This report discusses some problems that can arise when attempting to import PostScript images into R, when the PostScript image contains coordinate transformations that skew the image. There is a description of some new features in the ‘grImport’ package for R that allow these sorts of images to be imported into R successfully.

This report discusses some problems that can arise when attempting to import PostScript images into R, when the PostScript image contains coordinate transformations that skew the image. There is a description of some new features in the ‘grImport’ package for R that allow these sorts of images to be imported into R successfully.

Author: Andrew J. Felton
Date: 11/15/2024

This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis, and figure production for the study entitled:

"Global estimates of the storage and transit time of water through vegetation"

Please note that 'turnover' and 'transit' are used interchangeably. Also please note that this R project has been updated multiple times as the analysis has updated throughout the peer review process.

#Data information:

The data folder contains key data sets used for analysis. In particular:

"data/turnover_from_python/updated/august_2024_lc/" contains the core datasets used in this study including global arrays summarizing five year (2016-2020) averages of mean (annual) and minimum (monthly) transit time, storage, canopy transpiration, and number of months of data able as both an array (.nc) or data table (.csv). These data were produced in python using the python scripts found in the "supporting_code" folder. The remaining files in the "data" and "data/supporting_data" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here. The "supporting_data"" folder also contains annual (2016-2020) MODIS land cover data used in the analysis and contains separate filters containing the original data (.hdf) and then the final process (filtered) data in .nc format. The resulting annual land cover distributions were used in the pre-processing of data in python.

#Code information

Python scripts can be found in the "supporting_code" folder.

Each R script in this project has a role:

"01_start.R": This script sets the working directory, loads in the tidyverse package (the remaining packages in this project are called using the `::` operator), and can run two other scripts: one that loads the customized functions (02_functions.R) and one for importing and processing the key dataset for this analysis (03_import_data.R).

"02_functions.R": This script contains custom functions. Load this using the `source()` function in the 01_start.R script.

"03_import_data.R": This script imports and processes the .csv transit data. It joins the mean (annual) transit time data with the minimum (monthly) transit data to generate one dataset for analysis: annual_turnover_2. Load this using the
`source()` function in the 01_start.R script.

"04_figures_tables.R": This is the main workhouse for figure/table production and supporting analyses. This script generates the key figures and summary statistics used in the study that then get saved in the "manuscript_figures" folder. Note that all maps were produced using Python code found in the "supporting_code"" folder. Also note that within the "manuscript_figures" folder there is an "extended_data" folder, which contains tables of the summary statistics (e.g., quartiles and sample sizes) behind figures containing box plots or depicting regression coefficients.

"supporting_generate_data.R": This script processes supporting data used in the analysis, primarily the varying ground-based datasets of leaf water content.

"supporting_process_land_cover.R": This takes annual MODIS land cover distributions and processes them through a multi-step filtering process so that they can be used in preprocessing of datasets in python.

Please note this dataset is the most recent version of the Administrative Boundaries (AB). For previous versions of the AB please go to this url: https://data.gov.au/dataset/ds-dga-b4ad5702-ea2b-4f04-833c-d0229bfd689e/details?q=previous\r \r ----------------------------------\r \r Geoscape Administrative Boundaries is Australia’s most comprehensive national collection of boundaries, including government, statistical and electoral boundaries. It is built and maintained by Geoscape Australia using authoritative government data. Further information about contributors to Administrative Boundaries is available here.\r \r This dataset comprises seven Geoscape products:\r \r * Localities\r * Local Government Areas (LGAs)\r * Wards\r * Australian Bureau of Statistics (ABS) Boundaries\r * Electoral Boundaries\r * State Boundaries and\r * Town Points\r \r Updated versions of Administrative Boundaries are published on a quarterly basis.\r \r Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.\r \r Notable changes in the February 2025 release\r \r * There have been spatial changes (area) greater than 1 km2 to the localities ‘Koombooloomba’, ‘Isisford’, ‘Ilfracombe’ and ‘Glen Ruth’ in Queensland.\r \r * There have been spatial changes (area) greater than 1 km2 to the localities ‘Birdwood’ and ‘Forreston’ in South Australia.\r \r * Three new wards ‘Central Ward’, ’East Ward’ and ’West Ward’ have been added in Northern Territory.\r \r * ‘Anindilyakwa Ward’ has been retired in Northern Territory.\r \r IMPORTANT NOTE: correction of issues with the 22 November 2022 release\r \r * On 28 November 2022, the Administrative Boundaries dataset originally released on 22 November 2022 was amended and re-uploaded after Geoscape identified some issues with the original data for 'Electoral Boundaries'.\r * As a result of the error, some shapefiles were published in 3D rather than 2D, which may affect some users when importing data into GIS applications.\r * The error affected the Electoral Boundaries dataset, specifically the Commonwealth boundary data for Victoria and Western Australia, including 'All States'.\r * Only the ESRI Shapefile formats were affected (both GDA94 and GDA2020). The MapInfo TAB format was not affected.\r * Because the datasets are zipped into a single file, once the error was fixed by Geoscape all of Administrative Boundaries shapefiles had to be re-uploaded, rather than only the affected files.\r * If you downloaded either of the two Administrative Boundary ESRI Shapefiles between 22 November and 28 November 2022 and plan to use the Electoral Boundary component, you are advised to download the revised version dated 28 November 2022. Apologies for any inconvenience.\r \r Further information on Administrative Boundaries, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on Administrative Boundaries, including software solutions, consultancy and support.\r \r Note: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia. \r \r \r The Australian Government has negotiated the release of Administrative Boundaries to the whole economy under an open CCBY 4.0 licence.\r \r Users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).\r \r Users must also note the following attribution requirements:\r \r Preferred attribution for the Licensed Material:\r \r

