OSSL LUCAS soil vis-NIR and MIR. Origin database composed from 2 standardized v2.0 leaves: 2 spectral source(s), 13 declared target(s). Auto-generated from dataset_card.json (verify before publication).
Where the original data and publications come from. Follow these links to obtain the source and cite its authors. Reading guide
| Contributor | OSSL_NIRS |
|---|---|
| Conversion status | partial |
| Origin · url [open] | https://storage.googleapis.com/soilspec4gg-public/ossl_soillab_L1_v1.2.csv.gz |
| Origin · url [open] | https://storage.googleapis.com/soilspec4gg-public/ossl_visnir_L0_v1.2.csv.gz |
| Origin · url [open] | https://storage.googleapis.com/soilspec4gg-public/ossl_soilsite_L0_v1.2.csv.gz |
Source license, redistribution conditions, content version and identity hashes. Redistribution tier and original-source availability are separate. Reading guide
| Tier | private |
|---|---|
| License | LicenseRef-not-cleared |
| Permitted use | Research and benchmarking; private use only. |
| Access policy | Manual download / private-use-only per source. |
| Redistribution | Recovered from local initial-source exports; rights not cleared for redistribution. |
| Content version | 1.1.0 |
| Schema / protocol | 2.0 |
| Content hash | bb9bc03d8b56d1fa… |
| Processing hash | cebdc6b57c574487… |
| Metadata hash | 5dd9ff81db1c777a… |
Start with the profile axes to locate unusual variation, then inspect the measured values and evidence below. Scores are descriptive heuristics. Reading guide
The center is 0 and the outer ring is 1. Larger values indicate stronger anomaly or heterogeneity signals on that axis. Compare individual axes using the table; this is not an overall quality grade. Missing axes are left blank, and partial profiles have no filled polygon.
Dimensionless scores from 0 to 1 summarize each property; larger values indicate stronger signals. Unavailable axes are not zero.
Integrity ?Combines missing cells, zero columns and infinite values. Higher values call for checking data completeness. | 0.73 |
|---|---|
Noise ?Combines roughness and its signal-to-roughness proxy. Real narrow bands may also contribute. | unavailable |
Local artifacts ?Combines spike, jump and clipping heuristics. Inspect flagged spectral regions before attributing a cause. | unavailable |
Baseline/shape ?Summarizes baseline slope and excess edge variation relative to signal scale. | unavailable |
PCA outliers ?Combines relative PCA residual and score-distance tails. Valid rare spectra can produce a high value. | unavailable |
Distance to average spectrum ?Dispersion around this dataset's mean spectrum, using distance, shape and shift summaries. | unavailable |
Repeatability ?Dispersion among observations sharing an ID. Interpretation depends on the documented meaning of that ID. | unavailable |
Multi-regime structure ?Variation in local density in PCA space. May reflect legitimate batches or subpopulations. | unavailable |
Bars rank heuristic signatures from 0 to 1. They do not estimate the probability of a physical fault. On narrow screens, scroll the chart horizontally. Full labels, scores, evidence coverage, possible causes and required external checks are in the table below.
Heuristic signature scores are not calibrated probabilities of physical causes: 0.78 does not mean a 78% causal probability. Missing evidence remains unavailable. RMS/SAM describe dispersion around the dataset mean, without an independent external reference.
| Diagnostic | Score | Heuristic strength | Evidence coverage | Signals | Possible cause | Required external checks |
|---|---|---|---|---|---|---|
| Signature compatible with a spectral spliceX_visnir | 0.88 | strong | 100 % | Spike rate 1.00, Jump rate 1.00, RMS/SAM to mean spectrum 1.00 | Break at detector junctions, local calibration or different probe. | Check the white/black reference actually acquired and its metadata. Check instrument, probe, geometry, campaign and processing/resampling log. Check the repeat protocol before interpreting an intra-ID dispersion. |
| Offset compatible with a different white referenceX_visnir | 0.78 | strong | 100 % | Baseline/mean/area 1.00, RMS/SAM to mean spectrum 1.00, local artifacts 1.00 | Systematic discrepancy between campaigns, instruments or white reference. | Check the white/black reference actually acquired and its metadata. Check instrument, probe, geometry, campaign and processing/resampling log. Check the repeat protocol before interpreting an intra-ID dispersion. |
| Signature compatible with a different backgroundX_visnir | 0.72 | strong | 100 % | Baseline/mean/area 1.00, RMS/SAM to mean spectrum 1.00, PCA Q 0.71 | Systematic effect of support, white/black, transflectance or measurement environment. | Check the white/black reference actually acquired and its metadata. Check instrument, probe, geometry, campaign and processing/resampling log. Check the repeat protocol before interpreting an intra-ID dispersion. |
| Signature compatible with a population shiftX_visnir | 0.58 | medium | 100 % | RMS/SAM to mean spectrum 1.00, PCA structure 0.94, Mahalanobis / T2 0.62 | Different but physically plausible variety, species, batch or condition. | Check the white/black reference actually acquired and its metadata. Check instrument, probe, geometry, campaign and processing/resampling log. Check the repeat protocol before interpreting an intra-ID dispersion. |
| Signature compatible with clippingX_visnir | 0.46 | medium | 100 % | Baseline/mean/area 1.00, Jump rate 1.00, PCA Q 0.71 | Detector saturated or insufficient dynamic range. | Check the white/black reference actually acquired and its metadata. Check instrument, probe, geometry, campaign and processing/resampling log. Check the repeat protocol before interpreting an intra-ID dispersion. |
| High spectral roughness signatureX_visnir | 0.38 | weak | 100 % | Spike rate 1.00, RMS/SAM to mean spectrum 1.00, PCA Q 0.71 | Weak signal, detector problem, lamp or unstable acquisition. | Check the white/black reference actually acquired and its metadata. Check instrument, probe, geometry, campaign and processing/resampling log. Check the repeat protocol before interpreting an intra-ID dispersion. |
| Flat spectrum/weak signalX_visnir | 0.14 | weak | 100 % | PCA Q 0.71 | Bad contact, missing sample, degraded measurement or very low dynamic range. | Check the white/black reference actually acquired and its metadata. Check instrument, probe, geometry, campaign and processing/resampling log. Check the repeat protocol before interpreting an intra-ID dispersion. |
| Weak signatures observedX_mir | — | unknown | 20 % | Weak signatures on available metrics; does not certify either physical validity or the absence of defects. | Check the white/black reference actually acquired and its metadata. Check instrument, probe, geometry, campaign and processing/resampling log. Check the repeat protocol before interpreting an intra-ID dispersion. |
Each block is a separate instrument or spectral source. Read its signal summary before using PCA to explore variation between observations. Reading guide
| Source | Observations | Sample units |
|---|---|---|
| recovered_spectra | 40,175 | 40,175 |
| recovered_spectra | 40,175 | 40,175 |
40,175 sample units are measured by every source; 0 by a single source. Sources are joined by declared sample identity, never by row position.
Read the signal and variation summaries for this source. Axis unit: none; signal representation: not recorded.
The mean and median describe the typical signal at each channel; shaded bands show the 25th–75th and 5th–95th percentiles across observations. A wider band means greater variation, which can be biological or instrumental.
How many observations and spectral channels were exported, and how the wavelength axis is spaced. Reading guide
Wavelengths ?Number of spectral channels. A channel can represent wavelength, wavenumber or another feature axis. | 1,701 |
|---|---|
Axis range ?Lowest and highest recorded spectral-axis coordinates, in the stated unit. | 600–4,000 none |
Mean spacing ?Average difference between adjacent spectral-axis coordinates; compare only within the same unit. | 2 none |
Grid ?Uniform means adjacent axis spacings are approximately equal. An irregular grid may need care when applying derivatives. | uniform |
Observations ?Exported spectral rows in this source, including repeated measurements when present. | 40,175 |
Signal level and roughness summaries. These depend on the signal representation and are not instrument acceptance tests. Reading guide
X-outliers ?Number flagged by the qualification outlier method. An unusual spectrum can be valid and should not be discarded automatically. | 0 |
|---|
Missing values, narrow spikes and abrupt jumps to investigate in the original spectra. Reading guide
NaN ratio ?Missing spectral cells divided by all spectral cells, displayed as a percentage. | 100.00% |
|---|---|
Inf count ?Number of positive or negative infinite spectral values; investigate invalid calculations or export. | 0 |
Zero ratio ?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing. | 0.00% |
Spike count ?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged. | 0 |
Spike rate ?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage. | 0.00% |
Jump count ?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins. | 0 |
Jump rate ?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage. | 0.00% |
Variation relative to this dataset's mean spectrum; the mean is not an independently measured calibration reference. Reading guide
No values are available for this source in the identity card.
Unusual spectra and within-ID dispersion. Repeated IDs do not by themselves establish technical repetitions. Reading guide
Repeat groups ?Number of IDs with several observations. Source documentation is needed to decide whether these are technical repeats, positions or time points. | 0 |
|---|
Principal component analysis (PCA) compresses correlated wavelengths into components. This describes spectral variation, without using prediction targets. Reading guide
No values are available for this source in the identity card.
Value is the measured quantity; alert score is its heuristic mapping to 0–1. Levels are weak below 0.42, medium from 0.42, and strong from 0.72. These are display bands, not calibrated acceptance thresholds. A dash means unavailable, never zero. Possible causes are suggestions to investigate. Use each metric’s ? control for its meaning and scoring: hover for a hint, or expand it with a tap, Enter or Space.
