titanic5 Dataset Created by David Beltran del Rio March 2016.

Notes This is the final (for now) version of my update to the Titanic data. I think it’s finally ready for publishing if you’d like. What I did was to strip all the passenger and crew data from the Encyclopedia Titanica (ET) web pages (excluding channel crossing passengers), create a unique ID for each passenger and crew member (Name_ID), then (painstakingly and hopefully 100% correctly) match to your earlier titanic3 dataset, in order to compare the two and to get your sibsp and parch variables. Since the ET is updated occasionally the work put into the ID and matching can be reused and refined later. I did eventually hear back from the ET people, they are willing to make the underlying database available in the future, I have not yet taken them up on it.

The two datasets line up nicely, most of the differences in the newer titanic5 dataset are in the age variable, as I had mentioned before - the new set has less missing ages - 51 missing (vs 263) out of 1309.

I am in the process of refining my analysis of the data as well, based on your comments below and your Regression Modeling Strategies example.

titanic3_wID data can be matched to titanic5 using the Name_ID variable. Tab titanic5 Metadata has the variable descriptions and allowable values for Class and Class/Dept.

A note about the ages - instead of using the add 0.5 trick to indicate estimated birth day / date I have a flag that indicates how the “final” age (Age_F) was arrived at. It’s the Age_F_Code variable - the allowable values are in the Titanic5_metadata tab in the attached excel. The reason for this is that I already had some fractional ages for infants where I had age in months instead of years and I wanted to avoid confusion for 6 month old infants, although I don’t think there are any in the data! Also, I was thinking to make fractional ages or age in days for all passengers for whom I have DoB, but I have not yet done so.

Here’s what the tabs are:

Titanic5_all - all (mostly cleaned) Titanic passenger and crew records Titanic5_work - working dataset, crew removed, unnecessary variables removed - this is the one I import into SAS / R to work on Titanic5_metadata - Variable descriptions and allowable values titanic3_wID - Original Titanic3 dataset with Name_ID added for merging to Titanic5 I have a csv, R dataset, and SAS dataset, but the variable names are an older version, so I won’t send those along for now to avoid confusion.

If it helps send my contact info along to your student in case any questions arise. Gmail address probably best, on weekends for sure: davebdr@gmail.com

The tabs in titanic5.xls are

Titanic5_all Titanic5_passenger (the one to be used for analysis) Titanic5_metadata (used during analysis file creation) Titanic3_wID

Merging (in Table R) data published on https://www.data.gouv.fr/fr/datasets/ventes-de-pesticides-par-departement/, and joining two other sources of information associated with MAs: — uses: https://www.data.gouv.fr/fr/datasets/usages-des-produits-phytosanitaires/ — information on the “Biocontrol” status of the product, from document DGAL/SDQSPV/2020-784 published on 18/12/2020 at https://agriculture.gouv.fr/quest-ce-que-le-biocontrole

All the initial files (.csv transformed into.txt), the R code used to merge data and different output files are collected in a zip. enter image description here NB: 1) “YASCUB” for {year,AMM,Substance_active,Classification,Usage,Statut_“BioConttrol”}, substances not on the DGAL/SDQSPV list being coded NA. 2) The file of biocontrol products shall be cleaned from the duplicates generated by the marketing authorisations leading to several trade names.
3) The BNVD_BioC_DY3 table and the output file BNVD_BioC_DY3.txt contain the fields {Code_Region,Region,Dept,Code_Dept,Anne,Usage,Classification,Type_BioC,Quantite_substance)}

ABSTRACT Meta-analysis is an adequate statistical technique to combine results from different studies, and its use has been growing in the medical field. Thus, not only knowing how to interpret meta-analysis, but also knowing how to perform one, is fundamental today. Therefore, the objective of this article is to present the basic concepts and serve as a guide for conducting a meta-analysis using R and RStudio software. For this, the reader has access to the basic commands in the R and RStudio software, necessary for conducting a meta-analysis. The advantage of R is that it is a free software. For a better understanding of the commands, two examples were presented in a practical way, in addition to revising some basic concepts of this statistical technique. It is assumed that the data necessary for the meta-analysis has already been collected, that is, the description of methodologies for systematic review is not a discussed subject. Finally, it is worth remembering that there are many other techniques used in meta-analyses that were not addressed in this work. However, with the two examples used, the article already enables the reader to proceed with good and robust meta-analyses. Level of Evidence V, Expert Opinion.

