76 lines
2.4 KiB
Plaintext
76 lines
2.4 KiB
Plaintext
head 1.1;
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access;
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symbols;
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locks; strict;
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comment @# @;
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1.1
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date 2011.10.18.15.52.58; author KarenCranstn; state Exp;
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branches;
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next ;
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desc
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@none
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@
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1.1
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log
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@none
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@
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text
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@%META:TOPICINFO{author="KarenCranstn" date="1318953178" format="1.1" reprev="1.1" version="1.1"}%
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%META:TOPICPARENT{name="MIAPAWorkshop2011Ideas"}%
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---++Checklist for group 2:
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Members: Bill Piel, Jim Leebens-Mack, Karen Cranston, Teodor Georgiev
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Initial list of terms:
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* topology in digital format
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* branch lengths
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* support values
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* method of analysis (ML / MP / Bayes)
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* samples with valid taxon names
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* character data
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* excluded characters
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* character sets / partitions
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* biorepository collection code
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* specimen number
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* locality data
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* !GenBank accession number
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* tissue sequenced
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* molecular or morphological
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* alignment method
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* consensus method
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* software & version
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* character labels and states for morphological data
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* evolutionary model
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* heuristic search parameters / MCMC settings
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* random seed
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* input trees for consensus / composite trees
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Agreed-upon minimums
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* topology
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* support values
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* method of analysis (ML / MP / Bayes)
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* unique and valid OTU label (valid species name, or specimen identifier that could be found in a database somewhere - using some informatics magic)
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* alignment used to construct the tree
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* raw data (pre-cleaning and alignment, e.g. !GenBank ID)
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* alignment method
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* data assembly (how did we go from databased sequence data to final alignment); in the short to medium term, this would likely be text, not machine readable
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Discussed:
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* branch lengths with units: ideal, but not required; morphological data may not produce meaningful branch lengths
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* metadata about OTU label
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* specimen data: if a study did new sequencing, specimen data should be included
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Other interesting discussion:
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* distinction between what data should be available (the best practices) and how this information should be communicated (in text of paper, as digital object)
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* issue of something being required that might not be relevent for all studies (i.e. support values for a glommogram, valid species names for environmental samples)
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* best practices for analysis and data management: i.e. better to include all characters in matrix and exclude using software settings than removing those characters from the matrix itself
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-- Main.KarenCranstn - 18 Oct 2011
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@
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