104

samples

317

wavelengths

sources

100

targets

metadata

NIR

family

Dataset property explorer

Mean profile risk0.44

Highest axisArtefacts locaux · 1.00

Diagnostics8

Sources profiled1

Profile axes

Intégrité	0.00
Artefacts locaux	1.00
Bruit	0.05
Outliers PCA	0.61
Distance à la référence	0.84
Répétabilité	0.00
Baseline / forme	0.19
Structure multi-régimes	0.83

Diagnostic	Score	Force	Signaux	Interprétation probable
Splice / raccord détecteursX	0.82	forte	Spike rate 1.00, Jump rate 1.00, SNR non dégradé 0.92	Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Erreur interpolation / rééchantillonnageX	0.66	moyenne	Spike rate 1.00, Jump rate 1.00, Noise RMS faible 0.95	Artefacts numériques ou traitement spectral incorrect.
Signature VERA25-likeX	0.62	moyenne	Spike rate 1.00, Jump rate 1.00, RMS/SAM référence 0.84	Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Dataset multi-régimesX	0.51	moyenne	RMS/SAM référence 0.84, Structure PCA 0.83, PCA Q 0.61	Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.
Spectre hors domaine valideX	0.47	moyenne	RMS/SAM référence 0.84, Structure PCA 0.83, Mahalanobis / T2 0.47	Variété, espèce, lot ou condition différente mais physiquement plausible.
Erreur calibration / référence blancheX	0.45	moyenne	artefacts locaux 1.00, RMS/SAM référence 0.84, PCA Q 0.61	Décalage systématique entre campagnes, instruments ou référence blanche.
Différence de sonde / géométrieX	0.45	moyenne	RMS/SAM référence 0.84, PCA Q 0.61, Mahalanobis / T2 0.47	Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Fond différentX	0.38	faible	RMS/SAM référence 0.84, PCA Q 0.61, Mahalanobis / T2 0.47	Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.

Spectral sources

X

X · NIR

Sampling

Wavelengths	317
Axis range	414.9–2351 nm
Mean spacing	6.13 nm
Grid	irregular
Observations	104

Signal & quality

Value range	0.0172 – 0.518
Mean range	0.0244 – 0.371
Mean level	0.1954
Area	385.6
PTP	0.3463
Noise RMS	0.00077953
SNR	2.5e+02
SNR dB	5e+01 dB
Dynamic range	0.346
Smoothness	0.02058
Saturated	0.0%
X-outliers	41

Integrity & artefacts

NaN ratio	0.00%
Inf count	0
Zero ratio	0.00%
Spike count	2,843
Spike rate	8.68%
Jump count	551
Jump rate	1.68%
Clip fraction	0.01%

Shape & reference

Baseline slope	0.032218
Curvature RMS	0.020281
D1 RMS	0.014197
RMS to mean	0.023258
RMS p95	0.053935
SAM to mean	0.070037
SAM p95	0.18009
Affine offset p95	0.046848
Affine gain p95 Δ	0.33059
Affine residual p95	0.034841
Xcorr lag p95	0

Outliers & repeatability

PCA Q p95/median	4.9
Hotelling T2 p95/median	3.5
Mahalanobis H p95/median	1.9
Repeat groups	0

Dimensionality (PCA)

Effective rank	1.8
PCs → 95% var	2
PCs → 99% var	4
Top-10 cum. var	99.9%

Computed metric scores 29worst 1.00

Famille	Métrique calculée	Valeur	Score	Niveau	Interprétation dataset	Causes typiques	Calcul / scoring
Intégrité des données	`NaN ratio`integrity.nan_ratio	0%	0.00	faible	Spectre complet	Erreur acquisition/export	count(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des données	`Inf count`integrity.inf_count	0	0.00	faible	Normal	Calculs invalides	count(isinf(X))alert = min(1, inf_count / 1)
Intégrité des données	`Zero ratio`integrity.zero_ratio	0%	0.00	faible	Normal	Export, saturation	count(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globale	`Mean reflectance`amplitude.mean_reflectance	0.19544	0.19	faible	Trop sombre	Fond, géométrie	mean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globale	`Area under curve`amplitude.area_under_curve	385.58	0.19	faible	Normal	Distance sonde	trapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globale	`Peak-to-peak (PTP)`amplitude.peak_to_peak	0.34627	0.00	faible	Variabilité forte	Saturation	max(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globale	`Variance`amplitude.variance	0.013227	0.00	faible	Normal ou hétérogène	Mauvais contact	var(X finite)alert increases when variance/dynamic range is abnormally flat
Bruit	`Noise RMS`noise.noise_rms	0.00077953	0.05	faible	Stable	Lampe, détecteur	median MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
Bruit	`SNR`noise.snr	250.72	0.00	faible	Bon signal	Acquisition	mean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
Bruit	`Bandwise SNR`noise.bandwise_snr_min	8.3822	0.47	moyen	Zone problématique	Détecteur	min(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locaux	`Spike count`artefacts.spike_count	2,843	1.00	fort	Artefacts	Cosmic rays, splice	count robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locaux	`Spike rate`artefacts.spike_rate	8.68%	1.00	fort	Spectre suspect	Interpolation	spike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locaux	`Jump count`artefacts.jump_count	551	1.00	fort	Raccord détecteur	Splice	count robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locaux	`Jump rate`artefacts.jump_rate	1.68%	1.00	fort	Problème spectral	Calibration	jump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locaux	`Clip fraction`artefacts.clip_fraction	0.00607%	0.01	faible	Normal	Détecteur saturé	fraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectrale	`Baseline slope`shape.baseline_slope	0.032218	0.19	faible	Stable	Éclairement	linear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectrale	`Curvature RMS`shape.curvature_rms	0.020281	1.00	fort	Forme inhabituelle	Fond, splice	median RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectrale	`D1 RMS`shape.d1_rms	0.014197	0.82	fort	Spectre structuré	Biologie ou artefact	median RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariés	`PCA Q (SPE)`outliers.pca_q_ratio	4.905	0.61	moyen	Spectre atypique	Artefact, mélange	p95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariés	`Hotelling T²`outliers.hotelling_t2_ratio	3.4983	0.44	moyen	Extrême mais cohérent	Variabilité naturelle	p95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariés	`Mahalanobis H`outliers.mahalanobis_h_ratio	1.8703	0.47	moyen	Outlier global	Domaine différent	p95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référence	`RMS to mean spectrum`reference.rms_to_mean_spectrum_p95	0.053935	0.62	moyen	Spectre différent	Domain shift	p95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référence	`Spectral Angle Mapper (SAM)`reference.sam_to_mean_spectrum_p95	0.18009	0.51	moyen	Forme différente	Fond, géométrie	p95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
Répétabilité	`RMS intra-ID`repeatability.rms_intra_id	—	0.00	faible	Stable	Positionnement	median RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
Répétabilité	`SAM intra-ID`repeatability.sam_intra_id	—	0.00	faible	Stable	Acquisition	median SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
Répétabilité	`CV intra-ID`repeatability.cv_intra_id	—	0.00	faible	Stable	Opérateur	median within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du dataset	`PCA score density`structure.pca_score_density	8.417	0.83	fort	Sous-populations	Lots différents	1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du dataset	`Local Outlier Factor (LOF)`structure.local_outlier_factor_p95	2.4887	0.74	fort	Spectre isolé	Cas rares	p95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du dataset	`Isolation Forest score`structure.isolation_forest_score_p95	0.59212	0.83	fort	Spectre atypique	Diverses causes	p95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent

X-Y spectral correlation 20

Target	max \|r\|	axis @ max	mean \|r\|	\|r\| ≥ .5
Achi.mill	0.48	549	0.166	0.0%
Agri.eupa	0.294	1.75e+03	0.159	0.0%
Agro.capi	0.202	520	0.121	0.0%
Agro.vine	0.178	538	0.049	0.0%
Anth.odor	0.197	535	0.0491	0.0%
Anth.vuln	0.284	535	0.0919	0.0%
Apha.arve	0.39	614	0.259	0.0%
Arab.thal	0.26	705	0.148	0.0%
Arab.hirs	0.172	480	0.105	0.0%
Arrh.elat	0.418	1.7e+03	0.162	0.0%
Arte.absi	0.274	712	0.176	0.0%
Aspe.tinc	0.13	553	0.0426	0.0%
Bell.pere	0.231	1.31e+03	0.157	0.0%
Briz.medi	0.217	549	0.129	0.0%
Brom.erec	0.202	567	0.0742	0.0%
Brom.hord	0.421	2.05e+03	0.334	0.0%
Camp.pers	0.405	1.73e+03	0.193	0.0%
Camp.rotu	0.212	531	0.0968	0.0%
Caps.burs	0.403	1.77e+03	0.209	0.0%
Care.flac	0.317	1.03e+03	0.209	0.0%

Metric interpretation reference

Metric catalog 29

Famille	Métrique	Ce qu’elle détecte	Forte valeur =	Faible valeur =	Causes typiques	Calcul / score
Intégrité des données	`NaN ratio`	Données manquantes	Spectre corrompu	Spectre complet	Erreur acquisition/export	count(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des données	`Inf count`	Valeurs infinies	Corruption	Normal	Calculs invalides	count(isinf(X))alert = min(1, inf_count / 1)
Intégrité des données	`Zero ratio`	Colonnes ou cellules nulles	Spectre tronqué	Normal	Export, saturation	count(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globale	`Mean reflectance`	Niveau moyen	Trop clair / fond visible	Trop sombre	Fond, géométrie	mean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globale	`Area under curve`	Intensité globale	Différence d'éclairement	Normal	Distance sonde	trapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globale	`Peak-to-peak (PTP)`	Dynamique	Variabilité forte	Spectre plat	Saturation	max(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globale	`Variance`	Variabilité spectrale	Normal ou hétérogène	Spectre plat	Mauvais contact	var(X finite)alert increases when variance/dynamic range is abnormally flat
Bruit	`Noise RMS`	Bruit haute fréquence	Bruité	Stable	Lampe, détecteur	median MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
Bruit	`SNR`	Qualité signal	Bon signal	Mauvais signal	Acquisition	mean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
Bruit	`Bandwise SNR`	Bruit localisé	Zone fiable	Zone problématique	Détecteur	min(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locaux	`Spike count`	Pics étroits	Artefacts	Spectre propre	Cosmic rays, splice	count robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locaux	`Spike rate`	Densité de pics	Spectre suspect	Normal	Interpolation	spike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locaux	`Jump count`	Discontinuités	Raccord détecteur	Continu	Splice	count robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locaux	`Jump rate`	Fréquence de sauts	Problème spectral	Normal	Calibration	jump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locaux	`Clip fraction`	Saturation	Clipping	Normal	Détecteur saturé	fraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectrale	`Baseline slope`	Pente globale	Dérive	Stable	Éclairement	linear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectrale	`Curvature RMS`	Courbure	Forme inhabituelle	Lisse	Fond, splice	median RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectrale	`D1 RMS`	Variabilité locale	Spectre structuré	Plat	Biologie ou artefact	median RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariés	`PCA Q (SPE)`	Non expliqué par PCA	Spectre atypique	Conforme	Artefact, mélange	p95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariés	`Hotelling T²`	Extrême dans PCA	Extrême mais cohérent	Central	Variabilité naturelle	p95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariés	`Mahalanobis H`	Distance au nuage	Outlier global	Population normale	Domaine différent	p95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référence	`RMS to mean spectrum`	Distance moyenne	Spectre différent	Typique	Domain shift	p95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référence	`Spectral Angle Mapper (SAM)`	Différence de forme	Forme différente	Similaire	Fond, géométrie	p95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
Répétabilité	`RMS intra-ID`	Reproductibilité	Mauvaise répétabilité	Stable	Positionnement	median RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
Répétabilité	`SAM intra-ID`	Variation de forme	Instable	Stable	Acquisition	median SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
Répétabilité	`CV intra-ID`	Variabilité interne	Mauvais contrôle	Stable	Opérateur	median within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du dataset	`PCA score density`	Clusters	Sous-populations	Homogène	Lots différents	1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du dataset	`Local Outlier Factor (LOF)`	Anomalie locale	Spectre isolé	Population normale	Cas rares	p95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du dataset	`Isolation Forest score`	Anomalie globale	Spectre atypique	Normal	Diverses causes	p95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent

Technology-specific extensions

Technologie	Adaptations / métriques	Anomalies ciblées	Commentaire pratique
UV-Vis 300-1000 nm	Baseline, pente globale, dérive aux bords 300-350 et 900-1000; métriques par zones	Lumière parasite, mauvais blanc, saturation, faible signal aux extrémités	Les bords sont souvent instables; calculer aussi des scores edge/middle.
UV-Vis 300-1000 nm	Saturation / clipping proche absorbance max ou réflectance max	Signal écrêté	Très important si absorption forte.
UV-Vis 300-1000 nm	Red-edge, position de maximum, ratios de bandes si végétal	Décalage biologique ou artefact optique	Aide à distinguer changement réel et problème d'acquisition.
UV-Vis 300-1000 nm	Smoothness / roughness index	Bruit haute fréquence	Souvent plus informatif que le SNR seul.
MIR / ATR-FTIR	ATR contact quality index: intensité globale, aire totale, profondeur des bandes clés	Mauvais contact cristal-échantillon	Crucial: beaucoup d'anomalies viennent du contact ATR.
MIR / ATR-FTIR	CO2 / H2O atmospheric bands	Mauvaise correction atmosphérique	Pics parasites fréquents.
MIR / ATR-FTIR	Baseline curvature / rubber-band residual	Diffusion, contact, dérive baseline	Très utile avant PCA.
MIR / ATR-FTIR	Peak position shift	Mauvais alignement spectral / calibration	Important en FTIR car de petits shifts comptent.
MIR / ATR-FTIR	Band area ratios sur bandes connues	Spectre chimiquement incohérent	À adapter par matrice: polysaccharides, protéines, lipides, etc.
HS-MS	Total Ion Current (TIC), Base Peak Intensity (BPI)	Injection faible, ionisation instable	Équivalent MS du niveau global spectral.
HS-MS	Nombre de pics détectés	Spectre pauvre ou trop bruité	Trop peu = mauvais signal; trop = bruit/contamination.
HS-MS	Mass accuracy / m/z drift	Problème calibration masse	Fondamental en HRMS.
HS-MS	Retention time drift si LC/GC-MS	Dérive chromatographique	À suivre sur standards/QC pools.
HS-MS	Blank contamination score	Contaminants / carry-over	Comparer échantillons vs blancs.
HS-MS	Internal standard CV	Variabilité instrumentale	Très robuste si standards disponibles.
HS-MS	Missingness par feature	Instabilité de détection	Crucial pour filtrer les variables.
Avec répétitions	RMS intra-échantillon	Répétabilité globale	Applicable à toutes les technologies.
Avec répétitions	SAM / corrélation intra-échantillon	Répétabilité de forme	Très utile pour spectres.
Avec répétitions	CV intra-échantillon par bande / feature	Répétabilité locale	Détecte les zones instables.
Avec répétitions	ICC ou variance components	Part variance échantillon vs technique	Très utile si plusieurs répétitions par sample.
Avec répétitions	Distance au centroïde intra-ID	Répétition aberrante	Permet de flagger la mauvaise répétition plutôt que le sample entier.

Bug-hunting / supervised audits

Famille de bug potentiel	Méthodes à ajouter	Ce que ça détecte	État dans l’explorateur
Shift spectral global	Corrélation spectre moyen inter-dataset, DTW, cross-correlation, comparaison positions de pics	Décalage en longueur d'onde, mauvais alignement, interpolation différente	Partiellement calculé: cross-correlation lag et dispersion des positions de pics vs spectre moyen.
Baseline / offset / gain	Régression chaque spectre vs spectre moyen: x = a + b ref + residual; suivi de a, b, RMS résiduel	Offset additif, effet multiplicatif, dérive de baseline	Calculé dans reference.affine_*.
Mélange de lignes / mauvais appariement X-M-Y	Vérification index, hash des lignes, duplication ID, distance spectrale intra-ID, labels incohérents	Lignes mélangées, metadata mal alignées, Y attribué au mauvais spectre	Partiellement couvert par répétabilité intra-ID; checks index/hash à ajouter au pipeline canonical.
Fuite d'information / répétitions mal splitées	GroupKFold par sample_id vs StratifiedKFold random; audit des partitions par sample_id	Performance artificiellement bonne due aux répétitions	Nécessite splits et benchmark modèle; non calculé par la carte descriptive.
Label bugs	Échantillons proches en X mais Y différents, confident learning, erreurs systématiques FP/FN	Y inversés, erreurs de saisie, classes ambiguës	Nécessite Y et/ou modèle; recommandé pour l'explorateur supervisé.
Sous-domaines cachés	PCA/UMAP/t-SNE + clustering non supervisé + association avec dataset/Y/date/operator	Lots, campagnes, sondes, backgrounds non renseignés	Partiellement calculé par structure PCA/LOF; UMAP/t-SNE hors carte statique.
Artefacts localisés inconnus	Carte wavelength x dataset: différence moyenne, différence variance, KS par longueur d'onde	Régions spectrales anormales non anticipées	À calculer au niveau banque quand plusieurs datasets partagent un axe spectral.
Ruptures instrumentales	Discontinuités dans dérivées, changepoint detection	Splice, raccord détecteur, saut local non prévu	Calculé par jump/spike rates; changepoint plus avancé à ajouter.
Mélange / contamination spectrale	NMF / unmixing / reconstruction par convex hull	Composante externe: fond, plastique, sol	Non calculé automatiquement; nécessite hypothèses de composants ou grande bibliothèque.
Features instables mais prédictives	Importance modèle vs instabilité QC par variable	Modèle qui apprend un artefact plutôt qu'un signal biologique	Nécessite modèle supervisé; recommandé pour rapports de benchmark.

