/exploratory-data-analysis

Spectroscopy and Analytical Chemistry File Formats Reference

This reference covers file formats used in various spectroscopic techniques and analytical chemistry instrumentation.

NMR Spectroscopy

.fid - NMR Free Induction Decay

Description: Raw time-domain NMR data from Bruker, Agilent, JEOL Typical Data: Complex time-domain signal Use Cases: NMR spectroscopy, structure elucidation Python Libraries:

• nmrglue: nmrglue.bruker.read_fid('fid') or nmrglue.varian.read_fid('fid')
• nmrstarlib: NMR data handling EDA Approach:
• Time-domain signal decay
• Sampling rate and acquisition time
• Number of data points
• Signal-to-noise ratio estimation
• Baseline drift assessment
• Digital filter effects
• Acquisition parameter validation
• Apodization function selection

.ft / .ft1 / .ft2 - NMR Frequency Domain

Description: Fourier-transformed NMR spectrum Typical Data: Processed frequency-domain data Use Cases: NMR analysis, peak integration Python Libraries:

• nmrglue: Frequency domain reading
• Custom processing pipelines EDA Approach:
• Peak picking and integration
• Chemical shift range
• Baseline correction quality
• Phase correction assessment
• Reference peak identification
• Spectral resolution
• Artifacts detection
• Multiplicity analysis

.1r / .2rr - Bruker NMR Processed Data

Description: Bruker processed spectrum (real part) Typical Data: 1D or 2D processed NMR spectra Use Cases: NMR data analysis with Bruker software Python Libraries:

• nmrglue: Bruker format support EDA Approach:
• Processing parameters review
• Window function effects
• Zero-filling assessment
• Linear prediction validation
• Spectral artifacts

.dx - NMR JCAMP-DX

Description: JCAMP-DX format for NMR Typical Data: Standardized NMR spectrum Use Cases: Data exchange between software Python Libraries:

• jcamp: JCAMP reader
• nmrglue: Can import JCAMP EDA Approach:
• Format compliance
• Metadata completeness
• Peak table validation
• Integration values
• Compound identification info

.mnova - Mnova Format

Description: Mestrelab Research Mnova format Typical Data: NMR data with processing info Use Cases: Mnova software workflows Python Libraries:

• nmrglue: Limited Mnova support
• Conversion tools to standard formats EDA Approach:
• Multi-spectrum handling
• Processing pipeline review
• Quantification data
• Structure assignment

Mass Spectrometry

.mzML - Mass Spectrometry Markup Language

Description: Standard XML-based MS format Typical Data: MS spectra, chromatograms, metadata Use Cases: Proteomics, metabolomics, lipidomics Python Libraries:

• pymzml: pymzml.run.Reader('file.mzML')
• pyteomics.mzml: pyteomics.mzml.read('file.mzML')
• MSFileReader: Various wrappers EDA Approach:
• Scan count and MS level distribution
• Retention time range and TIC
• m/z range and resolution
• Precursor ion selection
• Fragmentation patterns
• Instrument configuration
• Quality control metrics
• Data completeness

.mzXML - Mass Spectrometry XML

Description: Legacy XML MS format Typical Data: Mass spectra and chromatograms Use Cases: Proteomics workflows (older) Python Libraries:

• pyteomics.mzxml
• pymzml: Can read mzXML EDA Approach:
• Similar to mzML
• Version compatibility
• Conversion quality assessment

.mzData - mzData Format

Description: Legacy PSI MS format Typical Data: Mass spectrometry data Use Cases: Legacy data archives Python Libraries:

• pyteomics: Limited support
• Conversion to mzML recommended EDA Approach:
• Format conversion validation
• Data completeness
• Metadata extraction

.raw - Vendor Raw Files (Thermo, Agilent, Bruker)

Description: Proprietary instrument data Typical Data: Raw mass spectra and metadata Use Cases: Direct instrument output Python Libraries:

• pymsfilereader: Thermo RAW files
• ThermoRawFileParser: CLI wrapper
• Vendor-specific APIs EDA Approach:
• Method parameter extraction
• Instrument performance metrics
• Calibration status
• Scan function analysis
• MS/MS quality metrics
• Dynamic exclusion evaluation

.d - Agilent Data Directory

Description: Agilent MS data folder Typical Data: LC-MS, GC-MS with methods Use Cases: Agilent MassHunter workflows Python Libraries:

• Community parsers
• Chemstation integration EDA Approach:
• Directory structure validation
• Method parameters
• Calibration curves
• Sequence metadata
• Signal quality metrics

.wiff - AB SCIEX Data

Description: AB SCIEX/SCIEX instrument format Typical Data: Mass spectrometry data Use Cases: SCIEX instrument workflows Python Libraries:

• Vendor SDKs (limited Python support)
• Conversion tools EDA Approach:
• Experiment type identification
• Scan properties
• Quantitation data
• Multi-experiment structure

.mgf - Mascot Generic Format

Description: Peak list format for MS/MS Typical Data: Precursor and fragment masses Use Cases: Peptide identification, database searches Python Libraries:

• pyteomics.mgf: pyteomics.mgf.read('file.mgf')
• pyopenms: MGF support EDA Approach:
• Spectrum count
• Charge state distribution
• Precursor m/z and intensity
• Fragment peak count
• Mass accuracy
• Title and metadata parsing

.pkl - Peak List (Binary)

Description: Binary peak list format Typical Data: Serialized MS/MS spectra Use Cases: Software-specific storage Python Libraries:

• pickle: Standard deserialization
• pyteomics: PKL support EDA Approach:
• Data structure inspection
• Conversion to standard formats
• Metadata preservation

.ms1 / .ms2 - MS1/MS2 Formats

Description: Simple text format for MS data Typical Data: MS1 and MS2 scans Use Cases: Database searching, proteomics Python Libraries:

• pyteomics.ms1 and ms2
• Simple text parsing EDA Approach:
• Scan count by level
• Retention time series
• Charge state analysis
• m/z range coverage

.pepXML - Peptide XML

Description: TPP peptide identification format Typical Data: Peptide-spectrum matches Use Cases: Proteomics search results Python Libraries:

• pyteomics.pepxml EDA Approach:
• Search result statistics
• Score distribution
• Modification analysis
• FDR assessment
• Enzyme specificity

.protXML - Protein XML

Description: TPP protein inference format Typical Data: Protein identifications Use Cases: Proteomics protein-level results Python Libraries:

• pyteomics.protxml EDA Approach:
• Protein group analysis
• Coverage statistics
• Confidence scoring
• Parsimony analysis

.msp - NIST MS Search Format

Description: NIST spectral library format Typical Data: Reference mass spectra Use Cases: Spectral library searching Python Libraries:

• matchms: Spectral library handling
• Custom parsers EDA Approach:
• Library size and coverage
• Metadata completeness
• Peak count statistics
• Compound annotation quality

Infrared and Raman Spectroscopy

.spc - Galactic SPC

Description: Thermo Galactic spectroscopy format Typical Data: IR, Raman, UV-Vis spectra Use Cases: Various spectroscopy instruments Python Libraries:

• spc: spc.File('file.spc')
• specio: Multi-format reader EDA Approach:
• Wavenumber/wavelength range
• Data point density
• Multi-spectrum handling
• Baseline characteristics
• Peak identification
• Absorbance/transmittance mode
• Instrument information

.spa - Thermo Nicolet

Description: Thermo Fisher FTIR format Typical Data: FTIR spectra Use Cases: OMNIC software data Python Libraries:

• Custom binary parsers
• Conversion to JCAMP or SPC EDA Approach:
• Interferogram vs spectrum
• Background spectrum validation
• Atmospheric compensation
• Resolution and scan number
• Sample information

.0 - Bruker OPUS

Description: Bruker OPUS FTIR format (numbered files) Typical Data: FTIR spectra and metadata Use Cases: Bruker FTIR instruments Python Libraries:

• brukeropusreader: OPUS format parser
• specio: OPUS support EDA Approach:
• Multiple block types (AB, ScSm, etc.)
• Sample and reference spectra
• Instrument parameters
• Optical path configuration
• Beam splitter and detector info

.dpt - Data Point Table

Description: Simple XY data format Typical Data: Generic spectroscopic data Use Cases: Renishaw Raman, generic exports Python Libraries:

• pandas: CSV-like reading
• Text parsing EDA Approach:
• X-axis type (wavelength, wavenumber, Raman shift)
• Y-axis units (intensity, absorbance, etc.)
• Data point spacing
• Header information
• Multi-column data handling

.wdf - Renishaw Raman

Description: Renishaw WiRE data format Typical Data: Raman spectra and maps Use Cases: Renishaw Raman microscopy Python Libraries:

• renishawWiRE: WDF reader
• Custom parsers for WDF format EDA Approach:
• Spectral vs mapping data
• Laser wavelength
• Accumulation and exposure time
• Spatial coordinates (mapping)
• Z-scan data
• Baseline and cosmic ray correction

.txt (Spectroscopy)

Description: Generic text export from instruments Typical Data: Wavelength/wavenumber and intensity Use Cases: Universal data exchange Python Libraries:

• pandas: Text file reading
• numpy: Simple array loading EDA Approach:
• Delimiter and format detection
• Header parsing
• Units identification
• Multiple spectrum handling
• Metadata extraction from comments

UV-Visible Spectroscopy

.asd / .asc - ASD Binary/ASCII

Description: ASD FieldSpec spectroradiometer Typical Data: Hyperspectral UV-Vis-NIR data Use Cases: Remote sensing, reflectance spectroscopy Python Libraries:

• spectral.io.asd: ASD format support
• Custom parsers EDA Approach:
• Wavelength range (UV to NIR)
• Reference spectrum validation
• Dark current correction
• Integration time
• GPS metadata (if present)
• Reflectance vs radiance

.sp - Perkin Elmer

Description: Perkin Elmer UV/Vis format Typical Data: UV-Vis spectrophotometer data Use Cases: PE Lambda instruments Python Libraries:

• Custom parsers
• Conversion to standard formats EDA Approach:
• Scan parameters
• Baseline correction
• Multi-wavelength scans
• Time-based measurements
• Sample/reference handling

.csv (Spectroscopy)

Description: CSV export from UV-Vis instruments Typical Data: Wavelength and absorbance/transmittance Use Cases: Universal format for UV-Vis data Python Libraries:

• pandas: Native CSV support EDA Approach:
• Lambda max identification
• Beer's law compliance
• Baseline offset
• Path length correction
• Concentration calculations

X-ray and Diffraction

.cif - Crystallographic Information File

Description: Crystal structure and diffraction data Typical Data: Unit cell, atomic positions, structure factors Use Cases: Crystallography, materials science Python Libraries:

• gemmi: gemmi.cif.read_file('file.cif')
• PyCifRW: CIF reading/writing
• pymatgen: Materials structure analysis EDA Approach:
• Crystal system and space group
• Unit cell parameters
• Atomic positions and occupancy
• Thermal parameters
• R-factors and refinement quality
• Completeness and redundancy
• Structure validation

.hkl - Reflection Data

Description: Miller indices and intensities Typical Data: Integrated diffraction intensities Use Cases: Crystallographic refinement Python Libraries:

• Custom parsers (format dependent)
• Crystallography packages (CCP4, etc.) EDA Approach:
• Resolution range
• Completeness by shell
• I/sigma distribution
• Systematic absences
• Twinning detection
• Wilson plot

.mtz - MTZ Format (CCP4)

Description: Binary crystallographic data Typical Data: Reflections, phases, structure factors Use Cases: Macromolecular crystallography Python Libraries:

• gemmi: MTZ support
• cctbx: Comprehensive crystallography EDA Approach:
• Column types and data
• Resolution limits
• R-factors (Rwork, Rfree)
• Phase probability distribution
• Map coefficients
• Batch information

.xy / .xye - Powder Diffraction

Description: 2-theta vs intensity data Typical Data: Powder X-ray diffraction patterns Use Cases: Phase identification, Rietveld refinement Python Libraries:

• pandas: Simple XY reading
• pymatgen: XRD pattern analysis EDA Approach:
• 2-theta range
• Peak positions and intensities
• Background modeling
• Peak width analysis (strain/size)
• Phase identification via matching
• Preferred orientation effects

.raw (XRD)

Description: Vendor-specific XRD raw data Typical Data: XRD patterns with metadata Use Cases: Bruker, PANalytical, Rigaku instruments Python Libraries:

• Vendor-specific parsers
• Conversion tools EDA Approach:
• Scan parameters (step size, time)
• Sample alignment
• Incident beam setup
• Detector configuration
• Background scan validation

.gsa / .gsas - GSAS Format

Description: General Structure Analysis System Typical Data: Powder diffraction for Rietveld Use Cases: Rietveld refinement Python Libraries:

• GSAS-II Python interface
• Custom parsers EDA Approach:
• Histogram data
• Instrument parameters
• Phase information
• Refinement constraints
• Profile function parameters

