{"schemaVersion":"1.1.0","slug":"tool-novel-object-recognition-calculator","identifier":"csdata:tool-novel-object-recognition-calculator:1.0.0","title":"ConductScience NOR Discrimination Index Calculator: method and validation record","shortDescription":"The calculation behind ConductScience's free novel object recognition discrimination index calculator, with three worked examples that check it.","description":"Method and validation record for the free NOR Discrimination Index Calculator at conductscience.com/tools/novel-object-recognition-calculator. From the time each animal explores the novel and the familiar object, the tool computes the discrimination index (N − F) / (N + F) and the preference index N / (N + F), with group means, SDs and SEMs. Three hand-worked examples, on the discrimination index of Ennaceur and Delacour (1988), cover a novel-object preference, a familiar-object preference, and an animal that explored neither object, which has no index. The record holds the calculation source files, the worked examples, a validation report with each file's SHA-256, and what changed on the tool page when the record was written.","creators":[{"name":"ConductScience","type":"Organization"}],"publishedAt":"2026-09-28","updatedAt":"2026-09-28","version":"1.0.0","doi":"10.55157/csd.tool.nor.2026.001","doiProvider":"Crossref","doiRegisteredAt":"2026-09-28T13:42:25Z","doiRegisteredLocalDate":"2026-09-28","doiRegistrationTimeZone":"America/New_York","doiVerifiedAt":"2026-09-28T14:13:25Z","access":"open","license":{"name":"MIT and CC BY 4.0"},"licenseLabel":"MIT (code); CC BY 4.0 (text, worked examples and reports)","rightsStatement":"The calculation source files in engine/ are released under the MIT License. The record text, worked examples and validation report are released under CC BY 4.0. Cite the record when you use the files.","subjects":["Research software","Method validation","Behavioral neuroscience","Learning and memory"],"methods":["Worked examples: each of the three cases gives its inputs, the expected outputs, the source and passage behind the expected answer, and the arithmetic. Expected answers are worked by hand from the published method, or from the tool's own rule where METHODS.md labels it a convention. Cases whose ids start with edge- test inputs at the limits of the method.","Check: each case was run in Node.js 24 through the tool's calculation source files, and every output was compared with its expected value, exactly or within the tolerance the case states. All three cases pass (validation-report.json), and ConductScience's test suite runs the same cases whenever the tool changes.","Fingerprint: the validation report gives the byte count and SHA-256 of each calculation file and of the files that ran the check, and engine/ holds those files, so anyone can rerun the cases and tell whether a later version of the calculation differs from this one."],"source":{"repository":"https://github.com/ConductScienceInc/csd-tool-novel-object-recognition-calculator","tag":"v1.0.0","commit":"e6a43fb01cbeacf46b39da54f3f1cb635933987f","releasedAt":"2026-09-28T13:12:27Z"},"basedOn":"https://conductscience.com/tools/novel-object-recognition-calculator","manifest":{"files":15,"bytes":18573,"sha256":"dccfa7341f85ebbdade4aa6388e4059f9bc31c2a83a881bb04739566a076535c"},"archive":{"entries":16,"bytes":12408,"sha256":"5881b13cca0a43766eb86c05a74402b1779ac442eec66820242b65799fd78c26"},"files":[{"name":"tool-novel-object-recognition-calculator-record-1.0.0.zip","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/tool-novel-object-recognition-calculator-record-1.0.0.zip","mediaType":"application/zip","sizeBytes":12408,"sha256":"5881b13cca0a43766eb86c05a74402b1779ac442eec66820242b65799fd78c26","description":"Complete frozen release: 15 source-manifest files plus MANIFEST.json."},{"name":"cases.csv","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/cases.csv","mediaType":"text/csv","sizeBytes":1321,"sha256":"c03c31a65afaffcdbca550916799000c37c815ee3fa49abe70b0ed118b167d35","description":"The worked examples, one row each: what the case checks, the source of the expected answer, the expected outputs and whether the engine matched them."},{"name":"cases.json","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/cases.json","mediaType":"application/json","sizeBytes":2300,"sha256":"38a081088e2566fb59edcf53705461c9aabed02a60abcb3dcdcb372eed2f1f55","description":"The worked examples in full: inputs, expected outputs, tolerances, source passage and the arithmetic behind each expected answer."},{"name":"validation-report.json","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/validation-report.json","mediaType":"application/json","sizeBytes":1839,"sha256":"db48d8939628188feab9d86f24bcfab46acad93f19e66bdded254ea45d3826a6","description":"The engine's output for every case, pass or fail, with the byte count and SHA-256 of each calculation and harness file."},{"name":"data_dictionary.csv","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/data_dictionary.csv","mediaType":"text/csv","sizeBytes":472,"sha256":"96b57ee5185ffcccc5397d98673c42bbc53ff77cc7f676fe5789ef04a7648090","description":"Columns of cases.csv."