NXem_ebsd

Status:

application definition, extends NXobject

Description:

Application definition for collecting and indexing Kikuchi pattern into orientation maps.

This NXem_ebsd application is a proposal how to represent data, metadata, and connections between these for the research field of electron microscopy. More specifically, exemplified here for electron backscatter diffraction (EBSD). The application definition solves two key documentation issues which are missing so far to document provenance of data and metadata in the field of EBSD. The application definition can be an example that is relevant for related workflows in orientation microscopy.

Firstly, an instance of NXem_ebsd (such as a NeXus/HDF5 file which is formatted according to the NXem_ebsd application definition) stores the connection between the microscope session and the key datasets which are considered typically results of the various processing steps involved when working with EBSD data.

Different groups in this application definition make connections to data artifacts which were collected when working with electron microscopes via the NXem partner application definition. Using a file which stores information according to the NXem application definition has the benefit that it connects the sample, references to the sample processing, the user operating the microscope, details about the microscope session, and details about the acquistion and eventual indexing of Kikuchi pattern, associated overview images, like secondary electron or backscattered electron images of the region-of-interest probed and many more pieces of information.

Secondly, this NXem_ebsd application definition connects and stores the conventions and reference frames which were used and are the key to mathematically correctly interpret every EBSD result. Otherwise, results would be ripped out of their context, as it is the situation with many traditional studies where EBSD data were indexed on-the-fly and shared with the community only via sharing the results file with some technology-partner-specific file but leaving important conventions out or relying on the assumptions that colleagues know these even though multiple definitions are possible.

This application definition covers experiments with one-, two-dimensional, and so-called three-dimensional EBSD datasets. The third dimension is either time (in the case of quasi in-situ experiments) or space (in the case of serial- sectioning) methods where a combination of mechanical or ion milling is used repetitively to measure the same region-of-interest at different depth increments. Material removal can be achieved with electron or ion polishing, using manual steps or using automated equipment like a robot system.

Three-dimensional experiments require to follow a sequence of specimen, surface preparation, and data collection steps. By nature these methods are destructive in that they either require the removal of the previously measured material region or that the sample surface can degrade due to e.g. contamination or other electron-matter interaction.

For three-dimensional EBSD, multiple two-dimensional EBSD orientation mappings are combined into one reconstructed stack. That is serial-sectioning is mainly a computational workflow. Users collect data for each serial sectioning step via an experiment. This assures that data for associated microscope sessions and steps of data processing stay connected and contextualized.

Eventual tomography methods also use such a workflow because first diffraction images are collected (e.g. with X-ray) and then these imagres are indexed and computed into a 3D orientation mapping. The here proposed NXem_ebsd application definition contains conceptual ideas how this splitting between measurement and post-processing can be granularized also for such X-ray-based techniques, whether it be 3DXRD or HEDM.

Symbols:

n_op: Number of arguments per orientation for given parameterization.

n_sc: Number of scan points.

n_z: Number of pixel along the slowest changing dimension for a rediscretized, i.e. standardized default orientation mapping.

n_y: Number of pixel along slow changing dimension for a rediscretized i.e. standardized default orientation mapping.

n_x: Number of pixel along fast changing dimension for a rediscretized i.e. standardized default orientation mapping.

Groups cited:

NX_TIME, NXcg_geodesic_mesh, NXdata, NXem_ebsd_conventions, NXem_ebsd_crystal_structure_model, NXentry, NXimage_set_em_kikuchi, NXprocess, NXprogram, NXtransformations, NXuser

Structure:

ENTRY: (required) NXentry

@version: (required) NX_CHAR

An at least as strong as SHA256 hashvalue of the file that specifies the application definition.

definition: (required) NX_CHAR

NeXus NXDL schema to which this file conforms.

Obligatory value: NXem_ebsd

workflow_identifier: (required) NX_CHAR

Ideally, a (globally) unique persistent identifier for referring to this workflow.

The identifier is usually defined/issued by the facility, laboratory, or the principle investigator. The identifier enables to link workflows/experiments to e.g. proposals.

workflow_description: (optional) NX_CHAR

Free-text description about the workflow.

Users are strongly advised to detail the sample history in the respective field and fill rather as completely as possible the fields of the application definition behind instead of filling in these details into the experiment_description free-text description field.

start_time: (recommended) NX_DATE_TIME

ISO 8601 time code with local time zone offset to UTC information included when the processing of the workflow started. If the application demands that time codes in this section of the application definition should only be used for specifying when the workflow was executed - and the exact duration is not relevant - this start_time field should be used.

Often though it is useful to specify a time interval with specifying both start_time and end_time to allow for more detailed bookkeeping and interpretation of the workflow.

end_time: (recommended) NX_DATE_TIME

ISO 8601 time code with local time zone offset to UTC included when the processing of the workflow ended.

PROGRAM: (required) NXprogram

Program which was used for creating the file instance which is formatted according to the NXem_ebsd application definition.

program_name: (required) NX_CHAR

@version: (required) NX_CHAR

USER: (optional) NXuser

Contact information and eventually details of at least one person involved in performing the workflow. This can be the principle investigator who performed this experiment. Adding multiple users if relevant is recommended.

name: (required) NX_CHAR

Given (first) name and surname of the user.

affiliation: (recommended) NX_CHAR

Name of the affiliation of the user at the point in time when the experiment was performed.

address: (recommended) NX_CHAR

Postal address of the affiliation.

email: (recommended) NX_CHAR

Email address of the user at the point in time when the experiment was performed. Writing the most permanently used email is recommended.

orcid: (recommended) NX_CHAR

Globally unique identifier of the user as offered by services like ORCID or ResearcherID. If this field is field the specific service should also be written in orcid_platform

orcid_platform: (recommended) NX_CHAR

Name of the OrcID or ResearcherID where the account under orcid is registered.

telephone_number: (optional) NX_CHAR

(Business) (tele)phone number of the user at the point in time when the experiment was performed.

