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EcoSIS Leaf Optical Properties Experiment Database (LOPEX93) (Equivalent_Water_Thickness_g_cm2)

leaf · NIR

EcoSIS Leaf Optical Properties Experiment Database (LOPEX93) (Equivalent_Water_Thickness_g_cm2). v2.0 standardized NIRS package: 1 spectral source(s), 1 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2leaf
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Private dataset. Full metadata and metrics are shown, but the bytes are not redistributed here — exporting the data requires a Dataverse token. The identity card carries no spectra, only descriptive statistics.
330
samples
2,101
wavelengths
1
sources
1
targets
55
metadata
NIR
family

Dataset property explorer

Mean profile risk0.48
Highest axisOutliers PCA · 1.00
Diagnostics8
Sources profiled1
EcoSIS Leaf Optical Properties Experiment Database (LOPEX93) (Equivalent_Water_Thickness_g_cm2) property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS Leaf Optical Properties Experiment Database (LOPEX93) (Equivalent_Water_Thickness_g_cm2) profileintegrity: 0.00noise: 0.03artefacts: 0.13baseline: 0.72PCA outliers: 1.00reference: 0.99repeatability: 0.00structure: 1.00EcoSIS Leaf Opt…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux0.13
Bruit0.03
Outliers PCA1.00
Distance à la référence0.99
Répétabilité0.00
Baseline / forme0.72
Structure multi-régimes1.00
Diagnostic hypotheses00.250.50.751hypothesis scoreSpectre hors domaine valideSpectre hors domaine valide: 0.910.91Fond différentFond différent: 0.800.80Dataset multi-régimesDataset multi-régimes: 0.720.72Erreur calibration / référenc…Erreur calibration / référence blanche: 0.700.70Mélange feuille + fondMélange feuille + fond: 0.700.70Différence de sonde / géométr…Différence de sonde / géométrie: 0.590.59Signature VERA25-likeSignature VERA25-like: 0.590.59Splice / raccord détecteursSplice / raccord détecteurs: 0.580.58
DiagnosticScoreForceSignauxInterprétation probable
Spectre hors domaine valideX0.91forteStructure PCA 1.00, RMS/SAM référence 0.99, Mahalanobis / T2 0.99Variété, espèce, lot ou condition différente mais physiquement plausible.
Fond différentX0.80fortePCA Q 1.00, RMS/SAM référence 0.99, Mahalanobis / T2 0.99Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.
Dataset multi-régimesX0.72moyenneStructure PCA 1.00, PCA Q 1.00, RMS/SAM référence 0.99Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.
Erreur calibration / référence blancheX0.70moyennePCA Q 1.00, RMS/SAM référence 0.99, Mahalanobis / T2 0.99Décalage systématique entre campagnes, instruments ou référence blanche.
Mélange feuille + fondX0.70moyennePCA Q 1.00, RMS/SAM référence 0.99, Mahalanobis / T2 0.99Couverture partielle du spot; contribution du fond ou du support.
Différence de sonde / géométrieX0.59moyennePCA Q 1.00, RMS/SAM référence 0.99, Mahalanobis / T2 0.99Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Signature VERA25-likeX0.59moyennePCA Q 1.00, RMS/SAM référence 0.99, Mahalanobis / T2 0.99Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Splice / raccord détecteursX0.58moyennePCA Q 1.00, SNR non dégradé 1.00, RMS/SAM référence 0.99Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.

Spectral sources

Lopex1993_Reflectance__Lopex_Refl

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

Sampling

Wavelengths2,101
Axis range400–2,500 nm
Mean spacing1 nm
Griduniform
Observations330

Signal & quality

Value range0.017 – 0.638
Mean range0.0427 – 0.47
Mean level0.2578
Area541.6
PTP0.4275
Noise RMS0.00060526
SNR4.3e+02
SNR dB5e+01 dB
Dynamic range0.427
Smoothness0.001717
Saturated0.0%
X-outliers153

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.00%
Spike count892
Spike rate0.13%
Jump count519
Jump rate0.07%
Clip fraction0.00%

Shape & reference

Baseline slope-0.1549
Curvature RMS0.0017077
D1 RMS0.001889
RMS to mean0.028085
RMS p950.081751
SAM to mean0.064367
SAM p950.21984
Affine offset p950.084585
Affine gain p95 Δ0.24918
Affine residual p950.050956
Xcorr lag p950

Outliers & repeatability

PCA Q p95/median9.3
Hotelling T2 p95/median7.9
Mahalanobis H p95/median2.8
Repeat groups0

Dimensionality (PCA)

