Description
Preserve first-seen included sample order, count distinct included replicate IDs per sample, and report every later duplicate sample/replicate pair's zero-based index.
Laboratory study records distinguish observations, sample identities, personnel, and supporting sources. Missingness and duplication depend on record scope; a metadata check cannot establish scientific validity or regulatory acceptance.
Preserve first-seen included sample order, count distinct included replicate IDs per sample, and report every later duplicate sample/replicate pair's zero-based index.
When to use this tool
Use GLP Data Record Technical Replicate Count when you need a laboratory-record consistency or source-support check.
How to use the tool and how it works
- Enter sampleIds, replicateIds, excluded. Keep parallel arrays aligned. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read samples, counts, duplicatePairIndices. Use the output convention below.
Require aligned sampleIds, replicateIds, and excluded flags. Preserve first-seen included sample order, count distinct included replicate IDs per sample, and report every later duplicate sample/replicate pair's zero-based index.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| sampleIds | Sample Ids | Required. Array of text; maximum UTF-16 length 8192; maximum items 256. |
| replicateIds | Replicate Ids | Required. Array of text; maximum UTF-16 length 8192; maximum items 256. |
| excluded | Excluded | Required. Array of boolean; maximum items 256. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| samples | Samples | Required. Array of text; maximum UTF-16 length 8192; maximum items 256. |
| counts | Counts | Required. Array of safe integer; maximum items 256. |
| duplicatePairIndices | Duplicate Pair Indices | Required. Array of safe integer; maximum items 256. |
{
"input": {
"sampleIds": [
"A",
"A",
"A",
"B"
],
"replicateIds": [
"1",
"2",
"1",
"1"
],
"excluded": [
false,
false,
false,
true
]
},
"output": {
"samples": [
"A"
],
"counts": [
2
],
"duplicatePairIndices": [
2
]
}
}Limitations and assumptions
- Excluded records do not contribute; technical-replicate counting does not establish biological independence, valid measurements, study power, or sample identity.
- Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
Alternative or Complementary methods
Review the controlled study records and audit trail for the complete evidentiary chain. Resolve duplicate identifiers, sampling units, and authorization rules before drawing study-level conclusions.
References
Similar or alternative tools
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