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

We have developed ProjecTILs, a computational approach to project new data sets into a reference map of T cells, enabling their direct comparison in a stable, annotated system of coordinates. Because new cells are embedded in the same space of the reference, ProjecTILs enables the classification of query cells into annotated, discrete states, but also over a continuous space of intermediate states. By comparing multiple samples over the same map, and across alternative embeddings, the method allows exploring the effect of cellular perturbations (e.g. as the result of therapy or genetic engineering) and identifying genetic programs significantly altered in the query compared to a control set or to the reference map. We illustrate the projection of several data sets from recent publications over two cross-study murine T cell reference atlases: the first describing tumor-infiltrating T lymphocytes (TILs), the second characterizing acute and chronic viral infection.To construct the reference TIL atlas, we obtained single-cell gene expression matrices from the following GEO entries: GSE124691, GSE116390, GSE121478, GSE86028; and entry E-MTAB-7919 from Array-Express. Data from GSE124691 contained samples from tumor and from tumor-draining lymph nodes, and were therefore treated as two separate datasets. For the TIL projection examples (OVA Tet+, miR-155 KO and Regnase-KO), we obtained the gene expression counts from entries GSE122713, GSE121478 and GSE137015, respectively.Prior to dataset integration, single-cell data from individual studies were filtered using TILPRED-1.0 (https://github.com/carmonalab/TILPRED), which removes cells not enriched in T cell markers (e.g. Cd2, Cd3d, Cd3e, Cd3g, Cd4, Cd8a, Cd8b1) and cells enriched in non T cell genes (e.g. Spi1, Fcer1g, Csf1r, Cd19). Dataset integration was performed using STACAS (https://github.com/carmonalab/STACAS), a batch-correction algorithm based on Seurat 3. For the TIL reference map, we specified 600 variable genes per dataset, excluding cell cycling genes, mitochondrial, ribosomal and non-coding genes, as well as genes expressed in less than 0.1% or more than 90% of the cells of a given dataset. For integration, a total of 800 variable genes were derived as the intersection of the 600 variable genes of individual datasets, prioritizing genes found in multiple datasets and, in case of draws, those derived from the largest datasets. We determined pairwise dataset anchors using STACAS with default parameters, and filtered anchors using an anchor score threshold of 0.8. Integration was performed using the IntegrateData function in Seurat3, providing the anchor set determined by STACAS, and a custom integration tree to initiate alignment from the largest and most heterogeneous datasets.Next, we performed unsupervised clustering of the integrated cell embeddings using the Shared Nearest Neighbor (SNN) clustering method implemented in Seurat 3 with parameters {resolution=0.6, reduction=”umap”, k.param=20}. We then manually annotated individual clusters (merging clusters when necessary) based on several criteria: i) average expression of key marker genes in individual clusters; ii) gradients of gene expression over the UMAP representation of the reference map; iii) gene-set enrichment analysis to determine over- and under- expressed genes per cluster using MAST. In order to have access to predictive methods for UMAP, we recomputed PCA and UMAP embeddings independently of Seurat3 using respectively the prcomp function from basic R package “stats”, and the “umap” R package (https://github.com/tkonopka/umap).

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

The biomarkers for thyroid cancer are still not known properly. For treating thyroid cancer these biomarkers can by be targeted specifically. Through this project, we identified and used bioinformatics tools to find biomarkers associated with thyroid cancer. Gene Expression Omnibus database (GEO) was used to find dataset related with thyroid cancer. Their expression profiles were downloaded. Four dataset GSE3467, GSE3678, GSE33630, and GSE53157 were identified from GEO database. The dataset GSE3467 contains nine thyroid tumor samples and nine normal thyroid tissue samples. The GSE3678 contains seven thyroid tumor samples and seven normal thyroid tissue samples. The GSE53157 contains twenty four thyroid tumor samples and three normal thyroid samples. The GSE33630 contains sixty thyroid tumor samples and forty five normal thyroid samples. These four datasets were analyzed individually and were integrated at the end to find the common genes among these four datasets. The microarray analysis of the datasets were performed using excel. T.Test analysis were performed for all the four datasets individually on a separate excel sheet. The data was normalized by converting normal value into log scale. Differential expression analysis of all the four datasets were done to identify differentially expresses genes (DEGs). Only upregulated genes were taken into account. Principal component analysis (PCA) of all the four dataset were performed using the raw data. The PCA analysis were performed using T-BioInfo server and the scatterplots were prepared using excel. RStudio was used to match the gene symbols with the corresponding probe ids using left join function. Inner join function in R was used to find integrated genes between the four datasets. Heatmaps of all the four datasets were performed using RStudio. To find number of intersection of Differentially expressed genes, an upset plot was prepared using RStudio. 74 genes with their corresponding probe ids were found to be common among all the four datasets. These genes are common to at least two datasets. These 74 common genes were analyzed using Database for Annotation, Visualization, and Integrated Discovery (DAVID), to study their Gene onotology (GO) functional annotations and pathways. According to the GO functional annotations result, most of the integrated upregulated genes were involved in protein binding, plasma membrane and integral component of membrane. Most common pathway include Extracellular matrix organization, Neutrophil degranulation, TGF-beta signaling pathway and Epithelial to mesenchymal transition in colorectal cancer. These 74 genes were introduced to STRING database to find protein-protein interactions between the genes. Interactions between the nodes were downloaded from STRING database and introduced to Sytoscape. Sytoscape analysis explained that only 19 genes showed protein-protein interactions between each other. Disease free survival analysis of the 13 genes that were common to three datasets were done using GEPIA. Boxplots of these 13 genes were also prepared using GEPIA. This showed that these differentially expressed genes showed different expression in normal thyroid tissue and thyroid tumor samples. Hence these 13 genes common to 3 datasets can be used as potential biomarkers for thyroid cancer. Among these 13 genes, four genes are implicated in cancer/cell proliferation can be probable target for treatment options.

