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EcoSIS NGEE Arctic Leaf Spectral Reflectance and Transmittance Data 2014 to 2016 Utqiagvik (Barrow) Alaska (transmittance)

ecosis · NIR

EcoSIS NGEE Arctic Leaf Spectral Reflectance and Transmittance Data 2014 to 2016 Utqiagvik (Barrow) Alaska (transmittance). v2.0 standardized NIRS package: 1 spectral source(s), 1 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2ecosis
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Private dataset. Full metadata and metrics are shown, but the bytes are not redistributed here — exporting the data requires a Dataverse token. The identity card carries no spectra, only descriptive statistics.
31
samples
2,151
wavelengths
1
sources
1
targets
27
metadata
NIR
family

Dataset property explorer

Mean profile risk0.37
Highest axisArtefacts locaux · 1.00
Diagnostics8
Sources profiled1
EcoSIS NGEE Arctic Leaf Spectral Reflectance and Transmittance Data 2014 to 2016 Utqiagvik (Barrow) Alaska (transmittance) property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS NGEE Arctic Leaf Spectral Reflectance and Transmittance Data 2014 to 2016 Utqiagvik (Barrow) Alaska (transmittance) profileintegrity: 0.00noise: 0.01artefacts: 1.00baseline: 0.46PCA outliers: 0.33reference: 0.68repeatability: 0.00structure: 0.51EcoSIS NGEE Arc…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux1.00
Bruit0.01
Outliers PCA0.33
Distance à la référence0.68
Répétabilité0.00
Baseline / forme0.46
Structure multi-régimes0.51
Diagnostic hypotheses00.250.50.751hypothesis scoreSplice / raccord détecteursSplice / raccord détecteurs: 0.730.73Erreur interpolation / réécha…Erreur interpolation / rééchantillonnage: 0.560.56Signature VERA25-likeSignature VERA25-like: 0.510.51Erreur calibration / référenc…Erreur calibration / référence blanche: 0.440.44Différence de sonde / géométr…Différence de sonde / géométrie: 0.400.40Fond différentFond différent: 0.360.36Spectre hors domaine valideSpectre hors domaine valide: 0.340.34Dataset multi-régimesDataset multi-régimes: 0.330.33
DiagnosticScoreForceSignauxInterprétation probable
Splice / raccord détecteursX0.73forteSpike rate 1.00, Jump rate 1.00, SNR non dégradé 1.00Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Erreur interpolation / rééchantillonnageX0.56moyenneSpike rate 1.00, Jump rate 1.00, SNR normal/élevé 1.00Artefacts numériques ou traitement spectral incorrect.
Signature VERA25-likeX0.51moyenneSpike rate 1.00, Jump rate 1.00, RMS/SAM référence 0.68Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Erreur calibration / référence blancheX0.44moyenneartefacts locaux 1.00, RMS/SAM référence 0.68, Baseline/mean/area 0.46Décalage systématique entre campagnes, instruments ou référence blanche.
Différence de sonde / géométrieX0.40faibleRMS/SAM référence 0.68, Baseline/mean/area 0.46, Mahalanobis / T2 0.33Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Fond différentX0.36faibleRMS/SAM référence 0.68, Baseline/mean/area 0.46, Mahalanobis / T2 0.33Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.
Spectre hors domaine valideX0.34faibleRMS/SAM référence 0.68, Structure PCA 0.51, Mahalanobis / T2 0.33Variété, espèce, lot ou condition différente mais physiquement plausible.
Dataset multi-régimesX0.33faibleRMS/SAM référence 0.68, Structure PCA 0.51, Mahalanobis / T2 0.33Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.

