← Back to the catalog
Public

EcoSIS Dessain project reflectance spectra (reflectance)

ecosis · NIR

EcoSIS Dessain project reflectance spectra (reflectance). v2.0 standardized NIRS package: 1 spectral source(s), 31 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2ecosis
200
samples
2,001
wavelengths
1
sources
31
targets
24
metadata
NIR
family

Dataset property explorer

Mean profile risk0.39
Highest axisArtefacts locaux · 1.00
Diagnostics8
Sources profiled1
EcoSIS Dessain project reflectance spectra (reflectance) property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS Dessain project reflectance spectra (reflectance) profileintegrity: 0.00noise: 0.00artefacts: 1.00baseline: 0.50PCA outliers: 0.47reference: 0.47repeatability: 0.00structure: 0.72EcoSIS Dessain …0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux1.00
Bruit0.00
Outliers PCA0.47
Distance à la référence0.47
Répétabilité0.00
Baseline / forme0.50
Structure multi-régimes0.72
Diagnostic hypotheses00.250.50.751hypothesis scoreSplice / raccord détecteursSplice / raccord détecteurs: 0.720.72Erreur interpolation / réécha…Erreur interpolation / rééchantillonnage: 0.610.61Signature VERA25-likeSignature VERA25-like: 0.540.54Erreur calibration / référenc…Erreur calibration / référence blanche: 0.460.46Différence de sonde / géométr…Différence de sonde / géométrie: 0.420.42Fond différentFond différent: 0.400.40Spectre hors domaine valideSpectre hors domaine valide: 0.380.38Dataset multi-régimesDataset multi-régimes: 0.380.38
DiagnosticScoreForceSignauxInterprétation probable
Splice / raccord détecteursX0.72moyenneSpike rate 1.00, Jump rate 1.00, SNR non dégradé 1.00Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Erreur interpolation / rééchantillonnageX0.61moyenneSpike 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, Mahalanobis / T2 0.47Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Erreur calibration / référence blancheX0.46moyenneartefacts locaux 1.00, Baseline/mean/area 0.50, Mahalanobis / T2 0.47Décalage systématique entre campagnes, instruments ou référence blanche.
Différence de sonde / géométrieX0.42faibleBaseline/mean/area 0.50, Mahalanobis / T2 0.47, RMS/SAM référence 0.47Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Fond différentX0.40faibleBaseline/mean/area 0.50, Mahalanobis / T2 0.47, RMS/SAM référence 0.47Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.
Spectre hors domaine valideX0.38faibleStructure PCA 0.72, Mahalanobis / T2 0.47, RMS/SAM référence 0.47Variété, espèce, lot ou condition différente mais physiquement plausible.
Dataset multi-régimesX0.38faibleStructure PCA 0.72, Mahalanobis / T2 0.47, RMS/SAM référence 0.47Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.