role: (recommended) NX_CHAR

Which role does the user have in the place and at the point in time when the experiment was performed? Technician operating the microscope. Student, postdoc, principle investigator, guest are common examples.

social_media_name: (optional) NX_CHAR

Account name that is associated with the user in social media platforms.

social_media_platform: (optional) NX_CHAR

Name of the social media platform where the account under social_media_name is registered.

conventions: (required) NXem_ebsd_conventions

rotation_conventions: (required) NXprocess

three_dimensional_rotation_handedness: (required) NX_CHAR

rotation_convention: (required) NX_CHAR

euler_angle_convention: (required) NX_CHAR

axis_angle_convention: (required) NX_CHAR

orientation_parameterization_sign_convention: (required) NX_CHAR

processing_reference_frame: (required) NXprocess

reference_frame_type: (required) NX_CHAR

xaxis_direction: (required) NX_CHAR

xaxis_alias: (required) NX_CHAR

yaxis_direction: (required) NX_CHAR

yaxis_alias: (required) NX_CHAR

zaxis_direction: (required) NX_CHAR

zaxis_alias: (required) NX_CHAR

origin: (required) NX_CHAR

sample_reference_frame: (required) NXprocess

reference_frame_type: (required) NX_CHAR

xaxis_direction: (required) NX_CHAR

yaxis_direction: (required) NX_CHAR

zaxis_direction: (required) NX_CHAR

origin: (required) NX_CHAR

detector_reference_frame: (required) NXprocess

reference_frame_type: (required) NX_CHAR

xaxis_direction: (required) NX_CHAR

yaxis_direction: (required) NX_CHAR

zaxis_direction: (required) NX_CHAR

origin: (required) NX_CHAR

gnomonic_projection_reference_frame: (required) NXprocess

reference_frame_type: (required) NX_CHAR

xaxis_direction: (required) NX_CHAR

yaxis_direction: (required) NX_CHAR

zaxis_direction: (required) NX_CHAR

origin: (required) NX_CHAR

pattern_centre: (required) NXprocess

xaxis_boundary_convention: (required) NX_CHAR

xaxis_normalization_direction: (required) NX_CHAR

yaxis_boundary_convention: (required) NX_CHAR

yaxis_normalization_direction: (required) NX_CHAR

simulation: (recommended) NXprocess

Details about simulations for Kikuchi pattern using kinematic or dynamic diffraction theory. Usually, the output of such computer simulations are spherical Kikuchi images which only when projected or observed in some region-of-interest will represent a set of rectangular Kikuchi pattern with the same rectangular shape and image size.

Therefore, these pattern should be stored. The spherical diffraction pattern can be stored as a set of triangulated geodesic meshes. The rectangular patterns should be stored as NXimage_set_em_kikuchi stack.

Do not store pattern in the simulation group if they have been measured are not simulated.

sequence_index: (required) NX_POSINT

PROGRAM: (optional) NXprogram

program_name: (required) NX_CHAR

@version: (required) NX_CHAR

CG_GEODESIC_MESH: (optional) NXcg_geodesic_mesh

IMAGE_SET_EM_KIKUCHI: (required) NXimage_set_em_kikuchi

stack: (required) NXdata

@signal: (required) NX_CHAR

@axes: (required) NX_CHAR

@AXISNAME_indices: (required) NX_CHAR

title: (required) NX_CHAR

data_counts: (required) NX_NUMBER

@long_name: (required) NX_CHAR

pattern_identifier: (required) NX_UINT

@long_name: (required) NX_CHAR

axis_y: (required) NX_NUMBER

@long_name: (required) NX_CHAR

axis_x: (required) NX_NUMBER

@long_name: (required) NX_CHAR

experiment: (optional) NXprocess

The experiment group captures relevant details about the conditions of and the tools used for collecting the Kikuchi diffraction pattern.

The most frequently collected EBSD data are captured as rectangular ROIs composed from square or hexagonally-shaped pixels. Substantially less frequently, because such experiments are more costly and technically demanding, correlated experiments are performed.

One important class of such correlated experiments are the so-called (quasi) in-situ experiments. Here the same or nearly the same ROI is analyzed via a cycles of thermomechanical treatment, sample preparation, measurement, on-the-fly-indexing. Phenomena investigated like this are recrystallization, strain accumulation, material damage. Post-processing is required to correlate and reidentify eventual features or local ROIs across several orientation maps.

Another important class of correlated experiments are the so-called serial-sectioning experiments. Here the same sample is repetitively measured and polished to create a stack of orientation data which can be reconstructed to a three-dimensional volume ROI.

time: (optional) NX_TIME

Physical time since the beginning of a timestamp that is required to be same for all experiments in the set. The purpose of this marker is to identify how all experiments in the set have have to be arranged sequentially based on the time elapsed. The time is relevant to sort e.g. experiments of consecutive quasi in-situ experiments where a measurement was e.g. taken after 0 minutes of annealing, 30 minutes, 6 hours, or 24 hours of annealing.

TRANSFORMATIONS: (optional) NXtransformations

Transformation which details where the region-of-interest described under indexing is located in absolute coordinates and rotation with respect to which coordinate system.

calibration: (recommended) NXprocess

The EBSD system, including components like the electron gun, pole-piece, stage tilting, EBSD detector, and the gnomonic projection have to be calibrated to achieve reliable results. Specifically, the gnomonic projection has to be calibrated.

In most practical cases, especially in engineering, there is a substantially larger number of sessions where such a calibrated system is used assuming that somebody has properly calibrated the system rather than that the user actively recalibrates it or is even allowed to do so. Especially the projection geometry has to calibrated which is usually achieved with measuring silicon, quartz or standards, and comparing against simulated diffraction pattern.

