← Back to the catalog
Private

EcoSIS NGEE Arctic Leaf Spectral Reflectance Utqiagvik (Barrow) Alaska 2013 (reflectance)

ecosis · NIR

EcoSIS NGEE Arctic Leaf Spectral Reflectance Utqiagvik (Barrow) Alaska 2013 (reflectance). v2.0 standardized NIRS package: 1 spectral source(s), 31 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2ecosis
🔒
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.
69
samples
2,151
wavelengths
1
sources
31
targets
27
metadata
NIR
family

Dataset property explorer

Mean profile risk0.43
Highest axisArtefacts locaux · 1.00
Diagnostics8
Sources profiled1
EcoSIS NGEE Arctic Leaf Spectral Reflectance Utqiagvik (Barrow) Alaska 2013 (reflectance) property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS NGEE Arctic Leaf Spectral Reflectance Utqiagvik (Barrow) Alaska 2013 (reflectance) profileintegrity: 0.00noise: 0.00artefacts: 1.00baseline: 0.70PCA outliers: 0.44reference: 0.64repeatability: 0.00structure: 0.69EcoSIS NGEE Arc…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux1.00
Bruit0.00
Outliers PCA0.44
Distance à la référence0.64
Répétabilité0.00
Baseline / forme0.70
Structure multi-régimes0.69
Diagnostic hypotheses00.250.50.751hypothesis scoreSplice / raccord détecteursSplice / raccord détecteurs: 0.740.74Erreur interpolation / réécha…Erreur interpolation / rééchantillonnage: 0.600.60Signature VERA25-likeSignature VERA25-like: 0.540.54Erreur calibration / référenc…Erreur calibration / référence blanche: 0.540.54Fond différentFond différent: 0.470.47Différence de sonde / géométr…Différence de sonde / géométrie: 0.460.46Spectre hors domaine valideSpectre hors domaine valide: 0.400.40Dataset multi-régimesDataset multi-régimes: 0.390.39
DiagnosticScoreForceSignauxInterprétation probable
Splice / raccord détecteursX0.74forteSpike rate 1.00, Jump rate 1.00, SNR non dégradé 1.00Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Erreur interpolation / rééchantillonnageX0.60moyenneSpike rate 1.00, Jump rate 1.00, SNR normal/élevé 1.00Artefacts numériques ou traitement spectral incorrect.
Signature VERA25-likeX0.54moyenneSpike rate 1.00, Jump rate 1.00, RMS/SAM référence 0.64Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Erreur calibration / référence blancheX0.54moyenneartefacts locaux 1.00, Baseline/mean/area 0.70, RMS/SAM référence 0.64Décalage systématique entre campagnes, instruments ou référence blanche.
Fond différentX0.47moyenneBaseline/mean/area 0.70, RMS/SAM référence 0.64, Mahalanobis / T2 0.44Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.
Différence de sonde / géométrieX0.46moyenneBaseline/mean/area 0.70, RMS/SAM référence 0.64, Mahalanobis / T2 0.44Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Spectre hors domaine valideX0.40faibleStructure PCA 0.69, RMS/SAM référence 0.64, Mahalanobis / T2 0.44Variété, espèce, lot ou condition différente mais physiquement plausible.
Dataset multi-régimesX0.39faibleStructure PCA 0.69, RMS/SAM référence 0.64, Mahalanobis / T2 0.44Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.

