010), Acta Cryst. D66, 133-144
[doi:10.1107/S0907444909047374](https://doi.org/10.1107/S0907444909047374).

**Profile fitting** with reweighted, de-biased variances is the Kabsch/Otwinowski iteration, from the
second XDS paper above and from Z. Otwinowski and W. Minor, "Processing of X-ray diffraction data
collected in oscillation mode" (1997), Methods Enzymol. 276, 307-326
[doi:10.1016/S0076-6879(97)76066-X](https://doi.org/10.1016/S0076-6879%2897%2976066-X).

**The two-dimensional integration architecture** — integrating each image in the detector plane
and only afterwards assembling a reflection's partials into a full across images, as against
three-dimensional profile fitting through the image stack — is the architecture of DENZO/SCALEPACK
and MOSFLM, and it is the one rugnux's rotation pipeline follows (per-image profile-fitted
integration, then partials combined into fulls; §9 and §10.6 of the analysis reference). The
Otwinowski & Minor citation above and the MOSFLM citations below carry the credit for the paradigm
as well as for the specifics taken from each.

**[DIALS](https://dials.github.io/)** — the resolution cutoff from the CC1/2 fall-off, per-observation
outlier rejection at merge, the scaling error model, and the treatment of a reflection whose
background is contaminated. Its published behaviour, and in places its source, settled several
choices here. G. Winter, D. G. Waterman, J. M. Parkhurst et al., "DIALS: implementation and
evaluation of a new integration package" (2018), Acta Cryst. D74, 85-97
[doi:10.1107/S2059798317017235](https://doi.org/10.1107/S2059798317017235); D. G. Waterman,
G. Winter, R. J. Gildea et al., "Diffraction-geometry refinement in the DIALS framework" (2016),
Acta Cryst. D72, 558-575 [doi:10.1107/S2059798316002187](https://doi.org/10.1107/S2059798316002187);
J. Beilsten-Edmands, G. Winter, R. Gildea et al., "Scaling diffraction data in the DIALS software
package: algorithms and new approaches for multi-crystal scaling" (2020), Acta Cryst. D76, 385-399
[doi:10.1107/S2059798320003198](https://doi.org/10.1107/S2059798320003198); J. M. Parkhurst,
G. Winter, D. G. Waterman et al., "Robust background modelling in DIALS" (2016), J. Appl. Cryst. 49,
1912-1921 [doi:10.1107/S1600576716013595](https://doi.org/10.1107/S1600576716013595).

**[POINTLESS](https://www.ccp4.ac.uk/)** (CCP4) — the space-group search. Stage A scores each
candidate rotation operator by the correlation of I(h) with I(Rh) on **resolution-normalised**
intensities (E²), as POINTLESS does — both arms of a symmetry pair sit at the same |s|, so on raw
intensities the resolution fall-off is variance shared between them and lifts a false operator's
correlation as much as a true one's; the screw-axis test scores a
predicted-absent class against the rest of its own axial row rather than against a global mean or a
fixed cut, and lets confidence fall away with the number of axial reflections instead of refusing
below a count. P. Evans, "Scaling and assessment of data quality" (2006), Acta Cryst. D62, 72-82
[doi:10.1107/S0907444905036693](https://doi.org/10.1107/S0907444905036693); P. R. Evans, "An
introduction to data reduction: space-group determination, scaling and intensity statistics" (2011),
Acta Cryst. D67, 282-292 [doi:10.1107/S090744491003982X](https://doi.org/10.1107/S090744491003982X);
P. R. Evans and G. N. Murshudov, "How good are my data and what is the resolution?" (2013), Acta
Cryst. D69, 1204-1214 [doi:10.1107/S0907444913000061](https://doi.org/10.1107/S0907444913000061);
J. Agirre, M. Atanasova, H. Bagdonas et al., "The CCP4 suite: integrative software for macromolecular
crystallography" (2023), Acta Cryst. D79, 449-461
[doi:10.1107/S2059798323003595](https://doi.org/10.1107/S2059798323003595).

