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EcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means

ecosis · NIR

EcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means. Origin database composed from 3 standardized v2.0 leaves: 1 spectral source(s), 1 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2ecosis
Original source available without a Dataverse token. The catalog uses the private tier for restricted or uncleared redistribution rights. You can obtain original resources from the source; their license still applies. Raw retrieval and automatic assembly are separate; use the loading example below.
80
samples
2,151
wavelengths
1
sources
1
targets
15
metadata
NIR
family

Provenance & citation

Where the original data and publications come from. Follow these links to obtain the source and cite its authors. Reading guide

ContributorPotato Virus Y Detection in Solanum tuberosum: Greenhouse Data
Conversion statuspartial
Origin · url [open]https://data.ecosis.org/dataset/potato-virus-y-detection-in-solanum-tuberosum--greenhouse-data-
Origin · url [open]https://data.ecosis.org/api/3/action/package_show?id=potato-virus-y-detection-in-solanum-tuberosum--greenhouse-data-
Origin · url [open]https://ecosis.org/package/potato-virus-y-detection-in-solanum-tuberosum--greenhouse-data-

Governance & integrity

Source license, redistribution conditions, content version and identity hashes. Redistribution tier and original-source availability are separate. Reading guide

Tierprivate
LicenseLicenseRef-not-cleared
Permitted useResearch and benchmarking.
Access policyManual download / private-use-only per source.
RedistributionEcoSIS CKAN API package_show verified open license: Other (Public Domain).
Content version1.1.0
Schema / protocol2.0
Content hashf4367959a640f929…
Processing hash929aed0cc29006f9…
Metadata hashc89b5b43bdc9788e…

Dataset property explorer

Start with the profile axes to locate unusual variation, then inspect the measured values and evidence below. Scores are descriptive heuristics. Reading guide

Mean available profile score0.42
Highest axisLocal artifacts · 1.00
Diagnostics8
Sources profiled1

How to read the profile

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.

EcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means profileEcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means — integrity: 0.00EcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means — noise: 0.00EcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means — artefacts: 1.00EcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means — baseline: 0.82EcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means — PCA outliers: 0.47EcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means — reference: 0.19EcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means — repeatability: 0.21EcoSIS Potato Virus Y detection in Solanum tuberosum, greenhouse plant-level means — structure: 0.64EcoSIS Potato V…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

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.
0.00
Noise
?Combines roughness and its signal-to-roughness proxy. Real narrow bands may also contribute.
0.00
Local artifacts
?Combines spike, jump and clipping heuristics. Inspect flagged spectral regions before attributing a cause.
1.00
Baseline/shape
?Summarizes baseline slope and excess edge variation relative to signal scale.
0.82
PCA outliers
?Combines relative PCA residual and score-distance tails. Valid rare spectra can produce a high value.
0.47
Distance to average spectrum
?Dispersion around this dataset's mean spectrum, using distance, shape and shift summaries.
0.19
Repeatability
?Dispersion among observations sharing an ID. Interpretation depends on the documented meaning of that ID.
0.21
Multi-regime structure
?Variation in local density in PCA space. May reflect legitimate batches or subpopulations.
0.64

Possible explanations to investigate

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.

Diagnostic hypotheses00.250.50.751hypothesis scoreSignature compatible with a spectral spliceSignature compatible with a spectral splice: 0.650.65Signature compatible with resamplingSignature compatible with resampling: 0.580.58Offset compatible with a different white referenceOffset compatible with a different white reference: 0.510.51Signature compatible with a change in geometrySignature compatible with a change in geometry: 0.440.44Signature compatible with a different backgroundSignature compatible with a different background: 0.440.44Signature compatible with clippingSignature compatible with clipping: 0.360.36Signature compatible with partial coverageSignature compatible with partial coverage: 0.330.33Multi-regime datasetMulti-regime dataset: 0.330.33

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 hypotheses and supporting evidence
DiagnosticScoreHeuristic strengthEvidence coverageSignalsPossible causeRequired external checks
Signature compatible with a spectral spliceX0.65medium100 %Spike rate 1.00, Jump rate 1.00, Undegraded roughness S/N proxy 1.00Break 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.
Signature compatible with resamplingX0.58medium100 %Spike rate 1.00, Jump rate 1.00, High S/N roughness proxy 1.00Digital artifacts or incorrect spectral processing.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 referenceX0.51medium100 %local artifacts 1.00, Baseline/mean/area 0.82, Mahalanobis / T2 0.47Systematic 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 change in geometryX0.44medium100 %Baseline/mean/area 0.82, Mahalanobis / T2 0.47, PCA Q 0.36Changing illumination, collection, angle or probe-sample distance.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 backgroundX0.44medium100 %Baseline/mean/area 0.82, Mahalanobis / T2 0.47, PCA Q 0.36Systematic 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 clippingX0.36weak100 %Jump rate 1.00, Baseline/mean/area 0.82, PCA Q 0.36Detector 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.
Signature compatible with partial coverageX0.33weak100 %Baseline/mean/area 0.82, Mahalanobis / T2 0.47, PCA Q 0.36Partial spot coverage; contribution from the background or support.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.
Multi-regime datasetX0.33weak100 %PCA structure 0.64, Mahalanobis / T2 0.47, PCA Q 0.36Mixture of campaigns, operators, batches, setups or spectral subpopulations.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.

Spectral sources

Each block is a separate instrument or spectral source. Read its signal summary before using PCA to explore variation between observations. Reading guide

plantlevel_means_df_PID_Time1

X · NIR

Read the signal and variation summaries for this source. Axis unit: nm; signal representation: absorbance.

Spectral signal summary

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.

