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Harvard Dataverse NIR spectra of Plasmodium falciparum infected Anopheles gambiae

harvard · NIR

Harvard Dataverse NIR spectra of Plasmodium falciparum infected Anopheles gambiae. Origin database composed from 2 standardized v2.0 leaves: 1 spectral source(s), 1 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2harvard
—
samples
2,151
wavelengths
1
sources
1
targets
8
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

ContributorMalaria Anopheles gambiae oocyst NIR
Conversion statusok
Origin · dataverse [open]10.7910/DVN/YD34OX — Harvard Dataverse

Governance & integrity

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

Tierpublic
LicenseCC-BY-4.0
Permitted useResearch and benchmarking.
Access policyOpen per source license.
RedistributionRights retained from source metadata; review before public redistribution.
Content version1.1.0
Schema / protocol2.0
Content hash5c4185984d596f73…
Processing hash21a1363144ceaf57…
Metadata hash508a52d7e9393eed…

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 score0.57
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.

Harvard Dataverse NIR spectra of Plasmodium falciparum infected Anopheles gambiae property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureHarvard Dataverse NIR spectra of Plasmodium falciparum infected Anopheles gambiae — integrity: 0.00Harvard Dataverse NIR spectra of Plasmodium falciparum infected Anopheles gambiae — noise: 0.00Harvard Dataverse NIR spectra of Plasmodium falciparum infected Anopheles gambiae — artefacts: 1.00Harvard Dataverse NIR spectra of Plasmodium falciparum infected Anopheles gambiae — baseline: 0.41Harvard Dataverse NIR spectra of Plasmodium falciparum infected Anopheles gambiae — PCA outliers: 0.71Harvard Dataverse NIR spectra of Plasmodium falciparum infected Anopheles gambiae — reference: 1.00Harvard Dataverse NIR spectra of Plasmodium falciparum infected Anopheles gambiae — structure: 0.88Harvard Dataver…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.
0.41
PCA outliers
?Combines relative PCA residual and score-distance tails. Valid rare spectra can produce a high value.
0.71
Distance to average spectrum
?Dispersion around this dataset's mean spectrum, using distance, shape and shift summaries.
1.00
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.
0.88

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: 0.880.88Offset compatible with a different white referenceOffset compatible with a different white reference: 0.570.57Signature compatible with a population shiftSignature compatible with a population shift: 0.520.52Signature compatible with a different backgroundSignature compatible with a different background: 0.500.50High spectral roughness signatureHigh spectral roughness signature: 0.390.39Signature compatible with clippingSignature compatible with clipping: 0.340.34Flat spectrum/weak signalFlat spectrum/weak signal: 0.140.14

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 spliceX0.88strong100 %Spike rate 1.00, Jump rate 1.00, RMS/SAM to mean spectrum 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.
Offset compatible with a different white referenceX0.57medium100 %RMS/SAM to mean spectrum 1.00, local artifacts 1.00, PCA Q 0.71Systematic 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 population shiftX0.52medium100 %RMS/SAM to mean spectrum 1.00, PCA structure 0.88, Mahalanobis / T2 0.48Different 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 a different backgroundX0.50medium100 %RMS/SAM to mean spectrum 1.00, PCA Q 0.71, Mahalanobis / T2 0.48Systematic 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.
High spectral roughness signatureX0.39weak100 %Spike rate 1.00, RMS/SAM to mean spectrum 1.00, PCA Q 0.71Weak 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.
Signature compatible with clippingX0.34weak100 %Jump rate 1.00, PCA Q 0.71, Baseline/mean/area 0.41Detector 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.
Flat spectrum/weak signalX0.14weak100 %PCA Q 0.71Bad 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—unknown90 %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.

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

malaria_nir

X · NIR · NIR

Read the signal and variation summaries for this source. Axis unit: none; signal representation: not recorded.

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.