| Family | Computed metric | Value | Alert score | Heuristic level | Descriptive interpretation | Possible causes to verify | Calculation / scoring |
|---|---|---|---|---|---|---|---|
| Data integrity | Invalid/masked cell count (owner report) ?Count of cells reported unavailable by the native owner under its null or validity mask. This count is distinct from the historical NaN ratio; consult the recorded population and denominator. Scoring: No alert mapping is defined for this metric; an unavailable score does not mean a zero measured value. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Missing data or validity mask; not evidence of an acquisition cause | Native Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio. |
| Data integrity | NaN ratio ?Missing spectral cells divided by all spectral cells, displayed as a percentage. Scoring: alert = min(1, nan_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 100% | 1.00 | strong | Corrupted spectrum | Acquisition/export error | count(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05) |
| Data integrity | Inf count ?Number of positive or negative infinite spectral values; investigate invalid calculations or export. Scoring: alert = min(1, inf_count / 1). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0 | 0.00 | weak | Normal | Invalid calculations | count(isinf(X))alert = min(1, inf_count / 1) |
| Data integrity | Zero ratio ?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing. Scoring: alert = min(1, zero_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0% | 0.00 | weak | Normal | Export, saturation | count(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05) |
| Overall amplitude | Mean reflectance ?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations. Scoring: alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Background, geometry | mean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent |
| Overall amplitude | Area under curve ?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit. Scoring: alert reuses baseline/shape drift because area scale depends on axis and units. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Probe distance | trapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units |
| Overall amplitude | Peak-to-peak (PTP) ?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum. Scoring: alert increases when dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Saturation | max(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat |
| Overall amplitude | Variance ?Variance of all finite spectral cells, in squared signal units. It combines variation across observations and channels; a small value can describe a flat signal. Scoring: alert increases when variance/dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Bad contact | var(X finite)alert increases when variance/dynamic range is abnormally flat |
| Noise | Roughness sigma proxy ?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise. Scoring: alert = noise_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Lamp, detector | median MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5% |
| Noise | Roughness S/N proxy ?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR. Scoring: alert decreases with SNR dB; >=40 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Acquisition | mean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert |
| Noise | Bandwise roughness S/N proxy ?Minimum channel-wise ratio of absolute mean signal to local second-difference roughness. A smaller ratio identifies a weak or rough spectral region; genuine narrow bands and near-zero signal can also lower it. This is not certified instrument SNR. Scoring: alert decreases with worst-band SNR dB; >=35 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Detector | min(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert |
| Local artifacts | Spike count ?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged. Scoring: alert follows spike_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0 | 0.00 | weak | Clean spectrum | Cosmic rays, splice | count robust outliers in second derivativealert follows spike_rate, saturated at 1% |
| Local artifacts | Spike rate ?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage. Scoring: alert = min(1, spike_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0% | 0.00 | weak | Normal | Interpolation | spike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01) |
| Local artifacts | Jump count ?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins. Scoring: alert follows jump_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0 | 0.00 | weak | Continuous | Splice | count robust outliers in first derivativealert follows jump_rate, saturated at 1% |
| Local artifacts | Jump rate ?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage. Scoring: alert = min(1, jump_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0% | 0.00 | weak | Normal | Calibration | jump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01) |
| Local artifacts | Clip fraction ?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation. Scoring: alert = min(1, clip_fraction / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Detector saturated | fraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01) |
| Spectral form | Baseline slope ?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit. Scoring: alert = abs(slope / signal_scale), saturated at 0.5. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Illumination | linear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5 |
| Spectral form | Curvature RMS ?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation. Scoring: alert = curvature_rms / signal_scale, saturated at 1%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Background, splice | median RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1% |
| Spectral form | D1 RMS ?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts. Scoring: alert = d1_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Biology or artifact | median RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5% |
| Multivariate outliers | PCA Q (SPE) ?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median. Scoring: alert = min(1, pca_q_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Artifact, mixture | p95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8) |
| Multivariate outliers | Hotelling T² ?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median. Scoring: alert = min(1, hotelling_t2_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Natural variability | p95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8) |
| Multivariate outliers | Mahalanobis H ?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra. Scoring: alert = min(1, mahalanobis_h_ratio / 4). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Different domain | p95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4) |
| Comparison to average spectrum | RMS to mean spectrum ?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value. Scoring: alert = RMS_p95 / signal_scale, saturated at 25%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Domain shift | p95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25% |
| Comparison to average spectrum | Spectral Angle Mapper (SAM) ?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS. Scoring: alert = min(1, SAM_p95 / 0.35 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Background, geometry | p95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad) |
| Repeatability | RMS intra-ID ?Within-ID root mean square deviation from the group's mean spectrum, in signal units. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = RMS_intra_ID / signal_scale, saturated at 10%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Positioning | median RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10% |
| Repeatability | SAM intra-ID ?Within-ID spectral angle to the group's mean spectrum, in radians. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, SAM_intra_ID / 0.15 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Acquisition | median SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad) |
| Repeatability | CV intra-ID ?Within-ID coefficient of variation: standard deviation relative to absolute mean; unstable near zero signal. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, CV_intra_ID / 0.25). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Operator | median within-ID band CValert = min(1, CV_intra_ID / 0.25) |
| Structure of the dataset | PCA score density ?Inverse median distance to nearby observations in retained PCA-score space. Larger raw values mean more closely spaced observations; scale depends on the PCA representation. The alert uses density variation, so raw density alone does not establish an anomaly. Scoring: alert follows density_cv/profile structure complexity, not raw density alone. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Different batches | 1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone |
| Structure of the dataset | kNN distance ratio (not LOF) ?95th percentile of mean k-nearest-neighbor distance divided by the population median of that distance, in PCA-score space. Values above 1 indicate an upper tail with more isolated observations; valid rare spectra can also be isolated. This distance ratio is not the Local Outlier Factor algorithm. Scoring: alert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Rare cases | p95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2) |
| Structure of the dataset | Isolation Forest score ?95th percentile of the negated Isolation Forest score in retained PCA-score space. Larger raw scores indicate easier isolation under that model; the scale is implementation-dependent. The displayed alert reuses structure complexity rather than directly mapping this raw score. Scoring: alert follows structure complexity; raw score is implementation-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Various causes | p95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent |
PC1 and PC2 summarize differences between observations without using targets; their coordinates are not wavelengths or target values.
A usable two-component observation projection is unavailable in this view.
Each bar reports the share of spectral variance explained by a component. Concentration in the first components indicates correlated wavelengths. This is not variance explained in the prediction target. This projection may use a different observation sample from the score plot.
Read the signal and variation summaries for this source. Axis unit: none; signal representation: absorbance.
The mean and median describe the typical signal at each channel; shaded bands show the 25th–75th and 5th–95th percentiles across observations. A wider band means greater variation, which can be biological or instrumental.
How many observations and spectral channels were exported, and how the wavelength axis is spaced. Reading guide
Wavelengths ?Number of spectral channels. A channel can represent wavelength, wavenumber or another feature axis. | 1,076 |
|---|---|
Axis range ?Lowest and highest recorded spectral-axis coordinates, in the stated unit. | 350–2,500 none |
Mean spacing ?Average difference between adjacent spectral-axis coordinates; compare only within the same unit. | 2 none |
Grid ?Uniform means adjacent axis spacings are approximately equal. An irregular grid may need care when applying derivatives. | uniform |
Observations ?Exported spectral rows in this source, including repeated measurements when present. | 40,175 |
Signal level and roughness summaries. These depend on the signal representation and are not instrument acceptance tests. Reading guide
Value range ?Minimum and maximum recorded spectral values, in the original signal representation. | 0.0247 – 0.869 |
|---|---|
Mean range ?Minimum and maximum of the mean spectrum across spectral channels. | 0.123 – 0.454 |
Mean level ?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations. | 0.3703 |
Area ?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit. | 777.8 |
PTP ?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum. | 0.3309 |
Noise RMS ?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise. | 6.0609e-06 |
Roughness S/N proxy ?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR. | 6.1e+04 |
Roughness S/N proxy dB ?20 log10 of the roughness signal-to-noise proxy. Units are decibels; compare only compatible signal representations. | 1e+02 dB |
Dynamic range ?Maximum minus minimum of the mean spectrum. Its magnitude depends on the signal representation. | 0.331 |
Saturated ?Fraction at the observed global maximum; a heuristic indicator, not a comparison with known detector limits. | 0.0% |
X-outliers ?Number flagged by the qualification outlier method. An unusual spectrum can be valid and should not be discarded automatically. | 20,367 |
Missing values, narrow spikes and abrupt jumps to investigate in the original spectra. Reading guide
NaN ratio ?Missing spectral cells divided by all spectral cells, displayed as a percentage. | 2.32% |
|---|---|
Inf count ?Number of positive or negative infinite spectral values; investigate invalid calculations or export. | 0 |
Zero ratio ?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing. | 0.00% |
Spike count ?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged. | 2,618,540 |
Spike rate ?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage. | 6.07% |
Jump count ?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins. | 619,269 |
Jump rate ?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage. | 1.43% |
Clip fraction ?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation. | 0.00% |
Variation relative to this dataset's mean spectrum; the mean is not an independently measured calibration reference. Reading guide
Baseline slope ?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit. | 0.23523 |
|---|---|
Curvature RMS ?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation. | 0.00020911 |
D1 RMS ?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts. | 0.0010669 |
RMS to mean ?Root mean square distance to this dataset's mean spectrum, in signal units. | 0.054386 |
RMS p95 ?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value. | 0.14809 |
SAM to mean ?Spectral Angle Mapper: angle to the mean spectrum, in radians. Smaller angles indicate more similar shapes. | 0.044466 |
SAM p95 ?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS. | 0.11534 |
Affine offset p95 ?95th percentile of absolute fitted offsets when matching each spectrum to the dataset mean. | 0.090201 |
Affine gain p95 Δ ?95th percentile of the absolute gain deviation from 1 in the fit to the mean spectrum. | 0.35377 |
Affine residual p95 ?95th percentile of residual RMS after gain and offset correction to the mean spectrum. | 0.031288 |
Xcorr lag p95 ?95th percentile of the absolute cross-correlation shift relative to the mean spectrum, in spectral channels. | 0 |
Unusual spectra and within-ID dispersion. Repeated IDs do not by themselves establish technical repetitions. Reading guide
PCA Q p95/median ?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median. | 5.7 |
|---|---|
Hotelling T2 p95/median ?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median. | 5 |
Mahalanobis H p95/median ?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra. | 2.2 |
Repeat groups ?Number of IDs with several observations. Source documentation is needed to decide whether these are technical repeats, positions or time points. | 0 |
Principal component analysis (PCA) compresses correlated wavelengths into components. This describes spectral variation, without using prediction targets. Reading guide
Effective rank ?Entropy-based summary of the variance distribution across components. Larger values indicate variation spread across more dimensions. | 1.4 |
|---|---|
PCs → 95% var ?Number of principal components needed to explain 95% of spectral variance, when reached by the computed projection. | 2 |
PCs → 99% var ?Number of principal components needed to explain 99% of spectral variance, when reached by the computed projection. | 4 |
Top-10 cum. var ?Fraction of spectral variance explained by up to the first ten computed principal components, displayed as a percentage. | 100.0% |
Value is the measured quantity; alert score is its heuristic mapping to 0–1. Levels are weak below 0.42, medium from 0.42, and strong from 0.72. These are display bands, not calibrated acceptance thresholds. A dash means unavailable, never zero. Possible causes are suggestions to investigate. Use each metric’s ? control for its meaning and scoring: hover for a hint, or expand it with a tap, Enter or Space.