In the first case, the user assumes that the principle geometry of the hardware components and the settings in the control and EBSD pattern acquisition software are calibrated. Consequently, users pick from an existent library of phase candidates. One example are the CRY or CIF files of the classical HKL/Channel 5/Flamenco software products. Each entry of the library of such phase candidates in this NeXus proposal is represented by one NXem_ebsd_crystal_structure_model base class. For each phase an instance of this base class is to be used to store crystallographic and simulation-relevant data.

Indexing is a data processing step performed after/during the beam scans the specimen (depends on configuration). Users load the specimen, and first collect a coarse image of the surface. Next, an approximate value for the calibrated working distance is chosen and the stage tilted. Users then may configure the microscope for collecting higher quality data and push in the EBSD detector. Subsequently, they fine tune the illumination and aberration settings and select one or multiple ROIs to machine off. The on-the-fly indexing parameter are defined and usually the automated measurement queue started.

Nowadays, this is usually an automated/unsupervised process. The pattern collection runs during the allocated session time slot which the user has booked ends or when the queue finishes prematurely. Kikuchi pattern surplus eventually multi-modal detector signals are collected and usually indexed on-the-fly. The Kikuchi patterns may or not be deleted directly after a solution was found (on-the-fly) so Kikuchi pattern are not always stored.

Results files are in many labs afterwards copied automatically for archival purposes to certain storage locations. The result of such an EBSD measurement/experiment is a set of usually proprietary or open files from technology partners (microscope and EBSD detector manufacturers).

In the second case, the system is being calibrated during the session using standards (silicon, quartz, or other common specimens). There is usually one person in each lab responsible for doing such calibrations. Important is that often this person or technician(s) are also in charge of configuring the graphical user interface and software with which most users control and perform their analyses. For EBSD this has key implications because, taking TSL OIM/EDAX as an example, the conventions how orientations are stored is affected by how reference frames are set up and this setup is made at the level of the GUI software. Unfortunately, these pieces of information are not necessarily stored in the results files. In effect, key conventions become disconnected from the data so it remains the users personal obligation to remember these settings, write them down in the lab notebook, or these metadata get lost. All these issues are a motivation and problem which NXem_ebsd solves.

sequence_index: (required) NX_POSINT

origin: (required) NX_CHAR

A link/cross reference to an existent instance of NXem_ebsd with ideally an associated instance of NXem detailed under measurement which informs about the calibration procedures.

@version: (required) NX_CHAR

Commit identifying this resource.

path: (required) NX_CHAR

Path which resolves which specific NXimage_set_em_kikuchi instance was used as the raw data to the EBSD data (post)-processing workflow when performing the calibration.

acquisition: (recommended) NXprocess

Relevant result of the session at the microscope for this experiment which enables to connect the measurement of the Kikuchi pattern and their processing into orientation microscopy maps.

sequence_index: (required) NX_POSINT

origin: (required) NX_CHAR

Name or link to an existent instance of an EBSD raw dataset ideally as an instance of an NXem application definition which has at least one NXimage_set_em_kikuchi instance i.e. one stack of Kikuchi pattern. The path to this instance in the origin has to be specified under path.

When NXem is not used or the aim is to rather explore first how community-specific files with EBSD data, such as ANG, CPR, or HDF5- based formats can be parsed from, inject here the name of that file.

The em_om parser will currently not interpret the majority of the many system- and technique-specific metadata which come with the files from e.g. technology partners. This is because the current culture in the EBSD community is that many of the metadata fields are neither in all cases fully documented nor use a standardized vocabulary although many people understand terms from different implementations and how these metadata can likely be compared to one another.

In addition, it is common practice in the research field of EBSD that users transcode their raw data into other (often text-based or HDF5) files with custom formatting to realize an information transfer between specific software tools including commercial software from technology partner, custom scripts in Matlab using tools like MTex, or Python scripting with tools like hyperspy, pyxem, orix, diffsims, kikuchipy, or EBSD data stack alignment tools like DREAM.3D. We have opted that in the first iteration this implementation of a RDMS-agnostic FAIR data schema for EBSD that we discard these metadata because these ad hoc file formats are not designed to communicate also specifically and most importantly the eventually different context of the metadata. Another reason for this choice was also to emphasize that in fact such challenges do exist in the community and thus pointing them out may support the discussion to arrive at eventually more complete solutions. As developing these solutions should not be our authority and necessarily demands feedback from the technology partners, we have opted for this intermediate approach to stimulate discussion.

@version: (required) NX_CHAR

Commit or e.g. at least SHA256 checksum identifying this resource.

path: (required) NX_CHAR

Path which resolves which specific NXimage_set_em_kikuchi instance was used as the raw data to this EBSD data (post)-processing workflow.

indexing: (recommended) NXprocess

OIM, orientation imaging microscopy. Post-processing of the Kikuchi patterns to obtain orientation per phase model and scan point. Fundamentally different algorithms can be used to index EBSD/EBSP pattern.

Common is that pattern indexing is a computational step of comparing simulated with measured diffraction pattern. Quality descriptors are defined based on which an indexing algorithm yields a quantitative measure of how similar measured and assumed/simulated pattern are, and thus if no, one, or multiple so-called solutions were found.

Assumed or simulated pattern use kinematical or dynamical electron diffraction theory. Hough transform (which is essentially a discretized Radon transform, for details see e.g A short introduction to the Radon and Hough transforms and how they relate by M. van Ginkel et al.). Recently, dictionary-based indexing methods are increasingly becoming used partly driven by the move to use artificial intelligence algorithms.

An inspection of publicly available EBSD datasets with an open-source license which are available on Zenodo was performed prior to implementing of the associated em_om parser for NXem_ebsd. This analysis revealed that EBSD data are in most cases stored in two ways: Case one was via a file in formats from technology partners. Examples are binary formats like OSC, H5OINA, OIP, EBSP, and many derived text-based formats like CPR, CRC, ANG, CTF, HKL and more. Recently, there is trend towards using HDF5-based formats.

