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EcoSIS Leaf Optical Properties Experiment database (LOPEX93)

ecosis · NIR

EcoSIS Leaf Optical Properties Experiment database (LOPEX93). Origin database composed from 3 standardized v2.0 leaves: 1 spectral source(s), 3 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2ecosis
Original source available without a Dataverse token. The catalog uses the private tier for restricted or uncleared redistribution rights. You can obtain original resources from the source; their license still applies. Raw retrieval and automatic assembly are separate; use the loading example below.
330
samples
2,101
wavelengths
1
sources
3
targets
44
metadata
NIR
family

Provenance & citation

Where the original data and publications come from. Follow these links to obtain the source and cite its authors. Reading guide

ContributorLeaf Optical Properties Experiment Database (LOPEX93)
Conversion statusok
Origin · url [open]https://data.ecosis.org/dataset/leaf-optical-properties-experiment-database--lopex93-
Origin · url [open]https://data.ecosis.org/api/3/action/package_show?id=leaf-optical-properties-experiment-database--lopex93-
Origin · url [open]https://ecosis.org/package/leaf-optical-properties-experiment-database--lopex93-

Governance & integrity

Source license, redistribution conditions, content version and identity hashes. Redistribution tier and original-source availability are separate. Reading guide

Tierprivate
LicenseLicenseRef-not-cleared
Permitted useResearch and benchmarking.
Access policyManual download / private-use-only per source.
RedistributionEcoSIS CKAN API package_show verified open license: Other (Open).
Content version1.1.0
Schema / protocol2.0
Content hashd862fb0b9ef4f505…
Processing hash9c29ae12d987cded…
Metadata hash0ffcb4a0ab63e3f3…

Dataset property explorer

Start with the profile axes to locate unusual variation, then inspect the measured values and evidence below. Scores are descriptive heuristics. Reading guide

Mean available profile score0.55
Highest axisPCA outliers · 1.00
Diagnostics8
Sources profiled1

How to read the profile

The center is 0 and the outer ring is 1. Larger values indicate stronger anomaly or heterogeneity signals on that axis. Compare individual axes using the table; this is not an overall quality grade. Missing axes are left blank, and partial profiles have no filled polygon.

EcoSIS Leaf Optical Properties Experiment database (LOPEX93) property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS Leaf Optical Properties Experiment database (LOPEX93) — integrity: 0.00EcoSIS Leaf Optical Properties Experiment database (LOPEX93) — noise: 0.03EcoSIS Leaf Optical Properties Experiment database (LOPEX93) — artefacts: 0.13EcoSIS Leaf Optical Properties Experiment database (LOPEX93) — baseline: 0.72EcoSIS Leaf Optical Properties Experiment database (LOPEX93) — PCA outliers: 1.00EcoSIS Leaf Optical Properties Experiment database (LOPEX93) — reference: 0.99EcoSIS Leaf Optical Properties Experiment database (LOPEX93) — structure: 1.00EcoSIS Leaf Opt…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Dimensionless scores from 0 to 1 summarize each property; larger values indicate stronger signals. Unavailable axes are not zero.

Integrity
?Combines missing cells, zero columns and infinite values. Higher values call for checking data completeness.
0.00
Noise
?Combines roughness and its signal-to-roughness proxy. Real narrow bands may also contribute.
0.03
Local artifacts
?Combines spike, jump and clipping heuristics. Inspect flagged spectral regions before attributing a cause.
0.13
Baseline/shape
?Summarizes baseline slope and excess edge variation relative to signal scale.
0.72
PCA outliers
?Combines relative PCA residual and score-distance tails. Valid rare spectra can produce a high value.
1.00
Distance to average spectrum
?Dispersion around this dataset's mean spectrum, using distance, shape and shift summaries.
0.99
Repeatability
?Dispersion among observations sharing an ID. Interpretation depends on the documented meaning of that ID.
unavailable
Multi-regime structure
?Variation in local density in PCA space. May reflect legitimate batches or subpopulations.
1.00

Possible explanations to investigate

Bars rank heuristic signatures from 0 to 1. They do not estimate the probability of a physical fault. On narrow screens, scroll the chart horizontally. Full labels, scores, evidence coverage, possible causes and required external checks are in the table below.

Diagnostic hypotheses00.250.50.751hypothesis scoreSignature compatible with a population shiftSignature compatible with a population shift: 0.910.91Signature compatible with a different backgroundSignature compatible with a different background: 0.800.80Offset compatible with a different white referenceOffset compatible with a different white reference: 0.700.70Signature compatible with a spectral spliceSignature compatible with a spectral splice: 0.580.58Signature compatible with clippingSignature compatible with clipping: 0.350.35High spectral roughness signatureHigh spectral roughness signature: 0.300.30Flat spectrum/weak signalFlat spectrum/weak signal: 0.290.29

Heuristic signature scores are not calibrated probabilities of physical causes: 0.78 does not mean a 78% causal probability. Missing evidence remains unavailable. RMS/SAM describe dispersion around the dataset mean, without an independent external reference.

Diagnostic hypotheses and supporting evidence
DiagnosticScoreHeuristic strengthEvidence coverageSignalsPossible causeRequired external checks
Signature compatible with a population shiftX0.91strong100 %PCA structure 1.00, RMS/SAM to mean spectrum 0.99, Mahalanobis / T2 0.99Different but physically plausible variety, species, batch or condition.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with a different backgroundX0.80strong100 %PCA Q 1.00, RMS/SAM to mean spectrum 0.99, Mahalanobis / T2 0.99Systematic effect of support, white/black, transflectance or measurement environment.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Offset compatible with a different white referenceX0.70medium100 %PCA Q 1.00, RMS/SAM to mean spectrum 0.99, Mahalanobis / T2 0.99Systematic discrepancy between campaigns, instruments or white reference.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with a spectral spliceX0.58medium100 %PCA Q 1.00, Undegraded roughness S/N proxy 1.00, RMS/SAM to mean spectrum 0.99Break at detector junctions, local calibration or different probe.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with clippingX0.35weak100 %PCA Q 1.00, Baseline/mean/area 0.72Detector saturated or insufficient dynamic range.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
High spectral roughness signatureX0.30weak100 %PCA Q 1.00, RMS/SAM to mean spectrum 0.99Weak signal, detector problem, lamp or unstable acquisition.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Flat spectrum/weak signalX0.29weak100 %PCA Q 1.00, weak artifacts 0.87Bad contact, missing sample, degraded measurement or very low dynamic range.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Weak signatures observedX—unknown90 %Weak signatures on available metrics; does not certify either physical validity or the absence of defects.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.

Spectral sources

Each block is a separate instrument or spectral source. Read its signal summary before using PCA to explore variation between observations. Reading guide

Lopex1993_Reflectance__Lopex_Refl

X · NIR

Read the signal and variation summaries for this source. Axis unit: nm; signal representation: absorbance.

Spectral signal summary

The mean and median describe the typical signal at each channel; shaded bands show the 25th–75th and 5th–95th percentiles across observations. A wider band means greater variation, which can be biological or instrumental.

Lopex1993_Reflectance__Lopex_Refl spectra0.00.20.40.601,0002,0003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm400nm — median 0.0412 (q25–q75 0.0374–0.0461)415nm — median 0.0438 (q25–q75 0.04002–0.0502)430nm — median 0.04665 (q25–q75 0.0418–0.053)445nm — median 0.0478 (q25–q75 0.04252–0.05495)460nm — median 0.0486 (q25–q75 0.043–0.05712)476nm — median 0.0486 (q25–q75 0.0432–0.05835)491nm — median 0.049 (q25–q75 0.0436–0.0595)506nm — median 0.0543 (q25–q75 0.04802–0.06677)521nm — median 0.07905 (q25–q75 0.0677–0.09413)536nm — median 0.107 (q25–q75 0.09155–0.128)551nm — median 0.113 (q25–q75 0.0963–0.136)566nm — median 0.102 (q25–q75 0.08515–0.122)581nm — median 0.08035 (q25–q75 0.067–0.09945)596nm — median 0.0728 (q25–q75 0.06063–0.0908)612nm — median 0.066 (q25–q75 0.056–0.08323)627nm — median 0.0602 (q25–q75 0.0517–0.07625)642nm — median 0.05665 (q25–q75 0.0487–0.07013)657nm — median 0.0515 (q25–q75 0.0446–0.0632)672nm — median 0.0472 (q25–q75 0.04188–0.05695)687nm — median 0.0496 (q25–q75 0.04365–0.06087)702nm — median 0.123 (q25–q75 0.106–0.1428)717nm — median 0.257 (q25–q75 0.2332–0.29)732nm — median 0.38 (q25–q75 0.357–0.408)747nm — median 0.4385 (q25–q75 0.419–0.469)763nm — median 0.456 (q25–q75 0.434–0.4867)778nm — median 0.459 (q25–q75 0.4373–0.49)793nm — median 0.46 (q25–q75 0.439–0.4907)808nm — median 0.464 (q25–q75 0.44–0.492)823nm — median 0.461 (q25–q75 0.439–0.491)838nm — median 0.463 (q25–q75 0.4395–0.493)853nm — median 0.463 (q25–q75 0.4393–0.495)868nm — median 0.463 (q25–q75 0.4385–0.4937)883nm — median 0.4625 (q25–q75 0.4405–0.494)899nm — median 0.462 (q25–q75 0.442–0.494)914nm — median 0.462 (q25–q75 0.4413–0.493)929nm — median 0.462 (q25–q75 0.441–0.492)944nm — median 0.461 (q25–q75 0.4382–0.489)959nm — median 0.456 (q25–q75 0.435–0.484)974nm — median 0.4555 (q25–q75 0.4342–0.483)989nm — median 0.4555 (q25–q75 0.4342–0.483)1,004nm — median 0.457 (q25–q75 0.436–0.485)1,019nm — median 0.459 (q25–q75 0.437–0.486)1,035nm — median 0.46 (q25–q75 0.438–0.488)1,050nm — median 0.461 (q25–q75 0.439–0.489)1,065nm — median 0.461 (q25–q75 0.4393–0.4907)1,080nm — median 0.461 (q25–q75 0.44–0.49)1,095nm — median 0.46 (q25–q75 0.4393–0.489)1,110nm — median 0.459 (q25–q75 0.4382–0.488)1,125nm — median 0.457 (q25–q75 0.4362–0.485)1,140nm — median 0.45 (q25–q75 0.43–0.475)1,155nm — median 0.4405 (q25–q75 0.4192–0.4647)1,171nm — median 0.436 (q25–q75 0.4152–0.46)1,186nm — median 0.433 (q25–q75 0.414–0.457)1,201nm — median 0.4335 (q25–q75 0.414–0.457)1,216nm — median 0.4345 (q25–q75 0.414–0.4578)1,231nm — median 0.4365 (q25–q75 0.416–0.4607)1,246nm — median 0.438 (q25–q75 0.418–0.4628)1,261nm — median 0.439 (q25–q75 0.418–0.464)1,276nm — median 0.4375 (q25–q75 0.418–0.4618)1,291nm — median 0.436 (q25–q75 0.4163–0.4598)1,306nm — median 0.431 (q25–q75 0.412–0.454)1,322nm — median 0.4225 (q25–q75 0.4033–0.444)1,337nm — median 0.41 (q25–q75 0.39–0.428)1,352nm — median 0.4 (q25–q75 0.3773–0.417)1,367nm — median 0.387 (q25–q75 0.365–0.403)1,382nm — median 0.353 (q25–q75 0.332–0.372)1,397nm — median 0.278 (q25–q75 0.2492–0.304)1,412nm — median 0.2165 (q25–q75 0.1853–0.246)1,427nm — median 0.188 (q25–q75 0.1583–0.2177)1,442nm — median 0.178 (q25–q75 0.1475–0.2068)1,458nm — median 0.176 (q25–q75 0.147–0.2057)1,473nm — median 0.1855 (q25–q75 0.157–0.2148)1,488nm — median 0.202 (q25–q75 0.1742–0.232)1,503nm — median 0.222 (q25–q75 0.195–0.25)1,518nm — median 0.243 (q25–q75 0.215–0.268)1,533nm — median 0.263 (q25–q75 0.234–0.2877)1,548nm — median 0.279 (q25–q75 0.2512–0.301)1,563nm — median 0.291 (q25–q75 0.266–0.312)1,578nm — median 0.302 (q25–q75 0.278–0.322)1,594nm — median 0.313 (q25–q75 0.2882–0.332)1,609nm — median 0.3205 (q25–q75 0.298–0.34)1,624nm — median 0.327 (q25–q75 0.305–0.346)1,639nm — median 0.3315 (q25–q75 0.3092–0.3508)1,654nm — median 0.334 (q25–q75 0.3123–0.353)1,669nm — median 0.332 (q25–q75 0.312–0.353)1,684nm — median 0.3315 (q25–q75 0.311–0.351)1,699nm — median 0.326 (q25–q75 0.306–0.3467)1,714nm — median 0.32 (q25–q75 0.301–0.3415)1,729nm — median 0.3145 (q25–q75 0.294–0.3357)1,745nm — median 0.309 (q25–q75 0.288–0.3287)1,760nm — median 0.3015 (q25–q75 0.279–0.32)1,775nm — median 0.297 (q25–q75 0.272–0.3158)1,790nm — median 0.295 (q25–q75 0.2692–0.3138)1,805nm — median 0.295 (q25–q75 0.2703–0.315)1,820nm — median 0.296 (q25–q75 0.272–0.3168)1,835nm — median 0.2935 (q25–q75 0.2683–0.314)1,850nm — median 0.281 (q25–q75 0.2543–0.3045)1,865nm — median 0.244 (q25–q75 0.2163–0.2698)1,881nm — median 0.158 (q25–q75 0.1312–0.184)1,896nm — median 0.0831 (q25–q75 0.06595–0.105)1,911nm — median 0.0543 (q25–q75 0.04323–0.06835)1,926nm — median 0.04855 (q25–q75 0.03885–0.06055)1,941nm — median 0.04935 (q25–q75 0.03953–0.06213)1,956nm — median 0.05395 (q25–q75 0.04265–0.06817)1,971nm — median 0.06055 (q25–q75 0.04743–0.07743)1,986nm — median 0.0703 (q25–q75 0.0552–0.08887)2,001nm — median 0.07995 (q25–q75 0.06383–0.102)2,017nm — median 0.0922 (q25–q75 0.07378–0.116)2,032nm — median 0.104 (q25–q75 0.08355–0.128)2,047nm — median 0.1135 (q25–q75 0.09195–0.137)2,062nm — median 0.1215 (q25–q75 0.101–0.147)2,077nm — median 0.132 (q25–q75 0.1092–0.157)2,092nm — median 0.1425 (q25–q75 0.1183–0.167)2,107nm — median 0.152 (q25–q75 0.127–0.1757)2,122nm — median 0.16 (q25–q75 0.135–0.184)2,137nm — median 0.1685 (q25–q75 0.142–0.191)2,153nm — median 0.173 (q25–q75 0.148–0.196)2,168nm — median 0.178 (q25–q75 0.152–0.2)2,183nm — median 0.182 (q25–q75 0.156–0.203)2,198nm — median 0.187 (q25–q75 0.1602–0.207)2,213nm — median 0.189 (q25–q75 0.163–0.2088)2,228nm — median 0.1885 (q25–q75 0.1633–0.2088)2,243nm — median 0.181 (q25–q75 0.157–0.201)2,258nm — median 0.1725 (q25–q75 0.1473–0.192)2,273nm — median 0.163 (q25–q75 0.139–0.1827)2,288nm — median 0.154 (q25–q75 0.13–0.173)2,304nm — median 0.144 (q25–q75 0.1212–0.164)2,319nm — median 0.1375 (q25–q75 0.115–0.156)2,334nm — median 0.1295 (q25–q75 0.107–0.148)2,349nm — median 0.1205 (q25–q75 0.09918–0.141)2,364nm — median 0.114 (q25–q75 0.09305–0.133)2,379nm — median 0.106 (q25–q75 0.08572–0.126)2,394nm — median 0.0967 (q25–q75 0.07813–0.116)2,409nm — median 0.0891 (q25–q75 0.07193–0.1078)2,424nm — median 0.08065 (q25–q75 0.06533–0.099)2,440nm — median 0.07275 (q25–q75 0.0584–0.0891)2,455nm — median 0.06575 (q25–q75 0.05278–0.0803)2,470nm — median 0.0588 (q25–q75 0.04648–0.0729)2,485nm — median 0.05475 (q25–q75 0.04322–0.0666)2,500nm — median 0.0528 (q25–q75 0.0418–0.06447)