Electron Spectroscopy

.vms - VG Scienta

Description: VG Scienta spectrometer format Typical Data: XPS, UPS, ARPES spectra Use Cases: Photoelectron spectroscopy Python Libraries:

• Custom parsers for VMS
• specio: Multi-format support EDA Approach:
• Binding energy calibration
• Pass energy and resolution
• Photoelectron line identification
• Satellite peak analysis
• Background subtraction quality
• Fermi edge position

.spe - WinSpec/SPE Format

Description: Princeton Instruments/Roper Scientific Typical Data: CCD spectra, Raman, PL Use Cases: Spectroscopy with CCD detectors Python Libraries:

• spe2py: SPE file reader
• spe_loader: Alternative parser EDA Approach:
• CCD frame analysis
• Wavelength calibration
• Dark frame subtraction
• Cosmic ray identification
• Readout noise
• Accumulation statistics

.pxt - Princeton PTI

Description: Photon Technology International Typical Data: Fluorescence, phosphorescence spectra Use Cases: Fluorescence spectroscopy Python Libraries:

• Custom parsers
• Text-based format variants EDA Approach:
• Excitation and emission spectra
• Quantum yield calculations
• Time-resolved measurements
• Temperature-dependent data
• Correction factors applied

.dat (Spectroscopy Generic)

Description: Generic binary or text spectroscopy data Typical Data: Various spectroscopic measurements Use Cases: Many instruments use .dat extension Python Libraries:

• Format-specific identification needed
• numpy, pandas for known formats EDA Approach:
• Format detection (binary vs text)
• Header identification
• Data structure inference
• Units and axis labels
• Instrument signature detection

Chromatography

.chrom - Chromatogram Data

Description: Generic chromatography format Typical Data: Retention time vs signal Use Cases: HPLC, GC, LC-MS Python Libraries:

• Vendor-specific parsers
• pandas for text exports EDA Approach:
• Retention time range
• Peak detection and integration
• Baseline drift
• Resolution between peaks
• Signal-to-noise ratio
• Tailing factor

.ch - ChemStation

Description: Agilent ChemStation format Typical Data: Chromatograms and method parameters Use Cases: Agilent HPLC and GC systems Python Libraries:

• agilent-chemstation: Community tools
• Binary format parsers EDA Approach:
• Method validation
• Integration parameters
• Calibration curve
• Sample sequence information
• Instrument status

.arw - Empower (Waters)

Description: Waters Empower format Typical Data: UPLC/HPLC chromatograms Use Cases: Waters instrument data Python Libraries:

• Vendor tools (limited Python access)
• Database extraction tools EDA Approach:
• Audit trail information
• Processing methods
• Compound identification
• Quantitation results
• System suitability tests

.lcd - Shimadzu LabSolutions

Description: Shimadzu chromatography format Typical Data: GC/HPLC data Use Cases: Shimadzu instruments Python Libraries:

• Vendor-specific parsers EDA Approach:
• Method parameters
• Peak purity analysis
• Spectral data (if PDA)
• Quantitative results

Other Analytical Techniques

.dta - DSC/TGA Data

Description: Thermal analysis data (TA Instruments) Typical Data: Temperature vs heat flow or mass Use Cases: Differential scanning calorimetry, thermogravimetry Python Libraries:

• Custom parsers for TA formats
• pandas for exported data EDA Approach:
• Transition temperature identification
• Enthalpy calculations
• Mass loss steps
• Heating rate effects
• Baseline determination
• Purity assessment

.run - ICP-MS/ICP-OES

Description: Elemental analysis data Typical Data: Element concentrations or counts Use Cases: Inductively coupled plasma MS/OES Python Libraries:

• Vendor-specific tools
• Custom parsers EDA Approach:
• Element detection and quantitation
• Internal standard performance
• Spike recovery
• Dilution factor corrections
• Isotope ratios
• LOD/LOQ calculations

.exp - Electrochemistry Data

Description: Electrochemical experiment data Typical Data: Potential vs current or charge Use Cases: Cyclic voltammetry, chronoamperometry Python Libraries:

• Custom parsers per instrument (CHI, Gamry, etc.)
• galvani: Biologic EC-Lab files EDA Approach:
• Redox peak identification
• Peak potential and current
• Scan rate effects
• Electron transfer kinetics
• Background subtraction
• Capacitance calculations