| Family | Computed metric | Value | Alert score | Heuristic level | Descriptive interpretation | Possible causes to verify | Calculation / scoring |
|---|---|---|---|---|---|---|---|
| Data integrity | Invalid/masked cell count (owner report) ?Count of cells reported unavailable by the native owner under its null or validity mask. This count is distinct from the historical NaN ratio; consult the recorded population and denominator. Scoring: No alert mapping is defined for this metric; an unavailable score does not mean a zero measured value. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Missing data or validity mask; not evidence of an acquisition cause | Native Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio. |
| Data integrity | NaN ratio ?Missing spectral cells divided by all spectral cells, displayed as a percentage. Scoring: alert = min(1, nan_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 2.32% | 0.46 | medium | Corrupted spectrum | Acquisition/export error | count(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05) |
| Data integrity | Inf count ?Number of positive or negative infinite spectral values; investigate invalid calculations or export. Scoring: alert = min(1, inf_count / 1). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0 | 0.00 | weak | Normal | Invalid calculations | count(isinf(X))alert = min(1, inf_count / 1) |
| Data integrity | Zero ratio ?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing. Scoring: alert = min(1, zero_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0% | 0.00 | weak | Normal | Export, saturation | count(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05) |
| Overall amplitude | Mean reflectance ?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations. Scoring: alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0.37027 | 1.00 | strong | Atypical value: Too light / background visible or Too dark | Background, geometry | mean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent |
| Overall amplitude | Area under curve ?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit. Scoring: alert reuses baseline/shape drift because area scale depends on axis and units. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 777.77 | 1.00 | strong | Atypical value: Illumination difference or Normal | Probe distance | trapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units |
| Overall amplitude | Peak-to-peak (PTP) ?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum. Scoring: alert increases when dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0.33086 | 0.00 | weak | Strong variability | Saturation | max(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat |
| Overall amplitude | Variance ?Variance of all finite spectral cells, in squared signal units. It combines variation across observations and channels; a small value can describe a flat signal. Scoring: alert increases when variance/dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0.014309 | 0.00 | weak | Normal or heterogeneous | Bad contact | var(X finite)alert increases when variance/dynamic range is abnormally flat |
| Noise | Roughness sigma proxy ?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise. Scoring: alert = noise_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 6.0609e-06 | 0.00 | weak | Stable | Lamp, detector | median MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5% |
| Noise | Roughness S/N proxy ?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR. Scoring: alert decreases with SNR dB; >=40 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 61092 | 0.00 | weak | Good signal | Acquisition | mean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert |
| Noise | Bandwise roughness S/N proxy ?Minimum channel-wise ratio of absolute mean signal to local second-difference roughness. A smaller ratio identifies a weak or rough spectral region; genuine narrow bands and near-zero signal can also lower it. This is not certified instrument SNR. Scoring: alert decreases with worst-band SNR dB; >=35 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 251.62 | 0.00 | weak | Reliable zone | Detector | min(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert |
| Local artifacts | Spike count ?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged. Scoring: alert follows spike_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 2,618,540 | 1.00 | strong | Artifacts | Cosmic rays, splice | count robust outliers in second derivativealert follows spike_rate, saturated at 1% |
| Local artifacts | Spike rate ?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage. Scoring: alert = min(1, spike_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 6.07% | 1.00 | strong | Suspicious spectrum | Interpolation | spike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01) |
| Local artifacts | Jump count ?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins. Scoring: alert follows jump_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 619,269 | 1.00 | strong | Detector connection | Splice | count robust outliers in first derivativealert follows jump_rate, saturated at 1% |
| Local artifacts | Jump rate ?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage. Scoring: alert = min(1, jump_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 1.43% | 1.00 | strong | Spectral problem | Calibration | jump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01) |
| Local artifacts | Clip fraction ?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation. Scoring: alert = min(1, clip_fraction / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 7.1e-06% | 0.00 | weak | Normal | Detector saturated | fraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01) |
| Spectral form | Baseline slope ?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit. Scoring: alert = abs(slope / signal_scale), saturated at 0.5. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0.23523 | 1.00 | strong | Drift | Illumination | linear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5 |
| Spectral form | Curvature RMS ?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation. Scoring: alert = curvature_rms / signal_scale, saturated at 1%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0.00020911 | 0.06 | weak | Smooth | Background, splice | median RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1% |
| Spectral form | D1 RMS ?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts. Scoring: alert = d1_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0.0010669 | 0.06 | weak | Flat | Biology or artifact | median RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5% |
| Multivariate outliers | PCA Q (SPE) ?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median. Scoring: alert = min(1, pca_q_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 5.6583 | 0.71 | medium | Atypical spectrum | Artifact, mixture | p95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8) |
| Multivariate outliers | Hotelling T² ?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median. Scoring: alert = min(1, hotelling_t2_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 4.9771 | 0.62 | medium | Extreme but consistent | Natural variability | p95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8) |
| Multivariate outliers | Mahalanobis H ?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra. Scoring: alert = min(1, mahalanobis_h_ratio / 4). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 2.2309 | 0.56 | medium | Global outlier | Different domain | p95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4) |
| Comparison to average spectrum | RMS to mean spectrum ?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value. Scoring: alert = RMS_p95 / signal_scale, saturated at 25%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0.14809 | 1.00 | strong | Different spectrum | Domain shift | p95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25% |
| Comparison to average spectrum | Spectral Angle Mapper (SAM) ?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS. Scoring: alert = min(1, SAM_p95 / 0.35 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0.11534 | 0.33 | weak | Similar | Background, geometry | p95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad) |
| Repeatability | RMS intra-ID ?Within-ID root mean square deviation from the group's mean spectrum, in signal units. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = RMS_intra_ID / signal_scale, saturated at 10%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Positioning | median RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10% |
| Repeatability | SAM intra-ID ?Within-ID spectral angle to the group's mean spectrum, in radians. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, SAM_intra_ID / 0.15 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Acquisition | median SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad) |