plantlevel_means_df_PID_Time1 spectra0.00.20.40.601,0002,0003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm350nm — median 0.07395 (q25–q75 0.06855–0.08038)365nm — median 0.0567 (q25–q75 0.05325–0.0609)381nm — median 0.0506 (q25–q75 0.04745–0.05462)396nm — median 0.04455 (q25–q75 0.04127–0.0476)412nm — median 0.0408 (q25–q75 0.03768–0.04377)427nm — median 0.03905 (q25–q75 0.036–0.0418)443nm — median 0.0385 (q25–q75 0.0354–0.041)458nm — median 0.0386 (q25–q75 0.0357–0.0415)474nm — median 0.03885 (q25–q75 0.036–0.0414)489nm — median 0.0392 (q25–q75 0.0363–0.04202)505nm — median 0.044 (q25–q75 0.03997–0.04752)520nm — median 0.06975 (q25–q75 0.06058–0.0771)536nm — median 0.1076 (q25–q75 0.0933–0.1188)551nm — median 0.1171 (q25–q75 0.1018–0.1296)567nm — median 0.09965 (q25–q75 0.08692–0.1105)582nm — median 0.07235 (q25–q75 0.06355–0.08105)597nm — median 0.06295 (q25–q75 0.05567–0.07063)613nm — median 0.054 (q25–q75 0.048–0.06033)628nm — median 0.048 (q25–q75 0.0431–0.05268)644nm — median 0.04335 (q25–q75 0.0393–0.04733)659nm — median 0.0388 (q25–q75 0.0361–0.0412)675nm — median 0.0387 (q25–q75 0.0364–0.0404)690nm — median 0.0476 (q25–q75 0.04437–0.0508)706nm — median 0.1614 (q25–q75 0.1424–0.1786)721nm — median 0.3332 (q25–q75 0.3076–0.3494)737nm — median 0.4567 (q25–q75 0.4398–0.4752)752nm — median 0.4984 (q25–q75 0.4833–0.5134)768nm — median 0.5073 (q25–q75 0.4928–0.521)783nm — median 0.5071 (q25–q75 0.4935–0.5211)799nm — median 0.5065 (q25–q75 0.4933–0.5206)814nm — median 0.5061 (q25–q75 0.4929–0.5197)829nm — median 0.5053 (q25–q75 0.4922–0.5185)845nm — median 0.5041 (q25–q75 0.4912–0.5173)860nm — median 0.5033 (q25–q75 0.4908–0.5166)876nm — median 0.5022 (q25–q75 0.49–0.5152)891nm — median 0.501 (q25–q75 0.489–0.5139)907nm — median 0.4995 (q25–q75 0.4876–0.5124)922nm — median 0.4975 (q25–q75 0.4857–0.51)938nm — median 0.4924 (q25–q75 0.481–0.5048)953nm — median 0.4832 (q25–q75 0.4727–0.4958)969nm — median 0.4765 (q25–q75 0.466–0.4879)984nm — median 0.4753 (q25–q75 0.4647–0.4869)1,000nm — median 0.477 (q25–q75 0.4657–0.4873)1,015nm — median 0.4786 (q25–q75 0.4675–0.49)1,031nm — median 0.483 (q25–q75 0.4718–0.4945)1,046nm — median 0.4852 (q25–q75 0.4737–0.4967)1,062nm — median 0.4858 (q25–q75 0.4742–0.4972)1,077nm — median 0.4849 (q25–q75 0.4732–0.4962)1,092nm — median 0.4832 (q25–q75 0.4713–0.4942)1,108nm — median 0.4803 (q25–q75 0.4686–0.491)1,123nm — median 0.4761 (q25–q75 0.4644–0.4863)1,139nm — median 0.4595 (q25–q75 0.4489–0.4697)1,154nm — median 0.4382 (q25–q75 0.4301–0.4491)1,170nm — median 0.4325 (q25–q75 0.4248–0.4433)1,185nm — median 0.4309 (q25–q75 0.423–0.4412)1,201nm — median 0.4305 (q25–q75 0.4228–0.4408)1,216nm — median 0.4325 (q25–q75 0.4246–0.4425)1,232nm — median 0.4346 (q25–q75 0.4264–0.4443)1,247nm — median 0.436 (q25–q75 0.4278–0.4458)1,263nm — median 0.4364 (q25–q75 0.4279–0.4461)1,278nm — median 0.4342 (q25–q75 0.4259–0.4438)1,294nm — median 0.4276 (q25–q75 0.4199–0.4375)1,309nm — median 0.4166 (q25–q75 0.4101–0.4266)1,324nm — median 0.3981 (q25–q75 0.3915–0.4076)1,340nm — median 0.3743 (q25–q75 0.3674–0.3832)1,355nm — median 0.3536 (q25–q75 0.3459–0.3619)1,371nm — median 0.3234 (q25–q75 0.3158–0.331)1,386nm — median 0.2522 (q25–q75 0.245–0.2598)1,402nm — median 0.1576 (q25–q75 0.1503–0.1644)1,417nm — median 0.1154 (q25–q75 0.1089–0.1213)1,433nm — median 0.1022 (q25–q75 0.09627–0.108)1,448nm — median 0.1003 (q25–q75 0.09438–0.1059)1,464nm — median 0.1046 (q25–q75 0.09828–0.1104)1,479nm — median 0.1178 (q25–q75 0.1113–0.1243)1,495nm — median 0.1384 (q25–q75 0.1317–0.1449)1,510nm — median 0.1604 (q25–q75 0.1536–0.1667)1,526nm — median 0.1841 (q25–q75 0.1777–0.1911)1,541nm — median 0.2062 (q25–q75 0.1998–0.2131)1,556nm — median 0.2261 (q25–q75 0.2196–0.2329)1,572nm — median 0.2443 (q25–q75 0.2379–0.2511)1,587nm — median 0.2591 (q25–q75 0.2525–0.2657)1,603nm — median 0.272 (q25–q75 0.2646–0.2782)1,618nm — median 0.2811 (q25–q75 0.2737–0.2873)1,634nm — median 0.2889 (q25–q75 0.2814–0.2952)1,649nm — median 0.2942 (q25–q75 0.2866–0.3007)1,665nm — median 0.2975 (q25–q75 0.2897–0.3039)1,680nm — median 0.297 (q25–q75 0.2896–0.3033)1,696nm — median 0.2938 (q25–q75 0.2864–0.2999)1,711nm — median 0.2887 (q25–q75 0.2816–0.295)1,727nm — median 0.2807 (q25–q75 0.274–0.2876)1,742nm — median 0.2702 (q25–q75 0.263–0.2766)1,758nm — median 0.256 (q25–q75 0.2493–0.263)1,773nm — median 0.2442 (q25–q75 0.2376–0.2508)1,788nm — median 0.2374 (q25–q75 0.2308–0.2443)1,804nm — median 0.2362 (q25–q75 0.2293–0.2431)1,819nm — median 0.2355 (q25–q75 0.2289–0.2426)1,835nm — median 0.2298 (q25–q75 0.223–0.2371)1,850nm — median 0.2114 (q25–q75 0.204–0.2188)1,866nm — median 0.1613 (q25–q75 0.1542–0.1679)1,881nm — median 0.0878 (q25–q75 0.08257–0.09315)1,897nm — median 0.04395 (q25–q75 0.0418–0.046)1,912nm — median 0.02725 (q25–q75 0.0257–0.0283)1,928nm — median 0.0256 (q25–q75 0.0242–0.02652)1,943nm — median 0.026 (q25–q75 0.0246–0.027)1,959nm — median 0.02785 (q25–q75 0.02628–0.02902)1,974nm — median 0.0308 (q25–q75 0.029–0.0323)1,990nm — median 0.03595 (q25–q75 0.03337–0.0379)2,005nm — median 0.04195 (q25–q75 0.03887–0.04453)2,021nm — median 0.0494 (q25–q75 0.04567–0.05242)2,036nm — median 0.0576 (q25–q75 0.0534–0.06112)2,051nm — median 0.06605 (q25–q75 0.06148–0.0701)2,067nm — median 0.07575 (q25–q75 0.07058–0.0804)2,082nm — median 0.08605 (q25–q75 0.08047–0.0907)2,098nm — median 0.0978 (q25–q75 0.0918–0.1028)2,113nm — median 0.1093 (q25–q75 0.1032–0.1146)2,129nm — median 0.1198 (q25–q75 0.1132–0.1255)2,144nm — median 0.1265 (q25–q75 0.12–0.1324)2,160nm — median 0.1325 (q25–q75 0.1261–0.1381)2,175nm — median 0.1352 (q25–q75 0.1291–0.141)2,191nm — median 0.1367 (q25–q75 0.1306–0.1426)2,206nm — median 0.138 (q25–q75 0.1318–0.144)2,222nm — median 0.1383 (q25–q75 0.1321–0.1442)2,237nm — median 0.1363 (q25–q75 0.1302–0.1423)2,253nm — median 0.1301 (q25–q75 0.1242–0.1359)2,268nm — median 0.1223 (q25–q75 0.1166–0.128)2,283nm — median 0.115 (q25–q75 0.1092–0.1204)2,299nm — median 0.1082 (q25–q75 0.1027–0.1134)2,314nm — median 0.101 (q25–q75 0.0956–0.106)2,330nm — median 0.093 (q25–q75 0.08768–0.0977)2,345nm — median 0.08495 (q25–q75 0.07982–0.0894)2,361nm — median 0.081 (q25–q75 0.076–0.0849)2,376nm — median 0.0782 (q25–q75 0.07385–0.0816)2,392nm — median 0.06525 (q25–q75 0.0608–0.0683)2,407nm — median 0.0576 (q25–q75 0.05357–0.0604)2,423nm — median 0.0524 (q25–q75 0.04888–0.0551)2,438nm — median 0.0495 (q25–q75 0.0462–0.0518)2,454nm — median 0.0439 (q25–q75 0.04127–0.04613)2,469nm — median 0.039 (q25–q75 0.03667–0.0411)2,485nm — median 0.03615 (q25–q75 0.0339–0.038)2,500nm — median 0.0351 (q25–q75 0.0331–0.0367)