Effective rank2.5
PCs → 95% var3
PCs → 99% var6
Top-10 cum. var99.9%
Computed metric scores 29worst 1.00
FamilleMétrique calculéeValeurScoreNiveauInterprétation datasetCauses typiquesCalcul / scoring
Intégrité des donnéesNaN ratiointegrity.nan_ratio0%0.00faibleSpectre completErreur acquisition/exportcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des donnéesInf countintegrity.inf_count00.00faibleNormalCalculs invalidescount(isinf(X))alert = min(1, inf_count / 1)
Intégrité des donnéesZero ratiointegrity.zero_ratio0%0.00faibleNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceamplitude.mean_reflectance0.25780.72fortValeur atypique: Trop clair / fond visible ou Trop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveamplitude.area_under_curve541.580.72fortValeur atypique: Différence d'éclairement ou NormalDistance sondetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globalePeak-to-peak (PTP)amplitude.peak_to_peak0.427460.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance0.0254240.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms0.000605260.03faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr425.930.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min32.6640.13faibleZone fiableDétecteurmin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locauxSpike countartefacts.spike_count8920.13faibleSpectre propreCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate0.129%0.13faibleNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count5190.07faibleContinuSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate0.0749%0.07faibleNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction0.000577%0.00faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope-0.15490.72fortDériveÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.00170770.40faibleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.0018890.09faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio9.33731.00fortSpectre atypiqueArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio7.93620.99fortExtrême mais cohérentVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio2.81560.70moyenOutlier globalDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p950.0817510.76fortSpectre différentDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référenceSpectral Angle Mapper (SAM)reference.sam_to_mean_spectrum_p950.219840.63moyenForme différenteFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDrepeatability.rms_intra_id—0.00faibleStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDrepeatability.sam_intra_id—0.00faibleStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDrepeatability.cv_intra_id—0.00faibleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densitystructure.pca_score_density3.00351.00fortSous-populationsLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)structure.local_outlier_factor_p954.00281.00fortSpectre isoléCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scorestructure.isolation_forest_score_p950.577041.00fortSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X PCA score plot-5051015-4-2024PC1 -0.27 · PC2 -0.6821PC1 -0.3668 · PC2 -0.5293PC1 -0.04133 · PC2 -0.5013PC1 0.1574 · PC2 -2.039PC1 0.0166 · PC2 -1.038PC1 -0.9978 · PC2 0.8297PC1 1.207 · PC2 -0.7776PC1 -0.9417 · PC2 1.248PC1 -1.392 · PC2 1.399PC1 -1.325 · PC2 1.19PC1 -0.7516 · PC2 0.1159PC1 -0.8222 · PC2 0.5074PC1 -0.8658 · PC2 0.3107PC1 -0.6437 · PC2 -0.1353PC1 -0.8759 · PC2 0.4457PC1 -0.481 · PC2 -0.1117PC1 -0.4707 · PC2 0.2014PC1 0.08086 · PC2 0.2518PC1 -0.5898 · PC2 -0.08961PC1 -0.9972 · PC2 0.2798PC1 -1.875 · PC2 -1.247PC1 -2.395 · PC2 -1.513PC1 -0.3024 · PC2 -2.109PC1 -0.9213 · PC2 -1.664PC1 -0.4134 · PC2 -0.9878PC1 -1.657 · PC2 -0.8991PC1 -1.249 · PC2 -1.319PC1 -1.529 · PC2 -0.2973PC1 -0.8682 · PC2 -1.444PC1 -1.586 · PC2 -0.5841PC1 -1.521 · PC2 1.025PC1 -0.303 · PC2 0.03736PC1 -1.419 · PC2 0.8918PC1 -1.337 · PC2 1.518PC1 -0.4196 · PC2 0.7828PC1 -0.7285 · PC2 -1.366PC1 -0.7011 · PC2 -0.7631PC1 -0.4665 · PC2 -0.6893PC1 -0.4197 · PC2 -0.8831PC1 -0.2538 · PC2 -1.457PC1 -1.029 · PC2 0.1043PC1 -0.9856 · PC2 -0.02554PC1 -0.9988 · PC2 -0.1969PC1 -1.272 · PC2 0.277PC1 -1.2 · PC2 -0.05355PC1 -1.417 · PC2 -0.2564PC1 -1.372 · PC2 -0.2617PC1 -1.091 · PC2 -0.7705PC1 -1.474 · PC2 -0.22PC1 -1.494 · PC2 -0.5781PC1 -1.689 · PC2 0.9533PC1 -1.824 · PC2 0.7806PC1 -0.1504 · PC2 -0.2701PC1 -1.996 · PC2 1.279PC1 -1.905 · PC2 1.157PC1 -1.395 · PC2 -1.042PC1 -1.707 · PC2 -1.364PC1 -0.676 · PC2 -1.895PC1 -1.601 · PC2 -1.716PC1 -0.9655 · PC2 -1.628PC1 1.035 · PC2 1.584PC1 1.081 · PC2 1.745PC1 0.7044 · PC2 1.055PC1 1.17 · PC2 1.78PC1 1.122 · PC2 1.596PC1 -0.04221 · PC2 0.01225PC1 0.3168 · PC2 0.1127PC1 -0.6679 · PC2 -0.7424PC1 -0.4927 · PC2 -0.1913PC1 -0.4593 · PC2 -0.4269PC1 -0.8194 · PC2 1.023PC1 0.2115 · PC2 0.9646PC1 0.07523 · PC2 0.6961PC1 -0.01001 · PC2 1.048PC1 -0.2321 · PC2 0.9806PC1 -0.628 · PC2 0.6753PC1 -0.7119 · PC2 1.396PC1 0.02595 · PC2 1.054PC1 -0.6042 · PC2 1.132PC1 0.007547 · PC2 2.389PC1 -1.438 · PC2 -0.4037PC1 -1.618 · PC2 -0.5177PC1 -1.747 · PC2 -0.4969PC1 -2.36 · PC2 -0.5978PC1 -1.4 · PC2 -0.427PC1 -0.6458 · PC2 0.9281PC1 -0.0431 · PC2 0.6774PC1 -0.02333 · PC2 0.8528PC1 0.2214 · PC2 0.9219PC1 0.2731 · PC2 0.8788PC1 -0.9474 · PC2 0.264PC1 -0.9738 · PC2 -0.7038PC1 -0.8962 · PC2 -0.5047PC1 -0.6503 · PC2 -0.9049PC1 -0.2333 · PC2 0.1567PC1 -2.833 · PC2 -0.1826PC1 -2.965 · PC2 0.3165PC1 -1.869 · PC2 -0.4538PC1 -3.334 · PC2 0.1759PC1 -2.013 · PC2 0.1079PC1 1.167 · PC2 0.1058PC1 0.8378 · PC2 0.9036PC1 0.9973 · PC2 0.4207PC1 1.947 · PC2 0.8424PC1 1.389 · PC2 0.1056PC1 0.232 · PC2 -0.5142PC1 0.6841 · PC2 -0.7496PC1 0.5772 · PC2 -1.329PC1 0.09522 · PC2 -0.8775PC1 0.7894 · PC2 -2.217PC1 -0.6329 · PC2 0.6811PC1 -2.099 · PC2 1.638PC1 -1.218 · PC2 0.3105PC1 -0.5889 · PC2 0.2753PC1 -0.5561 · PC2 0.5381PC1 0.06733 · PC2 0.0278PC1 0.6222 · PC2 -0.01401PC1 1.038 · PC2 -0.6738PC1 1.453 · PC2 -0.9749PC1 0.774 · PC2 -0.3953PC1 -0.3328 · PC2 1.36PC1 -0.532 · PC2 1.058PC1 -0.1525 · PC2 1.106PC1 -0.3208 · PC2 1.388PC1 0.1097 · PC2 1.279PC1 -0.8209 · PC2 0.4337PC1 -0.7512 · PC2 0.3496PC1 -1.119 · PC2 0.4597PC1 -0.979 · PC2 0.5055PC1 -1.052 · PC2 0.5045PC1 0.02117 · PC2 0.362PC1 -0.2486 · PC2 0.2343PC1 -0.3111 · PC2 -0.01913PC1 -0.617 · PC2 0.01056PC1 -0.2952 · PC2 -0.1314PC1 -2.001 · PC2 1.06PC1 -1.968 · PC2 0.8734PC1 -2.084 · PC2 1.08PC1 -1.95 · PC2 1.245PC1 -1.605 · PC2 0.7919PC1 0.2848 · PC2 -0.1016PC1 0.5461 · PC2 0.4623PC1 0.1954 · PC2 0.5555PC1 0.5042 · PC2 0.5937PC1 0.7129 · PC2 0.5691PC1 1.308 · PC2 0.3329PC1 2.156 · PC2 1.115PC1 1.784 · PC2 0.2353PC1 2.345 · PC2 0.8181PC1 1.501 · PC2 0.571PC1 -0.04434 · PC2 0.6031PC1 0.1061 · PC2 0.7284PC1 0.4553 · PC2 0.2821PC1 0.01487 · PC2 0.7029PC1 0.1215 · PC2 0.5537PC1 -0.8237 · PC2 -0.04007PC1 -0.8062 · PC2 1.219PC1 0.01825 · PC2 0.3632PC1 0.4564 · PC2 0.3669PC1 -0.2547 · PC2 0.4276PC1 -0.7496 · PC2 0.5786PC1 -0.3041 · PC2 0.6887PC1 -0.2152 · PC2 0.893PC1 -1.097 · PC2 0.346PC1 -0.914 · PC2 0.7161PC1 0.8615 · PC2 -1.174PC1 1.19 · PC2 -0.8763PC1 0.5417 · PC2 -0.6653PC1 0.8283 · PC2 -1.196PC1 0.981 · PC2 -0.8923PC1 0.3374 · PC2 -0.07828PC1 0.1891 · PC2 0.5669PC1 0.1859 · PC2 1.296PC1 0.1003 · PC2 0.1321PC1 1.034 · PC2 0.4253PC1 11.23 · PC2 -2.281PC1 11.24 · PC2 0.5807PC1 12.34 · PC2 -0.2802PC1 11.16 · PC2 -1.388PC1 10.99 · PC2 2.233PC1 0.1893 · PC2 0.5222PC1 -0.09848 · PC2 0.1939PC1 0.2661 · PC2 0.4304PC1 0.4677 · PC2 0.2659PC1 0.4448 · PC2 0.2661PC1 1.823 · PC2 -0.907PC1 0.2474 · PC2 -0.2625PC1 1.377 · PC2 -0.08371PC1 0.6323 · PC2 -0.07846PC1 2.472 · PC2 -0.9784PC1 1.572 · PC2 1.406PC1 2.556 · PC2 0.9085PC1 1.367 · PC2 1.771PC1 1.881 · PC2 2.672PC1 1.704 · PC2 1.318PC1 3.861 · PC2 0.1168PC1 5.222 · PC2 0.5645PC1 4.072 · PC2 1.033PC1 3.264 · PC2 1.321PC1 3.629 · PC2 0.9863PC1 0.2426 · PC2 1.689PC1 -0.0141 · PC2 1.249PC1 -0.004799 · PC2 1.785PC1 0.1166 · PC2 1.576PC1 0.05473 · PC2 1.581PC1 -0.676 · PC2 -1.502PC1 -0.5974 · PC2 -0.8628PC1 -0.4183 · PC2 -1.344PC1 -1.011 · PC2 -1.093PC1 -0.7978 · PC2 -1.418PC1 0.3191 · PC2 0.2416PC1 0.1322 · PC2 0.2468PC1 -0.3771 · PC2 -0.8622PC1 -0.08649 · PC2 -0.0534PC1 -0.3332 · PC2 0.1522PC1 0.117 · PC2 -0.4607PC1 0.4203 · PC2 -0.285PC1 0.3037 · PC2 -0.9215PC1 0.06482 · PC2 0.06468PC1 0.1732 · PC2 0.2431PC1 -1.743 · PC2 0.379PC1 -1.69 · PC2 -0.05758PC1 -1.216 · PC2 -0.4247PC1 -1.346 · PC2 0.1576PC1 -0.9392 · PC2 -0.3382PC1 1.162 · PC2 0.03787PC1 -0.3526 · PC2 0.2005PC1 0.8999 · PC2 0.4981PC1 0.1059 · PC2 -0.02386PC1 0.2119 · PC2 0.22PC1 -0.01355 · PC2 0.6453PC1 0.192 · PC2 0.6379PC1 0.1976 · PC2 0.497PC1 0.1708 · PC2 0.3876PC1 0.2266 · PC2 0.5834PC1 -1.208 · PC2 0.07508PC1 -0.6641 · PC2 0.515PC1 -0.8517 · PC2 0.1567PC1 -1.352 · PC2 0.4319PC1 -0.9239 · PC2 0.1567PC1 0.9828 · PC2 -0.05722PC1 0.8725 · PC2 1.387PC1 1.306 · PC2 0.8744PC1 0.9123 · PC2 0.3597PC1 1.446 · PC2 0.9731PC1 -0.08186 · PC2 -0.8679PC1 -0.2137 · PC2 -1.253PC1 -0.2287 · PC2 -1.591PC1 -0.0895 · PC2 -0.4567PC1 -0.3198 · PC2 -0.3209PC1 -0.6178 · PC2 0.9726PC1 -0.3797 · PC2 0.9795PC1 -0.5369 · PC2 1.14PC1 -0.1295 · PC2 0.8916PC1 -0.2655 · PC2 1.018PC1 -0.3335 · PC2 -0.2558PC1 -0.3867 · PC2 -0.1403PC1 0.1154 · PC2 -0.3456PC1 -0.1551 · PC2 -0.1515PC1 0.002472 · PC2 -0.3741PC1 1.91 · PC2 -0.9036PC1 2.93 · PC2 -1.114PC1 2.874 · PC2 -1.224PC1 3.896 · PC2 -1.553PC1 3.68 · PC2 -1.658PC1 -1.588 · PC2 -1.135PC1 -2.302 · PC2 -0.008971PC1 -2.003 · PC2 -0.9065PC1 -2.764 · PC2 -0.2066PC1 -1.47 · PC2 -1.198PC1 -2.192 · PC2 -2.626PC1 -2.394 · PC2 -2.891PC1 -1.794 · PC2 -2.818PC1 -1.964 · PC2 -2.4PC1 -1.832 · PC2 -3.064PC1 -1.04 · PC2 0.8082PC1 -1.011 · PC2 0.871PC1 -1.29 · PC2 0.8335PC1 -1.057 · PC2 1.245PC1 -0.7453 · PC2 0.8772PC1 -0.5668 · PC2 -0.4392PC1 -0.4663 · PC2 0.2605PC1 -0.3456 · PC2 0.1882PC1 0.0144 · PC2 0.04304PC1 -0.546 · PC2 0.5187PC1 -0.8985 · PC2 -0.3677PC1 -0.7791 · PC2 -0.8348PC1 -0.2676 · PC2 -0.8507PC1 0.5608 · PC2 -0.8984PC1 -1.126 · PC2 -0.5718PC1 0.2595 · PC2 -1.389PC1 -0.3148 · PC2 -0.797PC1 -0.3031 · PC2 -1.275PC1 -0.4424 · PC2 -0.978PC1 -0.3394 · PC2 -1.388PC1 -0.2586 · PC2 -0.5987PC1 -0.2645 · PC2 -0.6434PC1 -0.06935 · PC2 -0.6079PC1 -0.6085 · PC2 -0.5777PC1 -0.06423 · PC2 -0.3803PC1 0.3359 · PC2 0.3605PC1 0.1724 · PC2 0.2334PC1 0.6347 · PC2 0.6371PC1 -0.009597 · PC2 0.4409PC1 -0.1643 · PC2 0.4365PC1 0.2934 · PC2 -0.2367PC1 1.017 · PC2 -0.6886PC1 0.6358 · PC2 0.2104PC1 0.8371 · PC2 -0.625PC1 1.113 · PC2 -0.278PC1 -1.124 · PC2 -0.2316PC1 -0.3468 · PC2 0.2009PC1 -0.7291 · PC2 0.3533PC1 -1.52 · PC2 1.048PC1 -0.5832 · PC2 0.1451PC1 -1.648 · PC2 0.6802PC1 -1.368 · PC2 0.5061PC1 -1.231 · PC2 -0.06017PC1 -1.762 · PC2 0.545PC1 -2.545 · PC2 0.3822PC1 1.39 · PC2 -0.6479PC1 1.094 · PC2 -0.08603PC1 4.185 · PC2 -1.467PC1 2.449 · PC2 -1.405PC1 3.123 · PC2 -1.109PC1 0.5815 · PC2 -2.547PC1 1.605 · PC2 -1.156PC1 1.419 · PC2 -2.913PC1 0.9886 · PC2 -1.083PC1 1.123 · PC2 -2.185PC1 (71.5%)PC2 (19.0%)330 scores
PCA explained variance0%25%50%75%100%PC1: 71.5% (cumulative 71.5%)1PC2: 19.0% (cumulative 90.5%)2PC3: 5.4% (cumulative 95.9%)3PC4: 1.9% (cumulative 97.7%)4PC5: 1.0% (cumulative 98.8%)5PC6: 0.4% (cumulative 99.2%)6PC7: 0.3% (cumulative 99.5%)7PC8: 0.2% (cumulative 99.7%)8PC9: 0.1% (cumulative 99.8%)9PC10: 0.1% (cumulative 99.9%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
X-Y spectral correlation 1
X · Equivalent_Water_Thickness_g_cm2 spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
Equivalent_Water_Thickness_g_cm20.7341,3980.4655.5%