| Repeatability | CV intra-ID ?Within-ID coefficient of variation: standard deviation relative to absolute mean; unstable near zero signal. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, CV_intra_ID / 0.25). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | — | — | not calculable | Not enough information to score this metric on this dataset. | Operator | median within-ID band CValert = min(1, CV_intra_ID / 0.25) |
| Structure of the dataset | PCA score density ?Inverse median distance to nearby observations in retained PCA-score space. Larger raw values mean more closely spaced observations; scale depends on the PCA representation. The alert uses density variation, so raw density alone does not establish an anomaly. Scoring: alert follows density_cv/profile structure complexity, not raw density alone. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 6.463 | 0.94 | strong | Subpopulations | Different batches | 1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone |
| Structure of the dataset | kNN distance ratio (not LOF) ?95th percentile of mean k-nearest-neighbor distance divided by the population median of that distance, in PCA-score space. Values above 1 indicate an upper tail with more isolated observations; valid rare spectra can also be isolated. This distance ratio is not the Local Outlier Factor algorithm. Scoring: alert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 2.8141 | 0.91 | strong | Isolated spectrum | Rare cases | p95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2) |
| Structure of the dataset | Isolation Forest score ?95th percentile of the negated Isolation Forest score in retained PCA-score space. Larger raw scores indicate easier isolation under that model; the scale is implementation-dependent. The displayed alert reuses structure complexity rather than directly mapping this raw score. Scoring: alert follows structure complexity; raw score is implementation-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable. | 0.59038 | 0.94 | strong | Atypical spectrum | Various causes | p95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent |
Source recovered_spectra: 800 of 40,175 source observations are plotted. PC1 captures 94.2% and PC2 3.4% of the reported spectral variance: 97.7% together; 2.3% remains outside this view.
Principal component analysis (PCA) summarizes spectral differences without using targets. The horizontal PC1 axis captures the largest variation; vertical PC2 captures the next largest direction of variation. Each dot is one observation; repeated measurements need not be distinct samples. Scores are projection coordinates, not wavelengths or target values. Nearby dots have similar projections; separate groups or isolated dots are leads to inspect in the original spectra and acquisition metadata. Overlapping dots can differ in omitted dimensions. The dots have no class or batch labels, so this view cannot identify groups, confirm defective observations, explain a chemical cause or measure prediction accuracy.
Point numbers identify rows in this displayed coordinate list, not original observation or sample IDs. Non-integer coordinates use up to five significant digits. Original IDs, component loadings, preprocessing details and the observation selection method are not supplied with this plot.
| Point | PC1 score | PC2 score |
|---|---|---|
| 1 | 2.1657 | -0.038291 |
| 2 | -3.2338 | -0.42943 |
| 3 | 4.5114 | -0.21667 |
| 4 | 4.2448 | -0.30646 |
| 5 | 1.0972 | -0.028774 |
| 6 | 2.5088 | -0.017382 |
| 7 | -2.7362 | -0.20477 |
| 8 | 4.3578 | 0.20668 |
| 9 | 3.1064 | -0.085053 |
| 10 | 2.1995 | 0.21591 |
| 11 | 0.41254 | -0.24711 |
| 12 | 1.8337 | -0.18096 |
| 13 | 3.7522 | -0.26966 |
| 14 | -0.80887 | -0.057512 |
| 15 | 4.0217 | -0.21563 |
| 16 | 2.8518 | -0.028465 |
| 17 | 2.0461 | -0.13146 |
| 18 | 5.4288 | -0.31324 |
| 19 | -3.1731 | -0.47513 |
| 20 | 2.4243 | -0.55935 |
| 21 | 4.0489 | 0.5449 |
| 22 | 3.2867 | 0.51811 |
| 23 | -2.4386 | 0.45925 |
| 24 | -2.1323 | 0.28897 |
| 25 | 3.5448 | 0.26672 |
| 26 | 1.5431 | 0.29657 |
| 27 | 0.029081 | 0.6274 |
| 28 | 0.090869 | 0.30373 |
| 29 | -2.888 | 0.49905 |
| 30 | -0.31221 | -0.043791 |
| 31 | -0.55493 | -0.086619 |
| 32 | -0.86992 | -0.049611 |
| 33 | -1.2815 | 0.69951 |
| 34 | -6.6195 | -0.64516 |
| 35 | -0.39247 | 0.026983 |
| 36 | -2.5582 | -0.32236 |
| 37 | 0.92323 | -0.30736 |
| 38 | -4.6103 | 0.31548 |
| 39 | -5.0524 | -0.13918 |
| 40 | 0.65486 | -0.17637 |
| 41 | -0.66745 | 0.18204 |
| 42 | -2.057 | -0.19607 |
| 43 | -2.1171 | -0.61021 |
| 44 | -4.8921 | -0.023655 |
| 45 | -2.2894 | 0.19966 |
| 46 | 1.0578 | -0.60436 |
| 47 | -2.5043 | -0.41319 |
| 48 | -2.4745 | -0.29975 |
| 49 | 1.354 | -0.41607 |
| 50 | 0.75185 | -0.16631 |
| 51 | 0.8373 | -0.5629 |
| 52 | 2.2132 | 0.067714 |
| 53 | 3.8924 | -0.28105 |
| 54 | 0.33867 | 0.28307 |
| 55 | 0.43742 | 0.024036 |
| 56 | 4.4587 | -0.87634 |
| 57 | 3.1592 | -0.40914 |
| 58 | -0.3844 | 0.027441 |
| 59 | -3.2077 | -1.0624 |
| 60 | 2.0423 | -0.024816 |
| 61 | -2.3944 | -0.3222 |
| 62 | -1.8656 | -0.49728 |
| 63 | 1.4298 | -0.15159 |
| 64 | 4.7129 | -0.20531 |
| 65 | -1.4884 | 0.6368 |
| 66 | -0.17775 | -0.10863 |
| 67 | -1.2247 | 0.15704 |
| 68 | -1.7329 | 0.38206 |
| 69 | -0.10365 | -0.55313 |
| 70 | 2.5231 | -0.076292 |
| 71 | -3.3979 | 0.31039 |
| 72 | -0.51422 | 0.28609 |
| 73 | -0.65293 | -0.56166 |
| 74 | 1.7541 | -0.061414 |
| 75 | 0.4117 | -0.1892 |
| 76 | 1.6296 | -0.161 |
| 77 | -5.3434 | -0.043776 |
| 78 | -0.65496 | 0.18588 |
| 79 | -0.93765 | -0.021975 |
| 80 | -3.6193 | 0.2855 |
| 81 | -2.0633 | 0.73409 |
| 82 | -1.7181 | -0.42648 |
| 83 | -3.233 | 0.35137 |
| 84 | -0.91584 | -0.23604 |
| 85 | -0.53696 | -0.045965 |
| 86 | -2.8162 | 0.27393 |
| 87 | -1.272 | 0.32992 |
| 88 | -0.1958 | 0.12523 |
| 89 | -3.9629 | -0.11965 |
| 90 | -2.1477 | 0.32377 |
| 91 | 2.6095 | 0.02437 |
| 92 | 0.41384 | -0.26889 |
| 93 | -3.8217 | -0.18525 |
| 94 | 0.59091 | -0.085665 |
| 95 | 1.0619 | -0.19267 |
| 96 | -1.2391 | -0.33497 |
| 97 | 1.1326 | -0.14601 |
| 98 | 3.1986 | -0.38512 |
| 99 | 2.0236 | -0.27714 |
| 100 | 2.296 | 0.37591 |
| 101 | -1.7388 | 0.329 |
| 102 | 1.4412 | -0.023079 |
| 103 | -1.1699 | -0.30913 |
| 104 | -0.3833 | -0.14302 |
| 105 | -2.0915 | -0.82214 |
| 106 | -0.19177 | 0.077805 |
| 107 | -4.0527 | -0.38027 |
| 108 | 0.86724 | 0.15145 |
| 109 | -7.5362 | -0.64682 |
| 110 | -1.6192 | 0.15132 |
| 111 | -4.7096 | 0.48615 |
| 112 | -4.0813 | 0.39031 |
| 113 | 3.672 | -0.21385 |
| 114 | -4.8672 | -0.54364 |
| 115 | 1.6212 | -0.31872 |
| 116 | 5.2532 | 0.59178 |
| 117 | 5.5294 | 0.21097 |
| 118 | 2.6726 | -0.15381 |
| 119 | 3.1845 | 0.031419 |
| 120 | 2.8727 | 0.66894 |
| 121 | 3.8349 | 0.52976 |
| 122 | 3.236 | 0.37771 |
| 123 | 2.4158 | 0.74632 |
| 124 | -1.8925 | 1.2663 |
| 125 | 0.16454 | 0.64889 |
| 126 | 3.2044 | 0.75903 |
| 127 | -1.5135 | 0.029862 |
| 128 | -0.046743 | 0.59408 |
| 129 | 0.37112 | 0.089757 |
| 130 | 0.33354 | 0.63664 |
| 131 | 0.05965 | -0.32019 |
| 132 | 0.083744 | 0.76564 |
| 133 | -3.0646 | 0.28116 |
| 134 | -2.7455 | 0.2173 |
| 135 | -3.0159 | -0.081238 |
| 136 | -3.0252 | 0.28018 |
| 137 | -2.2316 | -0.074532 |
| 138 | -1.5949 | 0.68503 |
| 139 | -2.017 | 0.4409 |
| 140 | -4.26 | -0.54096 |
| 141 | -3.6181 | 0.34558 |
| 142 | -1.2692 | 0.24481 |
| 143 | -2.1059 | 0.32201 |
| 144 | 3.2759 | -0.13859 |
| 145 | -3.8286 | -0.34064 |
| 146 | 2.3918 | -0.13549 |
| 147 | 4.3497 | -0.60808 |
| 148 | -2.7767 | 0.42686 |
| 149 | -4.0645 | 0.93219 |
| 150 | 2.1492 | 0.033974 |
| 151 | 1.2719 | -0.19058 |
| 152 | -1.1334 | -0.12918 |
| 153 | 4.2158 | -0.38032 |
| 154 | 0.74194 | 0.32405 |
| 155 | -1.2315 | 0.68477 |
| 156 | 4.2814 | 0.53134 |
| 157 | 5.2076 | -0.2405 |
| 158 | 0.91158 | -0.2552 |
| 159 | -0.91996 | 0.6988 |
| 160 | 3.7648 | 0.12981 |
| 161 | 0.10882 | 0.48315 |
| 162 | -1.5119 | -0.73084 |
| 163 | -1.8155 | 0.4306 |
| 164 | -1.5062 | -1.5469 |
| 165 | 0.49609 | -1.0092 |
| 166 | -2.0473 | 0.49568 |
| 167 | 0.53221 | 0.1917 |
| 168 | 5.6105 | -0.54472 |
| 169 | -2.8591 | 0.7342 |
| 170 | 1.8355 | 0.046696 |
| 171 | -1.7454 | 1.3776 |
| 172 | 2.6133 | 0.07829 |
| 173 | 2.6042 | -0.40424 |
| 174 | -0.5273 | -0.052566 |
| 175 | 0.64501 | -0.79199 |
| 176 | -1.6629 | 0.15309 |
| 177 | 0.69798 | 0.37939 |
| 178 | 2.4572 | -1.1444 |
| 179 | -0.75322 | -0.0089234 |
| 180 | 1.5685 | -0.25634 |
| 181 | 1.4665 | -0.24021 |
| 182 | -2.668 | -0.3653 |
| 183 | -6.7565 | -0.43924 |
| 184 | 0.15743 | -0.64187 |
| 185 | 1.7623 | -0.17509 |
| 186 | 3.3219 | -0.048568 |
| 187 | 1.7606 | -0.47101 |
| 188 | -1.0834 | 0.32492 |
| 189 | 2.1022 | -0.00584 |
| 190 | 0.86727 | 0.10554 |
| 191 | 1.9476 | 0.1365 |
| 192 | 0.9353 | 0.068168 |
| 193 | -0.76506 | -0.37781 |
| 194 | -2.3949 | 0.1584 |
| 195 | -2.5021 | 0.27553 |
| 196 | -5.378 | 0.62035 |
| 197 | -0.10932 | -0.12851 |