malaria_nir spectra0.00.51.01.52.001,0002,0003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / none350none — median 0.9656 (q25–q75 0.7679–1.275)365none — median 0.9188 (q25–q75 0.7141–1.174)381none — median 0.8961 (q25–q75 0.7109–1.107)396none — median 0.8867 (q25–q75 0.7051–1.098)412none — median 0.8829 (q25–q75 0.7028–1.087)427none — median 0.8739 (q25–q75 0.6935–1.075)443none — median 0.8641 (q25–q75 0.6872–1.062)458none — median 0.8599 (q25–q75 0.6839–1.051)474none — median 0.8545 (q25–q75 0.6764–1.041)489none — median 0.8464 (q25–q75 0.6682–1.024)505none — median 0.8359 (q25–q75 0.6607–1.006)520none — median 0.8283 (q25–q75 0.6556–0.9947)536none — median 0.8186 (q25–q75 0.65–0.9821)551none — median 0.8103 (q25–q75 0.6466–0.9722)567none — median 0.8017 (q25–q75 0.6454–0.9638)582none — median 0.7903 (q25–q75 0.6388–0.9491)597none — median 0.7833 (q25–q75 0.6354–0.9407)613none — median 0.7741 (q25–q75 0.6302–0.9251)628none — median 0.7622 (q25–q75 0.6235–0.9103)644none — median 0.7492 (q25–q75 0.6149–0.8958)659none — median 0.7398 (q25–q75 0.6065–0.8798)675none — median 0.7278 (q25–q75 0.5976–0.8629)690none — median 0.7188 (q25–q75 0.5903–0.8474)706none — median 0.7063 (q25–q75 0.5815–0.8278)721none — median 0.6894 (q25–q75 0.5727–0.8095)737none — median 0.6758 (q25–q75 0.5643–0.789)752none — median 0.6609 (q25–q75 0.5549–0.7691)768none — median 0.6459 (q25–q75 0.5422–0.7487)783none — median 0.6298 (q25–q75 0.5319–0.7305)799none — median 0.6167 (q25–q75 0.5213–0.7104)814none — median 0.6026 (q25–q75 0.5101–0.6955)829none — median 0.5881 (q25–q75 0.5016–0.6763)845none — median 0.5766 (q25–q75 0.4914–0.6573)860none — median 0.5648 (q25–q75 0.4818–0.6395)876none — median 0.5517 (q25–q75 0.471–0.6223)891none — median 0.5401 (q25–q75 0.4627–0.6097)907none — median 0.5304 (q25–q75 0.4541–0.5952)922none — median 0.5185 (q25–q75 0.443–0.5801)938none — median 0.5074 (q25–q75 0.4315–0.5678)953none — median 0.498 (q25–q75 0.4233–0.5578)969none — median 0.4881 (q25–q75 0.4155–0.5468)984none — median 0.4776 (q25–q75 0.4048–0.5329)1,000none — median 0.4633 (q25–q75 0.3951–0.5174)1,015none — median 0.4528 (q25–q75 0.3905–0.5051)1,031none — median 0.4435 (q25–q75 0.3823–0.4938)1,046none — median 0.4335 (q25–q75 0.3744–0.4836)1,062none — median 0.4224 (q25–q75 0.3656–0.4735)1,077none — median 0.4127 (q25–q75 0.3578–0.4645)1,092none — median 0.405 (q25–q75 0.3516–0.4576)1,108none — median 0.3988 (q25–q75 0.3462–0.4516)1,123none — median 0.3935 (q25–q75 0.3429–0.4471)1,139none — median 0.3972 (q25–q75 0.3451–0.4493)1,154none — median 0.4054 (q25–q75 0.3521–0.4559)1,170none — median 0.4051 (q25–q75 0.3513–0.4552)1,185none — median 0.403 (q25–q75 0.3478–0.4521)1,201none — median 0.3985 (q25–q75 0.3443–0.4477)1,216none — median 0.3898 (q25–q75 0.3383–0.44)1,232none — median 0.3781 (q25–q75 0.3315–0.4312)1,247none — median 0.3724 (q25–q75 0.326–0.4245)1,263none — median 0.3675 (q25–q75 0.3218–0.4188)1,278none — median 0.3645 (q25–q75 0.3189–0.415)1,294none — median 0.3644 (q25–q75 0.3188–0.413)1,309none — median 0.3682 (q25–q75 0.3219–0.4155)1,324none — median 0.3776 (q25–q75 0.3276–0.4269)1,340none — median 0.3945 (q25–q75 0.3422–0.4438)1,355none — median 0.4096 (q25–q75 0.3558–0.4642)1,371none — median 0.4315 (q25–q75 0.3726–0.4947)1,386none — median 0.5012 (q25–q75 0.4185–0.5757)1,402none — median 0.6103 (q25–q75 0.4986–0.7303)1,417none — median 0.6614 (q25–q75 0.5395–0.8156)1,433none — median 0.6762 (q25–q75 0.5509–0.8369)1,448none — median 0.6767 (q25–q75 0.5491–0.8348)1,464none — median 0.6703 (q25–q75 0.5416–0.8217)1,479none — median 0.6484 (q25–q75 0.5274–0.7931)1,495none — median 0.6221 (q25–q75 0.506–0.7502)1,510none — median 0.5922 (q25–q75 0.4847–0.7068)1,526none — median 0.5645 (q25–q75 0.4668–0.6631)1,541none — median 0.5401 (q25–q75 0.4486–0.6281)1,556none — median 0.5148 (q25–q75 0.4308–0.5949)1,572none — median 0.4935 (q25–q75 0.4148–0.5709)1,587none — median 0.4815 (q25–q75 0.4065–0.5522)1,603none — median 0.4643 (q25–q75 0.3949–0.5349)1,618none — median 0.4535 (q25–q75 0.3871–0.522)1,634none — median 0.4453 (q25–q75 0.3815–0.51)1,649none — median 0.4384 (q25–q75 0.3769–0.5022)1,665none — median 0.436 (q25–q75 0.3763–0.5005)1,680none — median 0.4423 (q25–q75 0.3806–0.507)1,696none — median 0.4564 (q25–q75 0.3905–0.5234)1,711none — median 0.4682 (q25–q75 0.399–0.5371)1,727none — median 0.4814 (q25–q75 0.4076–0.5539)1,742none — median 0.4856 (q25–q75 0.4124–0.5579)1,758none — median 0.4981 (q25–q75 0.4221–0.5748)1,773none — median 0.5067 (q25–q75 0.4278–0.5855)1,788none — median 0.5114 (q25–q75 0.431–0.5933)1,804none — median 0.5312 (q25–q75 0.4456–0.6086)1,819none — median 0.526 (q25–q75 0.4433–0.606)1,835none — median 0.5269 (q25–q75 0.4446–0.6101)1,850none — median 0.5459 (q25–q75 0.4576–0.6343)1,866none — median 0.6111 (q25–q75 0.501–0.7305)1,881none — median 0.7152 (q25–q75 0.5776–0.8892)1,897none — median 0.8066 (q25–q75 0.6495–1.033)1,912none — median 0.8333 (q25–q75 0.6684–1.067)1,928none — median 0.8394 (q25–q75 0.6714–1.071)1,943none — median 0.8354 (q25–q75 0.6683–1.067)1,959none — median 0.8217 (q25–q75 0.6629–1.056)1,974none — median 0.8122 (q25–q75 0.6514–1.038)1,990none — median 0.7938 (q25–q75 0.6389–1.013)2,005none — median 0.7781 (q25–q75 0.6267–0.9876)2,021none — median 0.7643 (q25–q75 0.6141–0.9633)2,036none — median 0.7521 (q25–q75 0.6036–0.9467)2,051none — median 0.74 (q25–q75 0.593–0.9235)2,067none — median 0.7245 (q25–q75 0.5803–0.9046)2,082none — median 0.7097 (q25–q75 0.5699–0.8851)2,098none — median 0.6979 (q25–q75 0.5608–0.8652)2,113none — median 0.6884 (q25–q75 0.5512–0.8504)2,129none — median 0.6756 (q25–q75 0.5432–0.8401)2,144none — median 0.6718 (q25–q75 0.5388–0.8336)2,160none — median 0.67 (q25–q75 0.5369–0.8281)2,175none — median 0.6659 (q25–q75 0.5353–0.8232)2,191none — median 0.6604 (q25–q75 0.5309–0.8135)2,206none — median 0.6543 (q25–q75 0.5275–0.8041)2,222none — median 0.6509 (q25–q75 0.5262–0.8003)2,237none — median 0.6555 (q25–q75 0.5291–0.8069)2,253none — median 0.6712 (q25–q75 0.5396–0.8293)2,268none — median 0.6878 (q25–q75 0.5509–0.8492)2,283none — median 0.6986 (q25–q75 0.5603–0.8654)2,299none — median 0.7166 (q25–q75 0.5717–0.8898)2,314none — median 0.725 (q25–q75 0.5794–0.9027)2,330none — median 0.7308 (q25–q75 0.5822–0.9084)2,345none — median 0.7439 (q25–q75 0.5926–0.9316)2,361none — median 0.7508 (q25–q75 0.5966–0.942)2,376none — median 0.7594 (q25–q75 0.6042–0.9578)2,392none — median 0.7687 (q25–q75 0.6145–0.9799)2,407none — median 0.7792 (q25–q75 0.624–0.995)2,423none — median 0.7895 (q25–q75 0.6333–1.011)2,438none — median 0.7992 (q25–q75 0.6405–1.024)2,454none — median 0.8091 (q25–q75 0.6518–1.04)2,469none — median 0.8213 (q25–q75 0.6574–1.055)2,485none — median 0.8288 (q25–q75 0.6609–1.063)2,500none — median 0.8332 (q25–q75 0.6717–1.072)

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 none
Mean spacing
?Average difference between adjacent spectral-axis coordinates; compare only within the same unit.
1 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.
562

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.
0.209 – 10
Mean range
?Minimum and maximum of the mean spectrum across spectral channels.
0.369 – 1.17
Mean level
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations.
0.6326
Area
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit.
1360
PTP
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum.
0.7963
Noise RMS
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise.
3.8611e-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.
1.6e+04
Roughness S/N proxy dB
?20 log10 of the roughness signal-to-noise proxy. Units are decibels; compare only compatible signal representations.
8e+01 dB
Dynamic range
?Maximum minus minimum of the mean spectrum. Its magnitude depends on the signal representation.
0.796
Smoothness
?Mean per-spectrum RMS of second differences, in signal units. Smaller values indicate smoother spectra under this protocol.
0.008453
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.
292

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.
80,727
Spike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage.
6.68%
Jump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins.
34,897
Jump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage.
2.89%
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.
0.027749
Curvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation.
0.0035411
D1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts.
0.0030032
RMS to mean
?Root mean square distance to this dataset's mean spectrum, in signal units.
0.13613
RMS p95
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value.
0.28458
SAM to mean
?Spectral Angle Mapper: angle to the mean spectrum, in radians. Smaller angles indicate more similar shapes.
0.062956
SAM p95
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS.
0.13303
Affine offset p95
?95th percentile of absolute fitted offsets when matching each spectrum to the dataset mean.
0.30324
Affine gain p95 Δ
?95th percentile of the absolute gain deviation from 1 in the fit to the mean spectrum.
0.74756
Affine residual p95
?95th percentile of residual RMS after gain and offset correction to the mean spectrum.
0.058442
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.
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.
3.8
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.
0