Metric interpretation reference

Metric catalog 29
FamilleMétriqueCe qu’elle détecteForte valeur =Faible valeur =Causes typiquesCalcul / score
Intégrité des donnéesNaN ratioDonnées manquantesSpectre corrompuSpectre completErreur acquisition/exportcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des donnéesInf countValeurs infiniesCorruptionNormalCalculs invalidescount(isinf(X))alert = min(1, inf_count / 1)
Intégrité des donnéesZero ratioColonnes ou cellules nullesSpectre tronquéNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceNiveau moyenTrop clair / fond visibleTrop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveIntensité globaleDifférence d'éclairementNormalDistance sondetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globalePeak-to-peak (PTP)DynamiqueVariabilité forteSpectre platSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceVariabilité spectraleNormal ou hétérogèneSpectre platMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSBruit haute fréquenceBruitéStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRQualité signalBon signalMauvais signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRBruit localiséZone fiableZone problématiqueDétecteurmin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locauxSpike countPics étroitsArtefactsSpectre propreCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateDensité de picsSpectre suspectNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countDiscontinuitésRaccord détecteurContinuSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateFréquence de sautsProblème spectralNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionSaturationClippingNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopePente globaleDériveStableÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSCourbureForme inhabituelleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSVariabilité localeSpectre structuréPlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)Non expliqué par PCASpectre atypiqueConformeArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²Extrême dans PCAExtrême mais cohérentCentralVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis HDistance au nuageOutlier globalPopulation normaleDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumDistance moyenneSpectre différentTypiqueDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référenceSpectral Angle Mapper (SAM)Différence de formeForme différenteSimilaireFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDReproductibilitéMauvaise répétabilitéStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDVariation de formeInstableStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDVariabilité interneMauvais contrôleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densityClustersSous-populationsHomogèneLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)Anomalie localeSpectre isoléPopulation normaleCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scoreAnomalie globaleSpectre atypiqueNormalDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
Technology-specific extensions
TechnologieAdaptations / métriquesAnomalies cibléesCommentaire pratique
UV-Vis 300-1000 nmBaseline, pente globale, dérive aux bords 300-350 et 900-1000; métriques par zonesLumière parasite, mauvais blanc, saturation, faible signal aux extrémitésLes bords sont souvent instables; calculer aussi des scores edge/middle.
UV-Vis 300-1000 nmSaturation / clipping proche absorbance max ou réflectance maxSignal écrêtéTrès important si absorption forte.
UV-Vis 300-1000 nmRed-edge, position de maximum, ratios de bandes si végétalDécalage biologique ou artefact optiqueAide à distinguer changement réel et problème d'acquisition.
UV-Vis 300-1000 nmSmoothness / roughness indexBruit haute fréquenceSouvent plus informatif que le SNR seul.
MIR / ATR-FTIRATR contact quality index: intensité globale, aire totale, profondeur des bandes clésMauvais contact cristal-échantillonCrucial: beaucoup d'anomalies viennent du contact ATR.
MIR / ATR-FTIRCO2 / H2O atmospheric bandsMauvaise correction atmosphériquePics parasites fréquents.
MIR / ATR-FTIRBaseline curvature / rubber-band residualDiffusion, contact, dérive baselineTrès utile avant PCA.
MIR / ATR-FTIRPeak position shiftMauvais alignement spectral / calibrationImportant en FTIR car de petits shifts comptent.
MIR / ATR-FTIRBand area ratios sur bandes connuesSpectre chimiquement incohérentÀ adapter par matrice: polysaccharides, protéines, lipides, etc.
HS-MSTotal Ion Current (TIC), Base Peak Intensity (BPI)Injection faible, ionisation instableÉquivalent MS du niveau global spectral.
HS-MSNombre de pics détectésSpectre pauvre ou trop bruitéTrop peu = mauvais signal; trop = bruit/contamination.
HS-MSMass accuracy / m/z driftProblème calibration masseFondamental en HRMS.
HS-MSRetention time drift si LC/GC-MSDérive chromatographiqueÀ suivre sur standards/QC pools.
HS-MSBlank contamination scoreContaminants / carry-overComparer échantillons vs blancs.
HS-MSInternal standard CVVariabilité instrumentaleTrès robuste si standards disponibles.
HS-MSMissingness par featureInstabilité de détectionCrucial pour filtrer les variables.
Avec répétitionsRMS intra-échantillonRépétabilité globaleApplicable à toutes les technologies.
Avec répétitionsSAM / corrélation intra-échantillonRépétabilité de formeTrès utile pour spectres.
Avec répétitionsCV intra-échantillon par bande / featureRépétabilité localeDétecte les zones instables.
Avec répétitionsICC ou variance componentsPart variance échantillon vs techniqueTrès utile si plusieurs répétitions par sample.
Avec répétitionsDistance au centroïde intra-IDRépétition aberrantePermet de flagger la mauvaise répétition plutôt que le sample entier.
Bug-hunting / supervised audits
Famille de bug potentielMéthodes à ajouterCe que ça détecteÉtat dans l’explorateur
Shift spectral globalCorrélation spectre moyen inter-dataset, DTW, cross-correlation, comparaison positions de picsDécalage en longueur d'onde, mauvais alignement, interpolation différentePartiellement calculé: cross-correlation lag et dispersion des positions de pics vs spectre moyen.
Baseline / offset / gainRégression chaque spectre vs spectre moyen: x = a + b ref + residual; suivi de a, b, RMS résiduelOffset additif, effet multiplicatif, dérive de baselineCalculé dans reference.affine_*.
Mélange de lignes / mauvais appariement X-M-YVérification index, hash des lignes, duplication ID, distance spectrale intra-ID, labels incohérentsLignes mélangées, metadata mal alignées, Y attribué au mauvais spectrePartiellement couvert par répétabilité intra-ID; checks index/hash à ajouter au pipeline canonical.
Fuite d'information / répétitions mal splitéesGroupKFold par sample_id vs StratifiedKFold random; audit des partitions par sample_idPerformance artificiellement bonne due aux répétitionsNécessite splits et benchmark modèle; non calculé par la carte descriptive.
Label bugsÉchantillons proches en X mais Y différents, confident learning, erreurs systématiques FP/FNY inversés, erreurs de saisie, classes ambiguësNécessite Y et/ou modèle; recommandé pour l'explorateur supervisé.
Sous-domaines cachésPCA/UMAP/t-SNE + clustering non supervisé + association avec dataset/Y/date/operatorLots, campagnes, sondes, backgrounds non renseignésPartiellement calculé par structure PCA/LOF; UMAP/t-SNE hors carte statique.
Artefacts localisés inconnusCarte wavelength x dataset: différence moyenne, différence variance, KS par longueur d'ondeRégions spectrales anormales non anticipéesÀ calculer au niveau banque quand plusieurs datasets partagent un axe spectral.
Ruptures instrumentalesDiscontinuités dans dérivées, changepoint detectionSplice, raccord détecteur, saut local non prévuCalculé par jump/spike rates; changepoint plus avancé à ajouter.
Mélange / contamination spectraleNMF / unmixing / reconstruction par convex hullComposante externe: fond, plastique, solNon calculé automatiquement; nécessite hypothèses de composants ou grande bibliothèque.
Features instables mais prédictivesImportance modèle vs instabilité QC par variableModèle qui apprend un artefact plutôt qu'un signal biologiqueNécessite modèle supervisé; recommandé pour rapports de benchmark.

