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EcoSIS UW-BNL NASA HyspIRI Airborne Campaign Leaf and Canopy Spectra and Trait Data (N_mass_g_g)

leaf · NIR

EcoSIS UW-BNL NASA HyspIRI Airborne Campaign Leaf and Canopy Spectra and Trait Data (N_mass_g_g). v2.0 standardized NIRS package: 1 spectral source(s), 1 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2leaf
261
samples
2,151
wavelengths
1
sources
1
targets
101
metadata
NIR
family

Dataset property explorer

Mean profile risk0.50
Highest axisArtefacts locaux · 1.00
Diagnostics8
Sources profiled1
EcoSIS UW-BNL NASA HyspIRI Airborne Campaign Leaf and Canopy Spectra and Trait Data (N_mass_g_g) property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS UW-BNL NASA HyspIRI Airborne Campaign Leaf and Canopy Spectra and Trait Data (N_mass_g_g) profileintegrity: 0.00noise: 0.00artefacts: 1.00baseline: 0.98PCA outliers: 0.55reference: 0.60repeatability: 0.25structure: 0.64EcoSIS UW-BNL N…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux1.00
Bruit0.00
Outliers PCA0.55
Distance à la référence0.60
Répétabilité0.25
Baseline / forme0.98
Structure multi-régimes0.64
Diagnostic hypotheses00.250.50.751hypothesis scoreSplice / raccord détecteursSplice / raccord détecteurs: 0.770.77Erreur calibration / référenc…Erreur calibration / référence blanche: 0.650.65Erreur interpolation / réécha…Erreur interpolation / rééchantillonnage: 0.640.64Signature VERA25-likeSignature VERA25-like: 0.600.60Fond différentFond différent: 0.580.58Différence de sonde / géométr…Différence de sonde / géométrie: 0.550.55Mélange feuille + fondMélange feuille + fond: 0.450.45Dataset multi-régimesDataset multi-régimes: 0.430.43
DiagnosticScoreForceSignauxInterprétation probable
Splice / raccord détecteursX0.77forteSpike rate 1.00, Jump rate 1.00, SNR non dégradé 1.00Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Erreur calibration / référence blancheX0.65moyenneartefacts locaux 1.00, Baseline/mean/area 0.98, RMS/SAM référence 0.60Décalage systématique entre campagnes, instruments ou référence blanche.
Erreur interpolation / rééchantillonnageX0.64moyenneSpike rate 1.00, Jump rate 1.00, SNR normal/élevé 1.00Artefacts numériques ou traitement spectral incorrect.
Signature VERA25-likeX0.60moyenneSpike rate 1.00, Jump rate 1.00, RMS/SAM référence 0.60Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Fond différentX0.58moyenneBaseline/mean/area 0.98, RMS/SAM référence 0.60, PCA Q 0.55Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.
Différence de sonde / géométrieX0.55moyenneBaseline/mean/area 0.98, RMS/SAM référence 0.60, PCA Q 0.55Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Mélange feuille + fondX0.45moyenneBaseline/mean/area 0.98, RMS/SAM référence 0.60, PCA Q 0.55Couverture partielle du spot; contribution du fond ou du support.
Dataset multi-régimesX0.43moyenneStructure PCA 0.64, RMS/SAM référence 0.60, PCA Q 0.55Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.

Spectral sources

UW-BNL_NASA_HyspIRI_Airborne_Campaign_Compiled_Leaf_and_Canopy_Spectra

X · NIR · SpecEvo_PSM3500
UW-BNL_NASA_HyspIRI_Airborne_Campaign_Compiled_Leaf_and_Canopy_Spectra spectra0.00.20.40.60.801,0002,0003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm350nm — median 0.165 (q25–q75 0.06265–0.2229)365nm — median 0.1121 (q25–q75 0.05081–0.1503)381nm — median 0.07979 (q25–q75 0.04548–0.1053)396nm — median 0.05383 (q25–q75 0.04067–0.07562)412nm — median 0.05084 (q25–q75 0.03984–0.07212)427nm — median 0.04919 (q25–q75 0.03935–0.0718)443nm — median 0.05278 (q25–q75 0.041–0.0777)458nm — median 0.05438 (q25–q75 0.04242–0.07968)474nm — median 0.05576 (q25–q75 0.04286–0.08099)489nm — median 0.05673 (q25–q75 0.04317–0.08006)505nm — median 0.06705 (q25–q75 0.04853–0.09008)520nm — median 0.101 (q25–q75 0.0735–0.128)536nm — median 0.1383 (q25–q75 0.1083–0.1691)551nm — median 0.1494 (q25–q75 0.1188–0.1832)567nm — median 0.1353 (q25–q75 0.1053–0.1683)582nm — median 0.1111 (q25–q75 0.08309–0.1412)597nm — median 0.1017 (q25–q75 0.07397–0.13)613nm — median 0.09016 (q25–q75 0.06442–0.116)628nm — median 0.08238 (q25–q75 0.05932–0.105)644nm — median 0.07252 (q25–q75 0.05367–0.09586)659nm — median 0.06149 (q25–q75 0.04672–0.08014)675nm — median 0.05354 (q25–q75 0.04257–0.07165)690nm — median 0.076 (q25–q75 0.05727–0.09665)706nm — median 0.212 (q25–q75 0.1765–0.2483)721nm — median 0.3646 (q25–q75 0.3258–0.4045)737nm — median 0.4804 (q25–q75 0.4403–0.527)752nm — median 0.5227 (q25–q75 0.4775–0.5709)768nm — median 0.531 (q25–q75 0.4856–0.584)783nm — median 0.5307 (q25–q75 0.4867–0.5846)799nm — median 0.5313 (q25–q75 0.4873–0.5852)814nm — median 0.5314 (q25–q75 0.487–0.5858)829nm — median 0.531 (q25–q75 0.4873–0.5857)845nm — median 0.5301 (q25–q75 0.4876–0.5863)860nm — median 0.53 (q25–q75 0.488–0.5867)876nm — median 0.5297 (q25–q75 0.4875–0.5866)891nm — median 0.5281 (q25–q75 0.4875–0.5852)907nm — median 0.5264 (q25–q75 0.4866–0.5832)922nm — median 0.5248 (q25–q75 0.4854–0.5807)938nm — median 0.5209 (q25–q75 0.4818–0.5748)953nm — median 0.5112 (q25–q75 0.4758–0.5639)969nm — median 0.5041 (q25–q75 0.4678–0.5541)984nm — median 0.5019 (q25–q75 0.4655–0.5509)1,000nm — median 0.5002 (q25–q75 0.4634–0.5508)1,015nm — median 0.5072 (q25–q75 0.4699–0.5583)1,031nm — median 0.513 (q25–q75 0.473–0.5651)1,046nm — median 0.5163 (q25–q75 0.475–0.5694)1,062nm — median 0.5182 (q25–q75 0.4762–0.5715)1,077nm — median 0.5182 (q25–q75 0.4759–0.5714)1,092nm — median 0.5166 (q25–q75 0.4746–0.5697)1,108nm — median 0.5135 (q25–q75 0.4716–0.5661)1,123nm — median 0.5066 (q25–q75 0.4683–0.5596)1,139nm — median 0.4917 (q25–q75 0.4526–0.5377)1,154nm — median 0.4683 (q25–q75 0.4357–0.5073)1,170nm — median 0.4582 (q25–q75 0.4274–0.4936)1,185nm — median 0.4526 (q25–q75 0.4234–0.4863)1,201nm — median 0.4505 (q25–q75 0.4233–0.4832)1,216nm — median 0.4527 (q25–q75 0.4257–0.4859)1,232nm — median 0.4573 (q25–q75 0.429–0.4947)1,247nm — median 0.4612 (q25–q75 0.4308–0.5006)1,263nm — median 0.4636 (q25–q75 0.432–0.5038)1,278nm — median 0.462 (q25–q75 0.4308–0.5024)1,294nm — median 0.4578 (q25–q75 0.427–0.4953)1,309nm — median 0.4467 (q25–q75 0.4177–0.4819)1,324nm — median 0.4295 (q25–q75 0.4021–0.4587)1,340nm — median 0.4024 (q25–q75 0.3804–0.4314)1,355nm — median 0.3802 (q25–q75 0.3577–0.4078)1,371nm — median 0.3498 (q25–q75 0.3256–0.379)1,386nm — median 0.2912 (q25–q75 0.2607–0.323)1,402nm — median 0.2064 (q25–q75 0.1776–0.2324)1,417nm — median 0.1579 (q25–q75 0.133–0.177)1,433nm — median 0.1353 (q25–q75 0.1174–0.1523)1,448nm — median 0.1294 (q25–q75 0.1131–0.1455)1,464nm — median 0.1316 (q25–q75 0.115–0.1476)1,479nm — median 0.1413 (q25–q75 0.1247–0.1591)1,495nm — median 0.158 (q25–q75 0.1389–0.1764)1,510nm — median 0.1761 (q25–q75 0.1546–0.1948)1,526nm — median 0.1935 (q25–q75 0.1707–0.2139)1,541nm — median 0.2083 (q25–q75 0.1846–0.2301)1,556nm — median 0.2227 (q25–q75 0.1975–0.2454)1,572nm — median 0.2364 (q25–q75 0.2101–0.2583)1,587nm — median 0.2482 (q25–q75 0.2215–0.2707)1,603nm — median 0.2592 (q25–q75 0.2327–0.2822)1,618nm — median 0.269 (q25–q75 0.2425–0.2918)1,634nm — median 0.277 (q25–q75 0.2502–0.2992)1,649nm — median 0.281 (q25–q75 0.2537–0.303)1,665nm — median 0.281 (q25–q75 0.2527–0.3045)1,680nm — median 0.2786 (q25–q75 0.2495–0.3026)1,696nm — median 0.2715 (q25–q75 0.2416–0.2957)1,711nm — median 0.2638 (q25–q75 0.2342–0.2873)1,727nm — median 0.2558 (q25–q75 0.2258–0.2788)1,742nm — median 0.2501 (q25–q75 0.2196–0.2733)1,758nm — median 0.2413 (q25–q75 0.2113–0.2647)1,773nm — median 0.2343 (q25–q75 0.2062–0.2577)1,788nm — median 0.2315 (q25–q75 0.2052–0.255)1,804nm — median 0.233 (q25–q75 0.2066–0.2558)1,819nm — median 0.2346 (q25–q75 0.2083–0.257)1,835nm — median 0.2327 (q25–q75 0.207–0.2548)1,850nm — median 0.2204 (q25–q75 0.1938–0.2432)1,866nm — median 0.1869 (q25–q75 0.1596–0.2077)1,881nm — median 0.1233 (q25–q75 0.1001–0.14)1,897nm — median 0.05103 (q25–q75 0.04128–0.06283)1,912nm — median 0.03624 (q25–q75 0.02893–0.04781)1,928nm — median 0.03334 (q25–q75 0.02631–0.04473)1,943nm — median 0.03466 (q25–q75 0.02774–0.04664)1,959nm — median 0.03881 (q25–q75 0.03133–0.05169)1,974nm — median 0.0438 (q25–q75 0.03571–0.0578)1,990nm — median 0.05188 (q25–q75 0.0425–0.06611)2,005nm — median 0.06013 (q25–q75 0.04927–0.07371)2,021nm — median 0.06868 (q25–q75 0.05677–0.08334)2,036nm — median 0.07504 (q25–q75 0.06307–0.08973)2,051nm — median 0.08063 (q25–q75 0.06738–0.09658)2,067nm — median 0.08767 (q25–q75 0.07219–0.1034)2,082nm — median 0.09391 (q25–q75 0.07666–0.1102)2,098nm — median 0.09858 (q25–q75 0.0811–0.117)2,113nm — median 0.1037 (q25–q75 0.08568–0.1227)2,129nm — median 0.1091 (q25–q75 0.08948–0.1288)2,144nm — median 0.1136 (q25–q75 0.09295–0.1329)2,160nm — median 0.1182 (q25–q75 0.09783–0.1382)2,175nm — median 0.1218 (q25–q75 0.1015–0.142)2,191nm — median 0.1258 (q25–q75 0.1056–0.1465)2,206nm — median 0.1288 (q25–q75 0.1088–0.1493)2,222nm — median 0.1291 (q25–q75 0.1097–0.1503)2,237nm — median 0.1256 (q25–q75 0.1065–0.1473)2,253nm — median 0.117 (q25–q75 0.09755–0.1388)2,268nm — median 0.1082 (q25–q75 0.08795–0.1296)2,283nm — median 0.1017 (q25–q75 0.08236–0.1216)2,299nm — median 0.09346 (q25–q75 0.07605–0.1134)2,314nm — median 0.08845 (q25–q75 0.07139–0.1069)2,330nm — median 0.08628 (q25–q75 0.06887–0.1031)2,345nm — median 0.08041 (q25–q75 0.06489–0.09676)2,361nm — median 0.07685 (q25–q75 0.06184–0.09158)2,376nm — median 0.07208 (q25–q75 0.05769–0.086)2,392nm — median 0.06678 (q25–q75 0.05372–0.08018)2,407nm — median 0.06216 (q25–q75 0.04961–0.0748)2,423nm — median 0.05636 (q25–q75 0.04491–0.06887)2,438nm — median 0.05204 (q25–q75 0.04075–0.06403)2,454nm — median 0.04644 (q25–q75 0.03633–0.05848)2,469nm — median 0.04248 (q25–q75 0.03263–0.05496)2,485nm — median 0.04143 (q25–q75 0.03238–0.05301)2,500nm — median 0.04431 (q25–q75 0.0349–0.05598)

