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EcoSIS hyperspectral leaf reflectance, biochemistry and physiology of droughted and watered crops (leaf spectra)

ecosis · NIR

EcoSIS hyperspectral leaf reflectance, biochemistry and physiology of droughted and watered crops (leaf spectra). 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
665
samples
2,151
wavelengths
1
sources
3
targets
49
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

ContributorHyperspectral leaf reflectance, biochemistry, and physiology of droughted and watered crops
Conversion statusok
Origin · url [open]https://data.ecosis.org/dataset/hyperspectral-leaf-reflectance--biochemistry--and-physiology-of-droughted-and-watered-crops
Origin · url [open]https://data.ecosis.org/api/3/action/package_show?id=hyperspectral-leaf-reflectance--biochemistry--and-physiology-of-droughted-and-watered-crops
Origin · url [open]https://ecosis.org/package/hyperspectral-leaf-reflectance--biochemistry--and-physiology-of-droughted-and-watered-crops

Governance & integrity

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

Tierpublic
LicenseCC-BY-4.0
Permitted useResearch and benchmarking.
Access policyOpen per source license.
RedistributionEcoSIS CKAN API package_show verified open license: Creative Commons Attribution.
Content version1.1.0
Schema / protocol2.0
Content hashe27fe56e499c6c86…
Processing hashdb458ffe8684931a…
Metadata hash0ebe71c52bbf1767…

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 score0.51
Highest axisLocal artifacts · 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 hyperspectral leaf reflectance, biochemistry and physiology of droughted and watered crops (leaf spectra) property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS hyperspectral leaf reflectance, biochemistry and physiology of droughted and watered crops (leaf spectra) — integrity: 0.00EcoSIS hyperspectral leaf reflectance, biochemistry and physiology of droughted and watered crops (leaf spectra) — noise: 0.00EcoSIS hyperspectral leaf reflectance, biochemistry and physiology of droughted and watered crops (leaf spectra) — artefacts: 1.00EcoSIS hyperspectral leaf reflectance, biochemistry and physiology of droughted and watered crops (leaf spectra) — baseline: 0.74EcoSIS hyperspectral leaf reflectance, biochemistry and physiology of droughted and watered crops (leaf spectra) — PCA outliers: 0.44EcoSIS hyperspectral leaf reflectance, biochemistry and physiology of droughted and watered crops (leaf spectra) — reference: 0.55EcoSIS hyperspectral leaf reflectance, biochemistry and physiology of droughted and watered crops (leaf spectra) — structure: 0.83EcoSIS hyperspe…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.
0.00
Local artifacts
?Combines spike, jump and clipping heuristics. Inspect flagged spectral regions before attributing a cause.
1.00
Baseline/shape
?Summarizes baseline slope and excess edge variation relative to signal scale.
0.74
PCA outliers
?Combines relative PCA residual and score-distance tails. Valid rare spectra can produce a high value.
0.44
Distance to average spectrum
?Dispersion around this dataset's mean spectrum, using distance, shape and shift summaries.
0.55
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.
0.83

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 spectral spliceSignature compatible with a spectral splice: 0.720.72Offset compatible with a different white referenceOffset compatible with a different white reference: 0.540.54Signature compatible with a different backgroundSignature compatible with a different background: 0.470.47Signature compatible with a population shiftSignature compatible with a population shift: 0.400.40Signature compatible with clippingSignature compatible with clipping: 0.340.34High spectral roughness signatureHigh spectral roughness signature: 0.280.28Flat spectrum/weak signalFlat spectrum/weak signal: 0.070.07

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 spectral spliceX0.72strong100 %Spike rate 1.00, Jump rate 1.00, Undegraded roughness S/N proxy 1.00Break 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.
Offset compatible with a different white referenceX0.54medium100 %local artifacts 1.00, Baseline/mean/area 0.74, RMS/SAM to mean spectrum 0.55Systematic 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 different backgroundX0.47medium100 %Baseline/mean/area 0.74, RMS/SAM to mean spectrum 0.55, Mahalanobis / T2 0.44Systematic 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.
Signature compatible with a population shiftX0.40weak100 %PCA structure 0.83, RMS/SAM to mean spectrum 0.55, Mahalanobis / T2 0.44Different 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 clippingX0.34weak100 %Jump rate 1.00, Baseline/mean/area 0.74, PCA Q 0.37Detector 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 signatureX0.28weak100 %Spike rate 1.00, RMS/SAM to mean spectrum 0.55, PCA Q 0.37Weak 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 signalX0.07weak100 %PCA Q 0.37Bad 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

leaf_spectra

X · NIR · SE_PSR_Plus

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.

leaf_spectra spectra0.00.20.40.601,0002,0003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm350nm — median 0.2623 (q25–q75 0.2328–0.3125)365nm — median 0.2084 (q25–q75 0.1819–0.2512)381nm — median 0.1467 (q25–q75 0.1284–0.1832)396nm — median 0.1017 (q25–q75 0.08891–0.1256)412nm — median 0.0722 (q25–q75 0.06224–0.08857)427nm — median 0.06001 (q25–q75 0.05203–0.07484)443nm — median 0.0575 (q25–q75 0.04931–0.07267)458nm — median 0.04848 (q25–q75 0.04112–0.06177)474nm — median 0.04666 (q25–q75 0.03904–0.05873)489nm — median 0.04595 (q25–q75 0.03829–0.0581)505nm — median 0.04994 (q25–q75 0.04169–0.06199)520nm — median 0.0781 (q25–q75 0.06345–0.09416)536nm — median 0.1154 (q25–q75 0.09093–0.1347)551nm — median 0.124 (q25–q75 0.09754–0.1446)567nm — median 0.108 (q25–q75 0.08413–0.1289)582nm — median 0.08188 (q25–q75 0.06359–0.1029)597nm — median 0.07366 (q25–q75 0.05717–0.0932)613nm — median 0.0637 (q25–q75 0.05024–0.08109)628nm — median 0.05769 (q25–q75 0.04549–0.07239)644nm — median 0.05034 (q25–q75 0.04144–0.06305)659nm — median 0.04285 (q25–q75 0.0354–0.0538)675nm — median 0.0404 (q25–q75 0.03393–0.05132)690nm — median 0.05393 (q25–q75 0.04495–0.06499)706nm — median 0.1744 (q25–q75 0.1367–0.2043)721nm — median 0.3364 (q25–q75 0.2921–0.3636)737nm — median 0.4481 (q25–q75 0.4175–0.4875)752nm — median 0.4872 (q25–q75 0.4489–0.5316)768nm — median 0.496 (q25–q75 0.4561–0.5416)783nm — median 0.4976 (q25–q75 0.458–0.5428)799nm — median 0.499 (q25–q75 0.4581–0.5431)814nm — median 0.4993 (q25–q75 0.4578–0.5431)829nm — median 0.4992 (q25–q75 0.458–0.5427)845nm — median 0.4985 (q25–q75 0.4589–0.5428)860nm — median 0.4991 (q25–q75 0.4586–0.5429)876nm — median 0.4993 (q25–q75 0.4585–0.5422)891nm — median 0.4992 (q25–q75 0.4581–0.5419)907nm — median 0.499 (q25–q75 0.4573–0.5416)922nm — median 0.4982 (q25–q75 0.4565–0.5403)938nm — median 0.4954 (q25–q75 0.4526–0.5359)953nm — median 0.4887 (q25–q75 0.4475–0.5285)969nm — median 0.4845 (q25–q75 0.4412–0.5223)984nm — median 0.484 (q25–q75 0.4401–0.5199)1,000nm — median 0.4873 (q25–q75 0.4438–0.5238)1,015nm — median 0.4912 (q25–q75 0.4472–0.5277)1,031nm — median 0.4938 (q25–q75 0.4497–0.5318)1,046nm — median 0.4956 (q25–q75 0.4516–0.5345)1,062nm — median 0.4963 (q25–q75 0.452–0.5353)1,077nm — median 0.4961 (q25–q75 0.452–0.5349)1,092nm — median 0.4948 (q25–q75 0.4509–0.5339)1,108nm — median 0.4933 (q25–q75 0.449–0.5316)1,123nm — median 0.49 (q25–q75 0.4462–0.5275)1,139nm — median 0.479 (q25–q75 0.436–0.5134)1,154nm — median 0.4615 (q25–q75 0.4186–0.4956)1,170nm — median 0.4551 (q25–q75 0.4134–0.4906)1,185nm — median 0.454 (q25–q75 0.4125–0.4889)1,201nm — median 0.4535 (q25–q75 0.4117–0.4885)1,216nm — median 0.4552 (q25–q75 0.4131–0.4904)1,232nm — median 0.4575 (q25–q75 0.4158–0.4927)1,247nm — median 0.4587 (q25–q75 0.4171–0.4943)1,263nm — median 0.4593 (q25–q75 0.4174–0.4951)1,278nm — median 0.4577 (q25–q75 0.4163–0.4932)1,294nm — median 0.4524 (q25–q75 0.4121–0.4881)1,309nm — median 0.4439 (q25–q75 0.4043–0.4782)1,324nm — median 0.4277 (q25–q75 0.3899–0.4611)1,340nm — median 0.4057 (q25–q75 0.37–0.4388)1,355nm — median 0.3849 (q25–q75 0.3513–0.4188)1,371nm — median 0.357 (q25–q75 0.3276–0.3922)1,386nm — median 0.2933 (q25–q75 0.2662–0.3272)1,402nm — median 0.1974 (q25–q75 0.1692–0.2277)1,417nm — median 0.1494 (q25–q75 0.1244–0.1787)1,433nm — median 0.134 (q25–q75 0.1101–0.1616)1,448nm — median 0.1314 (q25–q75 0.1069–0.1587)1,464nm — median 0.1358 (q25–q75 0.1103–0.1634)1,479nm — median 0.1511 (q25–q75 0.1227–0.1803)1,495nm — median 0.1733 (q25–q75 0.1431–0.2043)1,510nm — median 0.1946 (q25–q75 0.1656–0.2283)1,526nm — median 0.2164 (q25–q75 0.1891–0.2536)1,541nm — median 0.2364 (q25–q75 0.2105–0.2746)1,556nm — median 0.2553 (q25–q75 0.2296–0.2944)1,572nm — median 0.2706 (q25–q75 0.2455–0.3108)1,587nm — median 0.2843 (q25–q75 0.2582–0.3236)1,603nm — median 0.2962 (q25–q75 0.2692–0.3347)1,618nm — median 0.3054 (q25–q75 0.2779–0.344)1,634nm — median 0.3133 (q25–q75 0.2847–0.3514)1,649nm — median 0.3199 (q25–q75 0.2891–0.3565)1,665nm — median 0.3222 (q25–q75 0.2912–0.3585)1,680nm — median 0.3223 (q25–q75 0.2911–0.3579)1,696nm — median 0.3184 (q25–q75 0.288–0.3529)1,711nm — median 0.3121 (q25–q75 0.2825–0.3473)1,727nm — median 0.3027 (q25–q75 0.2752–0.3404)1,742nm — median 0.2929 (q25–q75 0.2668–0.3308)1,758nm — median 0.2791 (q25–q75 0.2548–0.3181)1,773nm — median 0.2688 (q25–q75 0.2446–0.3084)1,788nm — median 0.2633 (q25–q75 0.239–0.3027)1,804nm — median 0.2621 (q25–q75 0.2382–0.3024)1,819nm — median 0.2618 (q25–q75 0.2379–0.3018)1,835nm — median 0.2569 (q25–q75 0.2339–0.297)1,850nm — median 0.2419 (q25–q75 0.218–0.2801)1,866nm — median 0.1987 (q25–q75 0.1734–0.2324)1,881nm — median 0.1231 (q25–q75 0.105–0.1454)1,897nm — median 0.05642 (q25–q75 0.04757–0.06803)1,912nm — median 0.0391 (q25–q75 0.03348–0.04721)1,928nm — median 0.0365 (q25–q75 0.03134–0.04413)1,943nm — median 0.03812 (q25–q75 0.03251–0.04636)1,959nm — median 0.04278 (q25–q75 0.03605–0.05233)1,974nm — median 0.04989 (q25–q75 0.04135–0.06129)1,990nm — median 0.05995 (q25–q75 0.04938–0.07296)2,005nm — median 0.07089 (q25–q75 0.05827–0.08605)2,021nm — median 0.08382 (q25–q75 0.06778–0.102)2,036nm — median 0.09612 (q25–q75 0.07948–0.1176)2,051nm — median 0.1073 (q25–q75 0.09–0.1321)2,067nm — median 0.1208 (q25–q75 0.1022–0.1482)2,082nm — median 0.1335 (q25–q75 0.1141–0.1644)2,098nm — median 0.1473 (q25–q75 0.1283–0.1812)2,113nm — median 0.159 (q25–q75 0.1392–0.1956)2,129nm — median 0.1727 (q25–q75 0.1527–0.2107)2,144nm — median 0.181 (q25–q75 0.1621–0.2203)2,160nm — median 0.1882 (q25–q75 0.1687–0.2273)2,175nm — median 0.1928 (q25–q75 0.173–0.2306)2,191nm — median 0.1972 (q25–q75 0.1771–0.2355)2,206nm — median 0.199 (q25–q75 0.1785–0.2377)2,222nm — median 0.1988 (q25–q75 0.178–0.2383)2,237nm — median 0.196 (q25–q75 0.1751–0.2344)2,253nm — median 0.1861 (q25–q75 0.1672–0.2243)2,268nm — median 0.1752 (q25–q75 0.1575–0.2115)2,283nm — median 0.1651 (q25–q75 0.1477–0.1996)2,299nm — median 0.1543 (q25–q75 0.1369–0.1877)2,314nm — median 0.1444 (q25–q75 0.1276–0.1761)2,330nm — median 0.1351 (q25–q75 0.1184–0.1655)2,345nm — median 0.1242 (q25–q75 0.1086–0.1529)2,361nm — median 0.1143 (q25–q75 0.09875–0.1411)2,376nm — median 0.1054 (q25–q75 0.08985–0.1299)2,392nm — median 0.09613 (q25–q75 0.08124–0.1184)2,407nm — median 0.08617 (q25–q75 0.07265–0.1066)2,423nm — median 0.07668 (q25–q75 0.06526–0.09506)2,438nm — median 0.0682 (q25–q75 0.05818–0.08423)2,454nm — median 0.05937 (q25–q75 0.05078–0.07437)2,469nm — median 0.05399 (q25–q75 0.04641–0.06707)2,485nm — median 0.04887 (q25–q75 0.04134–0.05968)2,500nm — median 0.04624 (q25–q75 0.03945–0.05605)