Variables

Targets 1

Equivalent_Water_Thickness_g_cm2

target · numeric
Equivalent_Water_Thickness_g_cm2 distribution02550750.0002927 – 0.002467: 100.002467 – 0.004642: 130.004642 – 0.006817: 550.006817 – 0.008992: 690.008992 – 0.01117: 600.01117 – 0.01334: 460.01334 – 0.01552: 250.01552 – 0.01769: 130.01769 – 0.01987: 80.01987 – 0.02204: 80.02204 – 0.02422: 70.02422 – 0.02639: 40.02639 – 0.02857: 30.02857 – 0.03074: 10.03074 – 0.03291: 00.03291 – 0.03509: 10.03509 – 0.03726: 30.03726 – 0.03944: 10.03944 – 0.04161: 00.04161 – 0.04379: 00.04379 – 0.04596: 10.04596 – 0.04814: 10.04814 – 0.05031: 00.05031 – 0.05249: 10.000.020.040.06
n / missing330 / 0
Mean ± SD0.01113 ± 0.00698
Median0.009486
Range0.0002927 – 0.05249
CV0.627
Skew / kurtosis2.4 / 8.3
Normal?no

Metadata 32

Refl_file

metadata · categorical
n / missing330 / 0
Classes330
Balance (entropy)1
Imbalance ratio1
Top classlo93r0001.txt (1)

English Name

metadata · categorical
English Name classesCornCorn: 3030European chestnutEuropean chestnut: 1515European ashEuropean ash: 1010Cherry laurelCherry laurel: 1010AlfalfaAlfalfa: 1010Black locustBlack locust: 1010Downy oakDowny oak: 1010Largeleaf lindenLargeleaf linden: 1010RiceRice: 1010SoybeanSoybean: 1010+10 more+10 more: 7575
n / missing330 / 0
Classes46
Balance (entropy)0.97
Imbalance ratio6
Top classCorn (30)

Latin Name

metadata · categorical
Latin Name classesZea mays L.Zea mays L.: 3030Castanea sativaCastanea sativa: 1515Fraxinus excelsior L.Fraxinus excelsior L.: 1010Prunus laurocerasus L.Prunus laurocerasus L.: 1010Medicago sativa L.Medicago sativa L.: 1010Robinia pseudoacacia L.Robinia pseudoacacia L.: 1010Quercus pubescensQuercus pubescens: 1010Tilia platyphyllosTilia platyphyllos: 1010Oryza sativa L.Oryza sativa L.: 1010Glycine max L.Glycine max L.: 1010+10 more+10 more: 7575
n / missing330 / 0
Classes46
Balance (entropy)0.97
Imbalance ratio6
Top classZea mays L. (30)

Plant Type

metadata · categorical
Plant Type classes22: 25025011: 8080
n / missing330 / 0
Classes2
Balance (entropy)0.8
Imbalance ratio3
Top class2 (250)

Structure parameter

metadata · numeric
Structure parameter distribution0200400-999 – -957.2: 10-957.2 – -915.5: 0-915.5 – -873.8: 0-873.8 – -832: 0-832 – -790.2: 0-790.2 – -748.5: 0-748.5 – -706.8: 0-706.8 – -665: 0-665 – -623.2: 0-623.2 – -581.5: 0-581.5 – -539.8: 0-539.8 – -498: 0-498 – -456.2: 0-456.2 – -414.5: 0-414.5 – -372.8: 0-372.8 – -331: 0-331 – -289.2: 0-289.2 – -247.5: 0-247.5 – -205.8: 0-205.8 – -164: 0-164 – -122.2: 0-122.2 – -80.5: 0-80.5 – -38.75: 0-38.75 – 3: 320-1,000-5000500
n / missing330 / 0
Mean ± SD-28.65 ± 172
Median1.604
Range-999 – 3
CV6
Skew / kurtosis-5.5 / 28
Normal?no

Chlorophyll_a (µg/cm²)

metadata · numeric
Chlorophyll_a (µg/cm²) distribution0100200-999 – -954.1: 10-954.1 – -909.3: 0-909.3 – -864.4: 0-864.4 – -819.6: 0-819.6 – -774.7: 0-774.7 – -729.9: 0-729.9 – -685: 0-685 – -640.1: 0-640.1 – -595.3: 0-595.3 – -550.4: 0-550.4 – -505.6: 0-505.6 – -460.7: 0-460.7 – -415.8: 0-415.8 – -371: 0-371 – -326.1: 0-326.1 – -281.3: 0-281.3 – -236.4: 0-236.4 – -191.6: 0-191.6 – -146.7: 0-146.7 – -101.8: 0-101.8 – -56.98: 0-56.98 – -12.12: 0-12.12 – 32.74: 13032.74 – 77.6: 190-1,000-5000500
n / missing330 / 0
Mean ± SD4.485 ± 178
Median33.42
Range-999 – 77.6
CV39.7
Skew / kurtosis-5.5 / 28
Normal?no

Chlorophyll_b (µg/cm²)

metadata · numeric
Chlorophyll_b (µg/cm²) distribution0200400-999 – -956.4: 10-956.4 – -913.8: 0-913.8 – -871.2: 0-871.2 – -828.6: 0-828.6 – -786: 0-786 – -743.4: 0-743.4 – -700.8: 0-700.8 – -658.2: 0-658.2 – -615.6: 0-615.6 – -573: 0-573 – -530.5: 0-530.5 – -487.9: 0-487.9 – -445.3: 0-445.3 – -402.7: 0-402.7 – -360.1: 0-360.1 – -317.5: 0-317.5 – -274.9: 0-274.9 – -232.3: 0-232.3 – -189.7: 0-189.7 – -147.1: 0-147.1 – -104.5: 0-104.5 – -61.91: 0-61.91 – -19.31: 0-19.31 – 23.28: 320-1,000-5000500
n / missing330 / 0
Mean ± SD-19.18 ± 174
Median10.85
Range-999 – 23.28
CV9.05
Skew / kurtosis-5.5 / 28
Normal?no

Chlorophyll_a+b (µg/cm²)

metadata · numeric
Chlorophyll_a+b (µg/cm²) distribution0100200300-999 – -953.3: 10-953.3 – -907.5: 0-907.5 – -861.8: 0-861.8 – -816: 0-816 – -770.3: 0-770.3 – -724.5: 0-724.5 – -678.8: 0-678.8 – -633.1: 0-633.1 – -587.3: 0-587.3 – -541.6: 0-541.6 – -495.8: 0-495.8 – -450.1: 0-450.1 – -404.4: 0-404.4 – -358.6: 0-358.6 – -312.9: 0-312.9 – -267.1: 0-267.1 – -221.4: 0-221.4 – -175.6: 0-175.6 – -129.9: 0-129.9 – -84.16: 0-84.16 – -38.42: 0-38.42 – 7.319: 57.319 – 53.06: 22053.06 – 98.8: 95-1,000-5000500
n / missing330 / 0
Mean ± SD15.57 ± 180
Median44.69
Range-999 – 98.8
CV11.6
Skew / kurtosis-5.4 / 28
Normal?no

Carotenoid (µg/cm²)

metadata · numeric
Carotenoid (µg/cm²) distribution0200400-999 – -956.2: 10-956.2 – -913.4: 0-913.4 – -870.6: 0-870.6 – -827.8: 0-827.8 – -785: 0-785 – -742.2: 0-742.2 – -699.4: 0-699.4 – -656.5: 0-656.5 – -613.7: 0-613.7 – -570.9: 0-570.9 – -528.1: 0-528.1 – -485.3: 0-485.3 – -442.5: 0-442.5 – -399.7: 0-399.7 – -356.9: 0-356.9 – -314.1: 0-314.1 – -271.3: 0-271.3 – -228.5: 0-228.5 – -185.7: 0-185.7 – -142.9: 0-142.9 – -100.1: 0-100.1 – -57.26: 0-57.26 – -14.46: 0-14.46 – 28.35: 320-1,000-5000500
n / missing330 / 0
Mean ± SD-20.28 ± 173
Median9.426
Range-999 – 28.35
CV8.55
Skew / kurtosis-5.5 / 28
Normal?no