These files contain some result and metadata to the numerical steps and the computational workflow which was performed to index Kikuchi pattern on-the-fly. Examples of metadata include scan point positions, indexing solutions per scan point, some quality descriptors for the solutions, as well as crystal structure and phase metadata.

Case two were raw pattern in some custom format, often text-based with some but in general no conclusive and interoperable representation of all relevant metadata. Often it remains unclear what individual fields and data arrays of these fields resolve and/or mean conceptually. For some fields, publications were referred to. However, software tools change over time and thus which specific data end in a file and which specific conceptual information is behind these data can change with software versions.

Other cases were storing results of custom post-processing steps and associated Kikuchi pattern. Testing of advanced indexing, pseudo-symmetry resolving methods, i.e. any sort of prototyping or alternative indexing strategies so far seem to require some flexibility for implementing rapid prototypic capabilities. The drawback of this is that such results come formatted on a case-by-case basis and are thus not interoperable.

Therefore, we first need to collect how these files have been generated and which metadata in these files (or database entries) represent which pieces of information conceptually. Ideally, one would do so by creating a complete set of information in e.g. an NXem application definition, such as a log of timestamped events and processing steps, metadata and data. Eventually even interactions with the graphical user interface of commercial software during the microscope session should be stored and become a part of the application definition.

Such a set of pieces of information could then be used via reading directly for the NXem application definition. However, in most cases such a data representation is not available yet.

sequence_index: (required) NX_POSINT

method: (required) NX_CHAR

Principal algorithm used for indexing.

Any of these values:

  • undefined

  • hough_transform

  • dictionary

  • radon_transform

  • other

status: (optional) NX_UINT (Rank: 1, Dimensions: [n_sc]) {units=NX_UNITLESS}

Which return value did the indexing algorithm yield for each scan point. Practically useful is to use an uint8 mask.

  • 0 - Not analyzed

  • 1 - Too high angular deviation

  • 2 - No solution

  • 100 - Success

  • 255 - Unexpected errors

n_phases_per_scan_point: (recommended) NX_UINT (Rank: 1, Dimensions: [n_sc]) {units=NX_UNITLESS}

How many phases i.e. crystal structure models were used to index each scan point if any? Let’s assume an example to explain how this field should be used: In the simplest case users collected one pattern for each scan point and have indexed using one phase, i.e. one instance of an NXem_ebsd_crystal_structure_model.

In another example users may have skipped some scan points (not indexed) them at all) and/or used differing numbers of phases for different scan points.

The cumulated of this array decodes how phase_identifier and phase_matching arrays have to be interpreted. In the simplest case (one pattern per scan point, and all scan points indexed using that same single phase model), phase_identifier has as many entries as scan points and phase_matching has also as many entries as scan points.

phase_identifier: (recommended) NX_UINT (Rank: 1, Dimensions: [i]) {units=NX_UNITLESS}

The array n_phases_per_scan_point details how the phase_identifier and the phase_matching arrays have to be interpreted.

For the example with a single phase phase_identifier has trivial values either 0 (no solution) or 1 (solution matching sufficiently significant with the model for phase 1).

When there are multiple phases, it is possible (although not frequently needed) that a pattern matches eventually (not equally well) sufficiently significant with multiple pattern. This can especially happen in cases of pseudosymmetry and more frequently with an improperly calibrated system or false or inaccurate phase models e.g. (ferrite, austenite). Having such field is especially relevant for recent machine learning or dictionary based indexing schemes because in combination with phase_matching these fields communicate the results in a model-agnostic way.

Depending on the n_phases_per_scan_point value phase_identifier and phase_matching arrays represent a collection of concatenated tuples, which are organized in sequence: The solutions for the 0-th scan point, the 1-th scan point, the n_sc - 1 th scan point and omitting tuples for those scan points with no phases according to n_phases_per_scan_point

phase_matching: (recommended) NX_NUMBER (Rank: 1, Dimensions: [i]) {units=NX_UNITLESS}

One-dimensional array, pattern by pattern labelling the solutions found. The array n_phases_per_scan_point has to be specified because it details how the phase_identifier and the phase_matching arrays have to be interpreted. See documentation of phase_identifier for further details.

phase_matching_descriptor: (recommended) NX_CHAR

Phase_matching is a descriptor for how well the solution matches or not. Examples can be confidence index (ci), mean angular deviation (mad), some AI-based matching probability (other), i.e. the details are implementation-specific.

Any of these values: undefined | ci | mad | other

orientation_parameterization: (recommended) NX_CHAR

How are orientations parameterized? Inspect euler_angle_convention in case of using euler to clarify the sequence of rotations assumed.

Any of these values:

  • euler

  • axis_angle

  • rodrigues

  • quaternion

  • homochoric

orientation: (recommended) NX_NUMBER (Rank: 2, Dimensions: [i, n_op]) {units=NX_ANY}

Matrix of parameterized orientations identified. The slow dimension iterates of the individual solutions as defined by n_phases_per_scan_point. Values for phases without a solution should be correctly identified as IEEE NaN.

scan_point_positions: (recommended) NX_NUMBER (Rank: 2, Dimensions: [n_sc, 2]) {units=NX_LENGTH}

Matrix of calibrated centre positions of each scan point in the sample surface reference system.

hit_rate: (optional) NX_NUMBER {units=NX_DIMENSIONLESS}

Fraction of successfully indexed pattern of the set averaged over entire set.

on_the_fly_indexing: (optional) NXprocess

Therefore, the on_the_fly_indexing group stores which source_file contains the results of the on-the-fly indexing. For commercial systems these files can be e.g. ANG, CPR/CRC, H5OINA, OSC. It is possible that the file or database entry which is referred to under origin is the same as the one under a given acquisition/origin in one of the experiment groups. This is because some commercial file formats make no clear distinction between which metadata are acquisition and/or indexing metadata.

origin: (required) NX_CHAR

Name of the file from which data relevant for creating default plots were taken in the case that the data in the experiment group were indexed on-the-fly.