The National Health and Nutrition Examination Survey (NHANES) provides data and have considerable potential to study the health and environmental exposure of the non-institutionalized US population. However, as NHANES data are plagued with multiple inconsistencies, processing these data is required before deriving new insights through large-scale analyses. Thus, we developed a set of curated and unified datasets by merging 614 separate files and harmonizing unrestricted data across NHANES III (1988-1994) and Continuous (1999-2018), totaling 135,310 participants and 5,078 variables. The variables conveydemographics (281 variables),dietary consumption (324 variables),physiological functions (1,040 variables),occupation (61 variables),questionnaires (1444 variables, e.g., physical activity, medical conditions, diabetes, reproductive health, blood pressure and cholesterol, early childhood),medications (29 variables),mortality information linked from the National Death Index (15 variables),survey weights (857 variables),environmental exposure biomarker measurements (598 variables), andchemical comments indicating which measurements are below or above the lower limit of detection (505 variables).csv Data Record: The curated NHANES datasets and the data dictionaries includes 23 .csv files and 1 excel file.The curated NHANES datasets involves 20 .csv formatted files, two for each module with one as the uncleaned version and the other as the cleaned version. The modules are labeled as the following: 1) mortality, 2) dietary, 3) demographics, 4) response, 5) medications, 6) questionnaire, 7) chemicals, 8) occupation, 9) weights, and 10) comments."dictionary_nhanes.csv" is a dictionary that lists the variable name, description, module, category, units, CAS Number, comment use, chemical family, chemical family shortened, number of measurements, and cycles available for all 5,078 variables in NHANES."dictionary_harmonized_categories.csv" contains the harmonized categories for the categorical variables.“dictionary_drug_codes.csv” contains the dictionary for descriptors on the drugs codes.“nhanes_inconsistencies_documentation.xlsx” is an excel file that contains the cleaning documentation, which records all the inconsistencies for all affected variables to help curate each of the NHANES modules.R Data Record: For researchers who want to conduct their analysis in the R programming language, only cleaned NHANES modules and the data dictionaries can be downloaded as a .zip file which include an .RData file and an .R file.“w - nhanes_1988_2018.RData” contains all the aforementioned datasets as R data objects. We make available all R scripts on customized functions that were written to curate the data.“m - nhanes_1988_2018.R” shows how we used the customized functions (i.e. our pipeline) to curate the original NHANES data.Example starter codes: The set of starter code to help users conduct exposome analysis consists of four R markdown files (.Rmd). We recommend going through the tutorials in order.“example_0 - merge_datasets_together.Rmd” demonstrates how to merge the curated NHANES datasets together.“example_1 - account_for_nhanes_design.Rmd” demonstrates how to conduct a linear regression model, a survey-weighted regression model, a Cox proportional hazard model, and a survey-weighted Cox proportional hazard model.“example_2 - calculate_summary_statistics.Rmd” demonstrates how to calculate summary statistics for one variable and multiple variables with and without accounting for the NHANES sampling design.“example_3 - run_multiple_regressions.Rmd” demonstrates how run multiple regression models with and without adjusting for the sampling design.

Overview

This dataset is the repository for the following paper submitted to Data in Brief:

Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023).

The Data in Brief article contains the supplement information and is the related data paper to:

Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023).

Description/abstract

The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.

Folder structure

The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:

“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.

“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.

“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).

“yield_productivity” contains .csv files of yield information for all countries listed above.

“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).

“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.

“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.

Code structure

1_MODIS_NDVI_hdf_file_extraction.R

This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.

2_MERGE_MODIS_tiles.R

In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory_MODIS/merged").

3_CROP_MODIS_merged_tiles.R

Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS.
The repository provides the already clipped and merged NDVI datasets.

4_TREND_analysis_NDVI.R

Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.
To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.

5_BUILT_UP_change_raster.R

Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.

6_POPULATION_numbers_plot.R

For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.

7_YIELD_plot.R

In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.

8_GLDAS_read_extract_trend

The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection).
Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.
From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).
From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.