Equivalent Water Thickness (g/cm²)

metadata · numeric
Equivalent Water Thickness (g/cm²) distribution02550750.0002927 – 0.002467: 100.002467 – 0.004642: 130.004642 – 0.006817: 550.006817 – 0.008992: 690.008992 – 0.01117: 600.01117 – 0.01334: 460.01334 – 0.01552: 250.01552 – 0.01769: 130.01769 – 0.01987: 80.01987 – 0.02204: 80.02204 – 0.02422: 70.02422 – 0.02639: 40.02639 – 0.02857: 30.02857 – 0.03074: 10.03074 – 0.03291: 00.03291 – 0.03509: 10.03509 – 0.03726: 30.03726 – 0.03944: 10.03944 – 0.04161: 00.04161 – 0.04379: 00.04379 – 0.04596: 10.04596 – 0.04814: 10.04814 – 0.05031: 00.05031 – 0.05249: 10.000.020.040.06
n / missing330 / 0
Mean ± SD0.01113 ± 0.00698
Median0.009486
Range0.0002927 – 0.05249
CV0.627
Skew / kurtosis2.4 / 8.3
Normal?no

Leaf mass per area (g/cm²)

metadata · numeric
Leaf mass per area (g/cm²) distribution02040600.001707 – 0.002292: 100.002292 – 0.002876: 310.002876 – 0.00346: 370.00346 – 0.004045: 450.004045 – 0.004629: 340.004629 – 0.005213: 300.005213 – 0.005798: 310.005798 – 0.006382: 270.006382 – 0.006966: 160.006966 – 0.007551: 210.007551 – 0.008135: 100.008135 – 0.00872: 70.00872 – 0.009304: 90.009304 – 0.009888: 50.009888 – 0.01047: 10.01047 – 0.01106: 30.01106 – 0.01164: 10.01164 – 0.01223: 40.01223 – 0.01281: 40.01281 – 0.01339: 10.01339 – 0.01398: 10.01398 – 0.01456: 00.01456 – 0.01515: 00.01515 – 0.01573: 20.0000.0050.0100.0150.020
n / missing330 / 0
Mean ± SD0.005296 ± 0.00247
Median0.004695
Range0.001707 – 0.01573
CV0.467
Skew / kurtosis1.3 / 2.2
Normal?no

Fresh Weight (g)

metadata · numeric
Fresh Weight (g) distribution02040600.0125 – 0.02198: 70.02198 – 0.03146: 130.03146 – 0.04094: 310.04094 – 0.05042: 460.05042 – 0.0599: 430.0599 – 0.06937: 530.06937 – 0.07885: 530.07885 – 0.08833: 210.08833 – 0.09781: 120.09781 – 0.1073: 70.1073 – 0.1168: 140.1168 – 0.1263: 80.1263 – 0.1357: 90.1357 – 0.1452: 20.1452 – 0.1547: 30.1547 – 0.1642: 20.1642 – 0.1736: 00.1736 – 0.1831: 20.1831 – 0.1926: 00.1926 – 0.2021: 10.2021 – 0.2116: 00.2116 – 0.221: 10.221 – 0.2305: 00.2305 – 0.24: 20.00.10.20.3
n / missing330 / 0
Mean ± SD0.06971 ± 0.0339
Median0.06515
Range0.0125 – 0.24
CV0.487
Skew / kurtosis1.7 / 4.5
Normal?no

Dry Weight (g)

metadata · numeric
Dry Weight (g) distribution02040600.007 – 0.009396: 80.009396 – 0.01179: 270.01179 – 0.01419: 340.01419 – 0.01658: 450.01658 – 0.01898: 360.01898 – 0.02138: 330.02138 – 0.02377: 320.02377 – 0.02617: 230.02617 – 0.02856: 190.02856 – 0.03096: 230.03096 – 0.03335: 70.03335 – 0.03575: 70.03575 – 0.03815: 90.03815 – 0.04054: 50.04054 – 0.04294: 10.04294 – 0.04533: 40.04533 – 0.04773: 10.04773 – 0.05013: 50.05013 – 0.05252: 40.05252 – 0.05492: 20.05492 – 0.05731: 20.05731 – 0.05971: 00.05971 – 0.0621: 00.0621 – 0.0645: 30.000.020.040.060.08
n / missing330 / 0
Mean ± SD0.0224 ± 0.0107
Median0.01965
Range0.007 – 0.0645
CV0.477
Skew / kurtosis1.4 / 2.2
Normal?no

Leaf Area (cm²)

metadata · numeric
Leaf Area (cm²) distribution01002003002.6 – 2.871: 22.871 – 3.142: 13.142 – 3.413: 13.413 – 3.683: 03.683 – 3.954: 23.954 – 4.225: 2914.225 – 4.496: 24.496 – 4.767: 44.767 – 5.037: 45.037 – 5.308: 35.308 – 5.579: 45.579 – 5.85: 25.85 – 6.121: 26.121 – 6.392: 16.392 – 6.662: 36.662 – 6.933: 16.933 – 7.204: 37.204 – 7.475: 17.475 – 7.746: 17.746 – 8.017: 18.017 – 8.287: 08.287 – 8.558: 08.558 – 8.829: 08.829 – 9.1: 112510
n / missing330 / 0
Mean ± SD4.26 ± 0.661
Median4.1
Range2.6 – 9.1
CV0.155
Skew / kurtosis3.9 / 18
Normal?no

Leaf Thickness (µm)

metadata · numeric
Leaf Thickness (µm) distribution05010070 – 107.4: 38107.4 – 144.8: 94144.8 – 182.2: 77182.2 – 219.7: 49219.7 – 257.1: 21257.1 – 294.5: 13294.5 – 331.9: 13331.9 – 369.3: 3369.3 – 406.8: 9406.8 – 444.2: 2444.2 – 481.6: 0481.6 – 519: 1519 – 556.4: 1556.4 – 593.8: 1593.8 – 631.2: 3631.2 – 668.7: 2668.7 – 706.1: 0706.1 – 743.5: 1743.5 – 780.9: 0780.9 – 818.3: 1818.3 – 855.8: 0855.8 – 893.2: 0893.2 – 930.6: 0930.6 – 968: 102505007501,000
n / missing330 / 0
Mean ± SD191.2 ± 114
Median162
Range70 – 968
CV0.595
Skew / kurtosis3.1 / 13
Normal?no

Carbon (% DW)

metadata · numeric
Carbon (% DW) distribution0200400-999 – -955.2: 25-955.2 – -911.5: 0-911.5 – -867.7: 0-867.7 – -824: 0-824 – -780.2: 0-780.2 – -736.5: 0-736.5 – -692.7: 0-692.7 – -649: 0-649 – -605.2: 0-605.2 – -561.5: 0-561.5 – -517.7: 0-517.7 – -474: 0-474 – -430.2: 0-430.2 – -386.4: 0-386.4 – -342.7: 0-342.7 – -298.9: 0-298.9 – -255.2: 0-255.2 – -211.4: 0-211.4 – -167.7: 0-167.7 – -123.9: 0-123.9 – -80.17: 0-80.17 – -36.42: 0-36.42 – 7.336: 07.336 – 51.09: 305-1,000-5000500
n / missing330 / 0
Mean ± SD-32.8 ± 277
Median46.37
Range-999 – 51.09
CV8.45
Skew / kurtosis-3.2 / 8.4
Normal?no

Hydrogen (% DW)

metadata · numeric
Hydrogen (% DW) distribution0200400-999 – -957.1: 25-957.1 – -915.1: 0-915.1 – -873.2: 0-873.2 – -831.2: 0-831.2 – -789.3: 0-789.3 – -747.3: 0-747.3 – -705.4: 0-705.4 – -663.4: 0-663.4 – -621.5: 0-621.5 – -579.5: 0-579.5 – -537.6: 0-537.6 – -495.6: 0-495.6 – -453.7: 0-453.7 – -411.7: 0-411.7 – -369.8: 0-369.8 – -327.8: 0-327.8 – -285.9: 0-285.9 – -243.9: 0-243.9 – -202: 0-202 – -160.1: 0-160.1 – -118.1: 0-118.1 – -76.16: 0-76.16 – -34.21: 0-34.21 – 7.74: 305-1,000-5000500
n / missing330 / 0
Mean ± SD-69.87 ± 266
Median6.17
Range-999 – 7.74
CV3.81
Skew / kurtosis-3.2 / 8.4
Normal?no

