{# General information# The script runs with R (Version 3.1.1; 2014-07-10) and packages plyr (Version 1.8.1), XLConnect (Version 0.2-9), utilsMPIO (Version 0.0.25), sp (Version 1.0-15), rgdal (Version 0.8-16), tools (Version 3.1.1) and lattice (Version 0.20-29)# --------------------------------------------------------------------------------------------------------# Questions can be directed to: Martin Bulla (bulla.mar@gmail.com)# -------------------------------------------------------------------------------------------------------- # Data collection and how the individual variables were derived is described in: #Steiger, S.S., et al., When the sun never sets: diverse activity rhythms under continuous daylight in free-living arctic-breeding birds. Proceedings of the Royal Society B: Biological Sciences, 2013. 280(1764): p. 20131016-20131016. # Dale, J., et al., The effects of life history and sexual selection on male and female plumage colouration. Nature, 2015. # Data are available as Rdata file # Missing values are NA. # --------------------------------------------------------------------------------------------------------# For better readability the subsections of the script can be collapsed # --------------------------------------------------------------------------------------------------------}{# Description of the method # 1 - data are visualized in an interactive actogram with time of day on x-axis and one panel for each day of data # 2 - red rectangle indicates the active field, clicking with the mouse in that field on the depicted light signal generates a data point that is automatically (via custom made function) saved in the csv file. For this data extraction I recommend, to click always on the bottom line of the red rectangle, as there is always data available due to a dummy variable ("lin") that creates continuous data at the bottom of the active panel. The data are captured only if greenish vertical bar appears and if new line of data appears in R console). # 3 - to extract incubation bouts, first click in the new plot has to be start of incubation, then next click depict end of incubation and the click on the same stop start of the incubation for the other sex. If the end and start of incubation are at different times, the data will be still extracted, but the sex, logger and bird_ID will be wrong. These need to be changed manually in the csv file. Similarly, the first bout for a given plot will be always assigned to male (if no data are present in the csv file) or based on previous data. Hence, whenever a data from a new plot are extracted, at a first mouse click it is worth checking whether the sex, logger and bird_ID information is correct and if not adjust it manually. # 4 - if all information from one day (panel) is extracted, right-click on the plot and choose "stop". This will activate the following day (panel) for extraction. # 5 - If you wish to end extraction before going through all the rectangles, just press "escape". }{# Annotations of data-files from turnstone_2009_Barrow_nest-t401_transmitter.RData dfr-- contains raw data on signal strength from radio tag attached to the rump of female and male, and information about when the birds where captured and incubation stage of the nest1. who: identifies whether the recording refers to female, male, capture or start of hatching2. datetime_: date and time of each recording3. logger: unique identity of the radio tag 4. signal_: signal strength of the radio tag5. sex: sex of the bird (f = female, m = male)6. nest: unique identity of the nest7. day: datetime_ variable truncated to year-month-day format8. time: time of day in hours9. datetime_utc: date and time of each recording, but in UTC time10. cols: colors assigned to "who"--------------------------------------------------------------------------------------------------------m-- contains metadata for a given nest1. sp: identifies species (RUTU = Ruddy turnstone)2. nest: unique identity of the nest3. year_: year of observation4. IDfemale: unique identity of the female5. IDmale: unique identity of the male6. lat: latitude coordinate of the nest7. lon: longitude coordinate of the nest8. hatch_start: date and time when the hatching of the eggs started 9. scinam: scientific name of the species10. breeding_site: unique identity of the breeding site (barr = Barrow, Alaska)11. logger: type of device used to record incubation (IT - radio tag)12. sampling: mean incubation sampling interval in seconds--------------------------------------------------------------------------------------------------------s-- contains metadata for the incubating parents1. year_: year of capture2. species: identifies species (RUTU = Ruddy turnstone)3. author: identifies the author who measured the bird4. nest: unique identity of the nest5. caught_date_time: date and time when the bird was captured6. recapture: was the bird capture before? (0 - no, 1 - yes)7. sex: sex of the bird (f = female, m = male)8. bird_ID: unique identity of the bird9. logger: unique identity of the radio tag --------------------------------------------------------------------------------------------------------}