Sampling

Wavelengths2,151
Axis range350–2,500 nm
Mean spacing1 nm
Griduniform
Observations358

Signal & quality

Value range0.0108 – 0.731
Mean range0.0375 – 0.539
Mean level0.243
Area522.6
PTP0.5014
Noise RMS3.1484e-05
SNR7.7e+03
SNR dB8e+01 dB
Dynamic range0.501
Smoothness0.0005686
Saturated0.0%
X-outliers166

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.00%
Spike count45,910
Spike rate5.97%
Jump count26,936
Jump rate3.50%
Clip fraction0.00%

Shape & reference

Baseline slope-0.24544
Curvature RMS0.00053237
D1 RMS0.0018393
RMS to mean0.034878
RMS p950.067422
SAM to mean0.07483
SAM p950.13442
Affine offset p950.047102
Affine gain p95 Δ0.23476
Affine residual p950.033995
Xcorr lag p953

Outliers & repeatability

PCA Q p95/median4.4
Hotelling T2 p95/median2.5
Mahalanobis H p95/median1.6
Repeat groups97
RMS intra-ID0.012719
SAM intra-ID0.029152
CV intra-ID0.048217

Dimensionality (PCA)

Effective rank3.2
PCs → 95% var4
PCs → 99% var7
Top-10 cum. var99.7%
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_reflectance0.243020.98fortValeur 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_curve522.650.98fortValeur 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_peak0.501410.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance0.0341050.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms3.1484e-050.00faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr77190.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min29.2350.16faibleZone 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_count45,9101.00fortArtefactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate5.97%1.00fortSpectre suspectInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count26,9361.00fortRaccord détecteurSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate3.5%1.00fortProblème spectralCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction0.00026%0.00faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope-0.245440.98fortDériveÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.000532370.11faibleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.00183930.07faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio4.37940.55moyenSpectre atypiqueArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio2.45230.31faibleCentralVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio1.5660.39faiblePopulation normaleDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p950.0674220.54moyenSpectre 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_p950.134420.38faibleSimilaireFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDrepeatability.rms_intra_id0.0127190.25faibleStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDrepeatability.sam_intra_id0.0291520.19faibleStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDrepeatability.cv_intra_id0.0482170.19faibleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densitystructure.pca_score_density1.86550.64moyenSous-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_p951.92590.46moyenSpectre 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_p950.541140.64moyenSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X PCA score plot-5.0-2.50.02.55.07.5-4-2024PC1 -1.583 · PC2 -1.181PC1 -0.4294 · PC2 -0.5169PC1 -0.4797 · PC2 0.2974PC1 -0.05471 · PC2 -0.552PC1 -0.6397 · PC2 -0.8076PC1 -0.817 · PC2 -0.812PC1 -0.9753 · PC2 -0.9458PC1 2.225 · PC2 -0.9065PC1 1.258 · PC2 -1.377PC1 1.5 · PC2 -1.652PC1 1.177 · PC2 -0.6061PC1 1.868 · PC2 -0.5413PC1 1.298 · PC2 -1.046PC1 1.351 · PC2 -0.8021PC1 1.784 · PC2 -0.831PC1 1.828 · PC2 -0.7323PC1 -0.03538 · PC2 -1.492PC1 0.9077 · PC2 -1.597PC1 0.558 · PC2 -0.5676PC1 1.897 · PC2 -0.3744PC1 1.199 · PC2 -1.151PC1 0.6486 · PC2 -0.8538PC1 0.7462 · PC2 -1.098PC1 1.095 · PC2 -0.8666PC1 -1.441 · PC2 -0.8963PC1 -1.366 · PC2 -0.6634PC1 -1.921 · PC2 -0.3725PC1 -2.847 · PC2 -1.194PC1 -2.091 · PC2 -1.826PC1 -3.506 · PC2 -1.347PC1 -3.26 · PC2 -1.661PC1 -1.296 · PC2 -1.688PC1 0.3941 · PC2 -0.7898PC1 0.4692 · PC2 -0.08168PC1 0.5191 · PC2 -0.9229PC1 0.8061 · PC2 -0.5932PC1 0.6379 · PC2 -0.4061PC1 0.2609 · PC2 -0.05037PC1 -1.238 · PC2 -0.1245PC1 -1.28 · PC2 -0.1586PC1 -1.025 · PC2 0.1482PC1 -0.2486 · PC2 0.135PC1 -0.6045 · PC2 -0.2236PC1 0.05778 · PC2 0.1743PC1 -1.812 · PC2 -1.034PC1 -1.24 · PC2 -0.3567PC1 -0.3927 · PC2 -1.039PC1 -0.331 · PC2 -1.044PC1 -1.387 · PC2 -1.378PC1 -0.5812 · PC2 -1.209PC1 -0.7582 · PC2 -0.6099PC1 -0.7079 · PC2 -0.5356PC1 -1.734 · PC2 -0.8763PC1 1.81 · PC2 -0.1386PC1 0.4959 · PC2 -0.4437PC1 2.245 · PC2 1.088PC1 0.9732 · PC2 0.6509PC1 0.5485 · PC2 -1.732PC1 0.8642 · PC2 -1.596PC1 0.04459 · PC2 -1.332PC1 0.6539 · PC2 -1.289PC1 0.7233 · PC2 -0.4751PC1 1.479 · PC2 -0.5873PC1 1.54 · PC2 -0.8397PC1 1.234 · PC2 -0.1357PC1 1.525 · PC2 -0.354PC1 0.02214 · PC2 -0.5204PC1 0.9778 · PC2 -0.6477PC1 0.9092 · PC2 -0.695PC1 -0.1438 · PC2 -0.8459PC1 -1.74 · PC2 -2.688PC1 -0.2616 · PC2 -2.197PC1 -0.6417 · PC2 -0.8355PC1 1.815 · PC2 -0.5968PC1 1.796 · PC2 0.1239PC1 1.448 · PC2 0.6151PC1 -0.8885 · PC2 0.1354PC1 0.5244 · PC2 -0.1594PC1 3.733 · PC2 -0.4084PC1 5.428 · PC2 0.149PC1 2.129 · PC2 0.6456PC1 -1.655 · PC2 -0.2436PC1 -1.331 · PC2 -1.244PC1 0.1068 · PC2 1.582PC1 1.978 · PC2 1.922PC1 -0.8896 · PC2 1.537PC1 -0.09496 · PC2 1.838PC1 -1.418 · PC2 1.961PC1 1.265 · PC2 1.623PC1 -1.866 · PC2 1.569PC1 -1.563 · PC2 1.075PC1 2.597 · PC2 2.127PC1 -1.039 · PC2 1.668PC1 -0.8238 · PC2 0.4242PC1 -0.4185 · PC2 1.515PC1 -0.4427 · PC2 1.277PC1 3.286 · PC2 1.479PC1 -0.7844 · PC2 1.557PC1 1.277 · PC2 0.7837PC1 -0.8531 · PC2 0.4494PC1 -0.9297 · PC2 -0.7746PC1 -0.8237 · PC2 0.2966PC1 0.6395 · PC2 0.3184PC1 -0.7453 · PC2 0.97PC1 -1.22 · PC2 1.218PC1 -0.4695 · PC2 0.825PC1 -2.785 · PC2 1.276PC1 -0.4143 · PC2 1.097PC1 5.018 · PC2 1.381PC1 -0.91 · PC2 1.267PC1 -0.6681 · PC2 0.205PC1 -0.3304 · PC2 0.1491PC1 -0.9015 · PC2 -1.08PC1 -0.234 · PC2 -0.1217PC1 -0.8846 · PC2 0.507PC1 -1.784 · PC2 0.5632PC1 -2.966 · PC2 0.6431PC1 -0.1048 · PC2 0.6908PC1 0.4825 · PC2 0.4609PC1 1.209 · PC2 -1.192PC1 -0.5643 · PC2 0.6907PC1 0.117 · PC2 -0.1245PC1 0.3445 · PC2 -0.2121PC1 -3.379 · PC2 -0.6256PC1 0.3994 · PC2 0.2309PC1 -2.31 · PC2 -0.6732PC1 0.8507 · PC2 -0.8552PC1 0.2082 · PC2 -0.7277PC1 2.03 · PC2 0.8121PC1 2.15 · PC2 0.4701PC1 -1.545 · PC2 0.369PC1 -1.331 · PC2 0.8687PC1 0.6636 · PC2 1.09PC1 1.654 · PC2 0.9917PC1 0.3744 · PC2 -0.6072PC1 -0.8625 · PC2 -0.5179PC1 -3.913 · PC2 -1.299PC1 0.2691 · PC2 0.7766PC1 1.501 · PC2 -0.8038PC1 0.905 · PC2 1.401PC1 -2.094 · PC2 0.9794PC1 -0.4145 · PC2 1.168PC1 -0.3756 · PC2 0.7771PC1 -1.383 · PC2 0.3459PC1 0.4715 · PC2 0.4955PC1 0.2639 · PC2 0.4269PC1 0.49 · PC2 -0.8906PC1 -0.09503 · PC2 -0.9561PC1 0.6294 · PC2 -1.246PC1 0.5193 · PC2 -1.023PC1 0.5595 · PC2 -0.6906PC1 0.7136 · PC2 -1.696PC1 0.8398 · PC2 -1.247PC1 1.178 · PC2 -0.9987PC1 1.337 · PC2 -0.1274PC1 1.062 · PC2 -0.3098PC1 1.629 · PC2 -0.2334PC1 1.578 · PC2 -0.9858PC1 1.154 · PC2 -1.261PC1 1.455 · PC2 -0.671PC1 -2.203 · PC2 -0.7248PC1 1.04 · PC2 -0.02099PC1 -1.847 · PC2 -0.3879PC1 3.24 · PC2 0.8389PC1 1.284 · PC2 0.1423PC1 0.4963 · PC2 -0.3486PC1 -0.5756 · PC2 -0.1534PC1 -0.2706 · PC2 0.7127PC1 2.26 · PC2 1.282PC1 -1.671 · PC2 0.5592PC1 1.196 · PC2 0.7335PC1 0.1412 · PC2 1.169PC1 1.128 · PC2 0.07019PC1 -0.2961 · PC2 -0.6271PC1 -0.7717 · PC2 -0.5322PC1 -0.2452 · PC2 -0.6748PC1 0.5674 · PC2 -0.9381PC1 1.636 · PC2 1.58PC1 0.9688 · PC2 1.833PC1 1.78 · PC2 1.896PC1 2.659 · PC2 1.49PC1 1.477 · PC2 0.962PC1 -0.4398 · PC2 1.452PC1 -0.06387 · PC2 0.9658PC1 -4.343 · PC2 0.925PC1 0.6004 · PC2 -0.8226PC1 0.1481 · PC2 1.282PC1 -1.358 · PC2 1.774PC1 -0.1922 · PC2 1.32PC1 0.463 · PC2 -0.251PC1 0.72 · PC2 -0.6185PC1 -2.572 · PC2 0.8847PC1 -1.758 · PC2 0.8573PC1 -2.029 · PC2 0.5145PC1 0.1433 · PC2 -0.2255PC1 -0.9502 · PC2 -0.9943PC1 -1.918 · PC2 1.238PC1 0.3772 · PC2 1.327PC1 -0.2984 · PC2 -0.8072PC1 0.111 · PC2 0.3449PC1 0.8743 · PC2 -0.69PC1 0.9067 · PC2 -0.5089PC1 0.8348 · PC2 -0.2553PC1 1.774 · PC2 -0.2958PC1 1.846 · PC2 -0.2831PC1 1.121 · PC2 -0.2963PC1 1.426 · PC2 0.3006PC1 0.7695 · PC2 1.082PC1 -0.9743 · PC2 1.439PC1 0.8691 · PC2 -0.4438PC1 1.366 · PC2 -0.1606PC1 1.496 · PC2 0.2122PC1 -3.374 · PC2 0.9974PC1 -1.108 · PC2 -0.6638PC1 -1.432 · PC2 -0.4129PC1 1.377 · PC2 0.3345PC1 1.05 · PC2 0.6101PC1 0.9321 · PC2 -0.1882PC1 1.632 · PC2 0.03002PC1 0.5524 · PC2 -0.2604PC1 0.4031 · PC2 -0.4465PC1 -0.2084 · PC2 -0.3056PC1 1.821 · PC2 -0.07736PC1 1.031 · PC2 0.6165PC1 1.148 · PC2 0.1PC1 1.399 · PC2 -0.4735PC1 0.0882 · PC2 -0.4727PC1 1.747 · PC2 -0.2245PC1 1.075 · PC2 0.05841PC1 -1.714 · PC2 -0.1979PC1 -0.2041 · PC2 -0.4265PC1 -0.9707 · PC2 -0.312PC1 -1.82 · PC2 -0.079PC1 -2.301 · PC2 0.05064PC1 -0.6099 · PC2 1.082PC1 -1.235 · PC2 -0.1212PC1 -2.703 · PC2 -0.599PC1 0.3881 · PC2 0.7882PC1 -1.865 · PC2 -0.4343PC1 -2.374 · PC2 -1.14PC1 -0.8384 · PC2 -0.6566PC1 -2.283 · PC2 -0.6239PC1 -1.298 · PC2 0.1498PC1 -2.612 · PC2 -0.2652PC1 -1.428 · PC2 -0.2602PC1 -2.862 · PC2 -0.8549PC1 0.1115 · PC2 -0.01291PC1 -2.974 · PC2 0.1553PC1 -1.892 · PC2 -0.2892PC1 -0.9977 · PC2 1.199PC1 -1.755 · PC2 -0.1602PC1 1.246 · PC2 0.2082PC1 -2.265 · PC2 -0.2532PC1 -2.132 · PC2 -0.4671PC1 -2.07 · PC2 -0.261PC1 -1.074 · PC2 -0.452PC1 0.5818 · PC2 0.3995PC1 1.368 · PC2 0.1858PC1 1.479 · PC2 0.1757PC1 -0.6422 · PC2 -0.6502PC1 1.261 · PC2 -1.386PC1 1.502 · PC2 -0.9761PC1 0.7907 · PC2 -1.009PC1 1.803 · PC2 -0.1723PC1 0.82 · PC2 0.202PC1 0.8746 · PC2 0.7321PC1 1.414 · PC2 -0.1647PC1 1.452 · PC2 -0.384PC1 1.215 · PC2 -2.17PC1 1.999 · PC2 1.257PC1 1.408 · PC2 0.7388PC1 1.218 · PC2 0.2188PC1 1.864 · PC2 0.3326PC1 -1.558 · PC2 -0.8291PC1 -1.828 · PC2 0.7496PC1 1.038 · PC2 -1.235PC1 0.5381 · PC2 -1.158PC1 0.6877 · PC2 -1.179PC1 2.039 · PC2 -0.04811PC1 1.23 · PC2 0.01374PC1 2.14 · PC2 0.3636PC1 1.372 · PC2 -0.02037PC1 1.733 · PC2 -0.4687PC1 0.4557 · PC2 -2.678PC1 1.992 · PC2 1.344PC1 1.591 · PC2 0.626PC1 0.9608 · PC2 0.1344PC1 -1.54 · PC2 0.5704PC1 -1.049 · PC2 0.3725PC1 -0.5444 · PC2 1.668PC1 0.363 · PC2 -0.2089PC1 -0.1905 · PC2 0.07459PC1 -1.515 · PC2 -0.01804PC1 -0.521 · PC2 1.348PC1 -1.42 · PC2 0.3996PC1 -1.814 · PC2 0.9296PC1 0.8559 · PC2 -0.6916PC1 -1.064 · PC2 0.8555PC1 -1.861 · PC2 1.049PC1 0.9011 · PC2 0.8094PC1 -0.4712 · PC2 -0.06467PC1 -3.79 · PC2 0.7786PC1 1.947 · PC2 -0.7337PC1 -3.495 · PC2 0.2296PC1 -1.11 · PC2 0.5906PC1 1.779 · PC2 0.4147PC1 0.5717 · PC2 1.034PC1 1.223 · PC2 -1.45PC1 2.571 · PC2 -0.1537PC1 1.535 · PC2 -0.3728PC1 1.087 · PC2 -2.328PC1 0.5678 · PC2 -0.7102PC1 1.678 · PC2 -2.36PC1 2.456 · PC2 0.8175PC1 1.194 · PC2 0.6637PC1 0.3593 · PC2 -0.1543PC1 -0.4404 · PC2 0.2705PC1 -1.559 · PC2 -0.3295PC1 -0.4105 · PC2 0.1772PC1 -0.7266 · PC2 -0.09749PC1 -0.4733 · PC2 1.258PC1 -0.5022 · PC2 1.404PC1 0.8897 · PC2 0.5383PC1 0.3738 · PC2 0.8146PC1 -0.5754 · PC2 1.411PC1 -0.4035 · PC2 -0.6119PC1 0.3834 · PC2 -0.6211PC1 -1.237 · PC2 0.8779PC1 -1.28 · PC2 1.214PC1 -0.9811 · PC2 1.353PC1 -1.017 · PC2 1.559PC1 -1.787 · PC2 1.421PC1 3.649 · PC2 1.411PC1 -0.01406 · PC2 0.1447PC1 -0.1887 · PC2 0.1427PC1 0.3819 · PC2 0.2702PC1 -0.2994 · PC2 0.8939PC1 -2.071 · PC2 -1.016PC1 1.498 · PC2 -0.0497PC1 -0.7103 · PC2 0.8473PC1 -1.399 · PC2 0.7273PC1 -2.111 · PC2 0.6727PC1 -2.316 · PC2 0.5776PC1 -2.262 · PC2 0.5787PC1 -1.472 · PC2 0.9362PC1 -2.423 · PC2 -0.057PC1 -0.6866 · PC2 0.01451PC1 0.2486 · PC2 -0.6095PC1 -1.293 · PC2 -0.2119PC1 -0.6055 · PC2 0.5703PC1 -0.6792 · PC2 0.849PC1 -0.07422 · PC2 0.8234PC1 0.029 · PC2 0.7429PC1 0.8163 · PC2 1.041PC1 0.7136 · PC2 0.7093PC1 -1.847 · PC2 0.467PC1 -0.6868 · PC2 0.7897PC1 0.2679 · PC2 0.7074PC1 (61.1%)PC2 (23.0%)358 scores
PCA explained variance0%25%50%75%100%PC1: 61.1% (cumulative 61.1%)1PC2: 23.0% (cumulative 84.1%)2PC3: 8.6% (cumulative 92.7%)3PC4: 3.1% (cumulative 95.8%)4PC5: 2.1% (cumulative 97.9%)5PC6: 0.7% (cumulative 98.6%)6PC7: 0.4% (cumulative 99.0%)7PC8: 0.3% (cumulative 99.3%)8PC9: 0.2% (cumulative 99.5%)9PC10: 0.1% (cumulative 99.7%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
X-Y spectral correlation 1
X · N_mass_g_g 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
N_mass_g_g0.5011,7240.2770.2%

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

N_mass_g_g

target · numeric
N_mass_g_g distribution020400.57 – 0.7471: 80.7471 – 0.9242: 240.9242 – 1.101: 361.101 – 1.278: 321.278 – 1.455: 191.455 – 1.632: 211.632 – 1.81: 141.81 – 1.987: 81.987 – 2.164: 172.164 – 2.341: 92.341 – 2.518: 72.518 – 2.695: 182.695 – 2.872: 142.872 – 3.049: 33.049 – 3.226: 33.226 – 3.403: 43.403 – 3.58: 13.58 – 3.757: 23.757 – 3.935: 03.935 – 4.112: 34.112 – 4.289: 64.289 – 4.466: 44.466 – 4.643: 44.643 – 4.82: 40246
n / missing261 / 0
Mean ± SD1.877 ± 1.02
Median1.56
Range0.57 – 4.82
CV0.541
Skew / kurtosis1.1 / 0.65
Normal?no

Metadata 69

Unique_Sample_ID

metadata · categorical
n / missing261 / 0
Classes261
Balance (entropy)1
Imbalance ratio1
Top classCVARS_Plot1_CAAN4_L1T1N_LC_RG_41430 (1)

Spectra_Name

metadata · categorical
Spectra_Name classesMCON_Tower_CADE27_L2T2chem_LC…MCON_Tower_CADE27_L2T2chem_LC_R: 22CVARS_Plot1_CAAN4_L1T1N_LC_RGCVARS_Plot1_CAAN4_L1T1N_LC_RG: 11CVARS_Plot1_CAAN4_L1T2N_LC_RGCVARS_Plot1_CAAN4_L1T2N_LC_RG: 11CVARS_Plot1_CAAN4_L2M1O_LC_RGCVARS_Plot1_CAAN4_L2M1O_LC_RG: 11CVARS_Plot2_CAAN4_L1T1N_LC_RGCVARS_Plot2_CAAN4_L1T1N_LC_RG: 11CVARS_Plot2_CAAN4_L2T2N_LC_RGCVARS_Plot2_CAAN4_L2T2N_LC_RG: 11CVARS_Plot1_Grape_L4_LC_RGCVARS_Plot1_Grape_L4_LC_RG: 11CVARS_na_Grape_L1T1chem_LC_RCVARS_na_Grape_L1T1chem_LC_R: 11CVARS_na_Grape_L2T1chem_LC_RCVARS_na_Grape_L2T1chem_LC_R: 11CVARS_na_Grape_L3T2chem_LC_RCVARS_na_Grape_L3T2chem_LC_R: 11+10 more+10 more: 1010
n / missing261 / 0
Classes260
Balance (entropy)1
Imbalance ratio2
Top classMCON_Tower_CADE27_L2T2chem_LC_R (2)