| 198 | -0.60376 | -0.6161 |
| 199 | -3.8667 | -0.22653 |
| 200 | -1.6834 | -0.30149 |
| 201 | -0.65254 | -0.42444 |
| 202 | -1.5915 | -0.011182 |
| 203 | -0.71332 | 0.021576 |
| 204 | -2.5126 | -0.38336 |
| 205 | -0.82635 | -0.09197 |
| 206 | -0.22982 | 0.37786 |
| 207 | -0.6883 | 0.34724 |
| 208 | -2.1766 | -0.55047 |
| 209 | -2.6704 | -0.064478 |
| 210 | -1.8392 | -0.70706 |
| 211 | 2.4546 | -0.60097 |
| 212 | 1.9878 | -0.5392 |
| 213 | 0.25813 | 0.12522 |
| 214 | -2.7511 | -0.85006 |
| 215 | 3.7716 | -0.82619 |
| 216 | -0.72363 | -0.35032 |
| 217 | -1.1406 | 0.18553 |
| 218 | 5.1998 | 0.32109 |
| 219 | 2.7546 | -0.35149 |
| 220 | 2.1034 | 0.15001 |
| 221 | 1.627 | -0.064961 |
| 222 | -1.8342 | 0.052737 |
| 223 | -1.8013 | -0.31438 |
| 224 | -1.6038 | 0.7774 |
| 225 | -2.9319 | 0.50245 |
| 226 | -3.7595 | 0.1863 |
| 227 | -1.396 | -0.2788 |
| 228 | -3.3095 | 0.99461 |
| 229 | -2.9017 | -0.48246 |
| 230 | -0.95073 | 0.58403 |
| 231 | -0.11895 | -0.17392 |
| 232 | -4.0665 | -0.73108 |
| 233 | -1.7629 | 0.21544 |
| 234 | -1.241 | 0.069138 |
| 235 | 1.5426 | 0.61557 |
| 236 | 1.5386 | 0.3449 |
| 237 | 5.958 | -0.66902 |
| 238 | 6.7015 | -0.10983 |
| 239 | 6.6486 | -1.3771 |
| 240 | 6.178 | -0.5694 |
| 241 | 8.59 | -1.48 |
| 242 | 5.7733 | -0.14948 |
| 243 | 1.9812 | 0.28336 |
| 244 | -0.88619 | 0.34066 |
| 245 | -0.48587 | -0.61994 |
| 246 | 6.4026 | -0.30387 |
| 247 | 0.41471 | 0.66342 |
| 248 | 2.783 | 0.46015 |
| 249 | 0.97231 | 0.11118 |
| 250 | 0.95751 | 0.32711 |
| 251 | 0.18071 | -0.1141 |
| 252 | 5.017 | -0.34354 |
| 253 | 2.1086 | -0.16156 |
| 254 | 4.7996 | 0.0045562 |
| 255 | 1.5224 | 0.63679 |
| 256 | 0.024697 | 0.19476 |
| 257 | 0.57511 | 0.54534 |
| 258 | 0.13059 | 0.010911 |
| 259 | 2.144 | -0.12227 |
| 260 | -4.7367 | 0.57575 |
| 261 | -1.2891 | -0.45325 |
| 262 | 2.7841 | -0.5888 |
| 263 | 2.6022 | -0.35816 |
| 264 | 4.436 | 0.27798 |
| 265 | 2.0009 | 0.62435 |
| 266 | 5.0205 | -0.51 |
| 267 | 5.0307 | -0.089557 |
| 268 | 2.1288 | 0.14763 |
| 269 | 5.7968 | -0.24392 |
| 270 | 1.6087 | -0.22891 |
| 271 | 1.2893 | -0.12664 |
| 272 | 0.7746 | 0.381 |
| 273 | 1.3352 | -0.36513 |
| 274 | 1.418 | 0.12187 |
| 275 | -0.91327 | -0.21288 |
| 276 | 3.5579 | -0.058046 |
| 277 | 0.49483 | -0.05512 |
| 278 | 0.28323 | -0.14177 |
| 279 | 2.9664 | 0.090563 |
| 280 | 1.2614 | 0.43633 |
| 281 | -0.74929 | 0.1488 |
| 282 | 1.3673 | 0.69228 |
| 283 | -0.75413 | -0.062356 |
| 284 | -0.78329 | -0.89333 |
| 285 | 1.2936 | -0.8686 |
| 286 | 0.95156 | -1.2215 |
| 287 | -0.40879 | -0.98315 |
| 288 | -2.4298 | -0.059258 |
| 289 | -2.4857 | -0.18252 |
| 290 | -3.8442 | -0.52067 |
| 291 | -3.5056 | 0.70051 |
| 292 | 1.5594 | -0.021079 |
| 293 | -2.6982 | 0.48596 |
| 294 | 1.7114 | 0.007101 |
| 295 | 1.3441 | -0.44311 |
| 296 | -2.34 | -0.080466 |
| 297 | -1.4255 | 0.14542 |
| 298 | -3.2632 | 0.57084 |
| 299 | -0.050644 | 0.24921 |
| 300 | 0.36628 | 0.25632 |
| 301 | -4.5727 | -0.10419 |
| 302 | 0.072257 | 0.32931 |
| 303 | 1.2844 | -0.0066464 |
| 304 | -5.7835 | -0.65417 |
| 305 | 1.4913 | -0.35786 |
| 306 | 2.5418 | -0.28438 |
| 307 | -1.9898 | 0.9009 |
| 308 | 3.0747 | -0.56447 |
| 309 | 0.020233 | 0.45514 |
| 310 | -1.468 | -0.70057 |
| 311 | -2.9444 | 0.79463 |
| 312 | 0.34627 | -0.015772 |
| 313 | -1.1163 | -0.1857 |
| 314 | 3.6021 | -0.23013 |
| 315 | 0.22691 | 0.21788 |
| 316 | 1.8521 | -0.33911 |
| 317 | -1.468 | -0.27469 |
| 318 | -2.4454 | -0.91895 |
| 319 | 1.0604 | 0.28601 |
| 320 | 1.0149 | -0.76431 |
| 321 | 0.28841 | 0.89676 |
| 322 | 2.4035 | 0.39463 |
| 323 | 3.2692 | 0.070083 |
| 324 | 2.8046 | -0.15782 |
| 325 | 4.0431 | -0.14456 |
| 326 | -2.6696 | 0.64481 |
| 327 | -0.30054 | 0.91151 |
| 328 | 1.7739 | 0.14918 |
| 329 | 0.15084 | -0.5873 |
| 330 | -2.6266 | 0.10065 |
| 331 | 0.95001 | 0.019995 |
| 332 | 2.5551 | -0.2085 |
| 333 | -0.2026 | -0.23848 |
| 334 | 1.5331 | -0.12907 |
| 335 | 7.0362 | -0.44129 |
| 336 | -2.195 | 0.45423 |
| 337 | -1.0181 | 0.1105 |
| 338 | 1.7652 | -0.042769 |
| 339 | -1.5174 | -0.10488 |
| 340 | -0.29028 | 0.84071 |
| 341 | 0.87679 | -0.64516 |
| 342 | 3.232 | -0.24969 |
| 343 | 2.3239 | 0.14411 |
| 344 | 0.75748 | -0.12591 |
| 345 | 3.5511 | -0.3476 |
| 346 | 3.4868 | -0.56919 |
| 347 | 3.0103 | -0.4386 |
| 348 | 4.852 | 0.20036 |
| 349 | 2.6034 | 0.027887 |
| 350 | 2.8835 | -0.73254 |
| 351 | 4.8873 | -0.36128 |
| 352 | 5.272 | -0.41209 |
| 353 | 2.1338 | 0.0027047 |
| 354 | 2.0918 | 0.078326 |
| 355 | -2.173 | -0.62954 |
| 356 | 3.8592 | 0.77946 |
| 357 | -2.3865 | -0.91497 |
| 358 | 3.7508 | 0.68926 |
| 359 | 3.2825 | 0.47479 |
| 360 | -0.46095 | 0.39178 |
| 361 | 0.94034 | 0.18229 |
| 362 | 1.3926 | 0.46296 |
| 363 | 5.1595 | 0.39838 |
| 364 | 3.3645 | -0.029382 |
| 365 | 2.8363 | 0.12099 |
| 366 | 1.5012 | -0.37402 |
| 367 | -3.5879 | 0.947 |
| 368 | 0.35427 | -0.030834 |
| 369 | 0.66922 | 1.1694 |
| 370 | 5.239 | 0.17819 |
| 371 | 3.4129 | 0.52865 |
| 372 | 4.2978 | -0.31592 |
| 373 | 0.42263 | 1.4078 |
| 374 | 3.1237 | 0.48369 |
| 375 | -1.0707 | 0.28138 |
| 376 | -1.8237 | 1.345 |
| 377 | 0.91709 | -0.049255 |
| 378 | -1.6586 | -0.17085 |
| 379 | 0.27806 | 0.86673 |
| 380 | 1.5521 | 0.39324 |
| 381 | 2.1111 | -0.25335 |
| 382 | 1.0055 | -0.28408 |
| 383 | 1.1826 | -0.55224 |
| 384 | 0.98527 | -0.17647 |
| 385 | 4.6129 | 0.4774 |
| 386 | 2.6948 | 0.029538 |
| 387 | 3.3067 | 0.66075 |
| 388 | 0.52281 | 1.2922 |
| 389 | 1.534 | 0.062958 |
| 390 | 2.2966 | 1.163 |
| 391 | 1.3703 | 0.51786 |
| 392 | 2.3809 | 0.70871 |
| 393 | 1.3538 | 0.59406 |
| 394 | 3.8828 | -0.11846 |
| 395 | 3.9312 | 0.19847 |
| 396 | 2.0143 | 0.12465 |
| 397 | 0.39507 | 0.014739 |
| 398 | 2.5238 | 0.19755 |
| 399 | 1.0965 | 0.2261 |
| 400 | 1.6426 | 0.52167 |
| 401 | 0.91197 | 0.050706 |
| 402 | 2.1213 | 0.83749 |
| 403 | -0.77993 | 0.84748 |
| 404 | 1.1447 | -0.35134 |
| 405 | 3.902 | -0.088772 |
| 406 | 1.9367 | -0.14636 |
| 407 | -0.81827 | 0.33459 |
| 408 | 0.37094 | 0.2642 |
| 409 | 0.73167 | -0.25489 |
| 410 | 3.3095 | 0.14417 |
| 411 | 4.2036 | -0.0032812 |
| 412 | 0.21282 | -0.15948 |
| 413 | -1.0777 | 0.029419 |
| 414 | -3.5571 | 0.36445 |
| 415 | -2.7677 | -1.8197 |
| 416 | -7.3384 | -2.049 |
| 417 | 3.3861 | 0.59596 |
| 418 | 2.5705 | -0.31123 |
| 419 | 1.7872 | -0.32841 |
| 420 | 0.22975 | 0.1358 |
| 421 | 4.6318 | -0.5752 |
| 422 | 0.87063 | -0.14392 |
| 423 | 2.6532 | 0.11388 |
| 424 | -1.0598 | -0.50216 |
| 425 | 2.1061 | 0.98971 |
| 426 | -0.30224 | 0.063784 |
| 427 | 2.7162 | 0.71028 |
| 428 | -1.9921 | -0.5726 |
| 429 | 4.2278 | -0.57296 |
| 430 | 0.27326 | 0.72929 |
| 431 | 5.6086 | -0.222 |
| 432 | 1.2266 | 0.56413 |
| 433 | -0.18749 | -0.81357 |
| 434 | 0.43885 | 0.70215 |
| 435 | -0.7434 | 0.45157 |
| 436 | 1.2687 | 0.49708 |
| 437 | 0.033822 | 0.30186 |
| 438 | 2.7984 | -0.26655 |
| 439 | 1.0332 | 0.049256 |
| 440 | -0.26279 | 0.39282 |
| 441 | 3.1761 | 0.23062 |
| 442 | 3.1303 | -0.32578 |
| 443 | 1.6065 | 0.50571 |
| 444 | 4.0064 | -0.68936 |
| 445 | 1.1972 | 0.46735 |
| 446 | 1.0279 | 0.0046093 |
| 447 | 4.2299 | -0.30246 |
| 448 | 1.0231 | 0.51014 |
| 449 | 3.2753 | 0.83756 |
| 450 | 2.2086 | 0.64056 |
| 451 | -5.2558 | -1.8029 |
| 452 | 1.3993 | -0.077644 |
| 453 | 0.67028 | 0.074218 |
| 454 | 2.0856 | 0.33963 |
| 455 | -0.39713 | -0.58017 |
| 456 | -7.8418 | -0.80237 |
| 457 | 1.3882 | 0.2905 |
| 458 | 1.6382 | 0.023272 |
| 459 | 2.9917 | 0.4839 |
| 460 | -1.4676 | -0.26905 |
| 461 | -2.3423 | -0.96934 |
| 462 | -3.1627 | -0.16402 |
| 463 | 2.7015 | -1.918 |
| 464 | 0.91488 | -0.70514 |
| 465 | -0.5266 | -0.43797 |
| 466 | 1.0974 | -0.44082 |
| 467 | 7.0072 | -0.26743 |
| 468 | -1.3522 | -0.09958 |
| 469 | -4.1773 | -0.14531 |
| 470 | -1.4278 | 0.41401 |
| 471 | -2.8032 | -0.059728 |
| 472 | -1.7409 | -0.060603 |
| 473 | 1.2071 | -0.2647 |
| 474 | 0.81027 | -0.14413 |
| 475 | 2.0971 | 0.13948 |
| 476 | -0.69907 | 0.33215 |
| 477 | -1.0851 | -0.034669 |
| 478 | 1.1565 | -0.25033 |
| 479 | -1.0997 | 0.14057 |
| 480 | -1.4946 | 0.278 |
| 481 | -2.7104 | -0.86693 |
| 482 | -3.0912 | -0.32467 |
| 483 | -1.9501 | -0.046542 |
| 484 | -1.0568 | -0.36682 |
| 485 | -4.2554 | -0.77596 |
| 486 | 2.6478 | -0.017246 |
| 487 | 2.066 | 0.33226 |
| 488 | -0.44829 | -0.18933 |
| 489 | -0.72304 | -0.035448 |
| 490 | 3.6729 | -0.13525 |
| 491 | -1.5091 | -0.23432 |
| 492 | 1.7604 | 0.05032 |
| 493 | 1.4981 | 0.62542 |
| 494 | 2.5666 | 0.19088 |
| 495 | 1.065 | 0.45014 |
| 496 | 2.0008 | 0.8393 |
| 497 | 1.4276 | -0.091256 |
| 498 | 3.7372 | 0.24596 |
| 499 | 1.2402 | 0.00053179 |
| 500 | -2.0322 | 1.0155 |
| 501 | 0.50195 | 0.34657 |
| 502 | 1.6917 | 0.38438 |
| 503 | 0.29573 | 0.31944 |
| 504 | -0.093177 | 0.045267 |
| 505 | 1.3717 | 0.023138 |
| 506 | -4.2937 | -0.75629 |
| 507 | -1.5484 | -0.073992 |
| 508 | -0.39874 | -0.022965 |
| 509 | -1.8148 | 0.42168 |
| 510 | -1.6781 | 0.90128 |
| 511 | 3.5994 | -0.16684 |
| 512 | 0.55903 | 0.45191 |
| 513 | -0.015171 | -0.25921 |
| 514 | 0.94611 | 0.15842 |
| 515 | 1.4325 | 0.064822 |