Spectral sources

Leaf_Spectra_Barrow_2013_20180824_ecosis.csv

X · NIR · Analytical Spectral Devices FieldSpec 3
Leaf_Spectra_Barrow_2013_20180824_ecosis.csv spectra0.00.20.40.601,0002,0003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm350nm — median 0.05224 (q25–q75 0.04411–0.06155)365nm — median 0.04173 (q25–q75 0.03235–0.04943)381nm — median 0.03598 (q25–q75 0.02613–0.0444)396nm — median 0.03281 (q25–q75 0.02339–0.03937)412nm — median 0.0319 (q25–q75 0.02356–0.03917)427nm — median 0.03276 (q25–q75 0.02714–0.04538)443nm — median 0.0367 (q25–q75 0.02999–0.05087)458nm — median 0.03806 (q25–q75 0.03018–0.05541)474nm — median 0.03798 (q25–q75 0.02998–0.0556)489nm — median 0.03804 (q25–q75 0.02984–0.05545)505nm — median 0.0437 (q25–q75 0.03426–0.06183)520nm — median 0.06456 (q25–q75 0.05222–0.0849)536nm — median 0.09667 (q25–q75 0.08008–0.1182)551nm — median 0.1079 (q25–q75 0.08906–0.1292)567nm — median 0.0954 (q25–q75 0.07703–0.1165)582nm — median 0.07795 (q25–q75 0.06296–0.09526)597nm — median 0.07018 (q25–q75 0.0566–0.08611)613nm — median 0.06228 (q25–q75 0.05035–0.08045)628nm — median 0.05607 (q25–q75 0.04547–0.07353)644nm — median 0.05212 (q25–q75 0.04007–0.06597)659nm — median 0.0426 (q25–q75 0.03274–0.05742)675nm — median 0.03883 (q25–q75 0.03092–0.05246)690nm — median 0.05279 (q25–q75 0.04084–0.06972)706nm — median 0.1713 (q25–q75 0.1517–0.1906)721nm — median 0.3226 (q25–q75 0.2955–0.3416)737nm — median 0.4242 (q25–q75 0.3958–0.4677)752nm — median 0.4591 (q25–q75 0.4377–0.5095)768nm — median 0.4663 (q25–q75 0.4439–0.5152)783nm — median 0.4676 (q25–q75 0.4429–0.5149)799nm — median 0.4684 (q25–q75 0.4426–0.5149)814nm — median 0.4694 (q25–q75 0.4427–0.5149)829nm — median 0.4698 (q25–q75 0.4427–0.5154)845nm — median 0.4707 (q25–q75 0.4428–0.5163)860nm — median 0.4717 (q25–q75 0.443–0.5172)876nm — median 0.4722 (q25–q75 0.443–0.5177)891nm — median 0.4724 (q25–q75 0.443–0.5176)907nm — median 0.4716 (q25–q75 0.4432–0.5172)922nm — median 0.4707 (q25–q75 0.4431–0.5163)938nm — median 0.4681 (q25–q75 0.4422–0.5129)953nm — median 0.4637 (q25–q75 0.4389–0.5049)969nm — median 0.4597 (q25–q75 0.4345–0.4981)984nm — median 0.4595 (q25–q75 0.4343–0.4977)1,000nm — median 0.4616 (q25–q75 0.4362–0.5007)1,015nm — median 0.4638 (q25–q75 0.4384–0.5047)1,031nm — median 0.4664 (q25–q75 0.4407–0.5088)1,046nm — median 0.4677 (q25–q75 0.4419–0.5119)1,062nm — median 0.4667 (q25–q75 0.4418–0.5134)1,077nm — median 0.4658 (q25–q75 0.4411–0.5131)1,092nm — median 0.4651 (q25–q75 0.4401–0.5114)1,108nm — median 0.4633 (q25–q75 0.4384–0.5085)1,123nm — median 0.4608 (q25–q75 0.4358–0.5044)1,139nm — median 0.4519 (q25–q75 0.4265–0.4916)1,154nm — median 0.4391 (q25–q75 0.4128–0.4731)1,170nm — median 0.4341 (q25–q75 0.4082–0.4672)1,185nm — median 0.4319 (q25–q75 0.4067–0.4627)1,201nm — median 0.4301 (q25–q75 0.4062–0.4622)1,216nm — median 0.4325 (q25–q75 0.4076–0.4642)1,232nm — median 0.4346 (q25–q75 0.4098–0.4685)1,247nm — median 0.4373 (q25–q75 0.4113–0.4721)1,263nm — median 0.4394 (q25–q75 0.4118–0.4739)1,278nm — median 0.4383 (q25–q75 0.4105–0.4729)1,294nm — median 0.4318 (q25–q75 0.4067–0.4675)1,309nm — median 0.4227 (q25–q75 0.3995–0.4551)1,324nm — median 0.4099 (q25–q75 0.3872–0.4354)1,340nm — median 0.3879 (q25–q75 0.3691–0.4118)1,355nm — median 0.3695 (q25–q75 0.3514–0.3925)1,371nm — median 0.3416 (q25–q75 0.3254–0.3659)1,386nm — median 0.2775 (q25–q75 0.2563–0.3129)1,402nm — median 0.1881 (q25–q75 0.1601–0.2234)1,417nm — median 0.1415 (q25–q75 0.1174–0.1789)1,433nm — median 0.1252 (q25–q75 0.1001–0.1602)1,448nm — median 0.1218 (q25–q75 0.09625–0.1587)1,464nm — median 0.1258 (q25–q75 0.09935–0.1638)1,479nm — median 0.1388 (q25–q75 0.1096–0.1762)1,495nm — median 0.1562 (q25–q75 0.1288–0.1931)1,510nm — median 0.1749 (q25–q75 0.1511–0.2136)1,526nm — median 0.1965 (q25–q75 0.1725–0.2358)1,541nm — median 0.2131 (q25–q75 0.1954–0.255)1,556nm — median 0.2291 (q25–q75 0.211–0.272)1,572nm — median 0.2426 (q25–q75 0.2284–0.2872)1,587nm — median 0.2566 (q25–q75 0.239–0.2996)1,603nm — median 0.2704 (q25–q75 0.2489–0.3111)1,618nm — median 0.2794 (q25–q75 0.2596–0.32)1,634nm — median 0.2891 (q25–q75 0.2677–0.3275)1,649nm — median 0.293 (q25–q75 0.273–0.3321)1,665nm — median 0.2961 (q25–q75 0.2752–0.334)1,680nm — median 0.2963 (q25–q75 0.2756–0.3327)1,696nm — median 0.293 (q25–q75 0.2723–0.3278)1,711nm — median 0.2877 (q25–q75 0.2672–0.3224)1,727nm — median 0.281 (q25–q75 0.2596–0.3158)1,742nm — median 0.2708 (q25–q75 0.2505–0.3083)1,758nm — median 0.2568 (q25–q75 0.2401–0.2981)1,773nm — median 0.2462 (q25–q75 0.2297–0.2902)1,788nm — median 0.2413 (q25–q75 0.2263–0.2871)1,804nm — median 0.2415 (q25–q75 0.2273–0.288)1,819nm — median 0.2421 (q25–q75 0.2282–0.2888)1,835nm — median 0.2387 (q25–q75 0.2256–0.2859)1,850nm — median 0.2237 (q25–q75 0.2124–0.2719)1,866nm — median 0.1815 (q25–q75 0.1624–0.2233)1,881nm — median 0.1082 (q25–q75 0.09159–0.1423)1,897nm — median 0.05025 (q25–q75 0.04355–0.06605)1,912nm — median 0.03738 (q25–q75 0.0323–0.04711)1,928nm — median 0.03578 (q25–q75 0.03041–0.04379)1,943nm — median 0.03659 (q25–q75 0.03188–0.04633)1,959nm — median 0.0404 (q25–q75 0.03546–0.05096)1,974nm — median 0.04569 (q25–q75 0.03925–0.05831)1,990nm — median 0.05346 (q25–q75 0.04471–0.06854)2,005nm — median 0.06133 (q25–q75 0.04906–0.07901)2,021nm — median 0.07 (q25–q75 0.05589–0.09176)2,036nm — median 0.07812 (q25–q75 0.06174–0.1031)2,051nm — median 0.08491 (q25–q75 0.06811–0.1134)2,067nm — median 0.09281 (q25–q75 0.07605–0.1248)2,082nm — median 0.101 (q25–q75 0.08438–0.1342)2,098nm — median 0.1084 (q25–q75 0.09244–0.1451)2,113nm — median 0.1157 (q25–q75 0.1001–0.1549)2,129nm — median 0.1236 (q25–q75 0.1068–0.1643)2,144nm — median 0.1298 (q25–q75 0.1117–0.1709)2,160nm — median 0.1352 (q25–q75 0.1177–0.1765)2,175nm — median 0.1393 (q25–q75 0.1224–0.1804)2,191nm — median 0.1437 (q25–q75 0.1271–0.1851)2,206nm — median 0.1469 (q25–q75 0.1307–0.1884)2,222nm — median 0.148 (q25–q75 0.1314–0.1897)2,237nm — median 0.145 (q25–q75 0.1287–0.1866)2,253nm — median 0.1374 (q25–q75 0.122–0.1782)2,268nm — median 0.1292 (q25–q75 0.1138–0.1692)2,283nm — median 0.1232 (q25–q75 0.1069–0.162)2,299nm — median 0.1162 (q25–q75 0.09976–0.1539)2,314nm — median 0.1084 (q25–q75 0.09309–0.1446)2,330nm — median 0.1017 (q25–q75 0.0873–0.1361)2,345nm — median 0.09518 (q25–q75 0.07961–0.1271)2,361nm — median 0.0887 (q25–q75 0.07345–0.119)2,376nm — median 0.08212 (q25–q75 0.06708–0.1106)2,392nm — median 0.07505 (q25–q75 0.06105–0.1012)2,407nm — median 0.06862 (q25–q75 0.05557–0.09252)2,423nm — median 0.06211 (q25–q75 0.05005–0.08282)2,438nm — median 0.05743 (q25–q75 0.04627–0.07494)2,454nm — median 0.05141 (q25–q75 0.04298–0.06607)2,469nm — median 0.04752 (q25–q75 0.03978–0.05924)2,485nm — median 0.04326 (q25–q75 0.03732–0.05463)2,500nm — median 0.04059 (q25–q75 0.03599–0.05158)