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.
2,151
Axis range
?Lowest and highest recorded spectral-axis coordinates, in the stated unit.
350–2,500 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.
665

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 – 0.649
Mean range
?Minimum and maximum of the mean spectrum across spectral channels.
0.039 – 0.503
Mean level
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations.
0.2537
Area
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit.
545.5
PTP
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum.
0.4637
Noise RMS
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise.
3.421e-05
Roughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR.
7.4e+03
Roughness S/N proxy dB
?20 log10 of the roughness signal-to-noise proxy. Units are decibels; compare only compatible signal representations.
8e+01 dB
Dynamic range
?Maximum minus minimum of the mean spectrum. Its magnitude depends on the signal representation.
0.464
Smoothness
?Mean per-spectrum RMS of second differences, in signal units. Smaller values indicate smoother spectra under this protocol.
0.0004016
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.
277

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.
83,460
Spike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage.
5.84%
Jump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins.
35,285
Jump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage.
2.47%
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.
-0.17168
Curvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation.
0.00041017
D1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts.
0.0019466
RMS to mean
?Root mean square distance to this dataset's mean spectrum, in signal units.
0.033718
RMS p95
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value.
0.063709
SAM to mean
?Spectral Angle Mapper: angle to the mean spectrum, in radians. Smaller angles indicate more similar shapes.
0.056234
SAM p95
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS.
0.14022
Affine offset p95
?95th percentile of absolute fitted offsets when matching each spectrum to the dataset mean.
0.05296
Affine gain p95 Δ
?95th percentile of the absolute gain deviation from 1 in the fit to the mean spectrum.
0.18405
Affine residual p95
?95th percentile of residual RMS after gain and offset correction to the mean spectrum.
0.029595
Xcorr lag p95
?95th percentile of the absolute cross-correlation shift relative to the mean spectrum, in spectral channels.
1

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.
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.
3.1
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.
1.8
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.
2.6
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.
99.8%
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		0.253690.74strongAtypical 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		545.530.74strongAtypical 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		0.463670.00weakStrong 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		0.0284150.00weakNormal 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		3.421e-050.00weakStableLamp, 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		7415.70.00weakGood 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		94.4060.00weakReliable 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		83,4601.00strongArtifactsCosmic 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		5.84%1.00strongSuspicious spectrumInterpolationspike_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		35,2851.00strongDetector connectionSplicecount 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		2.47%1.00strongSpectral problemCalibrationjump_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		0.00014%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		-0.171680.74strongDriftIlluminationlinear 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		0.000410170.09weakSmoothBackground, 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		0.00194660.08weakFlatBiology 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		2.99390.37weakCompliantArtifact, 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		3.14470.39weakCentralNatural 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		1.77330.44mediumGlobal 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		0.0637090.55mediumDifferent 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		0.140220.40weakSimilarBackground, 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		3.71110.83strongSubpopulationsDifferent 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		2.48260.74strongIsolated 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		0.558770.83strongAtypical spectrumVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent

PCA score plot — explore spectral variation

Source leaf_spectra: 665 of 665 source observations are plotted. PC1 captures 69.2% and PC2 20.4% of the reported spectral variance: 89.6% together; 10.4% 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.