| 516 | -2.1447 | 0.43284 |
| 517 | 0.71565 | -0.17154 |
| 518 | -4.6886 | -0.42495 |
| 519 | -2.1844 | -0.39898 |
| 520 | -0.74084 | 0.56372 |
| 521 | 0.52857 | -0.057347 |
| 522 | -2.9138 | 0.1313 |
| 523 | -0.84134 | -0.11575 |
| 524 | 0.89099 | -0.3004 |
| 525 | 1.8867 | -0.3808 |
| 526 | 1.6563 | 0.011065 |
| 527 | 4.0705 | -0.092981 |
| 528 | 3.4921 | 0.21608 |
| 529 | 0.33477 | 0.051682 |
| 530 | 4.4425 | 0.28559 |
| 531 | -1.749 | 0.09908 |
| 532 | 1.7887 | -0.36225 |
| 533 | 0.11535 | 0.44958 |
| 534 | -4.4378 | -0.18287 |
| 535 | -0.47487 | 0.42878 |
| 536 | -3.5011 | -0.68296 |
| 537 | 2.2812 | -0.14906 |
| 538 | -0.5317 | 0.69259 |
| 539 | -1.8793 | -0.34413 |
| 540 | 0.10667 | 0.16773 |
| 541 | 0.038559 | -0.25363 |
| 542 | 0.95444 | 0.1844 |
| 543 | 0.59732 | -0.12269 |
| 544 | -3.7988 | 0.57683 |
| 545 | -0.6986 | 0.013487 |
| 546 | 0.5089 | -0.5264 |
| 547 | -3.9773 | -0.2884 |
| 548 | -0.43462 | 1.1754 |
| 549 | -1.2585 | -0.0452 |
| 550 | -3.7027 | -0.73161 |
| 551 | -2.7466 | -1.2837 |
| 552 | -3.4833 | -0.16295 |
| 553 | -1.1327 | -0.022316 |
| 554 | 0.54299 | -0.61947 |
| 555 | 1.4792 | 0.31269 |
| 556 | -4.0765 | -0.64472 |
| 557 | -2.3575 | -0.54313 |
| 558 | -2.2791 | 0.065927 |
| 559 | -6.2509 | -0.74137 |
| 560 | 0.2582 | -0.20595 |
| 561 | 2.1001 | -0.63062 |
| 562 | 1.0517 | -0.43322 |
| 563 | 3.8675 | -0.62973 |
| 564 | 0.39602 | -0.084658 |
| 565 | -1.0757 | 0.15564 |
| 566 | -1.7579 | -0.81713 |
| 567 | -1.5922 | -0.40831 |
| 568 | 1.903 | -0.40116 |
| 569 | -3.1964 | 0.75597 |
| 570 | -0.82584 | 0.04107 |
| 571 | -2.6371 | 0.44469 |
| 572 | -0.22767 | -0.39782 |
| 573 | -0.35266 | 1.2179 |
| 574 | -1.2453 | 0.048822 |
| 575 | -0.42604 | -0.30864 |
| 576 | 2.8523 | -0.081282 |
| 577 | -0.20537 | 0.15692 |
| 578 | 1.5263 | 0.27399 |
| 579 | -4.6232 | -0.31279 |
| 580 | -3.5867 | 0.46271 |
| 581 | 0.95627 | -1.0476 |
| 582 | 2.4864 | -0.30357 |
| 583 | -1.0125 | -0.22914 |
| 584 | -0.052333 | 0.11302 |
| 585 | -5.3943 | 0.1324 |
| 586 | -3.0891 | -0.076147 |
| 587 | 2.9422 | -0.37951 |
| 588 | -1.5114 | 0.43315 |
| 589 | 0.21956 | -0.036458 |
| 590 | -1.1212 | -0.35506 |
| 591 | -0.83433 | -0.28901 |
| 592 | 0.28918 | 0.041881 |
| 593 | -4.9291 | -0.054836 |
| 594 | -4.6275 | 0.23361 |
| 595 | 1.3554 | -0.86752 |
| 596 | -2.4883 | -0.24994 |
| 597 | -2.8742 | 0.38898 |
| 598 | -1.2557 | 0.28234 |
| 599 | 0.33472 | -0.091466 |
| 600 | -0.77976 | -0.37387 |
| 601 | 2.3603 | 0.61698 |
| 602 | -0.41034 | -0.29138 |
| 603 | -3.8176 | 0.47205 |
| 604 | -1.4777 | -0.055459 |
| 605 | -1.2298 | 0.56517 |
| 606 | -0.50218 | 0.86647 |
| 607 | -2.3881 | -0.17317 |
| 608 | -2.1931 | 0.20519 |
| 609 | -0.88451 | 0.11081 |
| 610 | -1.3666 | -0.16894 |
| 611 | 0.076066 | -0.065202 |
| 612 | -0.50153 | 0.73289 |
| 613 | -1.631 | -0.068897 |
| 614 | 0.19828 | -0.58093 |
| 615 | 1.8129 | -0.62485 |
| 616 | -2.9262 | -0.21755 |
| 617 | -1.6352 | 1.2901 |
| 618 | -3.2951 | -0.15946 |
| 619 | 1.629 | -0.17731 |
| 620 | 1.3336 | 0.49903 |
| 621 | 3.6541 | -0.60896 |
| 622 | -1.1843 | -0.19208 |
| 623 | 2.2629 | -0.11327 |
| 624 | 1.0986 | 0.22006 |
| 625 | -2.2289 | 0.65431 |
| 626 | -1.2861 | 0.59013 |
| 627 | -0.22894 | 0.25549 |
| 628 | -1.942 | 0.42797 |
| 629 | -0.64235 | -0.42122 |
| 630 | -0.58439 | -0.22418 |
| 631 | -1.3368 | -0.028535 |
| 632 | -2.052 | 0.028864 |
| 633 | 0.53394 | 0.83915 |
| 634 | 3.0475 | -0.059502 |
| 635 | 0.40338 | 0.74936 |
| 636 | -1.1698 | 0.99934 |
| 637 | 2.988 | -0.52081 |
| 638 | -1.7577 | 0.087416 |
| 639 | -0.48414 | 0.19434 |
| 640 | 0.56134 | 0.25689 |
| 641 | -1.4655 | 0.27886 |
| 642 | 3.7673 | -0.448 |
| 643 | -2.8135 | 0.26919 |
| 644 | -4.1051 | -0.2621 |
| 645 | -1.3417 | -0.38956 |
| 646 | 1.0976 | 0.053815 |
| 647 | -1.1307 | -0.39542 |
| 648 | 0.37671 | 0.28138 |
| 649 | -2.2761 | -0.34813 |
| 650 | 0.14238 | 0.24525 |
| 651 | 0.58572 | -0.083595 |
| 652 | 0.16794 | 0.45866 |
| 653 | -2.7003 | -0.044389 |
| 654 | -0.10021 | -0.05166 |
| 655 | -1.5217 | 0.009094 |
| 656 | -2.7994 | 0.58223 |
| 657 | -1.6919 | -0.30973 |
| 658 | -3.4868 | 0.65639 |
| 659 | -1.3976 | 0.36079 |
| 660 | 0.90592 | 0.75716 |
| 661 | 2.3924 | -0.94644 |
| 662 | 0.23463 | -0.6158 |
| 663 | 2.0636 | -0.80292 |
| 664 | -1.9135 | 0.28572 |
| 665 | -3.6725 | -0.33532 |
| 666 | -1.2275 | 1.397 |
| 667 | -3.3061 | -0.23955 |
| 668 | -3.1766 | -0.39408 |
| 669 | 2.5393 | -0.51922 |
| 670 | 0.081594 | 0.52546 |
| 671 | -2.6795 | -0.1753 |
| 672 | 0.707 | -0.26999 |
| 673 | 0.62704 | 0.45243 |
| 674 | -2.241 | -1.0429 |
| 675 | -10.11 | -1.3972 |
| 676 | -0.013121 | -0.12838 |
| 677 | -1.0045 | -0.58984 |
| 678 | 0.80554 | -0.069642 |
| 679 | 3.3052 | -0.27453 |
| 680 | -3.4981 | 0.23306 |
| 681 | -3.1486 | -0.781 |
| 682 | -0.78958 | -0.30551 |
| 683 | 4.9081 | 0.14072 |
| 684 | -2.5919 | -0.10706 |
| 685 | 1.9707 | -0.16979 |
| 686 | -0.56316 | 0.15438 |
| 687 | 0.15244 | -0.46665 |
| 688 | -1.561 | -0.061347 |
| 689 | -0.36652 | 0.14694 |
| 690 | 0.7047 | 0.73473 |
| 691 | -1.7153 | -0.04691 |
| 692 | -1.6718 | -0.12888 |
| 693 | 0.1755 | -0.44048 |
| 694 | -0.53876 | -0.68034 |
| 695 | -6.1983 | 0.18493 |
| 696 | 2.7935 | -0.6504 |
| 697 | 1.6419 | 0.10552 |
| 698 | -0.61732 | -0.22014 |
| 699 | -1.0156 | -0.38752 |
| 700 | 2.1775 | -0.011386 |
| 701 | 0.015868 | -0.31773 |
| 702 | -5.4868 | -1.3862 |
| 703 | 0.91131 | 0.38013 |
| 704 | -6.3522 | 0.27299 |
| 705 | -3.3818 | -0.60946 |
| 706 | -3.1365 | 0.36164 |
| 707 | -3.1727 | -0.84113 |
| 708 | -0.47379 | -0.57592 |
| 709 | -3.6591 | 0.55765 |
| 710 | -2.1074 | -0.0023702 |
| 711 | 0.18185 | -0.76615 |
| 712 | 2.2613 | -0.11761 |
| 713 | -0.36941 | -0.22191 |
| 714 | 1.1071 | -0.52803 |
| 715 | -0.57099 | -0.57397 |
| 716 | -0.59338 | -0.025665 |
| 717 | -4.5608 | 0.48543 |
| 718 | 1.8742 | -0.25806 |
| 719 | 1.5048 | -0.1536 |
| 720 | -3.4664 | 0.025307 |
| 721 | 1.1669 | -0.32435 |
| 722 | -3.0028 | 0.26566 |
| 723 | 1.0405 | -0.90097 |
| 724 | -2.6071 | 0.046287 |
| 725 | -1.5099 | 0.69687 |
| 726 | -0.9475 | 1.0824 |
| 727 | -2.1664 | 0.44246 |
| 728 | -2.6034 | 0.85625 |
| 729 | -3.2708 | -0.59964 |
| 730 | -3.1573 | 0.32667 |
| 731 | -2.4469 | 0.010277 |
| 732 | -1.2747 | 0.46448 |
| 733 | 0.29233 | -0.078928 |
| 734 | -1.9107 | 0.24152 |
| 735 | 0.026165 | -0.048016 |
| 736 | 1.3749 | 0.3003 |
| 737 | -0.91304 | 0.7581 |
| 738 | 0.97078 | 0.044872 |
| 739 | -1.4362 | -0.35294 |
| 740 | -1.936 | -1.0751 |
| 741 | 2.5223 | -0.23818 |
| 742 | -0.91872 | -0.1247 |
| 743 | -3.2656 | 0.28785 |
| 744 | 1.6247 | -0.03783 |
| 745 | -2.2254 | 0.417 |
| 746 | -1.8231 | 0.93841 |
| 747 | -2.6332 | 0.19588 |
| 748 | 0.070956 | 0.003179 |
| 749 | -3.7438 | 0.30589 |
| 750 | -6.652 | -0.45279 |
| 751 | 0.57894 | 0.050181 |
| 752 | -1.0116 | 0.065132 |
| 753 | 0.38748 | 0.4417 |
| 754 | 0.36335 | 0.10561 |
| 755 | 0.39572 | 0.22301 |
| 756 | 0.99875 | -0.16675 |
| 757 | -2.6158 | 0.63882 |
| 758 | 0.67899 | 0.076536 |
| 759 | 1.2522 | 0.26198 |
| 760 | -5.8802 | -0.27132 |
| 761 | -5.3484 | -0.94446 |
| 762 | -1.0275 | 0.36253 |
| 763 | -3.2923 | -0.57877 |
| 764 | -3.3164 | 0.63565 |
| 765 | 2.3231 | 0.70086 |
| 766 | -0.4175 | 0.52882 |
| 767 | 0.65795 | 0.067093 |
| 768 | -3.522 | 0.039894 |
| 769 | 1.8776 | -0.18181 |
| 770 | -0.32807 | -0.096162 |
| 771 | 1.6774 | 0.35127 |
| 772 | 1.9932 | -0.17518 |
| 773 | -2.2364 | 0.14588 |
| 774 | -2.1799 | -0.037253 |
| 775 | -3.8097 | -0.63766 |
| 776 | 1.1056 | 1.188 |
| 777 | -1.5565 | 1.1902 |
| 778 | 1.5862 | -0.11928 |
| 779 | -0.027524 | -0.45736 |
| 780 | -1.3785 | -0.17416 |
| 781 | -0.84105 | 0.27864 |
| 782 | -2.7608 | 0.41997 |
| 783 | 1.8671 | -0.12094 |
| 784 | -0.96971 | -0.028928 |
| 785 | -2.0349 | -0.071874 |
| 786 | 0.27918 | 0.28825 |
| 787 | -0.44612 | 0.010293 |
| 788 | -0.93806 | -0.10174 |
| 789 | -1.1413 | 1.0117 |
| 790 | -1.731 | 0.66694 |
| 791 | 1.4105 | -0.32975 |
| 792 | -2.2719 | 0.14482 |
| 793 | -0.88276 | 1.1704 |
| 794 | -2.0365 | -0.25488 |
| 795 | -1.1339 | 0.28369 |
| 796 | -1.5478 | 1.0365 |
| 797 | 0.032181 | 0.57281 |
| 798 | -0.3937 | 0.76895 |
| 799 | -0.5476 | 0.73102 |
| 800 | -0.85794 | 0.011015 |
Each bar reports the share of spectral variance explained by a component. Concentration in the first components indicates correlated wavelengths. This is not variance explained in the prediction target. This projection may use a different observation sample from the score plot.
| Component | Spectral variance |
|---|---|
| PC1 | 93.46% |
| PC2 | 3.81% |
| PC3 | 1.68% |
| PC4 | 0.52% |
| PC5 | 0.25% |
| PC6 | 0.13% |
| PC7 | 0.04% |
| PC8 | 0.03% |
| PC9 | 0.02% |
| PC10 | 0.01% |
Pearson r measures the linear association between each spectral channel (X) and a numeric target (Y), from −1 to +1. The table reports peak and average absolute correlation and the share of channels with |r| ≥ 0.5. These are in-dataset associations, not validation scores or proof of causation.