Sampling

Wavelengths2,151
Axis range350–2,500 nm
Mean spacing1 nm
Griduniform
Observations69

Signal & quality

Value range0.0143 – 0.562
Mean range0.0316 – 0.467
Mean level0.2306
Area495.9
PTP0.4358
Noise RMS1.3308e-05
SNR1.7e+04
SNR dB8e+01 dB
Dynamic range0.436
Smoothness0.000168
Saturated0.0%
X-outliers22

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.00%
Spike count5,936
Spike rate4.00%
Jump count5,379
Jump rate3.63%
Clip fraction0.00%

Shape & reference

Baseline slope-0.15267
Curvature RMS0.00015966
D1 RMS0.0016417
RMS to mean0.035965
RMS p950.070215
SAM to mean0.07625
SAM p950.14264
Affine offset p950.060162
Affine gain p95 Δ0.27625
Affine residual p950.02848
Xcorr lag p950

Outliers & repeatability

PCA Q p95/median2.9
Hotelling T2 p95/median3.1
Mahalanobis H p95/median1.8
Repeat groups0

Dimensionality (PCA)

Effective rank2.2
PCs → 95% var2
PCs → 99% var4
Top-10 cum. var100.0%
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_reflectance0.230570.70moyenValeur 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_curve495.910.70moyenValeur 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_peak0.435810.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance0.0271840.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms1.3308e-050.00faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr173260.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min66.3350.00faibleZone 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_count5,9361.00fortArtefactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate4%1.00fortSpectre suspectInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count5,3791.00fortRaccord détecteurSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate3.63%1.00fortProblème spectralCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction0.00135%0.00faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope-0.152670.70moyenDériveÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.000159660.04faibleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.00164170.08faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio2.86860.36faibleConformeArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio3.1340.39faibleCentralVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio1.76880.44moyenOutlier globalDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p950.0702150.64moyenSpectre 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_p950.142640.41faibleSimilaireFond, 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_density1.62570.69moyenSous-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_p952.06550.53moyenSpectre 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_p950.58410.69moyenSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X PCA score plot-7.5-5.0-2.50.02.55.0-4-202PC1 -0.1748 · PC2 0.4324PC1 -1.031 · PC2 0.004772PC1 -0.6958 · PC2 0.2735PC1 -0.2361 · PC2 -0.07551PC1 -0.6647 · PC2 0.2684PC1 0.9076 · PC2 1.531PC1 0.151 · PC2 -0.1597PC1 -0.5517 · PC2 0.5527PC1 -0.4843 · PC2 1.184PC1 -0.5197 · PC2 0.807PC1 -0.1952 · PC2 0.5722PC1 -0.7778 · PC2 0.5669PC1 0.8808 · PC2 1.351PC1 1.102 · PC2 1.419PC1 0.8311 · PC2 1.713PC1 -3.766 · PC2 0.5368PC1 1.286 · PC2 1.477PC1 0.3317 · PC2 0.7342PC1 0.8899 · PC2 1.982PC1 -1.618 · PC2 0.8814PC1 0.5506 · PC2 1.54PC1 -5.687 · PC2 -0.8815PC1 -0.2965 · PC2 0.321PC1 0.9679 · PC2 0.1567PC1 -0.06099 · PC2 1.196PC1 -0.4135 · PC2 -0.5432PC1 -0.1913 · PC2 1.381PC1 -0.2623 · PC2 -0.9409PC1 1.149 · PC2 1.324PC1 -3.999 · PC2 -1.038PC1 -2.069 · PC2 -0.1772PC1 0.8193 · PC2 -1.916PC1 1.585 · PC2 -1.658PC1 0.4351 · PC2 -1.863PC1 -1.229 · PC2 -1.056PC1 1.383 · PC2 -2.07PC1 -0.9917 · PC2 -1.302PC1 1.959 · PC2 -2.166PC1 1.044 · PC2 1.37PC1 1.637 · PC2 -2.052PC1 0.6054 · PC2 1.082PC1 0.7923 · PC2 1.677PC1 -1.839 · PC2 -0.2212PC1 0.4032 · PC2 1.992PC1 -1.417 · PC2 -0.07547PC1 1.519 · PC2 1.441PC1 -3.134 · PC2 -0.2725PC1 -0.05397 · PC2 -0.003809PC1 -1.848 · PC2 -0.1462PC1 -3.018 · PC2 -0.2345PC1 -0.2013 · PC2 0.08916PC1 -0.485 · PC2 0.2275PC1 -1.503 · PC2 -0.2268PC1 -2.647 · PC2 -1.276PC1 0.7274 · PC2 -0.8522PC1 1.4 · PC2 -1.188PC1 0.6952 · PC2 -1.218PC1 0.2604 · PC2 -1.061PC1 3.163 · PC2 -0.9488PC1 -0.1218 · PC2 0.7155PC1 2.904 · PC2 -0.9221PC1 1.363 · PC2 -0.4325PC1 1.672 · PC2 -1.082PC1 2.406 · PC2 -0.3702PC1 0.4737 · PC2 -0.2769PC1 2.862 · PC2 -0.5836PC1 2.174 · PC2 -0.2674PC1 0.6824 · PC2 -0.4119PC1 0.1719 · PC2 -0.8292PC1 (67.1%)PC2 (29.4%)69 scores
PCA explained variance0%25%50%75%100%PC1: 67.1% (cumulative 67.1%)1PC2: 29.4% (cumulative 96.4%)2PC3: 2.3% (cumulative 98.7%)3PC4: 0.5% (cumulative 99.2%)4PC5: 0.3% (cumulative 99.5%)5PC6: 0.2% (cumulative 99.7%)6PC7: 0.1% (cumulative 99.8%)7PC8: 0.1% (cumulative 99.9%)8PC9: 0.0% (cumulative 99.9%)9PC10: 0.0% (cumulative 100.0%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
X-Y spectral correlation 20
X · Cmass_g_g spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale
X · Nmass_g_g spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale
X · LMA_g_m2 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
Cmass_g_g0.553530.2523.9%
Nmass_g_g0.5381,3600.3589.1%
LMA_g_m20.492,1490.2620.0%
C_area_g_m20.4112,1510.2150.0%
N_area_g_m20.4977610.2410.0%
CN_ratio0.4435530.2850.0%
Total_Leaf_Area0.3567010.2050.0%
A6520.4767020.1670.0%
A6650.4877020.1610.0%
A7500.3953730.1320.0%
A652bc0.4887020.1690.0%
A665bc0.4937020.1620.0%
Chl_a_L0.4947020.1590.0%
Chl_b_L0.4457010.1860.0%
Chl_a_area_L0.357030.1620.0%
Chl_b_area_L0.3367560.160.0%
Chl_ab_area_L0.3417020.1650.0%
Chl_a_b_ratio_L0.2137290.1190.0%
Chl_a_P0.4957020.1590.0%
Chl_b_P0.4487010.1850.0%