Spectral sources

Dessain_spectra.csv

X · NIR · Analytical Spectral Devices Field Spec 4
Dessain_spectra.csv spectra0.00.20.40.605001,0001,5002,0002,500q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm400nm — median 0.03511 (q25–q75 0.0297–0.03999)414nm — median 0.0369 (q25–q75 0.03306–0.04129)429nm — median 0.03938 (q25–q75 0.03602–0.04311)443nm — median 0.04038 (q25–q75 0.03687–0.04464)458nm — median 0.04104 (q25–q75 0.03765–0.04523)472nm — median 0.04137 (q25–q75 0.03774–0.04549)486nm — median 0.04134 (q25–q75 0.03798–0.04533)501nm — median 0.04416 (q25–q75 0.04087–0.04975)515nm — median 0.06009 (q25–q75 0.05369–0.06961)529nm — median 0.09519 (q25–q75 0.08307–0.1124)544nm — median 0.1131 (q25–q75 0.09697–0.1343)558nm — median 0.1134 (q25–q75 0.09637–0.135)573nm — median 0.08964 (q25–q75 0.07708–0.1079)587nm — median 0.07442 (q25–q75 0.06393–0.08956)601nm — median 0.06822 (q25–q75 0.05906–0.08224)616nm — median 0.05911 (q25–q75 0.05112–0.07011)630nm — median 0.0556 (q25–q75 0.0485–0.0648)645nm — median 0.04843 (q25–q75 0.04302–0.05593)659nm — median 0.04368 (q25–q75 0.04032–0.04877)673nm — median 0.04183 (q25–q75 0.0382–0.04601)688nm — median 0.04936 (q25–q75 0.04632–0.05493)702nm — median 0.1319 (q25–q75 0.1138–0.1509)717nm — median 0.2722 (q25–q75 0.2518–0.2924)731nm — median 0.3782 (q25–q75 0.3595–0.3924)745nm — median 0.4289 (q25–q75 0.4106–0.4462)760nm — median 0.4416 (q25–q75 0.4248–0.4624)774nm — median 0.443 (q25–q75 0.4255–0.4644)788nm — median 0.4431 (q25–q75 0.4253–0.4642)803nm — median 0.4425 (q25–q75 0.4249–0.4637)817nm — median 0.4419 (q25–q75 0.4243–0.4635)832nm — median 0.4415 (q25–q75 0.424–0.463)846nm — median 0.4424 (q25–q75 0.4247–0.4637)860nm — median 0.442 (q25–q75 0.4245–0.4634)875nm — median 0.4419 (q25–q75 0.4239–0.4627)889nm — median 0.4422 (q25–q75 0.4237–0.4627)904nm — median 0.4415 (q25–q75 0.4226–0.4617)918nm — median 0.4417 (q25–q75 0.4225–0.4616)932nm — median 0.4407 (q25–q75 0.4217–0.4608)947nm — median 0.438 (q25–q75 0.4197–0.4575)961nm — median 0.4344 (q25–q75 0.4169–0.4534)976nm — median 0.4344 (q25–q75 0.4159–0.4533)990nm — median 0.4343 (q25–q75 0.4162–0.4538)1,004nm — median 0.4358 (q25–q75 0.4176–0.4545)1,019nm — median 0.4377 (q25–q75 0.4193–0.457)1,033nm — median 0.4388 (q25–q75 0.4205–0.4584)1,047nm — median 0.4403 (q25–q75 0.4218–0.4598)1,062nm — median 0.4408 (q25–q75 0.4224–0.46)1,076nm — median 0.441 (q25–q75 0.4225–0.4603)1,091nm — median 0.4401 (q25–q75 0.4217–0.4593)1,105nm — median 0.439 (q25–q75 0.4204–0.4579)1,119nm — median 0.4383 (q25–q75 0.4194–0.4575)1,134nm — median 0.4341 (q25–q75 0.4147–0.4522)1,148nm — median 0.4247 (q25–q75 0.4051–0.4412)1,163nm — median 0.4181 (q25–q75 0.3987–0.4349)1,177nm — median 0.4157 (q25–q75 0.3964–0.4326)1,191nm — median 0.4143 (q25–q75 0.3953–0.4312)1,206nm — median 0.414 (q25–q75 0.3947–0.4312)1,220nm — median 0.4153 (q25–q75 0.3959–0.4329)1,235nm — median 0.4169 (q25–q75 0.3974–0.4343)1,249nm — median 0.4178 (q25–q75 0.3984–0.4357)1,263nm — median 0.4186 (q25–q75 0.3988–0.4366)1,278nm — median 0.4177 (q25–q75 0.3981–0.4355)1,292nm — median 0.4163 (q25–q75 0.3966–0.4333)1,306nm — median 0.4124 (q25–q75 0.3932–0.4294)1,321nm — median 0.4043 (q25–q75 0.3855–0.4195)1,335nm — median 0.3936 (q25–q75 0.3747–0.4086)1,350nm — median 0.3824 (q25–q75 0.3634–0.3967)1,364nm — median 0.3707 (q25–q75 0.3513–0.3856)1,378nm — median 0.3487 (q25–q75 0.33–0.3626)1,393nm — median 0.2928 (q25–q75 0.2687–0.3066)1,407nm — median 0.2289 (q25–q75 0.2065–0.2428)1,422nm — median 0.1901 (q25–q75 0.1698–0.2057)1,436nm — median 0.1754 (q25–q75 0.1543–0.189)1,450nm — median 0.1725 (q25–q75 0.1503–0.1847)1,465nm — median 0.1768 (q25–q75 0.1545–0.1893)1,479nm — median 0.1884 (q25–q75 0.1647–0.2005)1,494nm — median 0.2058 (q25–q75 0.1811–0.2189)1,508nm — median 0.2229 (q25–q75 0.198–0.2363)1,522nm — median 0.2396 (q25–q75 0.2161–0.2525)1,537nm — median 0.2553 (q25–q75 0.2333–0.2684)1,551nm — median 0.2683 (q25–q75 0.2463–0.2814)1,565nm — median 0.279 (q25–q75 0.2576–0.2909)1,580nm — median 0.2884 (q25–q75 0.2685–0.3008)1,594nm — median 0.2971 (q25–q75 0.2771–0.3079)1,609nm — median 0.3041 (q25–q75 0.2855–0.3157)1,623nm — median 0.3099 (q25–q75 0.2913–0.3216)1,637nm — median 0.3143 (q25–q75 0.2957–0.3262)1,652nm — median 0.3165 (q25–q75 0.2987–0.3284)1,666nm — median 0.317 (q25–q75 0.2993–0.3291)1,681nm — median 0.3155 (q25–q75 0.2973–0.3276)1,695nm — median 0.3131 (q25–q75 0.2935–0.3245)1,709nm — median 0.3081 (q25–q75 0.2888–0.3199)1,724nm — median 0.3034 (q25–q75 0.2836–0.3153)1,738nm — median 0.2977 (q25–q75 0.2785–0.3097)1,753nm — median 0.2911 (q25–q75 0.2725–0.3028)1,767nm — median 0.2858 (q25–q75 0.2659–0.297)1,781nm — median 0.2825 (q25–q75 0.2625–0.2937)1,796nm — median 0.2822 (q25–q75 0.2611–0.2935)1,810nm — median 0.283 (q25–q75 0.2626–0.295)1,824nm — median 0.2841 (q25–q75 0.2632–0.2959)1,839nm — median 0.2807 (q25–q75 0.2594–0.2926)1,853nm — median 0.2678 (q25–q75 0.243–0.2781)1,868nm — median 0.2266 (q25–q75 0.2032–0.2386)1,882nm — median 0.1539 (q25–q75 0.1332–0.1666)1,896nm — median 0.09013 (q25–q75 0.07665–0.0978)1,911nm — median 0.05967 (q25–q75 0.05124–0.06571)1,925nm — median 0.051 (q25–q75 0.04434–0.05722)1,940nm — median 0.05311 (q25–q75 0.04613–0.05929)1,954nm — median 0.05934 (q25–q75 0.0519–0.0653)1,968nm — median 0.06735 (q25–q75 0.05688–0.07349)1,983nm — median 0.07642 (q25–q75 0.06353–0.08369)1,997nm — median 0.08573 (q25–q75 0.07223–0.09436)2,012nm — median 0.09695 (q25–q75 0.08271–0.1066)2,026nm — median 0.1072 (q25–q75 0.09192–0.1175)2,040nm — median 0.1164 (q25–q75 0.1–0.1275)2,055nm — median 0.1259 (q25–q75 0.1085–0.1377)2,069nm — median 0.1334 (q25–q75 0.1159–0.1469)2,083nm — median 0.1411 (q25–q75 0.1238–0.1549)2,098nm — median 0.1488 (q25–q75 0.1318–0.1628)2,112nm — median 0.155 (q25–q75 0.1389–0.1696)2,127nm — median 0.1616 (q25–q75 0.1458–0.1766)2,141nm — median 0.166 (q25–q75 0.1514–0.1819)2,155nm — median 0.1707 (q25–q75 0.1556–0.1863)2,170nm — median 0.1749 (q25–q75 0.1591–0.1904)2,184nm — median 0.1791 (q25–q75 0.1632–0.1941)2,199nm — median 0.1833 (q25–q75 0.1674–0.1971)2,213nm — median 0.1852 (q25–q75 0.1699–0.1991)2,227nm — median 0.1844 (q25–q75 0.169–0.1986)2,242nm — median 0.1801 (q25–q75 0.1644–0.1937)2,256nm — median 0.1735 (q25–q75 0.1563–0.1874)2,271nm — median 0.1659 (q25–q75 0.1491–0.1808)2,285nm — median 0.1587 (q25–q75 0.1426–0.1744)2,299nm — median 0.1518 (q25–q75 0.1358–0.1672)2,314nm — median 0.1446 (q25–q75 0.1293–0.1597)2,328nm — median 0.1383 (q25–q75 0.1226–0.1534)2,342nm — median 0.1326 (q25–q75 0.1167–0.1473)2,357nm — median 0.1264 (q25–q75 0.1099–0.1406)2,371nm — median 0.1201 (q25–q75 0.1054–0.133)2,386nm — median 0.1133 (q25–q75 0.09816–0.1262)2,400nm — median 0.1069 (q25–q75 0.09276–0.1185)

Sampling

Wavelengths2,001
Axis range400–2,400 nm
Mean spacing1 nm
Griduniform
Observations200

Signal & quality

Value range-0.0282 – 0.553
Mean range0.0337 – 0.444
Mean level0.2517
Area503.6
PTP0.4103
Noise RMS1.3956e-05
SNR1.8e+04
SNR dB9e+01 dB
Dynamic range0.41
Smoothness0.0001633
Saturated0.0%
X-outliers71

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.00%
Spike count9,196
Spike rate2.30%
Jump count15,705
Jump rate3.93%
Clip fraction0.00%

Shape & reference

Baseline slope-0.10294
Curvature RMS0.00016355
D1 RMS0.0016059
RMS to mean0.020492
RMS p950.046846
SAM to mean0.045841
SAM p950.10706
Affine offset p950.040734
Affine gain p95 Δ0.21079
Affine residual p950.022975
Xcorr lag p950

Outliers & repeatability

PCA Q p95/median3.4
Hotelling T2 p95/median3.6
Mahalanobis H p95/median1.9
Repeat groups0

Dimensionality (PCA)

