Video on normalizing microbiome data from the Research Experiences in Microbiomes Network

Data for reproducing analysis in the manuscript:Normalizing and denoising protein expression data from droplet-based single cell profilinglink to manuscript: https://www.biorxiv.org/content/10.1101/2020.02.24.963603v1

Data deposited here are for the purposes of reproducing the analysis results and figures reported in the manuscript above. These data are all publicly available downloaded and converted to R datasets prior to Dec 4, 2020. For a full description of all the data included in this repository and instructions for reproducing all analysis results and figures, please see the repository: https://github.com/niaid/dsb_manuscript.

For usage of the dsb R package for normalizing CITE-seq data please see the repository: https://github.com/niaid/dsb

If you use the dsb R package in your work please cite:Mulè MP, Martins AJ, Tsang JS. Normalizing and denoising protein expression data from droplet-based single cell profiling. bioRxiv. 2020;2020.02.24.963603.

General contact: John Tsang (john.tsang AT nih.gov)

Questions about software/code: Matt Mulè (mulemp AT nih.gov)

Standardized data from Mobilise-D participants (YAR dataset) and pre-existing datasets (ICICLE, MSIPC2, Gait in Lab and real-life settings, MS project, UNISS-UNIGE) are provided in the shared folder, as an example of the procedures proposed in the publication "Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization" that is currently under review in Scientific data. Please refer to that publication for further information. Please cite that publication if using these data.

The code to standardize an example subject (for the ICICLE dataset) and to open the standardized Matlab files in other languages (Python, R) is available in github (https://github.com/luca-palmerini/Procedure-wearable-data-standardization-Mobilise-D).

The datasets can be opened using Microsoft Excel and R.

This repository is associated with NSF DBI 2033973, RAPID Grant: Rapid Creation of a Data Product for the World's Specimens of Horseshoe Bats and Relatives, a Known Reservoir for Coronaviruses (https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033973). Specifically, this repository contains (1) raw data from iDigBio (http://portal.idigbio.org) and GBIF (https://www.gbif.org), (2) R code for reproducible data wrangling and improvement, (3) protocols associated with data enhancements, and (4) enhanced versions of the dataset published at various project milestones. Additional code associated with this grant can be found in the BIOSPEX repository (https://github.com/iDigBio/Biospex). Long-term data management of the enhanced specimen data created by this project is expected to be accomplished by the natural history collections curating the physical specimens, a list of which can be found in this Zenodo resource.

Grant abstract: "The award to Florida State University will support research contributing to the development of georeferenced, vetted, and versioned data products of the world's specimens of horseshoe bats and their relatives for use by researchers studying the origins and spread of SARS-like coronaviruses, including the causative agent of COVID-19. Horseshoe bats and other closely related species are reported to be reservoirs of several SARS-like coronaviruses. Species of these bats are primarily distributed in regions where these viruses have been introduced to populations of humans. Currently, data associated with specimens of these bats are housed in natural history collections that are widely distributed both nationally and globally. Additionally, information tying these specimens to localities are mostly vague, or in many instances missing. This decreases the utility of the specimens for understanding the source, emergence, and distribution of SARS-COV-2 and similar viruses. This project will provide quality georeferenced data products through the consolidation of ancillary information linked to each bat specimen, using the extended specimen model. The resulting product will serve as a model of how data in biodiversity collections might be used to address emerging diseases of zoonotic origin. Results from the project will be disseminated widely in opensource journals, at scientific meetings, and via websites associated with the participating organizations and institutions. Support of this project provides a quality resource optimized to inform research relevant to improving our understanding of the biology and spread of SARS-CoV-2. The overall objectives are to deliver versioned data products, in formats used by the wider research and biodiversity collections communities, through an open-access repository; project protocols and code via GitHub and described in a peer-reviewed paper, and; sustained engagement with biodiversity collections throughout the project for reintegration of improved data into their local specimen data management systems improving long-term curation.

This RAPID award will produce and deliver a georeferenced, vetted and consolidated data product for horseshoe bats and related species to facilitate understanding of the sources, distribution, and spread of SARS-CoV-2 and related viruses, a timely response to the ongoing global pandemic caused by SARS-CoV-2 and an important contribution to the global effort to consolidate and provide quality data that are relevant to understanding emergent and other properties the current pandemic. This RAPID award is made by the Division of Biological Infrastructure (DBI) using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria."