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 31

Cmass_g_g

target · numeric
Cmass_g_g distribution051042.2 – 42.58: 242.58 – 42.96: 042.96 – 43.34: 443.34 – 43.72: 343.72 – 44.1: 244.1 – 44.48: 344.48 – 44.85: 244.85 – 45.23: 245.23 – 45.61: 245.61 – 45.99: 445.99 – 46.37: 546.37 – 46.75: 846.75 – 47.13: 647.13 – 47.51: 647.51 – 47.89: 447.89 – 48.27: 248.27 – 48.65: 148.65 – 49.02: 249.02 – 49.4: 249.4 – 49.78: 049.78 – 50.16: 450.16 – 50.54: 150.54 – 50.92: 350.92 – 51.3: 140.042.545.047.550.052.5
n / missing69 / 0
Mean ± SD46.64 ± 2.23
Median46.7
Range42.2 – 51.3
CV0.0478
Skew / kurtosis0.078 / -0.48
Normal?yes

Nmass_g_g

target · numeric
Nmass_g_g distribution05100.9 – 1.025: 11.025 – 1.15: 21.15 – 1.275: 11.275 – 1.4: 11.4 – 1.525: 21.525 – 1.65: 01.65 – 1.775: 11.775 – 1.9: 11.9 – 2.025: 92.025 – 2.15: 42.15 – 2.275: 22.275 – 2.4: 12.4 – 2.525: 32.525 – 2.65: 52.65 – 2.775: 52.775 – 2.9: 62.9 – 3.025: 53.025 – 3.15: 43.15 – 3.275: 43.275 – 3.4: 43.4 – 3.525: 43.525 – 3.65: 23.65 – 3.775: 13.775 – 3.9: 10.10.20.512510
n / missing69 / 0
Mean ± SD2.565 ± 0.701
Median2.7
Range0.9 – 3.9
CV0.273
Skew / kurtosis-0.43 / -0.46
Normal?yes

LMA_g_m2

target · numeric
LMA_g_m2 distribution05101541.25 – 47.22: 347.22 – 53.19: 753.19 – 59.16: 1359.16 – 65.13: 1565.13 – 71.1: 971.1 – 77.07: 377.07 – 83.04: 783.04 – 89.01: 789.01 – 94.98: 194.98 – 101: 3101 – 106.9: 0106.9 – 112.9: 0112.9 – 118.9: 0118.9 – 124.8: 0124.8 – 130.8: 0130.8 – 136.8: 0136.8 – 142.7: 0142.7 – 148.7: 0148.7 – 154.7: 0154.7 – 160.7: 0160.7 – 166.6: 0166.6 – 172.6: 0172.6 – 178.6: 0178.6 – 184.5: 1050100150200
n / missing69 / 0
Mean ± SD68.35 ± 19.9
Median64.28
Range41.25 – 184.5
CV0.291
Skew / kurtosis3 / 16
Normal?no

C_area_g_m2

target · numeric
C_area_g_m2 distribution0102019.24 – 22.35: 422.35 – 25.46: 825.46 – 28.56: 1728.56 – 31.67: 1631.67 – 34.78: 734.78 – 37.89: 337.89 – 41: 341 – 44.11: 644.11 – 47.22: 447.22 – 50.32: 050.32 – 53.43: 053.43 – 56.54: 056.54 – 59.65: 059.65 – 62.76: 062.76 – 65.87: 065.87 – 68.97: 068.97 – 72.08: 072.08 – 75.19: 075.19 – 78.3: 078.3 – 81.41: 081.41 – 84.52: 084.52 – 87.62: 087.62 – 90.73: 090.73 – 93.84: 1020406080100
n / missing69 / 0
Mean ± SD31.96 ± 10.2
Median29.98
Range19.24 – 93.84
CV0.319
Skew / kurtosis3.5 / 19
Normal?no

N_area_g_m2

target · numeric
N_area_g_m2 distribution0510150.66 – 0.7567: 30.7567 – 0.8533: 10.8533 – 0.95: 20.95 – 1.047: 01.047 – 1.143: 21.143 – 1.24: 31.24 – 1.337: 61.337 – 1.433: 21.433 – 1.53: 51.53 – 1.627: 61.627 – 1.723: 51.723 – 1.82: 111.82 – 1.917: 21.917 – 2.013: 32.013 – 2.11: 12.11 – 2.207: 62.207 – 2.303: 32.303 – 2.4: 22.4 – 2.497: 32.497 – 2.593: 12.593 – 2.69: 02.69 – 2.787: 12.787 – 2.883: 02.883 – 2.98: 10123
n / missing69 / 0
Mean ± SD1.702 ± 0.495
Median1.72
Range0.66 – 2.98
CV0.291
Skew / kurtosis0.034 / -0.013
Normal?yes

CN_ratio

target · numeric
CN_ratio distribution05101511.56 – 13.36: 613.36 – 15.15: 915.15 – 16.95: 1116.95 – 18.75: 1418.75 – 20.54: 720.54 – 22.34: 622.34 – 24.14: 724.14 – 25.93: 125.93 – 27.73: 127.73 – 29.53: 029.53 – 31.32: 031.32 – 33.12: 133.12 – 34.92: 134.92 – 36.71: 136.71 – 38.51: 038.51 – 40.31: 140.31 – 42.1: 142.1 – 43.9: 143.9 – 45.7: 045.7 – 47.49: 047.49 – 49.29: 049.29 – 51.09: 051.09 – 52.88: 052.88 – 54.68: 10204060
n / missing69 / 0
Mean ± SD20.09 ± 7.86
Median17.58
Range11.56 – 54.68
CV0.391
Skew / kurtosis2.3 / 6.1
Normal?no

