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Manual resolution needed for sparsedossa2/lib_pair_1 #579

Description

@EvaMart

This issue was created automatically by the metadata disambiguation pipeline because the associated block could not be resolved with high confidence.
Conflict Id: p:sparsedossa2/lib
Conflict File: https://github.com/inab/research-software-etl/blob/pair_wise_cache/human_annotations/conflicts/p:sparsedossa2/lib.json
Run Id: 20260205T170527Z-8c2d90e-test_9_0

A conflict has been detected between two software metadata entries with the same name but no shared repository or website. Please review the metadata and provide your decision using the format below.

Annotation Guidelines: refer to the annotation guidelines if needed.


Entry A - sparsedossa2

  • Name: sparsedossa2
  • ID: bioconductor/sparsedossa2/lib/0.99.1
  • Source: bioconductor
  • Version: None
  • Type: None
  • Repository:
  • Website:
  • Authors:
  • Publications: No publications listed.
  • License: No license information.
  • Documentation: No documentation available.
  • Description:
     SparseDOSSA 2 is an R package for fitting to and the simulation of realistic microbial abundance observations. It provides functionlaities for: a) generation of synthetic microbial observations, based on either pre-fitted template that the package provides, or user-trained results.  b) spiking-in of associations with metadata variables or between feature pairs, for e.g. benchmarking or power analysis purposes.  c) fitting the SparseDOSSA 2 model to real-world microbial abundance observations.
    

Entry B - sparsedossa2

  • Name: sparsedossa2
  • ID: biotools/sparsedossa2/lib/None
  • Source: biotools
  • Version: None
  • Type: None
  • Repository:
  • Website:
  • Authors:
  • Publications: No publications listed.
  • License: No license information.
  • Documentation:
  • Description:
     SparseDOSSA 2 is an upcoming software for the simulation of null and metadata-associated microbial abundance profiles. It has functionalities for a) fitting to user-provided microbial template datasets, b) synthesizing microbial abundances similar to either user-provided or pre-trained templates, and c) additionally simulating associations with sample metadata or among microbial features.
    

Your Decision (in a code block below)

Use the exact format shown in a single comment, replacing the values as needed:

{
  "decision": "same",  // options: "same", "different", "unclear"
  "explanation": "Brief explanation of your decision",
  "confidence": "high" // options: "high", "medium", "low",
}

Thank you! Your annotation will be automatically ingested and logged.

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