Metabolites are referenced in spectral, structural and pathway databases with a diverse array of schemas, including various internal database identifiers and large tables of common name synonyms. Cross-linking metabolite identifiers is a required step for meta-analysis of metabolomic results across studies but made difficult due to the lack of a consensus identifier system. We have implemented metLinkR, an R package that leverages RefMet and RaMP-DB to automate and simplify cross-linking metabolite identifiers across studies and generating common names. MetLinkR accepts as input metabolite common names and identifiers from five different databases (HMDB, KEGG, ChEBI, LIPIDMAPS and PubChem) to exhaustively search for possible overlap in supplied metabolites from input data sets. In an example of 13 metabolomic data sets totaling 10,400 metabolites, metLinkR identified and provided common names for 1377 metabolites in common between at least 2 data sets in less than 18 min and produced standardized names for 74.4% of the input metabolites. In another example comprising five data sets with 3512 metabolites, metLinkR identified 715 metabolites in common between at least two data sets in under 12 min and produced standardized names for 82.3% of the input metabolites. Outputs of MetLInR include output tables and metrics allowing users to readily double check the mappings and to get an overview of chemical classes represented. Overall, MetLinkR provides a streamlined solution for a common task in metabolomic epidemiology and other fields that meta-analyze metabolomic data. The R package, vignette and source code are freely downloadable at https://github.com/ncats/metLinkR.

This dryad repository contains data sets and R scripts for our Ecological Monographs (2025) paper: Linking climate variability to demography in cooperatively breeding meerkats. The aim of our study was to develop a mechanistic understanding of how variation in rainfall and temperature affect meerkat demography. Specifically, we:

Identified the critical period during which rainfall influences vegetation productivity at the field site. Investigated how vegetation productivity and temperature influence meerkat foraging performance. Examined how these climatic variables impact daily weight fluctuations and overall body condition. Analyzed conditions under which meerkats fail to accumulate enough mass during foraging to offset overnight losses. Explored the influence of body condition on reproduction and survival. Projected future temperature trends and their implications for meerkat populations up to the mid and end of the century.

To achieve these aims, our study included numerous data s..., The data was collected as part of a long-term study of meerkats in the southern Kalahari Desert. All methods of data collection are outlined in the paper and are also summarised in Clutton-Brock and Manser (2016). The project's data is stored on a centralised SQL database and was processed for analyses by the lead author using R.
All of the analyses are tied, directly or indirectly, to the effects of climate and/or vegetation productivity. Climate data included daily rainfall measured on-site, and temperature data taken from the NOAA Climate Prediction Centre. We also extracted future temperatures from the Coupled Model Intercomparison Project 6 (CMIP6) for two future pathways: a medium-risk scenario (SSP2-4.5) and a high-risk scenario (SSP5-8.5), which we obtained from the Copernicus Climate Change service (https://cds.climate.copernicus.eu/). We selected the EC-EARTH3-CC model from CMIP6. Vegetation productivity was assessed using NDVI from the MODIS MOD13Q1 product the GI..., # Data and R code for: Linking climate variability to demography in cooperatively breeding meerkats

📠Thorley_2025_EcologicalMonographs.zip

Dataset DOI: 10.5061/dryad.2ngf1vj11

Each analysis is provided in a separate R script, with the data sets needed for each script explained in detail.

(1) Part1_RainfallNDVI_Climwin.R

- **MeerkatNDVIMasked.csv **Average NDVI across the ranges of all meerkat groups (MODIS MOD13Q1). - date – dd/mm/yyyy - mean_ndvi_masked – NDVI is provided every 16 days

- **MeerkatRainReserve.csv **Daily rainfall at the field site. - date – dd/mm/yyyy - rain_method – Indicates how the daily value was collected (Manual / NOAA / Station) - rain_reserve – Total daily rainfall (mm)

(2) Part2_ForagingPerformance.R

- **MeerkatForagingData.csv **Historical data set of focal observations (1996–2001). - IndividID – Individual identity - GroupRef – Group identity - Focal_ID – Unique ident...,

A collection of datasets and python scripts for extraction and analysis of isograms (and some palindromes and tautonyms) from corpus-based word-lists, specifically Google Ngram and the British National Corpus (BNC).Below follows a brief description, first, of the included datasets and, second, of the included scripts.1. DatasetsThe data from English Google Ngrams and the BNC is available in two formats: as a plain text CSV file and as a SQLite3 database.1.1 CSV formatThe CSV files for each dataset actually come in two parts: one labelled ".csv" and one ".totals". The ".csv" contains the actual extracted data, and the ".totals" file contains some basic summary statistics about the ".csv" dataset with the same name.The CSV files contain one row per data point, with the colums separated by a single tab stop. There are no labels at the top of the files. Each line has the following columns, in this order (the labels below are what I use in the database, which has an identical structure, see section below):