Variables

Targets 100

Achi.mill

target · numeric

n / missing	104 / 0
Mean ± SD	0.7788 ± 0.417
Median	1
Range	0 – 1
CV	0.535
Skew / kurtosis	-1.4 / -0.14
Normal?	no

Agri.eupa

target · numeric

n / missing	104 / 0
Mean ± SD	0.1442 ± 0.353
Median	0
Range	0 – 1
CV	2.45
Skew / kurtosis	2.1 / 2.3
Normal?	no

Agro.capi

target · numeric

n / missing	104 / 0
Mean ± SD	0.3846 ± 0.489
Median	0
Range	0 – 1
CV	1.27
Skew / kurtosis	0.48 / -1.8
Normal?	no

Agro.vine

target · numeric

n / missing	104 / 0
Mean ± SD	0.1346 ± 0.343
Median	0
Range	0 – 1
CV	2.55
Skew / kurtosis	2.2 / 2.8
Normal?	no

Anth.odor

target · numeric

n / missing	104 / 0
Mean ± SD	0.1058 ± 0.309
Median	0
Range	0 – 1
CV	2.92
Skew / kurtosis	2.6 / 4.9
Normal?	no

Anth.vuln

target · numeric

n / missing	104 / 0
Mean ± SD	0.2692 ± 0.446
Median	0
Range	0 – 1
CV	1.66
Skew / kurtosis	1.1 / -0.9
Normal?	no

Apha.arve

target · numeric

n / missing	104 / 0
Mean ± SD	0.1154 ± 0.321
Median	0
Range	0 – 1
CV	2.78
Skew / kurtosis	2.4 / 4
Normal?	no

Arab.thal

target · numeric

n / missing	104 / 0
Mean ± SD	0.04808 ± 0.215
Median	0
Range	0 – 1
CV	4.47
Skew / kurtosis	4.3 / 17
Normal?	no

Arab.hirs

target · numeric

n / missing	104 / 0
Mean ± SD	0.2788 ± 0.451
Median	0
Range	0 – 1
CV	1.62
Skew / kurtosis	1 / -1
Normal?	no

Arrh.elat

target · numeric

n / missing	104 / 0
Mean ± SD	0.3173 ± 0.468
Median	0
Range	0 – 1
CV	1.47
Skew / kurtosis	0.8 / -1.4
Normal?	no

Arte.absi

target · numeric

n / missing	104 / 0
Mean ± SD	0.08654 ± 0.283
Median	0
Range	0 – 1
CV	3.26
Skew / kurtosis	3 / 7
Normal?	no

Aspe.tinc

target · numeric

n / missing	104 / 0
Mean ± SD	0.125 ± 0.332
Median	0
Range	0 – 1
CV	2.66
Skew / kurtosis	2.3 / 3.4
Normal?	no

Bell.pere

target · numeric

n / missing	104 / 0
Mean ± SD	0.5481 ± 0.5
Median	1
Range	0 – 1
CV	0.912
Skew / kurtosis	-0.2 / -2
Normal?	no

Briz.medi

target · numeric

n / missing	104 / 0
Mean ± SD	0.4135 ± 0.495
Median	0
Range	0 – 1
CV	1.2
Skew / kurtosis	0.36 / -1.9
Normal?	no

Brom.erec

target · numeric

n / missing	104 / 0
Mean ± SD	0.03846 ± 0.193
Median	0
Range	0 – 1
CV	5.02
Skew / kurtosis	4.9 / 22
Normal?	no

Brom.hord

target · numeric

n / missing	104 / 0
Mean ± SD	0.4519 ± 0.5
Median	0
Range	0 – 1
CV	1.11
Skew / kurtosis	0.2 / -2
Normal?	no

Camp.pers

target · numeric

n / missing	104 / 0
Mean ± SD	0.09615 ± 0.296
Median	0
Range	0 – 1
CV	3.08
Skew / kurtosis	2.8 / 5.8
Normal?	no

Camp.rotu

target · numeric

n / missing	104 / 0
Mean ± SD	0.2115 ± 0.41
Median	0
Range	0 – 1
CV	1.94
Skew / kurtosis	1.4 / 0.055
Normal?	no

Caps.burs

target · numeric

n / missing	104 / 0
Mean ± SD	0.2692 ± 0.446
Median	0
Range	0 – 1
CV	1.66
Skew / kurtosis	1.1 / -0.9
Normal?	no

Care.flac

target · numeric

n / missing	104 / 0
Mean ± SD	0.1346 ± 0.343
Median	0
Range	0 – 1
CV	2.55
Skew / kurtosis	2.2 / 2.8
Normal?	no

