HDF5 / NeXus data format

Jungfraujoch stores images and on-the-fly analysis results in HDF5 files that aim to be NXmx-compliant. On top of the NXmx application definition, Jungfraujoch records a substantial amount of derived metadata (spot finding, indexing, integration, azimuthal integration, per-image statistics, timing). These extra entries do not exist in NXmx and are documented here so that the layout is unambiguous and reusable.

This page documents the file layout and the data fields. The operational behaviour of the writer (running, republishing, file finalisation, CBF/TIFF output) is described in jfjoch_writer. The wire format that feeds the writer is described in CBOR messages; fields below frequently correspond one-to-one to CBOR message fields, and that document is a useful companion for their meaning.

1. Motivation: derived metadata and FAIR data

The goal of Jungfraujoch is not only to store high-throughput datasets efficiently, but to keep them findable, accessible, interoperable and reusable (FAIR). Jungfraujoch is used for both rotation macromolecular crystallography (single- and multi-crystal, including fine-sliced and helical scans) and serial crystallography (stills, grid scans); the same concerns apply to both:

• Findability. Raw diffraction images carry almost no descriptive metadata about content. Quantities such as background level, number of diffraction spots, or indexing outcome let a user judge the quality and relevance of a dataset before inspecting the raw images.

• Accessibility at scale. A single experiment can span tens to hundreds of terabytes. Standard retrieval (e.g. HTTP) makes a dataset available but not inspectable — users would otherwise have to download a large fraction of the data just to decide whether it is useful. Compact derived representations make discovery, assessment and reuse feasible.

Because Jungfraujoch couples acquisition with real-time analysis used to steer experiments, transparency and reproducibility of that analysis matter. As a minimum the writer therefore preserves spot-finding and indexing results together with the filters that were applied, and it can retain an unbiased, down-sampled reference set of unfiltered images for validation and reuse.

Two complementary layouts: per-image spots vs. a reflection table

Jungfraujoch stores analysis products in two shapes, matching how each is accessed.

Per-image spot finding / indexing. Spot finding and indexing are inherently image-centric — the natural query is “give me the spots for image n” — and this holds for serial stills and for rotation frames alike. For these products Jungfraujoch adopts a layout similar to the Coherent X-ray Imaging (CXI) data bank (Maia, 2012) and the convention understood by CrystFEL: spot properties (position, intensity, Miller index, …) are stored in fixed-size two-dimensional arrays indexed by image number, with each image allocated room for up to a predefined maximum number of spots. These dense arrays are addressed with ordinary HDF5 hyperslab reads, so the spots of a single image are retrieved without traversing variable-length structures. The cost is some storage overhead for unused slots (padded with sentinels), which is acceptable for the access pattern.

Integrated reflections. Integrated intensities are naturally a dataset-wide table, which is exactly the model of the NeXus NXreflections base class. This fits rotation crystallography well, and Jungfraujoch uses NXreflections for its integration results (see §4.2 below). We deliberately do not force spot finding/indexing into a single experiment-wide table: across the hundreds of thousands of patterns typical of serial — or fine-sliced rotation — experiments, that would require aggregating the whole experiment before the spots of one image can be read. We encourage the community to develop standardised NeXus application definitions for image-centric crystallography products that combine NeXus interoperability with the access patterns and scale of modern high-throughput experiments.

2. File layout

A run is written as one master file plus, depending on the format, one or more data files:

<prefix>_master.h5             # NXmx master file (metadata + links / virtual datasets)
<prefix>_data_000001.h5        # data file: images + per-image analysis
<prefix>_data_000002.h5
...

The master file is produced by writer/HDF5NXmx.cpp; data files by writer/HDF5DataFile.cpp and its plugins (writer/HDF5DataFilePlugin*.cpp). Files are written to a temporary *.<random>.tmp name and renamed on successful close.

Three master-file variants exist (set via file_format):

Format	Value	Master ↔ data linking
NXmxLegacy (default)	1	One external link in /entry/data per data file (data_000001, …). HDF5 1.8 compatible — works with Neggia/Durin XDS plugins and Albula 4.0.
NXmxVDS	2	A single virtual dataset /entry/data/data spans all data files; spot finding, azimuthal integration and reflections are linked the same way. Requires HDF5 1.10 / Albula 4.1+.
NXmxIntegrated	3	No separate data files — images and all metadata live in one file. Equivalent in content to the VDS format.