Spectral sources

NGEE-Arctic_Barrow_2015_SVCHR1024i_Leaf_Spectral_Transmittance.csv

X · NIR · Spectra Vista Corporation HR-1024i
NGEE-Arctic_Barrow_2015_SVCHR1024i_Leaf_Spectral_Transmittance.csv spectra-20020406001,0002,0003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm350nm — median 5.223 (q25–q75 -0.08344–23.41)365nm — median 0.4838 (q25–q75 -1.572–2.569)381nm — median 1.152 (q25–q75 -0.4299–2.062)396nm — median 0.03 (q25–q75 -0.3331–0.7797)412nm — median 0.1069 (q25–q75 -0.3068–0.4541)427nm — median 0.1679 (q25–q75 -0.05311–0.657)443nm — median 0.4187 (q25–q75 0.03286–0.6996)458nm — median 0.5213 (q25–q75 0.3388–1.1)474nm — median 0.6756 (q25–q75 0.3789–1.326)489nm — median 0.8707 (q25–q75 0.5003–1.305)505nm — median 1.384 (q25–q75 0.9386–2.124)520nm — median 3.522 (q25–q75 3.111–4.798)536nm — median 6.814 (q25–q75 5.966–7.691)551nm — median 7.953 (q25–q75 7.023–8.721)567nm — median 7.285 (q25–q75 6.648–8.009)582nm — median 5.59 (q25–q75 4.97–6.918)597nm — median 4.971 (q25–q75 4.542–6.91)613nm — median 4.161 (q25–q75 3.752–7.357)628nm — median 3.64 (q25–q75 2.999–7.15)644nm — median 2.764 (q25–q75 2.29–6.568)659nm — median 1.919 (q25–q75 1.349–5.056)675nm — median 1.051 (q25–q75 0.6263–3.286)690nm — median 2.825 (q25–q75 2.265–6.305)706nm — median 15.38 (q25–q75 13.71–20.84)721nm — median 27.57 (q25–q75 25.42–32.46)737nm — median 36.27 (q25–q75 34.38–40.73)752nm — median 39.28 (q25–q75 37.45–44.04)768nm — median 40.31 (q25–q75 38.31–45.08)783nm — median 40.72 (q25–q75 38.68–45.51)799nm — median 40.87 (q25–q75 38.87–45.74)814nm — median 41.17 (q25–q75 39.12–45.94)829nm — median 41.32 (q25–q75 39.23–46.16)845nm — median 41.41 (q25–q75 39.47–46.46)860nm — median 41.64 (q25–q75 39.66–46.44)876nm — median 41.77 (q25–q75 39.81–46.64)891nm — median 42.12 (q25–q75 39.92–46.72)907nm — median 41.78 (q25–q75 40.11–46.67)922nm — median 42.22 (q25–q75 40.17–47.13)938nm — median 42.17 (q25–q75 40.09–47.1)953nm — median 41.43 (q25–q75 39.89–46.63)969nm — median 41.1 (q25–q75 39.28–46.67)984nm — median 40.71 (q25–q75 39.08–46.29)1,000nm — median 41.05 (q25–q75 39.51–46.56)1,015nm — median 41.67 (q25–q75 40.08–47.03)1,031nm — median 42.33 (q25–q75 40.55–47.41)1,046nm — median 42.77 (q25–q75 40.92–47.86)1,062nm — median 42.98 (q25–q75 41.24–48.07)1,077nm — median 42.92 (q25–q75 41.28–48.21)1,092nm — median 43.02 (q25–q75 41.25–48.18)1,108nm — median 42.92 (q25–q75 41.13–48.2)1,123nm — median 42.68 (q25–q75 41.1–47.95)1,139nm — median 41.51 (q25–q75 39.72–46.94)1,154nm — median 40.2 (q25–q75 37.88–45.6)1,170nm — median 39.8 (q25–q75 37.2–45.15)1,185nm — median 39.61 (q25–q75 36.96–44.97)1,201nm — median 39.55 (q25–q75 36.92–44.95)1,216nm — median 39.75 (q25–q75 37.03–45.06)1,232nm — median 40.02 (q25–q75 37.38–45.4)1,247nm — median 40.19 (q25–q75 37.63–45.7)1,263nm — median 40.42 (q25–q75 37.82–45.85)1,278nm — median 40.38 (q25–q75 37.8–45.84)1,294nm — median 40.17 (q25–q75 37.49–45.56)1,309nm — median 39.56 (q25–q75 36.72–44.86)1,324nm — median 38.52 (q25–q75 35.48–43.65)1,340nm — median 37.09 (q25–q75 33.39–42.25)1,355nm — median 35.85 (q25–q75 31.96–41.24)1,371nm — median 33.98 (q25–q75 29.62–39.42)1,386nm — median 