Sampling

How many observations and spectral channels were exported, and how the wavelength axis is spaced. Reading guide

Wavelengths
?Number of spectral channels. A channel can represent wavelength, wavenumber or another feature axis.
2,101
Axis range
?Lowest and highest recorded spectral-axis coordinates, in the stated unit.
400–2,500 nm
Mean spacing
?Average difference between adjacent spectral-axis coordinates; compare only within the same unit.
1 nm
Grid
?Uniform means adjacent axis spacings are approximately equal. An irregular grid may need care when applying derivatives.
uniform
Observations
?Exported spectral rows in this source, including repeated measurements when present.
330

Signal & quality

Signal level and roughness summaries. These depend on the signal representation and are not instrument acceptance tests. Reading guide

Value range
?Minimum and maximum recorded spectral values, in the original signal representation.
0.017 – 0.638
Mean range
?Minimum and maximum of the mean spectrum across spectral channels.
0.0427 – 0.47
Mean level
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations.
0.2578
Area
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit.
541.6
PTP
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum.
0.4275
Noise RMS
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise.
0.00060526
Roughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR.
4.3e+02
Roughness S/N proxy dB
?20 log10 of the roughness signal-to-noise proxy. Units are decibels; compare only compatible signal representations.
5e+01 dB
Dynamic range
?Maximum minus minimum of the mean spectrum. Its magnitude depends on the signal representation.
0.427
Smoothness
?Mean per-spectrum RMS of second differences, in signal units. Smaller values indicate smoother spectra under this protocol.
0.001717
Saturated
?Fraction at the observed global maximum; a heuristic indicator, not a comparison with known detector limits.
0.0%
X-outliers
?Number flagged by the qualification outlier method. An unusual spectrum can be valid and should not be discarded automatically.
153

Integrity & artefacts

Missing values, narrow spikes and abrupt jumps to investigate in the original spectra. Reading guide

NaN ratio
?Missing spectral cells divided by all spectral cells, displayed as a percentage.
0.00%
Inf count
?Number of positive or negative infinite spectral values; investigate invalid calculations or export.
0
Zero ratio
?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing.
0.00%
Spike count
?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged.
892
Spike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage.
0.13%
Jump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins.
519
Jump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage.
0.07%
Clip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation.
0.00%

Shape & reference

Variation relative to this dataset's mean spectrum; the mean is not an independently measured calibration reference. Reading guide

Baseline slope
?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit.
-0.1549
Curvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation.
0.0017077
D1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts.
0.001889
RMS to mean
?Root mean square distance to this dataset's mean spectrum, in signal units.
0.028085
RMS p95
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value.
0.081751
SAM to mean
?Spectral Angle Mapper: angle to the mean spectrum, in radians. Smaller angles indicate more similar shapes.
0.064367
SAM p95
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS.
0.21984
Affine offset p95
?95th percentile of absolute fitted offsets when matching each spectrum to the dataset mean.
0.084585
Affine gain p95 Δ
?95th percentile of the absolute gain deviation from 1 in the fit to the mean spectrum.
0.24918
Affine residual p95
?95th percentile of residual RMS after gain and offset correction to the mean spectrum.
0.050956
Xcorr lag p95
?95th percentile of the absolute cross-correlation shift relative to the mean spectrum, in spectral channels.
0

Outliers & repeatability

Unusual spectra and within-ID dispersion. Repeated IDs do not by themselves establish technical repetitions. Reading guide

PCA Q p95/median
?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median.
9.3
Hotelling T2 p95/median
?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median.
7.9
Mahalanobis H p95/median
?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra.
2.8
Repeat groups
?Number of IDs with several observations. Source documentation is needed to decide whether these are technical repeats, positions or time points.
0

Dimensionality (PCA)

Principal component analysis (PCA) compresses correlated wavelengths into components. This describes spectral variation, without using prediction targets. Reading guide

Effective rank
?Entropy-based summary of the variance distribution across components. Larger values indicate variation spread across more dimensions.
2.5
PCs → 95% var
?Number of principal components needed to explain 95% of spectral variance, when reached by the computed projection.
3
PCs → 99% var
?Number of principal components needed to explain 99% of spectral variance, when reached by the computed projection.
6
Top-10 cum. var
?Fraction of spectral variance explained by up to the first ten computed principal components, displayed as a percentage.
99.9%
Computed metric scores 30highest alert 1.00

Value is the measured quantity; alert score is its heuristic mapping to 0–1. Levels are weak below 0.42, medium from 0.42, and strong from 0.72. These are display bands, not calibrated acceptance thresholds. A dash means unavailable, never zero. Possible causes are suggestions to investigate. Use each metric’s ? control for its meaning and scoring: hover for a hint, or expand it with a tap, Enter or Space.