In legacy/VDS mode, image-indexed analysis arrays live in the data files and are exposed in the master file through external links or virtual datasets; in integrated mode they are written directly into the single file. Throughout this document a “✓ in master” column marks entries that are visible (directly or via link/VDS) from the master file.

Images are stored chunked (one image per chunk) and compressed with bitshuffle + LZ4 or bitshuffle + Zstd; signed integer image datasets use INTx_MIN as the HDF5 fill value (the “masked / no-data” sentinel), unsigned use UINTx_MAX.

Reprocessing output: <prefix>_process.h5

The offline reprocessing tool jfjoch_process (tools/jfjoch_process.cpp) re-runs the full analysis pipeline (spot finding, indexing, refinement, integration, scaling) on an existing dataset and writes its results to a master file named <prefix>_process.h5. This file uses the integrated format, but instead of copying the images its /entry/data/data is a virtual dataset that links back to the original image files (hdf5_source_data → NXmx::LinkToData_ProcessingVDS). The result is a compact, self-describing companion file that holds all the derived analysis (everything in §4) plus a virtual view of the raw images — without duplicating terabytes of data.

This is a particularly FAIR-friendly artefact: it can be shared or archived alongside (or instead of) the raw data to convey what is in a dataset and how it processed, while the /entry/data/data VDS still resolves to the original images when they are available. jfjoch_process can also process an equally-spaced subset of images (start/end/stride), producing a down-sampled reference set.

3. NXmx-standard content

The entries below are part of, or valid base classes for, the NXmx application definition. “NXmx” = listed in the application definition; “base” = a valid field of the relevant NeXus base class (NXdetector, NXsample, NXsource) but not in the NXmx required/recommended subset.

/entry (NXentry)

Field	Std	Notes
definition	NXmx	value "NXmx"
start_time	NXmx	arming time
end_time, end_time_estimated	NXmx	approximate end time

File-level HDF5 attributes file_name, file_time, HDF5_Version are also set.

/entry/source (NXsource), /entry/instrument (NXinstrument)

Field	Std	Units
source/name, source/type	NXmx / base
source/current	base	A
instrument/name	NXmx

/entry/instrument/beam (NXbeam)

Field	Std	Units
incident_wavelength	NXmx	angstrom
incident_wavelength_spread	NXmx	angstrom (only if polychromatic)
total_flux	NXmx	Hz

/entry/instrument/attenuator (NXattenuator)

Field	Std
attenuator_transmission	NXmx

/entry/instrument/detector (NXdetector)

Field	Std	Units
depends_on	NXmx	→ transformations/rot3
beam_center_x, beam_center_y	NXmx	pixel
distance	NXmx	m
count_time, frame_time	NXmx	s
sensor_thickness	NXmx	m
sensor_material	NXmx
description	NXmx
threshold_energy	NXmx	eV (EIGER; written only for a single channel)
x_pixel_size, y_pixel_size	base	m
serial_number	base
bit_depth_readout	NXmx
saturation_value	NXmx
flatfield_applied	NXmx
pixel_mask, pixel_mask_applied	NXmx	pixel_mask is [y, x], hard-linked from detectorSpecific/pixel_mask
countrate_correction_applied	NXmx
number_of_cycles	base	frame-summation factor

/entry/instrument/detector/transformations (NXtransformations)

The NXtransformations mechanism (the depends_on chain, transformation_type, vector, offset attributes) is standard. The axis names follow the PyFAI PONI convention chosen by Jungfraujoch (see DETECTOR_GEOMETRY):

Axis	Type	Units	Depends on
translation	translation	m	.
rot1	rotation	rad	translation
rot2	rotation	rad	rot1
rot3	rotation	rad	rot2

/entry/instrument/detector/module (NXdetector_module)

data_origin, data_size, fast_pixel_direction, slow_pixel_direction, module_offset — all NXmx (fast/slow_pixel_direction and module_offset carry transformation attributes).