Effective rank2.8
PCs → 95% var3
PCs → 99% var6
Top-10 cum. var99.8%
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_reflectance0.25170.50moyenValeur 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_curve503.580.50moyenValeur 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_peak0.410330.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance0.0211750.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms1.3956e-050.00faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr180350.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min225.760.00faibleZone 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_count9,1961.00fortArtefactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate2.3%1.00fortSpectre suspectInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count15,7051.00fortRaccord détecteurSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate3.93%1.00fortProblème spectralCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction0.0005%0.00faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope-0.102940.50moyenDériveÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.000163550.04faibleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.00160590.08faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio3.40750.43moyenSpectre atypiqueArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio3.59860.45moyenExtrême mais cohérentVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio1.8970.47moyenOutlier globalDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p950.0468460.46moyenSpectre 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_p950.107060.31faibleSimilaireFond, 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_density2.90970.72moyenSous-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_p952.14840.57moyenSpectre 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_p950.562820.72moyenSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X PCA score plot-4-2024-2024PC1 -0.6129 · PC2 0.1017PC1 0.587 · PC2 -0.5042PC1 1.492 · PC2 0.3569PC1 0.07313 · PC2 -1.191PC1 0.6265 · PC2 1.123PC1 -0.4188 · PC2 0.7386PC1 -0.8236 · PC2 0.8477PC1 2.453 · PC2 -0.8913PC1 0.946 · PC2 -0.0696PC1 -0.2086 · PC2 -0.7491PC1 0.8851 · PC2 -1.29PC1 2.481 · PC2 -1.013PC1 0.3469 · PC2 -0.6084PC1 -0.3575 · PC2 -0.4257PC1 1.697 · PC2 -0.8981PC1 0.3331 · PC2 0.03888PC1 -0.06235 · PC2 -0.4139PC1 2.117 · PC2 -0.205PC1 2.131 · PC2 -0.82PC1 -0.4439 · PC2 -0.3456PC1 0.8061 · PC2 -1.153PC1 1.61 · PC2 -0.8326PC1 2 · PC2 -0.5764PC1 -0.5676 · PC2 -1.056PC1 -0.5426 · PC2 -0.4139PC1 0.198 · PC2 0.6715PC1 0.6707 · PC2 0.7733PC1 1.696 · PC2 -0.7351PC1 0.5891 · PC2 0.468PC1 -0.09701 · PC2 0.292PC1 1.623 · PC2 0.7312PC1 -0.9068 · PC2 0.166PC1 1.349 · PC2 0.1802PC1 0.6276 · PC2 0.1418PC1 -0.1942 · PC2 0.6098PC1 1.329 · PC2 0.3697PC1 0.6969 · PC2 0.003774PC1 0.9509 · PC2 -0.01468PC1 -1.199 · PC2 0.3773PC1 1.197 · PC2 0.06561PC1 0.1399 · PC2 1.652PC1 -0.3459 · PC2 -0.5429PC1 0.3601 · PC2 -0.4935PC1 1.252 · PC2 0.05254PC1 0.1798 · PC2 -0.09773PC1 2.216 · PC2 -0.0136PC1 0.4202 · PC2 -0.7118PC1 0.215 · PC2 0.356PC1 -0.07887 · PC2 -0.6708PC1 -0.01479 · PC2 -0.3819PC1 0.02143 · PC2 0.749PC1 0.5771 · PC2 0.7146PC1 0.7181 · PC2 0.5094PC1 -1.475 · PC2 0.02484PC1 0.8421 · PC2 0.7551PC1 -3.873 · PC2 -0.8219PC1 0.5807 · PC2 0.1047PC1 0.05862 · PC2 -0.01165PC1 -0.2523 · PC2 0.1295PC1 1.484 · PC2 0.4374PC1 -0.08296 · PC2 -0.4702PC1 1.2 · PC2 0.1127PC1 -1.306 · PC2 -0.1629PC1 1.026 · PC2 0.1566PC1 -0.467 · PC2 0.3436PC1 0.3758 · PC2 0.9702PC1 -0.8802 · PC2 -0.7581PC1 -0.02131 · PC2 0.7362PC1 -1.35 · PC2 0.3653PC1 -0.2628 · PC2 -0.08312PC1 -0.5453 · PC2 -0.0231PC1 -0.8932 · PC2 0.9958PC1 0.7342 · PC2 -0.1933PC1 -2.332 · PC2 0.4317PC1 -0.6894 · PC2 -0.3089PC1 0.3412 · PC2 -0.8826PC1 -0.6061 · PC2 0.6372PC1 -0.9243 · PC2 -0.2224PC1 0.2658 · PC2 -0.9855PC1 -0.1284 · PC2 0.4705PC1 -0.7983 · PC2 0.263PC1 -0.3131 · PC2 0.3174PC1 -0.4794 · PC2 -0.1763PC1 -0.1041 · PC2 -0.2423PC1 0.828 · PC2 0.06125PC1 0.6881 · PC2 -0.7149PC1 0.4598 · PC2 1.021PC1 0.8922 · PC2 1.469PC1 -2.528 · PC2 0.4357PC1 -1.03 · PC2 -0.02675PC1 0.004076 · PC2 -0.2512PC1 -1.686 · PC2 0.4542PC1 -0.3339 · PC2 0.04067PC1 0.9354 · PC2 0.5837PC1 0.07291 · PC2 -0.1755PC1 0.1053 · PC2 0.1308PC1 -0.4411 · PC2 -0.9653PC1 -0.2204 · PC2 -0.8683PC1 0.1497 · PC2 2.64PC1 -0.9324 · PC2 1.767PC1 0.5356 · PC2 1.059PC1 -1.332 · PC2 -1.089PC1 0.1412 · PC2 0.117PC1 -0.9596 · PC2 -0.0348PC1 -1.708 · PC2 -0.06497PC1 0.2132 · PC2 1.057PC1 -0.5113 · PC2 -0.04031PC1 -0.2607 · PC2 0.9797PC1 -0.2236 · PC2 0.39PC1 -1.048 · PC2 -0.5203PC1 0.566 · PC2 -0.5403PC1 -0.3988 · PC2 -0.05881PC1 -0.4609 · PC2 -0.1251PC1 0.8762 · PC2 -0.1233PC1 1.455 · PC2 0.6051PC1 -0.6356 · PC2 0.3413PC1 -1.333 · PC2 1.789PC1 -0.4642 · PC2 -0.3944PC1 0.2426 · PC2 0.1353PC1 -0.3165 · PC2 -0.3647PC1 0.5326 · PC2 0.5053PC1 1.131 · PC2 0.1022PC1 -0.8482 · PC2 -0.1174PC1 0.1247 · PC2 -0.7406PC1 0.5575 · PC2 0.9817PC1 0.307 · PC2 0.2634PC1 -0.7038 · PC2 -1.392PC1 -1.006 · PC2 -1.742PC1 -0.4473 · PC2 -0.2966PC1 0.2526 · PC2 -0.2197PC1 -0.5342 · PC2 0.168PC1 0.203 · PC2 -0.8276PC1 -1.315 · PC2 1.036PC1 0.02857 · PC2 0.5179PC1 -0.9751 · PC2 -1.384PC1 0.2401 · PC2 0.7025PC1 -0.7255 · PC2 0.02136PC1 0.1837 · PC2 -0.3128PC1 -0.04246 · PC2 -0.219PC1 1.272 · PC2 0.2417PC1 0.544 · PC2 0.776PC1 -0.1385 · PC2 0.03479PC1 0.4073 · PC2 0.1667PC1 0.3691 · PC2 -0.2939PC1 0.4056 · PC2 -0.6601PC1 0.1053 · PC2 0.506PC1 -0.2866 · PC2 -0.08897PC1 0.7263 · PC2 0.1689PC1 -0.7851 · PC2 -0.8353PC1 -0.3963 · PC2 -0.1049PC1 -0.6254 · PC2 0.2036PC1 0.417 · PC2 0.0819PC1 -0.8892 · PC2 0.4461PC1 0.4057 · PC2 0.06622PC1 0.3822 · PC2 0.8209PC1 -0.7357 · PC2 0.3828PC1 -0.888 · PC2 0.5648PC1 1.23 · PC2 0.7995PC1 -0.4412 · PC2 0.8362PC1 -0.631 · PC2 0.336PC1 -0.8892 · PC2 0.3817PC1 0.3277 · PC2 1.131PC1 1.114 · PC2 0.7775PC1 -0.5097 · PC2 0.4133PC1 -0.2013 · PC2 -0.01947PC1 -0.5909 · PC2 0.09131PC1 -0.5587 · PC2 0.6211PC1 -0.9991 · PC2 0.4222PC1 -0.1094 · PC2 -0.2929PC1 -0.415 · PC2 0.2698PC1 -0.35 · PC2 0.6892PC1 -1.732 · PC2 0.508PC1 -0.3848 · PC2 0.2565PC1 -0.01676 · PC2 -0.05697PC1 -0.7243 · PC2 -1.41PC1 0.6804 · PC2 -0.6115PC1 -0.6935 · PC2 -0.4802PC1 -0.4068 · PC2 -0.7907PC1 -0.5016 · PC2 -0.8353PC1 -0.8401 · PC2 0.1713PC1 -0.985 · PC2 -1.099PC1 0.1663 · PC2 -0.3955PC1 -0.3214 · PC2 0.3149PC1 -1.398 · PC2 -0.8425PC1 -0.1536 · PC2 -0.2105PC1 -0.3697 · PC2 -0.6728PC1 0.5455 · PC2 -0.9333PC1 -0.3651 · PC2 -0.417PC1 0.1918 · PC2 -0.1865PC1 0.8052 · PC2 0.3445PC1 -0.1479 · PC2 -0.3197PC1 0.03291 · PC2 -0.1346PC1 0.1197 · PC2 0.09378PC1 -0.2361 · PC2 -0.9526PC1 1.116 · PC2 -0.3196PC1 0.06166 · PC2 -0.7399PC1 0.5088 · PC2 -0.6078PC1 0.5573 · PC2 -0.5069PC1 0.179 · PC2 0.32PC1 -1.121 · PC2 -1.066PC1 (57.8%)PC2 (31.6%)200 scores
PCA explained variance0%25%50%75%100%PC1: 57.8% (cumulative 57.8%)1PC2: 31.6% (cumulative 89.3%)2PC3: 7.3% (cumulative 96.7%)3PC4: 1.4% (cumulative 98.0%)4PC5: 0.8% (cumulative 98.9%)5PC6: 0.4% (cumulative 99.3%)6PC7: 0.2% (cumulative 99.5%)7PC8: 0.2% (cumulative 99.7%)8PC9: 0.1% (cumulative 99.7%)9PC10: 0.1% (cumulative 99.8%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
X-Y spectral correlation 20
X · SLA spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation05001,0001,5002,0002,500|r|signed raxis · Pearson correlation scale
X · LMA spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation05001,0001,5002,0002,500|r|signed raxis · Pearson correlation scale
X · LDMC spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation05001,0001,5002,0002,500|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
SLA0.5548780.2925.9%
LMA0.4978810.2990.0%
LDMC0.5241,3910.2070.7%
EWT0.6081,4280.35413.4%
Cmass0.3821,3910.170.0%
Nmass0.3351,3910.1460.0%
solubles0.1741,1190.08390.0%
hemicellulose0.1954390.05280.0%
cellulose0.3845030.1240.0%
lignin0.3525240.140.0%
chlA0.3347120.1180.0%
chlB0.3047210.1450.0%
car0.1832,2810.09640.0%
Al_mass0.1935140.04880.0%
B_mass0.3111,8810.1250.0%
B.1_mass0.3161,8810.1240.0%
Ca_mass0.2737160.06370.0%
Cu_mass0.3561,3910.1650.0%
Fe_mass0.1722,1370.0720.0%
K_mass0.5641,4000.2275.5%