Label Data type Description

isogramy int The order of isogramy, e.g. "2" is a second order isogram

length int The length of the word in letters

word text The actual word/isogram in ASCII

source_pos text The Part of Speech tag from the original corpus

count int Token count (total number of occurences)

vol_count int Volume count (number of different sources which contain the word)

count_per_million int Token count per million words

vol_count_as_percent int Volume count as percentage of the total number of volumes

is_palindrome bool Whether the word is a palindrome (1) or not (0)

is_tautonym bool Whether the word is a tautonym (1) or not (0)

The ".totals" files have a slightly different format, with one row per data point, where the first column is the label and the second column is the associated value. The ".totals" files contain the following data:

Label

Data type

Description

!total_1grams

int

The total number of words in the corpus

!total_volumes

int

The total number of volumes (individual sources) in the corpus

!total_isograms

int

The total number of isograms found in the corpus (before compacting)

!total_palindromes

int

How many of the isograms found are palindromes

!total_tautonyms

int

How many of the isograms found are tautonyms

The CSV files are mainly useful for further automated data processing. For working with the data set directly (e.g. to do statistics or cross-check entries), I would recommend using the database format described below.1.2 SQLite database formatOn the other hand, the SQLite database combines the data from all four of the plain text files, and adds various useful combinations of the two datasets, namely:• Compacted versions of each dataset, where identical headwords are combined into a single entry.• A combined compacted dataset, combining and compacting the data from both Ngrams and the BNC.• An intersected dataset, which contains only those words which are found in both the Ngrams and the BNC dataset.The intersected dataset is by far the least noisy, but is missing some real isograms, too.The columns/layout of each of the tables in the database is identical to that described for the CSV/.totals files above.To get an idea of the various ways the database can be queried for various bits of data see the R script described below, which computes statistics based on the SQLite database.2. ScriptsThere are three scripts: one for tiding Ngram and BNC word lists and extracting isograms, one to create a neat SQLite database from the output, and one to compute some basic statistics from the data. The first script can be run using Python 3, the second script can be run using SQLite 3 from the command line, and the third script can be run in R/RStudio (R version 3).2.1 Source dataThe scripts were written to work with word lists from Google Ngram and the BNC, which can be obtained from http://storage.googleapis.com/books/ngrams/books/datasetsv2.html and [https://www.kilgarriff.co.uk/bnc-readme.html], (download all.al.gz).For Ngram the script expects the path to the directory containing the various files, for BNC the direct path to the *.gz file.2.2 Data preparationBefore processing proper, the word lists need to be tidied to exclude superfluous material and some of the most obvious noise. This will also bring them into a uniform format.Tidying and reformatting can be done by running one of the following commands:python isograms.py --ngrams --indir=INDIR --outfile=OUTFILEpython isograms.py --bnc --indir=INFILE --outfile=OUTFILEReplace INDIR/INFILE with the input directory or filename and OUTFILE with the filename for the tidied and reformatted output.2.3 Isogram ExtractionAfter preparing the data as above, isograms can be extracted from by running the following command on the reformatted and tidied files:python isograms.py --batch --infile=INFILE --outfile=OUTFILEHere INFILE should refer the the output from the previosu data cleaning process. Please note that the script will actually write two output files, one named OUTFILE with a word list of all the isograms and their associated frequency data, and one named "OUTFILE.totals" with very basic summary statistics.2.4 Creating a SQLite3 databaseThe output data from the above step can be easily collated into a SQLite3 database which allows for easy querying of the data directly for specific properties. The database can be created by following these steps:1. Make sure the files with the Ngrams and BNC data are named “ngrams-isograms.csv” and “bnc-isograms.csv” respectively. (The script assumes you have both of them, if you only want to load one, just create an empty file for the other one).2. Copy the “create-database.sql” script into the same directory as the two data files.3. On the command line, go to the directory where the files and the SQL script are. 4. Type: sqlite3 isograms.db 5. This will create a database called “isograms.db”.See the section 1 for a basic descript of the output data and how to work with the database.2.5 Statistical processingThe repository includes an R script (R version 3) named “statistics.r” that computes a number of statistics about the distribution of isograms by length, frequency, contextual diversity, etc. This can be used as a starting point for running your own stats. It uses RSQLite to access the SQLite database version of the data described above.

Description

The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) contains 7356 files (total size: 24.8 GB). The dataset contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. All conditions are available in three modality formats: Audio-only (16bit, 48kHz .wav), Audio-Video (720p H.264, AAC 48kHz, .mp4), and Video-only (no sound). Note, there are no song files for Actor_18.

The RAVDESS was developed by Dr Steven R. Livingstone, who now leads the Affective Data Science Lab, and Dr Frank A. Russo who leads the SMART Lab.

Citing the RAVDESS

The RAVDESS is released under a Creative Commons Attribution license, so please cite the RAVDESS if it is used in your work in any form. Published academic papers should use the academic paper citation for our PLoS1 paper. Personal works, such as machine learning projects/blog posts, should provide a URL to this Zenodo page, though a reference to our PLoS1 paper would also be appreciated.