Sampling

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.
2,151
Axis range
?Lowest and highest recorded spectral-axis coordinates, in the stated unit.
350–2,500 nm
Mean spacing
?Average difference between adjacent spectral-axis coordinates; compare only within the same unit.
1 nm
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.
240

Signal & quality

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.
0.0209 – 0.549
Mean range
?Minimum and maximum of the mean spectrum across spectral channels.
0.0254 – 0.508
Mean level
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations.
0.2259
Area
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit.
485.9
PTP
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum.
0.4824
Noise RMS
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise.
6.0519e-05
Roughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR.
3.7e+03
Roughness S/N proxy dB
?20 log10 of the roughness signal-to-noise proxy. Units are decibels; compare only compatible signal representations.
7e+01 dB
Dynamic range
?Maximum minus minimum of the mean spectrum. Its magnitude depends on the signal representation.
0.482
Smoothness
?Mean per-spectrum RMS of second differences, in signal units. Smaller values indicate smoother spectra under this protocol.
0.0004871
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.
87

Integrity & artefacts

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.
0.00%
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.
9,224
Spike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage.
1.79%
Jump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins.
18,941
Jump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage.
3.67%
Clip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation.
0.00%

Shape & reference

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.
-0.19755
Curvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation.
0.00048336
D1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts.
0.0018229
RMS to mean
?Root mean square distance to this dataset's mean spectrum, in signal units.
0.010211
RMS p95
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value.
0.018544
SAM to mean
?Spectral Angle Mapper: angle to the mean spectrum, in radians. Smaller angles indicate more similar shapes.
0.023544
SAM p95
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS.
0.04795
Affine offset p95
?95th percentile of absolute fitted offsets when matching each spectrum to the dataset mean.
0.014094
Affine gain p95 Δ
?95th percentile of the absolute gain deviation from 1 in the fit to the mean spectrum.
0.066758
Affine residual p95
?95th percentile of residual RMS after gain and offset correction to the mean spectrum.
0.011219
Xcorr lag p95
?95th percentile of the absolute cross-correlation shift relative to the mean spectrum, in spectral channels.
0

Outliers & repeatability

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.
2.9
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.
3.6
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.
1.9
Repeat groups
?Number of IDs with several observations. Source documentation is needed to decide whether these are technical repeats, positions or time points.
80
RMS intra-ID
?Within-ID root mean square deviation from the group's mean spectrum, in signal units.
0.009006
SAM intra-ID
?Within-ID spectral angle to the group's mean spectrum, in radians.
0.019909
CV intra-ID
?Within-ID coefficient of variation: standard deviation relative to absolute mean; unstable near zero signal.
0.053726

Dimensionality (PCA)

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.
2.8
PCs → 95% var
?Number of principal components needed to explain 95% of spectral variance, when reached by the computed projection.
3
PCs → 99% var
?Number of principal components needed to explain 99% of spectral variance, when reached by the computed projection.
5
Top-10 cum. var
?Fraction of spectral variance explained by up to the first ten computed principal components, displayed as a percentage.
99.7%
Computed metric scores 30highest alert 1.00

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.