Sample_Name

metadata · categorical
Sample_Name classesMCON_Tower_CADE27_L2T2chemMCON_Tower_CADE27_L2T2chem: 22SJJR_Tower_CADE27_ELKIC1TsunSJJR_Tower_CADE27_ELKIC1Tsun: 22SJJR_Tower_CADE27_ELKIC3BSJJR_Tower_CADE27_ELKIC3B: 22CVARS_Plot1_CAAN4_L1T1NCVARS_Plot1_CAAN4_L1T1N: 11CVARS_Plot1_CAAN4_L1T2NCVARS_Plot1_CAAN4_L1T2N: 11CVARS_Plot1_CAAN4_L2M1OCVARS_Plot1_CAAN4_L2M1O: 11CVARS_Plot2_CAAN4_L1T1NCVARS_Plot2_CAAN4_L1T1N: 11CVARS_Plot2_CAAN4_L2T2NCVARS_Plot2_CAAN4_L2T2N: 11CVARS_Plot1_Grape_L4CVARS_Plot1_Grape_L4: 11CVARS_na_Grape_L1T1chemCVARS_na_Grape_L1T1chem: 11+10 more+10 more: 1010
n / missing261 / 0
Classes258
Balance (entropy)1
Imbalance ratio2
Top classMCON_Tower_CADE27_L2T2chem (2)

Site

metadata · categorical
Site classesSJJRSJJR: 7070SJERSJER: 4747MCONMCON: 4141CVARSCVARS: 3737SoaprootSoaproot: 2525KAREKARE: 1515SCRecSCRec: 1212LomaRidgeLomaRidge: 1010KingsburgKingsburg: 44
n / missing261 / 0
Classes9
Balance (entropy)0.89
Imbalance ratio2e+01
Top classSJJR (70)

Site_Long

metadata · categorical
Site_Long classesSan Jacinto James ReserveSan Jacinto James Reserve: 7070San Joaquin Experimental RangeSan Joaquin Experimental Range: 4747Mixed Conifer - Providence Cr…Mixed Conifer - Providence Creek Sierra CZO: 4141Coachella Valley Agricultural…Coachella Valley Agricultural Research Station: 3737Soaproot Saddle - Sierra CZOSoaproot Saddle - Sierra CZO: 2525Kearney Agricultural Research…Kearney Agricultural Research and Extension Center: 1515South Coast Research and Exte…South Coast Research and Extension Center: 1212Loma Ridge Coastal SagebrushLoma Ridge Coastal Sagebrush: 1010KingsburgKingsburg: 44
n / missing261 / 0
Classes9
Balance (entropy)0.89
Imbalance ratio2e+01
Top classSan Jacinto James Reserve (70)

Sample_Date

metadata · categorical
Sample_Date classes6/16/136/16/13: 16166/9/136/9/13: 15156/23/136/23/13: 14146/20/136/20/13: 13133/27/133/27/13: 11113/28/133/28/13: 11116/10/146/10/14: 11114/16/144/16/14: 10103/26/133/26/13: 996/21/136/21/13: 99+10 more+10 more: 7676
n / missing261 / 0
Classes37
Balance (entropy)0.96
Imbalance ratio16
Top class6/16/13 (16)

USDA_Species_Code

metadata · categorical
USDA_Species_Code classesCADE27CADE27: 3131PILAPILA: 2626QUWI2QUWI2: 1919QUKEQUKE: 1717PIPOPIPO: 1717PISA2PISA2: 1515ABCOABCO: 1313PEAM3PEAM3: 1212CAAN4CAAN4: 1111ARCTO3ARCTO3: 1010+10 more+10 more: 7373
n / missing261 / 0
Classes29
Balance (entropy)0.91
Imbalance ratio31
Top classCADE27 (31)

Latitude

metadata · numeric
Latitude distribution05010015033.52 – 33.67: 2933.67 – 33.82: 9233.82 – 33.97: 033.97 – 34.12: 034.12 – 34.27: 034.27 – 34.42: 034.42 – 34.57: 034.57 – 34.72: 034.72 – 34.87: 034.87 – 35.02: 035.02 – 35.17: 035.17 – 35.32: 035.32 – 35.47: 035.47 – 35.62: 035.62 – 35.77: 035.77 – 35.92: 035.92 – 36.07: 036.07 – 36.22: 036.22 – 36.37: 036.37 – 36.52: 436.52 – 36.67: 1536.67 – 36.82: 036.82 – 36.97: 036.97 – 37.12: 113333435363738
n / missing261 / 8
Mean ± SD35.43 ± 1.65
Median36.59
Range33.52 – 37.12
CV0.0466
Skew / kurtosis-0.077 / -2
Normal?no

Longitude

metadata · numeric
Longitude distribution0255075-119.7 – -119.6: 47-119.6 – -119.4: 19-119.4 – -119.3: 0-119.3 – -119.1: 66-119.1 – -119: 0-119 – -118.8: 0-118.8 – -118.7: 0-118.7 – -118.5: 0-118.5 – -118.4: 0-118.4 – -118.2: 0-118.2 – -118.1: 0-118.1 – -117.9: 0-117.9 – -117.8: 0-117.8 – -117.6: 22-117.6 – -117.5: 0-117.5 – -117.3: 0-117.3 – -117.2: 0-117.2 – -117: 0-117 – -116.9: 0-116.9 – -116.7: 70-116.7 – -116.6: 0-116.6 – -116.4: 0-116.4 – -116.3: 0-116.3 – -116.1: 29-120-119-118-117-116
n / missing261 / 8
Mean ± SD-118.2 ± 1.38
Median-119.2
Range-119.7 – -116.1
CV0.0117
Skew / kurtosis0.21 / -1.7
Normal?no

Instrument

metadata · categorical
Instrument classesASD_FS3ASD_FS3: 131131SpecEvo_PSM3500SpecEvo_PSM3500: 108108ASD_FS4ASD_FS4: 2222
n / missing261 / 0
Classes3
Balance (entropy)0.84
Imbalance ratio6
Top classASD_FS3 (131)

GasExchange_Leaf

metadata · categorical
GasExchange_Leaf classesyesyes: 162162nono: 9999
n / missing261 / 0
Classes2
Balance (entropy)0.96
Imbalance ratio2
Top classyes (162)

Spectra

metadata · categorical
n / missing261 / 204
Classes57
Balance (entropy)1
Imbalance ratio1
Top classCVARS_Plot1_CAAN4_L1T1N_LC_RG (1)

SLA_cm2_g_DW

metadata · numeric
SLA_cm2_g_DW distribution051025.45 – 35.79: 935.79 – 46.12: 746.12 – 56.46: 356.46 – 66.8: 466.8 – 77.13: 777.13 – 87.47: 487.47 – 97.8: 197.8 – 108.1: 5108.1 – 118.5: 2118.5 – 128.8: 2128.8 – 139.1: 1139.1 – 149.5: 1149.5 – 159.8: 0159.8 – 170.2: 3170.2 – 180.5: 2180.5 – 190.8: 1190.8 – 201.2: 2201.2 – 211.5: 0211.5 – 221.8: 2221.8 – 232.2: 0232.2 – 242.5: 0242.5 – 252.8: 0252.8 – 263.2: 0263.2 – 273.5: 10100200300
n / missing261 / 204
Mean ± SD92.41 ± 59.7
Median72.05
Range25.45 – 273.5
CV0.646
Skew / kurtosis1 / 0.38
Normal?no

SLA_m2_kgDW

metadata · numeric
SLA_m2_kgDW distribution05102.545 – 3.579: 93.579 – 4.612: 74.612 – 5.646: 35.646 – 6.68: 46.68 – 7.713: 77.713 – 8.747: 48.747 – 9.78: 19.78 – 10.81: 510.81 – 11.85: 211.85 – 12.88: 212.88 – 13.91: 113.91 – 14.95: 114.95 – 15.98: 015.98 – 17.02: 317.02 – 18.05: 218.05 – 19.08: 119.08 – 20.12: 220.12 – 21.15: 021.15 – 22.18: 222.18 – 23.22: 023.22 – 24.25: 024.25 – 25.28: 025.28 – 26.32: 026.32 – 27.35: 10102030
n / missing261 / 204
Mean ± SD9.241 ± 5.97
Median7.205
Range2.545 – 27.35
CV0.646
Skew / kurtosis1 / 0.38
Normal?no

LMA_gDW_cm2

metadata · numeric
LMA_gDW_cm2 distribution05100.003656 – 0.005141: 30.005141 – 0.006626: 80.006626 – 0.008111: 30.008111 – 0.009595: 50.009595 – 0.01108: 30.01108 – 0.01257: 30.01257 – 0.01405: 60.01405 – 0.01553: 50.01553 – 0.01702: 10.01702 – 0.0185: 10.0185 – 0.01999: 20.01999 – 0.02147: 10.02147 – 0.02296: 10.02296 – 0.02444: 20.02444 – 0.02593: 10.02593 – 0.02741: 10.02741 – 0.0289: 30.0289 – 0.03038: 10.03038 – 0.03187: 00.03187 – 0.03335: 20.03335 – 0.03484: 20.03484 – 0.03632: 00.03632 – 0.03781: 20.03781 – 0.03929: 10.000.010.020.030.04
n / missing261 / 204
Mean ± SD0.01609 ± 0.00994
Median0.01388
Range0.003656 – 0.03929
CV0.617
Skew / kurtosis0.77 / -0.54
Normal?no

LMA_gDW_m2

metadata · numeric
LMA_gDW_m2 distribution051036.56 – 51.41: 351.41 – 66.26: 866.26 – 81.11: 381.11 – 95.95: 595.95 – 110.8: 3110.8 – 125.7: 3125.7 – 140.5: 6140.5 – 155.3: 5155.3 – 170.2: 1170.2 – 185: 1185 – 199.9: 2199.9 – 214.7: 1214.7 – 229.6: 1229.6 – 244.4: 2244.4 – 259.3: 1259.3 – 274.1: 1274.1 – 289: 3289 – 303.8: 1303.8 – 318.7: 0318.7 – 333.5: 2333.5 – 348.4: 2348.4 – 363.2: 0363.2 – 378.1: 2378.1 – 392.9: 10100200300400
n / missing261 / 204
Mean ± SD160.9 ± 99.4
Median138.8
Range36.56 – 392.9
CV0.617
Skew / kurtosis0.77 / -0.54
Normal?no

CHN_Sample_Name

metadata · categorical
CHN_Sample_Name classesMCON_Tower_CADE27_L2T2chemMCON_Tower_CADE27_L2T2chem: 22SJJR_Tower_CADE27_ELKIC1TsunSJJR_Tower_CADE27_ELKIC1Tsun: 22SJJR_Tower_CADE27_ELKIC3BSJJR_Tower_CADE27_ELKIC3B: 22CVARS_Plot1_CAAN4_L1T1NCVARS_Plot1_CAAN4_L1T1N: 11CVARS_Plot1_CAAN4_L1T2NCVARS_Plot1_CAAN4_L1T2N: 11CVARS_Plot1_CAAN4_L2M1OCVARS_Plot1_CAAN4_L2M1O: 11CVARS_Plot2_CAAN4_L1T1NCVARS_Plot2_CAAN4_L1T1N: 11CVARS_Plot2_CAAN4_L2T2NCVARS_Plot2_CAAN4_L2T2N: 11CVARS_Plot1_Grape_L4CVARS_Plot1_Grape_L4: 11CVARS_na_Grape_L1T1chemCVARS_na_Grape_L1T1chem: 11+10 more+10 more: 1010
n / missing261 / 0
Classes258
Balance (entropy)1
Imbalance ratio2
Top classMCON_Tower_CADE27_L2T2chem (2)

