11 Matching Annotations
  1. Dec 2021
  2. Nov 2021
  3. Oct 2021
    1. The analysis that I present is applicable to all branches of science whose models are based on continuous mathematics, such as algebraic, differential, or integral equations.

      I wonder if this could be applied also to the problem of reproducibility in the context of journalism and/or (h)ac(k)tivism. My approch has been to use relatively simple and self-contained connected infrastructures/tools and maybe pair them with functional package managers. The approach here could be complementary to it (still it's to early in the text to say)

  4. May 2021
  5. Mar 2021
  6. Feb 2021
  7. Jul 2018
    1. We used the program STRUCTURE v2.3.4 [16]

      Cited reference is open access and describes the method

    2. NA extractions were attempted for all taxa for which recent and sufficient tissues were available. Of the samples that were successfully extracted, at least two individuals for each taxon were included when available. This yielded 44 Malagasy Canarium accessions, to which we added an additional four Southeast Asian Canarium species to serve as outgroups. Genomic libraries were prepared for genotyping-by-sequencing (GBS) following the protocol described by Escudero et al. [14], but with the addition of a size selection step. After restriction digestion, fragment sizes were visualized on an Agilent 2100 Bioanalyzer and selected in the range of 300–800 bp using a Pippin Prep system. The final library containing 48 barcoded individuals was sequenced on two lanes of an Illumina HiSeq 2500 at Yale University’s Center for Genome Analysis to generate 75 bp single-end reads.

      Protocol not fully open. Based on closed access paper. Extension to method is clearly described.

    3. Fully reproducible code is available in the form of jupyter notebooks (https://jupyter.org) with instructions to install all necessary software, download sequence data, assemble it, and run genomic analyses (https://github.com/dereneaton/Canarium-GBS) (DOI 10.5281/zenodo.1273357). For all genomic analyses in this study we used the ipyrad Python API and the ipyrad-analysis toolkit (https://github.com/dereneaton/ipyrad) which provides a set of wrappers around common genomic analysis tools to run highly parallelized analyses using simple Python scripts.

      Well-described dry lab notebooks with open code, and Python analysis toolkit is open source.

  8. Apr 2016