Files included in this resource

9d4b9069-48c4-4212-90d8-4dd6f4b7f2a5.zip: Raw data from iDigBio, DwC-A format

0067804-200613084148143.zip: Raw data from GBIF, DwC-A format

0067806-200613084148143.zip: Raw data from GBIF, DwC-A format

1623690110.zip: Full export of this project's data (enhanced and raw) from BIOSPEX, CSV format

bionomia-datasets-attributions.zip: Directory containing 103 Frictionless Data packages for datasets that have attributions made containing Rhinolophids or Hipposiderids, each package also containing a CSV file for mismatches in person date of birth/death and specimen eventDate. File bionomia-datasets-attributions-key_2021-02-25.csv included in this directory provides a key between dataset identifier (how the Frictionless Data package files are named) and dataset name.

bionomia-problem-dates-all-datasets_2021-02-25.csv: List of 21 Hipposiderid or Rhinolophid records whose eventDate or dateIdentified mismatches a wikidata recipient’s date of birth or death across all datasets.

flagEventDate.txt: file containing term definition to reference in DwC-A

flagExclude.txt: file containing term definition to reference in DwC-A

flagGeoreference.txt: file containing term definition to reference in DwC-A

flagTaxonomy.txt: file containing term definition to reference in DwC-A

georeferencedByID.txt: file containing term definition to reference in DwC-A

identifiedByNames.txt: file containing term definition to reference in DwC-A

instructions-to-get-people-data-from-bionomia-via-datasetKey: instructions given to data providers

RAPID-code_collection-date.R: code associated with enhancing collection dates

RAPID-code_compile-deduplicate.R: code associated with compiling and deduplicating raw data

RAPID-code_external-linkages-bold.R: code associated with enhancing external linkages

RAPID-code_external-linkages-genbank.R: code associated with enhancing external linkages

RAPID-code_external-linkages-standardize.R: code associated with enhancing external linkages

RAPID-code_people.R: code associated with enhancing data about people

RAPID-code_standardize-country.R: code associated with standardizing country data

RAPID-data-dictionary.pdf: metadata about terms included in this project’s data, in PDF format

RAPID-data-dictionary.xlsx: metadata about terms included in this project’s data, in spreadsheet format

rapid-data-providers_2021-05-03.csv: list of data providers and number of records provided to rapid-joined-records_country-cleanup_2020-09-23.csv

rapid-final-data-product_2021-06-29.zip: Enhanced data from BIOSPEX, DwC-A format

rapid-final-gazetteer.zip: Gazetteer providing georeference data and metadata for 10,341 localities assessed as part of this project

rapid-joined-records_country-cleanup_2020-09-23.csv: data product initial version where raw data has been compiled and deduplicated, and country data has been standardized

RAPID-protocol_collection-date.pdf: protocol associated with enhancing collection dates

RAPID-protocol_compile-deduplicate.pdf: protocol associated with compiling and deduplicating raw data

RAPID-protocol_external-linkages.pdf: protocol associated with enhancing external linkages

RAPID-protocol_georeference.pdf: protocol associated with georeferencing

RAPID-protocol_people.pdf: protocol associated with enhancing data about people

RAPID-protocol_standardize-country.pdf: protocol associated with standardizing country data

RAPID-protocol_taxonomic-names.pdf: protocol associated with enhancing taxonomic name data

RAPIDAgentStrings1_archivedCopy_30March2021.ods: resource used in conjunction with RAPID people protocol

recordedByNames.txt: file containing term definition to reference in DwC-A

Rhinolophid-HipposideridAgentStrings_and_People2_archivedCopy_30March2021.ods: resource used in conjunction with RAPID people protocol

wikidata-notes-for-bat-collectors_leachman_2020: please see https://zenodo.org/record/4724139 for this resource

**Introduction ** This case study will be based on Cyclistic, a bike sharing company in Chicago. I will perform tasks of a junior data analyst to answer business questions. I will do this by following a process that includes the following phases: ask, prepare, process, analyze, share and act.

Background Cyclistic is a bike sharing company that operates 5828 bikes within 692 docking stations. The company has been around since 2016 and separates itself from the competition due to the fact that they offer a variety of bike services including assistive options. Lily Moreno is the director of the marketing team and will be the person to receive these insights from this analysis.