@version: (required) NX_CHAR

Hash of that file.

path: (required) NX_CHAR

TBD, path which resolves which specific NXimage_set_em_kikuchi instance was used as the raw data to this EBSD data (post)-processing workflow when performing the calibration.

PROGRAM: (required) NXprogram

Commercial program which was used to index the EBSD data incrementally after they have been captured and while the microscope was capturing (on-the-fly). This is the usual production workflow how EBSD data are collected in materials engineering, in industry, and academia.

program_name: (required) NX_CHAR

@version: (required) NX_CHAR

background_correction: (optional) NXprocess

Details about the background correction applied to each Kikuchi pattern.

sequence_index: (required) NX_POSINT

binning: (optional) NXprocess

Binning i.e. downsampling of the pattern.

sequence_index: (required) NX_POSINT

parameter: (optional) NXprocess

Specific parameter relevant only for certain algorithms used

sequence_index: (required) NX_POSINT

EM_EBSD_CRYSTAL_STRUCTURE_MODEL: (required) NXem_ebsd_crystal_structure_model

crystallographic_database_identifier: (recommended) NX_CHAR

crystallographic_database: (recommended) NX_CHAR

unit_cell_abc: (required) NX_FLOAT

unit_cell_alphabetagamma: (required) NX_FLOAT

space_group: (recommended) NX_CHAR

phase_identifier: (required) NX_UINT

phase_name: (recommended) NX_CHAR

atom_identifier: (recommended) NX_CHAR

atom: (recommended) NX_UINT

atom_positions: (recommended) NX_FLOAT

atom_occupancy: (recommended) NX_FLOAT

number_of_planes: (recommended) NX_UINT

plane_miller: (recommended) NX_NUMBER

dspacing: (recommended) NX_FLOAT

relative_intensity: (recommended) NX_FLOAT

region_of_interest: (required) NXprocess

An overview of the entire area which was scanned. For details about what defines the image contrast inspect descriptor.

descriptor: (required) NX_CHAR

Descriptor representing the image contrast.

Any of these values:

  • normalized_band_contrast

  • normalized_confidence_index

roi: (required) NXdata

Container holding a default plot of the region on the sample investigated with EBSD.

@signal: (required) NX_CHAR

@axes: (required) NX_CHAR

@AXISNAME_indices: (required) NX_CHAR

title: (required) NX_CHAR

data: (required) NX_NUMBER (Rank: 2, Dimensions: [n_y, n_x]) {units=NX_UNITLESS}

Descriptor values displaying the ROI.

@long_name: (required) NX_CHAR

Signal

axis_y: (required) NX_NUMBER (Rank: 1, Dimensions: [n_y]) {units=NX_LENGTH}

Calibrated center of mass of the pixel along the slow axis.

@long_name: (required) NX_CHAR

Label for the y axis

axis_x: (required) NX_NUMBER (Rank: 1, Dimensions: [n_x]) {units=NX_LENGTH}

Calibrated center of mass of the pixel along the fast axis.

@long_name: (required) NX_CHAR

Label for the x axis

PROCESS: (optional) NXprocess

Default inverse pole figure (IPF) plot of the data specific for each phase. No ipf_mapID instances for non-indexed scan points as these are by definition assigned the null phase with phase_identifier 0.

The IPF mapping is interpolated from the scan point data mapping onto a rectangular domain with square pixels and the orientations colored according to the coloring scheme used in the respective ipf_color_modelID/program.

The main purpose of the ipf_mapID group is not to keep raw data or scan point related data but offer a default way how a research data management system can display a preview of the dataset so that users working with the RDMS can get an overview of the dataset.

This matches the first aim of NXem_ebsd which is foremost to bring colleagues and users of EBSD together to discuss which pieces of information need to be stored together. We are convinced a step-by-step design and community-driven discussion about which pieces of information should and/or need to be included is a practical strategy to work towards an interoperable description and data model for exchanging data from EBSD between different tools and research data management systems (RDMS).

With this design the individual RDMS solutions and tools can still continue to support specific custom data analyses workflow and routes but at least there is then one common notation of understanding whereby also users not necessarily expert in all the details of the EBSD story can understand better these data and thus eventually this can motivate data reuse and repurposing.

It is important to mention that we cannot assume, at least for now, that the parser which writes to an NXem_ebsd-compliant file is also responsible or capable at all of computing the inverse pole figure color keys and maps itself. This cannot be assumed working because this mapping of orientation data uses involved mathematical algorithms and functions which not every tools used in the EBSD community is capable of using or is for sure not using in exactly the same way.

Currently, we assume it is the responsibilty of the tool used which generated the data under on_the_fly_indexing to compute these plots and deliver these to the parser.

Specific case studies have been explored by the experiment team of Area B of the FAIRmat project to realize and implement such mapping.

The first case study uses the H5OINA format and the pyxem/orix library. As orix is a Python library, the coloring is performed by the em_om parser.

The second case study uses MTex and its EBSD color coding model. As MTex is a Matlab tool, an intermediate format is written from MTex first which stores these pieces of information. The parser then pulls these data from the intermediate Matlab-agnostic representation and supplements the file with missing pieces of information as it is required by NXem_ebsd.

The third case study shows how a generic set of Kikuchi pattern can be loaded with the em_om parser. The pattern are loaded directly from a ZIP file and mapped to an simulation image section for now.

The fourth case study uses the DREAM.3D package which provides an own set of EBSD data post-processing procedures. DREAM.3D documents the processing steps with a pipeline file which is stored inside DREAM.3D output files. In this case study, the parser reads the DREAM.3D file and maps data relevant from the perspective of NXem_ebsd plus adds relevant IPF color maps as they were computed by DREAM.3D. Given that in this case the origin of the data is the DREAM.3D file again provenance is kept and more details can be followed upon when resolving origin.