Metric interpretation reference

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

Variables

Targets 31

species

target · categorical
species classesBetula alleghaniensis BrittonBetula alleghaniensis Britton: 66Acer rubrum LinnaeusAcer rubrum Linnaeus: 66Acer pensylvanicum LinnaeusAcer pensylvanicum Linnaeus: 66Rhus typhina LinnaeusRhus typhina Linnaeus: 55Populus tremuloides MichauxPopulus tremuloides Michaux: 55Fraxinus pennsylvanica Marsha…Fraxinus pennsylvanica Marshall: 55Populus deltoides W. Bartram …Populus deltoides W. Bartram ex Marshall: 55Phragmites australis (Cavanil…Phragmites australis (Cavanilles) Trinius ex Steudel: 55Fagus grandifolia EhrhartFagus grandifolia Ehrhart: 55Betula papyrifera MarshallBetula papyrifera Marshall: 55+10 more+10 more: 3939
n / missing200 / 0
Classes93
Balance (entropy)0.95
Imbalance ratio6
Top classBetula alleghaniensis Britton (6)

latin.genus

target · categorical
latin.genus classesAcerAcer: 3030PopulusPopulus: 1717BetulaBetula: 1414FraxinusFraxinus: 1010QuercusQuercus: 1010RubusRubus: 88PrunusPrunus: 77ViburnumViburnum: 66CornusCornus: 55RhusRhus: 55+10 more+10 more: 3333
n / missing200 / 0
Classes59
Balance (entropy)0.87
Imbalance ratio30
Top classAcer (30)

latin.species

target · categorical
latin.species classesamericanaamericana: 99alleghaniensisalleghaniensis: 66rubrumrubrum: 66pensylvanicumpensylvanicum: 66typhinatyphina: 55tremuloidestremuloides: 55pennsylvanicapennsylvanica: 55deltoidesdeltoides: 55virginianavirginiana: 55australisaustralis: 55+10 more+10 more: 4343
n / missing200 / 0
Classes82
Balance (entropy)0.95
Imbalance ratio9
Top classamericana (9)

growth.form

target · categorical
growth.form classestreetree: 107107shrubshrub: 5353herbherb: 3838vinevine: 22
n / missing200 / 0
Classes4
Balance (entropy)0.76
Imbalance ratio5e+01
Top classtree (107)

family

target · categorical
family classesSapindaceaeSapindaceae: 3030RosaceaeRosaceae: 2323BetulaceaeBetulaceae: 2020SalicaceaeSalicaceae: 2020FagaceaeFagaceae: 1515OleaceaeOleaceae: 1010AsteraceaeAsteraceae: 99AdoxaceaeAdoxaceae: 77CornaceaeCornaceae: 55AnacardiaceaeAnacardiaceae: 55+10 more+10 more: 2929
n / missing200 / 3
Classes37
Balance (entropy)0.83
Imbalance ratio30
Top classSapindaceae (30)

genus

target · categorical
genus classesAcerAcer: 3030PopulusPopulus: 1717BetulaBetula: 1414FraxinusFraxinus: 1010QuercusQuercus: 1010RubusRubus: 88PrunusPrunus: 77ViburnumViburnum: 66CornusCornus: 55RhusRhus: 55+10 more+10 more: 3333
n / missing200 / 3
Classes57
Balance (entropy)0.86
Imbalance ratio30
Top classAcer (30)