Care.sylv

target · numeric

n / missing	104 / 0
Mean ± SD	0.04808 ± 0.215
Median	0
Range	0 – 1
CV	4.47
Skew / kurtosis	4.3 / 17
Normal?	no

Cent.jace

target · numeric

n / missing	104 / 0
Mean ± SD	0.4135 ± 0.495
Median	0
Range	0 – 1
CV	1.2
Skew / kurtosis	0.36 / -1.9
Normal?	no

Cent.scab

target · numeric

n / missing	104 / 0
Mean ± SD	0.3558 ± 0.481
Median	0
Range	0 – 1
CV	1.35
Skew / kurtosis	0.61 / -1.7
Normal?	no

Cich.inty

target · numeric

n / missing	104 / 0
Mean ± SD	0.1058 ± 0.309
Median	0
Range	0 – 1
CV	2.92
Skew / kurtosis	2.6 / 4.9
Normal?	no

Cirs.acau

target · numeric

n / missing	104 / 0
Mean ± SD	0.1731 ± 0.38
Median	0
Range	0 – 1
CV	2.2
Skew / kurtosis	1.8 / 1.1
Normal?	no

Cirs.arve

target · numeric

n / missing	104 / 0
Mean ± SD	0.2019 ± 0.403
Median	0
Range	0 – 1
CV	2
Skew / kurtosis	1.5 / 0.28
Normal?	no

Cirs.vulg

target · numeric

n / missing	104 / 0
Mean ± SD	0.04808 ± 0.215
Median	0
Range	0 – 1
CV	4.47
Skew / kurtosis	4.3 / 17
Normal?	no

Conv.arve

target · numeric

n / missing	104 / 0
Mean ± SD	0.6154 ± 0.489
Median	1
Range	0 – 1
CV	0.794
Skew / kurtosis	-0.48 / -1.8
Normal?	no

Cyno.cris

target · numeric

n / missing	104 / 0
Mean ± SD	0.3558 ± 0.481
Median	0
Range	0 – 1
CV	1.35
Skew / kurtosis	0.61 / -1.7
Normal?	no

Dact.glom

target · numeric

n / missing	104 / 0
Mean ± SD	0.8654 ± 0.343
Median	1
Range	0 – 1
CV	0.396
Skew / kurtosis	-2.2 / 2.8
Normal?	no

Dauc.caro

target · numeric

n / missing	104 / 0
Mean ± SD	0.1923 ± 0.396
Median	0
Range	0 – 1
CV	2.06
Skew / kurtosis	1.6 / 0.52
Normal?	no

Dian.delt

target · numeric

n / missing	104 / 0
Mean ± SD	0.1346 ± 0.343
Median	0
Range	0 – 1
CV	2.55
Skew / kurtosis	2.2 / 2.8
Normal?	no

Drab.mura

target · numeric

n / missing	104 / 0
Mean ± SD	0.1154 ± 0.321
Median	0
Range	0 – 1
CV	2.78
Skew / kurtosis	2.4 / 4
Normal?	no

Elyt.repe

target · numeric

n / missing	104 / 0
Mean ± SD	0.4904 ± 0.502
Median	0
Range	0 – 1
CV	1.02
Skew / kurtosis	0.039 / -2
Normal?	no

Erod.cicu

target · numeric

n / missing	104 / 0
Mean ± SD	0.1154 ± 0.321
Median	0
Range	0 – 1
CV	2.78
Skew / kurtosis	2.4 / 4
Normal?	no

Erop.vern

target · numeric

n / missing	104 / 0
Mean ± SD	0.1923 ± 0.396
Median	0
Range	0 – 1
CV	2.06
Skew / kurtosis	1.6 / 0.52
Normal?	no

Fest.ovin

target · numeric

n / missing	104 / 0
Mean ± SD	0.4135 ± 0.495
Median	0
Range	0 – 1
CV	1.2
Skew / kurtosis	0.36 / -1.9
Normal?	no

Fest.prat

target · numeric

n / missing	104 / 0
Mean ± SD	0.2308 ± 0.423
Median	0
Range	0 – 1
CV	1.83
Skew / kurtosis	1.3 / -0.32
Normal?	no

Fest.rubr

target · numeric

n / missing	104 / 0
Mean ± SD	0.7115 ± 0.455
Median	1
Range	0 – 1
CV	0.64
Skew / kurtosis	-0.95 / -1.1
Normal?	no

Fili.vulg

target · numeric

n / missing	104 / 0
Mean ± SD	0.2308 ± 0.423
Median	0
Range	0 – 1
CV	1.83
Skew / kurtosis	1.3 / -0.32
Normal?	no

Gali.albu

target · numeric

n / missing	104 / 0
Mean ± SD	0.125 ± 0.332
Median	0
Range	0 – 1
CV	2.66
Skew / kurtosis	2.3 / 3.4
Normal?	no

Gali.bore

target · numeric

n / missing	104 / 0
Mean ± SD	0.1635 ± 0.372
Median	0
Range	0 – 1
CV	2.27
Skew / kurtosis	1.8 / 1.4
Normal?	no

Gali.veru

target · numeric

n / missing	104 / 0
Mean ± SD	0.6058 ± 0.491
Median	1
Range	0 – 1
CV	0.811
Skew / kurtosis	-0.44 / -1.8
Normal?	no

Gera.colu

target · numeric

n / missing	104 / 0
Mean ± SD	0.4135 ± 0.495
Median	0
Range	0 – 1
CV	1.2
Skew / kurtosis	0.36 / -1.9
Normal?	no