FamilyComputed metricValueAlert scoreHeuristic levelDescriptive interpretationPossible causes to verifyCalculation / scoring
Data integrityInvalid/masked cell count (owner report)
?Count of cells reported unavailable by the native owner under its null or validity mask. This count is distinct from the historical NaN ratio; consult the recorded population and denominator. Scoring: No alert mapping is defined for this metric; an unavailable score does not mean a zero measured value. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.missing_cells		——not calculableNot enough information to score this metric on this dataset.Missing data or validity mask; not evidence of an acquisition causeNative Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio.
Data integrityNaN ratio
?Missing spectral cells divided by all spectral cells, displayed as a percentage. Scoring: alert = min(1, nan_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.nan_ratio		0%0.00weakFull spectrumAcquisition/export errorcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Data integrityInf count
?Number of positive or negative infinite spectral values; investigate invalid calculations or export. Scoring: alert = min(1, inf_count / 1). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.inf_count		00.00weakNormalInvalid calculationscount(isinf(X))alert = min(1, inf_count / 1)
Data integrityZero ratio
?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing. Scoring: alert = min(1, zero_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.zero_ratio		0%0.00weakNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Overall amplitudeMean reflectance
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations. Scoring: alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.mean_reflectance		0.25780.72strongAtypical value: Too light / background visible or Too darkBackground, geometrymean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Overall amplitudeArea under curve
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit. Scoring: alert reuses baseline/shape drift because area scale depends on axis and units. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.area_under_curve		541.580.72strongAtypical value: Illumination difference or NormalProbe distancetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Overall amplitudePeak-to-peak (PTP)
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum. Scoring: alert increases when dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.peak_to_peak		0.427460.00weakStrong variabilitySaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Overall amplitudeVariance
?Variance of all finite spectral cells, in squared signal units. It combines variation across observations and channels; a small value can describe a flat signal. Scoring: alert increases when variance/dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.variance		0.0254240.00weakNormal or heterogeneousBad contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
NoiseRoughness sigma proxy
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise. Scoring: alert = noise_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.roughness_sigma_proxy		0.000605260.03weakStableLamp, detectormedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
NoiseRoughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR. Scoring: alert decreases with SNR dB; >=40 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.roughness_signal_to_noise_proxy		425.930.00weakGood signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
NoiseBandwise roughness S/N proxy
?Minimum channel-wise ratio of absolute mean signal to local second-difference roughness. A smaller ratio identifies a weak or rough spectral region; genuine narrow bands and near-zero signal can also lower it. This is not certified instrument SNR. Scoring: alert decreases with worst-band SNR dB; >=35 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.bandwise_roughness_signal_to_noise_proxy_min		32.6640.13weakReliable zoneDetectormin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Local artifactsSpike count
?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged. Scoring: alert follows spike_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.spike_count		8920.13weakClean spectrumCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Local artifactsSpike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage. Scoring: alert = min(1, spike_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.spike_rate		0.129%0.13weakNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Local artifactsJump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins. Scoring: alert follows jump_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.jump_count		5190.07weakContinuousSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Local artifactsJump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage. Scoring: alert = min(1, jump_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.jump_rate		0.0749%0.07weakNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Local artifactsClip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation. Scoring: alert = min(1, clip_fraction / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.clip_fraction		0.000577%0.00weakNormalDetector saturatedfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Spectral formBaseline slope
?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit. Scoring: alert = abs(slope / signal_scale), saturated at 0.5. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.baseline_slope		-0.15490.72strongDriftIlluminationlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Spectral formCurvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation. Scoring: alert = curvature_rms / signal_scale, saturated at 1%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.curvature_rms		0.00170770.40weakSmoothBackground, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Spectral formD1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts. Scoring: alert = d1_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.d1_rms		0.0018890.09weakFlatBiology or artifactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Multivariate outliersPCA Q (SPE)
?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median. Scoring: alert = min(1, pca_q_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.pca_q_ratio		9.33731.00strongAtypical spectrumArtifact, mixturep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Multivariate outliersHotelling T²
?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median. Scoring: alert = min(1, hotelling_t2_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.hotelling_t2_ratio		7.93620.99strongExtreme but consistentNatural variabilityp95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Multivariate outliersMahalanobis H
?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra. Scoring: alert = min(1, mahalanobis_h_ratio / 4). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.mahalanobis_h_ratio		2.81560.70mediumGlobal outlierDifferent domainp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparison to average spectrumRMS to mean spectrum
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value. Scoring: alert = RMS_p95 / signal_scale, saturated at 25%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
reference.rms_to_mean_spectrum_p95		0.0817510.76strongDifferent spectrumDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparison to average spectrumSpectral Angle Mapper (SAM)
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS. Scoring: alert = min(1, SAM_p95 / 0.35 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
reference.sam_to_mean_spectrum_p95		0.219840.63mediumDifferent shapeBackground, geometryp95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RepeatabilityRMS intra-ID
?Within-ID root mean square deviation from the group's mean spectrum, in signal units. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = RMS_intra_ID / signal_scale, saturated at 10%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.rms_intra_id		——not calculableNot enough information to score this metric on this dataset.Positioningmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RepeatabilitySAM intra-ID
?Within-ID spectral angle to the group's mean spectrum, in radians. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, SAM_intra_ID / 0.15 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.sam_intra_id		——not calculableNot enough information to score this metric on this dataset.Acquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RepeatabilityCV intra-ID
?Within-ID coefficient of variation: standard deviation relative to absolute mean; unstable near zero signal. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, CV_intra_ID / 0.25). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.cv_intra_id		——not calculableNot enough information to score this metric on this dataset.Operatormedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure of the datasetPCA score density
?Inverse median distance to nearby observations in retained PCA-score space. Larger raw values mean more closely spaced observations; scale depends on the PCA representation. The alert uses density variation, so raw density alone does not establish an anomaly. Scoring: alert follows density_cv/profile structure complexity, not raw density alone. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.pca_score_density		3.00351.00strongSubpopulationsDifferent batches1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure of the datasetkNN distance ratio (not LOF)
?95th percentile of mean k-nearest-neighbor distance divided by the population median of that distance, in PCA-score space. Values above 1 indicate an upper tail with more isolated observations; valid rare spectra can also be isolated. This distance ratio is not the Local Outlier Factor algorithm. Scoring: alert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.knn_distance_ratio_p95		4.00281.00strongIsolated spectrumRare casesp95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2)
Structure of the datasetIsolation Forest score
?95th percentile of the negated Isolation Forest score in retained PCA-score space. Larger raw scores indicate easier isolation under that model; the scale is implementation-dependent. The displayed alert reuses structure complexity rather than directly mapping this raw score. Scoring: alert follows structure complexity; raw score is implementation-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.isolation_forest_score_p95		0.577041.00strongAtypical spectrumVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent

PCA score plot — explore spectral variation

Source Lopex1993_Reflectance__Lopex_Refl: 330 of 330 source observations are plotted. PC1 captures 71.5% and PC2 19.0% of the reported spectral variance: 90.5% together; 9.5% remains outside this view.

Principal component analysis (PCA) summarizes spectral differences without using targets. The horizontal PC1 axis captures the largest variation; vertical PC2 captures the next largest direction of variation. Each dot is one observation; repeated measurements need not be distinct samples. Scores are projection coordinates, not wavelengths or target values. Nearby dots have similar projections; separate groups or isolated dots are leads to inspect in the original spectra and acquisition metadata. Overlapping dots can differ in omitted dimensions. The dots have no class or batch labels, so this view cannot identify groups, confirm defective observations, explain a chemical cause or measure prediction accuracy.

Lopex1993_Reflectance__Lopex_Refl PCA score plot-5051015-4-2024PC1 -0.27 · PC2 -0.6821PC1 -0.3668 · PC2 -0.5293PC1 -0.04133 · PC2 -0.5013PC1 0.1574 · PC2 -2.039PC1 0.0166 · PC2 -1.038PC1 -0.9978 · PC2 0.8297PC1 1.207 · PC2 -0.7776PC1 -0.9417 · PC2 1.248PC1 -1.392 · PC2 1.399PC1 -1.325 · PC2 1.19PC1 -0.7516 · PC2 0.1159PC1 -0.8222 · PC2 0.5074PC1 -0.8658 · PC2 0.3107PC1 -0.6437 · PC2 -0.1353PC1 -0.8759 · PC2 0.4457PC1 -0.481 · PC2 -0.1117PC1 -0.4707 · PC2 0.2014PC1 0.08086 · PC2 0.2518PC1 -0.5898 · PC2 -0.08961PC1 -0.9972 · PC2 0.2798PC1 -1.875 · PC2 -1.247PC1 -2.395 · PC2 -1.513PC1 -0.3024 · PC2 -2.109PC1 -0.9213 · PC2 -1.664PC1 -0.4134 · PC2 -0.9878PC1 -1.657 · PC2 -0.8991PC1 -1.249 · PC2 -1.319PC1 -1.529 · PC2 -0.2973PC1 -0.8682 · PC2 -1.444PC1 -1.586 · PC2 -0.5841PC1 -1.521 · PC2 1.025PC1 -0.303 · PC2 0.03736PC1 -1.419 · PC2 0.8918PC1 -1.337 · PC2 1.518PC1 -0.4196 · PC2 0.7828PC1 -0.7285 · PC2 -1.366PC1 -0.7011 · PC2 -0.7631PC1 -0.4665 · PC2 -0.6893PC1 -0.4197 · PC2 -0.8831PC1 -0.2538 · PC2 -1.457PC1 -1.029 · PC2 0.1043PC1 -0.9856 · PC2 -0.02554PC1 -0.9988 · PC2 -0.1969PC1 -1.272 · PC2 0.277PC1 -1.2 · PC2 -0.05355PC1 -1.417 · PC2 -0.2564PC1 -1.372 · PC2 -0.2617PC1 -1.091 · PC2 -0.7705PC1 -1.474 · PC2 -0.22PC1 -1.494 · PC2 -0.5781PC1 -1.689 · PC2 0.9533PC1 -1.824 · PC2 0.7806PC1 -0.1504 · PC2 -0.2701PC1 -1.996 · PC2 1.279PC1 -1.905 · PC2 1.157PC1 -1.395 · PC2 -1.042PC1 -1.707 · PC2 -1.364PC1 -0.676 · PC2 -1.895PC1 -1.601 · PC2 -1.716PC1 -0.9655 · PC2 -1.628PC1 1.035 · PC2 1.584PC1 1.081 · PC2 1.745PC1 0.7044 · PC2 1.055PC1 1.17 · PC2 1.78PC1 1.122 · PC2 1.596PC1 -0.04221 · PC2 0.01225PC1 0.3168 · PC2 0.1127PC1 -0.6679 · PC2 -0.7424PC1 -0.4927 · PC2 -0.1913PC1 -0.4593 · PC2 -0.4269PC1 -0.8194 · PC2 1.023PC1 0.2115 · PC2 0.9646PC1 0.07523 · PC2 0.6961PC1 -0.01001 · PC2 1.048PC1 -0.2321 · PC2 0.9806PC1 -0.628 · PC2 0.6753PC1 -0.7119 · PC2 1.396PC1 0.02595 · PC2 1.054PC1 -0.6042 · PC2 1.132PC1 0.007547 · PC2 2.389PC1 -1.438 · PC2 -0.4037PC1 -1.618 · PC2 -0.5177PC1 -1.747 · PC2 -0.4969PC1 -2.36 · PC2 -0.5978PC1 -1.4 · PC2 -0.427PC1 -0.6458 · PC2 0.9281PC1 -0.0431 · PC2 0.6774PC1 -0.02333 · PC2 0.8528PC1 0.2214 · PC2 0.9219PC1 0.2731 · PC2 0.8788PC1 -0.9474 · PC2 0.264PC1 -0.9738 · PC2 -0.7038PC1 -0.8962 · PC2 -0.5047PC1 -0.6503 · PC2 -0.9049PC1 -0.2333 · PC2 0.1567PC1 -2.833 · PC2 -0.1826PC1 -2.965 · PC2 0.3165PC1 -1.869 · PC2 -0.4538PC1 -3.334 · PC2 0.1759PC1 -2.013 · PC2 0.1079PC1 1.167 · PC2 0.1058PC1 0.8378 · PC2 0.9036PC1 0.9973 · PC2 0.4207PC1 1.947 · PC2 0.8424PC1 1.389 · PC2 0.1056PC1 0.232 · PC2 -0.5142PC1 0.6841 · PC2 -0.7496PC1 0.5772 · PC2 -1.329PC1 0.09522 · PC2 -0.8775PC1 0.7894 · PC2 -2.217PC1 -0.6329 · PC2 0.6811PC1 -2.099 · PC2 1.638PC1 -1.218 · PC2 0.3105PC1 -0.5889 · PC2 0.2753PC1 -0.5561 · PC2 0.5381PC1 0.06733 · PC2 0.0278PC1 0.6222 · PC2 -0.01401PC1 1.038 · PC2 -0.6738PC1 1.453 · PC2 -0.9749PC1 0.774 · PC2 -0.3953PC1 -0.3328 · PC2 1.36PC1 -0.532 · PC2 1.058PC1 -0.1525 · PC2 1.106PC1 -0.3208 · PC2 1.388PC1 0.1097 · PC2 1.279PC1 -0.8209 · PC2 0.4337PC1 -0.7512 · PC2 0.3496PC1 -1.119 · PC2 0.4597PC1 -0.979 · PC2 0.5055PC1 -1.052 · PC2 0.5045PC1 0.02117 · PC2 0.362PC1 -0.2486 · PC2 0.2343PC1 -0.3111 · PC2 -0.01913PC1 -0.617 · PC2 0.01056PC1 -0.2952 · PC2 -0.1314PC1 -2.001 · PC2 1.06PC1 -1.968 · PC2 0.8734PC1 -2.084 · PC2 1.08PC1 -1.95 · PC2 1.245PC1 -1.605 · PC2 0.7919PC1 0.2848 · PC2 -0.1016PC1 0.5461 · PC2 0.4623PC1 0.1954 · PC2 0.5555PC1 0.5042 · PC2 0.5937PC1 0.7129 · PC2 0.5691PC1 1.308 · PC2 0.3329PC1 2.156 · PC2 1.115PC1 1.784 · PC2 0.2353PC1 2.345 · PC2 0.8181PC1 1.501 · PC2 0.571PC1 -0.04434 · PC2 0.6031PC1 0.1061 · PC2 0.7284PC1 0.4553 · PC2 0.2821PC1 0.01487 · PC2 0.7029PC1 0.1215 · PC2 0.5537PC1 -0.8237 · PC2 -0.04007PC1 -0.8062 · PC2 1.219PC1 0.01825 · PC2 0.3632PC1 0.4564 · PC2 0.3669PC1 -0.2547 · PC2 0.4276PC1 -0.7496 · PC2 0.5786PC1 -0.3041 · PC2 0.6887PC1 -0.2152 · PC2 0.893PC1 -1.097 · PC2 0.346PC1 -0.914 · PC2 0.7161PC1 0.8615 · PC2 -1.174PC1 1.19 · PC2 -0.8763PC1 0.5417 · PC2 -0.6653PC1 0.8283 · PC2 -1.196PC1 0.981 · PC2 -0.8923PC1 0.3374 · PC2 -0.07828PC1 0.1891 · PC2 0.5669PC1 0.1859 · PC2 1.296PC1 0.1003 · PC2 0.1321PC1 1.034 · PC2 0.4253PC1 11.23 · PC2 -2.281PC1 11.24 · PC2 0.5807PC1 12.34 · PC2 -0.2802PC1 11.16 · PC2 -1.388PC1 10.99 · PC2 2.233PC1 0.1893 · PC2 0.5222PC1 -0.09848 · PC2 0.1939PC1 0.2661 · PC2 0.4304PC1 0.4677 · PC2 0.2659PC1 0.4448 · PC2 0.2661PC1 1.823 · PC2 -0.907PC1 0.2474 · PC2 -0.2625PC1 1.377 · PC2 -0.08371PC1 0.6323 · PC2 -0.07846PC1 2.472 · PC2 -0.9784PC1 1.572 · PC2 1.406PC1 2.556 · PC2 0.9085PC1 1.367 · PC2 1.771PC1 1.881 · PC2 2.672PC1 1.704 · PC2 1.318PC1 3.861 · PC2 0.1168PC1 5.222 · PC2 0.5645PC1 4.072 · PC2 1.033PC1 3.264 · PC2 1.321PC1 3.629 · PC2 0.9863PC1 0.2426 · PC2 1.689PC1 -0.0141 · PC2 1.249PC1 -0.004799 · PC2 1.785PC1 0.1166 · PC2 1.576PC1 0.05473 · PC2 1.581PC1 -0.676 · PC2 -1.502PC1 -0.5974 · PC2 -0.8628PC1 -0.4183 · PC2 -1.344PC1 -1.011 · PC2 -1.093PC1 -0.7978 · PC2 -1.418PC1 0.3191 · PC2 0.2416PC1 0.1322 · PC2 0.2468PC1 -0.3771 · PC2 -0.8622PC1 -0.08649 · PC2 -0.0534PC1 -0.3332 · PC2 0.1522PC1 0.117 · PC2 -0.4607PC1 0.4203 · PC2 -0.285PC1 0.3037 · PC2 -0.9215PC1 0.06482 · PC2 0.06468PC1 0.1732 · PC2 0.2431PC1 -1.743 · PC2 0.379PC1 -1.69 · PC2 -0.05758PC1 -1.216 · PC2 -0.4247PC1 -1.346 · PC2 0.1576PC1 -0.9392 · PC2 -0.3382PC1 1.162 · PC2 0.03787PC1 -0.3526 · PC2 0.2005PC1 0.8999 · PC2 0.4981PC1 0.1059 · PC2 -0.02386PC1 0.2119 · PC2 0.22PC1 -0.01355 · PC2 0.6453PC1 0.192 · PC2 0.6379PC1 0.1976 · PC2 0.497PC1 0.1708 · PC2 0.3876PC1 0.2266 · PC2 0.5834PC1 -1.208 · PC2 0.07508PC1 -0.6641 · PC2 0.515PC1 -0.8517 · PC2 0.1567PC1 -1.352 · PC2 0.4319PC1 -0.9239 · PC2 0.1567PC1 0.9828 · PC2 -0.05722PC1 0.8725 · PC2 1.387PC1 1.306 · PC2 0.8744PC1 0.9123 · PC2 0.3597PC1 1.446 · PC2 0.9731PC1 -0.08186 · PC2 -0.8679PC1 -0.2137 · PC2 -1.253PC1 -0.2287 · PC2 -1.591PC1 -0.0895 · PC2 -0.4567PC1 -0.3198 · PC2 -0.3209PC1 -0.6178 · PC2 0.9726PC1 -0.3797 · PC2 0.9795PC1 -0.5369 · PC2 1.14PC1 -0.1295 · PC2 0.8916PC1 -0.2655 · PC2 1.018PC1 -0.3335 · PC2 -0.2558PC1 -0.3867 · PC2 -0.1403PC1 0.1154 · PC2 -0.3456PC1 -0.1551 · PC2 -0.1515PC1 0.002472 · PC2 -0.3741PC1 1.91 · PC2 -0.9036PC1 2.93 · PC2 -1.114PC1 2.874 · PC2 -1.224PC1 3.896 · PC2 -1.553PC1 3.68 · PC2 -1.658PC1 -1.588 · PC2 -1.135PC1 -2.302 · PC2 -0.008971PC1 -2.003 · PC2 -0.9065PC1 -2.764 · PC2 -0.2066PC1 -1.47 · PC2 -1.198PC1 -2.192 · PC2 -2.626PC1 -2.394 · PC2 -2.891PC1 -1.794 · PC2 -2.818PC1 -1.964 · PC2 -2.4PC1 -1.832 · PC2 -3.064PC1 -1.04 · PC2 0.8082PC1 -1.011 · PC2 0.871PC1 -1.29 · PC2 0.8335PC1 -1.057 · PC2 1.245PC1 -0.7453 · PC2 0.8772PC1 -0.5668 · PC2 -0.4392PC1 -0.4663 · PC2 0.2605PC1 -0.3456 · PC2 0.1882PC1 0.0144 · PC2 0.04304PC1 -0.546 · PC2 0.5187PC1 -0.8985 · PC2 -0.3677PC1 -0.7791 · PC2 -0.8348PC1 -0.2676 · PC2 -0.8507PC1 0.5608 · PC2 -0.8984PC1 -1.126 · PC2 -0.5718PC1 0.2595 · PC2 -1.389PC1 -0.3148 · PC2 -0.797PC1 -0.3031 · PC2 -1.275PC1 -0.4424 · PC2 -0.978PC1 -0.3394 · PC2 -1.388PC1 -0.2586 · PC2 -0.5987PC1 -0.2645 · PC2 -0.6434PC1 -0.06935 · PC2 -0.6079PC1 -0.6085 · PC2 -0.5777PC1 -0.06423 · PC2 -0.3803PC1 0.3359 · PC2 0.3605PC1 0.1724 · PC2 0.2334PC1 0.6347 · PC2 0.6371PC1 -0.009597 · PC2 0.4409PC1 -0.1643 · PC2 0.4365PC1 0.2934 · PC2 -0.2367PC1 1.017 · PC2 -0.6886PC1 0.6358 · PC2 0.2104PC1 0.8371 · PC2 -0.625PC1 1.113 · PC2 -0.278PC1 -1.124 · PC2 -0.2316PC1 -0.3468 · PC2 0.2009PC1 -0.7291 · PC2 0.3533PC1 -1.52 · PC2 1.048PC1 -0.5832 · PC2 0.1451PC1 -1.648 · PC2 0.6802PC1 -1.368 · PC2 0.5061PC1 -1.231 · PC2 -0.06017PC1 -1.762 · PC2 0.545PC1 -2.545 · PC2 0.3822PC1 1.39 · PC2 -0.6479PC1 1.094 · PC2 -0.08603PC1 4.185 · PC2 -1.467PC1 2.449 · PC2 -1.405PC1 3.123 · PC2 -1.109PC1 0.5815 · PC2 -2.547PC1 1.605 · PC2 -1.156PC1 1.419 · PC2 -2.913PC1 0.9886 · PC2 -1.083PC1 1.123 · PC2 -2.185PC1 (71.5%)PC2 (19.0%)330 scores
View plotted PCA coordinates