Oxygen (% DW)

metadata · numeric
Oxygen (% DW) distribution0200400-999 – -955.5: 25-955.5 – -912.1: 0-912.1 – -868.6: 0-868.6 – -825.2: 0-825.2 – -781.7: 0-781.7 – -738.3: 0-738.3 – -694.8: 0-694.8 – -651.4: 0-651.4 – -607.9: 0-607.9 – -564.5: 0-564.5 – -521: 0-521 – -477.6: 0-477.6 – -434.1: 0-434.1 – -390.7: 0-390.7 – -347.2: 0-347.2 – -303.7: 0-303.7 – -260.3: 0-260.3 – -216.8: 0-216.8 – -173.4: 0-173.4 – -129.9: 0-129.9 – -86.48: 0-86.48 – -43.03: 0-43.03 – 0.4267: 00.4267 – 43.88: 305-1,000-5000500
n / missing330 / 0
Mean ± SD-39.99 ± 275
Median38.55
Range-999 – 43.88
CV6.88
Skew / kurtosis-3.2 / 8.4
Normal?no

Nitrogen (% DW)

metadata · numeric
Nitrogen (% DW) distribution0200400-999 – -957.1: 25-957.1 – -915.2: 0-915.2 – -873.4: 0-873.4 – -831.5: 0-831.5 – -789.6: 0-789.6 – -747.8: 0-747.8 – -705.9: 0-705.9 – -664: 0-664 – -622.1: 0-622.1 – -580.2: 0-580.2 – -538.4: 0-538.4 – -496.5: 0-496.5 – -454.6: 0-454.6 – -412.8: 0-412.8 – -370.9: 0-370.9 – -329: 0-329 – -287.1: 0-287.1 – -245.2: 0-245.2 – -203.4: 0-203.4 – -161.5: 0-161.5 – -119.6: 0-119.6 – -77.75: 0-77.75 – -35.88: 0-35.88 – 6: 305-1,000-5000500
n / missing330 / 0
Mean ± SD-72.79 ± 266
Median2.8
Range-999 – 6
CV3.65
Skew / kurtosis-3.2 / 8.4
Normal?no

Protein_1 (% DW)

metadata · numeric
Protein_1 (% DW) distribution020407.37 – 8.557: 108.557 – 9.743: 209.743 – 10.93: 1510.93 – 12.12: 512.12 – 13.3: 1013.3 – 14.49: 2014.49 – 15.68: 2515.68 – 16.86: 1016.86 – 18.05: 4018.05 – 19.24: 1519.24 – 20.42: 1520.42 – 21.61: 2521.61 – 22.8: 1522.8 – 23.98: 523.98 – 25.17: 2025.17 – 26.36: 2526.36 – 27.54: 027.54 – 28.73: 1028.73 – 29.92: 029.92 – 31.1: 531.1 – 32.29: 1532.29 – 33.48: 533.48 – 34.66: 034.66 – 35.85: 20010203040
n / missing330 / 0
Mean ± SD19.95 ± 7.48
Median18.37
Range7.37 – 35.85
CV0.375
Skew / kurtosis0.44 / -0.5
Normal?no

Protein_2 (% DW)

metadata · numeric
Protein_2 (% DW) distribution020407.08 – 8.317: 108.317 – 9.553: 09.553 – 10.79: 1010.79 – 12.03: 2012.03 – 13.26: 513.26 – 14.5: 2014.5 – 15.74: 4015.74 – 16.97: 3516.97 – 18.21: 3518.21 – 19.45: 3019.45 – 20.68: 1520.68 – 21.92: 1021.92 – 23.16: 1523.16 – 24.39: 1024.39 – 25.63: 1025.63 – 26.87: 1526.87 – 28.1: 528.1 – 29.34: 029.34 – 30.58: 530.58 – 31.81: 1031.81 – 33.05: 533.05 – 34.29: 534.29 – 35.52: 1035.52 – 36.76: 10010203040
n / missing330 / 0
Mean ± SD19.6 ± 7.06
Median17.55
Range7.08 – 36.76
CV0.36
Skew / kurtosis0.76 / -0.076
Normal?no

Cellulose_1 (% DW)

metadata · numeric
Cellulose_1 (% DW) distribution02040602.1 – 3.246: 103.246 – 4.392: 04.392 – 5.537: 05.537 – 6.683: 06.683 – 7.829: 07.829 – 8.975: 58.975 – 10.12: 2510.12 – 11.27: 5511.27 – 12.41: 3512.41 – 13.56: 513.56 – 14.7: 2514.7 – 15.85: 1515.85 – 17: 1517 – 18.14: 2018.14 – 19.29: 1019.29 – 20.43: 020.43 – 21.58: 1521.58 – 22.73: 3022.73 – 23.87: 1523.87 – 25.02: 3025.02 – 26.16: 1026.16 – 27.31: 027.31 – 28.45: 528.45 – 29.6: 50102030
n / missing330 / 0
Mean ± SD16.2 ± 6.21
Median14.95
Range2.1 – 29.6
CV0.383
Skew / kurtosis0.14 / -0.74
Normal?no

Cellulose_2 (% DW)

metadata · numeric
Cellulose_2 (% DW) distribution02040609.06 – 10.23: 510.23 – 11.4: 1511.4 – 12.57: 512.57 – 13.75: 4013.75 – 14.92: 3014.92 – 16.09: 3016.09 – 17.26: 2517.26 – 18.43: 1518.43 – 19.6: 2019.6 – 20.77: 1020.77 – 21.94: 2021.94 – 23.12: 523.12 – 24.29: 1024.29 – 25.46: 025.46 – 26.63: 4526.63 – 27.8: 1527.8 – 28.97: 028.97 – 30.14: 2030.14 – 31.31: 531.31 – 32.48: 032.48 – 33.66: 533.66 – 34.83: 534.83 – 36: 036 – 37.17: 5125102050100
n / missing330 / 0
Mean ± SD19.97 ± 6.58
Median18.37
Range9.06 – 37.17
CV0.329
Skew / kurtosis0.52 / -0.67
Normal?no

Lignin_1 (% DW)

metadata · numeric
Lignin_1 (% DW) distribution02040600.03 – 1: 101 – 1.97: 01.97 – 2.94: 552.94 – 3.91: 403.91 – 4.88: 354.88 – 5.85: 255.85 – 6.82: 256.82 – 7.79: 57.79 – 8.76: 158.76 – 9.73: 259.73 – 10.7: 2010.7 – 11.67: 1011.67 – 12.64: 1512.64 – 13.61: 2013.61 – 14.58: 514.58 – 15.55: 015.55 – 16.52: 516.52 – 17.49: 517.49 – 18.46: 018.46 – 19.43: 019.43 – 20.4: 1020.4 – 21.37: 021.37 – 22.34: 022.34 – 23.31: 50102030
n / missing330 / 0
Mean ± SD7.309 ± 5.03
Median5.745
Range0.03 – 23.31
CV0.689
Skew / kurtosis1.1 / 0.75
Normal?no