leaf_spectra PCA score plot-4-20246-4-2024PC1 -2.56 · PC2 -0.4566PC1 -3.124 · PC2 -0.7981PC1 -3.077 · PC2 -0.7926PC1 -2.958 · PC2 -0.5968PC1 -2.502 · PC2 -0.8158PC1 -2.229 · PC2 -0.7677PC1 -2.977 · PC2 -0.6614PC1 -2.946 · PC2 -0.5913PC1 -2.652 · PC2 -0.7329PC1 -2.395 · PC2 -0.5977PC1 -2.371 · PC2 -0.4459PC1 -2.249 · PC2 -0.7179PC1 -2.535 · PC2 -0.7647PC1 -2.048 · PC2 -0.8544PC1 -2.373 · PC2 -0.6612PC1 -2.299 · PC2 -0.5947PC1 -3.177 · PC2 -0.8296PC1 -2.554 · PC2 -0.5093PC1 -2.331 · PC2 -0.9239PC1 -2.441 · PC2 -1.525PC1 -1.92 · PC2 -0.9187PC1 -2.073 · PC2 -1.295PC1 -2.213 · PC2 -1.222PC1 -2.419 · PC2 -0.9535PC1 -2.494 · PC2 -0.933PC1 -2.04 · PC2 -1.527PC1 -2.748 · PC2 -1.04PC1 -2.435 · PC2 -1.691PC1 -2.032 · PC2 -0.6635PC1 -2.243 · PC2 -1.388PC1 -3.233 · PC2 -1.149PC1 -2.672 · PC2 -1.048PC1 -1.03 · PC2 0.3467PC1 -1.46 · PC2 0.7382PC1 -2.185 · PC2 0.2522PC1 -1.176 · PC2 0.7762PC1 -2.207 · PC2 0.932PC1 -1.732 · PC2 0.03219PC1 -2.712 · PC2 0.5164PC1 -0.6307 · PC2 -0.1705PC1 -2.055 · PC2 0.1881PC1 -2.241 · PC2 0.8065PC1 -2.647 · PC2 0.6096PC1 -1.901 · PC2 0.2795PC1 -2.268 · PC2 0.7078PC1 -1.716 · PC2 0.5269PC1 -1.328 · PC2 -0.06784PC1 -1.219 · PC2 -0.3324PC1 -2.156 · PC2 -0.737PC1 -1.981 · PC2 -0.8226PC1 -2.689 · PC2 -0.8202PC1 -1.879 · PC2 -0.8555PC1 -2.431 · PC2 -0.8174PC1 -2.4 · PC2 -0.8323PC1 -1.908 · PC2 -1.186PC1 -2.725 · PC2 -0.4367PC1 -1.336 · PC2 -0.5335PC1 -2.175 · PC2 -0.5693PC1 -1.666 · PC2 -0.5094PC1 -2.449 · PC2 -1.333PC1 -1.308 · PC2 -0.4227PC1 -1.945 · PC2 -0.6938PC1 -2.639 · PC2 -0.8791PC1 -2.015 · PC2 -0.8676PC1 -0.5429 · PC2 -1.549PC1 -0.911 · PC2 -1.462PC1 -1.148 · PC2 -0.9306PC1 -0.6203 · PC2 -1.158PC1 0.1233 · PC2 -1.484PC1 -1.217 · PC2 -1.836PC1 0.5092 · PC2 -1.759PC1 -1.436 · PC2 -1.943PC1 -0.7975 · PC2 -1.904PC1 -1.279 · PC2 -1.482PC1 -0.04526 · PC2 -1.946PC1 -0.4656 · PC2 -1.719PC1 -0.8018 · PC2 -1.825PC1 0.158 · PC2 -2.771PC1 -1.651 · PC2 -1.365PC1 0.2462 · PC2 -1.361PC1 -0.02126 · PC2 0.9706PC1 -1.378 · PC2 0.7356PC1 -1.707 · PC2 0.5833PC1 -1.328 · PC2 0.4811PC1 -1.4 · PC2 0.7248PC1 -0.9432 · PC2 0.4895PC1 -1.667 · PC2 0.875PC1 -0.2694 · PC2 0.3518PC1 -2.182 · PC2 0.5988PC1 -2.011 · PC2 0.8507PC1 -1.258 · PC2 0.4075PC1 -2.333 · PC2 0.5256PC1 -1.626 · PC2 0.7565PC1 -1.576 · PC2 0.7315PC1 -1.131 · PC2 0.3115PC1 -1.906 · PC2 0.2487PC1 1.254 · PC2 1.621PC1 0.9596 · PC2 1.239PC1 -0.8023 · PC2 0.5937PC1 -1.734 · PC2 0.5737PC1 -2.44 · PC2 0.7272PC1 -1.85 · PC2 0.55PC1 -3.045 · PC2 0.6093PC1 -2.023 · PC2 0.3921PC1 -1.038 · PC2 0.3891PC1 -1.577 · PC2 0.6499PC1 -1.71 · PC2 0.3699PC1 -1.51 · PC2 0.3201PC1 -1.504 · PC2 0.8583PC1 -0.7633 · PC2 0.3636PC1 -1.994 · PC2 0.5471PC1 -2.316 · PC2 0.5592PC1 0.1422 · PC2 -1.082PC1 0.1579 · PC2 -1.222PC1 -0.1012 · PC2 -1.351PC1 -0.244 · PC2 -0.9093PC1 -0.64 · PC2 -0.8174PC1 -1.08 · PC2 -1.733PC1 -0.6877 · PC2 -1.459PC1 -1.297 · PC2 -1.81PC1 0.3681 · PC2 -1.438PC1 -0.491 · PC2 -1.052PC1 -0.7573 · PC2 -1.169PC1 0.04086 · PC2 -1.312PC1 -0.5978 · PC2 -0.8721PC1 -0.7256 · PC2 -1.596PC1 0.3591 · PC2 -1.462PC1 0.08191 · PC2 -1.326PC1 -1.659 · PC2 -0.5806PC1 -1.633 · PC2 -0.2704PC1 -1.081 · PC2 -0.4961PC1 -1.722 · PC2 -0.7235PC1 -0.8995 · PC2 -0.6792PC1 -0.942 · PC2 -0.4281PC1 -1.86 · PC2 -0.575PC1 -1.262 · PC2 -0.5517PC1 -1.494 · PC2 -0.2587PC1 -0.811 · PC2 -0.5033PC1 -2.61 · PC2 -0.8442PC1 -0.8034 · PC2 -0.7011PC1 -2.247 · PC2 -0.6005PC1 -1.977 · PC2 -0.6732PC1 -1.553 · PC2 0.607PC1 -2.074 · PC2 0.5788PC1 -1.77 · PC2 0.4132PC1 -1.369 · PC2 0.9339PC1 -1.388 · PC2 -0.1491PC1 -1.538 · PC2 0.316PC1 -2.264 · PC2 1.419PC1 -1.474 · PC2 0.6486PC1 -1.884 · PC2 0.5658PC1 -1.743 · PC2 0.8281PC1 -0.2125 · PC2 1.106PC1 -2.15 · PC2 0.9329PC1 -1.86 · PC2 0.5589PC1 -2.438 · PC2 0.8611PC1 -0.2753 · PC2 -1.285PC1 1.165 · PC2 -1.57PC1 -0.2003 · PC2 -0.8616PC1 -0.7727 · PC2 -1.118PC1 -0.6417 · PC2 -0.7084PC1 -0.3656 · PC2 -1.531PC1 -0.01797 · PC2 -1.351PC1 -1.106 · PC2 -1.244PC1 1.673 · PC2 -2.076PC1 0.5668 · PC2 -1.238PC1 -0.8964 · PC2 -1.32PC1 0.2195 · PC2 -1.551PC1 0.2561 · PC2 -1.142PC1 -1.113 · PC2 -1.285PC1 0.9965 · PC2 -1.44PC1 0.5694 · PC2 -1.401PC1 -0.8963 · PC2 0.3749PC1 -1.196 · PC2 0.6155PC1 -1.338 · PC2 0.6366PC1 -0.7072 · PC2 0.3337PC1 -1.389 · PC2 0.6177PC1 -1.584 · PC2 0.4554PC1 -1.435 · PC2 0.4687PC1 -1.161 · PC2 0.4942PC1 -1.209 · PC2 0.4723PC1 -1.207 · PC2 0.4573PC1 -1.74 · PC2 0.6612PC1 -1.363 · PC2 0.7159PC1 -1.397 · PC2 0.619PC1 -1.682 · PC2 0.5131PC1 -1.448 · PC2 0.8567PC1 -1.539 · PC2 0.9355PC1 -1.567 · PC2 0.7676PC1 -1.06 · PC2 0.3756PC1 -1.326 · PC2 0.529PC1 -0.6822 · PC2 0.5235PC1 -0.7016 · PC2 0.03392PC1 -0.6076 · PC2 0.2678PC1 -0.6031 · PC2 0.3674PC1 -0.9155 · PC2 0.4063PC1 -1.084 · PC2 0.3683PC1 -0.6966 · PC2 0.4684PC1 -0.9279 · PC2 0.4477PC1 -1.348 · PC2 0.6921PC1 -1.299 · PC2 0.3138PC1 -0.93 · PC2 0.4508PC1 -1.091 · PC2 0.2405PC1 -1.509 · PC2 0.7764PC1 -1.539 · PC2 0.7733PC1 -1.224 · PC2 0.7269PC1 -1.341 · PC2 0.5623PC1 -1.66 · PC2 0.3661PC1 -1.551 · PC2 0.3952PC1 -1.048 · PC2 0.5908PC1 -1.429 · PC2 0.7029PC1 -0.6962 · PC2 0.5743PC1 -1.884 · PC2 0.8521PC1 -0.8656 · PC2 0.6591PC1 -1.671 · PC2 0.5642PC1 -1.366 · PC2 0.7861PC1 -1.237 · PC2 0.6078PC1 -1.193 · PC2 0.6611PC1 -1.048 · PC2 0.666PC1 -1.723 · PC2 0.7741PC1 -1.463 · PC2 0.7049PC1 -1.374 · PC2 0.7885PC1 -0.9774 · PC2 0.9761PC1 -1.207 · PC2 0.7919PC1 -1.617 · PC2 0.4537PC1 -1.446 · PC2 0.6929PC1 -1.174 · PC2 0.711PC1 -0.9184 · PC2 0.7147PC1 -1.435 · PC2 0.6079PC1 -0.8629 · PC2 0.7492PC1 -1.716 · PC2 0.6909PC1 -1.597 · PC2 0.7229PC1 -1.038 · PC2 0.6808PC1 -1.206 · PC2 0.5775PC1 -1.59 · PC2 0.6718PC1 -1.239 · PC2 0.6648PC1 -1.212 · PC2 0.638PC1 -1.446 · PC2 0.6864PC1 -1.16 · PC2 0.7036PC1 -1.805 · PC2 0.5302PC1 -0.6249 · PC2 0.5121PC1 -1.633 · PC2 0.8461PC1 -1.221 · PC2 0.6103PC1 -0.7908 · PC2 0.6491PC1 -0.316 · PC2 0.5089PC1 -0.9527 · PC2 0.6125PC1 -0.6432 · PC2 0.6317PC1 -1.037 · PC2 0.724PC1 -1.255 · PC2 0.6807PC1 -0.7727 · PC2 0.5905PC1 0.3419 · PC2 -0.049PC1 -0.2409 · PC2 0.9052PC1 1.809 · PC2 0.5148PC1 0.4318 · PC2 0.346PC1 1.218 · PC2 0.4362PC1 0.5625 · PC2 0.4148PC1 0.3449 · PC2 0.6646PC1 0.07742 · PC2 0.3096PC1 0.1748 · PC2 0.5009PC1 1.664 · PC2 0.4918PC1 0.7158 · PC2 0.4606PC1 -0.01774 · PC2 0.3347PC1 -0.2732 · PC2 0.3049PC1 0.06559 · PC2 -0.08944PC1 1.099 · PC2 -0.1045PC1 -0.1023 · PC2 0.3732PC1 0.6643 · PC2 0.7224PC1 0.3496 · PC2 0.5688PC1 0.7832 · PC2 0.3762PC1 0.7087 · PC2 0.6361PC1 0.03628 · PC2 0.6505PC1 2.329 · PC2 0.4745PC1 2.078 · PC2 0.6813PC1 -0.3038 · PC2 0.6126PC1 -0.2142 · PC2 0.5807PC1 -0.9798 · PC2 0.7074PC1 -0.8368 · PC2 0.6284PC1 -0.6611 · PC2 0.3594PC1 -1.036 · PC2 0.5198PC1 -1.242 · PC2 0.6297PC1 -0.9852 · PC2 0.6831PC1 -1.529 · PC2 0.8057PC1 -1.133 · PC2 0.7962PC1 -1.098 · PC2 0.8177PC1 -0.7376 · PC2 0.5635PC1 -1.173 · PC2 0.7069PC1 -0.7169 · PC2 0.7363PC1 -0.8657 · PC2 0.9407PC1 -1.699 · PC2 