Total_Leaf_Area

target · numeric
Total_Leaf_Area distribution05100.000525 – 0.0005603: 10.0005603 – 0.0005956: 10.0005956 – 0.0006309: 10.0006309 – 0.0006662: 30.0006662 – 0.0007015: 40.0007015 – 0.0007368: 60.0007368 – 0.0007721: 90.0007721 – 0.0008074: 50.0008074 – 0.0008427: 50.0008427 – 0.000878: 50.000878 – 0.0009133: 70.0009133 – 0.0009486: 10.0009486 – 0.0009839: 30.0009839 – 0.001019: 60.001019 – 0.001055: 60.001055 – 0.00109: 10.00109 – 0.001125: 40.001125 – 0.00116: 00.00116 – 0.001196: 00.001196 – 0.001231: 00.001231 – 0.001266: 00.001266 – 0.001302: 00.001302 – 0.001337: 00.001337 – 0.001372: 10.00010.00020.00050.0010.0020.0050.01
n / missing69 / 0
Mean ± SD0.000854 ± 0.000154
Median0.0008308
Range0.000525 – 0.001372
CV0.181
Skew / kurtosis0.52 / 0.55
Normal?yes

A652

target · numeric
A652 distribution02580.126 – 0.1371: 50.1371 – 0.1482: 20.1482 – 0.1594: 20.1594 – 0.1705: 20.1705 – 0.1816: 20.1816 – 0.1928: 30.1928 – 0.2039: 20.2039 – 0.215: 40.215 – 0.2261: 60.2261 – 0.2373: 20.2373 – 0.2484: 20.2484 – 0.2595: 40.2595 – 0.2706: 50.2706 – 0.2818: 70.2818 – 0.2929: 20.2929 – 0.304: 20.304 – 0.3151: 30.3151 – 0.3263: 30.3263 – 0.3374: 10.3374 – 0.3485: 30.3485 – 0.3596: 40.3596 – 0.3708: 20.3708 – 0.3819: 00.3819 – 0.393: 10.10.20.51
n / missing69 / 0
Mean ± SD0.25 ± 0.0696
Median0.254
Range0.126 – 0.393
CV0.279
Skew / kurtosis-0.012 / -0.87
Normal?yes

A665

target · numeric
A665 distribution02460.223 – 0.2423: 30.2423 – 0.2616: 30.2616 – 0.2809: 30.2809 – 0.3002: 10.3002 – 0.3195: 30.3195 – 0.3387: 30.3387 – 0.358: 10.358 – 0.3773: 50.3773 – 0.3966: 50.3966 – 0.4159: 30.4159 – 0.4352: 30.4352 – 0.4545: 40.4545 – 0.4738: 40.4738 – 0.4931: 40.4931 – 0.5124: 50.5124 – 0.5317: 20.5317 – 0.551: 20.551 – 0.5703: 20.5703 – 0.5895: 30.5895 – 0.6088: 20.6088 – 0.6281: 40.6281 – 0.6474: 30.6474 – 0.6667: 00.6667 – 0.686: 10.10.20.51
n / missing69 / 0
Mean ± SD0.4413 ± 0.121
Median0.448
Range0.223 – 0.686
CV0.275
Skew / kurtosis-0.0016 / -0.9
Normal?no

A750

target · numeric
A750 distribution01020-0.01 – -0.009292: 1-0.009292 – -0.008583: 2-0.008583 – -0.007875: 2-0.007875 – -0.007167: 0-0.007167 – -0.006458: 0-0.006458 – -0.00575: 0-0.00575 – -0.005042: 0-0.005042 – -0.004333: 1-0.004333 – -0.003625: 0-0.003625 – -0.002917: 2-0.002917 – -0.002208: 0-0.002208 – -0.0015: 1-0.0015 – -0.0007917: 4-0.0007917 – -8.333e-05: 0-8.333e-05 – 0.000625: 160.000625 – 0.001333: 30.001333 – 0.002042: 170.002042 – 0.00275: 00.00275 – 0.003458: 50.003458 – 0.004167: 60.004167 – 0.004875: 00.004875 – 0.005583: 40.005583 – 0.006292: 40.006292 – 0.007: 1-0.010-0.0050.0000.0050.010
n / missing69 / 0
Mean ± SD0.0009565 ± 0.0036
Median0.002
Range-0.01 – 0.007
CV3.77
Skew / kurtosis-1.3 / 2.1
Normal?no

A652bc

target · numeric
A652bc distribution02580.128 – 0.1389: 30.1389 – 0.1498: 30.1498 – 0.1608: 20.1608 – 0.1717: 40.1717 – 0.1826: 20.1826 – 0.1935: 20.1935 – 0.2044: 20.2044 – 0.2153: 50.2153 – 0.2263: 50.2263 – 0.2372: 10.2372 – 0.2481: 60.2481 – 0.259: 10.259 – 0.2699: 70.2699 – 0.2808: 50.2808 – 0.2918: 30.2918 – 0.3027: 20.3027 – 0.3136: 20.3136 – 0.3245: 30.3245 – 0.3354: 00.3354 – 0.3463: 50.3463 – 0.3573: 10.3573 – 0.3682: 40.3682 – 0.3791: 00.3791 – 0.39: 10.10.20.51
n / missing69 / 0
Mean ± SD0.2491 ± 0.0682
Median0.248
Range0.128 – 0.39
CV0.274
Skew / kurtosis0.029 / -0.87
Normal?yes

A665bc

target · numeric
A665bc distribution02460.225 – 0.2441: 30.2441 – 0.2632: 30.2632 – 0.2823: 20.2823 – 0.3013: 50.3013 – 0.3204: 10.3204 – 0.3395: 20.3395 – 0.3586: 10.3586 – 0.3777: 60.3777 – 0.3968: 40.3968 – 0.4158: 20.4158 – 0.4349: 40.4349 – 0.454: 40.454 – 0.4731: 40.4731 – 0.4922: 40.4922 – 0.5113: 50.5113 – 0.5303: 30.5303 – 0.5494: 20.5494 – 0.5685: 10.5685 – 0.5876: 30.5876 – 0.6067: 20.6067 – 0.6258: 30.6258 – 0.6448: 40.6448 – 0.6639: 00.6639 – 0.683: 10.10.20.51
n / missing69 / 0
Mean ± SD0.4404 ± 0.12
Median0.446
Range0.225 – 0.683
CV0.273
Skew / kurtosis0.021 / -0.9
Normal?no