Lignin_2 (% DW)

metadata · numeric
Lignin_2 (% DW) distribution020401.09 – 2.19: 202.19 – 3.289: 353.289 – 4.389: 154.389 – 5.488: 205.488 – 6.588: 356.588 – 7.688: 157.688 – 8.787: 58.787 – 9.887: 359.887 – 10.99: 1010.99 – 12.09: 1012.09 – 13.19: 2513.19 – 14.29: 3514.29 – 15.38: 515.38 – 16.48: 516.48 – 17.58: 2517.58 – 18.68: 518.68 – 19.78: 019.78 – 20.88: 020.88 – 21.98: 521.98 – 23.08: 1023.08 – 24.18: 024.18 – 25.28: 525.28 – 26.38: 526.38 – 27.48: 50102030
n / missing330 / 0
Mean ± SD10.22 ± 6.46
Median9.465
Range1.09 – 27.48
CV0.632
Skew / kurtosis0.71 / -0.065
Normal?no

Starch_1 (% DW)

metadata · numeric
Starch_1 (% DW) distribution0501001500 – 0.3925: 1500.3925 – 0.785: 50.785 – 1.177: 201.177 – 1.57: 351.57 – 1.963: 151.963 – 2.355: 252.355 – 2.748: 152.748 – 3.14: 103.14 – 3.533: 53.533 – 3.925: 53.925 – 4.317: 104.317 – 4.71: 154.71 – 5.103: 05.103 – 5.495: 05.495 – 5.888: 05.888 – 6.28: 06.28 – 6.673: 06.673 – 7.065: 57.065 – 7.458: 57.458 – 7.85: 07.85 – 8.242: 08.242 – 8.635: 08.635 – 9.027: 09.027 – 9.42: 100.02.55.07.510.0
n / missing330 / 0
Mean ± SD1.612 ± 2.16
Median1.065
Range0 – 9.42
CV1.34
Skew / kurtosis1.9 / 3.6
Normal?no

Starch_2 (% DW)

metadata · numeric
Starch_2 (% DW) distribution0501001500 – 0.4163: 1100.4163 – 0.8325: 350.8325 – 1.249: 301.249 – 1.665: 251.665 – 2.081: 52.081 – 2.498: 252.498 – 2.914: 52.914 – 3.33: 103.33 – 3.746: 153.746 – 4.162: 304.162 – 4.579: 04.579 – 4.995: 154.995 – 5.411: 55.411 – 5.828: 05.828 – 6.244: 06.244 – 6.66: 106.66 – 7.076: 07.076 – 7.492: 57.492 – 7.909: 07.909 – 8.325: 08.325 – 8.741: 08.741 – 9.158: 09.158 – 9.574: 09.574 – 9.99: 50.02.55.07.510.0
n / missing330 / 0
Mean ± SD1.966 ± 2.12
Median1.215
Range0 – 9.99
CV1.08
Skew / kurtosis1.4 / 1.9
Normal?no

Edited Variable Name

metadata · categorical
n / missing330 / 302
Classes28
Balance (entropy)1
Imbalance ratio1
Top classSample_# (1)

Original Variable Name

metadata · categorical
Original Variable Name classesC_star1C_star1: 22Sample_#Sample_#: 11English NameEnglish Name: 11Latin NameLatin Name: 11Plant TypePlant Type: 11NN: 11C_aC_a: 11C_bC_b: 11C_abC_ab: 11C_carC_car: 11+10 more+10 more: 1010
n / missing330 / 302
Classes27
Balance (entropy)1
Imbalance ratio2
Top classC_star1 (2)

Comments

metadata · categorical
n / missing330 / 302
Classes28
Balance (entropy)1
Imbalance ratio1
Top classNumber assigned to each sample in order to link reflectance and transmittance measurements (1)

raw_label

metadata · categorical
raw_label classes0.0122926829268292690.012292682926829269: 330.008292682926829270.00829268292682927: 330.0068536585365853670.006853658536585367: 330.0111219512195121960.011121951219512196: 330.00765853658536585350.0076585365853658535: 330.0088536585365853670.008853658536585367: 330.00665853658536585350.0066585365853658535: 330.0049512195121951220.004951219512195122: 330.0123902439024390250.012390243902439025: 220.0133902439024390240.013390243902439024: 22+10 more+10 more: 2020
n / missing330 / 0
Classes298
Balance (entropy)0.99
Imbalance ratio3
Top class0.012292682926829269 (3)

reference_value

metadata · numeric
reference_value distribution02550750.0002927 – 0.002467: 100.002467 – 0.004642: 130.004642 – 0.006817: 550.006817 – 0.008992: 690.008992 – 0.01117: 600.01117 – 0.01334: 460.01334 – 0.01552: 250.01552 – 0.01769: 130.01769 – 0.01987: 80.01987 – 0.02204: 80.02204 – 0.02422: 70.02422 – 0.02639: 40.02639 – 0.02857: 30.02857 – 0.03074: 10.03074 – 0.03291: 00.03291 – 0.03509: 10.03509 – 0.03726: 30.03726 – 0.03944: 10.03944 – 0.04161: 00.04161 – 0.04379: 00.04379 – 0.04596: 10.04596 – 0.04814: 10.04814 – 0.05031: 00.05031 – 0.05249: 10.000.020.040.06
n / missing330 / 0
Mean ± SD0.01113 ± 0.00698
Median0.009486
Range0.0002927 – 0.05249
CV0.627
Skew / kurtosis2.4 / 8.3
Normal?no
Constant metadata 19
  • SpectralRep1
  • Measurement_typereflectance
  • Anthocyanin-999
  • datasetEcoSIS
  • dataset_slugleaf-optical-properties-experiment-database--lopex93-
  • task_typeregression
  • trait_headerEquivalent_Water_Thickness_g_cm2
  • target_original_nameEquivalent Water Thickness (g/cm²)
  • measurement_hintLopex1993_Reflectance__Lopex_Refl
  • n_points_original2,101
  • dimensions1D
  • axis_notewavelengths_as_provided_by_EcoSIS_resource
  • official_data_urlhttps://data.ecosis.org/dataset/leaf-optical-properties-experiment-database--lopex93-
  • official_package_urlhttps://ecosis.org/package/leaf-optical-properties-experiment-database--lopex93-
  • licenseOther (Open)
  • rights_statusexplicit_open
  • usage_scopepublic_reuse_possible
  • public_release_allowedTrue
  • notesTask-specific EcoSIS target not present in active parent Y table, wavelengths kept as provided by EcoSIS resource, no project interpolation.

4 variable(s) omitted (no recorded values).

Alignment

Alignment levelsample
Sample id availableyes
Samples330
Observations (total)330
Reps per samplemin 1 · mean 1 · max 1

Provenance & citation

ContributorLeaf Optical Properties Experiment Database (LOPEX93)
Origin · url [open]https://data.ecosis.org/dataset/leaf-optical-properties-experiment-database--lopex93-
Origin · url [open]https://data.ecosis.org/api/3/action/package_show?id=leaf-optical-properties-experiment-database--lopex93-
Origin · url [open]https://ecosis.org/package/leaf-optical-properties-experiment-database--lopex93-
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)

Governance & integrity

Tierprivate
LicenseLicenseRef-not-cleared
Permitted useResearch and benchmarking.
Access policyManual download / private-use-only per source.
RedistributionEcoSIS CKAN API package_show verified open license: Other (Open).
Content version1.0.0
Schema / protocol2.0
Content hashb03ab74d2ca189cb…
Processing hash63d966f6140b63a7…
Metadata hashd4e8b924ea86ab47…

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

# private dataset — export requires a Dataverse token
ds = get("leaf_equivalent_water_thickness_g_cm2_reflectance_refl_optical_ecosis", token="…")
X, y = ds.x(), ds.y()
print(X.shape, y.shape)

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