28.77 (q25–q75 22.83–34.06)1,402nm — median 21.09 (q25–q75 14.38–25.29)1,417nm — median 16.5 (q25–q75 10.12–20.32)1,433nm — median 14.68 (q25–q75 8.156–17.81)1,448nm — median 14 (q25–q75 7.614–16.89)1,464nm — median 14.14 (q25–q75 7.619–17.06)1,479nm — median 15.18 (q25–q75 8.697–18.41)1,495nm — median 16.91 (q25–q75 10.34–20.39)1,510nm — median 18.75 (q25–q75 12.21–22.46)1,526nm — median 20.84 (q25–q75 14.3–24.82)1,541nm — median 22.85 (q25–q75 16.27–26.84)1,556nm — median 24.39 (q25–q75 17.92–28.45)1,572nm — median 25.91 (q25–q75 19.58–29.98)1,587nm — median 27.09 (q25–q75 20.95–31.36)1,603nm — median 28.26 (q25–q75 22.15–32.69)1,618nm — median 29.17 (q25–q75 23.11–33.72)1,634nm — median 29.91 (q25–q75 23.95–34.65)1,649nm — median 30.45 (q25–q75 24.72–35.37)1,665nm — median 30.71 (q25–q75 25.01–35.71)1,680nm — median 30.8 (q25–q75 25.23–35.77)1,696nm — median 30.59 (q25–q75 24.88–35.32)1,711nm — median 30.32 (q25–q75 24.57–34.79)1,727nm — median 29.71 (q25–q75 23.86–34.14)1,742nm — median 29.12 (q25–q75 23.03–33.52)1,758nm — median 28.29 (q25–q75 22.07–32.59)1,773nm — median 27.56 (q25–q75 21.11–31.96)1,788nm — median 27.48 (q25–q75 21.01–31.73)1,804nm — median 27.71 (q25–q75 21.16–32.05)1,819nm — median 27.94 (q25–q75 21.38–32.32)1,835nm — median 27.87 (q25–q75 21.21–32.26)1,850nm — median 26.45 (q25–q75 19.52–30.8)1,866nm — median 21.78 (q25–q75 15.07–26.01)1,881nm — median 14.32 (q25–q75 7.371–17.12)1,897nm — median 7.84 (q25–q75 3.291–9.235)1,912nm — median 3.516 (q25–q75 1.342–5.022)1,928nm — median 2.468 (q25–q75 0.5138–3.976)1,943nm — median 2.181 (q25–q75 0.5911–3.609)1,959nm — median 2.721 (q25–q75 0.2789–4.391)1,974nm — median 3.421 (q25–q75 0.75–4.843)1,990nm — median 4.763 (q25–q75 0.9512–6.901)2,005nm — median 6.257 (q25–q75 1.499–8.403)2,021nm — median 7.098 (q25–q75 2.19–9.829)2,036nm — median 8.526 (q25–q75 2.778–11.65)2,051nm — median 9.395 (q25–q75 3.637–13.23)2,067nm — median 10.38 (q25–q75 4.449–13.95)2,082nm — median 12.3 (q25–q75 5.123–15.08)2,098nm — median 12.59 (q25–q75 6.161–16.1)2,113nm — median 14.3 (q25–q75 7.917–18.09)2,129nm — median 15.44 (q25–q75 8.464–19.16)2,144nm — median 16.72 (q25–q75 9.375–19.8)2,160nm — median 17.14 (q25–q75 10.1–21.33)2,175nm — median 18.08 (q25–q75 11.21–21.84)2,191nm — median 18.1 (q25–q75 10.9–20.69)2,206nm — median 18.47 (q25–q75 12.47–22.52)2,222nm — median 19.12 (q25–q75 11.75–22.83)2,237nm — median 18.95 (q25–q75 11.59–22.42)2,253nm — median 18.08 (q25–q75 10.11–21.09)2,268nm — median 16.19 (q25–q75 9.553–20.47)2,283nm — median 15.84 (q25–q75 9.371–19.02)2,299nm — median 15.93 (q25–q75 8.69–19.65)2,314nm — median 14.49 (q25–q75 7.105–17.39)2,330nm — median 13.74 (q25–q75 7.526–16.63)2,345nm — median 11.65 (q25–q75 6.625–16.04)2,361nm — median 12.43 (q25–q75 5.75–16.32)2,376nm — median 11.27 (q25–q75 6.578–15.1)2,392nm — median 9.287 (q25–q75 3.81–13.06)2,407nm — median 7.609 (q25–q75 4.284–11.75)2,423nm — median 7.673 (q25–q75 3.775–11.11)2,438nm — median 7.264 (q25–q75 3.527–9.545)2,454nm — median 6.899 (q25–q75 5.388–8.13)2,469nm — median 5.06 (q25–q75 2.474–7.03)2,485nm — median 4.307 (q25–q75 2.573–6.917)2,500nm — median 3.834 (q25–q75 1.919–6.54)

