Données d'analyse pour : "Microbial community transfers across a pilot ripening cellar are increased by cheese wiping"
Notice bibliographique
Résumé
Quarto scripts that have be used for the publication Microbial community transfers across a pilot ripening cellar are increased by cheese wiping. The folder zip archive contains 4 folder: Metabarcoding analyses (16S) = `MetaB16S` Metabarcoding analyses (ITS) = `MetaBITSS` Analyses from culture-depend data and from gases data (CO2) = `Microbiology_gases` All data = `Raw_data` Metab16S In the MetaB16S folder ==\> 2 documents, the quarto file and the html version, related with analyses of bacterial composition and diversity from all environments (cheese, cloth, air and wall's cellars). The quarto file contains the following analyses. Cheese microbial characteristics Barpolt of composition of short-ripened cheese and long-ripened cheese (separeted by time and cellars) Alpha diversity was measured with the Shannon and Richness observed indexes by ripening stage, time and cellar Beta diversity analyses were generated by ripening stage, time and cellar (+ Permanova analyses and pairwise adonis when necessary) All env microbial characteristics Same analyses as for cheese microbial characteristics were performed to describe microbial evolution for all environments (cheese, cloths, air and wall'cellars) Flux - all environments Microbial transfers between all environments ==\> heatmap from ASVs at least shared with two environments and gather at species level and then at order level. In this heatmap, we can see the prevalence (Blue = weak, red = strong) of thoses ASVs at different relative abundance (absciss axe) for 6 environments (long-ripened cheese, short-ripened cheese, long-ripened cheese cloth, short-ripened cheese cloth, air and wall). This heatmap was generated for the three cellar separately. MetaBITS In the MetaBITS files there are two documents (quarto file with the script and the html version) related with bacterial composition of all environments (cheese, cloth, air and wall's cellars) In the quarto file, the phyloseq files was loaded to generate the following figures. Same analyses as MetaB16S were generated for fungal community. Differential analyses Differential analyses performed on cheese samples ==\> (short-ripened cheeses stored with vs without long-ripened cheeses) Microbiology_gases In this this barplot of levels of molds in the air according wiping activity ==\> comparison of 2 media (OGA vs MEA) A figure of the evolution of CO2 during one day in one cave was generated. On this figure we can see the concentration of CO2 increasing when cheeses were being wiped and its diminution. Then there is zoom on some specific period when the CO2 increased when cheese were not being wiped. Raw_data `2023-06-07_synthese_microbio_SN_1204`file (.xlsx) , sheet = Microbiologie ==\> data related with enumeration for each environment + metadata (date of sampling, cellar, wiping conditions...) `2024-10-10_Phyloseq_SN_16S` file (.Rdata) ==\> phyloseq file use to performed analyses MetaB16S `2024-10-10_Phyloseq_SN_ITS` file (.Rdata) ==\> phyloseq file use to performed analyses MetaBITS `2024-10-14_Synthese_gases_all_cellars1` file (.xlsx)==\> several sheets: `Evolution of CO2C2, Cave 3 and Cave 4` = Evolution of CO2, relative humidity, temperature and atmospheric pressure from 12/04/2023 to 17/05/2023. `CO2_suivi_C2, CO2_suivi_C3 and CO2_suivi_C4` = For each cellar (C2,C3 and C4) ==\> focus evolution on of CO2, relative humidity, temperature and atmospheric pressure on 1 day `NH3` = Evolution of NH3 from 12/04/2023 to 12/05/2023 [EBI BioProject](https://www.ebi.ac.uk/ena/browser/view/PRJEB91282)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,376 | 0,252 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».