Point numbers identify rows in this displayed coordinate list, not original observation or sample IDs. Non-integer coordinates use up to five significant digits. Original IDs, component loadings, preprocessing details and the observation selection method are not supplied with this plot.

Plotted observation positions (not original sample IDs)
PointPC1 scorePC2 score
1-0.26998-0.68208
2-0.36683-0.52926
3-0.041332-0.50131
40.15737-2.0394
50.016596-1.0378
6-0.997770.82965
71.2074-0.77764
8-0.941671.2484
9-1.39181.3993
10-1.32451.1904
11-0.751590.11589
12-0.822220.50741
13-0.865790.31066
14-0.64374-0.13534
15-0.875940.44572
16-0.48104-0.11173
17-0.470710.20143
180.080860.25181
19-0.58978-0.089615
20-0.99720.27977
21-1.8752-1.2474
22-2.3946-1.5129
23-0.30236-2.1087
24-0.9213-1.6638
25-0.41336-0.98777
26-1.6572-0.89906
27-1.2493-1.3193
28-1.5295-0.29725
29-0.86824-1.4443
30-1.5861-0.58409
31-1.52091.0249
32-0.302990.037364
33-1.41940.89176
34-1.33751.5176
35-0.419580.78279
36-0.72853-1.3663
37-0.7011-0.76314
38-0.46649-0.68935
39-0.41973-0.88307
40-0.25385-1.4572
41-1.02870.10426
42-0.98561-0.025544
43-0.9988-0.1969
44-1.27210.27699
45-1.2002-0.053547
46-1.4167-0.2564
47-1.3725-0.26175
48-1.0906-0.77046
49-1.474-0.22001
50-1.4937-0.57808
51-1.68890.95332
52-1.82370.78062
53-0.15043-0.27014
54-1.99561.2794
55-1.90541.1565
56-1.3948-1.0415
57-1.7071-1.3644
58-0.67603-1.8953
59-1.601-1.7164
60-0.96555-1.6277
611.03491.5843
621.08141.7454
630.704391.055
641.16971.7804
651.12211.5964
66-0.042210.012251
670.316780.11273
68-0.66785-0.74236
69-0.49271-0.19129
70-0.45934-0.42688
71-0.819391.0225
720.211470.96462
730.0752270.69606
74-0.0100081.0478
75-0.23210.98057
76-0.628040.67532
77-0.711911.3963
780.0259531.0541
79-0.604211.1318
800.00754682.3888
81-1.4384-0.40368
82-1.6181-0.51775
83-1.7473-0.49685
84-2.36-0.59775
85-1.3996-0.42696
86-0.645780.92812
87-0.0431040.67739
88-0.0233280.85281
890.221350.92187
900.273080.87878
91-0.947440.26402
92-0.97379-0.70376
93-0.89619-0.50468
94-0.65031-0.90487
95-0.233270.15666
96-2.8329-0.18264
97-2.96540.31648
98-1.8688-0.45381
99-3.33420.17589
100-2.01260.10791
1011.16660.10585
1020.837760.90364
1030.997280.42068
1041.94720.84239
1051.38930.10563
1060.23205-0.51423
1070.68407-0.74957
1080.57717-1.3289
1090.095215-0.87749
1100.78942-2.2167
111-0.632910.68106
112-2.09871.6381
113-1.21770.31046
114-0.588860.27528
115-0.55610.53815
1160.067330.027802
1170.62225-0.014008
1181.038-0.67378
1191.4526-0.97486
1200.77405-0.39525
121-0.332851.3603
122-0.531961.0576
123-0.152491.1061
124-0.320781.3879
1250.109651.2786
126-0.820940.43366
127-0.751170.3496
128-1.11920.45966
129-0.979040.50555
130-1.05190.50451
1310.0211740.36201
132-0.248580.23435
133-0.31112-0.019135
134-0.616970.010563
135-0.29516-0.13137
136-2.00141.0603
137-1.96810.87345
138-2.08411.0801
139-1.95041.2449
140-1.60490.7919
1410.28484-0.10157
1420.546130.46229
1430.195430.55551
1440.504250.59365
1450.712950.56911
1461.30780.33293
1472.15561.1149
1481.78450.23534
1492.34490.81808
1501.50080.57103
151-0.0443440.60307
1520.106130.72836
1530.455260.28209
1540.0148670.70294
1550.121540.55374
156-0.82366-0.040074
157-0.806221.2194
1580.0182490.36316
1590.456420.36693
160-0.254720.42762
161-0.749550.57865
162-0.304140.68867
163-0.215220.893
164-1.09690.34595
165-0.914030.71606
1660.86153-1.1739
1671.1896-0.87634
1680.54172-0.66525
1690.8283-1.1962
1700.98103-0.89229
1710.33741-0.078282
1720.189070.56694
1730.185921.2962
1740.100260.13214
1751.03370.42534
17611.228-2.2807
17711.2380.58075
17812.338-0.28015
17911.157-1.388
18010.9882.2327
1810.18930.52224
182-0.0984760.19389
1830.266110.43044
1840.46770.26589
1850.444750.26607
1861.823-0.90696
1870.24743-0.26254
1881.3767-0.083708
1890.63233-0.078463
1902.4723-0.97837
1911.57221.406
1922.55610.9085
1931.36671.7707
1941.8812.6721
1951.70451.3176
1963.86050.11681
1975.22160.56452
1984.0721.033
1993.26371.3212
2003.62870.98635
2010.242591.6893
202-0.0141021.249
203-0.00479851.7853
2040.116631.5757
2050.0547261.5806
206-0.67596-1.5024
207-0.59736-0.86282
208-0.41825-1.3437
209-1.0112-1.0927
210-0.79784-1.4184
2110.319120.24164
2120.132180.24684
213-0.37705-0.86224
214-0.086491-0.053402
215-0.333220.15222
2160.11702-0.46066
2170.42027-0.28504
2180.30373-0.92153
2190.0648250.064682
2200.173150.24307
221-1.7430.37904
222-1.6904-0.057584
223-1.2162-0.42467
224-1.34630.15756
225-0.93918-0.33822
2261.16220.037874
227-0.352620.20052
2280.899880.49807
2290.10594-0.023855
2300.211880.22
231-0.0135480.64529
2320.191960.63788
2330.197640.49702
2340.170820.38763
2350.226570.58345
236-1.2080.075083
237-0.664050.51502
238-0.851690.15669
239-1.35170.43192
240-0.923890.1567
2410.98285-0.057221
2420.872531.3868
2431.30620.87437
2440.912310.35972
2451.44640.97314
246-0.081864-0.86791
247-0.21373-1.253
248-0.22866-1.5909
249-0.0895-0.45671
250-0.31984-0.32087
251-0.61780.97259
252-0.379720.97949
253-0.536861.1397
254-0.129550.89161
255-0.265461.0177
256-0.33355-0.2558
257-0.3867-0.14033
2580.11536-0.34558
259-0.15508-0.15148
2600.0024723-0.37408
2611.9103-0.90363
2622.9299-1.114
2632.8743-1.224
2643.8957-1.5532
2653.6801-1.6576
266-1.5878-1.1353
267-2.302-0.0089707
268-2.0027-0.90649
269-2.7642-0.20658
270-1.4701-1.1975
271-2.1915-2.6256
272-2.3944-2.8914
273-1.7939-2.8183
274-1.9637-2.3997
275-1.832-3.0636
276-1.04020.80818
277-1.01080.871
278-1.28980.83355
279-1.05651.2449
280-0.745290.87722
281-0.56677-0.43924
282-0.466290.26049
283-0.34560.18817
2840.0143960.043044
285-0.545960.51872
286-0.8985-0.36772
287-0.77908-0.83482
288-0.26765-0.85072
2890.56077-0.89844
290-1.1258-0.57178
2910.25955-1.3891
292-0.31481-0.797
293-0.30313-1.2746
294-0.44242-0.97797
295-0.33942-1.3883
296-0.25859-0.59867
297-0.26449-0.64339
298-0.069347-0.60788
299-0.6085-0.57767
300-0.064234-0.38035
3010.335940.36052
3020.172440.23342
3030.634680.63709
304-0.00959730.44088
305-0.164290.43645
3060.29344-0.23666
3071.0171-0.68865
3080.635770.21039
3090.83711-0.62495
3101.1133-0.27799
311-1.1237-0.23159
312-0.346780.20086
313-0.729080.35331
314-1.51981.0481
315-0.583230.14507
316-1.64770.68023
317-1.36830.50612
318-1.2311-0.060169
319-1.76170.54499
320-2.54470.38223
3211.3904-0.64793
3221.0937-0.086025
3234.1849-1.4666
3242.4486-1.4048
3253.1234-1.1092
3260.58147-2.5469
3271.6054-1.156
3281.419-2.913
3290.98862-1.0834
3301.1225-2.1847