Background and methodsSystematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain steps in systematic reviews, such as data extraction, are labour-intensive, which hampers their feasibility, especially with the rapidly expanding body of biomedical literature. To bridge this gap, we aimed to develop a data mining tool in the R programming environment to automate data extraction from neuroscience in vivo publications. The function was trained on a literature corpus (n = 45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n = 31 publications; multiple sclerosis, n = 244 publications).ResultsOur data mining tool, STEED (STructured Extraction of Experimental Data), successfully extracted key experimental parameters such as animal models and species, as well as risk of bias items like randomization or blinding, from in vivo studies. Sensitivity and specificity were over 85% and 80%, respectively, for most items in both validation corpora. Accuracy and F1-score were above 90% and 0.9 for most items in the validation corpora, respectively. Time savings were above 99%.ConclusionsOur text mining tool, STEED, can extract key experimental parameters and risk of bias items from the neuroscience in vivo literature. This enables the tool’s deployment for probing a field in a research improvement context or replacing one human reader during data extraction, resulting in substantial time savings and contributing towards the automation of systematic reviews.

Replication pack, FSE2018 submission #164:
------------------------------------------

**Working title:** Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: 
A Case Study of the PyPI Ecosystem

**Note:** link to data artifacts is already included in the paper. 
Link to the code will be included in the Camera Ready version as well.


Content description
===================

- **ghd-0.1.0.zip** - the code archive. This code produces the dataset files 
 described below
- **settings.py** - settings template for the code archive.
- **dataset_minimal_Jan_2018.zip** - the minimally sufficient version of the dataset.
 This dataset only includes stats aggregated by the ecosystem (PyPI)
- **dataset_full_Jan_2018.tgz** - full version of the dataset, including project-level
 statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages
 themselves, which take around 2TB.
- **build_model.r, helpers.r** - R files to process the survival data 
  (`survival_data.csv` in **dataset_minimal_Jan_2018.zip**, 
  `common.cache/survival_data.pypi_2008_2017-12_6.csv` in 
  **dataset_full_Jan_2018.tgz**)
- **Interview protocol.pdf** - approximate protocol used for semistructured interviews.
- LICENSE - text of GPL v3, under which this dataset is published
- INSTALL.md - replication guide (~2 pages)

Replication guide
=================

Step 0 - prerequisites
----------------------

- Unix-compatible OS (Linux or OS X)
- Python interpreter (2.7 was used; Python 3 compatibility is highly likely)
- R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible)

Depending on detalization level (see Step 2 for more details):
- up to 2Tb of disk space (see Step 2 detalization levels)
- at least 16Gb of RAM (64 preferable)
- few hours to few month of processing time

Step 1 - software
----------------

- unpack **ghd-0.1.0.zip**, or clone from gitlab:

   git clone https://gitlab.com/user2589/ghd.git
   git checkout 0.1.0
 
 `cd` into the extracted folder. 
 All commands below assume it as a current directory.
  
- copy `settings.py` into the extracted folder. Edit the file:
  * set `DATASET_PATH` to some newly created folder path
  * add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` 
- install docker. For Ubuntu Linux, the command is 
  `sudo apt-get install docker-compose`
- install libarchive and headers: `sudo apt-get install libarchive-dev`
- (optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools`
 Without this dependency, you might get an error on the next step, 
 but it's safe to ignore.
- install Python libraries: `pip install --user -r requirements.txt` . 
- disable all APIs except GitHub (Bitbucket and Gitlab support were
 not yet implemented when this study was in progress): edit
 `scraper/init.py`, comment out everything except GitHub support
 in `PROVIDERS`.

Step 2 - obtaining the dataset
-----------------------------

The ultimate goal of this step is to get output of the Python function 
`common.utils.survival_data()` and save it into a CSV file:

  # copy and paste into a Python console
  from common import utils
  survival_data = utils.survival_data('pypi', '2008', smoothing=6)
  survival_data.to_csv('survival_data.csv')

Since full replication will take several months, here are some ways to speedup
the process:

####Option 2.a, difficulty level: easiest

Just use the precomputed data. Step 1 is not necessary under this scenario.