Chl_a_L

target · numeric
Chl_a_L distribution02462.59 – 2.809: 22.809 – 3.028: 43.028 – 3.247: 33.247 – 3.467: 33.467 – 3.686: 13.686 – 3.905: 23.905 – 4.124: 34.124 – 4.343: 44.343 – 4.562: 54.562 – 4.782: 24.782 – 5.001: 45.001 – 5.22: 45.22 – 5.439: 25.439 – 5.658: 65.658 – 5.877: 55.877 – 6.097: 36.097 – 6.316: 26.316 – 6.535: 16.535 – 6.754: 26.754 – 6.973: 46.973 – 7.192: 37.192 – 7.412: 37.412 – 7.631: 07.631 – 7.85: 112510
n / missing69 / 0
Mean ± SD5.082 ± 1.39
Median5.11
Range2.59 – 7.85
CV0.273
Skew / kurtosis0.023 / -0.91
Normal?no

Chl_b_L

target · numeric
Chl_b_L distribution02580.78 – 0.8688: 10.8688 – 0.9575: 20.9575 – 1.046: 41.046 – 1.135: 21.135 – 1.224: 31.224 – 1.312: 31.312 – 1.401: 41.401 – 1.49: 41.49 – 1.579: 21.579 – 1.667: 41.667 – 1.756: 71.756 – 1.845: 31.845 – 1.934: 41.934 – 2.022: 32.022 – 2.111: 62.111 – 2.2: 32.2 – 2.289: 12.289 – 2.377: 52.377 – 2.466: 12.466 – 2.555: 32.555 – 2.644: 12.644 – 2.732: 12.732 – 2.821: 02.821 – 2.91: 20.10.20.512510
n / missing69 / 0
Mean ± SD1.761 ± 0.518
Median1.74
Range0.78 – 2.91
CV0.294
Skew / kurtosis0.16 / -0.66
Normal?yes

Chl_a_area_L

target · numeric
Chl_a_area_L distribution0510192 – 203.5: 4203.5 – 214.9: 3214.9 – 226.4: 5226.4 – 237.8: 1237.8 – 249.3: 3249.3 – 260.8: 2260.8 – 272.2: 3272.2 – 283.7: 5283.7 – 295.1: 4295.1 – 306.6: 10306.6 – 318: 5318 – 329.5: 7329.5 – 341: 3341 – 352.4: 4352.4 – 363.9: 2363.9 – 375.3: 3375.3 – 386.8: 0386.8 – 398.2: 2398.2 – 409.7: 1409.7 – 421.2: 0421.2 – 432.6: 1432.6 – 444.1: 0444.1 – 455.5: 0455.5 – 467: 11002005001,000
n / missing69 / 0
Mean ± SD296.4 ± 58.7
Median300
Range192 – 467
CV0.198
Skew / kurtosis0.23 / 0.045
Normal?yes

Chl_b_area_L

target · numeric
Chl_b_area_L distribution025864 – 67.96: 267.96 – 71.92: 571.92 – 75.88: 575.88 – 79.83: 479.83 – 83.79: 283.79 – 87.75: 287.75 – 91.71: 391.71 – 95.67: 495.67 – 99.62: 199.62 – 103.6: 5103.6 – 107.5: 7107.5 – 111.5: 6111.5 – 115.5: 3115.5 – 119.4: 3119.4 – 123.4: 5123.4 – 127.3: 3127.3 – 131.3: 1131.3 – 135.2: 2135.2 – 139.2: 1139.2 – 143.2: 0143.2 – 147.1: 2147.1 – 151.1: 1151.1 – 155: 1155 – 159: 11020501002005001,000
n / missing69 / 0
Mean ± SD102.7 ± 23.4
Median105
Range64 – 159
CV0.228
Skew / kurtosis0.27 / -0.55
Normal?yes

Chl_ab_area_L

target · numeric
Chl_ab_area_L distribution0510262 – 277.2: 4277.2 – 292.3: 5292.3 – 307.5: 4307.5 – 322.7: 1322.7 – 337.8: 2337.8 – 353: 4353 – 368.2: 3368.2 – 383.3: 1383.3 – 398.5: 9398.5 – 413.7: 6413.7 – 428.8: 7428.8 – 444: 5444 – 459.2: 4459.2 – 474.3: 4474.3 – 489.5: 2489.5 – 504.7: 2504.7 – 519.8: 1519.8 – 535: 1535 – 550.2: 2550.2 – 565.3: 0565.3 – 580.5: 1580.5 – 595.7: 0595.7 – 610.8: 0610.8 – 626: 11002005001,000
n / missing69 / 0
Mean ± SD399.1 ± 80
Median401
Range262 – 626
CV0.2
Skew / kurtosis0.26 / -0.016
Normal?yes

Chl_a_b_ratio_L

target · numeric
Chl_a_b_ratio_L distribution05102.28 – 2.339: 22.339 – 2.398: 22.398 – 2.458: 32.458 – 2.517: 02.517 – 2.576: 32.576 – 2.635: 32.635 – 2.694: 52.694 – 2.753: 62.753 – 2.812: 22.812 – 2.872: 82.872 – 2.931: 42.931 – 2.99: 42.99 – 3.049: 63.049 – 3.108: 33.108 – 3.167: 13.167 – 3.227: 73.227 – 3.286: 13.286 – 3.345: 33.345 – 3.404: 03.404 – 3.463: 03.463 – 3.522: 03.522 – 3.582: 03.582 – 3.641: 53.641 – 3.7: 112510
n / missing69 / 0
Mean ± SD2.92 ± 0.337
Median2.88
Range2.28 – 3.7
CV0.115
Skew / kurtosis0.38 / -0.14
Normal?yes

Chl_a_P

target · numeric
Chl_a_P distribution02462.57 – 2.788: 32.788 – 3.006: 33.006 – 3.224: 33.224 – 3.442: 33.442 – 3.66: 13.66 – 3.877: 23.877 – 4.095: 34.095 – 4.313: 44.313 – 4.531: 54.531 – 4.749: 24.749 – 4.967: 44.967 – 5.185: 45.185 – 5.403: 25.403 – 5.621: 65.621 – 5.839: 55.839 – 6.057: 36.057 – 6.275: 26.275 – 6.492: 16.492 – 6.71: 36.71 – 6.928: 36.928 – 7.146: 37.146 – 7.364: 37.364 – 7.582: 07.582 – 7.8: 112510
n / missing69 / 0
Mean ± SD5.046 ± 1.38
Median5.08
Range2.57 – 7.8
CV0.273
Skew / kurtosis0.022 / -0.91
Normal?no