Sampling

Wavelengths2,151
Axis range350–2,500 nm
Mean spacing1 nm
Griduniform
Observations31

Signal & quality

Value range-33.1 – 191
Mean range-0.0891 – 44.4
Mean level22.11
Area4.755e+04
PTP44.48
Noise RMS0.011799
SNR1.9e+03
SNR dB7e+01 dB
Dynamic range44.5
Smoothness0.9324
Saturated0.0%
X-outliers10

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.03%
Spike count7,753
Spike rate11.64%
Jump count2,146
Jump rate3.22%
Clip fraction0.00%

Shape & reference

Baseline slope-10.208
Curvature RMS0.76373
D1 RMS0.7002
RMS to mean4.2997
RMS p957.581
SAM to mean0.10782
SAM p950.20058
Affine offset p955.0237
Affine gain p95 Δ0.14868
Affine residual p953.5957
Xcorr lag p952

Outliers & repeatability

PCA Q p95/median2
Hotelling T2 p95/median1.8
Mahalanobis H p95/median1.3
Repeat groups0

Dimensionality (PCA)

Effective rank2.3
PCs → 95% var3
PCs → 99% var14
Top-10 cum. var98.5%
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_ratio0.03%0.01faibleNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceamplitude.mean_reflectance22.110.46moyenValeur 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_curve475500.46moyenValeur 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_peak44.4780.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance259.250.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms0.0117990.01faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr1878.50.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min0.0523821.00fortZone problématiqueDétecteurmin(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_count7,7531.00fortArtefactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate11.6%1.00fortSpectre suspectInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count2,1461.00fortRaccord détecteurSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate3.22%1.00fortProblème spectralCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction0.003%0.00faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope-10.2080.46moyenDériveÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.763731.00fortForme inhabituelleFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.70020.31faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio1.97720.25faibleConformeArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio1.79070.22faibleCentralVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio1.33680.33faiblePopulation normaleDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p957.5810.68moyenSpectre 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_p950.200580.57moyenForme différenteFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDrepeatability.rms_intra_id—0.00faibleStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDrepeatability.sam_intra_id—0.00faibleStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDrepeatability.cv_intra_id—0.00faibleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densitystructure.pca_score_density0.0093070.51moyenSous-populationsLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)structure.local_outlier_factor_p951.53920.27faiblePopulation normaleCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scorestructure.isolation_forest_score_p950.540120.51moyenSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X PCA score plot-400-2000200400-200-1000100PC1 -181.5 · PC2 -91.08PC1 -259.2 · PC2 -123.5PC1 -117.2 · PC2 -120.4PC1 -82.69 · PC2 -139.7PC1 -141.7 · PC2 -66.19PC1 -115.8 · PC2 -58.11PC1 48.26 · PC2 -72.46PC1 122.5 · PC2 -103.7PC1 111.7 · PC2 -81.2PC1 -2.344 · PC2 -52.49PC1 -2.118 · PC2 -73.03PC1 233.3 · PC2 28.27PC1 249.8 · PC2 40.52PC1 325 · PC2 8.242PC1 263.6 · PC2 48.58PC1 170.9 · PC2 55.5PC1 206.7 · PC2 64.42PC1 136.3 · PC2 60.5PC1 204.1 · PC2 17.7PC1 354.4 · PC2 17.21PC1 255.9 · PC2 25.57PC1 117.9 · PC2 -53.87PC1 -183.6 · PC2 63.03PC1 -173.2 · PC2 87.1PC1 -70.03 · PC2 84.15PC1 -361.9 · PC2 53.44PC1 -193.8 · PC2 95.33PC1 -164.5 · PC2 86.7PC1 -335.3 · PC2 73.42PC1 -127.5 · PC2 44.12PC1 -287.9 · PC2 81.97PC1 (80.6%)PC2 (10.5%)31 scores
PCA explained variance0%25%50%75%100%PC1: 80.6% (cumulative 80.6%)1PC2: 10.5% (cumulative 91.1%)2PC3: 4.2% (cumulative 95.3%)3PC4: 0.9% (cumulative 96.3%)4PC5: 0.7% (cumulative 97.0%)5PC6: 0.5% (cumulative 97.4%)6PC7: 0.4% (cumulative 97.8%)7PC8: 0.3% (cumulative 98.1%)8PC9: 0.2% (cumulative 98.3%)9PC10: 0.2% (cumulative 98.5%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)