CHN_USDA_Species_Code

metadata · categorical
CHN_USDA_Species_Code classesCADE27CADE27: 3131PILAPILA: 2626QUWI2QUWI2: 1919QUKEQUKE: 1717PIPOPIPO: 1717PISA2PISA2: 1515ABCOABCO: 1313PEAM3PEAM3: 1212CAAN4CAAN4: 1111PICO3PICO3: 1010+10 more+10 more: 7171
n / missing261 / 0
Classes30
Balance (entropy)0.9
Imbalance ratio31
Top classCADE27 (31)

CHN_Tree_Plant_Number

metadata · numeric
CHN_Tree_Plant_Number distribution0501001501 – 1.292: 1011.292 – 1.583: 01.583 – 1.875: 01.875 – 2.167: 622.167 – 2.458: 02.458 – 2.75: 02.75 – 3.042: 133.042 – 3.333: 03.333 – 3.625: 03.625 – 3.917: 03.917 – 4.208: 64.208 – 4.5: 04.5 – 4.792: 04.792 – 5.083: 15.083 – 5.375: 05.375 – 5.667: 05.667 – 5.958: 05.958 – 6.25: 26.25 – 6.542: 06.542 – 6.833: 06.833 – 7.125: 17.125 – 7.417: 07.417 – 7.708: 07.708 – 8: 102468
n / missing261 / 74
Mean ± SD1.711 ± 1.1
Median1
Range1 – 8
CV0.642
Skew / kurtosis2.7 / 9.8
Normal?no

CHN_Leaf_Number

metadata · numeric
CHN_Leaf_Number distribution0501001501 – 1.583: 1021.583 – 2.167: 562.167 – 2.75: 02.75 – 3.333: 213.333 – 3.917: 03.917 – 4.5: 164.5 – 5.083: 145.083 – 5.667: 05.667 – 6.25: 96.25 – 6.833: 06.833 – 7.417: 47.417 – 8: 08 – 8.583: 28.583 – 9.167: 09.167 – 9.75: 09.75 – 10.33: 110.33 – 10.92: 010.92 – 11.5: 011.5 – 12.08: 012.08 – 12.67: 012.67 – 13.25: 013.25 – 13.83: 013.83 – 14.42: 014.42 – 15: 1051015
n / missing261 / 35
Mean ± SD2.363 ± 1.92
Median2
Range1 – 15
CV0.814
Skew / kurtosis2.3 / 8.8
Normal?no

CHN_Canopy_Position

metadata · categorical
CHN_Canopy_Position classesTT: 174174MM: 4545BB: 4141
n / missing261 / 1
Classes3
Balance (entropy)0.79
Imbalance ratio4
Top classT (174)

CHN_Leaf_Age

metadata · categorical
CHN_Leaf_Age classes11: 149149NewNew: 9595unknownunknown: 1111OldOld: 44
n / missing261 / 2
Classes4
Balance (entropy)0.64
Imbalance ratio4e+01
Top class1 (149)

CHN_Conifer_Broadleaf

metadata · categorical
CHN_Conifer_Broadleaf classesBB: 149149CC: 112112
n / missing261 / 0
Classes2
Balance (entropy)0.99
Imbalance ratio1
Top classB (149)

C_mass_g_g

metadata · numeric
C_mass_g_g distribution020406035.34 – 36.12: 136.12 – 36.89: 036.89 – 37.67: 037.67 – 38.45: 238.45 – 39.23: 239.23 – 40: 540 – 40.78: 740.78 – 41.56: 541.56 – 42.33: 342.33 – 43.11: 343.11 – 43.89: 943.89 – 44.67: 1344.67 – 45.44: 2345.44 – 46.22: 1646.22 – 47: 2547 – 47.77: 4247.77 – 48.55: 3448.55 – 49.33: 4049.33 – 50.1: 1650.1 – 50.88: 650.88 – 51.66: 451.66 – 52.44: 252.44 – 53.21: 253.21 – 53.99: 1102050100
n / missing261 / 0
Mean ± SD46.69 ± 2.93
Median47.33
Range35.34 – 53.99
CV0.0627
Skew / kurtosis-0.95 / 1.2
Normal?no

CN_Ratio

metadata · numeric
CN_Ratio distribution020408.061 – 11.6: 2011.6 – 15.15: 1215.15 – 18.69: 3818.69 – 22.23: 2322.23 – 25.77: 1925.77 – 29.32: 1129.32 – 32.86: 1632.86 – 36.4: 1936.4 – 39.95: 1839.95 – 43.49: 1743.49 – 47.03: 1847.03 – 50.57: 1550.57 – 54.12: 954.12 – 57.66: 657.66 – 61.2: 661.2 – 64.74: 764.74 – 68.29: 268.29 – 71.83: 171.83 – 75.37: 075.37 – 78.92: 178.92 – 82.46: 182.46 – 86: 086 – 89.54: 089.54 – 93.09: 20255075100
n / missing261 / 0
Mean ± SD32.53 ± 16.5
Median30.98
Range8.061 – 93.09
CV0.508
Skew / kurtosis0.7 / 0.26
Normal?no

CHN_Comments

metadata · categorical
n / missing261 / 249
Classes12
Balance (entropy)1
Imbalance ratio1
Top classLeaf N seems a bit high (1)

GE_Sample_Name

metadata · categorical
n / missing261 / 222
Classes39
Balance (entropy)1
Imbalance ratio1
Top classCVARS_Plot1_CAAN4_L1T1_GE (1)

GE_USDA_Species_Code

metadata · categorical
GE_USDA_Species_Code classesCAAN4CAAN4: 77GrapeGrape: 66PEAM3PEAM3: 66PHOEN2PHOEN2: 44PUGR2PUGR2: 44PIME4PIME4: 33PUNICPUNIC: 33CILI5CILI5: 22PRDUPRDU: 22PRPE3PRPE3: 22
n / missing261 / 222
Classes10
Balance (entropy)0.96
Imbalance ratio4
Top classCAAN4 (7)

GE_Tree_Plant_Number

metadata · numeric
GE_Tree_Plant_Number distribution02461 – 1.208: 41.208 – 1.417: 01.417 – 1.625: 01.625 – 1.833: 01.833 – 2.042: 52.042 – 2.25: 02.25 – 2.458: 02.458 – 2.667: 02.667 – 2.875: 02.875 – 3.083: 03.083 – 3.292: 03.292 – 3.5: 03.5 – 3.708: 03.708 – 3.917: 03.917 – 4.125: 14.125 – 4.333: 04.333 – 4.542: 04.542 – 4.75: 04.75 – 4.958: 04.958 – 5.167: 15.167 – 5.375: 05.375 – 5.583: 05.583 – 5.792: 05.792 – 6: 20246
n / missing261 / 248
Mean ± SD2.692 ± 1.89
Median2
Range1 – 6
CV0.701
Skew / kurtosis0.96 / -0.62
Normal?yes

GE_Leaf_Number

metadata · numeric
GE_Leaf_Number distribution0510151 – 1.292: 121.292 – 1.583: 01.583 – 1.875: 01.875 – 2.167: 52.167 – 2.458: 02.458 – 2.75: 02.75 – 3.042: 53.042 – 3.333: 03.333 – 3.625: 03.625 – 3.917: 03.917 – 4.208: 44.208 – 4.5: 04.5 – 4.792: 04.792 – 5.083: 35.083 – 5.375: 05.375 – 5.667: 05.667 – 5.958: 05.958 – 6.25: 26.25 – 6.542: 06.542 – 6.833: 06.833 – 7.125: 37.125 – 7.417: 07.417 – 7.708: 07.708 – 8: 202468
n / missing261 / 225
Mean ± SD3.25 ± 2.29
Median3
Range1 – 8
CV0.703
Skew / kurtosis0.72 / -0.69
Normal?yes

GE_Canopy_Position

metadata · categorical
GE_Canopy_Position classesTopTop: 3838BotBot: 11
n / missing261 / 222
Classes2
Balance (entropy)0.17
Imbalance ratio38
Top classTop (38)

GE_Rep

metadata · numeric
GE_Rep distribution020401 – 1.042: 371.042 – 1.083: 01.083 – 1.125: 01.125 – 1.167: 01.167 – 1.208: 01.208 – 1.25: 01.25 – 1.292: 01.292 – 1.333: 01.333 – 1.375: 01.375 – 1.417: 01.417 – 1.458: 01.458 – 1.5: 01.5 – 1.542: 01.542 – 1.583: 01.583 – 1.625: 01.625 – 1.667: 01.667 – 1.708: 01.708 – 1.75: 01.75 – 1.792: 01.792 – 1.833: 01.833 – 1.875: 01.875 – 1.917: 01.917 – 1.958: 01.958 – 2: 212510
n / missing261 / 222
Mean ± SD1.051 ± 0.223
Median1
Range1 – 2
CV0.213
Skew / kurtosis4.2 / 17
Normal?no

GE_Measurement_Date

metadata · categorical
GE_Measurement_Date classes4/12/144/12/14: 776/8/136/8/13: 444/15/144/15/14: 444/16/144/16/14: 446/13/146/13/14: 443/24/133/24/13: 446/5/136/5/13: 226/7/136/7/13: 224/17/144/17/14: 226/26/136/26/13: 22+3 more+3 more: 44
n / missing261 / 222
Classes13
Balance (entropy)0.95
Imbalance ratio7
Top class4/12/14 (7)

Mean_Tair

metadata · numeric
Mean_Tair distribution024616.04 – 16.69: 116.69 – 17.33: 017.33 – 17.98: 017.98 – 18.62: 018.62 – 19.27: 019.27 – 19.92: 119.92 – 20.56: 020.56 – 21.21: 121.21 – 21.85: 121.85 – 22.5: 022.5 – 23.15: 623.15 – 23.79: 423.79 – 24.44: 324.44 – 25.08: 425.08 – 25.73: 225.73 – 26.38: 126.38 – 27.02: 427.02 – 27.67: 227.67 – 28.31: 328.31 – 28.96: 028.96 – 29.61: 329.61 – 30.25: 130.25 – 30.9: 130.9 – 31.54: 1102050100
n / missing261 / 222
Mean ± SD25.17 ± 3.2
Median24.78
Range16.04 – 31.54
CV0.127
Skew / kurtosis-0.33 / 0.55
Normal?yes

Mean_Tleaf

metadata · numeric
Mean_Tleaf distribution024619.85 – 20.49: 120.49 – 21.13: 021.13 – 21.78: 021.78 – 22.42: 022.42 – 23.07: 523.07 – 23.71: 023.71 – 24.36: 024.36 – 25: 125 – 25.64: 225.64 – 26.29: 626.29 – 26.93: 126.93 – 27.58: 327.58 – 28.22: 328.22 – 28.87: 228.87 – 29.51: 429.51 – 30.15: 530.15 – 30.8: 030.8 – 31.44: 431.44 – 32.09: 032.09 – 32.73: 132.73 – 33.38: 033.38 – 34.02: 034.02 – 34.67: 034.67 – 35.31: 1102050100
n / missing261 / 222
Mean ± SD27.51 ± 3.13
Median27.73
Range19.85 – 35.31
CV0.114
Skew / kurtosis-0.13 / 0.23
Normal?yes