**[MOSFLM](https://www.mrc-lmb.cam.ac.uk/mosflm/)** — the Rossmann FFT autoindexing algorithm and
post-refinement practice, including which parameters are safe to refine per image and which must be
refined over a wedge. The autoindexing algorithm itself — projecting the reciprocal-space points
onto many directions and Fourier-transforming the 1D projection histograms — is I. Steller,
R. Bolotovsky and M. G. Rossmann, "An algorithm for automatic indexing of oscillation images using
Fourier analysis" (1997), J. Appl. Cryst. 30, 1036-1040
[doi:10.1107/S0021889897008777](https://doi.org/10.1107/S0021889897008777); MOSFLM is the
implementation whose practice is followed. A. G. W. Leslie and H. R. Powell, "Processing diffraction data with MOSFLM"
(2007), in *Evolving Methods for Macromolecular Crystallography*, NATO Science Series II, vol. 245,
41-51 [doi:10.1007/978-1-4020-6316-9_4](https://doi.org/10.1007/978-1-4020-6316-9_4);
T. G. G. Battye, L. Kontogiannis, O. Johnson, H. R. Powell and A. G. W. Leslie, "iMOSFLM: a new
graphical interface for diffraction-image processing with MOSFLM" (2011), Acta Cryst. D67, 271-281
[doi:10.1107/S0907444910048675](https://doi.org/10.1107/S0907444910048675); H. R. Powell,
T. G. G. Battye, L. Kontogiannis, O. Johnson and A. G. W. Leslie, "Integrating macromolecular X-ray
diffraction data with the graphical user interface iMosflm" (2017), Nat. Protoc. 12, 1310-1325
[doi:10.1038/nprot.2017.037](https://doi.org/10.1038/nprot.2017.037).

**[CrystFEL](https://www.desy.de/~twhite/crystfel/)** — spot finding, the three-ring integration
region, the serial/stills processing model, and the per-frame indexing acceptance test
(`indexing_peak_check()` in `peaks.c`). T. A. White, R. A. Kirian, A. V. Martin, A. Aquila, K. Nass,
A. Barty and H. N. Chapman, "CrystFEL: a software suite for snapshot serial crystallography" (2012),
J. Appl. Cryst. 45, 335-341 [doi:10.1107/S0021889812002312](https://doi.org/10.1107/S0021889812002312).
The self-calibrating spot finder's per-resolution-ring background statistics, with the Bragg peaks
excluded by iterated clipping, follow Cheetah's peakfinder8: A. Barty, R. A. Kirian,
F. R. N. C. Maia, M. Hantke, C. H. Yoon, T. A. White and H. N. Chapman, "Cheetah: software for
high-throughput reduction and analysis of serial femtosecond X-ray diffraction data" (2014),
J. Appl. Cryst. 47, 1118-1131
[doi:10.1107/S1600576714007626](https://doi.org/10.1107/S1600576714007626).

**[fast-feedback-indexer](https://github.com/paulscherrerinstitute/fast-feedback-indexer)** — the
known-cell indexer for serial stills (`-X ffbidx`) is PSI's fast-feedback-indexer library, linked at
build time (BSD-3-Clause; see [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md)), which implements
the TORO algorithm: P. Gasparotto, L. Barba, H.-C. Stadler, G. Assmann, H. Mendonça, A. W. Ashton,
M. Janousch, F. Leonarski and B. Béjar, "TORO Indexer: a PyTorch-based indexing algorithm for
kilohertz serial crystallography" (2024), J. Appl. Cryst. 57, 931-944
[doi:10.1107/S1600576724003182](https://doi.org/10.1107/S1600576724003182).

**[GEMMI](https://github.com/project-gemmi/gemmi)** — symmetry operations, unit-cell and
structure-factor machinery, and MTZ / XDS_ASCII I/O. Vendored in `gemmi_gph/`, so it also carries a
licence obligation. M. Wojdyr, "GEMMI: A library for structural biology" (2022), J. Open Source
Softw. 7, 4200 [doi:10.21105/joss.04200](https://doi.org/10.21105/joss.04200).