PCA explained variance — how many dimensions?

Each bar reports the share of spectral variance explained by a component. Concentration in the first components indicates correlated wavelengths. This is not variance explained in the prediction target. This projection may use a different observation sample from the score plot.

PCA explained variance0%25%50%75%100%PC1: 71.5% (cumulative 71.5%)1PC2: 19.0% (cumulative 90.5%)2PC3: 5.4% (cumulative 95.9%)3PC4: 1.9% (cumulative 97.7%)4PC5: 1.0% (cumulative 98.8%)5PC6: 0.4% (cumulative 99.2%)6PC7: 0.3% (cumulative 99.5%)7PC8: 0.2% (cumulative 99.7%)8PC9: 0.1% (cumulative 99.8%)9PC10: 0.1% (cumulative 99.9%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
View explained variance values
Variance per displayed principal component
ComponentSpectral variance
PC171.49%
PC219.04%
PC35.36%
PC41.86%
PC51.03%
PC60.40%
PC70.33%
PC80.21%
PC90.08%
PC100.06%
X-Y spectral correlation 3

Pearson r measures the linear association between each spectral channel (X) and a numeric target (Y), from −1 to +1. The table reports peak and average absolute correlation and the share of channels with |r| ≥ 0.5. These are in-dataset associations, not validation scores or proof of causation.

Spectral association with Equivalent_Water_Thickness_g_cm2

Read Pearson correlation (−1 to +1) along the spectral axis (nm); the solid curve shows |r| and the dashed curve shows signed r.

X · Equivalent_Water_Thickness_g_cm2 spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale

Spectral association with Leaf_mass_per_area_g_cm2

Read Pearson correlation (−1 to +1) along the spectral axis (nm); the solid curve shows |r| and the dashed curve shows signed r.

X · Leaf_mass_per_area_g_cm2 spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale

Spectral association with Nitrogen_pct_DW

Read Pearson correlation (−1 to +1) along the spectral axis (nm); the solid curve shows |r| and the dashed curve shows signed r.

X · Nitrogen_pct_DW spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
Equivalent_Water_Thickness_g_cm20.7341,3980.4655.5%
Leaf_mass_per_area_g_cm20.3588500.2370.0%
Nitrogen_pct_DW0.2538420.1360.0%

Metric interpretation reference

Definitions, formulas and possible explanations for the reported values. A possible cause requires verification against acquisition records. Reading guide

Metric catalog 30

Look up each measured quantity, its calculation and the heuristic alert mapping before interpreting a high or low value.

FamilyMetricWhat it measuresHigher values suggestLower values suggestPossible causesCalculation / score
Data integrityInvalid/masked cell count (owner report)Null or masked cells in the evaluated populationTo investigateComplete according to the declared maskMissing data or validity mask; not evidence of an acquisition causeNative Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio.
Data integrityNaN ratioMissing dataCorrupted spectrumFull spectrumAcquisition/export errorcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Data integrityInf countInfinite valuesCorruptionNormalInvalid calculationscount(isinf(X))alert = min(1, inf_count / 1)
Data integrityZero ratioNull columns or cellsTruncated spectrumNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Overall amplitudeMean reflectanceAverage levelToo light / background visibleToo darkBackground, geometrymean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Overall amplitudeArea under curveOverall intensityIllumination differenceNormalProbe distancetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Overall amplitudePeak-to-peak (PTP)Dynamic rangeStrong variabilityFlat spectrumSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Overall amplitudeVarianceSpectral variabilityNormal or heterogeneousFlat spectrumBad contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
NoiseRoughness sigma proxyHigh frequency noiseNoisyStableLamp, detectormedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
NoiseRoughness S/N proxySignal qualityGood signalBad signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
NoiseBandwise roughness S/N proxyLocalized noiseReliable zoneProblem areaDetectormin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Local artifactsSpike countNarrow peaksArtifactsClean spectrumCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Local artifactsSpike ratePeak densitySuspicious spectrumNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Local artifactsJump countDiscontinuitiesDetector connectionContinuousSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Local artifactsJump rateJump frequencySpectral problemNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Local artifactsClip fractionSaturationClippingNormalDetector saturatedfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Spectral formBaseline slopeOverall slopeDriftStableIlluminationlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Spectral formCurvature RMSCurvatureUnusual shapeSmoothBackground, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Spectral formD1 RMSLocal variabilityStructured spectrumFlatBiology or artifactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Multivariate outliersPCA Q (SPE)Not explained by PCAAtypical spectrumCompliantArtifact, mixturep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Multivariate outliersHotelling T²Extreme in PCAExtreme but consistentCentralNatural variabilityp95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Multivariate outliersMahalanobis HDistance to cloudGlobal outlierNormal populationDifferent domainp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparison to average spectrumRMS to mean spectrumAverage distanceDifferent spectrumTypicalDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparison to average spectrumSpectral Angle Mapper (SAM)Difference in shapeDifferent shapeSimilarBackground, geometryp95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RepeatabilityRMS intra-IDReproducibilityPoor repeatabilityStablePositioningmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RepeatabilitySAM intra-IDShape variationUnstableStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RepeatabilityCV intra-IDInternal variabilityBad controlStableOperatormedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure of the datasetPCA score densityClustersSubpopulationsHomogeneousDifferent batches1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure of the datasetkNN distance ratio (not LOF)Local anomalyIsolated spectrumNormal populationRare casesp95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2)
Structure of the datasetIsolation Forest scoreGlobal anomalyAtypical spectrumNormalVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
Technology-specific extensions

Find measurement-specific checks and acquisition context that can help interpret a source's spectral patterns.

TechnologyAdaptations / metricsTarget anomaliesPractical guidance
UV-Vis 300-1000 nmBaseline, overall slope, drift at edges 300-350 and 900-1000; metrics by zonesStray light, bad white, saturation, weak signal at the endsThe edges are often unstable; also calculate edge/middle scores.
UV-Vis 300-1000 nmSaturation / clipping near max absorbance or max reflectanceSignal clippedVery important if strong absorption.
UV-Vis 300-1000 nmRed-edge, peak position, band ratios for vegetationBiological shift or optical artifactHelps distinguish real change from acquisition problem.
UV-Vis 300-1000 nmSmoothness / roughness indexHigh frequency noiseOften more informative than the SNR alone.
MIR / ATR-FTIRATR contact quality index: overall intensity, total area, depth of key bandsPoor crystal-sample contactCrucial: many anomalies come from the ATR contact.
MIR / ATR-FTIRCO2 / H2O atmospheric bandsBad atmospheric correctionFrequent spurious peaks.
MIR / ATR-FTIRBaseline curvature / rubber-band residualDiffusion, contact, baseline driftVery useful before PCA.
MIR / ATR-FTIRPeak position shiftBad spectral alignment/calibrationImportant in FTIR because small shifts count.
MIR / ATR-FTIRBand area ratios on known bandsChemically incoherent spectrumTo be adapted by matrix: polysaccharides, proteins, lipids, etc.
HS-MSTotal Ion Current (TIC), Base Peak Intensity (BPI)Weak injection, unstable ionizationMS equivalent of the global spectral level.
HS-MSNumber of peaks detectedPoor or too noisy spectrumToo little = bad signal; too much = noise/contamination.
HS-MSMass accuracy / m/z driftMass calibration problemFundamental in HRMS.
HS-MSRetention time drift if LC/GC-MSChromatographic driftMonitor on standards/QC pools.
HS-MSBlank contamination scoreContaminants / carry-overCompare samples vs blanks.
HS-MSInternal standard CVInstrumental variabilityVery robust if standards available.
HS-MSMissingness by featureDetection instabilityCrucial for filtering variables.
With repetitionsIntra-sample RMSOverall repeatabilityApplicable to all technologies.
With repetitionsSAM / intra-sample correlationShape repeatabilityVery useful for spectra.
With repetitionsIntra-sample CV per band / featureLocal repeatabilityDetects unstable areas.
With repetitionsICC or variance componentsShare of sample vs. technical varianceVery useful if several repetitions per sample.
With repetitionsDistance to intra-ID centroidAberrant repetitionAllows you to flag the bad repetition rather than the entire sample.
Bug-hunting / supervised audits

Review suggested follow-up methods for suspected data issues and whether their results are available in this explorer.