SLA

target · numeric
SLA distribution01020309.425 – 10.99: 1110.99 – 12.55: 1712.55 – 14.12: 2514.12 – 15.68: 1415.68 – 17.24: 2517.24 – 18.81: 2118.81 – 20.37: 1720.37 – 21.93: 1221.93 – 23.5: 423.5 – 25.06: 1225.06 – 26.62: 726.62 – 28.19: 928.19 – 29.75: 429.75 – 31.31: 731.31 – 32.88: 232.88 – 34.44: 334.44 – 36.01: 236.01 – 37.57: 237.57 – 39.13: 239.13 – 40.7: 140.7 – 42.26: 042.26 – 43.82: 243.82 – 45.39: 045.39 – 46.95: 101020304050
n / missing200 / 0
Mean ± SD19.68 ± 7.38
Median18.06
Range9.425 – 46.95
CV0.375
Skew / kurtosis1.1 / 1.2
Normal?no

LMA

target · numeric
LMA distribution010200.0213 – 0.02483: 40.02483 – 0.02837: 60.02837 – 0.0319: 50.0319 – 0.03543: 110.03543 – 0.03897: 130.03897 – 0.0425: 150.0425 – 0.04603: 60.04603 – 0.04956: 110.04956 – 0.0531: 160.0531 – 0.05663: 160.05663 – 0.06016: 160.06016 – 0.0637: 140.0637 – 0.06723: 110.06723 – 0.07076: 30.07076 – 0.0743: 60.0743 – 0.07783: 170.07783 – 0.08136: 60.08136 – 0.0849: 90.0849 – 0.08843: 30.08843 – 0.09196: 20.09196 – 0.0955: 50.0955 – 0.09903: 20.09903 – 0.1026: 00.1026 – 0.1061: 30.0000.0250.0500.0750.1000.125
n / missing200 / 0
Mean ± SD0.05733 ± 0.019
Median0.05537
Range0.0213 – 0.1061
CV0.331
Skew / kurtosis0.32 / -0.55
Normal?no

LDMC

target · numeric
LDMC distribution01020125.2 – 140.7: 3140.7 – 156.1: 3156.1 – 171.6: 1171.6 – 187: 5187 – 202.5: 4202.5 – 217.9: 7217.9 – 233.4: 5233.4 – 248.9: 9248.9 – 264.3: 11264.3 – 279.8: 5279.8 – 295.2: 9295.2 – 310.7: 14310.7 – 326.2: 10326.2 – 341.6: 17341.6 – 357.1: 14357.1 – 372.5: 20372.5 – 388: 15388 – 403.4: 8403.4 – 418.9: 11418.9 – 434.4: 11434.4 – 449.8: 12449.8 – 465.3: 4465.3 – 480.7: 1480.7 – 496.2: 11002005001,000
n / missing200 / 0
Mean ± SD328.8 ± 81.3
Median339.6
Range125.2 – 496.2
CV0.247
Skew / kurtosis-0.41 / -0.49
Normal?no

EWT

target · numeric
EWT distribution02040600.05613 – 0.07271: 110.07271 – 0.0893: 290.0893 – 0.1059: 470.1059 – 0.1225: 410.1225 – 0.139: 340.139 – 0.1556: 130.1556 – 0.1722: 120.1722 – 0.1888: 20.1888 – 0.2054: 20.2054 – 0.222: 00.222 – 0.2386: 40.2386 – 0.2551: 00.2551 – 0.2717: 10.2717 – 0.2883: 00.2883 – 0.3049: 10.3049 – 0.3215: 00.3215 – 0.3381: 10.3381 – 0.3546: 00.3546 – 0.3712: 00.3712 – 0.3878: 00.3878 – 0.4044: 00.4044 – 0.421: 00.421 – 0.4376: 00.4376 – 0.4542: 20.00.10.20.30.40.5
n / missing200 / 0
Mean ± SD0.1206 ± 0.051
Median0.1095
Range0.05613 – 0.4542
CV0.423
Skew / kurtosis3.6 / 18
Normal?no

Cmass

target · numeric
Cmass distribution010203039.08 – 39.68: 239.68 – 40.29: 040.29 – 40.89: 140.89 – 41.5: 041.5 – 42.1: 142.1 – 42.71: 442.71 – 43.31: 643.31 – 43.92: 943.92 – 44.52: 1244.52 – 45.13: 2145.13 – 45.73: 1545.73 – 46.34: 2146.34 – 46.95: 1346.95 – 47.55: 2947.55 – 48.16: 2148.16 – 48.76: 2048.76 – 49.37: 1349.37 – 49.97: 749.97 – 50.58: 150.58 – 51.18: 051.18 – 51.78: 051.78 – 52.39: 152.39 – 53: 053 – 53.6: 1102050100
n / missing200 / 2
Mean ± SD46.45 ± 2.12
Median46.78
Range39.08 – 53.6
CV0.0457
Skew / kurtosis-0.34 / 0.89
Normal?no

Nmass

target · numeric
Nmass distribution01020301.49 – 1.669: 61.669 – 1.848: 71.848 – 2.026: 132.026 – 2.205: 242.205 – 2.383: 232.383 – 2.562: 182.562 – 2.741: 202.741 – 2.919: 182.919 – 3.098: 143.098 – 3.276: 83.276 – 3.455: 113.455 – 3.634: 93.634 – 3.812: 53.812 – 3.991: 73.991 – 4.169: 14.169 – 4.348: 24.348 – 4.527: 34.527 – 4.705: 34.705 – 4.884: 24.884 – 5.062: 25.062 – 5.241: 05.241 – 5.42: 05.42 – 5.598: 05.598 – 5.777: 212510
n / missing200 / 2
Mean ± SD2.786 ± 0.799
Median2.585
Range1.49 – 5.777
CV0.287
Skew / kurtosis1.1 / 1.4
Normal?no

solubles

target · numeric
solubles distribution010203038.53 – 40.52: 440.52 – 42.5: 142.5 – 44.49: 044.49 – 46.48: 246.48 – 48.47: 148.47 – 50.46: 050.46 – 52.45: 152.45 – 54.44: 154.44 – 56.43: 656.43 – 58.42: 358.42 – 60.4: 860.4 – 62.39: 1062.39 – 64.38: 1264.38 – 66.37: 1466.37 – 68.36: 668.36 – 70.35: 1070.35 – 72.34: 2172.34 – 74.33: 974.33 – 76.31: 1776.31 – 78.3: 1478.3 – 80.29: 2380.29 – 82.28: 1182.28 – 84.27: 1184.27 – 86.26: 9102050100
n / missing200 / 6
Mean ± SD70.71 ± 10.1
Median71.97
Range38.53 – 86.26
CV0.143
Skew / kurtosis-0.92 / 0.85
Normal?no

hemicellulose

target · numeric
hemicellulose distribution01020303.138 – 4.419: 104.419 – 5.7: 155.7 – 6.982: 256.982 – 8.263: 268.263 – 9.545: 299.545 – 10.83: 1710.83 – 12.11: 1312.11 – 13.39: 1913.39 – 14.67: 914.67 – 15.95: 815.95 – 17.23: 417.23 – 18.52: 518.52 – 19.8: 219.8 – 21.08: 321.08 – 22.36: 022.36 – 23.64: 223.64 – 24.92: 024.92 – 26.2: 026.2 – 27.49: 027.49 – 28.77: 128.77 – 30.05: 030.05 – 31.33: 231.33 – 32.61: 132.61 – 33.89: 3010203040
n / missing200 / 6
Mean ± SD10.66 ± 5.76
Median9.075
Range3.138 – 33.89
CV0.54
Skew / kurtosis1.9 / 4.7
Normal?no