Case Study and business task Lily Morenos perspective on how to generate more income by marketing Cyclistics services correctly includes converting casual riders (one day passes and/or pay per ride customers) into annual riders with a membership. Annual riders are more profitable than casual riders according to the finance analysts. She would rather see a campaign targeting casual riders into annual riders, instead of launching campaigns targeting new costumers. So her strategy as the manager of the marketing team is simply to maximize the amount of annual riders by converting casual riders.

In order to make a data driven decision, Moreno needs the following insights: - A better understanding of how casual riders and annual riders differ - Why would a casual rider become an annual one - How digital media can affect the marketing tactics

Moreno has directed me to the first question - how do casual riders and annual riders differ?

Stakeholders Lily Moreno, manager of the marketing team Cyclistic Marketing team Executive team

Data sources and organization Data used in this report is made available and is licensed by Motivate International Inc. Personal data is hidden to protect personal information. Data used is from the past 12 months (01/04/2021 – 31/03/2022) of bike share dataset.

By merging all 12 monthly bike share data provided, an extensive amount of data with 5,400,000 rows were returned and included in this analysis.

Data security and limitations: Personal information is secured and hidden to prevent unlawful use. Original files are backed up in folders and subfolders.

Tools and documentation of cleaning process The tools used for data verification and data cleaning are Microsoft Excel and R programming. The original files made accessible by Motivate International Inc. are backed up in their original format and in separate files.

Microsoft Excel is used to generally look through the dataset and get a overview of the content. I performed simple checks of the data by filtering, sorting, formatting and standardizing the data to make it easily mergeable.. In Excel, I also changed data type to have the right format, removed unnecessary data if its incomplete or incorrect, created new columns to subtract and reformat existing columns and deleting empty cells. These tasks are easily done in spreadsheets and provides an initial cleaning process of the data.

R will be used to perform queries of bigger datasets such as this one. R will also be used to create visualizations to answer the question at hand.

Limitations Microsoft Excel has a limitation of 1,048,576 rows while the data of the 12 months combined are over 5,500,000 rows. When combining the 12 months of data into one table/sheet, Excel is no longer efficient and I switched over to R programming.