**Křivý & Gruber's Niggli reduction, and the lattice-character table** — the reduction that puts
every candidate cell in a comparable form is I. Křivý and B. Gruber, "A unified algorithm for
determining the reduced (Niggli) cell" (1976), Acta Cryst. A32, 297-298
[doi:10.1107/S0567739476000636](https://doi.org/10.1107/S0567739476000636), used through GEMMI's
implementation; the table of lattice characters that maps a reduced cell to Bravais lattices and
centrings follows International Tables for Crystallography Vol. A, Table 9.2.5.1.

**Grosse-Kunstleve, Sauter & Adams's numerically stable cell reduction** - the magnitude-scaled
tolerance that decides the sign of a structurally-zero scalar product, and with it the Niggli type a
reduced cell is presented in. R. W. Grosse-Kunstleve, N. K. Sauter and P. D. Adams, "Numerically
stable algorithms for the computation of reduced unit cells" (2004), Acta Cryst. A60, 1-6
[doi:10.1107/S010876730302186X](https://doi.org/10.1107/S010876730302186X).

**Le Page's metric-symmetry search** - the obliquity of each of the 81 candidate two-folds of a
reduced cell, which is what tells a run that its lattice metric hosts more rotational symmetry than
the group its intensities supported, and the derivation of the conventional axes from that
rotation group, which is what the run then offers to the space-group search as a second lattice
candidate. The two-fold search is used through GEMMI's implementation of it. Y. Le Page, "The
derivation of the axes of the conventional unit cell from the dimensions of the Buerger-reduced
cell" (1982), J. Appl. Cryst. 15, 255-259
[doi:10.1107/S0021889882011959](https://doi.org/10.1107/S0021889882011959).

**Hexagonal-ice ring positions** — the eleven ring $d$ spacings from 3.895 to 1.522 Å that the
ice-ring score, the ice-ring flagging and the ice calibrant are all built on are taken from the
measurements of, not enumerated from a cell. D. W. Moreau, H. Atakisi and R. E. Thorne, "Ice in
biomolecular cryocrystallography" (2021), Acta Cryst. D77, 540-554
[doi:10.1107/S2059798321001170](https://doi.org/10.1107/S2059798321001170).

That list ends at 1.522 Å by its own scope, so the eight bands below it are calculated here rather
than taken from anyone: ice Ih structure factors on the oxygen sublattice, kept where they reach 3% of
the strongest line, which reproduces the eleven measured positions exactly. The lattice constants are
Röttger and co-workers'. K. Röttger, A. Endriss, J. Ihringer, S. Doyle and W. F. Kuhs, "Lattice
constants and thermal expansion of H2O and D2O ice Ih between 10 and 265 K" (1994), Acta Cryst. B50,
644-648 [doi:10.1107/S0108768194004933](https://doi.org/10.1107/S0108768194004933).

**The twinning L test** is Padilla and Yeates's: pairing each acentric reflection with a
symmetry-independent neighbour and reading the first and second moments of
L = (I1−I2)/(I1+I2) against their untwinned and perfect-twin values. J. E. Padilla and
T. O. Yeates, "A statistic for local intensity differences: robustness to anisotropy and
pseudo-centering and utility for detecting twinning" (2003), Acta Cryst. D59, 1124-1130
[doi:10.1107/S0907444903007947](https://doi.org/10.1107/S0907444903007947).