Potential issue familySuggested methodsWhat it detectsExplorer availability
Global spectral shiftInter-dataset average spectrum correlation, DTW, cross-correlation, comparison of peak positionsWavelength shift, misalignment, different interpolationPartially calculated: cross-correlation lag and dispersion of peak positions vs. average spectrum.
Baseline / offset / gainRegression each spectrum vs average spectrum: x = a + b ref + residual; followed by a, b, residual RMSAdditive offset, multiplicative effect, baseline driftCalculated in reference.affine_*.
Row mixing / X-M-Y mismatchIndex check, row hash, ID duplication, intra-ID spectral distance, inconsistent labelsMixed up rows, misaligned metadata, Y assigned to wrong spectrumPartially covered by intra-ID repeatability; checks index/hash to add to the canonical pipeline.
Information leak / poorly split repetitionsGroupKFold by sample_id vs StratifiedKFold random; auditing partitions by sample_idArtificially good performance due to repetitionsRequires splits and benchmark model; not calculated by the descriptive card.
Label bugsClose samples in X but different Y, confident learning, systematic FP/FN errorsInverted Y, input errors, ambiguous classesRequires Y and/or model; recommended for supervised explorer.
Hidden subdomainsPCA/UMAP/t-SNE + unsupervised clustering + association with dataset/Y/date/operatorBatches, campaigns, probes, backgrounds not providedPartially calculated by PCA and distance ratio kNN (not LOF); UMAP/t-SNE outside the static card.
Unknown localized artifactsWavelength x dataset map: mean difference, variance difference, KS per wavelengthUnanticipated anomalous spectral regionsTo be calculated at the bank level when several datasets share a spectral axis.
Instrumental breaksDiscontinuities in derivatives, changepoint detectionSplice, detector connection, local jump not providedCalculated by jump/spike rates; more advanced changepoint to add.
Spectral mixing/contaminationNMF / unmixing / reconstruction by convex hullExternal component: background, plastic, soilNot calculated automatically; requires component assumptions or large library.
Unstable but predictive featuresModel importance vs. QC instability by variableModel that learns an artifact rather than a biological signalRequires supervised model; recommended for benchmark reporting.

Curated scientific variables

Scientific roles derived from the source contract. M denotes experimental metadata; Y denotes informative response variables. Reading guide

3 experimental metadata fields (M) · 3 informative targets (Y).

VariableRoleDistinct valuesCompleteRange / balance
english_nameM46100%IR 6
latin_nameM46100%IR 6
plant_typeM2100%IR 3.125
equivalent_water_thickness_g_cm2Y272100%0.0002927 – 0.05249
leaf_mass_per_area_g_cm2Y233100%0.001707 – 0.01573
nitrogen_pct_dwY60100%-999 – 6

Identifiers and sampling groups remain available separately. Statistics count exported observations.

Variables

Declared prediction targets and recorded metadata, with distributions and missing-value counts. Identifiers are not automatically prediction targets. Reading guide

Targets 3

Equivalent_Water_Thickness_g_cm2

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Equivalent_Water_Thickness_g_cm2 distribution02550750.0002927 – 0.002467: 100.002467 – 0.004642: 130.004642 – 0.006817: 550.006817 – 0.008992: 690.008992 – 0.01117: 600.01117 – 0.01334: 460.01334 – 0.01552: 250.01552 – 0.01769: 130.01769 – 0.01987: 80.01987 – 0.02204: 80.02204 – 0.02422: 70.02422 – 0.02639: 40.02639 – 0.02857: 30.02857 – 0.03074: 10.03074 – 0.03291: 00.03291 – 0.03509: 10.03509 – 0.03726: 30.03726 – 0.03944: 10.03944 – 0.04161: 00.04161 – 0.04379: 00.04379 – 0.04596: 10.04596 – 0.04814: 10.04814 – 0.05031: 00.05031 – 0.05249: 10.000.020.040.06
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.01113 ± 0.00698
Median
?50th percentile: half the non-missing values lie at or below it.
0.009486
Range
?Smallest and largest recorded non-missing values.
0.0002927 – 0.05249
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.627
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.4 / 8.3
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Leaf_mass_per_area_g_cm2

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Leaf_mass_per_area_g_cm2 distribution02040600.001707 – 0.002292: 100.002292 – 0.002876: 310.002876 – 0.00346: 370.00346 – 0.004045: 450.004045 – 0.004629: 340.004629 – 0.005213: 300.005213 – 0.005798: 310.005798 – 0.006382: 270.006382 – 0.006966: 160.006966 – 0.007551: 210.007551 – 0.008135: 100.008135 – 0.00872: 70.00872 – 0.009304: 90.009304 – 0.009888: 50.009888 – 0.01047: 10.01047 – 0.01106: 30.01106 – 0.01164: 10.01164 – 0.01223: 40.01223 – 0.01281: 40.01281 – 0.01339: 10.01339 – 0.01398: 10.01398 – 0.01456: 00.01456 – 0.01515: 00.01515 – 0.01573: 20.0000.0050.0100.0150.020
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.005296 ± 0.00247
Median
?50th percentile: half the non-missing values lie at or below it.
0.004695
Range
?Smallest and largest recorded non-missing values.
0.001707 – 0.01573
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.467
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.3 / 2.2
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Nitrogen_pct_DW

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Nitrogen_pct_DW distribution0200400-999 – -957.1: 25-957.1 – -915.2: 0-915.2 – -873.4: 0-873.4 – -831.5: 0-831.5 – -789.6: 0-789.6 – -747.8: 0-747.8 – -705.9: 0-705.9 – -664: 0-664 – -622.1: 0-622.1 – -580.2: 0-580.2 – -538.4: 0-538.4 – -496.5: 0-496.5 – -454.6: 0-454.6 – -412.8: 0-412.8 – -370.9: 0-370.9 – -329: 0-329 – -287.1: 0-287.1 – -245.2: 0-245.2 – -203.4: 0-203.4 – -161.5: 0-161.5 – -119.6: 0-119.6 – -77.75: 0-77.75 – -35.88: 0-35.88 – 6: 305-1,000-5000500
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
-72.79 ± 266
Median
?50th percentile: half the non-missing values lie at or below it.
2.8
Range
?Smallest and largest recorded non-missing values.
-999 – 6
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
3.65
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-3.2 / 8.4
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Metadata 30

Refl_file

metadata · categorical

Compare recorded category counts and class balance. No distribution plot is available; use the recorded statistics below.

n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Classes
?Number of distinct recorded category values.
330
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
1
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
1
Top class
?Most frequent recorded category and its observation count.
lo93r0001.txt (1)

English Name

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

English Name classesCornCorn: 3030European chestnutEuropean chestnut: 1515European ashEuropean ash: 1010Cherry laurelCherry laurel: 1010AlfalfaAlfalfa: 1010Black locustBlack locust: 1010Downy oakDowny oak: 1010Largeleaf lindenLargeleaf linden: 1010RiceRice: 1010SoybeanSoybean: 1010+10 more+10 more: 7575
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Classes
?Number of distinct recorded category values.
46
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.97
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
6
Top class
?Most frequent recorded category and its observation count.
Corn (30)

Latin Name

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Latin Name classesZea mays L.Zea mays L.: 3030Castanea sativaCastanea sativa: 1515Fraxinus excelsior L.Fraxinus excelsior L.: 1010Prunus laurocerasus L.Prunus laurocerasus L.: 1010Medicago sativa L.Medicago sativa L.: 1010Robinia pseudoacacia L.Robinia pseudoacacia L.: 1010Quercus pubescensQuercus pubescens: 1010Tilia platyphyllosTilia platyphyllos: 1010Oryza sativa L.Oryza sativa L.: 1010Glycine max L.Glycine max L.: 1010+10 more+10 more: 7575
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Classes
?Number of distinct recorded category values.
46
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.97
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
6
Top class
?Most frequent recorded category and its observation count.
Zea mays L. (30)

Plant Type

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Plant Type classes22: 25025011: 8080
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.8
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
3
Top class
?Most frequent recorded category and its observation count.
2 (250)