ABSTRACT: The World Soil Information Service (WoSIS) provides quality-assessed and standardized soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the ‘WoSIS snapshot 2019’ many new soil data were shared with us, registered in the ISRIC data repository, and subsequently standardized in accordance with the licenses specified by the data providers. The source data were contributed by a wide range of data providers, therefore special attention was paid to the standardization of soil property definitions, soil analytical procedures and soil property values (and units of measurement). We presently consider the following soil chemical properties (organic carbon, total carbon, total carbonate equivalent, total Nitrogen, Phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures (aggregates) that are operationally comparable. For each profile we provide the original soil classification (FAO, WRB, USDA, and version) and horizon designations as far as these have been specified in the source databases. Three measures for 'fitness-for-intended-use' are provided: positional uncertainty (for site locations), time of sampling/description, and a first approximation for the uncertainty associated with the operationally defined analytical methods. These measures should be considered during digital soil mapping and subsequent earth system modelling that use the present set of soil data. DATA SET DESCRIPTION: The 'WoSIS 2023 snapshot' comprises data for 228k profiles from 217k geo-referenced sites that originate from 174 countries. The profiles represent over 900k soil layers (or horizons) and over 6 million records. The actual number of measurements for each property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. The data are provided in TSV (tab separated values) format and as GeoPackage. The zip-file (446 Mb) contains the following files: - Readme_WoSIS_202312_v2.pdf: Provides a short description of the dataset, file structure, column names, units and category values (this file is also available directly under 'online resources'). The pdf includes links to tutorials for downloading the TSV files into R respectively Excel. See also 'HOW TO READ TSV FILES INTO R AND PYTHON' in the next section. - wosis_202312_observations.tsv: This file lists the four to six letter codes for each observation, whether the observation is for a site/profile or layer (horizon), the unit of measurement and the number of profiles respectively layers represented in the snapshot. It also provides an estimate for the inferred accuracy for the laboratory measurements. - wosis_202312_sites.tsv: This file characterizes the site location where profiles were sampled. - wosis_2023112_profiles: Presents the unique profile ID (i.e. primary key), site_id, source of the data, country ISO code and name, positional uncertainty, latitude and longitude (WGS 1984), maximum depth of soil described and sampled, as well as information on the soil classification system and edition. Depending on the soil classification system used, the number of fields will vary . - wosis_202312_layers: This file characterises the layers (or horizons) per profile, and lists their upper and lower depths (cm). - wosis_202312_xxxx.tsv : This type of file presents results for each observation (e.g. “xxxx” = “BDFIOD” ), as defined under “code” in file wosis_202312_observation.tsv. (e.g. wosis_202311_bdfiod.tsv). - wosis_202312.gpkg: Contains the above datafiles in GeoPackage format (which stores the files within an SQLite database). HOW TO READ TSV FILES INTO R AND PYTHON: A) To read the data in R, please uncompress the ZIP file and specify the uncompressed folder. setwd("/YourFolder/WoSIS_2023_December/") ## For example: setwd('D:/WoSIS_2023_December/') Then use read_tsv to read the TSV files, specifying the data types for each column (c = character, i = integer, n = number, d = double, l = logical, f = factor, D = date, T = date time, t = time). observations = readr::read_tsv('wosis_202312_observations.tsv', col_types='cccciid') observations ## show columns and first 10 rows sites = readr::read_tsv('wosis_202312_sites.tsv', col_types='iddcccc') sites profiles = readr::read_tsv('wosis_202312_profiles.tsv', col_types='icciccddcccccciccccicccci') profiles layers = readr::read_tsv('wosis_202312_layers.tsv', col_types='iiciciiilcc') layers ## Do this for each observation 'XXXX', e.g. file 'Wosis_202312_orgc.tsv': orgc = readr::read_tsv('wosis_202312_orgc.tsv', col_types='iicciilccdccddccccc') orgc Note: One may also use the following R code (example is for file 'observations.tsv'): observations <- read.table("wosis_202312_observations.tsv", sep = "\t", header = TRUE, quote = "", comment.char = "", stringsAsFactors = FALSE ) B) To read the files into python first decompress the files to your selected folder. Then in python: # import the required library import pandas as pd # Read the observations data observations = pd.read_csv("wosis_202312_observations.tsv", sep="\t") # print the data frame header and some rows observations.head() # Read the sites data sites = pd.read_csv("wosis_202312_sites.tsv", sep="\t") # Read the profiles data profiles = pd.read_csv("wosis_202312_profiles.tsv", sep="\t") # Read the layers data layers = pd.read_csv("wosis_202312_layers.tsv", sep="\t") # Read the soil property data, e.g. 'cfvo' (do this for each observation) cfvo = pd.read_csv("wosis_202312_cfvo.tsv", sep="\t") CITATION: Calisto, L., de Sousa, L.M., Batjes, N.H., 2023. Standardised soil profile data for the world (WoSIS snapshot – December 2023), https://doi.org/10.17027/isric-wdcsoils-20231130 Supplement to: Batjes N.H., Calisto, L. and de Sousa L.M., 2023. Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023). Earth System Science Data, https://doi.org/10.5194/essd-16-4735-2024.

Simulation script 1This R script will simulate two populations of microbiome samples and compare normalization methods.Simulation script 2This R script will simulate two populations of microbiome samples and compare normalization methods via PcOAs.Sample.OTU.distributionOTU distribution used in the paper: Methods for normalizing microbiome data: an ecological perspective

To standardize NEON organismal data for major taxonomic groups, we first systematically reviewed NEON’s documentations for each taxonomic group. We then discussed as a group and with NEON staff to decide how to wrangle and standardize NEON organismal data. See Li et al. 2022 for more details. All R code to process NEON data products can be obtained through the R package ‘ecocomDP’. Once the data are in ecocomDP format, we further processed them to convert them into long data frames with code on Github (https://github.com/daijiang/neonDivData/tree/master/data-raw), which is also archived here.