Academic paper citation

Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. https://doi.org/10.1371/journal.pone.0196391.

Personal use citation

Include a link to this Zenodo page - https://zenodo.org/record/1188976

Commercial Licenses

Commercial licenses for the RAVDESS can be purchased. For more information, please visit our license page of fees, or contact us at ravdess@gmail.com.

Contact Information

If you would like further information about the RAVDESS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at ravdess@gmail.com.

Example Videos

Watch a sample of the RAVDESS speech and song videos.

Emotion Classification Users

If you're interested in using machine learning to classify emotional expressions with the RAVDESS, please see our new RAVDESS Facial Landmark Tracking data set [Zenodo project page].

Construction and Validation

Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - https://doi.org/10.1371/journal.pone.0196391.

The RAVDESS contains 7356 files. Each file was rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained adult research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity, interrater reliability, and test-retest intrarater reliability were reported. Validation data is open-access, and can be downloaded along with our paper from PLoS ONE.

Contents

Audio-only files

Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each):

• Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440.
• Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.

Audio-Visual and Video-only files

Video files are provided as separate zip downloads for each actor (01-24, ~500 MB each), and are split into separate speech and song downloads:

• Speech files (Video_Speech_Actor_01.zip to Video_Speech_Actor_24.zip) collectively contains 2880 files: 60 trials per actor x 2 modalities (AV, VO) x 24 actors = 2880.
• Song files (Video_Song_Actor_01.zip to Video_Song_Actor_24.zip) collectively contains 2024 files: 44 trials per actor x 2 modalities (AV, VO) x 23 actors = 2024.

File Summary

In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files).

File naming convention

Each of the 7356 RAVDESS files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics:

Filename identifiers

• Modality (01 = full-AV, 02 = video-only, 03 = audio-only).
• Vocal channel (01 = speech, 02 = song).
• Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).
• Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.
• Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").
• Repetition (01 = 1st repetition, 02 = 2nd repetition).
• Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).

Filename example: 02-01-06-01-02-01-12.mp4

1. Video-only (02)
2. Speech (01)
3. Fearful (06)
4. Normal intensity (01)
5. Statement "dogs" (02)
6. 1st Repetition (01)
7. 12th Actor (12)
8. Female, as the actor ID number is even.

License information

The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NC-SA 4.0

Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our license fee page, or contact us at ravdess@gmail.com.

Related Data sets

• RAVDESS Facial Landmark Tracking data set [Zenodo project page].

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

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Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

Dutch Ships and Sailors is a project that aims to provide an infrastructure for maritime historical datasets, linking correlating data through semantic web technology. It brings together datasets related to recruitment and shipping in the East-India trade (mainly 18th century) and in the shipping of the northern provinces of the Netherlands (mainly 19th century). For the northern provinces, the database contains data on the personnel recruited, the ships, and other variables (muster rolls of the northern provinces of the Netherlands).Dutch Ships and Sailors is a CLARIN IV-project, hosted by Huygens ING in collaboration with VU University Amsterdam, the International Institute of Social History and Scheepvaartmuseum Amsterdam.This prioject resulted in the Dutch Ships and Sailors (DSS) data cloud. This Linked Data cloud brings together four Dutch maritime historical datasets, each with its own datamodel. The data is available as five-star linked data making sharing and reuse possible. The data is integrated at a meta-level through common vocabularies and linked to generic external data sources allowing for new types of queries and analysis.The first two datasets ("Generale Zeemonsterrollen VOC" and "Noordelijke Monsterollen Databases") are modeled and converted in close collaboration with the historical researchers responsible for the source datasets and we describe them in more detail. The third ("VOC Opvarenden") and fourth ("Dutch Asiatic Shipping") datasets are conversions of previously published historical datasets and are described less elaborately. They were converted with the help of the historians. These four datasets are mapped to a "DSS interoperability layer", which describes common classes, properties and concepts. Links between datasets have been established through either this interoperability layer or through converting references in the original data into explicit links.The data is made available in RDF/turtle format, but is also live served at http://dutchshipsandsailors.nl/data or http://semanticweb.cs.vu.nl/dss/The five datasets of this project can be found via the links under 'relations'.

Reddit contents and complementary data regarding the r/The_Donald community and its main moderation interventions, used for the corresponding article indicated in the title.

An accompanying R notebook can be found in: https://github.com/amauryt/make_reddit_great_again

If you use this dataset please cite the related article.

The dataset timeframe of the Reddit contents (submissions and comments) spans from 30 weeks before Quarantine (2018-11-28) to 30 weeks after Restriction (2020-09-23). The original Reddit content was collected from the Pushshift monthly data files, transformed, and loaded into two SQLite databases.