FamilyComputed metricValueAlert scoreHeuristic levelDescriptive interpretationPossible causes to verifyCalculation / scoring
Data integrityInvalid/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.
integrity.missing_cells		——not calculableNot enough information to score this metric on this dataset.Missing data or validity mask; not evidence of an acquisition causeNative Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio.
Data integrityNaN 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.
integrity.nan_ratio		0%0.00weakFull spectrumAcquisition/export errorcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Data integrityInf 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.
integrity.inf_count		00.00weakNormalInvalid calculationscount(isinf(X))alert = min(1, inf_count / 1)
Data integrityZero 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.
integrity.zero_ratio		0%0.00weakNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Overall amplitudeMean 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.
amplitude.mean_reflectance		0.22590.82strongAtypical value: Too light / background visible or Too darkBackground, geometrymean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Overall amplitudeArea 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.
amplitude.area_under_curve		485.870.82strongAtypical value: Illumination difference or NormalProbe distancetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Overall amplitudePeak-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.
amplitude.peak_to_peak		0.482370.00weakStrong variabilitySaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Overall amplitudeVariance
?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.
amplitude.variance		0.0301480.00weakNormal or heterogeneousBad contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
NoiseRoughness 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.
noise.roughness_sigma_proxy		6.0519e-050.00weakStableLamp, detectormedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
NoiseRoughness 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.
noise.roughness_signal_to_noise_proxy		3732.80.00weakGood signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
NoiseBandwise 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.
noise.bandwise_roughness_signal_to_noise_proxy_min		28.7120.17weakReliable zoneDetectormin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Local artifactsSpike 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.
artefacts.spike_count		9,2241.00strongArtifactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Local artifactsSpike 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.
artefacts.spike_rate		1.79%1.00strongSuspicious spectrumInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Local artifactsJump 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.
artefacts.jump_count		18,9411.00strongDetector connectionSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Local artifactsJump 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.
artefacts.jump_rate		3.67%1.00strongSpectral problemCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Local artifactsClip 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.
artefacts.clip_fraction		0.00349%0.00weakNormalDetector saturatedfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Spectral formBaseline 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.
shape.baseline_slope		-0.197550.82strongDriftIlluminationlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Spectral formCurvature 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.
shape.curvature_rms		0.000483360.10weakSmoothBackground, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Spectral formD1 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.
shape.d1_rms		0.00182290.08weakFlatBiology or artifactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Multivariate outliersPCA 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.
outliers.pca_q_ratio		2.8770.36weakCompliantArtifact, mixturep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Multivariate outliersHotelling 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.
outliers.hotelling_t2_ratio		3.58650.45mediumExtreme but consistentNatural variabilityp95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Multivariate outliersMahalanobis 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.
outliers.mahalanobis_h_ratio		1.89380.47mediumGlobal outlierDifferent domainp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparison to average spectrumRMS 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.
reference.rms_to_mean_spectrum_p95		0.0185440.15weakTypicalDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparison to average spectrumSpectral 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.
reference.sam_to_mean_spectrum_p95		0.047950.14weakSimilarBackground, geometryp95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RepeatabilityRMS 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.
repeatability.rms_intra_id		0.0090060.19weakStablePositioningmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RepeatabilitySAM 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.
repeatability.sam_intra_id		0.0199090.13weakStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RepeatabilityCV 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.
repeatability.cv_intra_id		0.0537260.21weakStableOperatormedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure of the datasetPCA 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.
structure.pca_score_density		6.37370.64mediumSubpopulationsDifferent batches1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure of the datasetkNN 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.
structure.knn_distance_ratio_p95		1.91590.46mediumIsolated spectrumRare casesp95(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 datasetIsolation 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.
structure.isolation_forest_score_p95		0.564340.64mediumAtypical spectrumVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent

PCA score plot — explore spectral variation

Source plantlevel_means_df_PID_Time1: 240 of 240 source observations are plotted. PC1 captures 54.8% and PC2 33.7% of the reported spectral variance: 88.5% together; 11.5% 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.