DBNorm test script. Code of how we test DBNorm package. (TXT 2Â kb)

Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics. This technique has been available for a decade, yet the current state and standardization of publicly available computational tools for these data is bleak. We introduce XPRESSyourself, an analytical toolkit that eliminates barriers and bottlenecks associated with this specialized data type by filling gaps in the computational toolset for both experts and non-experts of ribosome profiling. XPRESSyourself automates and standardizes analysis procedures, decreasing time-to-discovery and increasing reproducibility. This toolkit acts as a reference implementation of current best practices in ribosome profiling analysis. We demonstrate this toolkit’s performance on publicly available ribosome profiling data by rapidly identifying hypothetical mechanisms related to neurodegenerative phenotypes and neuroprotective mechanisms of the small-molecule ISRIB during acute cellular stress. XPRESSyourself brings robust, rapid analysis of ribosome-profiling data to a broad and ever-expanding audience and will lead to more reproducible and accessible measurements of translation regulation. XPRESSyourself software is perpetually open-source under the GPL-3.0 license and is hosted at https://github.com/XPRESSyourself, where users can access additional documentation and report software issues.

DBNorm installation. Describes how to install DBNorm via devtools in R. (TXT 4Â kb)

Despite the improvements in wind turbine gearbox design and manufacturing practices, the wind industry is still challenged by premature wind turbine gearbox failures. To help address this industry-wide challenge, a consortium called the Gearbox Reliability Collaborative (GRC) was launched by the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) in 2007. It brings together the parties involved in the wind turbine gearbox supply chain to investigate possible root causes of premature wind turbine gearbox failures and research improvement strategies. One area of work under the GRC is a collection and analysis effort of failure data titled the Gearbox Reliability Database (GRD), which was started in 2009. The main objectives of the GRD are to categorize top wind turbine gearbox failure modes, identify possible root causes, and direct future wind turbine gearbox reliability research and development (R) activities. The GRD project has made huge progress in developing wind turbine gearbox reliability data collection tools, standardizing data recording practices, populating the database, and informing the industry with up-to-date R findings and statistics. The project currently has more than 20 data-sharing partners, including wind turbine and wind turbine gearbox manufacturers and owners/operators, gearbox rebuild shops, and operation and maintenance service providers. The assets represented by only owner/operator partners on this project comprised approximately 34% of the U.S. capacity at the end of 2013 (according to the American Wind Energy Association annual market report). The number and variety of partners and the assets they represent demonstrate the value and need of major wind turbine component data collection and analysis to the industry. The attached image shows the distribution of the damage component locations based on approximately 320 confirmable wind turbine gearbox damage records stored in the database. It is observed that wind turbine gearboxes could fail in drastically different ways. The majority of the damage occurs to bearings (64%), followed by gears (25%), and the other components account for 11% of the failures. Among the other components, lubrication and filtration system problems are dominant. Both bearing and gear faults are concentrated in the parallel section, which aligns with field observations made by wind turbine owner/operator partners. The top gearbox failure is axial cracks that occur to bearings located at the high- or intermediate-speed stage. This identification confirms the value and need for wind turbine gearbox R on bearing axial cracks root causes and mitigation methods, which is a joint research effort by Argonne National Laboratory and NREL funded by DOE's Wind and Water Power Program. The data-sharing partners highly value this project and recommend that NREL generate industry-wide reliability benchmarking statistics from the information contained in the database, which is currently not publicly available. Frequently, these reliability statistics are distorted and kept internally if they are generated by wind turbine original equipment manufacturers or owners/operators, which do not normally have a balanced representation of wind turbine makers and models. The GRD experiences provide the industry with valuable support to standardize reliability data collection for major wind turbine components and subsystems.

We present here the Bedmap3 ice thickness, bed and surface elevation standardised CSV data points that are used to create the Bedmap3 gridding products in addition to the previous data releases. The data consists of 50 million points acquired by 17 different data providers in Antarctica. The associated datasets consist of: - Bedmap1 standardised CSV data points: https://doi.org/10.5285/f64815ec-4077-4432-9f55-0ce230f46029 - Bedmap2 standardised CSV data points: https://doi.org/10.5285/2fd95199-365e-4da1-ae26-3b6d48b3e6ac - Bedmap3 statistically-summarised data points (shapefiles): https://doi.org/10.5285/a72a50c6-a829-4e12-9f9a-5a683a1acc4a This work is supported by the SCAR Bedmap project and the British Antarctic Survey's core programme: National Capability - Polar Expertise Supporting UK Research