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.
1.5
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.
99.9%
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		0.632620.41weakToo 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		1359.70.41weakNormalProbe 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		0.796340.00weakStrong 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		0.0554640.00weakNormal 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		3.8611e-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		163840.00weakGood 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		34.4690.12weakReliable 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		80,7271.00strongArtifactsCosmic 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		6.68%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		34,8971.00strongDetector 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		2.89%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		0.00116%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		0.0277490.07weakStableIlluminationlinear 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		0.00354110.44mediumUnusual shapeBackground, 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		0.00300320.08weakFlatBiology 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		5.68250.71mediumAtypical spectrumArtifact, 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		3.75010.47mediumExtreme 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		1.93650.48mediumGlobal 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		0.284581.00strongDifferent spectrumDomain 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		0.133030.38weakSimilarBackground, 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		——not calculableNot enough information to score this metric on this dataset.Positioningmedian 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		——not calculableNot enough information to score this metric on this dataset.Acquisitionmedian 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		——not calculableNot enough information to score this metric on this dataset.Operatormedian 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		1.9770.88strongSubpopulationsDifferent 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		2.64010.82strongIsolated 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		0.58180.88strongAtypical spectrumVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent

PCA score plot — explore spectral variation

Source malaria_nir: 562 of 562 source observations are plotted. PC1 captures 91.2% and PC2 4.2% of the reported spectral variance: 95.3% together; 4.7% 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.