- extract **dataset_minimal_Jan_2018.zip**
- get `survival_data.csv`, go to the next step

####Option 2.b, difficulty level: easy

Use precomputed longitudinal feature values to build the final table.
The whole process will take 15..30 minutes.

- create a folder `

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An R script to extract and parse the Postal Code Conversion File (PCCF) from Statistics Canada/Canada Post.

View details of Bilberry Extract Import Data of Tianjin Jianfeng Natural Product R And Huanghai Supplier to US with product description, price, date, quantity, major us ports, countries and more.

For more than 100 years, the Permian Basin has been an important source of oil and gas produced from conventional reservoirs; directional drilling combined with hydraulic fracturing has greatly increased production in the past 10 years to the extent that the Permian Basin is becoming one of the world’s largest continuous oil and gas (COG) producing fields (U.S. Energy Information Administration, 2020). These recent techniques extract oil and gas by directionally drilling and hydraulically fracturing the surrounding reservoir rock. The extraction of COG by using these techniques requires large volumes of water and estimates of the total water volume used in COG require a comprehensive assessment to determine the amount of water needed to extract reservoir resources. This data release contains the R scripts used to process input data (Ball and others, 2020) and the results (output data) produced by those scripts. Linear and quantile regression models of water use in relation to the number of oil and gas wells developed were fitted to the direct, indirect, and ancillary water-use categories for the Permian Basin. Confidence intervals for each parameter estimate (regression model coefficient) obtained from the linear regression models were computed as a measure of uncertainty. Together, these scripts and output data can be used to model water use associated with COG development in the Permian Basin, with estimates by individual well and by county. In March, 2022, U.S. Geological Survey staff noticed an incorrect version of a file that was not part of the Bureau approved data release was included within this data release by mistake. The data release has been updated by replacing the incorrect version of the file with the original Bureau approved version of the file. The file in question is located within the top-level "Model.zip" directory and is the "mungeDataRelease.R" script. The incorrect file had the same name as the correct file. First release: August 2021; revised April 2022 (version 2.0). The previous version can be obtained by contacting the USGS Oklahoma-Texas Water Science Center using the "Point of Contact" link on the landing page on ScienceBase.

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R-SCRIPT and EXAMPLE DATA to extract incubation from nest temperature measurements as described in

“Biparental incubation patterns in a high Arctic-breeding shorebird: how do pairs divide their duties?”by M. Bulla, M. Valcu, A. L. Rutten, and B. Kempenaerspublished in Behavioral Ecology, doi:10.1093/beheco/art098.

The R-script- is optimized for data sets with per 5 seconds temperature readings (example data: 5s.txt) as well as for data sets with per 2 minutes temperature readings (artificially spread to each 5 second; example data: 2m.txt)- runs with R (Version 2.15.13) and package zoo (Version 1.7-10) Data collection and how the individual variables and threshold values were derived is described in the paper and its supplementaries.

Example data sets are available as 2 text files (values are separated by semi-colon ).Both data set contain of following variables: nest (unique identity of the nest), datetime_ (date and time of the sampled datapoint), t_nest (nest incubation temperature in °C), t_ambient (tundra temperature next to the nest in °C).

WHEN USING THIS R-SCRIPT OR DATA, PLEACE CITE THE ORIGINAL PUBLICATION: Bulla M, Valcu M, Rutten AL, Kempenaers B (2014) Biparental incubation patterns in a high Arctic-breeding shorebird: how do pairs divide their duties? Behavioral Ecology 25(1): 152-164. doi:10.1093/beheco/art098 ADDITIONALLY, PLEASE CITE THE FIGSHARE.COM R-SCRIPT and DATA: Bulla M.R-SCRIPT and EXAMPLE DATA to extract incubation from temperature measurements. figshare.comAvailable at: http://figshare.com/articles/R_SCRIPT_and_EXAMPLE_DATA_to_extract_incubation_from_temperature_measurements/1037545