cellulose

target · numeric
cellulose distribution01020305.589 – 6.397: 56.397 – 7.205: 137.205 – 8.013: 298.013 – 8.821: 238.821 – 9.629: 189.629 – 10.44: 1410.44 – 11.24: 2411.24 – 12.05: 1812.05 – 12.86: 1412.86 – 13.67: 1113.67 – 14.48: 814.48 – 15.28: 415.28 – 16.09: 216.09 – 16.9: 316.9 – 17.71: 417.71 – 18.52: 218.52 – 19.32: 019.32 – 20.13: 020.13 – 20.94: 020.94 – 21.75: 121.75 – 22.56: 022.56 – 23.36: 023.36 – 24.17: 024.17 – 24.98: 5510152025
n / missing200 / 2
Mean ± SD10.76 ± 3.63
Median10.2
Range5.589 – 24.98
CV0.338
Skew / kurtosis1.7 / 4
Normal?no

lignin

target · numeric
lignin distribution01020301.145 – 2.04: 102.04 – 2.934: 92.934 – 3.828: 163.828 – 4.723: 174.723 – 5.617: 245.617 – 6.512: 136.512 – 7.406: 167.406 – 8.301: 208.301 – 9.195: 159.195 – 10.09: 1410.09 – 10.98: 910.98 – 11.88: 911.88 – 12.77: 512.77 – 13.67: 513.67 – 14.56: 514.56 – 15.46: 315.46 – 16.35: 016.35 – 17.25: 117.25 – 18.14: 418.14 – 19.03: 119.03 – 19.93: 019.93 – 20.82: 120.82 – 21.72: 021.72 – 22.61: 10102030
n / missing200 / 2
Mean ± SD7.503 ± 4.01
Median6.891
Range1.145 – 22.61
CV0.535
Skew / kurtosis0.92 / 0.91
Normal?no

chlA

target · numeric
chlA distribution0510151.846 – 2.415: 12.415 – 2.984: 02.984 – 3.553: 13.553 – 4.122: 24.122 – 4.691: 74.691 – 5.26: 55.26 – 5.829: 95.829 – 6.398: 66.398 – 6.967: 96.967 – 7.536: 127.536 – 8.105: 138.105 – 8.674: 108.674 – 9.243: 69.243 – 9.812: 89.812 – 10.38: 910.38 – 10.95: 410.95 – 11.52: 311.52 – 12.09: 512.09 – 12.66: 412.66 – 13.23: 313.23 – 13.8: 213.8 – 14.36: 014.36 – 14.93: 414.93 – 15.5: 205101520
n / missing200 / 75
Mean ± SD8.374 ± 2.84
Median8.036
Range1.846 – 15.5
CV0.34
Skew / kurtosis0.43 / -0.2
Normal?yes

chlB

target · numeric
chlB distribution0510150.4658 – 0.667: 10.667 – 0.8681: 00.8681 – 1.069: 01.069 – 1.27: 11.27 – 1.472: 21.472 – 1.673: 91.673 – 1.874: 91.874 – 2.075: 82.075 – 2.276: 102.276 – 2.477: 112.477 – 2.678: 112.678 – 2.879: 82.879 – 3.081: 103.081 – 3.282: 103.282 – 3.483: 43.483 – 3.684: 43.684 – 3.885: 13.885 – 4.086: 54.086 – 4.287: 64.287 – 4.488: 54.488 – 4.69: 54.69 – 4.891: 24.891 – 5.092: 15.092 – 5.293: 20246
n / missing200 / 75
Mean ± SD2.861 ± 1.01
Median2.689
Range0.4658 – 5.293
CV0.354
Skew / kurtosis0.46 / -0.47
Normal?yes

car

target · numeric
car distribution010200.4985 – 0.6169: 10.6169 – 0.7353: 00.7353 – 0.8537: 20.8537 – 0.972: 40.972 – 1.09: 51.09 – 1.209: 71.209 – 1.327: 71.327 – 1.446: 161.446 – 1.564: 121.564 – 1.682: 131.682 – 1.801: 71.801 – 1.919: 151.919 – 2.037: 42.037 – 2.156: 92.156 – 2.274: 42.274 – 2.393: 32.393 – 2.511: 32.511 – 2.629: 32.629 – 2.748: 22.748 – 2.866: 22.866 – 2.984: 22.984 – 3.103: 23.103 – 3.221: 03.221 – 3.34: 201234
n / missing200 / 75
Mean ± SD1.731 ± 0.55
Median1.624
Range0.4985 – 3.34
CV0.318
Skew / kurtosis0.71 / 0.52
Normal?no

Al_mass

target · numeric
Al_mass distribution02040600.013 – 0.02825: 160.02825 – 0.0435: 250.0435 – 0.05875: 410.05875 – 0.074: 250.074 – 0.08925: 190.08925 – 0.1045: 160.1045 – 0.1197: 70.1197 – 0.135: 80.135 – 0.1502: 120.1502 – 0.1655: 40.1655 – 0.1808: 50.1808 – 0.196: 60.196 – 0.2112: 20.2112 – 0.2265: 10.2265 – 0.2418: 30.2418 – 0.257: 10.257 – 0.2722: 20.2722 – 0.2875: 10.2875 – 0.3028: 00.3028 – 0.318: 00.318 – 0.3332: 00.3332 – 0.3485: 10.3485 – 0.3638: 00.3638 – 0.379: 20.00.10.20.30.4
n / missing200 / 3
Mean ± SD0.08944 ± 0.0652
Median0.068
Range0.013 – 0.379
CV0.728
Skew / kurtosis1.8 / 4.1
Normal?no

B_mass

target · numeric
B_mass distribution01020-0.103 – -0.08383: 3-0.08383 – -0.06467: 1-0.06467 – -0.0455: 3-0.0455 – -0.02633: 7-0.02633 – -0.007167: 5-0.007167 – 0.012: 80.012 – 0.03117: 120.03117 – 0.05033: 150.05033 – 0.0695: 120.0695 – 0.08867: 130.08867 – 0.1078: 150.1078 – 0.127: 170.127 – 0.1462: 120.1462 – 0.1653: 130.1653 – 0.1845: 150.1845 – 0.2037: 90.2037 – 0.2228: 150.2228 – 0.242: 70.242 – 0.2612: 30.2612 – 0.2803: 40.2803 – 0.2995: 40.2995 – 0.3187: 10.3187 – 0.3378: 20.3378 – 0.357: 2-0.20.00.2
n / missing200 / 2
Mean ± SD0.1146 ± 0.0939
Median0.1105
Range-0.103 – 0.357
CV0.819
Skew / kurtosis0.13 / -0.35
Normal?yes