Structure parameter

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Structure parameter distribution0200400-999 – -957.2: 10-957.2 – -915.5: 0-915.5 – -873.8: 0-873.8 – -832: 0-832 – -790.2: 0-790.2 – -748.5: 0-748.5 – -706.8: 0-706.8 – -665: 0-665 – -623.2: 0-623.2 – -581.5: 0-581.5 – -539.8: 0-539.8 – -498: 0-498 – -456.2: 0-456.2 – -414.5: 0-414.5 – -372.8: 0-372.8 – -331: 0-331 – -289.2: 0-289.2 – -247.5: 0-247.5 – -205.8: 0-205.8 – -164: 0-164 – -122.2: 0-122.2 – -80.5: 0-80.5 – -38.75: 0-38.75 – 3: 320-1,000-5000500
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
-28.65 ± 172
Median
?50th percentile: half the non-missing values lie at or below it.
1.604
Range
?Smallest and largest recorded non-missing values.
-999 – 3
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
6
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-5.5 / 28
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Chlorophyll_a (µg/cm²)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Chlorophyll_a (µg/cm²) distribution0100200-999 – -954.1: 10-954.1 – -909.3: 0-909.3 – -864.4: 0-864.4 – -819.6: 0-819.6 – -774.7: 0-774.7 – -729.9: 0-729.9 – -685: 0-685 – -640.1: 0-640.1 – -595.3: 0-595.3 – -550.4: 0-550.4 – -505.6: 0-505.6 – -460.7: 0-460.7 – -415.8: 0-415.8 – -371: 0-371 – -326.1: 0-326.1 – -281.3: 0-281.3 – -236.4: 0-236.4 – -191.6: 0-191.6 – -146.7: 0-146.7 – -101.8: 0-101.8 – -56.98: 0-56.98 – -12.12: 0-12.12 – 32.74: 13032.74 – 77.6: 190-1,000-5000500
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
4.485 ± 178
Median
?50th percentile: half the non-missing values lie at or below it.
33.42
Range
?Smallest and largest recorded non-missing values.
-999 – 77.6
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
39.7
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-5.5 / 28
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Chlorophyll_b (µg/cm²)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Chlorophyll_b (µg/cm²) distribution0200400-999 – -956.4: 10-956.4 – -913.8: 0-913.8 – -871.2: 0-871.2 – -828.6: 0-828.6 – -786: 0-786 – -743.4: 0-743.4 – -700.8: 0-700.8 – -658.2: 0-658.2 – -615.6: 0-615.6 – -573: 0-573 – -530.5: 0-530.5 – -487.9: 0-487.9 – -445.3: 0-445.3 – -402.7: 0-402.7 – -360.1: 0-360.1 – -317.5: 0-317.5 – -274.9: 0-274.9 – -232.3: 0-232.3 – -189.7: 0-189.7 – -147.1: 0-147.1 – -104.5: 0-104.5 – -61.91: 0-61.91 – -19.31: 0-19.31 – 23.28: 320-1,000-5000500
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
-19.18 ± 174
Median
?50th percentile: half the non-missing values lie at or below it.
10.85
Range
?Smallest and largest recorded non-missing values.
-999 – 23.28
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
9.05
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-5.5 / 28
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Chlorophyll_a+b (µg/cm²)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Chlorophyll_a+b (µg/cm²) distribution0100200300-999 – -953.3: 10-953.3 – -907.5: 0-907.5 – -861.8: 0-861.8 – -816: 0-816 – -770.3: 0-770.3 – -724.5: 0-724.5 – -678.8: 0-678.8 – -633.1: 0-633.1 – -587.3: 0-587.3 – -541.6: 0-541.6 – -495.8: 0-495.8 – -450.1: 0-450.1 – -404.4: 0-404.4 – -358.6: 0-358.6 – -312.9: 0-312.9 – -267.1: 0-267.1 – -221.4: 0-221.4 – -175.6: 0-175.6 – -129.9: 0-129.9 – -84.16: 0-84.16 – -38.42: 0-38.42 – 7.319: 57.319 – 53.06: 22053.06 – 98.8: 95-1,000-5000500
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
15.57 ± 180
Median
?50th percentile: half the non-missing values lie at or below it.
44.69
Range
?Smallest and largest recorded non-missing values.
-999 – 98.8
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
11.6
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-5.4 / 28
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Carotenoid (µg/cm²)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Carotenoid (µg/cm²) distribution0200400-999 – -956.2: 10-956.2 – -913.4: 0-913.4 – -870.6: 0-870.6 – -827.8: 0-827.8 – -785: 0-785 – -742.2: 0-742.2 – -699.4: 0-699.4 – -656.5: 0-656.5 – -613.7: 0-613.7 – -570.9: 0-570.9 – -528.1: 0-528.1 – -485.3: 0-485.3 – -442.5: 0-442.5 – -399.7: 0-399.7 – -356.9: 0-356.9 – -314.1: 0-314.1 – -271.3: 0-271.3 – -228.5: 0-228.5 – -185.7: 0-185.7 – -142.9: 0-142.9 – -100.1: 0-100.1 – -57.26: 0-57.26 – -14.46: 0-14.46 – 28.35: 320-1,000-5000500
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
-20.28 ± 173
Median
?50th percentile: half the non-missing values lie at or below it.
9.426
Range
?Smallest and largest recorded non-missing values.
-999 – 28.35
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
8.55
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-5.5 / 28
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Equivalent Water Thickness (g/cm²)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Equivalent Water Thickness (g/cm²) distribution02550750.0002927 – 0.002467: 100.002467 – 0.004642: 130.004642 – 0.006817: 550.006817 – 0.008992: 690.008992 – 0.01117: 600.01117 – 0.01334: 460.01334 – 0.01552: 250.01552 – 0.01769: 130.01769 – 0.01987: 80.01987 – 0.02204: 80.02204 – 0.02422: 70.02422 – 0.02639: 40.02639 – 0.02857: 30.02857 – 0.03074: 10.03074 – 0.03291: 00.03291 – 0.03509: 10.03509 – 0.03726: 30.03726 – 0.03944: 10.03944 – 0.04161: 00.04161 – 0.04379: 00.04379 – 0.04596: 10.04596 – 0.04814: 10.04814 – 0.05031: 00.05031 – 0.05249: 10.000.020.040.06
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.01113 ± 0.00698
Median
?50th percentile: half the non-missing values lie at or below it.
0.009486
Range
?Smallest and largest recorded non-missing values.
0.0002927 – 0.05249
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.627
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.4 / 8.3
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Leaf mass per area (g/cm²)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Leaf mass per area (g/cm²) distribution02040600.001707 – 0.002292: 100.002292 – 0.002876: 310.002876 – 0.00346: 370.00346 – 0.004045: 450.004045 – 0.004629: 340.004629 – 0.005213: 300.005213 – 0.005798: 310.005798 – 0.006382: 270.006382 – 0.006966: 160.006966 – 0.007551: 210.007551 – 0.008135: 100.008135 – 0.00872: 70.00872 – 0.009304: 90.009304 – 0.009888: 50.009888 – 0.01047: 10.01047 – 0.01106: 30.01106 – 0.01164: 10.01164 – 0.01223: 40.01223 – 0.01281: 40.01281 – 0.01339: 10.01339 – 0.01398: 10.01398 – 0.01456: 00.01456 – 0.01515: 00.01515 – 0.01573: 20.0000.0050.0100.0150.020
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.005296 ± 0.00247
Median
?50th percentile: half the non-missing values lie at or below it.
0.004695
Range
?Smallest and largest recorded non-missing values.
0.001707 – 0.01573
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.467
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.3 / 2.2
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Fresh Weight (g)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Fresh Weight (g) distribution02040600.0125 – 0.02198: 70.02198 – 0.03146: 130.03146 – 0.04094: 310.04094 – 0.05042: 460.05042 – 0.0599: 430.0599 – 0.06937: 530.06937 – 0.07885: 530.07885 – 0.08833: 210.08833 – 0.09781: 120.09781 – 0.1073: 70.1073 – 0.1168: 140.1168 – 0.1263: 80.1263 – 0.1357: 90.1357 – 0.1452: 20.1452 – 0.1547: 30.1547 – 0.1642: 20.1642 – 0.1736: 00.1736 – 0.1831: 20.1831 – 0.1926: 00.1926 – 0.2021: 10.2021 – 0.2116: 00.2116 – 0.221: 10.221 – 0.2305: 00.2305 – 0.24: 20.00.10.20.3
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.06971 ± 0.0339
Median
?50th percentile: half the non-missing values lie at or below it.
0.06515
Range
?Smallest and largest recorded non-missing values.
0.0125 – 0.24
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.487
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.7 / 4.5
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Dry Weight (g)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Dry Weight (g) distribution02040600.007 – 0.009396: 80.009396 – 0.01179: 270.01179 – 0.01419: 340.01419 – 0.01658: 450.01658 – 0.01898: 360.01898 – 0.02138: 330.02138 – 0.02377: 320.02377 – 0.02617: 230.02617 – 0.02856: 190.02856 – 0.03096: 230.03096 – 0.03335: 70.03335 – 0.03575: 70.03575 – 0.03815: 90.03815 – 0.04054: 50.04054 – 0.04294: 10.04294 – 0.04533: 40.04533 – 0.04773: 10.04773 – 0.05013: 50.05013 – 0.05252: 40.05252 – 0.05492: 20.05492 – 0.05731: 20.05731 – 0.05971: 00.05971 – 0.0621: 00.0621 – 0.0645: 30.000.020.040.060.08
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.0224 ± 0.0107
Median
?50th percentile: half the non-missing values lie at or below it.
0.01965
Range
?Smallest and largest recorded non-missing values.
0.007 – 0.0645
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.477
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.4 / 2.2
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Leaf Area (cm²)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Leaf Area (cm²) distribution01002003002.6 – 2.871: 22.871 – 3.142: 13.142 – 3.413: 13.413 – 3.683: 03.683 – 3.954: 23.954 – 4.225: 2914.225 – 4.496: 24.496 – 4.767: 44.767 – 5.037: 45.037 – 5.308: 35.308 – 5.579: 45.579 – 5.85: 25.85 – 6.121: 26.121 – 6.392: 16.392 – 6.662: 36.662 – 6.933: 16.933 – 7.204: 37.204 – 7.475: 17.475 – 7.746: 17.746 – 8.017: 18.017 – 8.287: 08.287 – 8.558: 08.558 – 8.829: 08.829 – 9.1: 112510
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
4.26 ± 0.661
Median
?50th percentile: half the non-missing values lie at or below it.
4.1
Range
?Smallest and largest recorded non-missing values.
2.6 – 9.1
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.155
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
3.9 / 18
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Leaf Thickness (µm)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Leaf Thickness (µm) distribution05010070 – 107.4: 38107.4 – 144.8: 94144.8 – 182.2: 77182.2 – 219.7: 49219.7 – 257.1: 21257.1 – 294.5: 13294.5 – 331.9: 13331.9 – 369.3: 3369.3 – 406.8: 9406.8 – 444.2: 2444.2 – 481.6: 0481.6 – 519: 1519 – 556.4: 1556.4 – 593.8: 1593.8 – 631.2: 3631.2 – 668.7: 2668.7 – 706.1: 0706.1 – 743.5: 1743.5 – 780.9: 0780.9 – 818.3: 1818.3 – 855.8: 0855.8 – 893.2: 0893.2 – 930.6: 0930.6 – 968: 102505007501,000
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
191.2 ± 114
Median
?50th percentile: half the non-missing values lie at or below it.
162
Range
?Smallest and largest recorded non-missing values.
70 – 968
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.595
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
3.1 / 13
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Carbon (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Carbon (% DW) distribution0200400-999 – -955.2: 25-955.2 – -911.5: 0-911.5 – -867.7: 0-867.7 – -824: 0-824 – -780.2: 0-780.2 – -736.5: 0-736.5 – -692.7: 0-692.7 – -649: 0-649 – -605.2: 0-605.2 – -561.5: 0-561.5 – -517.7: 0-517.7 – -474: 0-474 – -430.2: 0-430.2 – -386.4: 0-386.4 – -342.7: 0-342.7 – -298.9: 0-298.9 – -255.2: 0-255.2 – -211.4: 0-211.4 – -167.7: 0-167.7 – -123.9: 0-123.9 – -80.17: 0-80.17 – -36.42: 0-36.42 – 7.336: 07.336 – 51.09: 305-1,000-5000500
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
-32.8 ± 277
Median
?50th percentile: half the non-missing values lie at or below it.
46.37
Range
?Smallest and largest recorded non-missing values.
-999 – 51.09
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
8.45
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-3.2 / 8.4
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Hydrogen (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Hydrogen (% DW) distribution0200400-999 – -957.1: 25-957.1 – -915.1: 0-915.1 – -873.2: 0-873.2 – -831.2: 0-831.2 – -789.3: 0-789.3 – -747.3: 0-747.3 – -705.4: 0-705.4 – -663.4: 0-663.4 – -621.5: 0-621.5 – -579.5: 0-579.5 – -537.6: 0-537.6 – -495.6: 0-495.6 – -453.7: 0-453.7 – -411.7: 0-411.7 – -369.8: 0-369.8 – -327.8: 0-327.8 – -285.9: 0-285.9 – -243.9: 0-243.9 – -202: 0-202 – -160.1: 0-160.1 – -118.1: 0-118.1 – -76.16: 0-76.16 – -34.21: 0-34.21 – 7.74: 305-1,000-5000500
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
-69.87 ± 266
Median
?50th percentile: half the non-missing values lie at or below it.
6.17
Range
?Smallest and largest recorded non-missing values.
-999 – 7.74
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
3.81
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-3.2 / 8.4
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Oxygen (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Oxygen (% DW) distribution0200400-999 – -955.5: 25-955.5 – -912.1: 0-912.1 – -868.6: 0-868.6 – -825.2: 0-825.2 – -781.7: 0-781.7 – -738.3: 0-738.3 – -694.8: 0-694.8 – -651.4: 0-651.4 – -607.9: 0-607.9 – -564.5: 0-564.5 – -521: 0-521 – -477.6: 0-477.6 – -434.1: 0-434.1 – -390.7: 0-390.7 – -347.2: 0-347.2 – -303.7: 0-303.7 – -260.3: 0-260.3 – -216.8: 0-216.8 – -173.4: 0-173.4 – -129.9: 0-129.9 – -86.48: 0-86.48 – -43.03: 0-43.03 – 0.4267: 00.4267 – 43.88: 305-1,000-5000500
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
-39.99 ± 275
Median
?50th percentile: half the non-missing values lie at or below it.
38.55
Range
?Smallest and largest recorded non-missing values.
-999 – 43.88
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
6.88
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-3.2 / 8.4
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Nitrogen (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Nitrogen (% DW) distribution0200400-999 – -957.1: 25-957.1 – -915.2: 0-915.2 – -873.4: 0-873.4 – -831.5: 0-831.5 – -789.6: 0-789.6 – -747.8: 0-747.8 – -705.9: 0-705.9 – -664: 0-664 – -622.1: 0-622.1 – -580.2: 0-580.2 – -538.4: 0-538.4 – -496.5: 0-496.5 – -454.6: 0-454.6 – -412.8: 0-412.8 – -370.9: 0-370.9 – -329: 0-329 – -287.1: 0-287.1 – -245.2: 0-245.2 – -203.4: 0-203.4 – -161.5: 0-161.5 – -119.6: 0-119.6 – -77.75: 0-77.75 – -35.88: 0-35.88 – 6: 305-1,000-5000500
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
-72.79 ± 266
Median
?50th percentile: half the non-missing values lie at or below it.
2.8
Range
?Smallest and largest recorded non-missing values.
-999 – 6
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
3.65
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-3.2 / 8.4
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Protein_1 (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Protein_1 (% DW) distribution020407.37 – 8.557: 108.557 – 9.743: 209.743 – 10.93: 1510.93 – 12.12: 512.12 – 13.3: 1013.3 – 14.49: 2014.49 – 15.68: 2515.68 – 16.86: 1016.86 – 18.05: 4018.05 – 19.24: 1519.24 – 20.42: 1520.42 – 21.61: 2521.61 – 22.8: 1522.8 – 23.98: 523.98 – 25.17: 2025.17 – 26.36: 2526.36 – 27.54: 027.54 – 28.73: 1028.73 – 29.92: 029.92 – 31.1: 531.1 – 32.29: 1532.29 – 33.48: 533.48 – 34.66: 034.66 – 35.85: 20010203040
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
19.95 ± 7.48
Median
?50th percentile: half the non-missing values lie at or below it.
18.37
Range
?Smallest and largest recorded non-missing values.
7.37 – 35.85
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.375
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.44 / -0.5
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Protein_2 (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Protein_2 (% DW) distribution020407.08 – 8.317: 108.317 – 9.553: 09.553 – 10.79: 1010.79 – 12.03: 2012.03 – 13.26: 513.26 – 14.5: 2014.5 – 15.74: 4015.74 – 16.97: 3516.97 – 18.21: 3518.21 – 19.45: 3019.45 – 20.68: 1520.68 – 21.92: 1021.92 – 23.16: 1523.16 – 24.39: 1024.39 – 25.63: 1025.63 – 26.87: 1526.87 – 28.1: 528.1 – 29.34: 029.34 – 30.58: 530.58 – 31.81: 1031.81 – 33.05: 533.05 – 34.29: 534.29 – 35.52: 1035.52 – 36.76: 10010203040
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
19.6 ± 7.06
Median
?50th percentile: half the non-missing values lie at or below it.
17.55
Range
?Smallest and largest recorded non-missing values.
7.08 – 36.76
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.36
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.76 / -0.076
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Cellulose_1 (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Cellulose_1 (% DW) distribution02040602.1 – 3.246: 103.246 – 4.392: 04.392 – 5.537: 05.537 – 6.683: 06.683 – 7.829: 07.829 – 8.975: 58.975 – 10.12: 2510.12 – 11.27: 5511.27 – 12.41: 3512.41 – 13.56: 513.56 – 14.7: 2514.7 – 15.85: 1515.85 – 17: 1517 – 18.14: 2018.14 – 19.29: 1019.29 – 20.43: 020.43 – 21.58: 1521.58 – 22.73: 3022.73 – 23.87: 1523.87 – 25.02: 3025.02 – 26.16: 1026.16 – 27.31: 027.31 – 28.45: 528.45 – 29.6: 50102030
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
16.2 ± 6.21
Median
?50th percentile: half the non-missing values lie at or below it.
14.95
Range
?Smallest and largest recorded non-missing values.
2.1 – 29.6
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.383
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.14 / -0.74
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Cellulose_2 (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Cellulose_2 (% DW) distribution02040609.06 – 10.23: 510.23 – 11.4: 1511.4 – 12.57: 512.57 – 13.75: 4013.75 – 14.92: 3014.92 – 16.09: 3016.09 – 17.26: 2517.26 – 18.43: 1518.43 – 19.6: 2019.6 – 20.77: 1020.77 – 21.94: 2021.94 – 23.12: 523.12 – 24.29: 1024.29 – 25.46: 025.46 – 26.63: 4526.63 – 27.8: 1527.8 – 28.97: 028.97 – 30.14: 2030.14 – 31.31: 531.31 – 32.48: 032.48 – 33.66: 533.66 – 34.83: 534.83 – 36: 036 – 37.17: 5125102050100
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
19.97 ± 6.58
Median
?50th percentile: half the non-missing values lie at or below it.
18.37
Range
?Smallest and largest recorded non-missing values.
9.06 – 37.17
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.329
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.52 / -0.67
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Lignin_1 (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Lignin_1 (% DW) distribution02040600.03 – 1: 101 – 1.97: 01.97 – 2.94: 552.94 – 3.91: 403.91 – 4.88: 354.88 – 5.85: 255.85 – 6.82: 256.82 – 7.79: 57.79 – 8.76: 158.76 – 9.73: 259.73 – 10.7: 2010.7 – 11.67: 1011.67 – 12.64: 1512.64 – 13.61: 2013.61 – 14.58: 514.58 – 15.55: 015.55 – 16.52: 516.52 – 17.49: 517.49 – 18.46: 018.46 – 19.43: 019.43 – 20.4: 1020.4 – 21.37: 021.37 – 22.34: 022.34 – 23.31: 50102030
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
7.309 ± 5.03
Median
?50th percentile: half the non-missing values lie at or below it.
5.745
Range
?Smallest and largest recorded non-missing values.
0.03 – 23.31
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.689
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.1 / 0.75
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Lignin_2 (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Lignin_2 (% DW) distribution020401.09 – 2.19: 202.19 – 3.289: 353.289 – 4.389: 154.389 – 5.488: 205.488 – 6.588: 356.588 – 7.688: 157.688 – 8.787: 58.787 – 9.887: 359.887 – 10.99: 1010.99 – 12.09: 1012.09 – 13.19: 2513.19 – 14.29: 3514.29 – 15.38: 515.38 – 16.48: 516.48 – 17.58: 2517.58 – 18.68: 518.68 – 19.78: 019.78 – 20.88: 020.88 – 21.98: 521.98 – 23.08: 1023.08 – 24.18: 024.18 – 25.28: 525.28 – 26.38: 526.38 – 27.48: 50102030
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
10.22 ± 6.46
Median
?50th percentile: half the non-missing values lie at or below it.
9.465
Range
?Smallest and largest recorded non-missing values.
1.09 – 27.48
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.632
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.71 / -0.065
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Starch_1 (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Starch_1 (% DW) distribution0501001500 – 0.3925: 1500.3925 – 0.785: 50.785 – 1.177: 201.177 – 1.57: 351.57 – 1.963: 151.963 – 2.355: 252.355 – 2.748: 152.748 – 3.14: 103.14 – 3.533: 53.533 – 3.925: 53.925 – 4.317: 104.317 – 4.71: 154.71 – 5.103: 05.103 – 5.495: 05.495 – 5.888: 05.888 – 6.28: 06.28 – 6.673: 06.673 – 7.065: 57.065 – 7.458: 57.458 – 7.85: 07.85 – 8.242: 08.242 – 8.635: 08.635 – 9.027: 09.027 – 9.42: 100.02.55.07.510.0
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
1.612 ± 2.16
Median
?50th percentile: half the non-missing values lie at or below it.
1.065
Range
?Smallest and largest recorded non-missing values.
0 – 9.42
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.34
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.9 / 3.6
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Starch_2 (% DW)