R Usa S Export Sl Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The Clinical Practice Research Datalink (CPRD) is a large and widely used resource of electronic health records from the UK, linking primary care data to hospital data, death registration data, cancer registry data, deprivation data and mental health services data. Extraction and management of CPRD data is a computationally demanding process and requires a significant amount of work, in particular when using R. The rcprd package simplifies the process of extracting and processing CPRD data in order to build datasets ready for statistical analysis. Raw CPRD data is provided in thousands of.txt files, making querying this data cumbersome and inefficient. rcprd saves the relevant information into an SQLite database stored on the hard drive which can then be queried efficiently to extract required information about individuals. rcprd follows a four-stage process: 1) Definition of a cohort, 2) Read in medical/prescription data and save into an SQLite database, 3) Query this SQLite database for specific codes and tests to create variables for each individual in the cohort, 4) Combine extracted variables into a dataset ready for statistical analysis. Functions are available to extract common variable types (e.g., history of a condition, or time until an event occurs, relative to an index date), and more general functions for database queries, allowing users to define their own variables for extraction. The entire process can be done from within R, with no knowledge of SQL required. This manuscript showcases the functionality of rcprd by running through an example using simulated CPRD Aurum data. rcprd will reduce the duplication of time and effort among those using CPRD data for research, allowing more time to be focused on other aspects of research projects.

FETCH'R

## Overview

FETCH'R is a dataset for object detection tasks - it contains Objects annotations for 9,146 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Abstract \r

\r This dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement.\r \r The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.\r \r \r \r The Groundwater (GW) quantiles are extracted from the Groundwater modelling outputs. Dataset prepared for import into the Impact and Risk Analysis Database.\r \r

Dataset History \r

\r Drawdown percentile and exceedance probability values was extracted from groundwater model outputs. This was performed using a GIS routine to extract groundwater model raster values using the assessment units (as points) attributed with the regional water table aquifer layer and assigning the model value from the corresponding layer to each assessment unit.\r \r

Dataset Citation \r

\r XXXX XXX (2017) GAL GW Quantile Interpolation 20161013. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/49f20390-3340-4b08-b1dc-370fb919d34c.\r \r

Dataset Ancestors \r

\r * Derived From Surface Geology of Australia, 1:2 500 000 scale, 2012 edition\r \r * Derived From Galilee Drawdown Rasters\r \r * Derived From Galilee model HRV receptors gdb\r \r * Derived From Queensland petroleum exploration data - QPED\r \r * Derived From Galilee groundwater numerical modelling AEM models\r \r * Derived From Galilee drawdown grids\r \r * Derived From Three-dimensional visualisation of the Great Artesian Basin - GABWRA\r \r * Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale\r \r * Derived From Phanerozoic OZ SEEBASE v2 GIS\r \r * Derived From Galilee Hydrological Response Variable (HRV) model\r \r * Derived From QLD Department of Natural Resources and Mines Groundwater Database Extract 20142808\r \r * Derived From GAL Assessment Units 1000m 20160522 v01\r \r * Derived From Galilee Groundwater Model, Hydrogeological Formation Extents v01\r \r * Derived From BA ALL Assessment Units 1000m Reference 20160516_v01\r \r * Derived From GAL Aquifer Formation Extents v01\r \r * Derived From Queensland Geological Digital Data - Detailed state extent, regional. November 2012\r \r * Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01\r \r * Derived From GAL Aquifer Formation Extents v02\r \r

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Dataset and scripts used for manuscript: High consistency and repeatability in the breeding migrations of a benthic shark.

Project title: High consistency and repeatability in the breeding migrations of a benthic sharkDate:23/04/2024