These examples offer a first set of suggestions on how to make EBSD data injectable into research data management system using schemes which themselves are agnostic to the specific RDMS and interoperable. Steps of collecting the raw data and post-processing these with custom scripts like MTex or commercial tools so far are mainly undocumented. The limitation is that a program which consumes results or dump files from these tools may not have necessarily all the sufficient information available to check if the injected orientation data and color models are matching the conventions which a user or automated system has injected into an electronic lab notebook from which currently the em_om parser collects the conventions and stores them into this NXem_ebsd instance. The immediate benefit of the here presented NXem_ebsd concept though is that the conventions and reference frame definitions are expected in an ELN-agnostic representation to make NXem_ebsd a generally useful data scheme for EBSD.

Ideally, the em_om parser would load convention-compliant EBSD data and use subsequently a community library to transcode/convert orientation conventions and parameterized orientation values. Thereafter, convention- compliant default plot(s) could be created that would be truely interoperable.

However, given the variety of post-processing tools available surplus the fact that these are not usually executed along standardized post-processing workflows which perform exactly the same algorithmic steps, this is currently not a practically implementable option. Indeed, first developers who wish to implement this would first have to create a library for performing such tasks, mapping generally between conventions, i.e. map and rotate coordinate systems at the parser level.

The unfortunate situation in EBSD is that due to historical reasons and competitive strategies, different players in the field have implemented (slightly) different approaches each of which misses some part of a complete workflow description which is behind EBSD analyses: Sample preparation, measurement, indexing, post-processing, paper…

The here exemplified default plot do not so far apply relevant rotations but takes the orientation values as they come from the origin and using coloring them as they come. It is thus the scientists responsibility to enter and check if the respective dataset is rotation-conventions-wise consistent and fit for a particular task.

Ideally, with all conventions defined it can be possible to develop a converter which rotates the input data. This application definition does not assume this and users should be aware of this limitation.

The key point is that the conventions however are captured and this is the most important step to the development of such a generic transcoder for creating interoperable EBSD datasets.

Currently the conventions remain in the mind or manual lab book of the respective scientists or technicians instead of getting stored and communicated with research papers that are written based on specific dataset, i.e. database entries.

The default gridded representation of the data should not be misinterpreted as the only possible way how EBSD data and OIM maps can be created!

Indeed, the most general case is that patterns are collected for scan points. The scan generator of an electron microscope is instructed to steer the beam in such a way across the specimen surface that the beam illuminates certain positions for a certain amount time (usually equally-spaced and spending about the same amount of time at each position).

Therefore, scan positions can be due to such regular flight plans and represent sampling on lines, line stacks, rectangular regions-of- interests, but also could instruct spiral, random, or adaptive scans instead of tessellations with square or hexagonal pixels.

The majority of EBSD maps is though is reporting results for a regular grid (square, hexagon). What matters though in terms of damage induced by the electron beam and signal quality is the real electron dose history, i.e. for how long the beam exposed which location of the specimen. Especially when electron charging occurs (i.e. an excess amount of charge accumulates due to e.g. poor conducting away of this charge or an improper mounting, too high dose, etc. such details are relevant.

Specifically, the default visualization is an inverse pole-figure (IPF) map with the usual RGB color coding. Different strategies and normalization schemes are in use to define such color coding.

Finally, we should mention that each ipf_map represents data for scan points indexed as one phase. The alias/name of this phase should be stored in phase_name, the phase_identifier give an ID which must not be zero as this value is reserved for non-indexed / null model scan points.

phase_identifier: (required) NX_UINT {units=NX_UNITLESS}

Specifying which phase this IPF mapping visualizes.

phase_name: (required) NX_CHAR

Alias/name for the phase whose indexed scan points are displayed.

description: (optional) NX_CHAR

Which IPF definition computation according to backend.

projection_direction: (required) NX_NUMBER (Rank: 1, Dimensions: [3]) {units=NX_UNITLESS}

Along which axis to project? Typically [0, 0, 1] is chosen.

bitdepth: (required) NX_UINT {units=NX_UNITLESS}

Bitdepth used for the RGB color model. Usually 8 bit.

PROGRAM: (required) NXprogram

The tool/implementation used for creating the IPF color map from the orientation data. Effectively, this program is the backend which performs the computation of the inverse pole figure mappings which can be for some use cases the parser. Consider the explanations in the docstring of the ipf_mapID group.

program_name: (required) NX_CHAR

@version: (required) NX_CHAR

ipf_rgb_map: (required) NXdata

The RGB image which represents the IPF map.

@signal: (required) NX_CHAR

@axes: (required) NX_CHAR

@AXISNAME_indices: (required) NX_CHAR

title: (required) NX_CHAR

data: (required) NX_UINT (Rank: 3, Dimensions: [n_y, n_x, 3]) {units=NX_UNITLESS}

RGB array, with resolution per fastest changing value defined by bitdepth.

@long_name: (required) NX_CHAR

IPF color-coded orientation mapping

axis_y: (required) NX_NUMBER (Rank: 1, Dimensions: [n_y]) {units=NX_LENGTH}

Calibrated center of mass of the pixel along the slow axis.

@long_name: (required) NX_CHAR

Label for the y axis

axis_x: (required) NX_NUMBER (Rank: 1, Dimensions: [n_x])

Calibrated center of mass of the pixel along the fast axis.

@long_name: (required) NX_CHAR

Label for the x axis

ipf_rgb_color_model: (required) NXdata

For each stereographic standard triangle (SST), i.e. a rendering of the fundamental zone of the crystal-symmetry-reduced orientation space SO3, it is possible to define a color model which assigns each point in the fundamental zone a color. Different mapping models are in use and implement (slightly) different scaling relations. Differences are which base colors of the RGB color model are placed in which extremal position of the SST and where the white point is located. For further details see:

Details are implementation-specific and not standardized yet. Given that the SST has a complicated geometry, it cannot yet be visualized using tools like H5Web, which is why for now the em_om parsers takes a rasterized image which is rendered by the backend tool.