plantlevel_means_df_PID_Time1 PCA score plot-1.0-0.50.00.51.0-1012PC1 -0.3341 · PC2 0.3183PC1 -0.3003 · PC2 0.2488PC1 -0.4014 · PC2 0.8567PC1 -0.3379 · PC2 -0.03157PC1 -0.3663 · PC2 -0.0741PC1 -0.5 · PC2 0.4541PC1 -0.5557 · PC2 -0.07764PC1 -0.9473 · PC2 0.1565PC1 -0.6371 · PC2 0.06874PC1 -0.6858 · PC2 -0.1674PC1 -0.8531 · PC2 0.0189PC1 -0.465 · PC2 0.1212PC1 -0.1852 · PC2 0.1505PC1 -0.927 · PC2 0.1644PC1 -0.6366 · PC2 -0.4737PC1 -0.2806 · PC2 0.3948PC1 -0.8661 · PC2 -0.05387PC1 -0.5008 · PC2 0.05273PC1 -0.5054 · PC2 -0.2812PC1 0.06078 · PC2 0.1936PC1 -0.5493 · PC2 1.08PC1 -0.5794 · PC2 0.04835PC1 -0.5097 · PC2 1.354PC1 -0.2558 · PC2 0.2103PC1 -0.2229 · PC2 0.5193PC1 -0.327 · PC2 -0.01959PC1 -0.3798 · PC2 -0.02648PC1 -0.2962 · PC2 -0.1687PC1 -0.5645 · PC2 -0.4987PC1 -0.3571 · PC2 -0.1991PC1 -0.5739 · PC2 0.2896PC1 -0.1153 · PC2 0.527PC1 -0.5228 · PC2 -0.3447PC1 -0.6924 · PC2 0.1386PC1 -0.1817 · PC2 0.5636PC1 -0.3187 · PC2 -0.09942PC1 -0.113 · PC2 -0.0977PC1 0.6408 · PC2 0.3858PC1 0.5902 · PC2 0.09966PC1 0.1291 · PC2 -0.1881PC1 0.0822 · PC2 0.3854PC1 0.1182 · PC2 -0.4397PC1 0.1382 · PC2 0.05628PC1 0.7313 · PC2 -0.0105PC1 -0.4401 · PC2 -0.04038PC1 -0.6542 · PC2 0.09651PC1 -0.2719 · PC2 0.5767PC1 -0.6604 · PC2 -0.1457PC1 -0.3874 · PC2 0.4695PC1 -0.5316 · PC2 0.3299PC1 -0.5235 · PC2 0.2332PC1 -0.6439 · PC2 -0.01754PC1 -0.7191 · PC2 0.02579PC1 0.001172 · PC2 0.3929PC1 0.2912 · PC2 0.2188PC1 -0.5253 · PC2 0.7539PC1 -0.6 · PC2 0.009109PC1 -0.5961 · PC2 -0.3195PC1 0.4601 · PC2 0.03768PC1 0.6188 · PC2 0.301PC1 -0.009885 · PC2 0.3123PC1 0.006064 · PC2 -0.1404PC1 -0.1088 · PC2 0.4424PC1 0.02719 · PC2 0.05816PC1 0.2527 · PC2 0.2183PC1 -0.3497 · PC2 0.1725PC1 -0.5434 · PC2 0.07757PC1 -0.2637 · PC2 0.3672PC1 -0.1554 · PC2 0.9917PC1 0.3916 · PC2 0.2914PC1 -0.1165 · PC2 0.2315PC1 0.1234 · PC2 0.24PC1 -0.2208 · PC2 0.3308PC1 -0.2495 · PC2 0.7378PC1 -0.09073 · PC2 -0.05415PC1 -0.3567 · PC2 -0.005937PC1 -0.4014 · PC2 0.0953PC1 -0.379 · PC2 -0.1007PC1 -0.4048 · PC2 0.7956PC1 -0.602 · PC2 0.2404PC1 0.08603 · PC2 0.2188PC1 -0.05002 · PC2 -0.1575PC1 0.005638 · PC2 -0.2461PC1 0.07875 · PC2 0.2708PC1 -0.137 · PC2 -0.1492PC1 -0.4934 · PC2 0.004362PC1 -0.1033 · PC2 -0.6789PC1 -0.1853 · PC2 -0.3325PC1 0.02353 · PC2 -0.2809PC1 -0.1899 · PC2 -0.4653PC1 -0.1075 · PC2 -0.4327PC1 -0.2003 · PC2 -0.1942PC1 -0.2441 · PC2 -0.5846PC1 -0.4772 · PC2 -0.2338PC1 0.1191 · PC2 0.152PC1 -0.05428 · PC2 -0.09728PC1 -0.1745 · PC2 -0.02006PC1 -0.2909 · PC2 0.2857PC1 0.2605 · PC2 -0.2461PC1 -0.05065 · PC2 -0.2155PC1 0.1095 · PC2 0.1882PC1 -0.07384 · PC2 0.6673PC1 0.1869 · PC2 -0.08352PC1 -0.2314 · PC2 -0.6365PC1 -0.1393 · PC2 -0.3433PC1 0.3381 · PC2 0.3489PC1 -0.199 · PC2 -0.2084PC1 -0.08031 · PC2 -0.4136PC1 -0.2375 · PC2 -0.3637PC1 -0.4818 · PC2 -0.02362PC1 0.1523 · PC2 -0.0209PC1 0.361 · PC2 0.01544PC1 -0.008162 · PC2 0.01865PC1 0.2095 · PC2 0.4194PC1 -0.6561 · PC2 -0.3264PC1 -0.2822 · PC2 -0.2792PC1 0.07348 · PC2 -0.04803PC1 0.1352 · PC2 0.3399PC1 0.3182 · PC2 -0.2536PC1 -0.1216 · PC2 -0.3472PC1 -0.2735 · PC2 0.01593PC1 -0.08811 · PC2 -0.3557PC1 -0.09034 · PC2 -0.2342PC1 0.03322 · PC2 -0.2607PC1 -0.129 · PC2 -0.4314PC1 -0.5858 · PC2 -0.08178PC1 0.1923 · PC2 0.1884PC1 -0.282 · PC2 -0.6362PC1 0.08838 · PC2 -0.1849PC1 0.2414 · PC2 -0.1368PC1 -0.0158 · PC2 -0.4554PC1 -0.1608 · PC2 -0.05827PC1 0.003394 · PC2 -0.4189PC1 0.01949 · PC2 0.02765PC1 -0.6293 · PC2 -0.268PC1 -0.09845 · PC2 -0.3461PC1 0.05571 · PC2 -0.1201PC1 0.1874 · PC2 -0.3345PC1 -0.4631 · PC2 -0.05116PC1 -0.0922 · PC2 -0.07693PC1 -0.3279 · PC2 -0.3181PC1 -0.4897 · PC2 -0.4037PC1 0.07883 · PC2 -0.06324PC1 -0.2573 · PC2 -0.4376PC1 -0.4106 · PC2 -0.0548PC1 -0.2313 · PC2 -0.0476PC1 -0.1219 · PC2 -0.4299PC1 0.1499 · PC2 -0.03099PC1 -0.2143 · PC2 -0.2277PC1 -0.1645 · PC2 -0.1684PC1 -0.3757 · PC2 -0.2377PC1 -0.01431 · PC2 0.184PC1 0.07785 · PC2 0.07669PC1 0.1921 · PC2 0.04834PC1 -0.6469 · PC2 -0.0612PC1 -0.3584 · PC2 0.07875PC1 -0.1978 · PC2 -0.4676PC1 0.1672 · PC2 -0.2231PC1 -0.4839 · PC2 -0.2039PC1 -0.2628 · PC2 -0.1966PC1 0.5289 · PC2 0.1516PC1 0.5735 · PC2 -0.2179PC1 0.335 · PC2 -0.04148PC1 0.3688 · PC2 0.02473PC1 0.4531 · PC2 0.04896PC1 0.2151 · PC2 -0.1668PC1 0.1856 · PC2 -0.5163PC1 0.5935 · PC2 0.4681PC1 0.6065 · PC2 0.1089PC1 0.2858 · PC2 -0.8046PC1 0.5933 · PC2 0.08292PC1 0.3764 · PC2 -0.3726PC1 0.4345 · PC2 -0.08819PC1 0.03444 · PC2 0.004839PC1 0.4846 · PC2 -0.2406PC1 0.08836 · PC2 -0.2823PC1 0.2489 · PC2 0.1996PC1 -0.03294 · PC2 -0.3971PC1 0.5774 · PC2 -0.03635PC1 0.4462 · PC2 -0.07086PC1 0.2413 · PC2 -0.02961PC1 0.3817 · PC2 0.4904PC1 0.06196 · PC2 -0.2944PC1 0.3162 · PC2 -0.0201PC1 0.7454 · PC2 0.1406PC1 0.5571 · PC2 0.3048PC1 0.3197 · PC2 -0.1384PC1 0.5288 · PC2 0.07047PC1 0.6299 · PC2 -0.1397PC1 0.3858 · PC2 0.2399PC1 0.3612 · PC2 -0.1293PC1 0.06603 · PC2 0.08709PC1 0.4883 · PC2 0.1064PC1 0.5657 · PC2 0.1793PC1 0.5233 · PC2 0.06994PC1 0.4062 · PC2 0.4064PC1 0.3306 · PC2 -0.09363PC1 0.5131 · PC2 0.1094PC1 0.4092 · PC2 -0.1069PC1 0.3311 · PC2 -0.1548PC1 0.4709 · PC2 0.0644PC1 0.456 · PC2 -0.2263PC1 0.226 · PC2 -0.05915PC1 0.6071 · PC2 0.0447PC1 0.2466 · PC2 -0.0985PC1 0.507 · PC2 0.1379PC1 0.7503 · PC2 0.05525PC1 0.4137 · PC2 -0.5643PC1 0.4793 · PC2 -0.3842PC1 0.3069 · PC2 -0.2753PC1 0.2165 · PC2 -0.08539PC1 0.7216 · PC2 0.01094PC1 0.2424 · PC2 -0.3079PC1 0.3939 · PC2 -0.5063PC1 0.06968 · PC2 -0.3751PC1 0.4749 · PC2 -0.2042PC1 0.4902 · PC2 -0.1125PC1 0.6798 · PC2 0.2063PC1 0.3109 · PC2 -0.02253PC1 0.4264 · PC2 0.006065PC1 0.5511 · PC2 0.1341PC1 0.5434 · PC2 0.8378PC1 0.5615 · PC2 -0.1611PC1 0.4544 · PC2 0.1289PC1 0.8573 · PC2 -0.03125PC1 0.005244 · PC2 0.1931PC1 0.6079 · PC2 0.1509PC1 0.3819 · PC2 0.2039PC1 0.3361 · PC2 0.1497PC1 0.08161 · PC2 0.3622PC1 0.2499 · PC2 0.01859PC1 0.04509 · PC2 0.01774PC1 0.5061 · PC2 -0.07289PC1 0.1751 · PC2 0.08676PC1 0.594 · PC2 0.3207PC1 0.2685 · PC2 -0.2654PC1 0.4553 · PC2 -0.02022PC1 0.186 · PC2 -0.07308PC1 0.4871 · PC2 0.09685PC1 0.3811 · PC2 -0.005458PC1 (54.8%)PC2 (33.7%)240 scores
View plotted PCA coordinates

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.