Folders:- 1_Raw_data - Perpendicular_Point_068151, Sanctuary_Point_068088, SST raw data, sst_nc_files, IMOS_animal_measurements, IMOS_detections, PS&Syd&JB tags, rainfall_raw, sample_size, Point_Perpendicular_2013_2019, Sanctuary_Point_2013_2019, EAC_transport- 2_Processed_data - SST (anomaly, historic_sst, mean_sst_31_years, week_1992_sst:week_2022_sst including week_2019_complete_sst) - Rain (weekly_rain, weekly_rainfall_completed) - Clean (clean, cleaned_data, cleaned_gam, cleaned_pj_data)- 3_Script_processing_data - Plots(dual_axis_plot (Fig. 1 & Fig. 4).R, period_plot (Fig. 2).R, sd_plot (Fig. 5).R, sex_plot (Fig. 3).R - cleaned_data.R, cleaned_data_gam.R, weekly_rainfall_completed.R, descriptive_stats.R, sst.R, sst_2019b.R, sst_anomaly.R- 4_Script_analyses - gam.R, gam_eac.R, glm.R, lme.R, Repeatability.R- 5_Output_doc - Plots (arrival_dual_plot_with_anomaly (Fig. 1).png, period_plot (Fig.2).png, sex_arrival_departure (Fig. 3).png, departure_dual_plot_with_anomaly (Fig. 4).png, standard deviation plot (Fig. 5).png) - Tables (gam_arrival_eac_selection_table.csv (Table S2), gam_departure_eac_selection_table (Table S5), gam_arrival_selection_table (Table. S3), gam_departure_selection_table (Table. S6), glm_arrival_selection_table, glm_departure_selection_table, lme_arrival_anova_table, lme_arrival_selection_table (Table S4), lme_departure_anova_table, lme_departure_selection_table (Table. S8))

Descriptions of scripts and files used:- cleaned_data.R: script to extract detections of sharks at Jervis Bay. Calculate arrival and departure dates over the seven breeding seasons. Add sex and length for each individual. Extract moon phase (numerical value) and period of the day from arrival and departure times. - IMOS_detections.csv: raw data file with detections of Port Jackson sharks over different sites in Australia. - IMOS_animal_measurements.csv: raw data file with morphological data of Port Jackson sharks - PS&Syd&JB tags: file with measurements and sex identification of sharks (different from IMOS, it was used to complete missing sex and length). - cleaned_data.csv: file with arrival and departure dates of the final sample size of sharks (N=49) with missing sex and length for some individuals. - clean.csv: completed file using PS&Syd&JB tags, note: tag ID 117393679 was wrongly identified as a male in IMOS and correctly identified as a female in PS&Syd&JB tags file as indicated by its large size. - cleaned_pj_data: Final data file with arrival and departure dates, sex, length, moon phase (numerical) and period of the day.

• weekly_rainfall_completed.R: script to calculate average weekly rainfall and correlation between the two weather stations used (Point perpendicular and Sanctuary point). - weekly_rain.csv: file with the corresponding week number (1-28) for each date (01-06-2013 to 13-12-2019) - weekly_rainfall_completed.csv: file with week number (1-28), year (2013-2019) and weekly rainfall average completed with Sanctuary Point for week 2 of 2017 - Point_Perpendicular_2013_2019: Rainfall (mm) from 01-01-2013 to 31-12-2020 at the Point Perpendicular weather station - Sanctuary_Point_2013_2019: Rainfall (mm) from 01-01-2013 to 31-12-2020 at the Sanctuary Point weather station - IDCJAC0009_068088_2017_Data.csv: Rainfall (mm) from 01-01-2017 to 31-12-2017 at the Sanctuary Point weather station (to fill in missing value for average rainfall of week 2 of 2017)

• cleaned_data_gam.R: script to calculate weekly counts of sharks to run gam models and add weekly averages of rainfall and sst anomaly - cleaned_pj_data.csv - anomaly.csv: weekly (1-28) average sst anomalies for Jervis Bay (2013-2019) - weekly_rainfall_completed.csv: weekly (1-28) average rainfall for Jervis Bay (2013-2019_ - sample_size.csv: file with the number of sharks tagged (13-49) for each year (2013-2019)

• sst.R: script to extract daily and weekly sst from IMOS nc files from 01-05 until 31-12 for the following years: 1992:2022 for Jervis Bay - sst_raw_data: folder with all the raw weekly (1:28) csv files for each year (1992:2022) to fill in with sst data using the sst script - sst_nc_files: folder with all the nc files downloaded from IMOS from the last 31 years (1992-2022) at the sensor (IMOS - SRS - SST - L3S-Single Sensor - 1 day - night time – Australia). - SST: folder with the average weekly (1-28) sst data extracted from the nc files using the sst script for each of the 31 years (to calculate temperature anomaly).