malaria_nir PCA score plot-20-1001020-5.0-2.50.02.55.07.5PC1 -0.8482 · PC2 5.844PC1 -10.71 · PC2 0.9197PC1 -11.3 · PC2 -1.238PC1 -0.9241 · PC2 4.796PC1 -9.326 · PC2 -2.624PC1 12.28 · PC2 0.07238PC1 -10.61 · PC2 -1.438PC1 -1.93 · PC2 1.501PC1 1.338 · PC2 -1.035PC1 -6.827 · PC2 0.3615PC1 -8.132 · PC2 -0.5257PC1 -4.457 · PC2 -1.035PC1 -5.694 · PC2 -2.275PC1 10.96 · PC2 -1.728PC1 -6.676 · PC2 0.5394PC1 6.727 · PC2 -0.5313PC1 -1.537 · PC2 0.3911PC1 -0.9433 · PC2 2.919PC1 -11.61 · PC2 -2.083PC1 -11.23 · PC2 -2.06PC1 -6.067 · PC2 -0.09375PC1 -8.122 · PC2 0.7848PC1 7.985 · PC2 0.3903PC1 -10.72 · PC2 -1.051PC1 1.978 · PC2 0.5228PC1 1.904 · PC2 -0.7933PC1 -4.895 · PC2 -0.1696PC1 9.92 · PC2 -0.8062PC1 3.007 · PC2 -0.7578PC1 -8.333 · PC2 -2.257PC1 -12.92 · PC2 -2.476PC1 -9.218 · PC2 -0.1076PC1 2.436 · PC2 1.165PC1 -10.55 · PC2 -2.302PC1 -7.433 · PC2 3.562PC1 -6.272 · PC2 -1.364PC1 -2.773 · PC2 -2.414PC1 -1.548 · PC2 -0.6012PC1 -1.558 · PC2 -0.2254PC1 6.661 · PC2 -1.75PC1 -10.85 · PC2 -2.517PC1 -2.021 · PC2 -1.789PC1 -8.073 · PC2 -1.856PC1 -7.598 · PC2 -2.777PC1 -15.84 · PC2 -3.578PC1 9.627 · PC2 -1.483PC1 3.159 · PC2 -0.1493PC1 11.82 · PC2 -1.622PC1 5.905 · PC2 1.347PC1 10.79 · PC2 -1.218PC1 2.136 · PC2 -0.1673PC1 -7.628 · PC2 -1.667PC1 1.711 · PC2 0.1852PC1 -3.37 · PC2 -0.3072PC1 0.6093 · PC2 -0.9886PC1 -1.945 · PC2 -0.6316PC1 -7.645 · PC2 -2.102PC1 2.097 · PC2 -0.9273PC1 -0.663 · PC2 -1.081PC1 1.925 · PC2 1.505PC1 2.825 · PC2 1.271PC1 -0.3432 · PC2 -1.876PC1 -6.974 · PC2 -1.041PC1 -10.41 · PC2 -2.46PC1 -4.981 · PC2 -0.6669PC1 -11.81 · PC2 -1.449PC1 -1.628 · PC2 -1.365PC1 -4.584 · PC2 0.6739PC1 10.05 · PC2 -0.821PC1 3.093 · PC2 -1.589PC1 3.379 · PC2 2.519PC1 -5.499 · PC2 -1.307PC1 9.533 · PC2 0.2198PC1 4.42 · PC2 0.4881PC1 -7.366 · PC2 -2.113PC1 2.606 · PC2 -0.5925PC1 -5.339 · PC2 -0.9035PC1 -2.9 · PC2 -0.2852PC1 -6.042 · PC2 -2.128PC1 -9.396 · PC2 -1.803PC1 -1.038 · PC2 -1.046PC1 -10.83 · PC2 1.753PC1 -6.422 · PC2 0.639PC1 -0.1302 · PC2 -0.6612PC1 -0.5977 · PC2 -1.802PC1 -10.41 · PC2 -0.1875PC1 3.731 · PC2 -0.837PC1 -14.9 · PC2 -3.133PC1 -6.38 · PC2 -0.06988PC1 -4.16 · PC2 0.1284PC1 -3.178 · PC2 -0.4642PC1 7.298 · PC2 -0.05441PC1 -9.337 · PC2 -2.529PC1 13.09 · PC2 -1.217PC1 1.784 · PC2 -1.13PC1 4.573 · PC2 0.6212PC1 -5.612 · PC2 2.167PC1 -7.504 · PC2 -0.2633PC1 -16.84 · PC2 2.769PC1 5.637 · PC2 -0.764PC1 -1.595 · PC2 3.595PC1 -11.02 · PC2 4.504PC1 -7.414 · PC2 -1.206PC1 -16.93 · PC2 1.162PC1 -8.301 · PC2 1.772PC1 -0.325 · PC2 0.9354PC1 -9.402 · PC2 -0.5626PC1 7.294 · PC2 1.158PC1 11.39 · PC2 0.7452PC1 -3.196 · PC2 3.929PC1 -2.614 · PC2 -1.662PC1 -4.415 · PC2 -1.428PC1 -2.454 · PC2 0.6366PC1 -0.7111 · PC2 2.377PC1 -9.646 · PC2 1.161PC1 -9.183 · PC2 1.155PC1 3.438 · PC2 -0.5546PC1 -10.5 · PC2 1.646PC1 4.763 · PC2 0.5253PC1 4.537 · PC2 1.306PC1 1.917 · PC2 -1.801PC1 4.022 · PC2 1.003PC1 10.95 · PC2 -1.254PC1 3.932 · PC2 0.2704PC1 0.9001 · PC2 -2.044PC1 2.981 · PC2 0.7015PC1 0.1875 · PC2 2.309PC1 -2.702 · PC2 -0.9507PC1 -2.999 · PC2 1.284PC1 1.667 · PC2 -0.7176PC1 -3.597 · PC2 -1.573PC1 -13.1 · PC2 0.4953PC1 -12.39 · PC2 -2.615PC1 -12.22 · PC2 0.2831PC1 -0.9993 · PC2 2.133PC1 -4.737 · PC2 4.198PC1 5.997 · PC2 -1.278PC1 8.807 · PC2 0.2497PC1 2.359 · PC2 1.634PC1 2.415 · PC2 -0.8007PC1 -5.597 · PC2 -1.142PC1 6.529 · PC2 -0.7346PC1 -9.529 · PC2 1.647PC1 0.277 · PC2 1.152PC1 7.987 · PC2 0.1884PC1 -1.769 · PC2 -1.065PC1 -6.793 · PC2 1.983PC1 0.3829 · PC2 1.837PC1 4.279 · PC2 0.8793PC1 -3.07 · PC2 1.438PC1 -1.427 · PC2 1.554PC1 5.225 · PC2 -1.979PC1 -8.516 · PC2 -0.782PC1 6.383 · PC2 0.8017PC1 7.392 · PC2 0.686PC1 10.61 · PC2 -0.3105PC1 1.235 · PC2 1.817PC1 -5.966 · PC2 -0.3704PC1 5.447 · PC2 3.68PC1 -3.074 · PC2 0.1613PC1 -4.035 · PC2 0.5179PC1 3.66 · PC2 2.375PC1 -0.1836 · PC2 -1.108PC1 -0.9823 · PC2 0.7556PC1 -4.173 · PC2 2.437PC1 2.043 · PC2 0.5834PC1 -4.19 · PC2 1.655PC1 -3.361 · PC2 2.079PC1 8.481 · PC2 0.2458PC1 1.368 · PC2 -0.8279PC1 -4.849 · PC2 2.86PC1 2.31 · PC2 1.465PC1 5.511 · PC2 1.821PC1 -0.9018 · PC2 0.7403PC1 0.09027 · PC2 -2.245PC1 8.138 · PC2 0.9951PC1 -8.535 · PC2 -1.665PC1 4.145 · PC2 -1.538PC1 3.007 · PC2 3.49PC1 5.535 · PC2 0.2772PC1 2.497 · PC2 -0.8362PC1 -0.4958 · PC2 0.3624PC1 -8.74 · PC2 2.467PC1 -1.095 · PC2 2.786PC1 -0.2018 · PC2 3.456PC1 -5.296 · PC2 1.222PC1 2.792 · PC2 -1.086PC1 3.749 · PC2 1.515PC1 -0.5456 · PC2 2.004PC1 -15.74 · PC2 0.7972PC1 -10.22 · PC2 3.96PC1 -3.036 · PC2 3.09PC1 -4.76 · PC2 0.6319PC1 6.731 · PC2 -0.9994PC1 -6.365 · PC2 1.631PC1 -9.023 · PC2 -1.424PC1 -15.2 · PC2 2.683PC1 1.633 · PC2 1.425PC1 -5.81 · PC2 -0.5594PC1 -10.83 · PC2 -0.4247PC1 -9.916 · PC2 0.3965PC1 -6.512 · PC2 -1.602PC1 -6.831 · PC2 -0.8637PC1 9.909 · PC2 -0.4776PC1 -8.393 · PC2 -2.018PC1 0.6102 · PC2 1.757PC1 3.309 · PC2 -0.2369PC1 -9.292 · PC2 -1.471PC1 -10.66 · PC2 -1.961PC1 -4.997 · PC2 1.425PC1 -5.084 · PC2 -1.48PC1 2.472 · PC2 -0.9133PC1 -8.125 · PC2 -1.643PC1 -3.775 · PC2 -1.306PC1 -0.9024 · PC2 -1.33PC1 -7.168 · PC2 0.3717PC1 -14.2 · PC2 -2.736PC1 -0.4555 · PC2 0.4636PC1 -10.15 · PC2 -1.144PC1 3.748 · PC2 -0.2587PC1 -6.927 · PC2 -1.794PC1 3.164 · PC2 -0.6993PC1 -1.487 · PC2 2.25PC1 -11.93 · PC2 -2.67PC1 -9.399 · PC2 4.17PC1 -4.727 · PC2 0.4666PC1 -6.238 · PC2 2.831PC1 -7.742 · PC2 -0.5475PC1 -10.01 · PC2 0.7209PC1 6.144 · PC2 1.488PC1 0.2476 · PC2 1.152PC1 -9.661 · PC2 0.1175PC1 5.222 · PC2 -0.6901PC1 -3.938 · PC2 1.718PC1 7.04 · PC2 0.6974PC1 -2.593 · PC2 1.105PC1 -9.236 · PC2 -1.482PC1 -11.65 · PC2 0.5108PC1 -14.35 · PC2 -1.481PC1 -3.609 · PC2 0.1062PC1 -9.178 · PC2 1.824PC1 9.282 · PC2 -0.1225PC1 1.337 · PC2 1.396PC1 2.155 · PC2 0.6321PC1 -2.831 · PC2 2.344PC1 7.858 · PC2 -0.09011PC1 -9.112 · PC2 -0.4494PC1 -0.3492 · PC2 2.181PC1 9.636 · PC2 0.3616PC1 3.265 · PC2 0.4033PC1 -6.459 · PC2 -1.802PC1 9.933 · PC2 0.3066PC1 4.329 · PC2 1.277PC1 14.54 · PC2 -1.325PC1 5.08 · PC2 0.7366PC1 0.8432 · PC2 0.3308PC1 -11.18 · PC2 -2.419PC1 -5.134 · PC2 3.145PC1 8.674 · PC2 1.68PC1 -12.82 · PC2 6.218PC1 8.93 · PC2 -0.5059PC1 -1.599 · PC2 -0.05863PC1 -6.024 · PC2 0.4337PC1 3.097 · PC2 3.107PC1 -10.22 · PC2 1.155PC1 12.99 · PC2 -0.633PC1 -3.712 · PC2 -0.2603PC1 8.421 · PC2 0.1394PC1 4.924 · PC2 0.7321PC1 8.032 · PC2 -0.6871PC1 9.183 · PC2 0.2297PC1 -4.561 · PC2 -1.699PC1 -4.216 · PC2 0.4816PC1 -5.751 · PC2 3.477PC1 -0.8482 · PC2 5.844PC1 -10.71 · PC2 0.9197PC1 -11.3 · PC2 -1.238PC1 -0.9241 · PC2 4.796PC1 -9.326 · PC2 -2.624PC1 12.28 · PC2 0.07238PC1 -10.61 · PC2 -1.438PC1 -1.93 · PC2 1.501PC1 1.338 · PC2 -1.035PC1 -6.827 · PC2 0.3615PC1 -8.132 · PC2 -0.5257PC1 -4.457 · PC2 -1.035PC1 -5.694 · PC2 -2.275PC1 10.96 · PC2 -1.728PC1 -6.676 · PC2 0.5394PC1 6.727 · PC2 -0.5313PC1 -1.537 · PC2 0.3911PC1 -0.9433 · PC2 2.919PC1 -11.61 · PC2 -2.083PC1 -11.23 · PC2 -2.06PC1 -6.067 · PC2 -0.09375PC1 -8.122 · PC2 0.7848PC1 7.985 · PC2 0.3903PC1 -10.72 · PC2 -1.051PC1 1.978 · PC2 0.5228PC1 1.904 · PC2 -0.7933PC1 -4.895 · PC2 -0.1696PC1 9.92 · PC2 -0.8062PC1 3.007 · PC2 -0.7578PC1 -8.333 · PC2 -2.257PC1 -12.92 · PC2 -2.476PC1 -9.218 · PC2 -0.1076PC1 2.436 · PC2 1.165PC1 -10.55 · PC2 -2.302PC1 -7.433 · PC2 3.562PC1 -6.272 · PC2 -1.364PC1 -2.773 · PC2 -2.414PC1 -1.548 · PC2 -0.6012PC1 -1.558 · PC2 -0.2254PC1 6.661 · PC2 -1.75PC1 -10.85 · PC2 -2.517PC1 -2.021 · PC2 -1.789PC1 -8.073 · PC2 -1.856PC1 -7.598 · PC2 -2.777PC1 -15.84 · PC2 -3.578PC1 9.627 · PC2 -1.483PC1 3.159 · PC2 -0.1493PC1 11.82 · PC2 -1.622PC1 5.905 · PC2 1.347PC1 10.79 · PC2 -1.218PC1 2.136 · PC2 -0.1673PC1 -7.628 · PC2 -1.667PC1 1.711 · PC2 0.1852PC1 -3.37 · PC2 -0.3072PC1 0.6093 · PC2 -0.9886PC1 -1.945 · PC2 -0.6316PC1 -7.645 · PC2 -2.102PC1 2.097 · PC2 -0.9273PC1 -0.663 · PC2 -1.081PC1 4.838 · PC2 -0.4394PC1 8.969 · PC2 -0.1159PC1 3.484 · PC2 -2.636PC1 4.481 · PC2 -0.1646PC1 1.491 · PC2 0.536PC1 8.922 · PC2 0.4543PC1 -1.184 · PC2 2.883PC1 -12.19 · PC2 -0.2691PC1 -15.1 · PC2 1.737PC1 2.675 · PC2 1.157PC1 0.194 · PC2 1.22PC1 5.673 · PC2 0.1548PC1 9.738 · PC2 -1.754PC1 2.083 · PC2 -2.476PC1 3.086 · PC2 0.8808PC1 8.74 · PC2 0.1131PC1 -0.8671 · PC2 -1.082PC1 -2.388 · PC2 0.5463PC1 2.161 · PC2 0.06815PC1 9.772 · PC2 0.0288PC1 8.05 · PC2 0.8128PC1 5.803 · PC2 0.06138PC1 10.41 · PC2 -0.5241PC1 -0.6307 · PC2 1.554PC1 6.139 · PC2 -0.5069PC1 -2.642 · PC2 -1.88PC1 -0.3209 · PC2 1.236PC1 0.775 · PC2 0.07215PC1 -10.33 · PC2 -0.3431PC1 2.804 · PC2 1.348PC1 10.06 · PC2 -0.2384PC1 -2.971 · PC2 0.8398PC1 1.731 · PC2 0.5329PC1 -2.267 · PC2 0.7795PC1 10.17 · PC2 -0.4313PC1 2.387 · PC2 0.3291PC1 9.053 · PC2 0.01136PC1 5.842 · PC2 -0.619PC1 12.64 · PC2 -1.126PC1 1.698 · PC2 -1.238PC1 6.099 · PC2 -0.7782PC1 3.437 · PC2 -1.272PC1 13.8 · PC2 -0.8959PC1 6.757 · PC2 -1.451PC1 -1.248 · PC2 1.746PC1 -11.31 · PC2 0.6574PC1 10.95 · PC2 0.7831PC1 -8.493 · PC2 0.1165PC1 9.738 · PC2 -1.316PC1 -9.672 · PC2 0.2611PC1 2.53 · PC2 -0.01653PC1 -0.226 · PC2 -2.072PC1 5.792 · PC2 -2.241PC1 6.234 · PC2 0.4464PC1 0.3603 · PC2 -1.9PC1 7.801 · PC2 -0.6301PC1 10.24 · PC2 -1.385PC1 -2.716 · PC2 -0.9087PC1 9.872 · PC2 0.01049PC1 10.11 · PC2 -1.847PC1 4.16 · PC2 -0.747PC1 8.789 · PC2 0.4368PC1 4.818 · PC2 0.5843PC1 6.05 · PC2 -0.3085PC1 8.837 · PC2 0.4466PC1 -8.681 · PC2 -0.33PC1 7.003 · PC2 1.178PC1 6.412 · PC2 0.2793PC1 10.69 · PC2 0.4221PC1 15.63 · PC2 -1.371PC1 3 · PC2 0.8262PC1 13.52 · PC2 -1.787PC1 6.482 · PC2 -0.3825PC1 9.727 · PC2 0.9692PC1 11.31 · PC2 -0.706PC1 5.488 · PC2 -1.42PC1 12.22 · PC2 -0.8388PC1 3.371 · PC2 -1.2PC1 1.856 · PC2 -2.128PC1 6.902 · PC2 0.9248PC1 13.69 · PC2 -1.465PC1 10.14 · PC2 0.4474PC1 1.299 · PC2 -0.1634PC1 8.899 · PC2 2.154PC1 -10.46 · PC2 -3.039PC1 -10.71 · PC2 -2.328PC1 8.602 · PC2 0.8627PC1 -1.731 · PC2 1.729PC1 5.326 · PC2 0.7579PC1 -6.272 · PC2 2.389PC1 -1.685 · PC2 1.581PC1 -7.742 · PC2 -0.5475PC1 -10.01 · PC2 0.7209PC1 6.144 · PC2 1.488PC1 0.2476 · PC2 1.152PC1 -9.661 · PC2 0.1175PC1 5.222 · PC2 -0.6901PC1 -3.938 · PC2 1.718PC1 7.04 · PC2 0.6974PC1 -2.593 · PC2 1.105PC1 -9.236 · PC2 -1.482PC1 -11.65 · PC2 0.5108PC1 -14.35 · PC2 -1.481PC1 -3.609 · PC2 0.1062PC1 -9.178 · PC2 1.824PC1 9.282 · PC2 -0.1225PC1 1.337 · PC2 1.396PC1 2.155 · PC2 0.6321PC1 -2.831 · PC2 2.344PC1 7.858 · PC2 -0.09011PC1 -9.112 · PC2 -0.4494PC1 -0.3492 · PC2 2.181PC1 9.636 · PC2 0.3616PC1 3.265 · PC2 0.4033PC1 -6.459 · PC2 -1.802PC1 9.933 · PC2 0.3066PC1 4.329 · PC2 1.277PC1 14.54 · PC2 -1.325PC1 5.08 · PC2 0.7366PC1 0.8432 · PC2 0.3308PC1 -11.18 · PC2 -2.419PC1 -5.134 · PC2 3.145PC1 8.674 · PC2 1.68PC1 -12.82 · PC2 6.218PC1 8.93 · PC2 -0.5059PC1 -1.599 · PC2 -0.05863PC1 -6.024 · PC2 0.4337PC1 3.097 · PC2 3.107PC1 -10.22 · PC2 1.155PC1 12.99 · PC2 -0.633PC1 -3.712 · PC2 -0.2603PC1 8.421 · PC2 0.1394PC1 4.924 · PC2 0.7321PC1 8.032 · PC2 -0.6871PC1 9.183 · PC2 0.2297PC1 -4.561 · PC2 -1.699PC1 -4.216 · PC2 0.4816PC1 -5.751 · PC2 3.477PC1 4.838 · PC2 -0.4394PC1 8.969 · PC2 -0.1159PC1 3.484 · PC2 -2.636PC1 4.481 · PC2 -0.1646PC1 1.491 · PC2 0.536PC1 8.922 · PC2 0.4543PC1 -1.184 · PC2 2.883PC1 -12.19 · PC2 -0.2691PC1 -15.1 · PC2 1.737PC1 2.675 · PC2 1.157PC1 0.194 · PC2 1.22PC1 5.673 · PC2 0.1548PC1 9.738 · PC2 -1.754PC1 2.083 · PC2 -2.476PC1 3.086 · PC2 0.8808PC1 8.74 · PC2 0.1131PC1 -0.8671 · PC2 -1.082PC1 -2.388 · PC2 0.5463PC1 2.161 · PC2 0.06815PC1 9.772 · PC2 0.0288PC1 8.05 · PC2 0.8128PC1 5.803 · PC2 0.06138PC1 10.41 · PC2 -0.5241PC1 -0.6307 · PC2 1.554PC1 6.139 · PC2 -0.5069PC1 -2.642 · PC2 -1.88PC1 -0.3209 · PC2 1.236PC1 0.775 · PC2 0.07215PC1 -10.33 · PC2 -0.3431PC1 2.804 · PC2 1.348PC1 10.06 · PC2 -0.2384PC1 -2.971 · PC2 0.8398PC1 1.731 · PC2 0.5329PC1 -2.267 · PC2 0.7795PC1 10.17 · PC2 -0.4313PC1 2.387 · PC2 0.3291PC1 9.053 · PC2 0.01136PC1 5.842 · PC2 -0.619PC1 12.64 · PC2 -1.126PC1 1.698 · PC2 -1.238PC1 6.099 · PC2 -0.7782PC1 3.437 · PC2 -1.272PC1 13.8 · PC2 -0.8959PC1 6.757 · PC2 -1.451PC1 -1.248 · PC2 1.746PC1 -11.31 · PC2 0.6574PC1 10.95 · PC2 0.7831PC1 -8.493 · PC2 0.1165PC1 9.738 · PC2 -1.316PC1 -9.672 · PC2 0.2611PC1 2.53 · PC2 -0.01653PC1 -0.226 · PC2 -2.072PC1 5.792 · PC2 -2.241PC1 6.234 · PC2 0.4464PC1 0.3603 · PC2 -1.9PC1 7.801 · PC2 -0.6301PC1 10.24 · PC2 -1.385PC1 -2.716 · PC2 -0.9087PC1 9.872 · PC2 0.01049PC1 10.11 · PC2 -1.847PC1 4.16 · PC2 -0.747PC1 8.789 · PC2 0.4368PC1 4.818 · PC2 0.5843PC1 6.05 · PC2 -0.3085PC1 8.837 · PC2 0.4466PC1 -8.681 · PC2 -0.33PC1 7.003 · PC2 1.178PC1 6.412 · PC2 0.2793PC1 10.69 · PC2 0.4221PC1 15.63 · PC2 -1.371PC1 3 · PC2 0.8262PC1 13.52 · PC2 -1.787PC1 6.482 · PC2 -0.3825PC1 9.727 · PC2 0.9692PC1 11.31 · PC2 -0.706PC1 5.488 · PC2 -1.42PC1 12.22 · PC2 -0.8388PC1 3.371 · PC2 -1.2PC1 1.856 · PC2 -2.128PC1 6.902 · PC2 0.9248PC1 13.69 · PC2 -1.465PC1 10.14 · PC2 0.4474PC1 1.299 · PC2 -0.1634PC1 8.899 · PC2 2.154PC1 -10.46 · PC2 -3.039PC1 -10.71 · PC2 -2.328PC1 8.602 · PC2 0.8627PC1 -1.731 · PC2 1.729PC1 5.326 · PC2 0.7579PC1 -6.272 · PC2 2.389PC1 -1.685 · PC2 1.581PC1 (91.2%)PC2 (4.2%)562 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
1-0.848215.8437
2-10.7080.9197
3-11.297-1.2375
4-0.92414.7959
5-9.3263-2.6245
612.2830.072383
7-10.608-1.438
8-1.92961.5009
91.3383-1.0349
10-6.82670.36147
11-8.1323-0.52574
12-4.4569-1.0349
13-5.6944-2.2745
1410.957-1.7279
15-6.67630.53938
166.7271-0.53133
17-1.53690.39113
18-0.943332.9194
19-11.611-2.0828
20-11.23-2.0596
21-6.0671-0.09375
22-8.12230.78478
237.98480.3903
24-10.722-1.0513
251.97850.5228
261.9036-0.79329
27-4.8951-0.1696
289.9199-0.8062
293.0073-0.75784
30-8.3325-2.2572
31-12.924-2.4763
32-9.2179-0.10763
332.43631.1652
34-10.553-2.302
35-7.43283.562
36-6.2722-1.3643
37-2.7729-2.4137
38-1.5476-0.6012
39-1.558-0.22537
406.6606-1.7502
41-10.853-2.5171
42-2.021-1.7889
43-8.0735-1.8562
44-7.5981-2.7772
45-15.839-3.5779
469.6274-1.4834
473.1594-0.14927
4811.82-1.622
495.90461.3466
5010.789-1.2182
512.136-0.16732
52-7.6281-1.6673
531.7110.18522
54-3.37-0.30716
550.60932-0.98862
56-1.9453-0.6316
57-7.6451-2.1015
582.0972-0.92728
59-0.66299-1.0813
601.92481.5046
612.82521.2706
62-0.34325-1.8759
63-6.9737-1.0413
64-10.409-2.4601
65-4.9811-0.66688
66-11.813-1.4491
67-1.6285-1.3653
68-4.5840.67392
6910.05-0.82097
703.0934-1.5888
713.37872.519
72-5.4992-1.307
739.53290.21979
744.42010.48813
75-7.3659-2.1127
762.6056-0.59254
77-5.3393-0.90352
78-2.9003-0.28516
79-6.0424-2.1279
80-9.3959-1.8031
81-1.0381-1.0458
82-10.831.753
83-6.42150.63905
84-0.13022-0.66117
85-0.59772-1.8019
86-10.413-0.18747
873.7313-0.837
88-14.9-3.1328
89-6.38-0.069876
90-4.16030.12838
91-3.1784-0.4642
927.2977-0.054415
93-9.3373-2.5291
9413.085-1.217
951.7836-1.1301
964.57320.62124
97-5.61242.167
98-7.5036-0.26328
99-16.8372.7693
1005.6368-0.76402
101-1.59483.595
102-11.024.504
103-7.4135-1.2057
104-16.9331.1616
105-8.30081.7721
106-0.325040.93538
107-9.4023-0.56259
1087.29421.1582
10911.3880.74521
110-3.19623.9289
111-2.6144-1.6625
112-4.4148-1.4283
113-2.4540.63661
114-0.711092.3769
115-9.64621.1608
116-9.18331.155
1173.4382-0.55463
118-10.4991.6464
1194.76260.52527
1204.53741.3061
1211.9166-1.8011
1224.02171.0027
12310.952-1.254
1243.93180.27037
1250.9001-2.0435
1262.9810.70152
1270.187542.3093
128-2.7018-0.95071
129-2.9991.2839
1301.667-0.71763
131-3.5972-1.573
132-13.0980.49525
133-12.387-2.6153
134-12.220.28308
135-0.999342.1335
136-4.73724.198
1375.9965-1.2777
1388.80660.24965
1392.35871.6344
1402.4149-0.80067
141-5.597-1.1422
1426.5294-0.73459
143-9.52871.6467
1440.277011.1525
1457.98750.18837
146-1.7688-1.065
147-6.79311.9828
1480.382941.8374
1494.27880.87929
150-3.07031.4379
151-1.4271.5541
1525.225-1.9787
153-8.5158-0.78199
1546.3830.80167
1557.39170.68599
15610.609-0.31046
1571.23521.8175
158-5.9665-0.37036
1595.44713.6802
160-3.07380.1613
161-4.03530.5179
1623.662.3755
163-0.1836-1.1085
164-0.982350.75558
165-4.17352.4366
1662.0430.58336
167-4.19031.655
168-3.3612.0787
1698.48150.24576
1701.3676-0.82789
171-4.84922.8596
1722.30951.4652
1735.51121.8206
174-0.901810.74026
1750.090275-2.2448
1768.13840.99505
177-8.5353-1.6648
1784.1449-1.5376
1793.00743.49
1805.53490.27717
1812.4966-0.83617
182-0.495810.3624
183-8.73962.4672
184-1.09512.7855
185-0.201793.4559
186-5.2961.2219
1872.7917-1.0861
1883.74911.5152
189-0.545632.0042
190-15.7440.79717
191-10.2213.9598
192-3.03653.0902
193-4.75990.63191
1946.7309-0.99944
195-6.36521.631
196-9.0226-1.4238
197-15.1972.6829
1981.63261.4249
199-5.8095-0.55938
200-10.829-0.42467
201-9.91630.39645
202-6.5124-1.6019
203-6.8313-0.86367
2049.9088-0.47759
205-8.3932-2.0181
2060.610211.7568
2073.3092-0.2369
208-9.2916-1.4712
209-10.663-1.9609
210-4.99731.4249
211-5.0841-1.4803
2122.4722-0.91328
213-8.1246-1.6431
214-3.7748-1.3057
215-0.90243-1.3301
216-7.16750.37173
217-14.203-2.7357
218-0.455550.46357
219-10.149-1.1438
2203.7482-0.25865
221-6.927-1.7942
2223.1643-0.6993
223-1.48722.2496
224-11.934-2.6698
225-9.39914.1702
226-4.72740.46664
227-6.23812.831
228-7.7423-0.54746
229-10.0110.72091
2306.14381.4883
2310.247631.1522
232-9.66050.1175
2335.2222-0.69014
234-3.9381.7181
2357.04050.69737
236-2.59251.1049
237-9.2355-1.482
238-11.6470.5108
239-14.346-1.481
240-3.60920.10621