**Diffraction anisotropy** — the description of the overall fall-off by a single anisotropic
displacement tensor, its symmetry constraints, and the fact that only its deviatoric part is
determined (the isotropic part being degenerate with the overall scale) are Sheriff and Hendrickson's.
The estimator fits that tensor to the observed intensity distribution, taking sigma(I) into account,
in the sense of Popov and Bourenkov. The directional diffraction limits - <I/sigma(I)> in a cone about
each principal direction, and the reporting of the anisotropic deltaB as the range of the principal
components - follow AIMLESS. rugnux reports these; it corrects no intensity and removes no reflection
on a directional criterion. S. Sheriff and W. A. Hendrickson, "Description of overall anisotropy in
diffraction from macromolecular crystals" (1987), Acta Cryst. A43, 118-121
[doi:10.1107/S010876738709977X](https://doi.org/10.1107/S010876738709977X); A. N. Popov and
G. P. Bourenkov, "Choice of data-collection parameters based on statistic modelling" (2003), Acta
Cryst. D59, 1145-1153 [doi:10.1107/S0907444903008163](https://doi.org/10.1107/S0907444903008163);
P. R. Evans and G. N. Murshudov, "How good are my data and what is the resolution?" (2013), Acta
Cryst. D69, 1204-1214 [doi:10.1107/S0907444913000061](https://doi.org/10.1107/S0907444913000061).

**[ANODE](https://doi.org/10.1107/S0021889811041768)** — reading the anomalous difference map at the
atoms of a supplied model and reporting the strongest sites by name, instead of searching the map for
blobs. The map itself is the textbook anomalous difference Fourier; what is taken from ANODE is that
reading: A. Thorn and G. M. Sheldrick, "ANODE: anomalous and heavy-atom density calculation" (2011),
J. Appl. Cryst. 44, 1285-1287
[doi:10.1107/S0021889811041768](https://doi.org/10.1107/S0021889811041768).

**sigma_A map coefficients** — the maps written by `--model` are weighted by a maximum-likelihood
sigma_A estimated per resolution shell, giving 2mFo-DFc and mFo-DFc rather than 2Fo-Fc and Fo-Fc.
What is taken is the formalism itself: the Rice and Woolfson likelihoods of |Fo| given |Fc| and
sigma_A, the figure of merit m and the scale D that follow from it, and the result that the
bias-corrected coefficient is 2mFo-DFc for an acentric reflection and mFo for a centric one.
R. J. Read, "Improved Fourier coefficients for maps using phases from partial structures with
errors" (1986), Acta Cryst. A42, 140-149
[doi:10.1107/S0108767386099622](https://doi.org/10.1107/S0108767386099622).

**Uniform random rotations** — the null a supplied model is scored against re-orients that model at
random about its own centroid, and the rotations are drawn uniformly from SO(3) through a uniform
random unit quaternion. K. Shoemake, "Uniform Random Rotations", in *Graphics Gems III*, ed. D. Kirk,
Academic Press (1992), 124-132 (no DOI).

**Data-quality statistics** follow the established conventions rather than any one program: R_meas
and R_pim, CC1/2 and CC\*, and the reporting of I/sigma(I). K. Diederichs and P. A. Karplus, "Improved
R-factors for diffraction data analysis in macromolecular crystallography" (1997), Nat. Struct. Biol.
4, 269-275 [doi:10.1038/nsb0497-269](https://doi.org/10.1038/nsb0497-269); P. A. Karplus and
K. Diederichs, "Linking crystallographic model and data quality" (2012), Science 336, 1030-1033
[doi:10.1126/science.1218231](https://doi.org/10.1126/science.1218231); K. Diederichs and
P. A. Karplus, "Better models by discarding data?" (2013), Acta Cryst. D69, 1215-1222
[doi:10.1107/S0907444913001121](https://doi.org/10.1107/S0907444913001121).

**Uncertainty conventions** follow the IUCr Commission on Crystallographic Nomenclature:
D. Schwarzenbach, S. C. Abrahams, H. D. Flack et al., "Statistical descriptors in crystallography:
Report of the IUCr Subcommittee on Statistical Descriptors" (1989), Acta Cryst. A45, 63-75
[doi:10.1107/S0108767388009596](https://doi.org/10.1107/S0108767388009596); D. Schwarzenbach,
S. C. Abrahams, H. D. Flack, E. Prince and A. J. C. Wilson, "Statistical descriptors in
crystallography. II. Report of a Working Group on Expression of Uncertainty in Measurement" (1995),
Acta Cryst. A51, 565-569 [doi:10.1107/S0108767395002340](https://doi.org/10.1107/S0108767395002340).