1. Plant fire syndromes are usually defined as combinations of fire response traits, the most common being resprouting (R) and seeding (S). Plant flammability (F), on the other hand, refers to a plant’s effects on communities and ecosystems. Despite its important ecological and evolutionary implications, F has rarely been considered to define plant fire syndromes and, if so, usually separated from response syndromes.
2. We propose a three-dimensional model that combines R, S and F, encapsulating both plant response to fire regimes and the capacity to promote them. Each axis is divided into three possible standardized categories, reflecting low, medium and high values of each variable, with a total of 27 possible combinations of R, S and F.
3. We hypothesized that different fire histories should be reflected in the position of species within the three-dimensional space and that this should help assess the importance of fire as an evolutionary force in determining R-S-F syndromes.
4. To illustrate our approach we compiled information on the fire syndromes of 24 dominant species of different growth forms from the Chaco seasonally-dry forest of central Argentina, and we compared them to 33 species from different Mediterranean-type climate ecosystems (MTCEs) of the world.
5. Chaco and MTCEs species differed in the range (seven syndromes vs. thirteen syndromes, respectively) and proportion of extreme syndromes (i.e. species with extreme values of R, S and/or F) representing 29% of species in the Chaco vs. 45% in the MTCEs.
6. Additionally, we explored the patterns of R, S and F of 4032 species from seven regions with contrasting fire histories, and found significantly higher frequencies of extreme values (predominantly high) of all three variables in MTCEs compared to the other regions, where intermediate and low values predominated, broadly supporting our general hypothesis.
7. The proposed three-dimensional approach should help standardize comparisons of fire syndromes across taxa, growth forms and regions with different fire histories. This will contribute to the understanding of the role of fire in the evolution of plant traits and assist vegetation modelling in the face of changes in fire regimes. Methods Data collection for Chaco species From previous studies, we compiled data on post-fire resprouting (R) (Jaureguiberry 2012; Jaureguibery et al. 2020), germination capacity after heat shock treatments (S) (Jaureguiberry & Díaz) and flammability (F) (Jaureguiberry et al. 2011) of 24 dominant species of the seasonally-dry Chaco forest of central Argentina (hereafter Chaco). We then transformed the original data from the mentioned studies into three possible categorical ordinal values: 1, 2 or 3, indicating low, medium and high values of each variable, respectively. To do so, we used the following criteria: 1) For R data: we focused on the survival percentage recorded for each species (Jaureguiberry et al., 2020) as a proxy for resprouting capacity (Pérez-Harguindeguy et al., 2013). This was because this variable is widely used in fire studies and has a standard scale and range of values, therefore facilitating comparisons between species from different regions. Survival percentages were assigned to one of three possible intervals: 0 to 33 %; 34 to 66 % and from 67 to 100%, and then each interval was assigned the value 1, 2 or 3 respectively, indicating low, medium and high values of resprouting capacity. 2) For S data: based on germination response to heat shock treatments we classified species as heat-sensitive (germination lower than the control), heat-tolerant (germination similar to the control) or heat-stimulated (germination higher than the control) (see details in Jaureguiberry and Díaz 2015). Each of these categories was respectively assigned a value of 1, 2 or 3. 3) For F data: while original measurements included burning rate, maximum temperature and biomass consumed (see details in Jaureguiberry et al. 2011), with the purpose of comparing Chaco species with species from other regions, and considering that burning rate is rarely reported, data of the two latter variables were collected from studies that followed Jaureguiberry et al. (2011). A PCA followed by cluster analysis allowed classifying species into the following categories: 1=low flammability; 2=moderate flammability; and 3=high flammability.