241-9.17751.8242
2429.2817-0.12251
2431.33691.3957
2442.15550.63215
245-2.83052.3445
2467.8582-0.090108
247-9.1123-0.44943
248-0.349192.1812
2499.63590.36161
2503.26550.40331
251-6.4587-1.8024
2529.93330.30664
2534.32941.2766
25414.538-1.3252
2555.08030.73659
2560.843180.33083
257-11.18-2.419
258-5.13433.1453
2598.67381.6801
260-12.8246.2185
2618.9304-0.50586
262-1.599-0.058629
263-6.02360.43371
2643.09713.1074
265-10.2151.155
26612.993-0.63304
267-3.7116-0.26031
2688.42120.13944
2694.9240.73212
2708.0325-0.68705
2719.18320.22965
272-4.5615-1.6989
273-4.21590.48157
274-5.75113.4771
275-0.848215.8437
276-10.7080.9197
277-11.297-1.2375
278-0.92414.7959
279-9.3263-2.6245
28012.2830.072383
281-10.608-1.438
282-1.92961.5009
2831.3383-1.0349
284-6.82670.36147
285-8.1323-0.52574
286-4.4569-1.0349
287-5.6944-2.2745
28810.957-1.7279
289-6.67630.53938
2906.7271-0.53133
291-1.53690.39113
292-0.943332.9194
293-11.611-2.0828
294-11.23-2.0596
295-6.0671-0.09375
296-8.12230.78478
2977.98480.3903
298-10.722-1.0513
2991.97850.5228
3001.9036-0.79329
301-4.8951-0.1696
3029.9199-0.8062
3033.0073-0.75784
304-8.3325-2.2572
305-12.924-2.4763
306-9.2179-0.10763
3072.43631.1652
308-10.553-2.302
309-7.43283.562
310-6.2722-1.3643
311-2.7729-2.4137
312-1.5476-0.6012
313-1.558-0.22537
3146.6606-1.7502
315-10.853-2.5171
316-2.021-1.7889
317-8.0735-1.8562
318-7.5981-2.7772
319-15.839-3.5779
3209.6274-1.4834
3213.1594-0.14927
32211.82-1.622
3235.90461.3466
32410.789-1.2182
3252.136-0.16732
326-7.6281-1.6673
3271.7110.18522
328-3.37-0.30716
3290.60932-0.98862
330-1.9453-0.6316
331-7.6451-2.1015
3322.0972-0.92728
333-0.66299-1.0813
3344.8376-0.43936
3358.9686-0.11586
3363.4839-2.6356
3374.4811-0.16465
3381.49080.53599
3398.92190.45428
340-1.18422.8828
341-12.186-0.26908
342-15.1011.7372
3432.67531.157
3440.194021.2202
3455.67280.1548
3469.7379-1.754
3472.0826-2.4761
3483.0860.88085
3498.73990.11311
350-0.86711-1.0817
351-2.38760.54627
3522.16140.068148
3539.77210.028795
3548.04970.81278
3555.80260.061381
35610.409-0.52413
357-0.630691.5544
3586.1395-0.50694
359-2.642-1.8804
360-0.32091.2358
3610.774970.072151
362-10.334-0.34306
3632.80361.3483
36410.06-0.23839
365-2.97130.83984
3661.73140.53285
367-2.26720.77953
36810.175-0.43125
3692.38650.32906
3709.05320.011355
3715.842-0.61897
37212.641-1.1262
3731.6978-1.2381
3746.0985-0.77824
3753.437-1.2724
37613.801-0.89588
3776.7573-1.4511
378-1.24831.7464
379-11.3140.65736
38010.9540.78311
381-8.49340.11645
3829.7384-1.3159
383-9.67170.26111
3842.5305-0.016533
385-0.226-2.0716
3865.792-2.241
3876.23440.44638
3880.36034-1.8999
3897.8008-0.63007
39010.242-1.3847
391-2.716-0.90874
3929.87220.010488
39310.109-1.8472
3944.1598-0.74699
3958.78870.43684
3964.81850.5843
3976.0504-0.30846
3988.83690.44655
399-8.6807-0.33002
4007.00261.1779
4016.41250.27931
40210.6890.42209
40315.626-1.3709
4043.00010.82618
40513.523-1.7866
4066.4823-0.38245
4079.72670.96916
40811.311-0.70596
4095.4883-1.4196
41012.219-0.83884
4113.3709-1.1999
4121.8563-2.1284
4136.90160.92477
41413.688-1.4652
41510.1410.44741
4161.2994-0.16336
4178.89872.1536
418-10.455-3.0387
419-10.712-2.3277
4208.60190.8627
421-1.73081.729
4225.32630.75793
423-6.27152.3893
424-1.68511.581
425-7.7423-0.54746
426-10.0110.72091
4276.14381.4883
4280.247631.1522
429-9.66050.1175
4305.2222-0.69014
431-3.9381.7181
4327.04050.69737
433-2.59251.1049
434-9.2355-1.482
435-11.6470.5108
436-14.346-1.481
437-3.60920.10621
438-9.17751.8242
4399.2817-0.12251
4401.33691.3957
4412.15550.63215
442-2.83052.3445
4437.8582-0.090108
444-9.1123-0.44943
445-0.349192.1812
4469.63590.36161
4473.26550.40331
448-6.4587-1.8024
4499.93330.30664
4504.32941.2766
45114.538-1.3252
4525.08030.73659
4530.843180.33083
454-11.18-2.419
455-5.13433.1453
4568.67381.6801
457-12.8246.2185
4588.9304-0.50586
459-1.599-0.058629
460-6.02360.43371
4613.09713.1074
462-10.2151.155
46312.993-0.63304
464-3.7116-0.26031
4658.42120.13944
4664.9240.73212
4678.0325-0.68705
4689.18320.22965
469-4.5615-1.6989
470-4.21590.48157
471-5.75113.4771
4724.8376-0.43936
4738.9686-0.11586
4743.4839-2.6356
4754.4811-0.16465
4761.49080.53599
4778.92190.45428
478-1.18422.8828
479-12.186-0.26908
480-15.1011.7372
4812.67531.157
4820.194021.2202
4835.67280.1548
4849.7379-1.754
4852.0826-2.4761
4863.0860.88085
4878.73990.11311
488-0.86711-1.0817
489-2.38760.54627
4902.16140.068148
4919.77210.028795
4928.04970.81278
4935.80260.061381
49410.409-0.52413
495-0.630691.5544
4966.1395-0.50694
497-2.642-1.8804
498-0.32091.2358
4990.774970.072151
500-10.334-0.34306
5012.80361.3483
50210.06-0.23839
503-2.97130.83984
5041.73140.53285
505-2.26720.77953
50610.175-0.43125
5072.38650.32906
5089.05320.011355
5095.842-0.61897
51012.641-1.1262
5111.6978-1.2381
5126.0985-0.77824
5133.437-1.2724
51413.801-0.89588
5156.7573-1.4511
516-1.24831.7464
517-11.3140.65736
51810.9540.78311
519-8.49340.11645
5209.7384-1.3159
521-9.67170.26111
5222.5305-0.016533
523-0.226-2.0716
5245.792-2.241
5256.23440.44638
5260.36034-1.8999
5277.8008-0.63007
52810.242-1.3847
529-2.716-0.90874
5309.87220.010488
53110.109-1.8472
5324.1598-0.74699
5338.78870.43684
5344.81850.5843
5356.0504-0.30846
5368.83690.44655
537-8.6807-0.33002
5387.00261.1779
5396.41250.27931
54010.6890.42209
54115.626-1.3709
5423.00010.82618
54313.523-1.7866
5446.4823-0.38245
5459.72670.96916
54611.311-0.70596
5475.4883-1.4196
54812.219-0.83884
5493.3709-1.1999
5501.8563-2.1284
5516.90160.92477
55213.688-1.4652
55310.1410.44741
5541.2994-0.16336
5558.89872.1536
556-10.455-3.0387
557-10.712-2.3277
5588.60190.8627
559-1.73081.729
5605.32630.75793
561-6.27152.3893
562-1.68511.581