},{"name":"README.md","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/README.md","mediaType":"text/markdown","sizeBytes":1708,"sha256":"9e50156521f399a644d3e407d991de5f03ed9752224c981743302f38fa98d126","description":"What the tool computes, the files, and how to cite the record."},{"name":"METHODS.md","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/METHODS.md","mediaType":"text/markdown","sizeBytes":1948,"sha256":"2c64a4d36ffca3f26586f6bf26d3eafdd8782730e88f8debfa4914ad25b9a822","description":"Formulas, input rules, conventions, references and what changed on the tool page."},{"name":"MANIFEST.json","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/MANIFEST.json","mediaType":"application/json","sizeBytes":2362,"sha256":"dccfa7341f85ebbdade4aa6388e4059f9bc31c2a83a881bb04739566a076535c","description":"Byte count and SHA-256 for every file in the source release."},{"name":"CITATION.cff","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/CITATION.cff","mediaType":"text/yaml","sizeBytes":431,"sha256":"544469fb387aea0ada95c784d33a663bdff716133b4c998f9e9f56545ee2c4e1","description":"Citation metadata."},{"name":"LICENSE","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/LICENSE","mediaType":"text/plain","sizeBytes":1361,"sha256":"d2dca2297cc985f3577b9376fbf5a72231ac88be401776098486f6d1e8a4d87a","description":"MIT License for the calculation source files; CC BY 4.0 for everything else."},{"name":"deposit-receipt.json","url":"/data/records/tool-novel-object-recognition-calculator/1.0.0/deposit-receipt.json","mediaType":"application/json","sizeBytes":3083,"sha256":"6dd526a0a096a8a932ffffb5e8f3ee0c764bd1d9b339483dba4e0b426a190f83","description":"ConductScience verification and packaging receipt."}],"previewFile":"cases.csv","preview":{"columns":["id","title","source_type","expected","pass"],"rows":[["hand-worked-novel-preference","An animal that spends three quarters of its exploration on the novel object","hand-worked","{\"di\":0.5,\"pi\":0.75}","true"],["hand-worked-familiar-preference","An animal that prefers the familiar object","hand-worked","{\"di\":-0.5,\"pi\":0.25}","true"],["edge-no-exploration","An animal that explored neither object","hand-worked","{\"di\":null,\"pi\":null}","true"]]},"dataDictionary":[{"file":"cases.csv","description":"One row per worked example.","columns":[{"name":"id","type":"string","values":"e.g. hand-worked-novel-preference","description":"Case identifier; edge cases start with \"edge-\""},{"name":"title","type":"string","values":"","description":"What the case checks"},{"name":"source_type","type":"string","values":"hand-worked","description":"Where the expected answer comes from: published, hand-worked, analytic or cross-check"},{"name":"citation","type":"string","values":"","description":"Source of the method or the expected answer"},{"name":"locator","type":"string","values":"","description":"Section, formula or table of the source that the case relies on"},{"name":"expected","type":"JSON object","values":"null: not computable","description":"Expected outputs as JSON; null means not computable"},{"name":"pass","type":"boolean","values":"true, false","description":"true when every expected output matched the engine within the stated tolerance"}]},{"file":"cases.json","description":"The worked examples in full: inputs, expected outputs, tolerances, source passage and the arithmetic behind each expected answer.","columns":[]},{"file":"validation-report.json","description":"The engine's output for every case, pass or fail, with the byte count and SHA-256 of each calculation and harness file.","columns":[]},{"file":"engine/behavioral/nor-metrics.ts","description":"Calculation source (MIT). Discrimination index (N − F) / (N + F) and preference index N / (N + F).","columns":[]},{"file":"engine/behavioral/types.ts","description":"Calculation source (MIT). Shared types, and group statistics that leave out values that are not computable.","columns":[]},{"file":"engine/pilot-tools.ts","description":"Harness source (MIT). Slugs of the ten tools with validation records; the check accepts only these.","columns":[]},{"file":"engine/validation/run.ts","description":"Harness source (MIT). Runs one case: passes its inputs to the tool's adapter and compares each output with the expected value within the tolerance.","columns":[]},{"file":"engine/validation/tools/novel-object-recognition-calculator.ts","description":"Harness source (MIT). Adapter from a case's inputs to this tool's calculation, returning the outputs the cases check.","columns":[]},{"file":"engine/validation/types.ts","description":"Harness source (MIT). Types for cases, tolerances and results.","columns":[]}],"citation":"ConductScience. (2026). ConductScience NOR Discrimination Index Calculator: method and validation record (Version 1.0.0) [Data set]. ConductScience Data. https://doi.org/10.55157/csd.tool.nor.2026.001","relatedResearch":[{"title":"ConductScience NOR Discrimination Index Calculator (live tool)","url":"https://conductscience.com/tools/novel-object-recognition-calculator","relation":"Documents"},{"title":"Ennaceur and Delacour (1988). A new one-trial test for neurobiological studies of memory in rats. 1: Behavioral data","url":"https://doi.org/10.1016/0166-4328(88)90157-x","relation":"Reference source"}]}