metadata · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Starch_2 (% DW) distribution0501001500 – 0.4163: 1100.4163 – 0.8325: 350.8325 – 1.249: 301.249 – 1.665: 251.665 – 2.081: 52.081 – 2.498: 252.498 – 2.914: 52.914 – 3.33: 103.33 – 3.746: 153.746 – 4.162: 304.162 – 4.579: 04.579 – 4.995: 154.995 – 5.411: 55.411 – 5.828: 05.828 – 6.244: 06.244 – 6.66: 106.66 – 7.076: 07.076 – 7.492: 57.492 – 7.909: 07.909 – 8.325: 08.325 – 8.741: 08.741 – 9.158: 09.158 – 9.574: 09.574 – 9.99: 50.02.55.07.510.0
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
1.966 ± 2.12
Median
?50th percentile: half the non-missing values lie at or below it.
1.215
Range
?Smallest and largest recorded non-missing values.
0 – 9.99
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.08
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.4 / 1.9
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Edited Variable Name

metadata · categorical

Compare recorded category counts and class balance. No distribution plot is available; use the recorded statistics below.

n / missing
?Recorded observation count and number of missing values for this variable.
330 / 302
Classes
?Number of distinct recorded category values.
28
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
1
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
1
Top class
?Most frequent recorded category and its observation count.
Sample_# (1)

Original Variable Name

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Original Variable Name classesC_star1C_star1: 22Sample_#Sample_#: 11English NameEnglish Name: 11Latin NameLatin Name: 11Plant TypePlant Type: 11NN: 11C_aC_a: 11C_bC_b: 11C_abC_ab: 11C_carC_car: 11+10 more+10 more: 1010
n / missing
?Recorded observation count and number of missing values for this variable.
330 / 302
Classes
?Number of distinct recorded category values.
27
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
1
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
2
Top class
?Most frequent recorded category and its observation count.
C_star1 (2)

Comments

metadata · categorical

Compare recorded category counts and class balance. No distribution plot is available; use the recorded statistics below.

n / missing
?Recorded observation count and number of missing values for this variable.
330 / 302
Classes
?Number of distinct recorded category values.
28
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
1
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
1
Top class
?Most frequent recorded category and its observation count.
Number assigned to each sample in order to link reflectance and transmittance measurements (1)
Constant metadata 14

Fields with one recorded value provide context for every observation; they have no distribution to compare.

  • SpectralRep1
  • Measurement_typereflectance
  • Anthocyanin-999
  • datasetEcoSIS
  • n_points_original2,101
  • dimensions1D
  • axis_notewavelengths_as_provided_by_EcoSIS_resource
  • official_data_urlhttps://data.ecosis.org/dataset/leaf-optical-properties-experiment-database--lopex93-
  • official_package_urlhttps://ecosis.org/package/leaf-optical-properties-experiment-database--lopex93-
  • licenseOther (Open)
  • rights_statusexplicit_open
  • usage_scopepublic_reuse_possible
  • public_release_allowedTrue
  • notesTask-specific EcoSIS target not present in active parent Y table, wavelengths kept as provided by EcoSIS resource, no project interpolation.

Alignment

How observations join to physical sample IDs. Multiple exported rows are not necessarily independent samples or technical repetitions. Reading guide

Alignment levelsample
Sample id availableyes
Samples330
Observations (total)330
Exported observations per unitmin 1 · mean 1 · max 1
Identity statusavailable
Documented technical repetitionsND
Source unitMultiplicity of the declared identifier; protocol not established

Splits

Partitions supplied by the original dataset. They are documented here and are not applied automatically when loading. Reading guide

No original partition summaries are recorded. Loading does not create a train/test split.

Load this dataset

Use the appropriate route below: an assembled dataset, original raw resources, or the documented access procedure. Reading guide

# An open original source is documented in Provenance & citation.
# Automatic retrieval is not implemented for this source yet.
# Follow the original source links to obtain its files.

Metadata downloads are available for public datasets only. The dataset bytes are never served here — fetch them from the origin / DOI above.

Read the documentation

Access and rights are separate

Start with provenance and governance at the top of a card. Provenance links to the original resources and publications. Governance records the license, redistribution conditions and content identity.

Open source access means an open original-source route is recorded. Restricted redistribution is the display label for the catalog's private tier: rights are restricted or not cleared for redistribution. It does not mean that the original source is necessarily private.

Raw reproducible identifies a retrieval route. Assembly incomplete means raw resources can be available while dataset-specific conversion is missing. The loading example uses retrieve(id, prepare=False) for these raw downloads; it returns resource metadata rather than X/Y arrays. A complete original-source recipe uses get(id). Two Harvard datasets require explicit guestbook answers.

Token required means no open automatic route is recorded. A Dataverse token works only once the canonical data have a configured hosted DOI. Manual access requires the original provider's documented steps. Availability labels describe the catalog routes, not a fresh live check of every URL.

Why is PCA on a dataset card?

Spectra have many correlated wavelengths. Principal component analysis (PCA) compresses their variation into a few orthogonal directions, called principal components (PCs). It is an exploratory summary of X, calculated without using the target Y.

The score plot places each plotted observation on PC1 and PC2. Nearby dots have similar projections; groups can reveal batches, instruments or natural subpopulations. Axis percentages show explained spectral variance. Large sources are sampled to at most 800 observations. Similar positions in two dimensions do not guarantee similar full spectra; an isolated dot does not prove an acquisition fault.

The scree plot reports spectral variance per component, helping you see whether variation is concentrated in a few dimensions. It is not explained target variance or predictive accuracy. The scree projection and score-plot projection may use different row samples; their percentages need not match.

Q / SPE measures residual variation outside the retained PCA subspace. Hotelling T² measures an observation's scaled distance within that subspace; Mahalanobis H summarizes PCA-space distance. The displayed p95/median ratios compare the distribution's upper tail with its center, without an absolute rejection threshold.

Historical profiles replace missing cells with column medians before PCA, center channels, and use bounded row/component counts. Loadings and distances depend on signal units and preprocessing. Compare compatible sources and inspect the original spectra before acting.

Scores, evidence and missing values

Read measured values first. An alert score maps selected metrics to a heuristic 0–1 scale. The display bands are weak below 0.42, medium from 0.42 to below 0.72, and strong from 0.72. They are not universally validated pass/fail thresholds.

The radar's center is 0 and its outer ring is 1: outward means a stronger anomaly or heterogeneity signal for that axis. Its table gives values rounded to two decimals and explains each axis. Missing axes are left blank; partial profiles have no filled polygon. The mean profile score averages only available axes and is not an overall quality grade.

Diagnostic bars rank possible explanations from heuristic signatures. A score of 0.78 does not mean a 78% causal probability. Evidence coverage is the proportion of required signals available, not confidence in the cause. A dash or “unavailable” means the quantity could not be computed; zero is an actual recorded value.

RMS (root mean square distance) and SAM (Spectral Angle Mapper, in radians) use this dataset's mean spectrum as their reference. They do not compare with an independently acquired white or dark reference. Repeated IDs require a documented protocol before within-ID spread can be called technical repeatability.

X–Y plots show Pearson correlation within this dataset. They identify linear associations; they are not cross-validated predictions and do not establish causal relationships. Targets, metadata, observation counts and physical sample counts have different meanings.

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