Read Pearson correlation (−1 to +1) along the spectral axis (none); the solid curve shows |r| and the dashed curve shows signed r.
Read Pearson correlation (−1 to +1) along the spectral axis (none); the solid curve shows |r| and the dashed curve shows signed r.
Read Pearson correlation (−1 to +1) along the spectral axis (none); the solid curve shows |r| and the dashed curve shows signed r.
| Target | max |r| | axis @ max | mean |r| | |r| ≥ .5 |
|---|---|---|---|---|
| caco3_usda_a54_w_pct | 0.403 | 416 | 0.357 | 0.0% |
| cec_usda_a723_cmolc_kg | 0.44 | 2,500 | 0.274 | 0.0% |
| cf_usda_c236_w_pct | 0.0659 | 2,500 | 0.018 | 0.0% |
| clay_tot_usda_a334_w_pct | 0.321 | 602 | 0.196 | 0.0% |
| ec_usda_a364_ds_m | 0.251 | 2,488 | 0.183 | 0.0% |
| k_ext_usda_a725_cmolc_kg | 0.176 | 2,500 | 0.0488 | 0.0% |
| n_tot_usda_a623_w_pct | 0.535 | 614 | 0.431 | 9.9% |
| oc_usda_c729_w_pct | 0.504 | 606 | 0.383 | 2.6% |
| p_ext_usda_a274_mg_kg | 0.189 | 614 | 0.157 | 0.0% |
| ph_cacl2_usda_a481_index | 0.284 | 602 | 0.21 | 0.0% |
| ph_h2o_usda_a268_index | 0.302 | 604 | 0.23 | 0.0% |
| sand_tot_usda_c60_w_pct | 0.21 | 1,134 | 0.153 | 0.0% |
| silt_tot_usda_c62_w_pct | 0.454 | 616 | 0.377 | 0.0% |
Definitions, formulas and possible explanations for the reported values. A possible cause requires verification against acquisition records. Reading guide
Look up each measured quantity, its calculation and the heuristic alert mapping before interpreting a high or low value.
| Family | Metric | What it measures | Higher values suggest | Lower values suggest | Possible causes | Calculation / score |
|---|---|---|---|---|---|---|
| Data integrity | Invalid/masked cell count (owner report) | Null or masked cells in the evaluated population | To investigate | Complete according to the declared mask | Missing data or validity mask; not evidence of an acquisition cause | Native Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio. |
| Data integrity | NaN ratio | Missing data | Corrupted spectrum | Full spectrum | Acquisition/export error | count(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05) |
| Data integrity | Inf count | Infinite values | Corruption | Normal | Invalid calculations | count(isinf(X))alert = min(1, inf_count / 1) |
| Data integrity | Zero ratio | Null columns or cells | Truncated spectrum | Normal | Export, saturation | count(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05) |
| Overall amplitude | Mean reflectance | Average level | Too light / background visible | Too dark | Background, geometry | mean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent |
| Overall amplitude | Area under curve | Overall intensity | Illumination difference | Normal | Probe distance | trapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units |
| Overall amplitude | Peak-to-peak (PTP) | Dynamic range | Strong variability | Flat spectrum | Saturation | max(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat |
| Overall amplitude | Variance | Spectral variability | Normal or heterogeneous | Flat spectrum | Bad contact | var(X finite)alert increases when variance/dynamic range is abnormally flat |
| Noise | Roughness sigma proxy | High frequency noise | Noisy | Stable | Lamp, detector | median MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5% |
| Noise | Roughness S/N proxy | Signal quality | Good signal | Bad signal | Acquisition | mean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert |
| Noise | Bandwise roughness S/N proxy | Localized noise | Reliable zone | Problem area | Detector | min(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert |
| Local artifacts | Spike count | Narrow peaks | Artifacts | Clean spectrum | Cosmic rays, splice | count robust outliers in second derivativealert follows spike_rate, saturated at 1% |
| Local artifacts | Spike rate | Peak density | Suspicious spectrum | Normal | Interpolation | spike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01) |
| Local artifacts | Jump count | Discontinuities | Detector connection | Continuous | Splice | count robust outliers in first derivativealert follows jump_rate, saturated at 1% |
| Local artifacts | Jump rate | Jump frequency | Spectral problem | Normal | Calibration | jump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01) |
| Local artifacts | Clip fraction | Saturation | Clipping | Normal | Detector saturated | fraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01) |
| Spectral form | Baseline slope | Overall slope | Drift | Stable | Illumination | linear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5 |
| Spectral form | Curvature RMS | Curvature | Unusual shape | Smooth | Background, splice | median RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1% |
| Spectral form | D1 RMS | Local variability | Structured spectrum | Flat | Biology or artifact | median RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5% |
| Multivariate outliers | PCA Q (SPE) | Not explained by PCA | Atypical spectrum | Compliant | Artifact, mixture | p95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8) |
| Multivariate outliers | Hotelling T² | Extreme in PCA | Extreme but consistent | Central | Natural variability | p95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8) |
| Multivariate outliers | Mahalanobis H | Distance to cloud | Global outlier | Normal population | Different domain | p95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4) |
| Comparison to average spectrum | RMS to mean spectrum | Average distance | Different spectrum | Typical | Domain shift | p95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25% |
| Comparison to average spectrum | Spectral Angle Mapper (SAM) | Difference in shape | Different shape | Similar | Background, geometry | p95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad) |
| Repeatability | RMS intra-ID | Reproducibility | Poor repeatability | Stable | Positioning | median RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10% |
| Repeatability | SAM intra-ID | Shape variation | Unstable | Stable | Acquisition | median SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad) |
| Repeatability | CV intra-ID | Internal variability | Bad control | Stable | Operator | median within-ID band CValert = min(1, CV_intra_ID / 0.25) |
| Structure of the dataset | PCA score density | Clusters | Subpopulations | Homogeneous | Different batches | 1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone |
| Structure of the dataset | kNN distance ratio (not LOF) | Local anomaly | Isolated spectrum | Normal population | Rare cases | p95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2) |
| Structure of the dataset | Isolation Forest score | Global anomaly | Atypical spectrum | Normal | Various causes | p95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent |
Find measurement-specific checks and acquisition context that can help interpret a source's spectral patterns.
| Technology | Adaptations / metrics | Target anomalies | Practical guidance |
|---|---|---|---|
| UV-Vis 300-1000 nm | Baseline, overall slope, drift at edges 300-350 and 900-1000; metrics by zones | Stray light, bad white, saturation, weak signal at the ends | The edges are often unstable; also calculate edge/middle scores. |
| UV-Vis 300-1000 nm | Saturation / clipping near max absorbance or max reflectance | Signal clipped | Very important if strong absorption. |
| UV-Vis 300-1000 nm | Red-edge, peak position, band ratios for vegetation | Biological shift or optical artifact | Helps distinguish real change from acquisition problem. |
| UV-Vis 300-1000 nm | Smoothness / roughness index | High frequency noise | Often more informative than the SNR alone. |
| MIR / ATR-FTIR | ATR contact quality index: overall intensity, total area, depth of key bands | Poor crystal-sample contact | Crucial: many anomalies come from the ATR contact. |
| MIR / ATR-FTIR | CO2 / H2O atmospheric bands | Bad atmospheric correction | Frequent spurious peaks. |
| MIR / ATR-FTIR | Baseline curvature / rubber-band residual | Diffusion, contact, baseline drift | Very useful before PCA. |
| MIR / ATR-FTIR | Peak position shift | Bad spectral alignment/calibration | Important in FTIR because small shifts count. |
| MIR / ATR-FTIR | Band area ratios on known bands | Chemically incoherent spectrum | To be adapted by matrix: polysaccharides, proteins, lipids, etc. |
| HS-MS | Total Ion Current (TIC), Base Peak Intensity (BPI) | Weak injection, unstable ionization | MS equivalent of the global spectral level. |
| HS-MS | Number of peaks detected | Poor or too noisy spectrum | Too little = bad signal; too much = noise/contamination. |
| HS-MS | Mass accuracy / m/z drift | Mass calibration problem | Fundamental in HRMS. |
| HS-MS | Retention time drift if LC/GC-MS | Chromatographic drift | Monitor on standards/QC pools. |
| HS-MS | Blank contamination score | Contaminants / carry-over | Compare samples vs blanks. |
| HS-MS | Internal standard CV | Instrumental variability | Very robust if standards available. |
| HS-MS | Missingness by feature | Detection instability | Crucial for filtering variables. |
| With repetitions | Intra-sample RMS | Overall repeatability | Applicable to all technologies. |
| With repetitions | SAM / intra-sample correlation | Shape repeatability | Very useful for spectra. |
| With repetitions | Intra-sample CV per band / feature | Local repeatability | Detects unstable areas. |
| With repetitions | ICC or variance components | Share of sample vs. technical variance | Very useful if several repetitions per sample. |
| With repetitions | Distance to intra-ID centroid | Aberrant repetition | Allows you to flag the bad repetition rather than the entire sample. |
Review suggested follow-up methods for suspected data issues and whether their results are available in this explorer.
| Potential issue family | Suggested methods | What it detects | Explorer availability |
|---|---|---|---|
| Global spectral shift | Inter-dataset average spectrum correlation, DTW, cross-correlation, comparison of peak positions | Wavelength shift, misalignment, different interpolation | Partially calculated: cross-correlation lag and dispersion of peak positions vs. average spectrum. |
| Baseline / offset / gain | Regression each spectrum vs average spectrum: x = a + b ref + residual; followed by a, b, residual RMS | Additive offset, multiplicative effect, baseline drift | Calculated in reference.affine_*. |
| Row mixing / X-M-Y mismatch | Index check, row hash, ID duplication, intra-ID spectral distance, inconsistent labels | Mixed up rows, misaligned metadata, Y assigned to wrong spectrum | Partially covered by intra-ID repeatability; checks index/hash to add to the canonical pipeline. |
| Information leak / poorly split repetitions | GroupKFold by sample_id vs StratifiedKFold random; auditing partitions by sample_id | Artificially good performance due to repetitions | Requires splits and benchmark model; not calculated by the descriptive card. |
| Label bugs | Close samples in X but different Y, confident learning, systematic FP/FN errors | Inverted Y, input errors, ambiguous classes | Requires Y and/or model; recommended for supervised explorer. |
| Hidden subdomains | PCA/UMAP/t-SNE + unsupervised clustering + association with dataset/Y/date/operator | Batches, campaigns, probes, backgrounds not provided | Partially calculated by PCA and distance ratio kNN (not LOF); UMAP/t-SNE outside the static card. |
| Unknown localized artifacts | Wavelength x dataset map: mean difference, variance difference, KS per wavelength | Unanticipated anomalous spectral regions | To be calculated at the bank level when several datasets share a spectral axis. |
| Instrumental breaks | Discontinuities in derivatives, changepoint detection | Splice, detector connection, local jump not provided | Calculated by jump/spike rates; more advanced changepoint to add. |
| Spectral mixing/contamination | NMF / unmixing / reconstruction by convex hull | External component: background, plastic, soil | Not calculated automatically; requires component assumptions or large library. |
| Unstable but predictive features | Model importance vs. QC instability by variable | Model that learns an artifact rather than a biological signal | Requires supervised model; recommended for benchmark reporting. |
Scientific roles derived from the source contract. M denotes experimental metadata; Y denotes informative response variables. Reading guide
5 experimental metadata fields (M) · 13 informative targets (Y).