Gera.moll

target · numeric

n / missing	104 / 0
Mean ± SD	0.5385 ± 0.501
Median	1
Range	0 – 1
CV	0.93
Skew / kurtosis	-0.16 / -2
Normal?	no

Geum.urba

target · numeric

n / missing	104 / 0
Mean ± SD	0.1346 ± 0.343
Median	0
Range	0 – 1
CV	2.55
Skew / kurtosis	2.2 / 2.8
Normal?	no

Heli.numm

target · numeric

n / missing	104 / 0
Mean ± SD	0.2115 ± 0.41
Median	0
Range	0 – 1
CV	1.94
Skew / kurtosis	1.4 / 0.055
Normal?	no

Heli.oela

target · numeric

n / missing	104 / 0
Mean ± SD	0.05769 ± 0.234
Median	0
Range	0 – 1
CV	4.06
Skew / kurtosis	3.8 / 13
Normal?	no

Heli.prat

target · numeric

n / missing	104 / 0
Mean ± SD	0.2596 ± 0.441
Median	0
Range	0 – 1
CV	1.7
Skew / kurtosis	1.1 / -0.78
Normal?	no

Heli.pube

target · numeric

n / missing	104 / 0
Mean ± SD	0.3846 ± 0.489
Median	0
Range	0 – 1
CV	1.27
Skew / kurtosis	0.48 / -1.8
Normal?	no

Hype.perf

target · numeric

n / missing	104 / 0
Mean ± SD	0.2981 ± 0.46
Median	0
Range	0 – 1
CV	1.54
Skew / kurtosis	0.9 / -1.2
Normal?	no

Hypo.radi

target · numeric

n / missing	104 / 0
Mean ± SD	0.09615 ± 0.296
Median	0
Range	0 – 1
CV	3.08
Skew / kurtosis	2.8 / 5.8
Normal?	no

Knau.arve

target · numeric

n / missing	104 / 0
Mean ± SD	0.4135 ± 0.495
Median	0
Range	0 – 1
CV	1.2
Skew / kurtosis	0.36 / -1.9
Normal?	no

Leuc.vulg

target · numeric

n / missing	104 / 0
Mean ± SD	0.09615 ± 0.296
Median	0
Range	0 – 1
CV	3.08
Skew / kurtosis	2.8 / 5.8
Normal?	no

Linu.cath

target · numeric

n / missing	104 / 0
Mean ± SD	0.1346 ± 0.343
Median	0
Range	0 – 1
CV	2.55
Skew / kurtosis	2.2 / 2.8
Normal?	no

Loli.pere

target · numeric

n / missing	104 / 0
Mean ± SD	0.6731 ± 0.471
Median	1
Range	0 – 1
CV	0.7
Skew / kurtosis	-0.75 / -1.5
Normal?	no

Lotu.corn

target · numeric

n / missing	104 / 0
Mean ± SD	0.5 ± 0.502
Median	0.5
Range	0 – 1
CV	1
Skew / kurtosis	0 / -2
Normal?	no

Luzu.camp

target · numeric

n / missing	104 / 0
Mean ± SD	0.3846 ± 0.489
Median	0
Range	0 – 1
CV	1.27
Skew / kurtosis	0.48 / -1.8
Normal?	no

Medi.lupu

target · numeric

n / missing	104 / 0
Mean ± SD	0.6731 ± 0.471
Median	1
Range	0 – 1
CV	0.7
Skew / kurtosis	-0.75 / -1.5
Normal?	no

Oxyt.camp

target · numeric

n / missing	104 / 0
Mean ± SD	0.04808 ± 0.215
Median	0
Range	0 – 1
CV	4.47
Skew / kurtosis	4.3 / 17
Normal?	no

Phle.phle

target · numeric

n / missing	104 / 0
Mean ± SD	0.2212 ± 0.417
Median	0
Range	0 – 1
CV	1.89
Skew / kurtosis	1.4 / -0.14
Normal?	no

Phle.prat

target · numeric

n / missing	104 / 0
Mean ± SD	0.5962 ± 0.493
Median	1
Range	0 – 1
CV	0.827
Skew / kurtosis	-0.4 / -1.9
Normal?	no

Plan.lanc

target · numeric

n / missing	104 / 0
Mean ± SD	0.8462 ± 0.363
Median	1
Range	0 – 1
CV	0.428
Skew / kurtosis	-1.9 / 1.8
Normal?	no

Plan.majo

target · numeric

n / missing	104 / 0
Mean ± SD	0.05769 ± 0.234
Median	0
Range	0 – 1
CV	4.06
Skew / kurtosis	3.8 / 13
Normal?	no

Poa.annu

target · numeric

n / missing	104 / 0
Mean ± SD	0.1058 ± 0.309
Median	0
Range	0 – 1
CV	2.92
Skew / kurtosis	2.6 / 4.9
Normal?	no

Poa.comp

target · numeric

n / missing	104 / 0
Mean ± SD	0.6154 ± 0.489
Median	1
Range	0 – 1
CV	0.794
Skew / kurtosis	-0.48 / -1.8
Normal?	no

Poly.como

target · numeric

n / missing	104 / 0
Mean ± SD	0.125 ± 0.332
Median	0
Range	0 – 1
CV	2.66
Skew / kurtosis	2.3 / 3.4
Normal?	no

Poly.vulg

target · numeric

n / missing	104 / 0
Mean ± SD	0.07692 ± 0.268
Median	0
Range	0 – 1
CV	3.48
Skew / kurtosis	3.2 / 8.5
Normal?	no

Poly.avic

target · numeric

n / missing	104 / 0
Mean ± SD	0.1635 ± 0.372
Median	0
Range	0 – 1
CV	2.27
Skew / kurtosis	1.8 / 1.4
Normal?	no

