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Published 2025 | Version v5
Software Open

vdhooftcompmet/MS2LDA 2.0

Authors/Creators

  • 1. ROR icon Wageningen University & Research
  • 2. ROR icon University of Glasgow

Contributors

Project leader:

Project member:

  • 1. ROR icon Wageningen University & Research
  • 2. ROR icon University of Glasgow

Description

For running the automated guidance annotation (SAG) in MS2LDA 2.0 you need to download the following files:

150225_CleanedLibraries_Spec2Vec_pos_embeddings.npy, 150225_Spec2Vec_pos_CleanedLibraries.model, 150225_Spec2Vec_pos_CleanedLibraries.model.wv.vectors.npy, 150225_CombLibraries_spectra.db, and 150225_Spec2Vec_pos_CleanedLibraries.model.syn1neg.npy.

It is recommended to have all this files a single folder for Spec2Vec/positive_mode/*

The rest of the files are the results for the future MS2LDA 2.0 manuscript.

Files

CaseStudy_Results_Mushroom_200_ms2lda_viz.json

Files (7.9 GB)

Name Size Download all
md5:5d0703a756a06f5968dce1740e25a136
1.0 GB Download
md5:e1e798efc43143e63eddc339e4b9993c
4.4 GB Download
md5:02570a11628c5ae71152ff5d10b32183
9.9 MB Download
md5:aa247e479168bc2b710d712ba722992d
333.4 MB Download
md5:5fb41d7495691d297c673198b1cab19e
333.4 MB Download
md5:ee208734928449eac14176a769692ef7
45.3 MB Preview Download
md5:aae08b4ebb833688f58ecce6a16e5fbd
33.7 MB Download
md5:af2e2f5105e25168eb5ae286e044028f
44.0 MB Download
md5:24393aad20d6c57636bd7f3fbfc99b8b
70.6 MB Preview Download
md5:ea0adc887dfec8a32ff2413dfd6ce4e4
1.7 GB Download

Additional details

Related works

Is supplement to
Software: https://github.com/vdhooftcompmet/MS2LDA/tree/1.0.1 (URL)

Software

Repository URL
https://github.com/vdhooftcompmet/MS2LDA