0.7772PC1 -0.9862 · PC2 0.6628PC1 -1.337 · PC2 0.8057PC1 -1.073 · PC2 0.4899PC1 -1.081 · PC2 0.7206PC1 -1.836 · PC2 0.4761PC1 -1.073 · PC2 0.6588PC1 -0.9551 · PC2 0.5666PC1 -1.73 · PC2 0.4396PC1 -1.426 · PC2 0.8558PC1 -0.7036 · PC2 0.5193PC1 -1.766 · PC2 0.7261PC1 -2.089 · PC2 0.8486PC1 -1.287 · PC2 0.6473PC1 -1.649 · PC2 0.6351PC1 -0.8153 · PC2 0.6516PC1 0.2729 · PC2 1.05PC1 -0.4719 · PC2 1.261PC1 0.2617 · PC2 1.054PC1 0.8524 · PC2 -0.06284PC1 0.4588 · PC2 0.8389PC1 0.8589 · PC2 0.8578PC1 0.4838 · PC2 0.807PC1 1.349 · PC2 1.197PC1 0.4284 · PC2 0.04026PC1 1.835 · PC2 0.7163PC1 2.136 · PC2 0.03905PC1 1.056 · PC2 0.6225PC1 1.578 · PC2 0.3132PC1 1.151 · PC2 0.9638PC1 0.9741 · PC2 0.6218PC1 0.55 · PC2 0.9195PC1 0.0744 · PC2 0.9108PC1 1.693 · PC2 0.5839PC1 0.9023 · PC2 0.905PC1 0.271 · PC2 0.4842PC1 0.1715 · PC2 0.7822PC1 2.68 · PC2 1.623PC1 0.4398 · PC2 0.9836PC1 4.231 · PC2 1.166PC1 -1.266 · PC2 0.7194PC1 -1.088 · PC2 0.7503PC1 -2.372 · PC2 1.318PC1 -1.349 · PC2 0.5511PC1 -1.233 · PC2 0.5583PC1 -1.409 · PC2 0.3359PC1 -1.781 · PC2 0.7594PC1 -0.9304 · PC2 0.6726PC1 -0.9916 · PC2 0.6233PC1 -0.7735 · PC2 0.4807PC1 -1.023 · PC2 0.7481PC1 -0.5124 · PC2 0.5243PC1 -0.9444 · PC2 0.5844PC1 -1.061 · PC2 0.5092PC1 -1.474 · PC2 0.5872PC1 -0.8104 · PC2 0.51PC1 1.302 · PC2 1.177PC1 0.3141 · PC2 1.501PC1 1.215 · PC2 1.17PC1 1.006 · PC2 0.8531PC1 0.9052 · PC2 0.8488PC1 0.02652 · PC2 1.346PC1 0.2153 · PC2 1.221PC1 0.2604 · PC2 1.249PC1 0.6317 · PC2 0.4756PC1 1.178 · PC2 0.8406PC1 1.343 · PC2 0.9222PC1 0.8559 · PC2 1.112PC1 0.7407 · PC2 0.7061PC1 1.035 · PC2 0.9792PC1 0.5508 · PC2 0.6514PC1 -0.03331 · PC2 1.04PC1 0.4335 · PC2 1.11PC1 1.027 · PC2 0.8906PC1 0.6656 · PC2 0.8042PC1 0.8128 · PC2 0.4637PC1 -0.2667 · PC2 1.199PC1 0.7597 · PC2 1.05PC1 0.9139 · PC2 0.9784PC1 1.953 · PC2 1.794PC1 1.577 · PC2 -1.395PC1 0.9071 · PC2 -0.854PC1 1.322 · PC2 0.2531PC1 0.941 · PC2 -1.428PC1 0.6489 · PC2 -0.9619PC1 0.8122 · PC2 -0.8275PC1 -1.104 · PC2 -1.244PC1 -0.02878 · PC2 -0.6928PC1 0.07441 · PC2 -1.319PC1 -0.4481 · PC2 -2.085PC1 0.8274 · PC2 -1.821PC1 1.468 · PC2 -1.955PC1 1.367 · PC2 -1.552PC1 -0.384 · PC2 -1.335PC1 0.7175 · PC2 0.1055PC1 -0.3028 · PC2 -1.371PC1 2.15 · PC2 0.2811PC1 0.7185 · PC2 -1.375PC1 -0.6536 · PC2 -1.179PC1 0.4715 · PC2 -2.01PC1 -1.455 · PC2 -0.9952PC1 -0.581 · PC2 -1.413PC1 0.1614 · PC2 -0.9641PC1 -0.2088 · PC2 -1.219PC1 0.4112 · PC2 1.181PC1 -0.7234 · PC2 0.9457PC1 3.727 · PC2 3.038PC1 -0.000569 · PC2 0.2187PC1 0.3648 · PC2 0.8124PC1 1.42 · PC2 1.293PC1 0.4128 · PC2 0.6348PC1 0.1295 · PC2 0.9787PC1 0.8358 · PC2 1.064PC1 0.9331 · PC2 1.06PC1 0.5143 · PC2 1.499PC1 0.8612 · PC2 0.7432PC1 0.5189 · PC2 0.6452PC1 0.6264 · PC2 1.185PC1 0.1197 · PC2 0.7567PC1 -0.6627 · PC2 1.144PC1 0.4833 · PC2 1.021PC1 0.3185 · PC2 0.8732PC1 1.216 · PC2 0.3141PC1 0.2617 · PC2 0.9553PC1 0.4646 · PC2 1.583PC1 1.108 · PC2 1.295PC1 0.3065 · PC2 0.801PC1 0.9842 · PC2 -1.105PC1 1.665 · PC2 -1.19PC1 -0.08592 · PC2 -0.07293PC1 0.5888 · PC2 -0.7194PC1 0.4524 · PC2 -0.5375PC1 0.7449 · PC2 -1.473PC1 0.1947 · PC2 -1.211PC1 -0.1967 · PC2 -1.07PC1 0.167 · PC2 -1.54PC1 -0.4712 · PC2 -1.503PC1 0.7288 · PC2 -1.616PC1 1.018 · PC2 -1.715PC1 0.7128 · PC2 -1.014PC1 2.001 · PC2 -0.268PC1 1.857 · PC2 -0.1PC1 0.6678 · PC2 -1.407PC1 0.7447 · PC2 0.1791PC1 0.8565 · PC2 -1.469PC1 0.9383 · PC2 -1.235PC1 -1.102 · PC2 -0.9188PC1 0.3099 · PC2 -1.6PC1 -0.3718 · PC2 -1.076PC1 -0.1467 · PC2 -0.7682PC1 -0.6266 · PC2 -1.181PC1 1.546 · PC2 1.02PC1 -1.052 · PC2 1.394PC1 1.198 · PC2 1.156PC1 0.3734 · PC2 0.7386PC1 1.026 · PC2 0.9499PC1 0.1173 · PC2 1.05PC1 1.746 · PC2 0.8992PC1 2.194 · PC2 1.925PC1 0.7654 · PC2 1.19PC1 0.716 · PC2 1.084PC1 -0.5534 · PC2 1.083PC1 -1.333 · PC2 1.076PC1 0.9963 · PC2 0.9967PC1 3.805 · PC2 1.414PC1 0.5194 · PC2 0.9723PC1 -0.3757 · PC2 1.206PC1 1.628 · PC2 0.9308PC1 2.344 · PC2 2.368PC1 -0.01662 · PC2 -0.6876PC1 -0.2141 · PC2 -0.7565PC1 -0.4969 · PC2 -0.1004PC1 3.453 · PC2 -1.152PC1 0.4405 · PC2 -0.6415PC1 -1.356 · PC2 -0.3399PC1 1.109 · PC2 -0.1815PC1 -0.1742 · PC2 0.4981PC1 0.1527 · PC2 -0.5507PC1 -0.6749 · PC2 -0.4296PC1 -1.74 · PC2 -0.2618PC1 -1.092 · PC2 -0.4942PC1 0.3142 · PC2 -1.322PC1 -0.304 · PC2 -0.9791PC1 -0.6123 · PC2 -0.5233PC1 -0.8331 · PC2 -0.6842PC1 -1.049 · PC2 -0.4214PC1 0.3015 · PC2 -0.168PC1 -0.7455 · PC2 -0.7716PC1 0.171 · PC2 -1.16PC1 -0.6221 · PC2 -0.08741PC1 1.132 · PC2 0.8921PC1 1.825 · PC2 0.311PC1 0.963 · PC2 -0.9264PC1 2.834 · PC2 -0.3482PC1 1.727 · PC2 -0.8602PC1 -0.02324 · PC2 -0.2673PC1 4.09 · PC2 0.1759PC1 1.58 · PC2 -0.1839PC1 0.8701 · PC2 -0.6793PC1 3.106 · PC2 0.4173PC1 0.1064 · PC2 0.2297PC1 1.077 · PC2 -0.5522PC1 -0.05944 · PC2 -0.1116PC1 0.4518 · PC2 -1.264PC1 0.5582 · PC2 -0.7389PC1 0.3826 · PC2 -1.33PC1 0.4197 · PC2 -0.8513PC1 3.568 · PC2 -0.7835PC1 -0.05945 · PC2 -0.5467PC1 3.583 · PC2 -0.1755PC1 3.12 · PC2 -0.6014PC1 2.784 · PC2 0.06297PC1 4.25 · PC2 -0.6733PC1 1.636 · PC2 -0.652PC1 2.129 · PC2 0.1484PC1 1.323 · PC2 0.1663PC1 2.849 · PC2 -0.9322PC1 0.7606 · PC2 -0.9069PC1 1.813 · PC2 -1.048PC1 1.187 · PC2 -1.169PC1 3.777 · PC2 -0.6387PC1 2.923 · PC2 -0.2331PC1 1.904 · PC2 -0.006927PC1 1.195 · PC2 -0.7068PC1 2.49 · PC2 -0.4823PC1 1.47 · PC2 -0.5947PC1 1.902 · PC2 -0.8549PC1 1.41 · PC2 -0.7442PC1 1.045 · PC2 -0.4115PC1 0.9582 · PC2 -0.4867PC1 0.8123 · PC2 -0.6853PC1 0.4956 · PC2 -0.3727PC1 -1.563 · PC2 0.569PC1 2.781 · PC2 -0.3204PC1 2.134 · PC2 -0.3675PC1 1.828 · PC2 -0.8187PC1 1.714 · PC2 -0.1865PC1 1.157 · PC2 -0.6418PC1 2.212 · PC2 -0.1386PC1 1.343 · PC2 -0.71PC1 0.8006 · PC2 -0.6043PC1 1.319 · PC2 -0.7761PC1 2.804 · PC2 -0.5418PC1 2.469 · PC2 -0.4433PC1 2.485 · PC2 -0.4994PC1 2.098 · PC2 -0.2508PC1 0.4301 · PC2 -0.384PC1 1.158 · PC2 -0.7854PC1 1.947 · PC2 -0.4585PC1 3.084 · PC2 -0.01329PC1 2.557 · PC2 -0.358PC1 -0.08287 · PC2 -0.1531PC1 1.075 · PC2 0.1858PC1 0.4028 · PC2 -0.03477PC1 0.6122 · PC2 -0.2563PC1 1.217 · PC2 0.1114PC1 1.324 · PC2 -0.112PC1 0.4801 · PC2 -0.1524PC1 1.033 · PC2 -0.3835PC1 1.944 · PC2 -0.005794PC1 1.652 · PC2 0.04963PC1 0.1283 · PC2 -0.214PC1 1.096 · PC2 -0.1696PC1 0.7469 · PC2 0.02411PC1 0.7846 · PC2 0.05351PC1 0.1637 · PC2 0.1553PC1 1.54 · PC2 0.09015PC1 2.136 · PC2 -0.3205PC1 2.712 · PC2 0.09023PC1 1.27 · PC2 -0.3461PC1 1.043 · PC2 -0.1711PC1 1.906 · PC2 -0.3311PC1 1.774 · PC2 -0.3313PC1 1.053 · PC2 -0.4954PC1 1.405 · PC2 -0.2757PC1 0.2696 · PC2 0.1134PC1 1.604 · PC2 -0.1078PC1 1.333 · PC2 -0.1623PC1 2.374 · PC2 0.06919PC1 2.662 · PC2 0.1268PC1 2.236 · PC2 -0.3282PC1 0.8872 · PC2 0.09679PC1 2.026 · PC2 -0.2704PC1 1.262 · PC2 0.08872PC1 2.259 · PC2 -0.009655PC1 2.397 · PC2 0.03986PC1 2.193 · PC2 -0.2239PC1 1.181 · PC2 0.1076PC1 1.2 · PC2 -0.1951PC1 2.18 · PC2 0.07798PC1 2.166 · PC2 -0.2089PC1 2.672 · PC2 -0.4766PC1 2.735 · PC2 -0.2366PC1 2.162 · PC2 0.2199PC1 1.177 · PC2 -0.3206PC1 1.441 · PC2 -0.3406PC1 