The first database, the_donald.sqlite, contains all the available content from r/The_Donald created during the dataset timeframe, with the last content being posted several weeks before the timeframe upper limit. It only has two tables: submissions and comments. It should be noted that the IDs of contents are on base 10 (numeric integer), unlike the original base 36 (alphanumeric) used on Reddit and Pushshift. This is for efficient storage and processing. If necessary, many programming languages or libraries can easily convert IDs from one base to another.

The second database, core_the_donald.sqlite, contains all the available content from core users of r/The_Donald made platform-wise (i.e., within and without the subreddit) during the dataset timeframe. Core users are defined as those who authored either a submission or a comment a week in r/The_Donald during the 30 weeks prior to the subreddit's Quarantine. The database has four tables: submissions, comments, subreddits, and perspective_scores. The subreddits table contains the names of the subreddits to which submissions and comments were made (their IDs are also on base 10). The perspective_scores table contains comment toxicity scores.

The Perspective API was used to score comments based on the attributes toxicity and severe_toxicity. It should be noted that not all of the comments in core_the_donald have a score because the comment body was blank or because the Perspective API returned a request error (after three tries). However, the percentage of missing scores is minuscule.

A third file, mbfc_scores.csv, contains the bias and factual reporting accuracy collected in October 2021 from Media Bias / Fact Check (MBFC). Both attributes are scored on a Likert-like manner. One can associate submissions to MBFC scores by doing a join by the domain column.

This dataset contains the results reported for the Trends in Forest Tree Seed Use indicator published on Environmental Reporting BC. The “Methods” section in the link above provides a summary of: - The source data and methods used to develop these results; and - The R code used to create the indicator graphs. The two datasets published here are linked to the two measures investigated in the indicator: - Forest Regeneration by Tree Seed Source in B.C. - bcregen.csv - Select Seed Use by Natural Resource District in B.C. - districtregen.csv

Abstract

The 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. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This metadata contains data for two models: AWRA-L and AWRA-R. In the Macquarie-Tuggerah-Lake (MTL) coastal subregion, only AWRA-L modelling was conducted; in the Hunter subregion both modelling were conducted and AWRA-L flow outputs provided as model inputs for AWRA-R.

AWRA-L

The metadata within the dataset contains the workflow, processes, inputs and outputs data. The workflow pptx file under the top folder provides the top level summary of the modelling framework, including three slides. The first slide explains how to generate global definition file; the second slide outlines the calibration and simulation for AWRA-L model run; the third slide shows AWRA-L model post-processing for getting streamflow under baseline and coal mine resources development.

The exectable model framework is under the Application subfolder

Other subfolders, including model calibration, model simulation, post processing, contain the associated files used for model calibration, simulation and post processing, respectively.

Documentation about the implementation of AWRA-L in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

AWRA-R

The metadata within the dataset contains the workflow, processes, input and output data and instructions to implement the Hunter AWRA-R model for model calibration or simulation.

Each sub-folder in the associated data has a readme file indicating folder contents and providing general instructions about the workflow performed.

Detailed documentation of the AWRA-R model, is provided in: https://publications.csiro.au/rpr/download?pid=csiro:EP154523&dsid=DS2

Documentation about the implementation of AWRA-R in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

Purpose

BA surface water modelling in the Hunter bioregion

Dataset History

There are two sections, the first section describing AWRA-L, the second section describing AWRA-R.

Section 1 - AWRA-L

The directories within contain the input and output data of the Hunter AWRA-L model for model calibration, simulation and post-processing.

The calibration folder contains the input and output subfolders used for two model calibration schemes: lowflow and normal. The lowflow model calibration puts more weight on median and low streamflow; the normal model calibration puts more weight on high streamflow.

The simulation folder contains only one replicate of model input and output as an example.

The post-processing folder contains three subfolders: inputs, outputs and scripts used for generating streamflow under the baseline and coal mine resources development conditions. It contains the post-processing for the two subregions (MTL and HUN). In the MTL coastal subregion, the AWRA-L postprocessing results were final outputs, while in the HUN subregion AWRA-L flow outputs were model inputs for AWRA-R (Details below).

Input and output files are the daily data covering the period of 1953 to 2102, with the first 30 years (1953-1982) for model spin-up.