@signal: (required) NX_CHAR

@axes: (required) NX_CHAR

@AXISNAME_indices: (required) NX_CHAR

title: (required) NX_CHAR

data: (required) NX_UINT (Rank: 3, Dimensions: [n_y, n_x, 3]) {units=NX_UNITLESS}

RGB array, with resolution per fastest changing value defined by bitdepth.

@long_name: (required) NX_CHAR

IPF color key in stereographic standard triangle (SST)

axis_y: (required) NX_NUMBER (Rank: 1, Dimensions: [n_y]) {units=NX_ANY}

Pixel coordinate along the slow axis.

@long_name: (required) NX_CHAR

Label for the y axis

axis_x: (required) NX_NUMBER (Rank: 1, Dimensions: [n_x]) {units=NX_ANY}

Pixel coordinate along the fast axis.

@long_name: (required) NX_CHAR

Label for the x axis

correlation: (optional) NXprocess

This application definition also enables to describe a workflow where several EBSD datasets are not only documented but also correlated based on time, position (spatial), or both (spatiotemporal).

Spatial correlations between repetitively characterized regions-of-interests are typically correlated using image registration and alignment algorithms. For this typically so-called landmarks are used. These can be grains with a very large size or specific shape, i.e. grains which are qualitatively different enough to be used as a guide how images are shifted relative to one another. Other commonly used landmarks are fiducial marks which are milled into the specimen surface using focus-ion beam milling and/or various types of indentation methods.

As far as the same physical region-of-interest is just measured several times, the additional issue of the depth increment is not a concern. However, correct assumptions for the depth increment, amount of material removed along the milling direction is relevant for accurate and precise three-dimensional (serial-sectioning) correlations. For these studies it can be tricky though to assume or estimate useful depth increments. Different strategies have been proposed like calibrations, wedged-shaped landmarks and computer simulation assisted assumption making.

Despite the use of landmarks, there are many practical issues which make the processing of correlations imprecise and inaccurate. Among these are drift and shift of the specimen, instabilities of the holder, the beam, irrespective of the source of the drift, charging effects, here specifically causing local image distortions and rotations which may require special processing algorithms to reduce such imprecisions.

Time correlations face all of the above-mentioned issues surplus the challenge that specific experimental protocols have to be used to ensure the material state is observed at specific physical time. The example of quasi in-situ characterization of crystal growth phenomena, a common topic in engineering or modern catalysis research makes it necessary to consider that e.g. the target value for the desired annealing temperature is not just gauged based on macroscopic arguments but considers that transient effects take place. Heating or quenching a sample might thus might not have been executed under conditions in the interaction volume as they are documented and/or assumed.

These issue cause that correlations have an error margin as to how accurately respective datasets were not only just synced based on the geometry of the region-of-interests and the time markers but also to asssure which physical conditions the specimen experienced over the course of the measurements.

The fourth example of the em_om reference implementation explores the use of the correlation group with a serial-sectioning datasets that was collected by the classical Inconel 100 dataset collected by M. D. Uchic and colleagues (M. Groeber M, Haley BK, Uchic MD, Dimiduk DM, Ghosh S 3d reconstruction and characterization of polycrystalline microstructures using a fib-sem system data set. Mater Charac 2006, 57 259–273. 10.1016/j.matchar.2006.01.019M).

This dataset was specifically relevant in driving forward the implementation of the DREAM.3D software. DREAM.3D is an open-source software project for post-processing and reconstructing, i.e. correlating sets of orientation microscopy data foremost spatially. One focus of the software is the (post-)processing of EBSD datasets. Another cutting edge tool with similar scope but a commercial solution by Bruker is QUBE which was developed by P. Konijnenberg and coworkers.

Conceptually, software like DREAM.3D supports users with creating linear workflows of post-processing tasks. Workflows can be instructed via the graphical user interface or via so-called pipeline processing via command line calls. DREAM.3D is especially useful because its internal system documents all input, output, and parameter of the processing steps. This makes DREAM.3D a good candidate to interface with tools like em_om parser. Specifically, DREAM.3D documents numerical results via a customized HDF5 file format called DREAM3D. Workflow steps and settings are stored as nested dictionaries in JSON syntax inside a supplementary JSON file or alongside the data in the DREAM3D file. DREAM.3D has a few hundred algorithms implemented. These are called filters in DREAM.3D terminology.

Users configure a workflow which instructs DREAM.3D to send the data through a chain of predefined and configured filters. Given that for each analysis the filter is documented via its version tags surplus its parameter and setting via a controlled vocabulary, interpreting the content of a DREAM3D HDF5 file is possible in an automated manner using a parser. This makes DREAM.3D analyses repeatable and self-descriptive. A key limitation though is that most frequently the initial set of input data come from commercial files like ANG. This missing link between the provenance of these input files, their associated creation as electron microscope session, is also what NXem_ebsd solves.

Nevertheless, as this can be solved with e.g. NXem_ebsd we are convinced that the DREAM.3D and the em_om parser can work productively together to realize RDMS-agnostic parsing of serial-section analyses.

The internal documentation of the DREAM.3D workflow also simplifies the provenance tracking represented by an instance of NXem_ebsd as not every intermediate results has to be stored. Therefore, the fourth example focuses on the key result obtained from DREAM.3D - the reconstructed and aligned three-dimensional orientation map.

Usually, this result is the starting point for further post-processing and characterization of structural features. As here orientation microscopy is insofar scale invariant using DREAM.3D, NXem_ebsd, and em_om should be useful for different characterization methods, such as EBSD, Transmission Kikuchi Diffraction (TKD), Automated Crystal Orientation Mapping (ACOM), Nanobeam Electron Diffraction (using commercial systems like NanoMegas ASTAR) or open-source implementations of these techniques (such as via pyxem/orix).