2.353 · PC2 -0.1337PC1 1.392 · PC2 -0.102PC1 1.834 · PC2 -0.04099PC1 1.894 · PC2 0.4723PC1 2.167 · PC2 0.2681PC1 1.971 · PC2 0.3221PC1 0.8872 · PC2 -0.1057PC1 0.81 · PC2 -0.2357PC1 1.754 · PC2 -0.2813PC1 2.001 · PC2 -0.2555PC1 2.196 · PC2 -0.3147PC1 1.77 · PC2 -0.1433PC1 2.018 · PC2 -0.004629PC1 1.35 · PC2 -0.6443PC1 1.726 · PC2 -0.3804PC1 1.635 · PC2 -0.4632PC1 1.611 · PC2 -0.2015PC1 1.884 · PC2 0.1138PC1 2.266 · PC2 -0.2404PC1 1.967 · PC2 -0.647PC1 1.701 · PC2 -0.3668PC1 2.582 · PC2 0.001277PC1 1.835 · PC2 -0.1763PC1 3.888 · PC2 -0.1139PC1 2.508 · PC2 -0.2476PC1 1.489 · PC2 -0.07823PC1 1.635 · PC2 0.2415PC1 0.868 · PC2 -0.1425PC1 1.633 · PC2 0.4207PC1 1.773 · PC2 0.3262PC1 2.223 · PC2 0.009183PC1 2.017 · PC2 -0.04213PC1 0.9913 · PC2 -0.3431PC1 1.309 · PC2 0.3683PC1 1.393 · PC2 0.6009PC1 1.48 · PC2 0.4019PC1 1.598 · PC2 0.1837PC1 1.427 · PC2 0.2109PC1 1.945 · PC2 0.2178PC1 1.186 · PC2 0.1366PC1 1.588 · PC2 0.361PC1 1.686 · PC2 0.08386PC1 1.061 · PC2 -0.2274PC1 2.291 · PC2 -0.04941PC1 1.486 · PC2 -0.3816PC1 0.885 · PC2 -0.1511PC1 2.167 · PC2 -0.1767PC1 1.416 · PC2 -0.1252PC1 1.082 · PC2 -0.2042PC1 1.314 · PC2 0.1988PC1 1.335 · PC2 0.06821PC1 1.421 · PC2 -0.005232PC1 0.6898 · PC2 0.1465PC1 1.349 · PC2 0.06506PC1 1.582 · PC2 -0.03855PC1 1.261 · PC2 0.05056PC1 1.518 · PC2 0.5091PC1 1.522 · PC2 0.01272PC1 1.22 · PC2 -0.2138PC1 1.221 · PC2 -0.2956PC1 1.118 · PC2 0.1076PC1 1.184 · PC2 0.1709PC1 1.153 · PC2 0.6451PC1 1.497 · PC2 -0.2222PC1 2.018 · PC2 0.07374PC1 1.62 · PC2 0.205PC1 2.192 · PC2 0.4567PC1 0.7745 · PC2 0.07581PC1 0.612 · PC2 -0.2041PC1 1.479 · PC2 -0.1406PC1 1.012 · PC2 -0.07184PC1 0.8042 · PC2 0.1528PC1 0.7875 · PC2 -0.02694PC1 1.316 · PC2 0.07893PC1 1.276 · PC2 0.4204PC1 1.259 · PC2 0.4038PC1 1.281 · PC2 0.2573PC1 0.9062 · PC2 0.08203PC1 0.521 · PC2 -0.07683PC1 1.141 · PC2 -0.2059PC1 1.437 · PC2 0.06795PC1 (69.2%)PC2 (20.4%)665 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
1-2.5595-0.4566
2-3.1243-0.79809
3-3.077-0.79263
4-2.9583-0.59685
5-2.5016-0.81583
6-2.2292-0.76766
7-2.9769-0.66143
8-2.9459-0.59126
9-2.6523-0.73291
10-2.3946-0.59772
11-2.3708-0.44591
12-2.2488-0.71789
13-2.5354-0.76473
14-2.0481-0.85438
15-2.3728-0.66117
16-2.2988-0.59472
17-3.1771-0.82959
18-2.5543-0.50934
19-2.3314-0.92394
20-2.4405-1.5246
21-1.9203-0.9187
22-2.0729-1.2954
23-2.213-1.2218
24-2.4189-0.95345
25-2.4937-0.93302
26-2.0405-1.5267
27-2.7477-1.0401
28-2.4349-1.6905
29-2.0318-0.66349
30-2.2433-1.3879
31-3.2334-1.1493
32-2.6725-1.0477
33-1.02970.34671
34-1.46020.73817
35-2.18520.25216
36-1.17590.77616
37-2.20730.93196
38-1.73250.032187
39-2.71210.51641
40-0.63068-0.17051
41-2.05480.18813
42-2.24080.80649
43-2.64670.60964
44-1.90110.27947
45-2.26820.70783
46-1.7160.52694
47-1.3278-0.067843
48-1.2194-0.33245
49-2.1557-0.737
50-1.9807-0.82263
51-2.6886-0.82016
52-1.8789-0.85545
53-2.4312-0.81739
54-2.4-0.83231
55-1.9081-1.1865
56-2.7251-0.43671
57-1.3364-0.53355
58-2.1745-0.56925
59-1.6655-0.50942
60-2.4486-1.3331
61-1.3078-0.42271
62-1.9448-0.69376
63-2.6386-0.87911
64-2.0154-0.86757
65-0.54289-1.549
66-0.91099-1.4618
67-1.1482-0.93063
68-0.62031-1.1576
690.12327-1.4841
70-1.2167-1.8363
710.50922-1.7585
72-1.4365-1.9428
73-0.79748-1.9044
74-1.2787-1.4823
75-0.045263-1.9462
76-0.46555-1.7193
77-0.80182-1.825
780.15801-2.771
79-1.6515-1.3653
800.24625-1.3612
81-0.0212640.97063
82-1.37780.73559
83-1.70690.58334
84-1.32810.48113
85-1.39950.72476
86-0.943220.48951
87-1.66720.87496
88-0.269410.35181
89-2.1820.5988
90-2.01060.85069
91-1.25750.40752
92-2.33290.52557
93-1.62650.75647
94-1.57550.73151
95-1.13110.31147
96-1.90590.24868
971.25411.6207
980.959571.2389
99-0.802290.59375
100-1.73440.5737
101-2.44010.72719
102-1.85050.54996
103-3.04480.60929
104-2.02270.3921
105-1.0380.38914
106-1.5770.64993
107-1.71010.36995
108-1.51030.32008
109-1.50390.85828
110-0.763310.36362
111-1.9940.54709
112-2.31650.55917
1130.14221-1.0822
1140.15795-1.2221
115-0.10119-1.3514
116-0.24403-0.90935
117-0.64002-0.81735
118-1.0799-1.7328
119-0.68772-1.4586
120-1.2966-1.8098
1210.36806-1.4385
122-0.49105-1.0525
123-0.75734-1.1693
1240.040863-1.3125
125-0.59777-0.87206
126-0.72565-1.5958
1270.35909-1.4618
1280.081912-1.326
129-1.6585-0.58056
130-1.6333-0.27036
131-1.0812-0.49614
132-1.7217-0.72349
133-0.89955-0.67924
134-0.942-0.42806
135-1.8599-0.575
136-1.2617-0.55171
137-1.4943-0.25871
138-0.81101-0.50326
139-2.6096-0.84423
140-0.8034-0.70113
141-2.2474-0.60054
142-1.9771-0.67323
143-1.5530.60699
144-2.07370.57878
145-1.77040.41318
146-1.36870.93393
147-1.3884-0.14913
148-1.5380.31603
149-2.26431.4191
150-1.47350.64864
151-1.88410.56583
152-1.74280.82806
153-0.212481.1055
154-2.15010.93291
155-1.86050.55895
156-2.4380.86107
157-0.27527-1.2854
1581.1654-1.5705
159-0.2003-0.86159
160-0.77274-1.118
161-0.64169-0.70841
162-0.36563-1.5315
163-0.017973-1.3506
164-1.1056-1.2437
1651.6733-2.0765
1660.5668-1.2383
167-0.89644-1.3201
1680.21948-1.5508
1690.25611-1.1421
170-1.1126-1.2848
1710.99649-1.4401
1720.56937-1.4006
173-0.896290.37485
174-1.19630.6155
175-1.33820.6366
176-0.70720.33374
177-1.3890.61774
178-1.58420.45545
179-1.43550.46866
180-1.16130.49415
181-1.20870.47225
182-1.20710.45729
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4000.129540.97872
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4040.861220.74321
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408-0.662661.1441
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450-0.553431.0834
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521-1.56310.56895
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540-0.082866-0.15306
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5460.48008-0.1524
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5491.65210.049626
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5640.269610.11339
5651.6045-0.10776
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5700.887230.096788
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5732.2594-0.0096553
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5831.1772-0.32061
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6211.59780.18367
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6381.34880.065057
6391.5819-0.038547
6401.26140.05056
6411.51850.5091
6421.52210.012723
6431.22-0.21383
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6471.15310.64511
6481.4973-0.22219
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6501.61990.20503
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6520.774520.075806
6530.612-0.20411
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6551.012-0.071837
6560.804240.15283
6570.78745-0.026936
6581.31560.07893
6591.27620.42044
6601.25880.40384
6611.28070.25732
6620.906190.082033
6630.52096-0.07683
6641.1414-0.20585
6651.43740.067947