Plotted observation positions (not original sample IDs)
PointPC1 scorePC2 score
1-0.334110.31833
2-0.300320.24885
3-0.401360.85672
4-0.33794-0.031575
5-0.36631-0.074097
6-0.499950.45413
7-0.55571-0.077638
8-0.947330.15646
9-0.637150.068743
10-0.68576-0.16742
11-0.853150.018901
12-0.465040.12122
13-0.185250.1505
14-0.926990.16443
15-0.63658-0.47373
16-0.280570.39479
17-0.86615-0.053867
18-0.500750.052735
19-0.50538-0.28116
200.0607830.19361
21-0.549251.0801
22-0.579370.048352
23-0.509671.354
24-0.255760.21026
25-0.222920.51927
26-0.327-0.019588
27-0.37979-0.026484
28-0.29621-0.16869
29-0.56449-0.4987
30-0.35715-0.19907
31-0.573910.28958
32-0.115290.52703
33-0.52281-0.34474
34-0.69240.13864
35-0.181690.56363
36-0.31867-0.099422
37-0.11301-0.097698
380.64080.38583
390.590210.099665
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440.73131-0.010499
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600.618840.30103
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94-0.47725-0.23377
950.119120.15196
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97-0.17446-0.020058
98-0.290920.28565
990.26055-0.24611
100-0.050654-0.2155
1010.109530.18816
102-0.0738450.6673
1030.18691-0.083522
104-0.23137-0.63652
105-0.13926-0.34333
1060.338110.34886
107-0.19904-0.20843
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110-0.48181-0.023623
1110.1523-0.020897
1120.361020.015436
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1270.192260.18844
128-0.28196-0.63624
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1900.385810.23988
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1920.0660330.087085
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1940.565660.17927
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1960.406240.40639
1970.33057-0.093631
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2020.45598-0.22631
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2120.721650.010939
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2180.67980.20635
2190.31088-0.022527
2200.426360.0060654
2210.551080.13407
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2230.56147-0.16112
2240.454370.12894
2250.85732-0.031251
2260.00524380.19314
2270.607850.15086
2280.381910.20388
2290.336090.14972
2300.0816120.36223
2310.249890.01859
2320.0450870.017742
2330.50611-0.072891
2340.175080.086762
2350.594020.32071
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2370.45531-0.020222
2380.18599-0.073076
2390.487050.096848
2400.38114-0.0054579

PCA explained variance — how many dimensions?

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.

PCA explained variance0%25%50%75%100%PC1: 54.8% (cumulative 54.8%)1PC2: 33.7% (cumulative 88.5%)2PC3: 9.2% (cumulative 97.8%)3PC4: 0.6% (cumulative 98.4%)4PC5: 0.6% (cumulative 99.0%)5PC6: 0.3% (cumulative 99.3%)6PC7: 0.2% (cumulative 99.5%)7PC8: 0.1% (cumulative 99.6%)8PC9: 0.1% (cumulative 99.7%)9PC10: 0.1% (cumulative 99.7%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
View explained variance values
Variance per displayed principal component
ComponentSpectral variance
PC154.80%
PC233.74%
PC39.22%
PC40.64%
PC50.63%
PC60.25%
PC70.22%
PC80.12%
PC90.07%
PC100.06%
X-Y spectral correlation 1

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.

Spectral association with PVY_Strain

Read Pearson correlation (−1 to +1) along the spectral axis (nm); the solid curve shows |r| and the dashed curve shows signed r.

X · PVY_Strain spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
PVY_Strain0.2943580.1070.0%

Metric interpretation reference

Definitions, formulas and possible explanations for the reported values. A possible cause requires verification against acquisition records. Reading guide

Metric catalog 30

Look up each measured quantity, its calculation and the heuristic alert mapping before interpreting a high or low value.

FamilyMetricWhat it measuresHigher values suggestLower values suggestPossible causesCalculation / score
Data integrityInvalid/masked cell count (owner report)Null or masked cells in the evaluated populationTo investigateComplete according to the declared maskMissing data or validity mask; not evidence of an acquisition causeNative Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio.
Data integrityNaN ratioMissing dataCorrupted spectrumFull spectrumAcquisition/export errorcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Data integrityInf countInfinite valuesCorruptionNormalInvalid calculationscount(isinf(X))alert = min(1, inf_count / 1)
Data integrityZero ratioNull columns or cellsTruncated spectrumNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Overall amplitudeMean reflectanceAverage levelToo light / background visibleToo darkBackground, geometrymean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Overall amplitudeArea under curveOverall intensityIllumination differenceNormalProbe distancetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Overall amplitudePeak-to-peak (PTP)Dynamic rangeStrong variabilityFlat spectrumSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Overall amplitudeVarianceSpectral variabilityNormal or heterogeneousFlat spectrumBad contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
NoiseRoughness sigma proxyHigh frequency noiseNoisyStableLamp, detectormedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
NoiseRoughness S/N proxySignal qualityGood signalBad signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
NoiseBandwise roughness S/N proxyLocalized noiseReliable zoneProblem areaDetectormin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Local artifactsSpike countNarrow peaksArtifactsClean spectrumCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Local artifactsSpike ratePeak densitySuspicious spectrumNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Local artifactsJump countDiscontinuitiesDetector connectionContinuousSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Local artifactsJump rateJump frequencySpectral problemNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Local artifactsClip fractionSaturationClippingNormalDetector saturatedfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Spectral formBaseline slopeOverall slopeDriftStableIlluminationlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Spectral formCurvature RMSCurvatureUnusual shapeSmoothBackground, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Spectral formD1 RMSLocal variabilityStructured spectrumFlatBiology or artifactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Multivariate outliersPCA Q (SPE)Not explained by PCAAtypical spectrumCompliantArtifact, mixturep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Multivariate outliersHotelling T²Extreme in PCAExtreme but consistentCentralNatural variabilityp95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Multivariate outliersMahalanobis HDistance to cloudGlobal outlierNormal populationDifferent domainp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparison to average spectrumRMS to mean spectrumAverage distanceDifferent spectrumTypicalDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparison to average spectrumSpectral Angle Mapper (SAM)Difference in shapeDifferent shapeSimilarBackground, geometryp95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RepeatabilityRMS intra-IDReproducibilityPoor repeatabilityStablePositioningmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RepeatabilitySAM intra-IDShape variationUnstableStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RepeatabilityCV intra-IDInternal variabilityBad controlStableOperatormedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure of the datasetPCA score densityClustersSubpopulationsHomogeneousDifferent batches1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure of the datasetkNN distance ratio (not LOF)Local anomalyIsolated spectrumNormal populationRare casesp95(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 datasetIsolation Forest scoreGlobal anomalyAtypical spectrumNormalVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
Technology-specific extensions

Find measurement-specific checks and acquisition context that can help interpret a source's spectral patterns.

