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| [Term] | |
| id: MS:4000216 | |
| name: targeted compound retention time deviation quantiles | |
| def: "From the distribution of deviations between the observed and the expected retention time of a set of targeted/expected compounds, the quantiles. I.e. a value triplet represents the quartiles Q1, Q2, Q3. The compounds need to be identified, possibly by retention time matching." [PSI:MS] | |
| comment: A tight distribution close to zero indicates a stable chromatography; a shifted Q2 indicates a systematic retention time drift, a wide Q3-Q1 spread indicates compound-dependent deviations. | |
| is_a: MS:4000004 ! n-tuple | |
| relationship: has_metric_category MS:4000008 ! ID based metric | |
| relationship: has_metric_category MS:4000012 ! single run based metric | |
| relationship: has_metric_category MS:4000016 ! retention time metric |
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| #!/usr/bin/env python3 | |
| """ | |
| immich_external_album.py | |
| Add all external library assets in Immich to a single album. | |
| License: CC0 | |
| Features: | |
| - Dry-run mode via --dry-run (no changes made) | |
| - Diagnostic output: counts, sample asset IDs | |
| - Pagination-safe for large libraries |
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| #!/bin/bash | |
| did="did:plc:oxbo2ifc5i7shah4bvai2pbi" | |
| cursor="" | |
| total=0 | |
| while true; do | |
| url="https://public.api.bsky.app/xrpc/app.bsky.graph.getFollowers?actor=$did" | |
| if [ -n "$cursor" ]; then | |
| url="$url&cursor=$cursor" |
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| #!/bin/bash | |
| chmo2definedterm () { | |
| CURIE=`echo "$1" | sed -e 's/.*\(CHMO:[0-9]*\).*/\1/'` | |
| wget -q -O- "https://service.tib.eu/ts4tib/api/ontologies/chmo/terms?obo_id=$CURIE" |\ | |
| jq '{ measurementTechnique: { "@type": "DefinedTerm", "@id": ._embedded.terms[0].iri, "termCode": ._embedded.terms[0].obo_id, "name": ._embedded.terms[0].label, "url": ("https://terminology.nfdi4chem.de/ts/ontologies/"+._embedded.terms[0].ontology_name+"/terms?iri="+._embedded.terms[0].iri), "inDefinedTermSet": { "@type": "DefinedTermSet", "@id": ._embedded.terms[0].ontology_iri, "name": ._embedded.terms[0].ontology_name, } } }' |\ | |
| grep -v '^[{}]$' | |
| echo "," | |
| } |
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| #!/bin/bash | |
| BASEURL=https://www.chemotion-repository.net/api/v1/public/metadata/publications | |
| LIMIT=500 | |
| MAXPARALLEL=25 | |
| MAXRECORDS=99999999 | |
| #MAXRECORDS=2000 | |
| # There are currently ~6000 Samples, ~25000 Containers and ~5000 Reactions |
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| #!/bin/bash | |
| BASEURL=https://www.chemotion-repository.net/api/v1/public/metadata/publications | |
| LIMIT=500 | |
| MAXPARALLEL=25 | |
| MAXRECORDS=99999999 | |
| MAXRECORDS=2000 | |
| # There are currently ~6000 Samples, ~25000 Containers and ~5000 Reactions |
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| { | |
| "@context": "https://schema.org", | |
| "@type": "Study", | |
| "@id": "https://dx.doi.org/10.14272/reaction/SA-FUHFF-UHFFFADPSC-UWHQATUHQV-UHFFFADPSC-NUHFF-NUHFF-NUHFF-ZZZ", | |
| "identifier": "CCR-33542", | |
| "dct:conformsTo": { | |
| "@type": "CreativeWork", | |
| "@id": "https://bioschemas.org/types/Study/0.3-DRAFT" | |
| }, | |
| "url": "https://www.chemotion-repository.net/inchikey/reaction/SA-FUHFF-UHFFFADPSC-UWHQATUHQV-UHFFFADPSC-NUHFF-NUHFF-NUHFF-ZZZ", |
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| #!/usr/bin/env bash | |
| export BACKEND=http://localhost:8081/oai-backend | |
| jq -c '.[]' DataDump-IPB.jsonld | \ | |
| while read i; do | |
| IDENTIFIER=`echo "$i" | jq '.identifier'` | |
| ( |
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| ## 1) Get paginated list of Molecules from Chemotion | |
| ## 2) extract DOI and internal chemotion ID for each molecule with `jq` | |
| ## 3) Query each molecule and convert with `jq` to (bio)schemas | |
| ## 4) Embedd the JSON in a CDATA in a minimalistic XML | |
| ## 5) Pipe the XML to the FIZ-OAI backend running at $BACKEND, e.g. localhost | |
| export BACKEND=http://localhost:8081/oai-backend | |
| for I in 1 2 3 4 5 6 7 8 9 10; do | |
| wget -O- "https://www.chemotion-repository.net/api/v1/public/molecules.json?page=$I&per_page=100" |\ |
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| library(rmzTabM) ## https://lifs-tools.github.io/rmzTab-m/ | |
| library(metabolighteR) ## https://aberhrml.github.io/metabolighteR/ | |
| convertMzTab2MAF <- function(mzTabfile, MAFfile) { | |
| mzTabTable <- readMzTab(mzTabfile) | |
| ## To turn these tables into objects, use the R6 class constructor method `new()`: | |
| mzTabObject <- MzTab$new()$fromDataFrame(mzTabTable) | |
| metadata <- mzTabObject$metadata |
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