• sst_2019b.R: script to extract daily and weekly sst from IMOS nc file for 2019 (missing value for week 19) for Jervis Bay - week_2019_sst: weekly average sst 2019 with a missing value for week 19 - week_2019b_sst: sst data from 2019 with another sensor (IMOS – SRS – MODIS - 01 day - Ocean Colour-SST) to fill in the gap of week 19 - week_2019_complete_sst: completed average weekly sst data from the year 2019 for weeks 1-28.

• sst_anomaly.R: script to calculate mean weekly sst anomaly for the study period (2013-2019) using mean historic weekly sst (1992-2022) - historic_sst.csv: mean weekly (1-28) and yearly (1992-2022) sst for Jervis Bay - mean_sst_31_years.csv: mean weekly (1-28) sst across all years (1992-2022) for Jervis Bay - anomaly.csv: mean weekly and yearly sst anomalies for the study period (2013-2019)

• Descriptive_stats.R: script to calculate minimum and maximum length of sharks, mean Julian arrival and departure dates per individual per year, mean Julian arrival and departure dates per year for all sharks (Table. S10), summary of standard deviation of julian arrival dates (Table. S9) - cleaned_pj_data.csv

• gam.R: script used to run the Generalized additive model for rainfall and sea surface temperature - cleaned_gam.csv

• glm.R: script used to run the Generalized linear mixed models for the period of the day and moon phase - cleaned_pj_data.csv - sample_size.csv

• lme.R: script used to run the Linear mixed model for sex and size - cleaned_pj_data.csv

• Repeatability.R: script used to run the Repeatability for Julian arrival and Julian departure dates - cleaned_pj_data.csv