Metric interpretation reference

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

Variables

Targets 1

USDA_Species_Code

target · categorical
USDA_Species_Code classesPEFR5PEFR5: 1010ARFU2ARFU2: 99CAAQCAAQ: 66ERAN6ERAN6: 66
n / missing31 / 0
Classes4
Balance (entropy)0.98
Imbalance ratio2
Top classPEFR5 (10)

Metadata 1

date

metadata · categorical
date classes2015071220150712: 21212015072020150720: 1010
n / missing31 / 0
Classes2
Balance (entropy)0.91
Imbalance ratio2
Top class20150712 (21)
Constant metadata 19
  • ecosis_resource_id4367a915-4354-4162-b97e-b8e99d9e17c1
  • siteBarrow (Utqiagvik) Environmental Observatory
  • coordinate_precision_notessource-provided coordinates when available
  • year2,019
  • plant_partLeaf
  • canopy_or_leafleaf
  • instrumentSpectra Vista Corporation HR-1024i
  • acquisition_modeContact
  • signal_typetransmittance
  • axis_unitnm
  • axis_min350
  • axis_max2,500
  • n_points_original2,151
  • publication_doi10.5440/1336809 | 10.5440/1336812 | 10.5440/1437044 | 10.5440/1482338
  • citationShawn Serbin Wil Lieberman-Cribbin Kim Ely Alistair Rogers. 2019. NGEE Arctic Leaf Spectral Reflectance and Transmittance Data 2014 to 2016 Utqiagvik (Barrow) Alaska. Data set. Available on-line [http://ecosis.org] from the Ecological Spectral Information System (EcoSIS). https://doi.org/10.5440/1437044
  • licensenot specified
  • rights_statusmanual_review_needed
  • usage_scopeprivate_use_only
  • notesEcoSIS package ngee-arctic-leaf-spectral-reflectance-and-transmittance-data-2014-to-2016-utqiagvik--barrow--alaska, no interpolation applied by project.

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

Alignment

Alignment levelobservation
Sample id availableyes
Samples31
Observations (total)31
Reps per samplemin 1 · mean 1 · max 1

Provenance & citation

ContributorNGEE Arctic Leaf Spectral Reflectance and Transmittance Data 2014 to 2016 Utqiagvik (Barrow) Alaska
Origin · url [open]https://data.ecosis.org/dataset/ngee-arctic-leaf-spectral-reflectance-and-transmittance-data-2014-to-2016-utqiagvik--barrow--alaska
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)
Publication10.5440/1437044 — NGEE Arctic Leaf Spectral Reflectance and Transmittance, Barrow, Alaska, 2014-2016
Publication10.5440/1336812 — Leaf Mass Area, Leaf Carbon and Nitrogen Content, Barrow, Alaska, 2012-2016
Publication10.5440/1336809 — Leaf Photosynthetic Parameters Vcmax and Jmax and Supporting Gas Exchange Data, Barrow, Alaska, 2012-2016
Publication10.5440/1482338 — Leaf Photosynthetic Parameters: Quantum Yield, Convexity, Respiration, Gross CO2 Assimilation Rate and Raw Gas Exchange Data, Barrow, Alaska, 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 hash54c3e9e6396b49c0…
Processing hash0d246a0123fc6914…
Metadata hash6d510e82e4033f79…

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_and_transmittance_transmittance_nirs", token="…")
X, y = ds.x(), ds.y()
print(X.shape, y.shape)

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