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: 69.2% (cumulative 69.2%)1PC2: 20.4% (cumulative 89.6%)2PC3: 6.8% (cumulative 96.4%)3PC4: 1.4% (cumulative 97.8%)4PC5: 0.9% (cumulative 98.6%)5PC6: 0.4% (cumulative 99.0%)6PC7: 0.3% (cumulative 99.3%)7PC8: 0.2% (cumulative 99.6%)8PC9: 0.1% (cumulative 99.7%)9PC10: 0.1% (cumulative 99.8%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
View explained variance values
Variance per displayed principal component
ComponentSpectral variance
PC169.22%
PC220.35%
PC36.84%
PC41.36%
PC50.86%
PC60.37%
PC70.34%
PC80.22%
PC90.12%
PC100.09%
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 LMA

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

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

Spectral association with Protein

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

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

Spectral association with Starch

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

X · Starch 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
LMA0.3937760.2720.0%
Protein0.4157130.1080.0%
Starch0.4087230.2170.0%

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

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

VariableRoleDistinct valuesCompleteRange / balance
co2sM13821.05%378.7 – 400.3
h2osM13321.05%4 – 18.31
rhsM13721.05%10.17 – 58.53
flowM7221.05%299.5 – 500.6
qinM12521.05%1498 – 1802
locationM2100%IR 3.156
plotM1224.06%IR 1.667
speciesM6100%IR 5.667
treatmentM2100%IR 1.046
plant_ageM21100%35 – 127
days_into_treatmentM22100%3 – 37
measurement_dateM31100%IR 2.571
lmaY568100%14.93 – 92.29
proteinY65898.95%2.78 – 33.25
starchY65498.35%0.00614 – 200.9

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

LMA

target · numeric

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

LMA distribution025507514.93 – 18.16: 318.16 – 21.38: 1421.38 – 24.6: 2224.6 – 27.83: 3627.83 – 31.05: 5231.05 – 34.27: 7234.27 – 37.5: 6137.5 – 40.72: 6540.72 – 43.94: 4843.94 – 47.17: 3547.17 – 50.39: 2050.39 – 53.61: 2353.61 – 56.84: 3156.84 – 60.06: 2460.06 – 63.28: 4563.28 – 66.51: 3166.51 – 69.73: 2169.73 – 72.95: 2572.95 – 76.18: 2176.18 – 79.4: 779.4 – 82.62: 482.62 – 85.85: 085.85 – 89.07: 189.07 – 92.29: 4020406080100
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
45.44 ± 16
Median
?50th percentile: half the non-missing values lie at or below it.
41.46
Range
?Smallest and largest recorded non-missing values.
14.93 – 92.29
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.351
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.5 / -0.68
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Protein

target · numeric

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

Protein distribution01002002.78 – 4.049: 154.049 – 5.318: 755.318 – 6.588: 1716.588 – 7.857: 1647.857 – 9.127: 1049.127 – 10.4: 7210.4 – 11.67: 3211.67 – 12.93: 1712.93 – 14.2: 514.2 – 15.47: 115.47 – 16.74: 116.74 – 18.01: 018.01 – 19.28: 019.28 – 20.55: 020.55 – 21.82: 021.82 – 23.09: 023.09 – 24.36: 024.36 – 25.63: 025.63 – 26.9: 026.9 – 28.17: 028.17 – 29.44: 029.44 – 30.71: 030.71 – 31.98: 031.98 – 33.25: 1010203040
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 7
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
7.418 ± 2.27
Median
?50th percentile: half the non-missing values lie at or below it.
7.11
Range
?Smallest and largest recorded non-missing values.
2.78 – 33.25
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.306
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.7 / 25
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Starch

target · numeric

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

Starch distribution01002003000.00614 – 8.378: 2938.378 – 16.75: 6716.75 – 25.12: 4025.12 – 33.49: 4333.49 – 41.86: 3241.86 – 50.23: 3950.23 – 58.61: 3258.61 – 66.98: 3266.98 – 75.35: 1675.35 – 83.72: 1083.72 – 92.09: 1692.09 – 100.5: 13100.5 – 108.8: 5108.8 – 117.2: 6117.2 – 125.6: 1125.6 – 133.9: 4133.9 – 142.3: 3142.3 – 150.7: 0150.7 – 159.1: 1159.1 – 167.4: 0167.4 – 175.8: 0175.8 – 184.2: 0184.2 – 192.5: 0192.5 – 200.9: 10100200300
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 11
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
27.2 ± 31.7
Median
?50th percentile: half the non-missing values lie at or below it.
11.77
Range
?Smallest and largest recorded non-missing values.
0.00614 – 200.9
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.17
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.5 / 2.2
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Metadata 32

uniquefield

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.
665 / 0
Classes
?Number of distinct recorded category values.
665
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.
2018-03-19_17 (1)

Glucose

metadata · numeric

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

Glucose distribution0501001500.01264 – 1.022: 671.022 – 2.03: 1132.03 – 3.039: 883.039 – 4.048: 594.048 – 5.057: 375.057 – 6.066: 476.066 – 7.075: 357.075 – 8.084: 288.084 – 9.093: 259.093 – 10.1: 2410.1 – 11.11: 2511.11 – 12.12: 1912.12 – 13.13: 1613.13 – 14.14: 1714.14 – 15.15: 1115.15 – 16.15: 916.15 – 17.16: 817.16 – 18.17: 1118.17 – 19.18: 519.18 – 20.19: 220.19 – 21.2: 421.2 – 22.21: 022.21 – 23.22: 223.22 – 24.23: 10102030
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 12
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
5.806 ± 4.96
Median
?50th percentile: half the non-missing values lie at or below it.
4.047
Range
?Smallest and largest recorded non-missing values.
0.01264 – 24.23
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.855
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.1 / 0.52
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Fructose

metadata · numeric

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

Fructose distribution0501001500.2065 – 0.763: 1040.763 – 1.319: 1391.319 – 1.876: 981.876 – 2.432: 632.432 – 2.989: 742.989 – 3.545: 433.545 – 4.102: 264.102 – 4.658: 324.658 – 5.215: 195.215 – 5.771: 185.771 – 6.328: 116.328 – 6.884: 126.884 – 7.441: 67.441 – 7.997: 47.997 – 8.554: 58.554 – 9.11: 19.11 – 9.667: 19.667 – 10.22: 010.22 – 10.78: 010.78 – 11.34: 011.34 – 11.89: 011.89 – 12.45: 012.45 – 13.01: 013.01 – 13.56: 1051015
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 8
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
2.355 ± 1.83
Median
?50th percentile: half the non-missing values lie at or below it.
1.786
Range
?Smallest and largest recorded non-missing values.
0.2065 – 13.56
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.778
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.5 / 3
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Sucrose

metadata · numeric

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

Sucrose distribution01002000.8231 – 2.643: 452.643 – 4.463: 1874.463 – 6.283: 1286.283 – 8.103: 648.103 – 9.923: 349.923 – 11.74: 1711.74 – 13.56: 2213.56 – 15.38: 1615.38 – 17.2: 1217.2 – 19.02: 1619.02 – 20.84: 2420.84 – 22.66: 1922.66 – 24.48: 1824.48 – 26.3: 1826.3 – 28.12: 1828.12 – 29.94: 629.94 – 31.76: 331.76 – 33.58: 233.58 – 35.4: 135.4 – 37.22: 137.22 – 39.04: 039.04 – 40.86: 040.86 – 42.68: 042.68 – 44.5: 20204060
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 12
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
9.424 ± 8
Median
?50th percentile: half the non-missing values lie at or below it.
5.847
Range
?Smallest and largest recorded non-missing values.
0.8231 – 44.5
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.849
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.4 / 1.1
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

LDP_Fructan

metadata · numeric

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

LDP_Fructan distribution0510150.01275 – 0.09582: 110.09582 – 0.1789: 40.1789 – 0.262: 70.262 – 0.345: 80.345 – 0.4281: 40.4281 – 0.5112: 50.5112 – 0.5942: 20.5942 – 0.6773: 00.6773 – 0.7604: 20.7604 – 0.8435: 20.8435 – 0.9265: 30.9265 – 1.01: 11.01 – 1.093: 21.093 – 1.176: 11.176 – 1.259: 21.259 – 1.342: 01.342 – 1.425: 01.425 – 1.508: 01.508 – 1.591: 01.591 – 1.674: 01.674 – 1.757: 01.757 – 1.84: 01.84 – 1.923: 01.923 – 2.006: 10.00.51.01.52.02.5
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 610
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.4505 ± 0.405
Median
?50th percentile: half the non-missing values lie at or below it.
0.3295
Range
?Smallest and largest recorded non-missing values.
0.01275 – 2.006
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.899
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.5 / 2.8
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

HDP_Fructan

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.
665 / 657
Classes
?Number of distinct recorded category values.
8
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.
1.10284464 (1)

Amino_Acids

metadata · numeric

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

Amino_Acids distribution01002003000.1126 – 1.615: 1581.615 – 3.116: 2323.116 – 4.618: 1314.618 – 6.12: 456.12 – 7.622: 167.622 – 9.124: 129.124 – 10.63: 610.63 – 12.13: 912.13 – 13.63: 213.63 – 15.13: 415.13 – 16.63: 616.63 – 18.14: 318.14 – 19.64: 219.64 – 21.14: 421.14 – 22.64: 322.64 – 24.14: 624.14 – 25.65: 025.65 – 27.15: 127.15 – 28.65: 028.65 – 30.15: 330.15 – 31.65: 031.65 – 33.16: 133.16 – 34.66: 234.66 – 36.16: 2010203040
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 17
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
4.102 ± 5.13
Median
?50th percentile: half the non-missing values lie at or below it.
2.713
Range
?Smallest and largest recorded non-missing values.
0.1126 – 36.16
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.25
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
3.5 / 14
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