Administrative Boundaries © Geoscape Australia licensed by the Commonwealth of Australia under Creative Commons Attribution 4.0 International license (CC BY 4.0).\r \r Preferred attribution for Adapted Material:\r \r Incorporates or developed using Administrative Boundaries © Geoscape Australia licensed by the Commonwealth of Australia under Creative Commons Attribution 4.0 International licence (CC BY 4.0).\r \r

What to Expect When You Download Administrative Boundaries\r

\r Administrative Boundaries is large dataset (around 1.5GB unpacked), made up of seven themes each containing multiple layers.\r \r Users are advised to read the technical documentation including the product change notices and the individual product descriptions before downloading and using the product.\r \r Please note this dataset is the most recent version of the Administrative Boundaries (AB). For previous versions of the AB please go to this url: https://data.gov.au/dataset/ds-dga-b4ad5702-ea2b-4f04-833c-d0229bfd689e/details?q=previous\r

License Information\r

\r

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM for Europe at 1000 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 1000 m, bilinear resampling was performed in GRASS GIS (using r.proj) and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.

In many birds, there is significant variation in egg size both across and within clutches that remains to be explained. Birds lay one egg per day and in hot climates, the first laid eggs may start to develop before the laying of the rest of the clutch is complete, through warming by the ambient air temperature. Here, we test the hypothesis that in hot conditions, skews in egg size across the laying sequence may be more pronounced, as females use egg size to compensate for hatching asynchrony, providing a higher level of provisioning to the later laid eggs that would hatch later due to ambient incubation. We have focused on the zebra finch (Taeniopygia guttata), a species that typically breeds over an extended period of the year, and therefore across a particularly wide range of ambient temperatures. We characterised the variation in egg size using data from over 700 clutches, including historical specimens, a wild population, and both domesticated and wild birds breeding in captivity, in addition to clutches produced experimentally in controlled-temperature rooms. Here, we document significant variation in egg size between and within clutches, with eggs increasing in size over the laying order, with both maternal identity and population differences playing an important role (domesticated birds laid eggs that were much larger than their wild counterparts). However, we found no support for the idea that variation in egg size either within a clutch, and across clutches and populations, is related to variation in ambient temperature, despite the large range of thermal environments experienced during laying. In conclusion, whilst egg size is clearly a labile characteristic there is no evidence this is flexibly adjusted to local ambient temperatures before and during laying.

Methods Details in full paper

Usage Notes File contains all the data to run the Analyses that are described in the supplementary R code file "Griffith et al_Egg size_R_code_190814.Rmd". There is one data frame in the supplementary data file (Sup_Data_ZBs_EggSize_190814.xlsx) that can be saved into an individual CSV file using the same file name as the tab name so that the supplementary code can be easily followed for importing the data to R.

Find more details in read me in second worksheet of file.

Original data, R script (code) and code output for the paper published on Journal of Dairy Science. For best use, replicate analysis using R. Importing data using the .csv file may cause some variables (columns of the spreadsheet) to be imported with the wrong format. Any issues, do not hesitate in contact. Happy coding!