/entry/sample (NXsample)

Field	Std	Units / notes
name	NXmx
depends_on	NXmx	points at the last goniometer / grid-scan axis, or . for stills
temperature	NXmx	K
transformations/ (NXtransformations)	NXmx	rotation axis (e.g. omega) or grid-scan translation; hard-linked as /entry/sample/goniometer
unit_cell	base	[a, b, c, α, β, γ]
ub_matrix	base	[1, 3, 3], Angstrom⁻¹

For a rotation scan the goniometer axis is written as a per-image angle array <axis> plus <axis>_end, scalar <axis>_range_average, <axis>_range_total, and for helical scans <axis>_helical_x/_y/_z. These extra goniometer datasets beyond the bare axis array are Jungfraujoch conveniences.

/entry/data (NXdata)

data (3-D image stack, [n_images, y, x]) with image_nr_low / image_nr_high attributes. In legacy mode this group instead contains one external link data_000001, … per data file.

4. Extensions beyond NXmx

Everything in this section is outside the NXmx standard. Each group is declared with NX_class = NXcollection (the NeXus-sanctioned container for non-standardised content) unless noted. The per-image arrays are indexed by image number, padded to the run length and filled with a sentinel (NaN for floats, -1/0 for integer indices) where a quantity is absent.

4.1 /entry/MX — spot finding and indexing (CXI-style)

The flagship extension. Spot (“peak”) properties are stored as fixed-size [n_images, max_spots] arrays (CXI layout, recognised by CrystFEL); scalar-per-image quantities as [n_images] vectors. In legacy/VDS mode these live in the data files and are linked/virtual-stacked into the master.

Per-spot arrays [n_images, max_spots]:

Dataset	Units	Meaning	Indexing only
peakXPosRaw, peakYPosRaw	pixel	spot position (raw detector frame)
peakTotalIntensity	photons	spot intensity
peakIceRingRes		spot lies in an ice-ring resolution band
peakH, peakK, peakL		Miller indices of the (indexed) spot	✓
peakDistEwaldSphere	Å⁻¹	distance of the spot from the Ewald sphere	✓
peakIndexed		spot fits the indexing solution	✓
peakLattice		lattice the spot belongs to (-1 = unindexed)	✓

Per-image vectors [n_images]:

Dataset	Units	Meaning
nPeaks		number of spots stored for the image (CXI)
strongPixels		strong-pixel count (first spot-finding stage)
peakCountUnfiltered		spots found before filtering
peakCountLowRes		low-resolution spots
peakCountIceRingRes		spots inside ice-ring bands
peakCountIndexed		spots fitting the indexing solution
imageIndexed		image was indexed (0/1)
indexingLatticeCount		number of lattices found for the image
niggliClass		Niggli class of the indexed Bravais lattice (see International Tables for Crystallography A (2016), Vol. A, Table 3.1.3.1)
bravaisLattice		Bravais lattice short code, e.g. aP, mC, oF, tI, hP, hR, cF
profileRadius	Å⁻¹	crystal profile radius
mosaicity	deg	mosaicity estimate
bFactor	Ų	per-image B-factor estimate
resolutionEstimate	Å	diffraction resolution estimate
integratedReflections		number of integrated reflections
bkgEstimate	photons	mean background in the 3–5 Å resolution band
beam_corr_x, beam_corr_y	pixel	beam-center correction applied during processing
imageScaleFactor		on-the-fly per-image scale factor g
imageScaleCC		on-the-fly scaling correlation coefficient
imageScaleMosaicity	deg	scaling-model mosaicity
imageScaleBFactor	Ų	scaling-model B-factor

Per-image lattices: latticeIndexed [n_images, 9] (Å) — the real-space lattice (flattened 3×3); latticeIndexedExtra [n_images, max_extra_lattices, 9] (Å) — additional orientation variants.

Run-level summaries (written into the master /entry/MX at finalisation):

Dataset	Units	Meaning
indexing_algorithm		FFBIDX / FFT (CUDA) / FFT (FFTW)
geom_refinement_algorithm		e.g. beam_center
rotationLatticeIndexed	Å	whole-run rotation-indexing lattice ([9])
rotationLatticeIndexedExtra	Å	additional whole-run lattices ([m, 9])
rotationLatticeNiggliClass		Niggli class of the run lattice
imageIndexedMean		mean indexing rate over the run
bkgEstimateMean	photons	mean background over the run
indexedLatticeCount		per-image lattice count summary (master). Note: data files use indexingLatticeCount; readers accept either.