PercCV_Tleaf

metadata · numeric
PercCV_Tleaf distribution05100.01202 – 0.08085: 80.08085 – 0.1497: 10.1497 – 0.2185: 00.2185 – 0.2873: 40.2873 – 0.3562: 40.3562 – 0.425: 10.425 – 0.4938: 10.4938 – 0.5626: 20.5626 – 0.6315: 10.6315 – 0.7003: 10.7003 – 0.7691: 40.7691 – 0.838: 40.838 – 0.9068: 10.9068 – 0.9756: 20.9756 – 1.044: 01.044 – 1.113: 01.113 – 1.182: 01.182 – 1.251: 01.251 – 1.32: 11.32 – 1.389: 11.389 – 1.457: 01.457 – 1.526: 21.526 – 1.595: 01.595 – 1.664: 10.00.51.01.52.0
n / missing261 / 222
Mean ± SD0.5667 ± 0.457
Median0.5077
Range0.01202 – 1.664
CV0.807
Skew / kurtosis0.7 / -0.21
Normal?yes

Mean_RH_R

metadata · numeric
Mean_RH_R distribution024624.91 – 27.11: 127.11 – 29.3: 029.3 – 31.5: 031.5 – 33.69: 133.69 – 35.89: 035.89 – 38.08: 238.08 – 40.28: 340.28 – 42.48: 142.48 – 44.67: 344.67 – 46.87: 046.87 – 49.06: 249.06 – 51.26: 151.26 – 53.45: 553.45 – 55.65: 255.65 – 57.84: 357.84 – 60.04: 160.04 – 62.23: 362.23 – 64.43: 364.43 – 66.63: 066.63 – 68.82: 268.82 – 71.02: 171.02 – 73.21: 173.21 – 75.41: 175.41 – 77.6: 3102050100
n / missing261 / 222
Mean ± SD54.47 ± 13
Median54.66
Range24.91 – 77.6
CV0.239
Skew / kurtosis-0.081 / -0.6
Normal?yes

Mean_RH_S

metadata · numeric
Mean_RH_S distribution02432.12 – 34.32: 134.32 – 36.52: 036.52 – 38.71: 038.71 – 40.91: 140.91 – 43.11: 043.11 – 45.31: 245.31 – 47.51: 147.51 – 49.71: 449.71 – 51.9: 351.9 – 54.1: 154.1 – 56.3: 056.3 – 58.5: 258.5 – 60.7: 360.7 – 62.9: 362.9 – 65.09: 265.09 – 67.29: 367.29 – 69.49: 469.49 – 71.69: 171.69 – 73.89: 173.89 – 76.09: 176.09 – 78.28: 078.28 – 80.48: 280.48 – 82.68: 382.68 – 84.88: 1102050100
n / missing261 / 222
Mean ± SD61.28 ± 13.1
Median61.43
Range32.12 – 84.88
CV0.213
Skew / kurtosis-0.055 / -0.63
Normal?yes

Mean_VpdA

metadata · numeric
Mean_VpdA distribution05100.6327 – 0.7373: 10.7373 – 0.8419: 20.8419 – 0.9465: 20.9465 – 1.051: 41.051 – 1.156: 11.156 – 1.26: 11.26 – 1.365: 31.365 – 1.47: 91.47 – 1.574: 31.574 – 1.679: 21.679 – 1.783: 21.783 – 1.888: 21.888 – 1.993: 01.993 – 2.097: 12.097 – 2.202: 12.202 – 2.306: 02.306 – 2.411: 22.411 – 2.516: 12.516 – 2.62: 02.62 – 2.725: 02.725 – 2.829: 02.829 – 2.934: 12.934 – 3.039: 03.039 – 3.143: 101234
n / missing261 / 222
Mean ± SD1.516 ± 0.571
Median1.413
Range0.6327 – 3.143
CV0.376
Skew / kurtosis1 / 1.1
Normal?no

Mean_VpdL

metadata · numeric
Mean_VpdL distribution02580.4792 – 0.604: 10.604 – 0.7288: 00.7288 – 0.8536: 10.8536 – 0.9784: 10.9784 – 1.103: 31.103 – 1.228: 01.228 – 1.353: 21.353 – 1.478: 71.478 – 1.603: 01.603 – 1.727: 61.727 – 1.852: 41.852 – 1.977: 31.977 – 2.102: 42.102 – 2.227: 12.227 – 2.351: 02.351 – 2.476: 22.476 – 2.601: 12.601 – 2.726: 02.726 – 2.851: 02.851 – 2.975: 02.975 – 3.1: 13.1 – 3.225: 03.225 – 3.35: 03.35 – 3.475: 201234
n / missing261 / 222
Mean ± SD1.768 ± 0.638
Median1.706
Range0.4792 – 3.475
CV0.361
Skew / kurtosis0.86 / 1.4
Normal?no

PercCV_VpdL

metadata · numeric
PercCV_VpdL distribution02580.37 – 0.8447: 40.8447 – 1.319: 31.319 – 1.794: 71.794 – 2.269: 32.269 – 2.743: 32.743 – 3.218: 23.218 – 3.692: 23.692 – 4.167: 34.167 – 4.642: 34.642 – 5.116: 05.116 – 5.591: 15.591 – 6.066: 16.066 – 6.54: 16.54 – 7.015: 17.015 – 7.49: 07.49 – 7.964: 17.964 – 8.439: 08.439 – 8.914: 28.914 – 9.388: 09.388 – 9.863: 09.863 – 10.34: 010.34 – 10.81: 010.81 – 11.29: 111.29 – 11.76: 1051015
n / missing261 / 222
Mean ± SD3.615 ± 2.87
Median2.609
Range0.37 – 11.76
CV0.794
Skew / kurtosis1.3 / 1.1
Normal?no

Mean_PARi

metadata · numeric
Mean_PARi distribution02581999 – 1999: 11999 – 1999: 01999 – 1999: 11999 – 1999: 01999 – 1999: 01999 – 1999: 21999 – 1999: 11999 – 1999: 01999 – 1999: 11999 – 2000: 22000 – 2000: 42000 – 2000: 02000 – 2000: 52000 – 2000: 32000 – 2000: 12000 – 2000: 12000 – 2000: 72000 – 2000: 12000 – 2000: 12000 – 2000: 22000 – 2000: 02000 – 2001: 12001 – 2001: 22001 – 2001: 31,9981,9992,0002,001
n / missing261 / 222
Mean ± SD2000 ± 0.489
Median2000
Range1999 – 2001
CV0.000245
Skew / kurtosis-0.26 / -0.16
Normal?yes

Mean_Ci_over_Ca

metadata · numeric
Mean_Ci_over_Ca distribution02580.5262 – 0.5437: 10.5437 – 0.5612: 00.5612 – 0.5787: 00.5787 – 0.5963: 00.5963 – 0.6138: 00.6138 – 0.6313: 00.6313 – 0.6488: 20.6488 – 0.6664: 10.6664 – 0.6839: 10.6839 – 0.7014: 00.7014 – 0.7189: 70.7189 – 0.7365: 60.7365 – 0.754: 20.754 – 0.7715: 10.7715 – 0.7891: 40.7891 – 0.8066: 20.8066 – 0.8241: 40.8241 – 0.8416: 10.8416 – 0.8592: 20.8592 – 0.8767: 20.8767 – 0.8942: 10.8942 – 0.9117: 10.9117 – 0.9293: 00.9293 – 0.9468: 10.10.20.51
n / missing261 / 222
Mean ± SD0.7628 ± 0.0817
Median0.742
Range0.5262 – 0.9468
CV0.107
Skew / kurtosis-0.2 / 0.81
Normal?yes

PercCV_Ci_Ca

metadata · numeric
PercCV_Ci_Ca distribution0248.431 – 10.29: 310.29 – 12.15: 112.15 – 14.01: 314.01 – 15.87: 115.87 – 17.73: 417.73 – 19.59: 319.59 – 21.46: 221.46 – 23.32: 323.32 – 25.18: 125.18 – 27.04: 327.04 – 28.9: 128.9 – 30.76: 230.76 – 32.62: 332.62 – 34.48: 134.48 – 36.34: 236.34 – 38.2: 138.2 – 40.06: 040.06 – 41.92: 241.92 – 43.78: 043.78 – 45.64: 045.64 – 47.5: 147.5 – 49.36: 149.36 – 51.23: 051.23 – 53.09: 10204060
n / missing261 / 222
Mean ± SD25.16 ± 11.2
Median22.9
Range8.431 – 53.09
CV0.445
Skew / kurtosis0.61 / -0.16
Normal?yes

Mean_Cond

metadata · numeric
Mean_Cond distribution05100.1003 – 0.1272: 80.1272 – 0.154: 50.154 – 0.1808: 90.1808 – 0.2077: 60.2077 – 0.2345: 20.2345 – 0.2613: 20.2613 – 0.2882: 00.2882 – 0.315: 00.315 – 0.3419: 00.3419 – 0.3687: 10.3687 – 0.3955: 10.3955 – 0.4224: 10.4224 – 0.4492: 10.4492 – 0.476: 00.476 – 0.5029: 00.5029 – 0.5297: 00.5297 – 0.5566: 00.5566 – 0.5834: 00.5834 – 0.6102: 10.6102 – 0.6371: 10.6371 – 0.6639: 00.6639 – 0.6907: 00.6907 – 0.7176: 00.7176 – 0.7444: 10.00.20.40.60.8
n / missing261 / 222
Mean ± SD0.2228 ± 0.151
Median0.1733
Range0.1003 – 0.7444
CV0.676
Skew / kurtosis2.1 / 4
Normal?no

PercCV_Cond

metadata · numeric
PercCV_Cond distribution02462.071 – 3.423: 23.423 – 4.776: 34.776 – 6.129: 36.129 – 7.482: 17.482 – 8.834: 28.834 – 10.19: 610.19 – 11.54: 411.54 – 12.89: 512.89 – 14.25: 114.25 – 15.6: 015.6 – 16.95: 116.95 – 18.3: 318.3 – 19.66: 119.66 – 21.01: 021.01 – 22.36: 022.36 – 23.72: 123.72 – 25.07: 125.07 – 26.42: 126.42 – 27.77: 227.77 – 29.13: 129.13 – 30.48: 030.48 – 31.83: 031.83 – 33.18: 033.18 – 34.54: 1010203040
n / missing261 / 222
Mean ± SD13.12 ± 7.92
Median11.31
Range2.071 – 34.54
CV0.604
Skew / kurtosis0.96 / 0.29
Normal?no

Calculated_VIS_Leaf_Abs

metadata · numeric
Calculated_VIS_Leaf_Abs distribution0240.8367 – 0.8417: 10.8417 – 0.8467: 10.8467 – 0.8517: 00.8517 – 0.8568: 20.8568 – 0.8618: 10.8618 – 0.8668: 20.8668 – 0.8718: 30.8718 – 0.8769: 10.8769 – 0.8819: 10.8819 – 0.8869: 20.8869 – 0.8919: 20.8919 – 0.8969: 00.8969 – 0.902: 30.902 – 0.907: 20.907 – 0.912: 00.912 – 0.917: 00.917 – 0.9221: 20.9221 – 0.9271: 10.9271 – 0.9321: 30.9321 – 0.9371: 10.9371 – 0.9422: 40.9422 – 0.9472: 20.9472 – 0.9522: 10.9522 – 0.9572: 40.800.850.900.951.00
n / missing261 / 222
Mean ± SD0.9054 ± 0.0359
Median0.9052
Range0.8367 – 0.9572
CV0.0396
Skew / kurtosis-0.19 / -1.3
Normal?no