ABA

metadata · numeric

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

ABA distribution02004006000.2882 – 2806: 5652806 – 5612: 225612 – 8417: 88417 – 1.122e+04: 21.122e+04 – 1.403e+04: 21.403e+04 – 1.683e+04: 01.683e+04 – 1.964e+04: 11.964e+04 – 2.245e+04: 12.245e+04 – 2.525e+04: 02.525e+04 – 2.806e+04: 12.806e+04 – 3.086e+04: 03.086e+04 – 3.367e+04: 13.367e+04 – 3.647e+04: 03.647e+04 – 3.928e+04: 03.928e+04 – 4.208e+04: 04.208e+04 – 4.489e+04: 04.489e+04 – 4.77e+04: 04.77e+04 – 5.05e+04: 05.05e+04 – 5.331e+04: 05.331e+04 – 5.611e+04: 05.611e+04 – 5.892e+04: 05.892e+04 – 6.172e+04: 06.172e+04 – 6.453e+04: 06.453e+04 – 6.733e+04: 1020,00040,00060,00080,000
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 61
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
1017 ± 3.66e+03
Median
?50th percentile: half the non-missing values lie at or below it.
242.8
Range
?Smallest and largest recorded non-missing values.
0.2882 – 6.733e+04
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
3.59
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
12 / 2e+02
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Proline

metadata · numeric

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

Proline distribution02004006000.1427 – 2.005: 4052.005 – 3.868: 1653.868 – 5.731: 465.731 – 7.593: 97.593 – 9.456: 99.456 – 11.32: 1111.32 – 13.18: 413.18 – 15.04: 215.04 – 16.91: 016.91 – 18.77: 118.77 – 20.63: 220.63 – 22.49: 022.49 – 24.36: 124.36 – 26.22: 026.22 – 28.08: 128.08 – 29.94: 029.94 – 31.81: 031.81 – 33.67: 033.67 – 35.53: 135.53 – 37.4: 037.4 – 39.26: 039.26 – 41.12: 041.12 – 42.98: 042.98 – 44.85: 10204060
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 7
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
2.222 ± 3.54
Median
?50th percentile: half the non-missing values lie at or below it.
1.01
Range
?Smallest and largest recorded non-missing values.
0.1427 – 44.85
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.59
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
5.8 / 50
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

RWC

metadata · numeric

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

RWC distribution05010015041.52 – 43.7: 143.7 – 45.89: 045.89 – 48.08: 148.08 – 50.27: 050.27 – 52.46: 152.46 – 54.65: 254.65 – 56.84: 256.84 – 59.03: 959.03 – 61.22: 1361.22 – 63.41: 1363.41 – 65.6: 1965.6 – 67.79: 2567.79 – 69.98: 3069.98 – 72.17: 1572.17 – 74.36: 1674.36 – 76.55: 2676.55 – 78.74: 5678.74 – 80.93: 8380.93 – 83.12: 11283.12 – 85.3: 7485.3 – 87.49: 6887.49 – 89.68: 5489.68 – 91.87: 3591.87 – 94.06: 9102050100
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 1
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
79.41 ± 8.55
Median
?50th percentile: half the non-missing values lie at or below it.
81.47
Range
?Smallest and largest recorded non-missing values.
41.52 – 94.06
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.108
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-1 / 0.77
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Elemental_C

metadata · numeric

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

Elemental_C distribution02550750.4387 – 2.387: 242.387 – 4.335: 04.335 – 6.283: 16.283 – 8.231: 48.231 – 10.18: 3410.18 – 12.13: 5012.13 – 14.08: 7014.08 – 16.02: 6816.02 – 17.97: 4017.97 – 19.92: 2219.92 – 21.87: 1221.87 – 23.82: 1323.82 – 25.77: 2125.77 – 27.71: 3727.71 – 29.66: 3929.66 – 31.61: 2531.61 – 33.56: 1333.56 – 35.51: 1335.51 – 37.45: 337.45 – 39.4: 339.4 – 41.35: 141.35 – 43.3: 043.3 – 45.25: 145.25 – 47.2: 10204060
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 170
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
18.46 ± 8.76
Median
?50th percentile: half the non-missing values lie at or below it.
15.88
Range
?Smallest and largest recorded non-missing values.
0.4387 – 47.2
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.475
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.31 / -0.42
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Elemental_N

metadata · numeric

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

Elemental_N distribution02550750.6373 – 0.8668: 460.8668 – 1.096: 751.096 – 1.326: 431.326 – 1.556: 471.556 – 1.785: 731.785 – 2.015: 562.015 – 2.244: 492.244 – 2.474: 202.474 – 2.703: 122.703 – 2.933: 32.933 – 3.162: 83.162 – 3.392: 113.392 – 3.622: 73.622 – 3.851: 113.851 – 4.081: 74.081 – 4.31: 34.31 – 4.54: 44.54 – 4.769: 54.769 – 4.999: 84.999 – 5.228: 25.228 – 5.458: 25.458 – 5.688: 15.688 – 5.917: 15.917 – 6.147: 102468
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 170
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
1.88 ± 1.04
Median
?50th percentile: half the non-missing values lie at or below it.
1.662
Range
?Smallest and largest recorded non-missing values.
0.6373 – 6.147
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.552
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.5 / 2.2
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Asat

metadata · numeric

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

Asat distribution051015-0.46 – 1.363: 41.363 – 3.185: 53.185 – 5.008: 45.008 – 6.83: 46.83 – 8.652: 88.652 – 10.47: 610.47 – 12.3: 912.3 – 14.12: 714.12 – 15.94: 1115.94 – 17.77: 417.77 – 19.59: 919.59 – 21.41: 1421.41 – 23.23: 923.23 – 25.05: 925.05 – 26.88: 626.88 – 28.7: 428.7 – 30.52: 630.52 – 32.34: 932.34 – 34.17: 234.17 – 35.99: 435.99 – 37.81: 137.81 – 39.63: 039.63 – 41.46: 241.46 – 43.28: 3-200204060
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
18.91 ± 10.1
Median
?50th percentile: half the non-missing values lie at or below it.
19.27
Range
?Smallest and largest recorded non-missing values.
-0.46 – 43.28
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.535
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.19 / -0.52
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
yes

gs

metadata · numeric

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

gs distribution01020300.01 – 0.04167: 130.04167 – 0.07333: 50.07333 – 0.105: 220.105 – 0.1367: 140.1367 – 0.1683: 150.1683 – 0.2: 100.2 – 0.2317: 100.2317 – 0.2633: 30.2633 – 0.295: 50.295 – 0.3267: 110.3267 – 0.3583: 40.3583 – 0.39: 50.39 – 0.4217: 70.4217 – 0.4533: 30.4533 – 0.485: 10.485 – 0.5167: 30.5167 – 0.5483: 20.5483 – 0.58: 20.58 – 0.6117: 10.6117 – 0.6433: 00.6433 – 0.675: 20.675 – 0.7067: 00.7067 – 0.7383: 10.7383 – 0.77: 10.00.20.40.60.8
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.2199 ± 0.162
Median
?50th percentile: half the non-missing values lie at or below it.
0.17
Range
?Smallest and largest recorded non-missing values.
0.01 – 0.77
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.738
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.1 / 0.79
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Ci

metadata · numeric

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

Ci distribution02040-157.9 – -129.8: 3-129.8 – -101.8: 0-101.8 – -73.72: 1-73.72 – -45.67: 1-45.67 – -17.63: 1-17.63 – 10.42: 410.42 – 38.47: 838.47 – 66.51: 766.51 – 94.56: 894.56 – 122.6: 6122.6 – 150.7: 3150.7 – 178.7: 2178.7 – 206.7: 3206.7 – 234.8: 17234.8 – 262.8: 23262.8 – 290.9: 32290.9 – 318.9: 14318.9 – 347: 4347 – 375: 1375 – 403.1: 0403.1 – 431.1: 0431.1 – 459.2: 0459.2 – 487.2: 1487.2 – 515.3: 1-2000200400600
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
199.9 ± 120
Median
?50th percentile: half the non-missing values lie at or below it.
245.4
Range
?Smallest and largest recorded non-missing values.
-157.9 – 515.3
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.598
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.83 / 0.49
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Tr

metadata · numeric

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

Tr distribution0510150.21 – 0.6621: 60.6621 – 1.114: 51.114 – 1.566: 61.566 – 2.018: 52.018 – 2.47: 72.47 – 2.922: 132.922 – 3.375: 123.375 – 3.827: 143.827 – 4.279: 124.279 – 4.731: 94.731 – 5.183: 75.183 – 5.635: 65.635 – 6.087: 56.087 – 6.539: 56.539 – 6.991: 46.991 – 7.443: 57.443 – 7.895: 37.895 – 8.348: 28.348 – 8.8: 48.8 – 9.252: 49.252 – 9.704: 09.704 – 10.16: 410.16 – 10.61: 110.61 – 11.06: 1051015
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
4.386 ± 2.49
Median
?50th percentile: half the non-missing values lie at or below it.
3.89
Range
?Smallest and largest recorded non-missing values.
0.21 – 11.06
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.567
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.59 / -0.24
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

VPDleaf

metadata · numeric

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

VPDleaf distribution010201.04 – 1.172: 21.172 – 1.304: 11.304 – 1.436: 71.436 – 1.568: 41.568 – 1.7: 121.7 – 1.833: 71.833 – 1.965: 71.965 – 2.097: 182.097 – 2.229: 102.229 – 2.361: 142.361 – 2.493: 82.493 – 2.625: 92.625 – 2.757: 32.757 – 2.889: 72.889 – 3.021: 53.021 – 3.153: 23.153 – 3.285: 43.285 – 3.417: 43.417 – 3.55: 43.55 – 3.682: 33.682 – 3.814: 13.814 – 3.946: 23.946 – 4.078: 54.078 – 4.21: 112510
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
2.375 ± 0.723
Median
?50th percentile: half the non-missing values lie at or below it.
2.24
Range
?Smallest and largest recorded non-missing values.
1.04 – 4.21
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.305
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.64 / -0.22
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Tleaf

metadata · numeric

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

Tleaf distribution0102023.03 – 23.38: 323.38 – 23.73: 123.73 – 24.09: 424.09 – 24.44: 524.44 – 24.79: 624.79 – 25.14: 525.14 – 25.49: 925.49 – 25.84: 1125.84 – 26.2: 1226.2 – 26.55: 426.55 – 26.9: 1226.9 – 27.25: 727.25 – 27.6: 1627.6 – 27.95: 627.95 – 28.3: 528.3 – 28.66: 428.66 – 29.01: 429.01 – 29.36: 429.36 – 29.71: 929.71 – 30.06: 330.06 – 30.41: 130.41 – 30.77: 330.77 – 31.12: 531.12 – 31.47: 1102050100
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
26.98 ± 1.95
Median
?50th percentile: half the non-missing values lie at or below it.
26.86
Range
?Smallest and largest recorded non-missing values.
23.03 – 31.47
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.0722
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.29 / -0.58
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
yes