Data collection for other regions We performed an unstructured literature review of fire-related traits relevant to our model. Whenever possible, we searched for the same or similar variables to those used for the Chaco, namely survival percentage, germination response to heat shock, and variables related to flammability (e.g. maximum temperature, biomass consumed and burning rate), as proxies for R, S and F, respectively. Classification into different R intervals was based either on quantitative data on survival percentage, or on qualitative information from major databases. For example, resprouting capacity reported as “low”, or “high” (e.g. Tavşanoğlu & Pausas, 2018) were assigned R values of 1 and 3, respectively. For Southern Australian species, those reported as “fire killed” and “weak resprouting” (Falster et al., 2021) were assigned a value of 1, while those reported as “intermediate resprouting” and “strong resprouting” were assigned values of 2 and 3, respectively. The vast majority of records in our dataset refer to resprouting of individuals one growing season after the fire. Flammability data for most of the species were based on quantitative measurements that have used the method of Jaureguiberry et al. (2011), which was standardised following the criteria explained earlier. However, for some species, classification was based either on other quantitative measures that followed other methodologies (e.g. measures based on plant parts such as twigs or leaves, or fuel beds) or on qualitative classifications reported in the literature (most of which are in turn based on reviews of quantitative measurements from previous studies). We standardised the original data collected for the other regions following the same approach as for the Chaco. We then built contingency tables to analyse each region and to compare between regions. The curated total number of records from our literature review was 4411 (records for R, S and F, were 3399, 678 and 334, respectively) for 4,032 species (many species had information on two variables, and very few on the three variables). The database covers a wide taxonomic range, encompassing species from approximately 1,250 genera and 180 botanical families, belonging to ten different growth forms, and coming from seven major regions with a wide range of evolutionary histories of fire, from long and intense (Mediterranean-Type Climate Ecosystems) to very recent (New Zealand).

Australian rangelands ecosystems cover 81% of the continent but are understudied and continental-scale research has been limited in part by a lack of precise data that are standardised between jurisdictions. We present a new dataset from AusPlots Rangelands that enables integrative rangelands analysis due to its geographic scope and standardised methodology. The method provides data on vegetation and soils, enabling comparison of a suite of metrics including fractional vegetation cover, basal area, and species richness, diversity, and composition. Cover estimates are robust and repeatable, allowing comparisons among environments and detection of modest change. The 442 field plots presented here span a rainfall gradient of 129–1437 mm Mean annual precipitation with varying seasonality. Vegetation measurements include vouchered vascular plant species, growth form, basal area, height, cover and substrate type from 1010 point intercepts as well as systematically recorded absences, which are useful for predictive modelling and validation of remote sensing applications. Leaf and soil samples are sampled for downstream chemical and genomic analysis. We overview the sampling of vegetation parameters and environments, applying the data to the question of how species abundance distributions (SADs) vary over climatic gradients, a key question for the influence of environmental change on ecosystem processes. We found linear relationships between SAD shape and rainfall within grassland and shrubland communities, indicating more uneven abundance in deserts and suggesting relative abundance may shift as a consequence of climate change, resulting in altered diversity and ecosystem function. The standardised data of AusPlots enables such analyses at large spatial scales, and the testing of predictions through time with longitudinal sampling. In future, the AusPlots field program will be directed towards improving coverage of space, under-represented environments, vegetation types and fauna and, increasingly, re-sampling of established plots. Providing up-to-date data access methods to enhance re-use is also a priority.

Means, standard deviations and standardised factor loadings for PDSS-R data.

a) Raw data of PLIN2 from Figure 3a and t-WB analysis for Fig 3b, 3c. b) Raw data of β-actin from Fig 3a and t-WB analysis for Figure 3d,e. c) PLIN2 normalization to β-actin expressed as fold change compared to control at 2.5 μg protein loading for Fig 3f (left panel). d) PLIN2 normalization to β-actin expressed as fold change compared to control at 5 μg protein loading for Fig 3f (middle panel). e) PLIN2 normalization to β-actin expressed as fold change compared to control at 10 μg protein loading for Fig 3f (right panel). (XLSX)

a) Raw data of LDLR from Figure 1c and 2a and t-WB analysis for Fig 2b, 2c. b) LDLR signals normalized to WB1 at 20 μg (red) and t-WB analysis for S3 Fig 1a, 1b. c) LDLR signals normalized to WB1 at 40 μg (red) and t-WB analysis for S3 Fig 1c, 1d. d) LDLR signals normalized to WB1 at 60 μg (red) and t-WB analysis for Fig 2d, 2e. (XLSX)

a) Arbitrary values and t-WB analysis used for schematic in Fig 1b. b) Raw data of LDLR from Fig 1c and t-WB analysis for Fig 1d. c) Arbitrary values and t-WB analysis used for schematic in Fig 1e. d) Raw data of PLIN3 from Fig 1f and t-WB analysis for Fig 1g. e) Arbitrary values and t-WB analysis used for schematic in Fig 1h. f) Raw data of HSC70 from Fig 1i and t-WB analysis for Fig 1j. (XLSX)