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This report describes three different approaches to communicating between R and MetaPost: importing the PostScript output from MetaPost with the 'grImport' package; calling the mpost program to solve MetaPost paths with the 'metapost' package; and calling the mplib library to solve MetaPost paths with the 'mplib' package.

[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

Here we provide a mosaic of the Copernicus DEM 30m for Europe and the corresponding hillshade derived from the GLO-30 public instance of the Copernicus DEM. The CRS is the same as the original Copernicus DEM CRS: EPSG:4326. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM for Europe at 30 m in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt The pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.

Goods may only be landed at certain points. International vessels must comply with the Biosecurity Act 2015 and the First Point of Entry Biosecurity Determinations when entering an Australian port.\r \r Approval for the landing of goods must be granted from the department.\r \r - Vessels with ships pets are required to arrive at a port that is a first point of entry.\r - View more information about biosecurity requirements for importing goods is available at http://www.agriculture.gov.au/import \r - For more details on ship sanitation and certification refer to http://www.agriculture.gov.au/biosecurity/avm/vessels/commercial-vessels/sanitation\r \r Please be aware that as of 16 June 2019 there have been changes to the determination of ports which may impact what classes of vessels and goods are permitted to arrive at a FPOE. Please see the first point of entry determination for each port for current permissions at: http://www.agriculture.gov.au/biosecurity/avm/vessels/first-point-entry-and-non-first-point-entry/seaport-locations.\r \r Ports that demonstrated full compliance with regulatory requirements were granted ongoing first point of entry determination. Certain ports had their transitional first point of entry determination extended and can continue to operate as usual during their extension period.\r \r The ports of Carnarvon, Exmouth, Port Huon, Spring Bay and Stanley have had their determinations lapse. As these ports are no longer FPOE they are not listed in the table.

EEG data analysis repository for Shalu et al. Similar but different: ERP evidence on the processing of mental and physical
experiencer verbs in Malayalam. The preprocessed data from https://doi.org/10.5281/zenodo.14986232 was imported in R (Version 4.4.2; R Core Team, 2024) using the eeguana package (Version 0.1.11.9001; Nicenboim, 2018) for epoching and statistical analysis. Single trial EEG epochs were used for statistically analysing the mean amplitudes in selected time-windows of interest by fitting linear mixed effects models (LMEM) using the lme4 package (Bates et al., 2015) in R.

The names of the zipped folders describe their respective contents. The Code_In_Context_Analysis_Output_R_Notebooks folder contains the R Notebooks of the LMEM analyses of the behavioural and ERP data. These notebooks show the code, data and output in context, and provide full model summaries and details for all the models reported in the article. The R scripts themselves reside in a folder of their own, which also includes the version of the eeguana package we used for the analyses, and the custom-made helper scripts for processing the EEG data, epoching them and extracting mean amplitudes from them. The EEG and Epochs data resulting from the ERP analysis, as well as the single-trial mean amplitudes and prestimulus mean amplitudes extracted for each time-window of interest are in EEG_And_Epochs_Data_Files. Since the LMEM models computed were quite complex, the model objects are provided as RDS files for easily importing them into R without having to compute the models again. The plots generated at various stages of the analysis are in the Plots folder.

References

Bates, D., Mächler, M., Bolker, B., and Walker, S. (2015). Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1. doi: 10.18637/jss.v067.i01.

Nicenboim, B. (2018). eeguana: A package for manipulating EEG data in R. Version 0.1.11.9001. Retrieved from https://github.com/bnicenboim/eeguana. http://doi.org/10.5281/zenodo.2533138.

R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org.

Shalu, S., Choudhary, K. K., & Muralikrishnan, R. (2025). Dataset with EEG data from Malayalam speakers. (Shalu et al., 2025) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.14986232

The Hopkins U.S. System Index (HUSSI) is an information resource for forest entomologists, systematic entomologists, pest management specialists, foresters, and students. It is a collection of notes on thousands of insect and damage specimens from forests or wood products taken mainly in the United States, with some from Canada, Mexico, Central America, South America and other regions. Specimens related to the records are in collections at several USDA Forest Service installations; at the U.S. National Museum, Smithsonian Institution, Washington, DC; and at several universities. The paper-based system, conceptualized by Dr. A.D. Hopkins in 1894 and formally initiated by the USDA in 1902, now contains over 160,000 written records. Some of these records have been digitized as follows. The database includes information on location, date, taxon, insect and plant host association; other searches, measurements, and quantitative data; and other information in tabular or narrative form. The original database file was designed for importing into dBase, Access, FoxBase, RBase, Paradox, and other XBase-type programs. The data dictionary describes information entered in the 16 fields abstracted from the Hopkins U.S. System records. Then you can structure specific queries and reports that show:

Plant hosts Insect hosts Parasites & predators Geographic distribution Collection dates and collectors Location of original written notes Location of insect or damage specimens Resources in this dataset:Resource Title: Data files rezipped October 2015. File Name: allwest2.zipResource Description: The original allwest.exe data package offered by U.S. Forest Service was opened using WinZip 15 (Windows 7) and saved as a zip archive suitable for opening with typical archive utilities on both Windows and Macintosh. Downloaded in October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml.