CO2s

metadata · numeric

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

CO2s distribution051015378.7 – 379.6: 2379.6 – 380.5: 0380.5 – 381.4: 2381.4 – 382.3: 2382.3 – 383.2: 0383.2 – 384.1: 5384.1 – 385: 6385 – 385.9: 8385.9 – 386.8: 7386.8 – 387.7: 10387.7 – 388.6: 8388.6 – 389.5: 8389.5 – 390.4: 13390.4 – 391.3: 5391.3 – 392.2: 13392.2 – 393.1: 7393.1 – 394: 8394 – 394.9: 7394.9 – 395.8: 9395.8 – 396.7: 6396.7 – 397.6: 3397.6 – 398.5: 7398.5 – 399.4: 0399.4 – 400.3: 4370380390400410
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
390.5 ± 4.69
Median
?50th percentile: half the non-missing values lie at or below it.
390.3
Range
?Smallest and largest recorded non-missing values.
378.7 – 400.3
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.012
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.064 / -0.53
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
yes

H2Os

metadata · numeric

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

H2Os distribution010204 – 4.596: 34.596 – 5.192: 45.192 – 5.789: 55.789 – 6.385: 06.385 – 6.981: 46.981 – 7.577: 77.577 – 8.174: 28.174 – 8.77: 18.77 – 9.366: 49.366 – 9.962: 79.962 – 10.56: 510.56 – 11.15: 611.15 – 11.75: 611.75 – 12.35: 1212.35 – 12.94: 712.94 – 13.54: 713.54 – 14.14: 1114.14 – 14.73: 2014.73 – 15.33: 1315.33 – 15.92: 315.92 – 16.52: 416.52 – 17.12: 117.12 – 17.71: 317.71 – 18.31: 505101520
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
12.02 ± 3.51
Median
?50th percentile: half the non-missing values lie at or below it.
12.75
Range
?Smallest and largest recorded non-missing values.
4 – 18.31
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.292
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.54 / -0.46
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

RHs

metadata · numeric

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

RHs distribution0102010.17 – 12.19: 412.19 – 14.2: 814.2 – 16.21: 416.21 – 18.23: 618.23 – 20.25: 420.25 – 22.26: 222.26 – 24.27: 124.27 – 26.29: 226.29 – 28.3: 728.3 – 30.32: 230.32 – 32.34: 532.34 – 34.35: 1134.35 – 36.37: 836.37 – 38.38: 738.38 – 40.4: 840.4 – 42.41: 442.41 – 44.43: 1144.43 – 46.44: 1646.44 – 48.46: 1548.46 – 50.47: 250.47 – 52.49: 352.49 – 54.5: 154.5 – 56.52: 456.52 – 58.53: 50204060
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
36.03 ± 12.8
Median
?50th percentile: half the non-missing values lie at or below it.
38.24
Range
?Smallest and largest recorded non-missing values.
10.17 – 58.53
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.354
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.44 / -0.75
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

flow

metadata · numeric

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

flow distribution050100299.5 – 307.9: 48307.9 – 316.2: 0316.2 – 324.6: 0324.6 – 333: 0333 – 341.4: 0341.4 – 349.8: 0349.8 – 358.1: 0358.1 – 366.5: 0366.5 – 374.9: 0374.9 – 383.3: 0383.3 – 391.7: 0391.7 – 400: 0400 – 408.4: 0408.4 – 416.8: 0416.8 – 425.2: 0425.2 – 433.6: 0433.6 – 441.9: 0441.9 – 450.3: 0450.3 – 458.7: 0458.7 – 467.1: 0467.1 – 475.5: 0475.5 – 483.8: 0483.8 – 492.2: 0492.2 – 500.6: 921002005001,000
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
431.4 ± 95.3
Median
?50th percentile: half the non-missing values lie at or below it.
500
Range
?Smallest and largest recorded non-missing values.
299.5 – 500.6
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.221
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.67 / -1.6
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Qin

metadata · numeric

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

Qin distribution0501001498 – 1511: 481511 – 1523: 01523 – 1536: 01536 – 1549: 01549 – 1561: 01561 – 1574: 01574 – 1587: 01587 – 1599: 01599 – 1612: 01612 – 1625: 01625 – 1637: 01637 – 1650: 01650 – 1663: 01663 – 1675: 01675 – 1688: 01688 – 1701: 01701 – 1713: 01713 – 1726: 01726 – 1739: 01739 – 1751: 01751 – 1764: 01764 – 1777: 01777 – 1789: 01789 – 1802: 921,4001,5001,6001,7001,8001,900
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 525
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
1697 ± 143
Median
?50th percentile: half the non-missing values lie at or below it.
1799
Range
?Smallest and largest recorded non-missing values.
1498 – 1802
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.0841
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.67 / -1.6
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Location

metadata · categorical

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

Location classesglasshouseglasshouse: 505505fieldfield: 160160
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 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.
glasshouse (505)

Plant

metadata · numeric

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

Plant distribution020406017 – 23.29: 2823.29 – 29.58: 2229.58 – 35.88: 2235.88 – 42.17: 2842.17 – 48.46: 2448.46 – 54.75: 054.75 – 61.04: 061.04 – 67.33: 967.33 – 73.62: 1873.62 – 79.92: 1879.92 – 86.21: 4986.21 – 92.5: 4792.5 – 98.79: 3298.79 – 105.1: 0105.1 – 111.4: 0111.4 – 117.7: 0117.7 – 124: 12124 – 130.2: 32130.2 – 136.5: 29136.5 – 142.8: 29142.8 – 149.1: 30149.1 – 155.4: 24155.4 – 161.7: 24161.7 – 168: 28050100150200
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 160
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
96.02 ± 45.9
Median
?50th percentile: half the non-missing values lie at or below it.
91
Range
?Smallest and largest recorded non-missing values.
17 – 168
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.478
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.13 / -1.2
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Plot

metadata · categorical

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

Plot classes2.02.0: 15153.03.0: 15155.05.0: 15156.06.0: 15157.07.0: 151510.010.0: 15159.09.0: 141413.013.0: 121214.014.0: 121216.016.0: 1212+2 more+2 more: 2020
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 505
Classes
?Number of distinct recorded category values.
12
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.
2.0 (15)

Rep

metadata · categorical

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

Rep classes1.01.0: 54543.03.0: 54542.02.0: 5252
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 505
Classes
?Number of distinct recorded category values.
3
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.
1.0 (54)

Species

metadata · categorical

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

Species classesCUPECUPE: 272272POCA19POCA19: 125125CAAN4CAAN4: 9696HEAN3HEAN3: 6262SOBI2SOBI2: 6262RASA2RASA2: 4848
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 0
Classes
?Number of distinct recorded category values.
6
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.89
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.
CUPE (272)

Treatment

metadata · categorical

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

Treatment classeswateredwatered: 340340droughteddroughted: 325325
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 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.
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.
watered (340)

Plant_Age

metadata · numeric

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

Plant_Age distribution05010035 – 38.83: 2338.83 – 42.67: 9442.67 – 46.5: 2446.5 – 50.33: 3650.33 – 54.17: 054.17 – 58: 5658 – 61.83: 3961.83 – 65.67: 065.67 – 69.5: 069.5 – 73.33: 073.33 – 77.17: 077.17 – 81: 081 – 84.83: 1684.83 – 88.67: 088.67 – 92.5: 4792.5 – 96.33: 096.33 – 100.2: 64100.2 – 104: 16104 – 107.8: 76107.8 – 111.7: 0111.7 – 115.5: 95115.5 – 119.3: 15119.3 – 123.2: 40123.2 – 127: 241020501002005001,000
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
82.85 ± 30.9
Median
?50th percentile: half the non-missing values lie at or below it.
92
Range
?Smallest and largest recorded non-missing values.
35 – 127
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.373
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.23 / -1.6
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Days_Into_Treatment

metadata · numeric

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

Days_Into_Treatment distribution0501001503 – 4.417: 164.417 – 5.833: 165.833 – 7.25: 387.25 – 8.667: 08.667 – 10.08: 1610.08 – 11.5: 5811.5 – 12.92: 012.92 – 14.33: 13214.33 – 15.75: 1615.75 – 17.17: 2417.17 – 18.58: 1418.58 – 20: 020 – 21.42: 10821.42 – 22.83: 1622.83 – 24.25: 024.25 – 25.67: 025.67 – 27.08: 6227.08 – 28.5: 4928.5 – 29.92: 1429.92 – 31.33: 1631.33 – 32.75: 3932.75 – 34.17: 1534.17 – 35.58: 035.58 – 37: 16010203040
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
19.13 ± 8.58
Median
?50th percentile: half the non-missing values lie at or below it.
20
Range
?Smallest and largest recorded non-missing values.
3 – 37
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.449
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.15 / -0.91
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Measurement_Date

metadata · categorical

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

Measurement_Date classes7/19/197/19/19: 36367/26/197/26/19: 36367/16/197/16/19: 34348/2/198/2/19: 33333/29/183/29/18: 32324/3/184/3/18: 32327/19/187/19/18: 24247/23/187/23/18: 24247/31/187/31/18: 24248/3/188/3/18: 2424+10 more+10 more: 197197
n / missing
?Recorded observation count and number of missing values for this variable.
665 / 0
Classes
?Number of distinct recorded category values.
31
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.99
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.
7/19/19 (36)
Constant metadata 17

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

  • SpectralRep1
  • Paired_Spectraleaf
  • InstrumentSE_PSR_Plus
  • Pre_or_Post_Treatmentpost
  • datasetEcoSIS
  • n_points_original2,151
  • dimensions1D
  • axis_notewavelengths_as_provided_by_EcoSIS_resource
  • official_data_urlhttps://data.ecosis.org/dataset/hyperspectral-leaf-reflectance--biochemistry--and-physiology-of-droughted-and-watered-crops
  • official_package_urlhttps://ecosis.org/package/hyperspectral-leaf-reflectance--biochemistry--and-physiology-of-droughted-and-watered-crops
  • licenseCreative Commons Attribution
  • license_urlhttp://www.opendefinition.org/licenses/cc-by
  • rights_statusexplicit_open
  • usage_scopepublic_reuse_possible
  • public_release_allowedTrue
  • parent_standardized_dataset_idecosis_hyperspectral_leaf_reflectance_biochemistry_and_physiol_reflectance_nirs
  • 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
Samples665
Observations (total)665
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

# pip install nirs4all-datasets
from nirs4all_datasets import get

ds = get("ecosis_hyperspectral_leaf_reflectance_biochemistry_and_physiol_leaf_spectra_reflectance_nirs")            # DOI-pinned, checksum-verified, cached
X, y = ds.x(), ds.y()
print(X.shape, y.shape)
card.jsoncroissant.jsonIdentity metadata only — the dataset bytes live at the origin / DOI.

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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