Supplementary Table 3.Profiles of individuals in the eLIFE cohort who replied "No have never tried reproducing any published results" stratified by how they responded in each of the questions 13, 15, 16 and 17.

Supplementary Table 1: The number of respondents who were able and willing to reproduce successfully and unsuccessfully published experiments and their training received

Supplementary Table 2 showing the success and willingness in reproducing published experiments stratified by the frequency of using bioinformatics tools. Chi square statistic: 0.53333, df = 3, p-value = 0.9115Conclusion: There was no evidence for a difference in the ability and willingness to reproduce published results between the respondents who use bioinformatics tools often and those who use them rarely or never.

Supplementary Table 4 - Analysis of Question 5 answers - Knowledge and attitudes among life scientists towards reproducibility within journal articles.Excel File.

Raw datafile of the survey data collected from the survey distributed to collect knowledge and attitudes among life scientists towards reproducibility within journal articles.

Data for figure 3 of the manuscript:"Knowledge and attitudes among life scientists towards reproducibility within journal articles: a research survey". Figure 3. Preferred features for the interactive figure. Responses to question 9: Respondents were asked to rank in order of preference the above features, with 1 most preferred feature, to 11 the least preferred feature. The average score for each feature was calculated in order of preference as selected by the respondents from both NBI and eLIFE surveys. The lower the average score value (x- axis), the more preferred the feature (y-axis).

scripts to reproduce the figure4 h results

Pre-print of my article describing how git can be used to improve science.

Excel file showing the table and the graph for figure 1 in the manuscript: "Knowledge and attitudes among life scientists towards reproducibility within journal articles: a research survey."

This is a sample output CSV file from the Deus ex machina tool.

The Deus ex machina code is available on GitHub via this link: https://github.com/code56/ontologiesreproducibility.git

Briefly, the code performs semantic annotations on PDF articles and their associated ArrayExpress XML files and computes a Reproducibility Metric Score (RMS) based on various assessment parameters.

The case studies are in the field of wheat crop transcriptomics.

Reproducibility Project: Cancer Biology (https://osf.io/e81xl/wiki/home/) aims to reproduce the key experiments from 50 landmark papers in cancer research. As a follow up to the previously published study, which showed a lack of indentifiability of research resources in the published biomedical literature (Vasilevsky, et al. 2014, PeerJ 1:e148), we analyzed 6 resource types reported in these papers to determine the identifiability of these resources. The resource types included antibodies, cell lines, constructs, knockdown reagents, model organisms and software. The results showed an average 85% of the resources were identifiable, and the ability to identify the resources varied amongst the resource types.

Methods S1 in nbviewer: http://goo.gl/KzFAvj The reproducibility of experiments is key to the scientific process, and particularly necessary for accurate reporting of analyses in data-rich fields such as phylogenomics. We present ReproPhylo, a phylogenomic analysis environment developed to ensure experimental reproducibility, to facilitate the handling of large-scale data, and to assist methodological experimentation. Reproducibility, and instantaneous repeatability, is built in to the ReproPhylo system, and does not require user intervention or configuration because the it stores the experimental workflow as a single, serialized Python object that contains explicit provenance and environment information. This ‘single file’ approach ensures the persistence of provenance across iterations of the analysis with changes automatically, managed by the version control program Git. ReproPhylo produces an extensive human-readable report, and generates a comprehensive experimental archive file, both of which are suitable for submission with publications. The system facilitates thorough experimental exploration of both parameters and data. ReproPhylo is a platform independent CC0 python module, and is easily installed as a Docker image, with an IPython Notebook GUI, or as a slimmer version in a Galaxy distribution.

This is an html file of the blog post "Modest reproducibility success: a reanalysis of two early branching Metazoa datasets using ReproPhylo" as in the link below.

treebase======== An R package for discovery, access and manipulation of online phylogenies - Publication in Methods in Ecology and Evolution- Development version source code on github- HTML package documentation- Report issues, bugs or feature requests Installation------------ 'treebase' is available from CRAN. You can install the latest version from the development website on github using the 'devtools' package from within R. Make sure you have the latest version for the best experience. '''rlibrary(devtools)install_github("treebase", "ropensci")''' Getting Started--------------- Use of the 'treebase' package should be relatively straight forward: '''rlibrary(treebase)Phylogenies_from_Huelsenbeck

Poster presented at ASM 2015 General Meeting

This is the archived git repostiory for this paper: git can facilitate greater reproducibility and increased transparency in science

Paper is currently in press in Source Code in Medicine and Biology and this zipped repo gives you access to the full history of the paper's development.

Backup in case SRA Tools doesn't work.

Building the annotation file, consisting of protein (entity)-gene ontology process map extracted from the GOA UniProt dataset at ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/UNIPROT/goa_uniprot_all.gaf.gz. This protein-process map file is used to generate protein pairs used for testing the PySML library. Semantic similarity scores produced are also included.

SD indicates Standard Deviation, means are the Cycle Threshold values (Ct), and CV indicates the Coefficient of Variation. Each assay extract used independent DNA extracts from a given fecal sample. The Ct mean of an assay extract is the result of five replicate real-time PCR measures.

Deus ex machina tool - Confusion matrix computation analyses

The Deus ex machina code is available on GitHub via this link: https://github.com/code56/ontologiesreproducibility.git Briefly, the code performs semantic annotations on PDF articles and their associated ArrayExpress XML files and computes a Reproducibility Metric Score (RMS) based on various assessment parameters. The case studies are in the field of wheat crop transcriptomics. The accuracy of the Deus ex machina tool of correctly annotating the Plant Ontology terms within wheat transcriptomic articles, was assessed by comparing the manually annotated results and the automatically annotated results, produced by the Deus ex machina tool, with the help of a confusion matrix. The Excel file has 3 tabs that show in detail all the computation analyses (manual annotation of the papers) and the automatic annotation results by the Deus ex machina tool. Some terms were not possible to assign a specific ontology term with confidence (this is the issue where authors did not clarify the terms and describe their research using standardised ontology terms), and these terms are denoted clearly in the computation analysis file. Nonetheless, as the terms were only a few, they do not affect the results of the confusion matrix to any significant degree.