TechnologyAdaptations / metricsTarget anomaliesPractical guidance
UV-Vis 300-1000 nmBaseline, overall slope, drift at edges 300-350 and 900-1000; metrics by zonesStray light, bad white, saturation, weak signal at the endsThe edges are often unstable; also calculate edge/middle scores.
UV-Vis 300-1000 nmSaturation / clipping near max absorbance or max reflectanceSignal clippedVery important if strong absorption.
UV-Vis 300-1000 nmRed-edge, peak position, band ratios for vegetationBiological shift or optical artifactHelps distinguish real change from acquisition problem.
UV-Vis 300-1000 nmSmoothness / roughness indexHigh frequency noiseOften more informative than the SNR alone.
MIR / ATR-FTIRATR contact quality index: overall intensity, total area, depth of key bandsPoor crystal-sample contactCrucial: many anomalies come from the ATR contact.
MIR / ATR-FTIRCO2 / H2O atmospheric bandsBad atmospheric correctionFrequent spurious peaks.
MIR / ATR-FTIRBaseline curvature / rubber-band residualDiffusion, contact, baseline driftVery useful before PCA.
MIR / ATR-FTIRPeak position shiftBad spectral alignment/calibrationImportant in FTIR because small shifts count.
MIR / ATR-FTIRBand area ratios on known bandsChemically incoherent spectrumTo be adapted by matrix: polysaccharides, proteins, lipids, etc.
HS-MSTotal Ion Current (TIC), Base Peak Intensity (BPI)Weak injection, unstable ionizationMS equivalent of the global spectral level.
HS-MSNumber of peaks detectedPoor or too noisy spectrumToo little = bad signal; too much = noise/contamination.
HS-MSMass accuracy / m/z driftMass calibration problemFundamental in HRMS.
HS-MSRetention time drift if LC/GC-MSChromatographic driftMonitor on standards/QC pools.
HS-MSBlank contamination scoreContaminants / carry-overCompare samples vs blanks.
HS-MSInternal standard CVInstrumental variabilityVery robust if standards available.
HS-MSMissingness by featureDetection instabilityCrucial for filtering variables.
With repetitionsIntra-sample RMSOverall repeatabilityApplicable to all technologies.
With repetitionsSAM / intra-sample correlationShape repeatabilityVery useful for spectra.
With repetitionsIntra-sample CV per band / featureLocal repeatabilityDetects unstable areas.
With repetitionsICC or variance componentsShare of sample vs. technical varianceVery useful if several repetitions per sample.
With repetitionsDistance to intra-ID centroidAberrant repetitionAllows you to flag the bad repetition rather than the entire sample.
Bug-hunting / supervised audits

Review suggested follow-up methods for suspected data issues and whether their results are available in this explorer.

Potential issue familySuggested methodsWhat it detectsExplorer availability
Global spectral shiftInter-dataset average spectrum correlation, DTW, cross-correlation, comparison of peak positionsWavelength shift, misalignment, different interpolationPartially calculated: cross-correlation lag and dispersion of peak positions vs. average spectrum.
Baseline / offset / gainRegression each spectrum vs average spectrum: x = a + b ref + residual; followed by a, b, residual RMSAdditive offset, multiplicative effect, baseline driftCalculated in reference.affine_*.
Row mixing / X-M-Y mismatchIndex check, row hash, ID duplication, intra-ID spectral distance, inconsistent labelsMixed up rows, misaligned metadata, Y assigned to wrong spectrumPartially covered by intra-ID repeatability; checks index/hash to add to the canonical pipeline.
Information leak / poorly split repetitionsGroupKFold by sample_id vs StratifiedKFold random; auditing partitions by sample_idArtificially good performance due to repetitionsRequires splits and benchmark model; not calculated by the descriptive card.
Label bugsClose samples in X but different Y, confident learning, systematic FP/FN errorsInverted Y, input errors, ambiguous classesRequires Y and/or model; recommended for supervised explorer.
Hidden subdomainsPCA/UMAP/t-SNE + unsupervised clustering + association with dataset/Y/date/operatorBatches, campaigns, probes, backgrounds not providedPartially calculated by PCA and distance ratio kNN (not LOF); UMAP/t-SNE outside the static card.
Unknown localized artifactsWavelength x dataset map: mean difference, variance difference, KS per wavelengthUnanticipated anomalous spectral regionsTo be calculated at the bank level when several datasets share a spectral axis.
Instrumental breaksDiscontinuities in derivatives, changepoint detectionSplice, detector connection, local jump not providedCalculated by jump/spike rates; more advanced changepoint to add.
Spectral mixing/contaminationNMF / unmixing / reconstruction by convex hullExternal component: background, plastic, soilNot calculated automatically; requires component assumptions or large library.
Unstable but predictive featuresModel importance vs. QC instability by variableModel that learns an artifact rather than a biological signalRequires supervised model; recommended for benchmark reporting.

Curated scientific variables

Scientific roles derived from the source contract. M denotes experimental metadata; Y denotes informative response variables. Reading guide

1 experimental metadata fields (M) · 1 informative targets (Y).

VariableRoleDistinct valuesCompleteRange / balance
measurement_timeM3100%IR 1
pvy_strainY4100%0 – 3

Identifiers and sampling groups remain available separately. Statistics count exported observations.