CrystFEL can read the spots directly with:

peak_list = /entry/MX
peak_list_type = cxi

4.2 /entry/reflections — integrated reflections (NXreflections)

Integrated reflections are stored per image as /entry/reflections/image_NNNNNN groups, each declared NX_class = NXreflections. The columns map mostly onto the standard NXreflections base class:

Dataset	Units	NXreflections	Meaning
h, k, l		standard	Miller indices
d	Å	standard	resolution
int_sum	photons	standard	integrated intensity (summation)
int_err	photons	non-standard name	σ of the intensity (standard equivalent: int_sum_errors)
background_mean	photons	standard	mean background under the peak
predicted_x, predicted_y	pixel	name standard, units differ	predicted position. NXreflections predicted_x/_y are physical lengths; the pixel datasets are predicted_px_x/_y
observed_x, observed_y	pixel	name standard, units differ	observed centroid (pixels; standard pixel form is observed_px_x/_y)
observed_frame		standard	image number of the reflection
lp		standard	Lorentz–polarization factor (stored as 1/rlp)
partiality		standard	recorded fraction of the reflection
delta_phi	deg	extension	XDS Δφ: offset from the centre of the current frame
zeta		extension	Lorentz ζ factor (reciprocal-space geometry term)
image_scale_corr		extension	per-image scale correction; I_true = image_scale_corr · int_sum

In the master file these per-image groups are exposed through /entry/reflections external links (VDS/integrated formats).

4.3 /entry/azint — azimuthal integration

Dataset	Shape	Units	Meaning
bin_to_q	[φ_bins, q_bins]	Å⁻¹	q value of each bin
bin_to_two_theta	[φ_bins, q_bins]	deg	2θ of each bin
bin_to_phi	[φ_bins, q_bins]	deg	azimuthal angle of each bin
image	[n_images, φ_bins, q_bins]		per-image integrated profile (NaN for empty bins)
image_std	[n_images, φ_bins, q_bins]		per-bin standard deviation
image_count	[n_images, φ_bins, q_bins]		pixels contributing per bin
map	[y, x]		pixel→bin mapping (master file only)

4.4 /entry/roi — regions of interest (per-image results)

/entry/roi/<roi_name> has one sub-group per configured ROI, holding the per-image result vectors [n_images]. These are written into the data files; in VDS mode they are exposed from the master file through virtual datasets, and in integrated mode they are in the single file. (In legacy mode they remain only in the data files.)

Dataset	Meaning
max	maximum pixel value in the ROI
sum	sum of pixel values
sum_sq	sum of squared pixel values
npixel	number of valid pixels
x, y	intensity-weighted centroid

4.4.1 /entry/roi_defs — ROI definitions (master file)

The dataset-wide ROI definitions (geometry, fixed for the whole acquisition) live in the master file under a separate /entry/roi_defs group — kept apart from /entry/roi above so that older readers, which iterate /entry/roi, are unaffected by these entries. One sub-group /entry/roi_defs/<roi_name> per ROI:

Dataset	Meaning
bit_index	which bit of roi_map (below) marks this ROI
type	box, circle or azim
min_x_pxl, max_x_pxl, min_y_pxl, max_y_pxl	box bounds (type box)
center_x_pxl, center_y_pxl, radius_pxl	circle (type circle)
q_min_recipA, q_max_recipA	Q range (type azim)
phi_min_deg, phi_max_deg	azimuthal-angle sector (type azim, omitted for a full ring)

/entry/roi_defs/roi_map [y, x] is a uint16 per-pixel bitmask: bit bit_index is set for every pixel belonging to that ROI, so an ROI’s footprint can be recovered exactly.

4.5 /entry/image — per-image pixel statistics

[n_images] vectors: max_value, min_value (viable min/max, excluding error/saturated pixels), error_pixels, saturated_pixels, pixel_sum. Surfaced in the master file under /entry/image.

4.6 /entry/profiling — per-image timing

[n_images] vectors in seconds: spotFindingTime, indexingTime, integrationTime, refinementTime, processingTime, braggPredictionTime, preprocessingTime, compressionTime, azIntTime, indexAnalysisTime, imageScaleTime.

4.7 /entry/detector — acquisition diagnostics (data file)

A convenience NXcollection in the data file (note: distinct from the standard /entry/instrument/detector). In integrated format these datasets are written under /entry/instrument/detector/detectorSpecific instead.