| Variable | Role | Distinct values | Complete | Range / balance |
|---|---|---|---|---|
| longitude | M | 40,175 | 100% | -10.05 – 34.03 |
| latitude | M | 40,175 | 100% | 34.69 – 69.96 |
| campaign_date_begin | M | 2 | 100% | IR 1.184 |
| campaign_date_end | M | 2 | 100% | IR 1.184 |
| campaign_year | M | 2 | 100% | 2,009 – 2,015 |
| caco3_usda_a54_w_pct | Y | 800 | 100% | 0 – 97.6 |
| cec_usda_a723_cmolc_kg | Y | 749 | 45.78% | 1 – 234 |
| cf_usda_c236_w_pct | Y | 87 | 56.37% | 0 – 90 |
| clay_tot_usda_a334_w_pct | Y | 80 | 56.38% | 0 – 79 |
| ec_usda_a364_ds_m | Y | 3,059 | 54.22% | 0.0031 – 9.69 |
| k_ext_usda_a725_cmolc_kg | Y | 6,564 | 100% | 0 – 51.31 |
| n_tot_usda_a623_w_pct | Y | 322 | 100% | 0 – 3.86 |
| oc_usda_c729_w_pct | Y | 3,179 | 100% | 0.01 – 58.68 |
| p_ext_usda_a274_mg_kg | Y | 1,650 | 100% | 0 – 1366 |
| ph_cacl2_usda_a481_index | Y | 547 | 100% | 2.57 – 10 |
| ph_h2o_usda_a268_index | Y | 559 | 100% | 3.17 – 10.37 |
| sand_tot_usda_c60_w_pct | Y | 101 | 56.38% | 0 – 100 |
| silt_tot_usda_c62_w_pct | Y | 91 | 56.38% | 0 – 92 |
Identifiers and sampling groups remain available separately. Statistics count exported observations.
Declared prediction targets and recorded metadata, with distributions and missing-value counts. Identifiers are not automatically prediction targets. Reading guide
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 5.486 ± 13.1 |
Median ?50th percentile: half the non-missing values lie at or below it. | 0.1 |
Range ?Smallest and largest recorded non-missing values. | 0 – 97.6 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 2.39 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 2.9 / 8.8 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 43,564 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 15.63 ± 14.1 |
Median ?50th percentile: half the non-missing values lie at or below it. | 12.3 |
Range ?Smallest and largest recorded non-missing values. | 1 – 234 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 0.903 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 4.2 / 35 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 35,054 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 14.73 ± 12 |
Median ?50th percentile: half the non-missing values lie at or below it. | 11 |
Range ?Smallest and largest recorded non-missing values. | 0 – 90 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 0.813 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 1.5 / 2.6 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 35,050 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 17.97 ± 12.8 |
Median ?50th percentile: half the non-missing values lie at or below it. | 16 |
Range ?Smallest and largest recorded non-missing values. | 0 – 79 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 0.711 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 0.83 / 0.55 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 36,786 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 0.261 ± 0.334 |
Median ?50th percentile: half the non-missing values lie at or below it. | 0.1724 |
Range ?Smallest and largest recorded non-missing values. | 0.0031 – 9.69 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 1.28 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 6 / 73 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 0.9994 ± 1.15 |
Median ?50th percentile: half the non-missing values lie at or below it. | 0.7156 |
Range ?Smallest and largest recorded non-missing values. | 0 – 51.31 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 1.15 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 11 / 3e+02 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 0.3016 ± 0.369 |
Median ?50th percentile: half the non-missing values lie at or below it. | 0.19 |
Range ?Smallest and largest recorded non-missing values. | 0 – 3.86 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 1.22 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 3.8 / 18 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 4.63 ± 8.36 |
Median ?50th percentile: half the non-missing values lie at or below it. | 2.06 |
Range ?Smallest and largest recorded non-missing values. | 0.01 – 58.68 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 1.81 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 4 / 17 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 31.54 ± 32.7 |
Median ?50th percentile: half the non-missing values lie at or below it. | 23.2 |
Range ?Smallest and largest recorded non-missing values. | 0 – 1366 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 1.04 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 5.2 / 1.1e+02 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 5.675 ± 1.41 |
Median ?50th percentile: half the non-missing values lie at or below it. | 5.7 |
Range ?Smallest and largest recorded non-missing values. | 2.57 – 10 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 0.249 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | -0.15 / -1.3 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 6.16 ± 1.35 |
Median ?50th percentile: half the non-missing values lie at or below it. | 6.13 |
Range ?Smallest and largest recorded non-missing values. | 3.17 – 10.37 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 0.219 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | -0.031 / -1.3 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 35,050 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 39.9 ± 25.9 |
Median ?50th percentile: half the non-missing values lie at or below it. | 38 |
Range ?Smallest and largest recorded non-missing values. | 0 – 100 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 0.649 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 0.26 / -0.98 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 35,050 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 37.5 ± 18.9 |
Median ?50th percentile: half the non-missing values lie at or below it. | 38 |
Range ?Smallest and largest recorded non-missing values. | 0 – 92 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 0.505 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | -0.067 / -0.5 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Classes ?Number of distinct recorded category values. | 40,175 |
Balance (entropy) ?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies. | 1 |
Imbalance ratio ?Largest class count divided by smallest class count; 1 indicates equal counts. | 1 |
Top class ?Most frequent recorded category and its observation count. | b27ab24c14a301c1f2be8279db5dc60b (2) |
Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Classes ?Number of distinct recorded category values. | 2 |
Balance (entropy) ?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies. | 1 |
Imbalance ratio ?Largest class count divided by smallest class count; 1 indicates equal counts. | 1 |
Top class ?Most frequent recorded category and its observation count. | ossl_visnir (40,175) |
Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Classes ?Number of distinct recorded category values. | 2 |
Balance (entropy) ?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies. | 1 |
Imbalance ratio ?Largest class count divided by smallest class count; 1 indicates equal counts. | 1 |
Top class ?Most frequent recorded category and its observation count. | visnir (40,175) |
Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Classes ?Number of distinct recorded category values. | 40,175 |
Balance (entropy) ?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies. | 1 |
Imbalance ratio ?Largest class count divided by smallest class count; 1 indicates equal counts. | 1 |
Top class ?Most frequent recorded category and its observation count. | 2009.47142646 (2) |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 1388 ± 313 |
Median ?50th percentile: half the non-missing values lie at or below it. | 1388 |
Range ?Smallest and largest recorded non-missing values. | 1,076 – 1,701 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 0.225 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 0 / -2 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Classes ?Number of distinct recorded category values. | 2 |
Balance (entropy) ?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies. | 1 |
Imbalance ratio ?Largest class count divided by smallest class count; 1 indicates equal counts. | 1 |
Top class ?Most frequent recorded category and its observation count. | ossl_visnir_350_2500_nm_step_2 (40,175) |
Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Classes ?Number of distinct recorded category values. | 2 |
Balance (entropy) ?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies. | 1 |
Imbalance ratio ?Largest class count divided by smallest class count; 1 indicates equal counts. | 1 |
Top class ?Most frequent recorded category and its observation count. | ossl_regression_743303ac | ossl_soil_clay_tot_usda_a334_w_pct_lucas_nirs_ossl (40,175) |
Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Classes ?Number of distinct recorded category values. | 25,228 |
Balance (entropy) ?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies. | 1 |
Imbalance ratio ?Largest class count divided by smallest class count; 1 indicates equal counts. | 2 |
Top class ?Most frequent recorded category and its observation count. | 47182946 (4) |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 10.3 ± 10.8 |
Median ?50th percentile: half the non-missing values lie at or below it. | 11.85 |
Range ?Smallest and largest recorded non-missing values. | -10.05 – 34.03 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 1.04 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | -0.073 / -1.2 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 49.42 ± 7.56 |
Median ?50th percentile: half the non-missing values lie at or below it. | 48.58 |
Range ?Smallest and largest recorded non-missing values. | 34.69 – 69.96 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 0.153 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | 0.43 / -0.62 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Classes ?Number of distinct recorded category values. | 2 |
Balance (entropy) ?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies. | 0.99 |
Imbalance ratio ?Largest class count divided by smallest class count; 1 indicates equal counts. | 1 |
Top class ?Most frequent recorded category and its observation count. | 2015-03-01 (43,564) |
Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Classes ?Number of distinct recorded category values. | 2 |
Balance (entropy) ?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies. | 0.99 |
Imbalance ratio ?Largest class count divided by smallest class count; 1 indicates equal counts. | 1 |
Top class ?Most frequent recorded category and its observation count. | 2015-10-01 (43,564) |
Read the recorded value distribution and summary statistics; no measurement unit is recorded.
n / missing ?Recorded observation count and number of missing values for this variable. | 80,350 / 0 |
|---|---|
Mean ± SD ?Arithmetic mean and standard deviation in the variable's stated unit. | 2012 ± 2.99 |
Median ?50th percentile: half the non-missing values lie at or below it. | 2,015 |
Range ?Smallest and largest recorded non-missing values. | 2,009 – 2,015 |
CV ?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero. | 0.00149 |
Skew / kurtosis ?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0. | -0.17 / -2 |
Normal? ?Descriptive normality-test result. A non-normal target is not automatically a defective variable. | no |
How observations join to physical sample IDs. Multiple exported rows are not necessarily independent samples or technical repetitions. Reading guide
| Alignment level | sample |
|---|---|
| Sample id available | yes |
| Samples | 40,175 |
| Observations (total) | 80,350 |
| Exported observations per unit | min 1 · mean 1 · max 1 |
| Identity status | available |
| Documented technical repetitions | ND |
| Source unit | Multiplicity of the declared identifier; protocol not established |
Partitions supplied by the original dataset. They are documented here and are not applied automatically when loading. Reading guide
| original | all: 40,175 documented · not applied |
|---|
Use the appropriate route below: an assembled dataset, original raw resources, or the documented access procedure. Reading guide
# Download original raw resources; no Dataverse token required from nirs4all_datasets import retrieve result = retrieve("ossl_lucas_soil_all_y", prepare=False) print(result["resources"]) # Raw resources may need dataset-specific assembly before analysis
Metadata downloads are available for public datasets only. The dataset bytes are never served here — fetch them from the origin / DOI above.