B.1_mass

target · numeric
B.1_mass distribution0102030-0.093 – -0.0745: 3-0.0745 – -0.056: 1-0.056 – -0.0375: 4-0.0375 – -0.019: 6-0.019 – -0.0005: 6-0.0005 – 0.018: 140.018 – 0.0365: 130.0365 – 0.055: 120.055 – 0.0735: 130.0735 – 0.092: 210.092 – 0.1105: 170.1105 – 0.129: 110.129 – 0.1475: 110.1475 – 0.166: 160.166 – 0.1845: 110.1845 – 0.203: 140.203 – 0.2215: 80.2215 – 0.24: 60.24 – 0.2585: 20.2585 – 0.277: 30.277 – 0.2955: 20.2955 – 0.314: 20.314 – 0.3325: 00.3325 – 0.351: 2-0.20.00.2
n / missing200 / 2
Mean ± SD0.1054 ± 0.0862
Median0.1005
Range-0.093 – 0.351
CV0.817
Skew / kurtosis0.21 / -0.24
Normal?yes

Ca_mass

target · numeric
Ca_mass distribution020401.639 – 3.716: 83.716 – 5.793: 165.793 – 7.869: 247.869 – 9.946: 309.946 – 12.02: 3312.02 – 14.1: 3114.1 – 16.18: 1016.18 – 18.25: 1018.25 – 20.33: 1120.33 – 22.41: 622.41 – 24.48: 524.48 – 26.56: 226.56 – 28.64: 228.64 – 30.71: 230.71 – 32.79: 132.79 – 34.87: 234.87 – 36.94: 136.94 – 39.02: 239.02 – 41.1: 041.1 – 43.17: 043.17 – 45.25: 145.25 – 47.33: 047.33 – 49.41: 049.41 – 51.48: 10204060
n / missing200 / 2
Mean ± SD12.94 ± 7.72
Median11.21
Range1.639 – 51.48
CV0.597
Skew / kurtosis1.8 / 4.8
Normal?no

Cu_mass

target · numeric
Cu_mass distribution020400.004 – 0.006792: 220.006792 – 0.009583: 380.009583 – 0.01237: 370.01237 – 0.01517: 300.01517 – 0.01796: 130.01796 – 0.02075: 170.02075 – 0.02354: 30.02354 – 0.02633: 110.02633 – 0.02912: 60.02912 – 0.03192: 40.03192 – 0.03471: 30.03471 – 0.0375: 10.0375 – 0.04029: 10.04029 – 0.04308: 00.04308 – 0.04587: 20.04587 – 0.04867: 30.04867 – 0.05146: 20.05146 – 0.05425: 10.05425 – 0.05704: 00.05704 – 0.05983: 00.05983 – 0.06262: 00.06262 – 0.06542: 00.06542 – 0.06821: 10.06821 – 0.071: 10.000.020.040.060.08
n / missing200 / 4
Mean ± SD0.01594 ± 0.0112
Median0.013
Range0.004 – 0.071
CV0.7
Skew / kurtosis2.2 / 5.7
Normal?no

Fe_mass

target · numeric
Fe_mass distribution01020300.017 – 0.02721: 70.02721 – 0.03742: 100.03742 – 0.04763: 90.04763 – 0.05783: 150.05783 – 0.06804: 170.06804 – 0.07825: 290.07825 – 0.08846: 120.08846 – 0.09867: 150.09867 – 0.1089: 180.1089 – 0.1191: 90.1191 – 0.1293: 110.1293 – 0.1395: 90.1395 – 0.1497: 90.1497 – 0.1599: 70.1599 – 0.1701: 40.1701 – 0.1803: 20.1803 – 0.1905: 10.1905 – 0.2007: 30.2007 – 0.211: 20.211 – 0.2212: 10.2212 – 0.2314: 30.2314 – 0.2416: 20.2416 – 0.2518: 00.2518 – 0.262: 10.00.10.20.3
n / missing200 / 4
Mean ± SD0.09639 ± 0.0487
Median0.0875
Range0.017 – 0.262
CV0.505
Skew / kurtosis0.93 / 0.76
Normal?no

K_mass

target · numeric
K_mass distribution020403.402 – 5.074: 105.074 – 6.745: 356.745 – 8.417: 278.417 – 10.09: 2010.09 – 11.76: 2311.76 – 13.43: 1613.43 – 15.1: 1215.1 – 16.78: 1416.78 – 18.45: 918.45 – 20.12: 320.12 – 21.79: 821.79 – 23.46: 123.46 – 25.13: 125.13 – 26.81: 426.81 – 28.48: 128.48 – 30.15: 130.15 – 31.82: 031.82 – 33.49: 333.49 – 35.16: 235.16 – 36.84: 236.84 – 38.51: 238.51 – 40.18: 040.18 – 41.85: 041.85 – 43.52: 30204060
n / missing200 / 3
Mean ± SD12.83 ± 8.19
Median10.53
Range3.402 – 43.52
CV0.639
Skew / kurtosis1.8 / 3.1
Normal?no

Mg_mass

target · numeric
Mg_mass distribution010201.118 – 1.323: 61.323 – 1.528: 51.528 – 1.733: 161.733 – 1.938: 191.938 – 2.143: 182.143 – 2.349: 172.349 – 2.554: 152.554 – 2.759: 122.759 – 2.964: 132.964 – 3.169: 143.169 – 3.374: 143.374 – 3.579: 53.579 – 3.784: 93.784 – 3.989: 33.989 – 4.194: 74.194 – 4.399: 54.399 – 4.604: 44.604 – 4.809: 24.809 – 5.015: 15.015 – 5.22: 25.22 – 5.425: 25.425 – 5.63: 45.63 – 5.835: 25.835 – 6.04: 202468
n / missing200 / 3
Mean ± SD2.828 ± 1.1
Median2.624
Range1.118 – 6.04
CV0.387
Skew / kurtosis0.91 / 0.38
Normal?no

Mn_mass

target · numeric
Mn_mass distribution01002000.005 – 0.1607: 1520.1607 – 0.3163: 180.3163 – 0.472: 90.472 – 0.6277: 40.6277 – 0.7833: 30.7833 – 0.939: 70.939 – 1.095: 11.095 – 1.25: 01.25 – 1.406: 01.406 – 1.562: 01.562 – 1.717: 21.717 – 1.873: 01.873 – 2.029: 02.029 – 2.184: 02.184 – 2.34: 02.34 – 2.496: 02.496 – 2.651: 02.651 – 2.807: 02.807 – 2.963: 02.963 – 3.118: 03.118 – 3.274: 13.274 – 3.43: 03.43 – 3.585: 03.585 – 3.741: 101234
n / missing200 / 2
Mean ± SD0.1794 ± 0.422
Median0.0455
Range0.005 – 3.741
CV2.35
Skew / kurtosis5.7 / 40
Normal?no

Na_mass

target · numeric
Na_mass distribution02040-0.121 – 0.1092: 80.1092 – 0.3395: 90.3395 – 0.5697: 190.5697 – 0.8: 300.8 – 1.03: 331.03 – 1.26: 331.26 – 1.491: 341.491 – 1.721: 101.721 – 1.951: 61.951 – 2.181: 12.181 – 2.412: 12.412 – 2.642: 12.642 – 2.872: 02.872 – 3.102: 03.102 – 3.333: 23.333 – 3.563: 03.563 – 3.793: 33.793 – 4.024: 24.024 – 4.254: 04.254 – 4.484: 14.484 – 4.714: 14.714 – 4.944: 24.944 – 5.175: 05.175 – 5.405: 1-20246
n / missing200 / 3
Mean ± SD1.169 ± 0.893
Median1.026
Range-0.121 – 5.405
CV0.764
Skew / kurtosis2.4 / 7
Normal?no