Includes:

README.TXT : Instructions from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml

TITLPAGE.TXT : Title page.

HUSINTRO.TXT : Background information on the Hopkins U.S. System and the Hopkins U.S. System Index (HUSSI).

HUSSTAT.TXT : Description of HUSSI files at each repository.

HUSREPOS.TXT : List of repositories (as of 1986) for Hopkins U.S. System records described in HUSSI.

HUSDTDIC.TXT : Data dictionary for HUSSI records.

DBDESAW2.TXT : Description of ALLWEST2 database.

ALLWEST2.DBF : HUSSI records from all western USDA Forest Service repositories (as of 1986), except PSWNB records from notebooks at the Pacific Southwest Experiment Station, Berkeley, CA. PSWNB records are in a seperate archive.Resource Title: Flat version of the HUSSI database. File Name: ALLWEST2.csvResource Description: The file ALLWEST2.DBF from ALLWEST.EXE was converted to a comma separated values file using LibreOffice 5.0.2.2. This appears to include all 37,198 records with 16 columns as described in the data dictionary. Suitable for use with most applications that can handle CSV input.Resource Title: Original text version of HUSSI data dictionary. File Name: HUSDTDIC.TXTResource Description: Included in ALLWEST archive downloaded from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml Title: Original list of repositories (as of 1986) for Hopkins U.S. System records described in HUSSI.. File Name: HUSREPOS.TXTResource Description: Included in ALLWEST2 archive downloaded from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml

Expands all the acronyms of the repositories holding physical cards represented in the database.Resource Title: Original README.TXT from the ALLWEST archive. File Name: README.TXTResource Description: Original README.TXT from the ALLWEST archive. The explanations appear in the zipped archive, and have been used as a basis for this dataset description. Includes obsolete instructions for using self-extracting archive on Windows 95 and Windows 3.x operating systems.Resource Title: Original Database Description from ALLWEST2 archive. File Name: DBDESAW2.TXTResource Description: Included in ALLWEST2 archive downloaded in October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml. Title: Original introductory text from ALLWEST2 archive. File Name: HUSINTRO.TXTResource Description: Included in ALLWEST archive downloaded in October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml Title: Original title page for HUSSI. File Name: TITLPAGE.TXTResource Description: Included in ALLWEST archive downloaded from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml Title: Original statistics file for HUSSI records . File Name: HUSSTAT.TXTResource Description: A description of record types for Hopkins U.S. System files and number of HUSSI records for each repository as of March 1991. Part of the ALLWEST2 archive downloaded October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml

This repository provides the R-MRIO database for the years 1995–2005 of the study "A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress".

https://doi.org/10.1016/j.scitotenv.2020.142587

The code to resolve the database and the data for the years 2006–2015 are stored under the repository http://doi.org/10.5281/zenodo.3993659

The folders "R-MRIO_year" provide the following files (*.mat-files) for each year from 1995–2005:
A_RMRIO: the coefficient matrix
Y_RMRIO: the final demand matrix
Ext_RMRIO and Ext_hh_RMRIO: the satellite matrix of the economy and the final demand
TotalOut_RMRIO: the total output vector
The labels of the matrices are provided by the separate folder "Labels_RMRIO "

A script for importing and indexing the RMRIO database files in Python as Pandas DataFrames can be found here:

https://github.com/jbnsn/RMRIO-database-py-import

These data files are used for analyses in manuscript Ellis, N.M. and Leroux, S.J. [submitted]. A test of browser impacts on ecosystem function.This data includes information on the plant community characteristics, litter quantity, soil quality, and litter decomposition rates, as well as abiotic characteristics in Newfoundland, Canada. The file also contains R code used for analyses of this data. When importing new data (i.e. sw_sites2.csv, ind_spec.csv, height1.csv, litter.csv, soil_quality.csv, decomposition.csv, correlations.csv) while running the R code, be sure to set the