Dataset	Meaning
timestamp, exptime	per-image timestamp and exposure time
number	image number (original number if image rejection was used)
det_info	JUNGFRAU debug field
storage_cell_image	storage-cell number
rcv_delay, rcv_free_send_buffers	receiver internal diagnostics
packets_expected, packets_received	UDP packets per image
data_collection_efficiency_image	received / expected packet ratio

4.8 /entry/xfel — pulsed-source metadata

[n_images] vectors pulseID and eventCode, written for pulsed sources (e.g. SwissFEL).

4.9 Other collections

Path	Class	Content
/entry/instrument/detector/detectorSpecific	NXcollection	Dectris-style detector metadata + Jungfraujoch fields: x_pixels_in_detector, y_pixels_in_detector, nimages, ntrigger, nimages_collected, nimages_written, data_collection_efficiency, max_receiver_delay, storage_cell_number, storage_cell_delay [ns], software_git_commit, software_git_date, jfjoch_release, jfjoch_writer_release, summation_mode, detect_ice_rings, gain_file_names, data_reduction_factor_serialmx, adu_histogram/, data_collection_efficiency_image
/entry/instrument/detector/calibration	NXcollection	per-channel pedestal / calibration images (bitshuffle-compressed)
/entry/instrument/fluorescence	NXcollection	XRF spectrum: energy [eV], data
/entry/user	NXcollection	scalar values supplied under header_appendix.hdf5

4.10 Non-standard fields inside the NXmx detector group

A few extension scalars are written inside the otherwise-standard /entry/instrument/detector group for compatibility with existing tooling:

Field	Units	Meaning
detector_distance	m	duplicate of distance (Dectris/Neggia compatibility)
detector_number		detector identifier (Dectris convention)
error_value		masked/error pixel sentinel (NXmx standard would be underload_value)
bit_depth_image		stored image bit depth (NXmx standard is bit_depth_readout)
acquisition_type		always triggered (Dectris convention)
jungfrau_conversion_applied		JUNGFRAU photon/keV conversion applied
jungfrau_conversion_factor	eV	conversion factor
geometry_transformation_applied		module→full-detector geometry applied

4.11 User-supplied metadata: header_appendix and image_appendix

Facilities frequently need to attach metadata that Jungfraujoch does not model explicitly. Two free-form JSON fields in the /start request (broker/jfjoch_api.yaml) provide this without any schema change; both accept any valid JSON:

Field	Carried in	Persisted to HDF5?
header_appendix	the start message, under user_data.user (see CBOR)	no — except the hdf5 sub-object (below)
image_appendix	every image message, as user_data	no

Both are forwarded verbatim through the ZeroMQ/CBOR stream to every downstream consumer (writer, republished analysis, viewers), so they are the recommended channel for facility- or beamline-specific provenance (proposal, operator, optics state, per-image trigger info, …) that has no dedicated API field.

Persisting selected values to HDF5. header_appendix is normally not written to the master file. As an exception, if it contains a key hdf5 whose value is a JSON object of scalars (strings and numbers — no arrays or nested objects), the writer stores each entry under /entry/user/<key>.

For example, a /start request containing:

{
  "header_appendix": {
    "proposal": "p20001",
    "operator": "jdoe",
    "hdf5": { "beamline": "X06SA", "ring_mode": "top-up", "attenuator_foils": 2 }
  },
  "image_appendix": { "trigger_source": "external" }
}

forwards the whole header_appendix as user_data.user on the start message and {"trigger_source": "external"} as user_data on every image message, and writes three scalars into the master file:

/entry/user/beamline          = "X06SA"
/entry/user/ring_mode         = "top-up"
/entry/user/attenuator_foils  = 2

5. Notes

• Units are written as the HDF5 units attribute on the dataset (e.g. m, eV, deg, Angstrom, Angstrom^-1, Angstrom^2, pixel, s).

• Sentinels. Missing per-image values are NaN (floats) or -1/0 (integer indices); image pixels use INTx_MIN / UINTx_MAX.

• Master vs data file. In legacy/VDS formats the analysis arrays physically live in the data files; the master file links to them (external links in legacy, virtual datasets in VDS). In the integrated format there are no data files and everything is in one place.

• CXI / CrystFEL. /entry/MX follows the CXI peak-list convention; see CXI file format.