P_mass

target · numeric
P_mass distribution01020300.962 – 1.176: 91.176 – 1.391: 181.391 – 1.605: 221.605 – 1.819: 181.819 – 2.033: 112.033 – 2.248: 172.248 – 2.462: 232.462 – 2.676: 102.676 – 2.891: 162.891 – 3.105: 83.105 – 3.319: 73.319 – 3.534: 73.534 – 3.748: 73.748 – 3.962: 53.962 – 4.176: 64.176 – 4.391: 04.391 – 4.605: 44.605 – 4.819: 24.819 – 5.034: 15.034 – 5.248: 35.248 – 5.462: 15.462 – 5.676: 05.676 – 5.891: 15.891 – 6.105: 202468
n / missing200 / 2
Mean ± SD2.47 ± 1.08
Median2.295
Range0.962 – 6.105
CV0.435
Skew / kurtosis1.1 / 1
Normal?no

Zn_mass

target · numeric
Zn_mass distribution0501001500.005 – 0.03175: 1150.03175 – 0.0585: 330.0585 – 0.08525: 40.08525 – 0.112: 90.112 – 0.1388: 40.1388 – 0.1655: 60.1655 – 0.1923: 50.1923 – 0.219: 40.219 – 0.2457: 30.2457 – 0.2725: 30.2725 – 0.2993: 30.2993 – 0.326: 20.326 – 0.3528: 20.3528 – 0.3795: 10.3795 – 0.4062: 20.4062 – 0.433: 00.433 – 0.4597: 00.4597 – 0.4865: 00.4865 – 0.5132: 00.5132 – 0.54: 00.54 – 0.5667: 10.5667 – 0.5935: 00.5935 – 0.6202: 00.6202 – 0.647: 10.00.20.40.60.8
n / missing200 / 2
Mean ± SD0.06992 ± 0.1
Median0.027
Range0.005 – 0.647
CV1.43
Skew / kurtosis2.7 / 9
Normal?no

Metadata 4

site

metadata · categorical
site classesireqaireqa: 4848sblacsblac: 4747jbmcbjbmcb: 4343jbmarjbmar: 1717pnbsmpnbsm: 1414jbmtbjbmtb: 1313sutodsutod: 99ireqbireqb: 99
n / missing200 / 0
Classes8
Balance (entropy)0.9
Imbalance ratio5
Top classireqa (48)

latitude

metadata · numeric
latitude distribution025507545.12 – 45.16: 945.16 – 45.19: 045.19 – 45.23: 045.23 – 45.27: 045.27 – 45.3: 045.3 – 45.34: 045.34 – 45.37: 045.37 – 45.41: 045.41 – 45.45: 045.45 – 45.48: 045.48 – 45.52: 045.52 – 45.56: 045.56 – 45.59: 7345.59 – 45.63: 7145.63 – 45.66: 045.66 – 45.7: 045.7 – 45.74: 045.74 – 45.77: 045.77 – 45.81: 045.81 – 45.84: 045.84 – 45.88: 045.88 – 45.92: 045.92 – 45.95: 045.95 – 45.99: 4745.0045.2545.5045.7546.00
n / missing200 / 0
Mean ± SD45.66 ± 0.207
Median45.62
Range45.12 – 45.99
CV0.00454
Skew / kurtosis0.081 / 0.63
Normal?yes

longitude

metadata · numeric
longitude distribution0255075-74.01 – -73.95: 47-73.95 – -73.89: 0-73.89 – -73.84: 0-73.84 – -73.78: 0-73.78 – -73.72: 0-73.72 – -73.67: 0-73.67 – -73.61: 0-73.61 – -73.56: 70-73.56 – -73.5: 3-73.5 – -73.44: 14-73.44 – -73.39: 36-73.39 – -73.33: 21-73.33 – -73.27: 0-73.27 – -73.22: 0-73.22 – -73.16: 0-73.16 – -73.1: 0-73.1 – -73.05: 0-73.05 – -72.99: 0-72.99 – -72.93: 0-72.93 – -72.88: 0-72.88 – -72.82: 0-72.82 – -72.77: 0-72.77 – -72.71: 0-72.71 – -72.65: 9-74.5-74.0-73.5-73.0-72.5
n / missing200 / 0
Mean ± SD-73.57 ± 0.305
Median-73.56
Range-74.01 – -72.65
CV0.00415
Skew / kurtosis0.59 / 1.8
Normal?no

date

metadata · categorical
date classes8/16/20178/16/2017: 21216/1/20176/1/2017: 16167/26/20177/26/2017: 15158/3/20178/3/2017: 14145/31/20175/31/2017: 13135/26/20175/26/2017: 12126/7/20176/7/2017: 11116/14/20176/14/2017: 11117/27/20177/27/2017: 11118/15/20178/15/2017: 1111+7 more+7 more: 6565
n / missing200 / 0
Classes17
Balance (entropy)0.99
Imbalance ratio3
Top class8/16/2017 (21)
Constant metadata 17
  • ecosis_resource_id99529cc2-5cd8-4379-ba2f-c9d6e012566a
  • coordinate_precision_notessource-provided coordinates when available
  • year2,022
  • plant_partLeaf
  • instrumentAnalytical Spectral Devices Field Spec 4
  • acquisition_modeContact
  • signal_typereflectance
  • axis_unitnm
  • axis_min400
  • axis_max2,400
  • n_points_original2,001
  • publication_doi10.1080/00103624.2016.1228952 | 10.1101/2022.07.01.498461 | 10.21232/VYJzNBEy | 10.21232/deP7jVyq | 10.21232/dep7jvyq
  • citationShan Kothari, Aurlie Dessain, Rosalie Beauchamp-Rioux, Florence Blanchard, Anna L. Crofts, Alize Girard, Xavier Guilbeault-Mayers, Paul W. Hacker, Juliana Pardo, Anna K. Schweiger, Sabrina Demers-Thibeault, Anne Bruneau, Nicholas C. Coops, Margaret Kalacska, Mark Vellend and Etienne Lalibert. 2022. Dessain project reflectance spectra. Data set. Available on-line [http://ecosis.org] from the Ecological Spectral Information System (EcoSIS). 10.21232/VYJzNBEy
  • licenseCreative Commons Attribution Share-Alike
  • rights_statusexplicit_open
  • usage_scopepublic_reuse_possible
  • notesEcoSIS package dessain-project-reflectance-spectra, no interpolation applied by project.

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

Alignment

Alignment levelobservation
Sample id availableyes
Samples200
Observations (total)200
Reps per samplemin 1 · mean 1 · max 1

Provenance & citation

ContributorDessain project reflectance spectra
Origin · url [open]https://data.ecosis.org/dataset/dessain-project-reflectance-spectra
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)
Publication10.1101/2022.07.01.498461 — https://www.biorxiv.org/content/10.1101/2022.07.01.498461v2
Publication10.21232/VYJzNBEy — Dessain project reflectance spectra
Publication10.1080/00103624.2016.1228952
Publication10.21232/deP7jVyq
Publication10.21232/dep7jvyq

Governance & integrity

Tierpublic
LicenseCC-BY-SA-4.0
Permitted useResearch and benchmarking.
Access policyOpen per source license.
RedistributionEcoSIS CKAN metadata exposes an open license.
Content version1.0.0
Schema / protocol2.0
Content hash59d391f8a4cee62c…
Processing hash16db6194fcda60ce…
Metadata hash9b026bb67f200bcf…

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

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