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In molecular phylogenetics, partition models and mixture models provide different approaches to accommodating heterogeneity in genomic sequencing data. Both types of models generally give a superior fit to data than models that assume the process of sequence evolution is homogeneous across sites and lineages. The Akaike Information Criterion (AIC), an estimator of Kullback-Leibler divergence, and the Bayesian Information Criterion (BIC) are popular tools to select models in phylogenetics. Recent work suggests AIC should not be used for comparing mixture and partition models. In this work, we clarify that this difficulty is not fully explained by AIC misestimating the Kullback-Leibler divergence. We also investigate the performance of the AIC and BIC by comparing amongst mixture models and amongst partition models. We find that under non-standard conditions (i.e. when some edges have a small expected number of changes), AIC underestimates the expected Kullback-Leibler divergence. Under such conditions, AIC preferred the complex mixture models and BIC preferred the simpler mixture models. The mixture models selected by AIC had a better performance in estimating the edge length, while the simpler models selected by BIC performed better in estimating the base frequencies and substitution rate parameters. In contrast, AIC and BIC both prefer simpler partition models over more complex partition models under non-standard conditions, despite the fact that the more complex partition model was the generating model. We also investigated how mispartitioning (i.e. grouping sites that have not evolved under the same process) affects both the performance of partition models compared to mixture models and the model selection process. We found that as the level of mispartitioning increases, the bias of AIC in estimating the expected Kullback-Leibler divergence remains the same, and the branch lengths and evolutionary parameters estimated by partition models become less accurate. We recommend that researchers be cautious when using AIC and BIC to select among partition and mixture models; other alternatives, such as cross-validation and bootstrapping should be explored, but may suffer similar limitations. Methods This document records the pipeline used in data analyses in ``Performance of Akaike Information Criterion and Bayesian Information Criterion in selecting partition models and mixture models''. The main processes included generating alignments, fitting four different partition and mixture models, and analysing results. The data were generated under Seq-Gen-1.3.4 (Rambaut and Grass 1997). The model fitting was performed IQ-TREE2 (Minh et al. 2020) on a Linux system. The results were analysed using the R package phangorn in R (version 3.6.2) (Schliep 2011, R Core Team 2019). We wrote custom bash scripts to extract relevant parts of the results from IQ-TREE2, and these results were processed in R. The zip files contain four folders: "bash-scripts", "data", "R-codes", and "results-IQTREE2". The bash-scripts folder contains all the bash scripts for simulating alignments and performing model fitting. The "data" folder contains two child folders: "sequence-data" and "Rdata". The child folder "sequence-data" contains the alignments created for the simulations. The other child folder, "Rdata", contains the files created by R to store the results extracted from "IQTREE2" and the results calculated in R. The "R-codes" folder includes the R codes for analysing the results from "IQTREE2". The folder "results-IQTREE2" stores all the results from the fitted models. The three simulations we performed were essentially the same. We used the same parameters of the evolutionary models, and the trees with the same topologies but different edge lengths to generate the sequences. The steps we used were: simulating alignments, model fitting and extracting results, and processing the extracted results. The first two steps were performed on a Linux system using bash scripts, and the last step was performed in R. Simulating Alignment To simulate heterogeneous data we created two multiple sequence alignments (MSAs) under simple homogeneous models with each model comprising a substitution model and an edge-weighted phylogenetic tree (the tree topology was fixed). Each MSA contained eight taxa and 1000 sites. This was performed using the bash script “step1_seqgen_data.sh” in Linux. These two MSAs were then concatenated together giving a MSA with 2000 sites. This was equivalent to generating the concatenated MSA under a two-block unlinked edge lengths partition model (P-UEL). This was performed using the bash script “step2_concat_data.sh”. This created the 0% group of MSAs. In order to simulate a situation where the initial choice of blocks does not properly account for the heterogeneity in the concatenated MSA (i.e., mispartitioning), we randomly selected a proportion of 0%, 5%, 10%, 15%, …, up to 50% of sites from each block and swapped them. That is, the sites drawn from the first block were placed in the second block, and the sites drawn from the second block were placed in the first block. This process was repeated 100 times for each proportion of mispartitioned sites giving a total of 1100 MSAs. This process involved two steps. The first step was to generate ten sets of different amounts of numbers without duplicates from each of the two intervals [1,1000] and [1001,2000]. The amounts of numbers were based on the proportions of incorrectly partitioning sites. For example, the first set has 50 numbers on each interval, and the second set has 100 numbers on each interval, etc. The first step was performed in R, and the R code was not provided but the random number text files were included. The second step was to select sites from the concatenated MSAs from the locations based on the numbers created in the first step. This created 5%, 10%, 15%, …, 50% groups of MSAs. The second step used the following bash scripts: “step3_1_mixmatch_pre_data.sh” and “step3_2_mixmatch_data.sh”. The MSAs used in the simulations were created and stored in the “data” folder. Model Fitting and Extracting Results The next steps were to fit four different partition and mixture models to the data in IQ-TREE2 and extract the results. The models used were P-LEL partition model, P-UEL partition model, M-UGP mixture model, and M-LGP mixture model. For the partition models, the partitioning schemes were the same: the first 1000 sites as a block and the second 1000 sites as another. For the groups of MSAs with different proportions of mispartitioned sites, this was equivalent to fitting the partition models with an incorrect partitioning scheme. The partitioning scheme was called “parscheme.nex”. The bash scripts for model fitting were stored in the “bash-scripts” folder. To run the bash scripts, users can follow the order which was shown in the names of these bash scripts. The inferred trees, estimated base frequencies, estimated rate matrices, estimated weight factors and AIC values, and BIC values were extracted from the IQTREE2 results. These extracted results were stored in the “results-IQTREE2” folder and used to evaluate the performance of AIC, BIC, and models in R. Processing Extracted Results in R To evaluate the performance of AIC, BIC, and the performance of fitted partition models and mixture models, we calculated the following measures: the rEKL values, the bias of AIC in estimating the rEKL, BIC values, and the branch scores (bs). We also compared the distribution of the estimated model parameters (i.e. base frequencies and rate matrices) to the generating model parameters. These processes were performed in R. The first step was to read in the inferred trees, estimated base frequencies, estimated rate matrices, estimated weight factors, AIC values, and BIC values that were extracted from IQTREE2 results. These R scripts were stored in the “R-codes” folder, and the names of these scripts started with “readpara_...” (e.g. “readpara_MLGP_standard”). After reading in all the parameters for each model, we estimated the measures mentioned above using the corresponding R scripts that were also in the “R-codes” folder. The functions used in these R scripts were stored in the “R_functions_simulation”. It is worth noting that the directories need to be changed if users want to run these R scripts on their computers.

516 Global export shipment records of R Coat with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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