Pote.arge

target · numeric

n / missing	104 / 0
Mean ± SD	0.5 ± 0.502
Median	0.5
Range	0 – 1
CV	1
Skew / kurtosis	0 / -2
Normal?	no

Pote.rept

target · numeric

n / missing	104 / 0
Mean ± SD	0.4135 ± 0.495
Median	0
Range	0 – 1
CV	1.2
Skew / kurtosis	0.36 / -1.9
Normal?	no

Pote.tabe

target · numeric

n / missing	104 / 0
Mean ± SD	0.3846 ± 0.489
Median	0
Range	0 – 1
CV	1.27
Skew / kurtosis	0.48 / -1.8
Normal?	no

Prim.veri

target · numeric

n / missing	104 / 0
Mean ± SD	0.2788 ± 0.451
Median	0
Range	0 – 1
CV	1.62
Skew / kurtosis	1 / -1
Normal?	no

Puls.prat

target · numeric

n / missing	104 / 0
Mean ± SD	0.2019 ± 0.403
Median	0
Range	0 – 1
CV	2
Skew / kurtosis	1.5 / 0.28
Normal?	no

Ranu.acri

target · numeric

n / missing	104 / 0
Mean ± SD	0.125 ± 0.332
Median	0
Range	0 – 1
CV	2.66
Skew / kurtosis	2.3 / 3.4
Normal?	no

Ranu.bulb

target · numeric

n / missing	104 / 0
Mean ± SD	0.8654 ± 0.343
Median	1
Range	0 – 1
CV	0.396
Skew / kurtosis	-2.2 / 2.8
Normal?	no

Ranu.repe

target · numeric

n / missing	104 / 0
Mean ± SD	0.03846 ± 0.193
Median	0
Range	0 – 1
CV	5.02
Skew / kurtosis	4.9 / 22
Normal?	no

Rume.acet

target · numeric

n / missing	104 / 0
Mean ± SD	0.2115 ± 0.41
Median	0
Range	0 – 1
CV	1.94
Skew / kurtosis	1.4 / 0.055
Normal?	no

Rume.acel

target · numeric

n / missing	104 / 0
Mean ± SD	0.04808 ± 0.215
Median	0
Range	0 – 1
CV	4.47
Skew / kurtosis	4.3 / 17
Normal?	no

Sang.mino

target · numeric

n / missing	104 / 0
Mean ± SD	0.1538 ± 0.363
Median	0
Range	0 – 1
CV	2.36
Skew / kurtosis	1.9 / 1.8
Normal?	no

Satu.acin

target · numeric

n / missing	104 / 0
Mean ± SD	0.1154 ± 0.321
Median	0
Range	0 – 1
CV	2.78
Skew / kurtosis	2.4 / 4
Normal?	no

Saxi.gran

target · numeric

n / missing	104 / 0
Mean ± SD	0.08654 ± 0.283
Median	0
Range	0 – 1
CV	3.26
Skew / kurtosis	3 / 7
Normal?	no

Sedu.acre

target · numeric

n / missing	104 / 0
Mean ± SD	0.2885 ± 0.455
Median	0
Range	0 – 1
CV	1.58
Skew / kurtosis	0.95 / -1.1
Normal?	no

Sene.jaco

target · numeric

n / missing	104 / 0
Mean ± SD	0.2885 ± 0.455
Median	0
Range	0 – 1
CV	1.58
Skew / kurtosis	0.95 / -1.1
Normal?	no

Sesl.caer

target · numeric

n / missing	104 / 0
Mean ± SD	0.08654 ± 0.283
Median	0
Range	0 – 1
CV	3.26
Skew / kurtosis	3 / 7
Normal?	no

Stel.medi

target · numeric

n / missing	104 / 0
Mean ± SD	0.05769 ± 0.234
Median	0
Range	0 – 1
CV	4.06
Skew / kurtosis	3.8 / 13
Normal?	no

Thym.serp

target · numeric

n / missing	104 / 0
Mean ± SD	0.1346 ± 0.343
Median	0
Range	0 – 1
CV	2.55
Skew / kurtosis	2.2 / 2.8
Normal?	no

Trag.prat

target · numeric

n / missing	104 / 0
Mean ± SD	0.1346 ± 0.343
Median	0
Range	0 – 1
CV	2.55
Skew / kurtosis	2.2 / 2.8
Normal?	no

Trif.arve

target · numeric

n / missing	104 / 0
Mean ± SD	0.1827 ± 0.388
Median	0
Range	0 – 1
CV	2.13
Skew / kurtosis	1.7 / 0.79
Normal?	no

Trif.prat

target · numeric

n / missing	104 / 0
Mean ± SD	0.4904 ± 0.502
Median	0
Range	0 – 1
CV	1.02
Skew / kurtosis	0.039 / -2
Normal?	no

Trif.repe

target · numeric

n / missing	104 / 0
Mean ± SD	0.7019 ± 0.46
Median	1
Range	0 – 1
CV	0.655
Skew / kurtosis	-0.9 / -1.2
Normal?	no

Vero.cham

target · numeric

n / missing	104 / 0
Mean ± SD	0.4519 ± 0.5
Median	0
Range	0 – 1
CV	1.11
Skew / kurtosis	0.2 / -2
Normal?	no

Vero.offi

target · numeric

n / missing	104 / 0
Mean ± SD	0.06731 ± 0.252
Median	0
Range	0 – 1
CV	3.74
Skew / kurtosis	3.5 / 10
Normal?	no

Vero.serp

target · numeric

n / missing	104 / 0
Mean ± SD	0.2212 ± 0.417
Median	0
Range	0 – 1
CV	1.89
Skew / kurtosis	1.4 / -0.14
Normal?	no