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: 91.2% (cumulative 91.2%)1PC2: 4.2% (cumulative 95.3%)2PC3: 2.3% (cumulative 97.6%)3PC4: 1.4% (cumulative 99.0%)4PC5: 0.4% (cumulative 99.4%)5PC6: 0.3% (cumulative 99.7%)6PC7: 0.1% (cumulative 99.8%)7PC8: 0.1% (cumulative 99.9%)8PC9: 0.0% (cumulative 99.9%)9PC10: 0.0% (cumulative 99.9%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
View explained variance values
Variance per displayed principal component
ComponentSpectral variance
PC191.19%
PC24.16%
PC32.28%
PC41.38%
PC50.44%
PC60.28%
PC70.07%
PC80.05%
PC90.04%
PC100.02%
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 parasite_load

Read Pearson correlation (−1 to +1) along the spectral axis (none); the solid curve shows |r| and the dashed curve shows signed r.

X · parasite_load 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
parasite_load0.06885510.040.0%

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
infection_stageM2100%IR 1.454
parasite_loadY309100%0 – 235,864

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

parasite_load

target · numeric

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

parasite_load distribution02004006000 – 9828: 4849828 – 1.966e+04: 301.966e+04 – 29,483: 1729,483 – 3.931e+04: 103.931e+04 – 4.914e+04: 44.914e+04 – 58,966: 458,966 – 6.879e+04: 46.879e+04 – 7.862e+04: 17.862e+04 – 88,449: 188,449 – 9.828e+04: 19.828e+04 – 1.081e+05: 11.081e+05 – 117,932: 1117,932 – 1.278e+05: 01.278e+05 – 1.376e+05: 21.376e+05 – 147,415: 1147,415 – 1.572e+05: 01.572e+05 – 1.671e+05: 01.671e+05 – 176,898: 0176,898 – 1.867e+05: 01.867e+05 – 1.966e+05: 01.966e+05 – 206,381: 0206,381 – 2.162e+05: 02.162e+05 – 2.26e+05: 02.26e+05 – 235,864: 10100,000200,000300,000
n / missing
?Recorded observation count and number of missing values for this variable.
562 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
6241 ± 1.89e+04
Median
?50th percentile: half the non-missing values lie at or below it.
122.5
Range
?Smallest and largest recorded non-missing values.
0 – 235,864
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
3.03
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
6.3 / 54
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Metadata 6

ID1

metadata · categorical

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

ID1 classesinfection/S2161infection/S2161: 1818infection/S214infection/S214: 1616infection/S2141infection/S2141: 1616infection/S2151infection/S2151: 1616infection/S216infection/S216: 1616Minfection/113G3Minfection/113G3: 1414Minfection/114Minfection/114: 1414Minfection/1141Minfection/1141: 1414infection/S314infection/S314: 1414infection/S2142infection/S2142: 1414+10 more+10 more: 101101
n / missing
?Recorded observation count and number of missing values for this variable.
562 / 0
Classes
?Number of distinct recorded category values.
90
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.96
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
18
Top class
?Most frequent recorded category and its observation count.
infection/S2161 (18)

ID2

metadata · numeric

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

ID2 distribution02550750 – 0.375: 580.375 – 0.75: 00.75 – 1.125: 651.125 – 1.5: 01.5 – 1.875: 01.875 – 2.25: 662.25 – 2.625: 02.625 – 3: 03 – 3.375: 573.375 – 3.75: 03.75 – 4.125: 634.125 – 4.5: 04.5 – 4.875: 04.875 – 5.25: 435.25 – 5.625: 05.625 – 6: 06 – 6.375: 606.375 – 6.75: 06.75 – 7.125: 567.125 – 7.5: 07.5 – 7.875: 07.875 – 8.25: 608.25 – 8.625: 08.625 – 9: 340.02.55.07.510.0
n / missing
?Recorded observation count and number of missing values for this variable.
562 / 0
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
4.222 ± 2.8
Median
?50th percentile: half the non-missing values lie at or below it.
4
Range
?Smallest and largest recorded non-missing values.
0 – 9
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.664
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.092 / -1.2
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

ID

metadata · categorical

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

ID classesMinfection/11310Minfection/11310: 22Minfection/11311Minfection/11311: 22Minfection/11313Minfection/11313: 22Minfection/1133Minfection/1133: 22Minfection/1134Minfection/1134: 22Minfection/1136Minfection/1136: 22Minfection/1137Minfection/1137: 22Minfection/113G1Minfection/113G1: 22Minfection/113G10Minfection/113G10: 22Minfection/113G15Minfection/113G15: 22+10 more+10 more: 2020
n / missing
?Recorded observation count and number of missing values for this variable.
562 / 0
Classes
?Number of distinct recorded category values.
365
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.
2
Top class
?Most frequent recorded category and its observation count.
Minfection/11310 (2)

Oocytes

metadata · numeric

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

Oocytes distribution01002003000 – 2540: 2712540 – 5080: 275080 – 7620: 87620 – 1.016e+04: 71.016e+04 – 1.27e+04: 01.27e+04 – 1.524e+04: 61.524e+04 – 1.778e+04: 21.778e+04 – 2.032e+04: 22.032e+04 – 2.286e+04: 02.286e+04 – 2.54e+04: 22.54e+04 – 2.794e+04: 12.794e+04 – 30,479: 230,479 – 3.302e+04: 33.302e+04 – 3.556e+04: 03.556e+04 – 3.81e+04: 03.81e+04 – 4.064e+04: 04.064e+04 – 4.318e+04: 04.318e+04 – 4.572e+04: 04.572e+04 – 4.826e+04: 04.826e+04 – 5.08e+04: 05.08e+04 – 5.334e+04: 05.334e+04 – 5.588e+04: 15.588e+04 – 5.842e+04: 05.842e+04 – 60,958: 1020,00040,00060,00080,000
n / missing
?Recorded observation count and number of missing values for this variable.
562 / 229
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
2386 ± 6.74e+03
Median
?50th percentile: half the non-missing values lie at or below it.
38.01
Range
?Smallest and largest recorded non-missing values.
0 – 60,958
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
2.83
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
5.1 / 32
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

replaces_dataset_id

metadata · categorical

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

replaces_dataset_id classesbacon_malaria_oocist_333_maia…bacon_malaria_oocist_333_maia_classification: 333333bacon_malaria_sporozoite_229_…bacon_malaria_sporozoite_229_maia_classification: 229229
n / missing
?Recorded observation count and number of missing values for this variable.
562 / 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.
0.98
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.
bacon_malaria_oocist_333_maia_classification (333)

infection_stage

metadata · categorical

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

infection_stage classesoocystoocyst: 333333sporozoitesporozoite: 229229
n / missing
?Recorded observation count and number of missing values for this variable.
562 / 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.
0.98
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.
oocyst (333)
Constant metadata 2

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

  • active_statusactive
  • active_replacement_source_noterebuilt_from_existing_standardized_export_with_official_source_documented

Alignment

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

Alignment levelobservation
Sample id availableno
Samples—
Observations (total)562
Identity statusunavailable
Documented technical repetitionsND
Source unit274 ID strings for 333 rows

Splits

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

originaltest: 197, train: 365 documented · not applied

Load this dataset

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

# pip install nirs4all-datasets
from nirs4all_datasets import get

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

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