Variables

Declared prediction targets and recorded metadata, with distributions and missing-value counts. Identifiers are not automatically prediction targets. Reading guide

Targets 1

PVY_Strain

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

PVY_Strain distribution02040600 – 0.125: 600.125 – 0.25: 00.25 – 0.375: 00.375 – 0.5: 00.5 – 0.625: 00.625 – 0.75: 00.75 – 0.875: 00.875 – 1: 01 – 1.125: 601.125 – 1.25: 01.25 – 1.375: 01.375 – 1.5: 01.5 – 1.625: 01.625 – 1.75: 01.75 – 1.875: 01.875 – 2: 02 – 2.125: 602.125 – 2.25: 02.25 – 2.375: 02.375 – 2.5: 02.5 – 2.625: 02.625 – 2.75: 02.75 – 2.875: 02.875 – 3: 600123
n / missing
?Recorded observation count and number of missing values for this variable.
240 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
1.5 ± 1.12
Median
?50th percentile: half the non-missing values lie at or below it.
1.5
Range
?Smallest and largest recorded non-missing values.
0 – 3
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.747
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0 / -1.4
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Metadata 3

Plant_ID

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Plant_ID classesP56P56: 33P77P77: 33P54P54: 33P42P42: 33P48P48: 33P43P43: 33P60P60: 33P61P61: 33P62P62: 33P63P63: 33+10 more+10 more: 3030
n / missing
?Recorded observation count and number of missing values for this variable.
240 / 0
Classes
?Number of distinct recorded category values.
80
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.
P56 (3)

idstr

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

idstr classes56_mean56_mean: 3377_mean77_mean: 3354_mean54_mean: 3342_mean42_mean: 3348_mean48_mean: 3343_mean43_mean: 3360_mean60_mean: 3361_mean61_mean: 3362_mean62_mean: 3363_mean63_mean: 33+10 more+10 more: 3030
n / missing
?Recorded observation count and number of missing values for this variable.
240 / 0
Classes
?Number of distinct recorded category values.
80
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.
56_mean (3)

measurement_time

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

measurement_time classestime1time1: 8080time2time2: 8080time4time4: 8080
n / missing
?Recorded observation count and number of missing values for this variable.
240 / 0
Classes
?Number of distinct recorded category values.
3
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.
time1 (80)
Constant metadata 12

Fields with one recorded value provide context for every observation; they have no distribution to compare.

  • SpectralRep1
  • datasetEcoSIS
  • n_points_original2,151
  • dimensions1D
  • axis_notewavelengths_as_provided_by_EcoSIS_resource
  • official_data_urlhttps://data.ecosis.org/dataset/potato-virus-y-detection-in-solanum-tuberosum--greenhouse-data-
  • official_package_urlhttps://ecosis.org/package/potato-virus-y-detection-in-solanum-tuberosum--greenhouse-data-
  • licenseOther (Public Domain)
  • rights_statusexplicit_open
  • usage_scopepublic_reuse_possible
  • public_release_allowedTrue
  • notesTask-specific EcoSIS target not present in active parent Y table, wavelengths kept as provided by EcoSIS resource, no project interpolation.

Alignment

How observations join to physical sample IDs. Multiple exported rows are not necessarily independent samples or technical repetitions. Reading guide

Alignment levelsample
Sample id availableyes
Samples80
Observations (total)240
Exported observations per unitmin 3 · mean 3 · max 3
Identity statusavailable
Documented technical repetitionsND
Source unitMultiplicity of the declared identifier; protocol not established

Splits

Partitions supplied by the original dataset. They are documented here and are not applied automatically when loading. Reading guide

No original partition summaries are recorded. Loading does not create a train/test split.

Load this dataset

Use the appropriate route below: an assembled dataset, original raw resources, or the documented access procedure. Reading guide

# An open original source is documented in Provenance & citation.
# Automatic retrieval is not implemented for this source yet.
# Follow the original source links to obtain its files.

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

Read the documentation

Access and rights are separate

Start with provenance and governance at the top of a card. Provenance links to the original resources and publications. Governance records the license, redistribution conditions and content identity.

Open source access means an open original-source route is recorded. Restricted redistribution is the display label for the catalog's private tier: rights are restricted or not cleared for redistribution. It does not mean that the original source is necessarily private.

Raw reproducible identifies a retrieval route. Assembly incomplete means raw resources can be available while dataset-specific conversion is missing. The loading example uses retrieve(id, prepare=False) for these raw downloads; it returns resource metadata rather than X/Y arrays. A complete original-source recipe uses get(id). Two Harvard datasets require explicit guestbook answers.

Token required means no open automatic route is recorded. A Dataverse token works only once the canonical data have a configured hosted DOI. Manual access requires the original provider's documented steps. Availability labels describe the catalog routes, not a fresh live check of every URL.

Why is PCA on a dataset card?

Spectra have many correlated wavelengths. Principal component analysis (PCA) compresses their variation into a few orthogonal directions, called principal components (PCs). It is an exploratory summary of X, calculated without using the target Y.

The score plot places each plotted observation on PC1 and PC2. Nearby dots have similar projections; groups can reveal batches, instruments or natural subpopulations. Axis percentages show explained spectral variance. Large sources are sampled to at most 800 observations. Similar positions in two dimensions do not guarantee similar full spectra; an isolated dot does not prove an acquisition fault.

The scree plot reports spectral variance per component, helping you see whether variation is concentrated in a few dimensions. It is not explained target variance or predictive accuracy. The scree projection and score-plot projection may use different row samples; their percentages need not match.

Q / SPE measures residual variation outside the retained PCA subspace. Hotelling T² measures an observation's scaled distance within that subspace; Mahalanobis H summarizes PCA-space distance. The displayed p95/median ratios compare the distribution's upper tail with its center, without an absolute rejection threshold.

Historical profiles replace missing cells with column medians before PCA, center channels, and use bounded row/component counts. Loadings and distances depend on signal units and preprocessing. Compare compatible sources and inspect the original spectra before acting.

Scores, evidence and missing values

Read measured values first. An alert score maps selected metrics to a heuristic 0–1 scale. The display bands are weak below 0.42, medium from 0.42 to below 0.72, and strong from 0.72. They are not universally validated pass/fail thresholds.

The radar's center is 0 and its outer ring is 1: outward means a stronger anomaly or heterogeneity signal for that axis. Its table gives values rounded to two decimals and explains each axis. Missing axes are left blank; partial profiles have no filled polygon. The mean profile score averages only available axes and is not an overall quality grade.

Diagnostic bars rank possible explanations from heuristic signatures. A score of 0.78 does not mean a 78% causal probability. Evidence coverage is the proportion of required signals available, not confidence in the cause. A dash or “unavailable” means the quantity could not be computed; zero is an actual recorded value.

RMS (root mean square distance) and SAM (Spectral Angle Mapper, in radians) use this dataset's mean spectrum as their reference. They do not compare with an independently acquired white or dark reference. Repeated IDs require a documented protocol before within-ID spread can be called technical repeatability.

X–Y plots show Pearson correlation within this dataset. They identify linear associations; they are not cross-validated predictions and do not establish causal relationships. Targets, metadata, observation counts and physical sample counts have different meanings.

Open the full reading guide