Vero.spic

target · numeric

n / missing	104 / 0
Mean ± SD	0.2404 ± 0.429
Median	0
Range	0 – 1
CV	1.79
Skew / kurtosis	1.2 / -0.49
Normal?	no

Vici.sati.sat

target · numeric

n / missing	104 / 0
Mean ± SD	0.09615 ± 0.296
Median	0
Range	0 – 1
CV	3.08
Skew / kurtosis	2.8 / 5.8
Normal?	no

Vici.crac

target · numeric

n / missing	104 / 0
Mean ± SD	0.04808 ± 0.215
Median	0
Range	0 – 1
CV	4.47
Skew / kurtosis	4.3 / 17
Normal?	no

Vici.hirs

target · numeric

n / missing	104 / 0
Mean ± SD	0.1058 ± 0.309
Median	0
Range	0 – 1
CV	2.92
Skew / kurtosis	2.6 / 4.9
Normal?	no

Vici.tetr

target · numeric

n / missing	104 / 0
Mean ± SD	0.03846 ± 0.193
Median	0
Range	0 – 1
CV	5.02
Skew / kurtosis	4.9 / 22
Normal?	no

Viol.hirt

target · numeric

n / missing	104 / 0
Mean ± SD	0.1346 ± 0.343
Median	0
Range	0 – 1
CV	2.55
Skew / kurtosis	2.2 / 2.8
Normal?	no

Metadata 6

plot_id

metadata · categorical

n / missing	104 / 0
Classes	104
Balance (entropy)	1
Imbalance ratio	1
Top class	int01 (1)

plot_id_info

metadata · categorical

n / missing	104 / 0
Classes	104
Balance (entropy)	1
Imbalance ratio	1
Top class	int_01 (1)

site

metadata · numeric

n / missing	104 / 8
Mean ± SD	31.27 ± 19.5
Median	31.5
Range	0 – 63
CV	0.622
Skew / kurtosis	0.0065 / -1.3
Normal?	no

age

metadata · categorical

n / missing	104 / 0
Classes	3
Balance (entropy)	1
Imbalance ratio	1
Top class	int (36)

swe99tm_x

metadata · numeric

n / missing	104 / 0
Mean ± SD	5.955e+05 ± 1.15e+03
Median	5.954e+05
Range	5.934e+05 – 5.983e+05
CV	0.00194
Skew / kurtosis	0.64 / 0.25
Normal?	no

swe99tm_y

metadata · numeric

n / missing	104 / 0
Mean ± SD	6.283e+06 ± 1.06e+03
Median	6.283e+06
Range	6.281e+06 – 6.285e+06
CV	0.000168
Skew / kurtosis	0.082 / -0.55
Normal?	yes

Constant metadata 5

manual_downloadTrue
manual_download_source_dirD:\VS projects\nirs_db_vf\manual_download\grassland
manual_download_filedoi_10_5061_dryad_cfxpnvx9w__v20230517.zip::Hyspex.csv
official_source_urlhttps://datadryad.org/dataset/doi:10.5061/dryad.cfxpnvx9w
standardization_batchv0.8.57

Alignment

Alignment level	sample
Sample id available	no
Samples	104
Observations (total)	104
Reps per sample	min 1 · mean 1 · max 1

Provenance & citation

Contributor	DRYAD grassland
Origin · url [open]	10.5061/dryad.cfxpnvx9w — Dryad
Origin · script [manual]	source_to_standard.py — standardization script (maintainer-only)

Governance & integrity

Tier	public
License	CC0-1.0
Permitted use	Research and benchmarking.
Access policy	Open per source license.
Redistribution	Dryad source recorded as CC0-1.0 in blocked inventory; manual download retained as provenance.
Content version	1.0.0
Schema / protocol	2.0
Content hash	`99e0b1899c9cce5c…`
Processing hash	`4398583990f2d250…`
Metadata hash	`3363a16a0682dc9a…`

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

ds = get("dryad_grassland_hyspex_species_manual")            # DOI-pinned, checksum-verified, cached
X, y = ds.x(), ds.y()
print(X.shape, y.shape)

card.json croissant.jsonIdentity metadata only — the dataset bytes live at the origin / DOI.

Grassland Hyspex spectra and plant species data

Dataset property explorer

Profile axes

Spectral sources

X

Sampling

Signal & quality

Integrity & artefacts

Shape & reference

Outliers & repeatability

Dimensionality (PCA)

Metric interpretation reference

Variables

Targets 100

Achi.mill

Agri.eupa

Agro.capi

Agro.vine

Anth.odor

Anth.vuln

Apha.arve

Arab.thal

Arab.hirs

Arrh.elat

Arte.absi

Aspe.tinc

Bell.pere

Briz.medi

Brom.erec

Brom.hord

Camp.pers

Camp.rotu

Caps.burs

Care.flac

Care.sylv

Cent.jace

Cent.scab

Cich.inty

Cirs.acau

Cirs.arve

Cirs.vulg

Conv.arve

Cyno.cris

Dact.glom

Dauc.caro

Dian.delt

Drab.mura

Elyt.repe

Erod.cicu

Erop.vern

Fest.ovin

Fest.prat

Fest.rubr

Fili.vulg

Gali.albu

Gali.bore

Gali.veru

Gera.colu

Gera.moll

Geum.urba

Heli.numm

Heli.oela

Heli.prat

Heli.pube

Hype.perf

Hypo.radi

Knau.arve

Leuc.vulg

Linu.cath

Loli.pere

Lotu.corn

Luzu.camp

Medi.lupu

Oxyt.camp

Phle.phle

Phle.prat

Plan.lanc

Plan.majo

Poa.annu

Poa.comp