Chl_b_P

target · numeric
Chl_b_P distribution02460.74 – 0.8221: 10.8221 – 0.9042: 20.9042 – 0.9862: 40.9862 – 1.068: 21.068 – 1.15: 41.15 – 1.232: 11.232 – 1.315: 41.315 – 1.397: 51.397 – 1.479: 21.479 – 1.561: 51.561 – 1.643: 41.643 – 1.725: 51.725 – 1.807: 31.807 – 1.889: 41.889 – 1.971: 51.971 – 2.053: 42.053 – 2.135: 12.135 – 2.217: 52.217 – 2.3: 12.3 – 2.382: 12.382 – 2.464: 22.464 – 2.546: 22.546 – 2.628: 02.628 – 2.71: 20.10.20.512510
n / missing69 / 0
Mean ± SD1.656 ± 0.483
Median1.65
Range0.74 – 2.71
CV0.292
Skew / kurtosis0.15 / -0.69
Normal?yes

Chl_a_area_P

target · numeric
Chl_a_area_P distribution051015191 – 202.3: 4202.3 – 213.7: 3213.7 – 225: 5225 – 236.3: 1236.3 – 247.7: 3247.7 – 259: 2259 – 270.3: 3270.3 – 281.7: 5281.7 – 293: 3293 – 304.3: 11304.3 – 315.7: 5315.7 – 327: 7327 – 338.3: 3338.3 – 349.7: 4349.7 – 361: 2361 – 372.3: 3372.3 – 383.7: 0383.7 – 395: 2395 – 406.3: 1406.3 – 417.7: 0417.7 – 429: 1429 – 440.3: 0440.3 – 451.7: 0451.7 – 463: 11002005001,000
n / missing69 / 0
Mean ± SD294.3 ± 58.2
Median297
Range191 – 463
CV0.198
Skew / kurtosis0.23 / 0.035
Normal?yes

Chl_b_area_P

target · numeric
Chl_b_area_P distribution051061 – 64.71: 264.71 – 68.42: 668.42 – 72.12: 772.12 – 75.83: 175.83 – 79.54: 379.54 – 83.25: 283.25 – 86.96: 286.96 – 90.67: 490.67 – 94.38: 394.38 – 98.08: 698.08 – 101.8: 5101.8 – 105.5: 5105.5 – 109.2: 3109.2 – 112.9: 3112.9 – 116.6: 8116.6 – 120.3: 0120.3 – 124: 3124 – 127.8: 0127.8 – 131.5: 1131.5 – 135.2: 2135.2 – 138.9: 1138.9 – 142.6: 0142.6 – 146.3: 1146.3 – 150: 11020501002005001,000
n / missing69 / 0
Mean ± SD96.54 ± 21.8
Median98
Range61 – 150
CV0.225
Skew / kurtosis0.27 / -0.51
Normal?yes

Chl_ab_area_P

target · numeric
Chl_ab_area_P distribution0510256 – 270.9: 4270.9 – 285.8: 5285.8 – 300.6: 4300.6 – 315.5: 1315.5 – 330.4: 2330.4 – 345.2: 4345.2 – 360.1: 3360.1 – 375: 1375 – 389.9: 8389.9 – 404.8: 6404.8 – 419.6: 7419.6 – 434.5: 6434.5 – 449.4: 4449.4 – 464.2: 4464.2 – 479.1: 2479.1 – 494: 2494 – 508.9: 1508.9 – 523.8: 1523.8 – 538.6: 2538.6 – 553.5: 0553.5 – 568.4: 1568.4 – 583.2: 0583.2 – 598.1: 0598.1 – 613: 11002005001,000
n / missing69 / 0
Mean ± SD390.9 ± 78.2
Median393
Range256 – 613
CV0.2
Skew / kurtosis0.25 / -0.013
Normal?yes

Chl_a_b_ratio_P

target · numeric
Chl_a_b_ratio_P distribution02582.43 – 2.49: 22.49 – 2.549: 12.549 – 2.609: 42.609 – 2.668: 02.668 – 2.728: 32.728 – 2.788: 12.788 – 2.847: 62.847 – 2.907: 52.907 – 2.966: 42.966 – 3.026: 63.026 – 3.085: 43.085 – 3.145: 63.145 – 3.205: 53.205 – 3.264: 43.264 – 3.324: 03.324 – 3.383: 73.383 – 3.443: 23.443 – 3.502: 33.502 – 3.562: 03.562 – 3.622: 03.622 – 3.681: 03.681 – 3.741: 03.741 – 3.8: 53.8 – 3.86: 112510
n / missing69 / 0
Mean ± SD3.082 ± 0.34
Median3.04
Range2.43 – 3.86
CV0.11
Skew / kurtosis0.35 / -0.18
Normal?yes

Chl_a_mol_P

target · numeric
Chl_a_mol_P distribution02462.88 – 3.123: 33.123 – 3.367: 33.367 – 3.61: 33.61 – 3.853: 33.853 – 4.097: 14.097 – 4.34: 24.34 – 4.583: 34.583 – 4.827: 44.827 – 5.07: 55.07 – 5.313: 25.313 – 5.557: 45.557 – 5.8: 45.8 – 6.043: 26.043 – 6.287: 66.287 – 6.53: 56.53 – 6.773: 36.773 – 7.017: 27.017 – 7.26: 17.26 – 7.503: 27.503 – 7.747: 47.747 – 7.99: 37.99 – 8.233: 38.233 – 8.477: 08.477 – 8.72: 112510
n / missing69 / 0
Mean ± SD5.645 ± 1.54
Median5.68
Range2.88 – 8.72
CV0.273
Skew / kurtosis0.023 / -0.91
Normal?no

Chl_b_mol_P

target · numeric
Chl_b_mol_P distribution02460.82 – 0.9104: 10.9104 – 1.001: 31.001 – 1.091: 31.091 – 1.182: 31.182 – 1.272: 31.272 – 1.363: 21.363 – 1.453: 41.453 – 1.543: 41.543 – 1.634: 21.634 – 1.724: 51.724 – 1.815: 41.815 – 1.905: 51.905 – 1.995: 41.995 – 2.086: 32.086 – 2.176: 52.176 – 2.267: 42.267 – 2.357: 12.357 – 2.448: 52.448 – 2.538: 12.538 – 2.628: 12.628 – 2.719: 22.719 – 2.809: 22.809 – 2.9: 02.9 – 2.99: 20.10.20.512510
n / missing69 / 0
Mean ± SD1.826 ± 0.533
Median1.82
Range0.82 – 2.99
CV0.292
Skew / kurtosis0.15 / -0.68
Normal?yes

