PublicationPrimahadi Wijaya R., Gede. 2014. Visualisation of diachronic constructional change using Motion Chart. In Zane Goebel, J. Herudjati Purwoko, Suharno, M. Suryadi & Yusuf Al Aried (eds.). Proceedings: International Seminar on Language Maintenance and Shift IV (LAMAS IV), 267-270. Semarang: Universitas Diponegoro. doi: https://doi.org/10.4225/03/58f5c23dd8387Description of R codes and data files in the repositoryThis repository is imported from its GitHub repo. Versioning of this figshare repository is associated with the GitHub repo's Release. So, check the Releases page for updates (the next version is to include the unified version of the codes in the first release with the tidyverse).The raw input data consists of two files (i.e. will_INF.txt and go_INF.txt). They represent the co-occurrence frequency of top-200 infinitival collocates for will and be going to respectively across the twenty decades of Corpus of Historical American English (from the 1810s to the 2000s).These two input files are used in the R code file 1-script-create-input-data-raw.r. The codes preprocess and combine the two files into a long format data frame consisting of the following columns: (i) decade, (ii) coll (for "collocate"), (iii) BE going to (for frequency of the collocates with be going to) and (iv) will (for frequency of the collocates with will); it is available in the input_data_raw.txt. Then, the script 2-script-create-motion-chart-input-data.R processes the input_data_raw.txt for normalising the co-occurrence frequency of the collocates per million words (the COHA size and normalising base frequency are available in coha_size.txt). The output from the second script is input_data_futurate.txt.Next, input_data_futurate.txt contains the relevant input data for generating (i) the static motion chart as an image plot in the publication (using the script 3-script-create-motion-chart-plot.R), and (ii) the dynamic motion chart (using the script 4-script-motion-chart-dynamic.R).The repository adopts the project-oriented workflow in RStudio; double-click on the Future Constructions.Rproj file to open an RStudio session whose working directory is associated with the contents of this repository.

Abstract [Related Publication]:

Models of dispersal potential are required to predict connectivity between populations of sessile organisms. However, to date, such models do not allow for time‐varying rates of acquisition and loss of competence to settle and metamorphose, and permit only a limited range of possible survivorship curves. We collect high‐resolution observations of coral larval survival and metamorphosis, and apply a piecewise modeling approach that incorporates a broad range of temporally‐varying rates of mortality and loss of competence. Our analysis identified marked changes in competence loss and mortality rates, whose timing implicates developmental failure and depletion of energy reserves. Asymmetric demographic rates suggest more intermediate‐range dispersal, less local retention, and less long‐distance dispersal than predicted by previously‐employed non‐piecewise models. Because vital rates are likely temporally asymmetric, at least for non‐feeding broadcast‐spawned larvae, piecewise analysis of demographic rates will likely yield more reliable predictions of dispersal potential.

Usage Notes [Dryad]:

TenuisGBR2012longtermsurvival.csv
Empirical long term survival data for A. Tenuis larvae. "temp" is the temperature that the larvae were raised at, "rep" is the replicate number, "day" is the date the observation was taken, "age (h)" is the larval age (h) at the time of the observation, "age (d)" is the larval age (d) at the time of the observation, "larvae" is the number of larvae at the beginning of the experiment, "surv" is the number of larvae alive at the time of the observation.

A.tenuisGBR2012metamorphosis.csv
Empirical metamorphosis data for A. Tenuis larvae. "temp" is the temperature that the larvae were raised at, "date" is the date the observation was taken, "hour" is the hour of the day that the observation was taken, "age (h)" is the larval age (h) at the time of the observation, "age (d)" is the larval age (d) at the time of the observation, "rep" is the replicate number, "larvae" is the number of larvae at the beginning of the experiment, "meta" is the number of larvae that had metamorphosed by the time of the observation, "swimming" is the number of larvae swimming at the time of the observation.

Survival model fit R code
This code fits the best-fitting survival model to the data.
(survival model code.R)

Competence model fit R code
This code fits the best-fitting competence model to the data.
(competence model code.R)

The dataset also includes 2 README files (MS Word format) for the R codes.

These directories contain the scripts (shell and R) and data required for the primary analysis of Sokolowski et al., 2023. Title: Transcriptomic effects of the foraging gene shed light on pathways of pleiotropy and plasticity. Athors: Authors: Dustin J. Sokolowski1,2*, Oscar E. Vasquez3*, Michael D. Wilson1,2, Marla B. Sokolowski3,4, Ina Anreiter5

Author Affiliations *Authors contributed equally 1Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada 2Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada 3Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada 4Program in Child and Brain Development, Canadian Institute for Advanced Research, ON, Canada. 5Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada Corresponding Author For Manuscript: ina-dot-anreiter-at-utoronto-dot-ca Data analysis and figshare generator: dustin-dot-sokolowski-at-sickkids-dot-ca - alternative email (for when moved on from PhD) - djsokolowski95-at-gmail-dot-com

Each folder has it's own readme explaining the contents within. Directories have not been changed. Therefore to re-generate any output, they would need to be switched for the indivudal The raw and processed data (outside of the fastq, bw, and bam files) to regenerate the major analyses of each paper are the in /data subdirectory already. This includes all of the DEGs, pathways, varimax genes, scMappR outputs, SNP vcf files etc.

The sequencing information associated with these data can be found on arrayexpress https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-12688?query=foraging

R packages used: - scMappR - DESeq2 - gplots - edgeR - reshape2 - ggplot2 - TxDb.Dmelanogaster.UCSC.dm6.ensGene - org.Dm.eg.db - ChIPseeker - ggfortify - scales - xlsx - ActivePathways - DEET - Seurat - WGCNA - pheatmap - gProfileR - dplyr - reshape

Genomics command-line modules used: - fastqc - STAR - trimmomatic - bwa - samtools - bedtools - bcftools - gatk (picard) - featurecounts - qualimap - DeepTools