The result of orientation microscopy methods are maps of local orientation and thermodynamic phase (crystal structure) pieces of information. Virtually all post-processing of such results for structural features includes again a workflow of steps which are covered though by the NXms partner application definition. The respective source of the data in an instance of NXms can again be a link or reference to an instance of NXem_ebsd to complete the chain of provenance.

sequence_index: (required) NX_POSINT

EM_EBSD_CRYSTAL_STRUCTURE_MODEL: (required) NXem_ebsd_crystal_structure_model

crystallographic_database_identifier: (recommended) NX_CHAR

crystallographic_database: (recommended) NX_CHAR

unit_cell_abc: (required) NX_FLOAT

unit_cell_alphabetagamma: (required) NX_FLOAT

space_group: (recommended) NX_CHAR

phase_identifier: (required) NX_UINT

phase_name: (recommended) NX_CHAR

region_of_interest: (required) NXprocess

An overview of the entire reconstructed volume. For details about what defines the image contrast inspect descriptor.

descriptor: (required) NX_CHAR

Descriptor representing the image contrast.

roi: (required) NXdata

Container holding a default plot of the reconstructed volume.

@signal: (required) NX_CHAR

@axes: (required) NX_CHAR

@AXISNAME_indices: (required) NX_CHAR

title: (required) NX_CHAR

data: (required) NX_NUMBER (Rank: 3, Dimensions: [n_z, n_y, n_x]) {units=NX_UNITLESS}

Descriptor values displaying the ROI.

@long_name: (required) NX_CHAR

Signal

axis_z: (required) NX_NUMBER (Rank: 1, Dimensions: [n_z]) {units=NX_LENGTH}

Calibrated center of mass of the pixel along the slow axis.

@long_name: (required) NX_CHAR

Label for the z axis

axis_y: (required) NX_NUMBER (Rank: 1, Dimensions: [n_y]) {units=NX_LENGTH}

Calibrated center of mass of the pixel along the fast axis.

@long_name: (required) NX_CHAR

Label for the y axis

axis_x: (required) NX_NUMBER (Rank: 1, Dimensions: [n_x]) {units=NX_LENGTH}

Calibrated center of mass of the pixel along the fastest axis.

@long_name: (required) NX_CHAR

Label for the x axis

PROCESS: (optional) NXprocess

Default inverse pole figure (IPF) plot of the data specific for each phase. No ipf_mapID instances for non-indexed scan points as these are by definition assigned the null phase with phase_identifier 0. The same comments apply as to the two-dimensional representation.

phase_identifier: (required) NX_UINT {units=NX_UNITLESS}

Specifying which phase this IPF mapping visualizes.

phase_name: (required) NX_CHAR

Alias/name for the phase whose indexed scan points are displayed.

description: (optional) NX_CHAR

Which IPF definition computation according to backend.

projection_direction: (required) NX_NUMBER (Rank: 1, Dimensions: [3]) {units=NX_UNITLESS}

Along which axis to project? Typically [0, 0, 1] is chosen.

bitdepth: (required) NX_UINT {units=NX_UNITLESS}

Bitdepth used for the RGB color model. Usually 8 bit.

PROGRAM: (required) NXprogram

The tool/implementation used for creating the IPF color map from the orientation data. Effectively, this program is the backend which performs the computation of the inverse pole figure mappings which can be for some use cases the parser. Consider the explanations in the docstring of the ipf_mapID group.

program_name: (required) NX_CHAR

@version: (required) NX_CHAR

ipf_rgb_map: (required) NXdata

The RGB image which represents the IPF map.

@signal: (required) NX_CHAR

@axes: (required) NX_CHAR

@AXISNAME_indices: (required) NX_CHAR

title: (required) NX_CHAR

data: (required) NX_UINT (Rank: 4, Dimensions: [n_z, n_y, n_x, 3]) {units=NX_UNITLESS}

RGB array, with resolution per fastest changing value defined by bitdepth.

@long_name: (required) NX_CHAR

IPF color-coded orientation mapping

axis_z: (required) NX_NUMBER (Rank: 1, Dimensions: [n_z]) {units=NX_LENGTH}

Calibrated center of mass of the pixel along the slow axis.

@long_name: (required) NX_CHAR

Label for the z axis

axis_y: (required) NX_NUMBER (Rank: 1, Dimensions: [n_y]) {units=NX_LENGTH}

Calibrated center of mass of the pixel along the faster axis.

@long_name: (required) NX_CHAR

Label for the y axis

axis_x: (required) NX_NUMBER (Rank: 1, Dimensions: [n_x])

Calibrated center of mass of the pixel along the fastest axis.

@long_name: (required) NX_CHAR

Label for the x axis

ipf_rgb_color_model: (required) NXdata

Same comments as for the two-dimensional case apply.

@signal: (required) NX_CHAR

@axes: (required) NX_CHAR

@AXISNAME_indices: (required) NX_CHAR

title: (required) NX_CHAR

data: (required) NX_UINT (Rank: 3, Dimensions: [n_y, n_x, 3]) {units=NX_UNITLESS}

RGB array, with resolution per fastest changing value defined by bitdepth.

@long_name: (required) NX_CHAR

IPF color key in stereographic standard triangle (SST)

axis_y: (required) NX_NUMBER (Rank: 1, Dimensions: [n_y]) {units=NX_ANY}

Pixel coordinate along the slow axis.

@long_name: (required) NX_CHAR

Label for the y axis

axis_x: (required) NX_NUMBER (Rank: 1, Dimensions: [n_x]) {units=NX_ANY}

Pixel coordinate along the fast axis.

@long_name: (required) NX_CHAR

Label for the x axis

Hypertext Anchors

List of hypertext anchors for all groups, fields, attributes, and links defined in this class.

NXDL Source:

https://github.com/FAIRmat-Experimental/nexus_definitions/tree/fairmat/contributed_definitions/NXem_ebsd.nxdl.xml