Mean_Fs

metadata · numeric
Mean_Fs distribution0246538 – 568.8: 1568.8 – 599.6: 1599.6 – 630.4: 5630.4 – 661.1: 3661.1 – 691.9: 3691.9 – 722.7: 4722.7 – 753.5: 2753.5 – 784.3: 4784.3 – 815.1: 0815.1 – 845.8: 2845.8 – 876.6: 2876.6 – 907.4: 4907.4 – 938.2: 1938.2 – 969: 3969 – 999.8: 1999.8 – 1031: 11031 – 1061: 01061 – 1092: 01092 – 1123: 01123 – 1154: 01154 – 1184: 01184 – 1215: 01215 – 1246: 11246 – 1277: 11002005001,0002,0005,00010,000
n / missing261 / 222
Mean ± SD789.6 ± 167
Median765.5
Range538 – 1277
CV0.212
Skew / kurtosis0.98 / 1.1
Normal?no

Mean_qP

metadata · numeric
Mean_qP distribution02460.3208 – 0.3375: 10.3375 – 0.3542: 20.3542 – 0.3709: 20.3709 – 0.3875: 50.3875 – 0.4042: 00.4042 – 0.4209: 20.4209 – 0.4376: 40.4376 – 0.4543: 30.4543 – 0.471: 20.471 – 0.4877: 20.4877 – 0.5044: 00.5044 – 0.5211: 10.5211 – 0.5378: 50.5378 – 0.5544: 10.5544 – 0.5711: 00.5711 – 0.5878: 10.5878 – 0.6045: 00.6045 – 0.6212: 10.6212 – 0.6379: 10.6379 – 0.6546: 00.6546 – 0.6713: 30.6713 – 0.688: 10.688 – 0.7047: 00.7047 – 0.7213: 20.10.20.51
n / missing261 / 222
Mean ± SD0.4887 ± 0.113
Median0.4615
Range0.3208 – 0.7213
CV0.231
Skew / kurtosis0.56 / -0.74
Normal?yes

Mean_Fv_Prime_over_Fm_Prime

metadata · numeric
Mean_Fv_Prime_over_Fm_Prime distribution0240.2735 – 0.2826: 10.2826 – 0.2918: 00.2918 – 0.3009: 00.3009 – 0.31: 10.31 – 0.3192: 10.3192 – 0.3283: 00.3283 – 0.3374: 10.3374 – 0.3466: 10.3466 – 0.3557: 30.3557 – 0.3648: 30.3648 – 0.374: 20.374 – 0.3831: 00.3831 – 0.3923: 30.3923 – 0.4014: 20.4014 – 0.4105: 30.4105 – 0.4197: 00.4197 – 0.4288: 30.4288 – 0.4379: 30.4379 – 0.4471: 20.4471 – 0.4562: 40.4562 – 0.4653: 20.4653 – 0.4745: 20.4745 – 0.4836: 00.4836 – 0.4928: 20.10.20.51
n / missing261 / 222
Mean ± SD0.4033 ± 0.0527
Median0.4078
Range0.2735 – 0.4928
CV0.131
Skew / kurtosis-0.4 / -0.46
Normal?yes

Mean_PhiPS2

metadata · numeric
Mean_PhiPS2 distribution02460.1063 – 0.1166: 10.1166 – 0.1268: 20.1268 – 0.137: 10.137 – 0.1473: 40.1473 – 0.1575: 20.1575 – 0.1677: 40.1677 – 0.178: 10.178 – 0.1882: 60.1882 – 0.1984: 20.1984 – 0.2087: 10.2087 – 0.2189: 10.2189 – 0.2291: 30.2291 – 0.2394: 20.2394 – 0.2496: 20.2496 – 0.2598: 00.2598 – 0.27: 00.27 – 0.2803: 10.2803 – 0.2905: 00.2905 – 0.3007: 10.3007 – 0.311: 20.311 – 0.3212: 10.3212 – 0.3314: 00.3314 – 0.3417: 10.3417 – 0.3519: 10.10.20.51
n / missing261 / 222
Mean ± SD0.2015 ± 0.0631
Median0.1853
Range0.1063 – 0.3519
CV0.313
Skew / kurtosis0.74 / -0.23
Normal?yes

Max_ETR

metadata · numeric
Max_ETR distribution024127 – 137.1: 2137.1 – 147.3: 2147.3 – 157.5: 1157.5 – 167.7: 3167.7 – 177.9: 2177.9 – 188: 3188 – 198.2: 4198.2 – 208.4: 3208.4 – 218.6: 1218.6 – 228.8: 0228.8 – 238.9: 1238.9 – 249.1: 1249.1 – 259.3: 1259.3 – 269.5: 1269.5 – 279.7: 0279.7 – 289.8: 1289.8 – 300: 1300 – 310.2: 0310.2 – 320.4: 0320.4 – 330.6: 0330.6 – 340.7: 1340.7 – 350.9: 2350.9 – 361.1: 0361.1 – 371.3: 31002005001,000
n / missing261 / 228
Mean ± SD224 ± 75.2
Median197
Range127 – 371.3
CV0.335
Skew / kurtosis0.8 / -0.59
Normal?yes

Max_Fs

metadata · numeric
Max_Fs distribution0246544.8 – 576.9: 1576.9 – 609: 1609 – 641.1: 2641.1 – 673.1: 3673.1 – 705.2: 2705.2 – 737.3: 1737.3 – 769.4: 5769.4 – 801.5: 1801.5 – 833.6: 2833.6 – 865.7: 2865.7 – 897.7: 1897.7 – 929.8: 1929.8 – 961.9: 1961.9 – 994: 3994 – 1026: 11026 – 1058: 41058 – 1090: 01090 – 1122: 01122 – 1154: 01154 – 1186: 01186 – 1219: 01219 – 1251: 01251 – 1283: 01283 – 1315: 21002005001,0002,0005,00010,000
n / missing261 / 228
Mean ± SD843.4 ± 188
Median805.7
Range544.8 – 1315
CV0.223
Skew / kurtosis0.73 / 0.31
Normal?yes

Max_qP

metadata · numeric
Max_qP distribution0240.3733 – 0.3887: 40.3887 – 0.404: 10.404 – 0.4194: 10.4194 – 0.4348: 30.4348 – 0.4502: 30.4502 – 0.4656: 00.4656 – 0.481: 10.481 – 0.4964: 10.4964 – 0.5118: 20.5118 – 0.5272: 40.5272 – 0.5426: 10.5426 – 0.558: 10.558 – 0.5734: 10.5734 – 0.5888: 10.5888 – 0.6042: 10.6042 – 0.6196: 00.6196 – 0.635: 10.635 – 0.6504: 00.6504 – 0.6658: 00.6658 – 0.6812: 10.6812 – 0.6965: 30.6965 – 0.7119: 10.7119 – 0.7273: 00.7273 – 0.7427: 20.10.20.51
n / missing261 / 228
Mean ± SD0.5269 ± 0.114
Median0.5142
Range0.3733 – 0.7427
CV0.216
Skew / kurtosis0.48 / -0.97
Normal?yes

Max_Fv_Prime_over_Fm_Prime

metadata · numeric
Max_Fv_Prime_over_Fm_Prime distribution01230.3636 – 0.3719: 20.3719 – 0.3803: 00.3803 – 0.3886: 10.3886 – 0.397: 00.397 – 0.4054: 30.4054 – 0.4137: 20.4137 – 0.4221: 20.4221 – 0.4304: 20.4304 – 0.4388: 20.4388 – 0.4471: 10.4471 – 0.4555: 00.4555 – 0.4639: 20.4639 – 0.4722: 30.4722 – 0.4806: 00.4806 – 0.4889: 20.4889 – 0.4973: 20.4973 – 0.5057: 20.5057 – 0.514: 00.514 – 0.5224: 10.5224 – 0.5307: 10.5307 – 0.5391: 10.5391 – 0.5475: 20.5475 – 0.5558: 00.5558 – 0.5642: 20.10.20.51
n / missing261 / 228
Mean ± SD0.4608 ± 0.0566
Median0.4621
Range0.3636 – 0.5642
CV0.123
Skew / kurtosis0.15 / -0.93
Normal?yes

Max_PhiPS2

metadata · numeric
Max_PhiPS2 distribution0240.1472 – 0.1585: 30.1585 – 0.1698: 10.1698 – 0.1812: 30.1812 – 0.1925: 20.1925 – 0.2038: 30.2038 – 0.2152: 40.2152 – 0.2265: 30.2265 – 0.2378: 00.2378 – 0.2491: 20.2491 – 0.2605: 00.2605 – 0.2718: 20.2718 – 0.2831: 00.2831 – 0.2944: 20.2944 – 0.3058: 10.3058 – 0.3171: 00.3171 – 0.3284: 10.3284 – 0.3397: 00.3397 – 0.3511: 00.3511 – 0.3624: 10.3624 – 0.3737: 20.3737 – 0.3851: 00.3851 – 0.3964: 10.3964 – 0.4077: 10.4077 – 0.419: 10.10.20.51
n / missing261 / 228
Mean ± SD0.2469 ± 0.0787
Median0.2204
Range0.1472 – 0.419
CV0.319
Skew / kurtosis0.82 / -0.45
Normal?yes

Meas_Photo_at_CO2R_400

metadata · numeric
Meas_Photo_at_CO2R_400 distribution02589.783 – 10.63: 710.63 – 11.48: 011.48 – 12.33: 212.33 – 13.19: 313.19 – 14.04: 414.04 – 14.89: 214.89 – 15.74: 215.74 – 16.59: 216.59 – 17.44: 517.44 – 18.29: 318.29 – 19.14: 219.14 – 19.99: 019.99 – 20.84: 120.84 – 21.69: 021.69 – 22.54: 022.54 – 23.39: 223.39 – 24.24: 024.24 – 25.09: 125.09 – 25.94: 125.94 – 26.79: 026.79 – 27.64: 027.64 – 28.5: 128.5 – 29.35: 029.35 – 30.2: 1125102050100
n / missing261 / 222
Mean ± SD16.16 ± 5.08
Median15.65
Range9.783 – 30.2
CV0.314
Skew / kurtosis0.98 / 0.64
Normal?no

Meas_CiCa_at_CO2R_400

metadata · numeric
Meas_CiCa_at_CO2R_400 distribution0240.391 – 0.4113: 20.4113 – 0.4317: 20.4317 – 0.452: 00.452 – 0.4723: 10.4723 – 0.4927: 10.4927 – 0.513: 30.513 – 0.5333: 10.5333 – 0.5537: 10.5537 – 0.574: 20.574 – 0.5943: 40.5943 – 0.6147: 30.6147 – 0.635: 20.635 – 0.6553: 20.6553 – 0.6757: 20.6757 – 0.696: 40.696 – 0.7163: 10.7163 – 0.7367: 20.7367 – 0.757: 10.757 – 0.7773: 10.7773 – 0.7977: 10.7977 – 0.818: 10.818 – 0.8383: 10.8383 – 0.8587: 00.8587 – 0.879: 10.10.20.51
n / missing261 / 222
Mean ± SD0.617 ± 0.12
Median0.6079
Range0.391 – 0.879
CV0.195
Skew / kurtosis0.039 / -0.43
Normal?yes