Chl_a_mol_area_P

target · numeric
Chl_a_mol_area_P distribution051015214 – 226.7: 4226.7 – 239.3: 4239.3 – 252: 4252 – 264.7: 1264.7 – 277.3: 3277.3 – 290: 2290 – 302.7: 3302.7 – 315.3: 5315.3 – 328: 3328 – 340.7: 11340.7 – 353.3: 5353.3 – 366: 7366 – 378.7: 3378.7 – 391.3: 4391.3 – 404: 2404 – 416.7: 3416.7 – 429.3: 0429.3 – 442: 2442 – 454.7: 1454.7 – 467.3: 0467.3 – 480: 1480 – 492.7: 0492.7 – 505.3: 0505.3 – 518: 11002005001,000
n / missing69 / 0
Mean ± SD329.3 ± 65.2
Median333
Range214 – 518
CV0.198
Skew / kurtosis0.23 / 0.032
Normal?yes

Chl_b_mol_area_P

target · numeric
Chl_b_mol_area_P distribution025867 – 71.08: 271.08 – 75.17: 675.17 – 79.25: 579.25 – 83.33: 383.33 – 87.42: 387.42 – 91.5: 291.5 – 95.58: 295.58 – 99.67: 499.67 – 103.8: 3103.8 – 107.8: 3107.8 – 111.9: 6111.9 – 116: 6116 – 120.1: 4120.1 – 124.2: 4124.2 – 128.2: 7128.2 – 132.3: 0132.3 – 136.4: 1136.4 – 140.5: 2140.5 – 144.6: 1144.6 – 148.7: 0148.7 – 152.8: 2152.8 – 156.8: 1156.8 – 160.9: 1160.9 – 165: 11020501002005001,000
n / missing69 / 0
Mean ± SD106.5 ± 24
Median108
Range67 – 165
CV0.225
Skew / kurtosis0.28 / -0.51
Normal?yes

Chl_ab_mol_area_P

target · numeric
Chl_ab_mol_area_P distribution0510286 – 302.5: 4302.5 – 319.1: 5319.1 – 335.6: 4335.6 – 352.2: 1352.2 – 368.7: 2368.7 – 385.2: 4385.2 – 401.8: 3401.8 – 418.3: 1418.3 – 434.9: 8434.9 – 451.4: 7451.4 – 468: 6468 – 484.5: 6484.5 – 501: 4501 – 517.6: 4517.6 – 534.1: 2534.1 – 550.7: 2550.7 – 567.2: 1567.2 – 583.8: 1583.8 – 600.3: 2600.3 – 616.8: 0616.8 – 633.4: 1633.4 – 649.9: 0649.9 – 666.5: 0666.5 – 683: 11002005001,000
n / missing69 / 0
Mean ± SD435.7 ± 87.1
Median437
Range286 – 683
CV0.2
Skew / kurtosis0.25 / -0.014
Normal?yes

Chl_a_b_ratio_mol_P

target · numeric
Chl_a_b_ratio_mol_P distribution02582.47 – 2.53: 22.53 – 2.591: 32.591 – 2.651: 22.651 – 2.712: 02.712 – 2.772: 32.772 – 2.833: 32.833 – 2.893: 52.893 – 2.953: 62.953 – 3.014: 23.014 – 3.074: 63.074 – 3.135: 43.135 – 3.195: 63.195 – 3.255: 63.255 – 3.316: 33.316 – 3.376: 03.376 – 3.437: 73.437 – 3.497: 23.497 – 3.558: 33.558 – 3.618: 03.618 – 3.678: 03.678 – 3.739: 03.739 – 3.799: 03.799 – 3.86: 53.86 – 3.92: 112510
n / missing69 / 0
Mean ± SD3.129 ± 0.344
Median3.09
Range2.47 – 3.92
CV0.11
Skew / kurtosis0.35 / -0.18
Normal?yes

USDA_Species

target · categorical
USDA_Species classesSAPU15SAPU15: 1010PEFR5PEFR5: 1010SAPU6SAPU6: 1010DUFIDUFI: 1010ERAN6ERAN6: 1010CAAQCAAQ: 88ARFU2ARFU2: 55ARLA2ARLA2: 55VAVIVAVI: 11
n / missing69 / 0
Classes9
Balance (entropy)0.95
Imbalance ratio10
Top classSAPU15 (10)
Constant metadata 19
  • ecosis_resource_id6ef5b4b1-4d6a-4770-9b3d-8390f49d44e0
  • locationUtqiagvik, Alaska
  • coordinate_precision_notessource-provided coordinates when available
  • year2,018
  • plant_partLeaf
  • canopy_or_leafleaf
  • instrumentAnalytical Spectral Devices FieldSpec 3
  • acquisition_modeContact
  • signal_typereflectance
  • axis_unitnm
  • axis_min350
  • axis_max2,500
  • n_points_original2,151
  • publication_doi10.5440/1336812 | 10.5440/1429875 | 10.5440/1441203
  • citationShawn Serbin Alistair Rogers Jennifer Liebig. 2018. NGEE Arctic Leaf Spectral Reflectance Utqiagvik (Barrow) Alaska 2013. Data set. Available on-line [http://ecosis.org] from the Ecological Spectral Information System (EcoSIS). 10.5440/1441203
  • licensenot specified
  • rights_statusmanual_review_needed
  • usage_scopeprivate_use_only
  • notesEcoSIS package ngee-arctic-leaf-spectral-reflectance-utqiagvik--barrow--alaska-2013, no interpolation applied by project.

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

Alignment

Alignment levelobservation
Sample id availableyes
Samples69
Observations (total)69
Reps per samplemin 1 · mean 1 · max 1

Provenance & citation

ContributorNGEE Arctic Leaf Spectral Reflectance Utqiagvik (Barrow) Alaska 2013
Origin · url [open]https://data.ecosis.org/dataset/ngee-arctic-leaf-spectral-reflectance-utqiagvik--barrow--alaska-2013
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)
Publication10.5440/1441203 — NGEE Arctic Leaf Spectral Reflectance, Barrow, Alaska 2013
Publication10.5440/1429875 — Leaf Chlorophyll and Total Carotenoid Content, Barrow, Alaska, 2013-2015
Publication10.5440/1336812 — Leaf Mass Area, Leaf Carbon and Nitrogen Content, Barrow, Alaska, 2012-2016

Governance & integrity

Tierprivate
LicenseLicenseRef-not-cleared
Permitted useResearch and benchmarking; private use only.
Access policyManual download / private-use-only per source.
RedistributionEcoSIS license is missing or unclear; private/internal conversion only by v0.5 policy.
Content version1.0.0
Schema / protocol2.0
Content hashd67b0e01840b66ec…
Processing hash521d45c3a966afba…
Metadata hash4457a2a4182d9379…

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

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

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