Mod_Photo_at_Ci_388

metadata · numeric
Mod_Photo_at_Ci_388 distribution024610.11 – 12.18: 212.18 – 14.24: 314.24 – 16.3: 316.3 – 18.36: 318.36 – 20.43: 220.43 – 22.49: 122.49 – 24.55: 124.55 – 26.62: 526.62 – 28.68: 428.68 – 30.74: 330.74 – 32.81: 132.81 – 34.87: 034.87 – 36.93: 136.93 – 38.99: 438.99 – 41.06: 141.06 – 43.12: 043.12 – 45.18: 245.18 – 47.25: 047.25 – 49.31: 149.31 – 51.37: 151.37 – 53.44: 053.44 – 55.5: 055.5 – 57.56: 057.56 – 59.63: 10204060
n / missing261 / 222
Mean ± SD27.56 ± 11.8
Median26.35
Range10.11 – 59.63
CV0.429
Skew / kurtosis0.66 / 0.085
Normal?yes

Mod_Photo_at_Ci_388_times_CiCa

metadata · numeric
Mod_Photo_at_Ci_388_times_CiCa distribution02467.769 – 8.791: 48.791 – 9.812: 29.812 – 10.83: 210.83 – 11.86: 311.86 – 12.88: 112.88 – 13.9: 313.9 – 14.92: 214.92 – 15.94: 515.94 – 16.96: 316.96 – 17.99: 417.99 – 19.01: 319.01 – 20.03: 120.03 – 21.05: 021.05 – 22.07: 122.07 – 23.09: 123.09 – 24.12: 224.12 – 25.14: 025.14 – 26.16: 126.16 – 27.18: 027.18 – 28.2: 028.2 – 29.22: 029.22 – 30.24: 030.24 – 31.27: 031.27 – 32.29: 1125102050100
n / missing261 / 222
Mean ± SD15.61 ± 5.38
Median15.75
Range7.769 – 32.29
CV0.345
Skew / kurtosis0.84 / 1.2
Normal?no

Est_Photo_stomata_limit

metadata · numeric
Est_Photo_stomata_limit distribution02461.844 – 3.269: 43.269 – 4.694: 34.694 – 6.12: 46.12 – 7.545: 37.545 – 8.97: 58.97 – 10.4: 310.4 – 11.82: 311.82 – 13.25: 313.25 – 14.67: 014.67 – 16.1: 216.1 – 17.52: 017.52 – 18.95: 118.95 – 20.37: 020.37 – 21.8: 221.8 – 23.22: 023.22 – 24.65: 324.65 – 26.07: 026.07 – 27.5: 027.5 – 28.93: 028.93 – 30.35: 030.35 – 31.78: 131.78 – 33.2: 133.2 – 34.63: 034.63 – 36.05: 1010203040
n / missing261 / 222
Mean ± SD11.95 ± 8.71
Median9.522
Range1.844 – 36.05
CV0.728
Skew / kurtosis1.2 / 0.93
Normal?no

Est_Ci_colimit

metadata · numeric
Est_Ci_colimit distribution051038 – 74.21: 974.21 – 110.4: 1110.4 – 146.6: 1146.6 – 182.8: 0182.8 – 219: 2219 – 255.2: 3255.2 – 291.5: 5291.5 – 327.7: 1327.7 – 363.9: 3363.9 – 400.1: 1400.1 – 436.3: 0436.3 – 472.5: 0472.5 – 508.7: 0508.7 – 544.9: 1544.9 – 581.1: 1581.1 – 617.3: 0617.3 – 653.5: 0653.5 – 689.8: 0689.8 – 726: 1726 – 762.2: 0762.2 – 798.4: 0798.4 – 834.6: 1834.6 – 870.8: 2870.8 – 907: 102505007501,000
n / missing261 / 228
Mean ± SD308.1 ± 263
Median258
Range38 – 907
CV0.853
Skew / kurtosis1.1 / 0.24
Normal?no

Fitted_Vcmax

metadata · numeric
Fitted_Vcmax distribution024632.69 – 42.88: 342.88 – 53.07: 253.07 – 63.25: 463.25 – 73.44: 173.44 – 83.63: 583.63 – 93.82: 293.82 – 104: 2104 – 114.2: 2114.2 – 124.4: 5124.4 – 134.6: 3134.6 – 144.8: 0144.8 – 154.9: 0154.9 – 165.1: 1165.1 – 175.3: 2175.3 – 185.5: 1185.5 – 195.7: 0195.7 – 205.9: 0205.9 – 216.1: 1216.1 – 226.3: 2226.3 – 236.4: 0236.4 – 246.6: 0246.6 – 256.8: 1256.8 – 267: 0267 – 277.2: 20100200300
n / missing261 / 222
Mean ± SD118.6 ± 66.5
Median108.6
Range32.69 – 277.2
CV0.561
Skew / kurtosis0.98 / 0.22
Normal?no

Fitted_Jmax

metadata · numeric
Fitted_Jmax distribution012396.99 – 104.8: 2104.8 – 112.6: 1112.6 – 120.5: 2120.5 – 128.3: 1128.3 – 136.1: 3136.1 – 144: 3144 – 151.8: 2151.8 – 159.6: 3159.6 – 167.4: 2167.4 – 175.3: 1175.3 – 183.1: 3183.1 – 190.9: 1190.9 – 198.8: 0198.8 – 206.6: 0206.6 – 214.4: 0214.4 – 222.2: 3222.2 – 230.1: 0230.1 – 237.9: 0237.9 – 245.7: 1245.7 – 253.6: 0253.6 – 261.4: 0261.4 – 269.2: 1269.2 – 277: 1277 – 284.9: 31020501002005001,000
n / missing261 / 228
Mean ± SD174.1 ± 55.8
Median159.5
Range96.99 – 284.9
CV0.321
Skew / kurtosis0.76 / -0.49
Normal?yes

Fitted_Rd

metadata · numeric
Fitted_Rd distribution0246-0.9303 – -0.7131: 1-0.7131 – -0.4959: 0-0.4959 – -0.2786: 0-0.2786 – -0.06138: 0-0.06138 – 0.1559: 00.1559 – 0.3731: 00.3731 – 0.5903: 00.5903 – 0.8076: 00.8076 – 1.025: 31.025 – 1.242: 41.242 – 1.459: 51.459 – 1.677: 41.677 – 1.894: 31.894 – 2.111: 42.111 – 2.328: 42.328 – 2.545: 52.545 – 2.763: 52.763 – 2.98: 02.98 – 3.197: 03.197 – 3.414: 03.414 – 3.632: 03.632 – 3.849: 03.849 – 4.066: 04.066 – 4.283: 1-20246
n / missing261 / 222
Mean ± SD1.831 ± 0.812
Median1.847
Range-0.9303 – 4.283
CV0.443
Skew / kurtosis-0.32 / 3.9
Normal?no

RMSE_DEoptim

metadata · numeric
RMSE_DEoptim distribution0240.02592 – 0.1126: 30.1126 – 0.1994: 20.1994 – 0.2861: 30.2861 – 0.3728: 20.3728 – 0.4595: 40.4595 – 0.5463: 40.5463 – 0.633: 30.633 – 0.7197: 20.7197 – 0.8064: 20.8064 – 0.8931: 30.8931 – 0.9799: 40.9799 – 1.067: 01.067 – 1.153: 01.153 – 1.24: 11.24 – 1.327: 31.327 – 1.413: 01.413 – 1.5: 01.5 – 1.587: 11.587 – 1.674: 01.674 – 1.76: 01.76 – 1.847: 01.847 – 1.934: 01.934 – 2.021: 12.021 – 2.107: 10123
n / missing261 / 222
Mean ± SD0.6984 ± 0.488
Median0.6041
Range0.02592 – 2.107
CV0.698
Skew / kurtosis1.1 / 1.3
Normal?no

RMSE_photo

metadata · numeric
RMSE_photo distribution05100.2116 – 0.5082: 40.5082 – 0.8047: 10.8047 – 1.101: 21.101 – 1.398: 01.398 – 1.694: 21.694 – 1.991: 01.991 – 2.287: 22.287 – 2.584: 62.584 – 2.881: 102.881 – 3.177: 43.177 – 3.474: 13.474 – 3.77: 13.77 – 4.067: 14.067 – 4.363: 04.363 – 4.66: 14.66 – 4.956: 24.956 – 5.253: 05.253 – 5.549: 15.549 – 5.846: 05.846 – 6.143: 06.143 – 6.439: 06.439 – 6.736: 06.736 – 7.032: 07.032 – 7.329: 102468
n / missing261 / 222
Mean ± SD2.629 ± 1.46
Median2.609
Range0.2116 – 7.329
CV0.554
Skew / kurtosis0.73 / 1.9
Normal?no

raw_label

metadata · categorical
raw_label classes1.121.12: 550.980.98: 552.862.86: 442.62.6: 441.251.25: 440.960.96: 441.081.08: 441.071.07: 441.991.99: 331.561.56: 33+10 more+10 more: 2929
n / missing261 / 0
Classes171
Balance (entropy)0.98
Imbalance ratio5
Top class1.12 (5)

reference_value

metadata · numeric
reference_value distribution020400.57 – 0.7471: 80.7471 – 0.9242: 240.9242 – 1.101: 361.101 – 1.278: 321.278 – 1.455: 191.455 – 1.632: 211.632 – 1.81: 141.81 – 1.987: 81.987 – 2.164: 172.164 – 2.341: 92.341 – 2.518: 72.518 – 2.695: 182.695 – 2.872: 142.872 – 3.049: 33.049 – 3.226: 33.226 – 3.403: 43.403 – 3.58: 13.58 – 3.757: 23.757 – 3.935: 03.935 – 4.112: 34.112 – 4.289: 64.289 – 4.466: 44.466 – 4.643: 44.643 – 4.82: 40246
n / missing261 / 0
Mean ± SD1.877 ± 1.02
Median1.56
Range0.57 – 4.82
CV0.541
Skew / kurtosis1.1 / 0.65
Normal?no
Constant metadata 30
  • SpectralRep1
  • Acquisition_MethodLC
  • Measurement_QuantityReflectance
  • Leaf_or_Canopyleaf
  • Target_TypeVegetation
  • Spectra_UnitsRatio
  • Wavelength_Unitsnanometers
  • Spectral_Resolution1nm
  • CHN_Year_Analyzed2,014
  • CHN_QC1.0
  • GE_Leaf_AgeNew
  • Leaf_Area_sq_cm2
  • datasetEcoSIS
  • dataset_sluguw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
  • task_typeregression
  • trait_headerN_mass_g_g
  • target_original_nameN_mass_g_g
  • measurement_hintUW-BNL_NASA_HyspIRI_Airborne_Campaign_Compiled_Leaf_and_Canopy_Spectra
  • n_points_original2,151
  • dimensions1D
  • axis_notewavelengths_as_provided_by_EcoSIS_resource
  • official_data_urlhttps://data.ecosis.org/dataset/uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
  • official_package_urlhttps://ecosis.org/package/uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
  • 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_uw_bnl_nasa_hyspiri_airborne_campaign_leaf_and_canopy_s_reflectance_nirs
  • notesTask-specific EcoSIS target not present in active parent Y table, wavelengths kept as provided by EcoSIS resource, no project interpolation.

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

Alignment

Alignment levelsample
Sample id availableyes
Samples261
Observations (total)358
Reps per samplemin 1 · mean 1.372 · max 2

Provenance & citation

ContributorUW-BNL NASA HyspIRI Airborne Campaign Leaf and Canopy Spectra and Trait Data
Origin · url [open]https://data.ecosis.org/dataset/uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
Origin · url [open]https://data.ecosis.org/api/3/action/package_show?id=uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
Origin · url [open]https://ecosis.org/package/uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)

Governance & integrity

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 version1.0.0
Schema / protocol2.0
Content hasha35904706b77889a…
Processing hashb783a4728d1cc2d6…
Metadata hash3d5a668f8496637d…

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

ds = get("leaf_n_mass_g_g_uw_bnl_ecosis")            # 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.