Documentation about the implementation of AWRA-L in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

Data details are below

Model calibrations

1. Climate forcings are under '... AWRAL_Metadata\model calibration\inputs\Climate\'

2. Lowflow calibration data including catchment location, global definition mapping, objective definition and optimiser definition under '... AWRAL_Metadata\model calibration\inputs\lowflow\'

3. Higflow calibration data including catchment location, global definition mapping, objective definition and optimiser definition under '... AWRAL_Metadata\model calibration\inputs ormal\'

4. Observed streamflow data used for model calibrations are under '... AWRAL_Metadata\model calibration\inputs\Streamflow\'

Model simulations

1. Climate forcings are under '... AWRAL_Metadata\model simulation\inputs\Climate\'

2. Global definition file used in csv output mode data is under '... AWRAL_Metadata\model simulation\inputs\csv_Model_1\'

3. Global definition file used in netcdf output mode data is under '... AWRAL_Metadata\model simulation\inputs\Netcdf_Model_1\'

4. Output files used in csv output mode data contain Dd, dgw, E0, Qg, Qtot, Rain, Sg outputs, which is used for AWRA-R model input and is under '... AWRAL_Metadata\model simulation\outputs\csv_Model_1\'

5. Output files used in netcdf output mode data contain Qg and Qtot outputs, which is used for AWRA-L postprocessing and is under '... AWRAL_Metadata\model simulation\outputs\Netcdf_Model_1\'

Post-processing

1. Input data include AWRA-L streamflow, ground water baseflow input and mine footprint data, stored at '... AWRAL_Metadata\post processing\Inputs\'

2. Output data include streamflow outputs under crdp and baseline for the HUN and MTL subregions, stored at '... AWRAL_Metadata\post processing\Outputs\'

3. Scripts for use for post-processing AWRA-L streamflow and ground water baseflow, is under '... AWRAL_Metadata\model simulation\post processing\Scripts\'

Section 2 - AWRA-R

The directories within contain the input data and outputs of the Hunter AWRA-R model for model calibration or simulation. The folders were calibration data stored is used as an example,

simulation uses mirror files of these data, albeit with longer time-series depending on the simualtion period.

Detailed documentation of the AWRA-R model, is provided in: https://publications.csiro.au/rpr/download?pid=csiro:EP154523&dsid=DS2

Documentation about the implementation of AWRA-R in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

Additional data needed to generate some of the inputs needed to implement AWRA-R are detailed in the corresponding metadata statement as stated below.

Input data needed:

1. Gauge/node topological information in '...\model calibration\HUN4_low\gis\sites\AWRARv5.00_reaches.csv'.

2. Look up table for soil thickness in '...\model calibration\HUN4_low\gis\ASRIS_soil_properties\HUN_AWRAR_ASRIS_soil_thickness_v5.00.csv'. (check metadata statement)

3. Look up tables of AWRA-LG groundwater parameters in '...\model calibration\HUN4_low\gis\AWRA-LG_gw_parameters\'.

4. Look up table of AWRA-LG catchment grid cell contribution in '...model calibration\HUN4_low\gis\catchment-boundary\AWRA-R_catchment_x_AWRA-L_weight.csv'. (check metadata statement)

5. Look up tables of link lengths for main river, tributaries and distributaries within a reach in \model calibration\HUN4_low\gis\rivers\'. (check metadata statement)

6. Time series data of AWRA-LG outputs: evaporation, rainfall, runoff and depth to groundwater.

7. Gridded data of AWRA-LG groundwater parameters, refer to explanation in '...'\model calibration\HUN4_low\rawdata\AWRA_LG_output\gw_parameters\README.txt'.

8. Time series of observed or simulated reservoir level, volume and surface area for reservoirs used in the simulation: Glenbawn Dam and Glennies Creek Dam.

   located in '...\model calibration\HUN4_low\rawdata\reservoirs\'.

9. Gauge station cross sections in '...\model calibration\HUN4_low\rawdata\Site_Station_Sections\'. (check metadata statement)

10. Daily Streamflow and level time-series in'...\model calibration\HUN4_low\rawdata\streamflow_and_level_all_processed\'.

11. Irrigation input, configuration and parameter files in '...\AWRAR_Metadata\model calibration\HUN4_low\inputs\HUN\irrigation\'. These come from the separate calibration of the AWRA-R irrigation module in:

    '...\irrigation calibration\', refer to explanation in readme.txt file therein.

12. Dam simulation script '\AWRAR_Metadata\dam model calibration simulation\scripts\Hunter_dam_run_2.R' and configuration files in

    '\AWRAR_Metadata\dam model calibration simulation\scripts\Hunter_dam_config_2.csv'. The config file comes from a separate calibration of AWRA-R dam module in

    '\AWRAR_Metadata\dam model calibration simulation\', refer to the explanation in the readme.txt file therein

Relevant ouputs include:

1. AWRA-R time series of stores and fluxes in river reaches ('...\AWRAR_Metadata\model calibration\HUN4_low\outputs\jointcalibration\v01\HUN\simulations\')

   including simulated streamflow in files denoted XXXXXX_full_period_states_nonrouting.csv where XXXXXX denotes gauge or node ID.

2. AWRA-R time series of stores and fluxes for irrigation/mining in the same directory as above in files XXXXXX_irrigation_states.csv

3. AWRA-R calibration validation goodness of fit metrics ('...\AWRAR_Metadata\model calibration\HUN4_low\outputs\jointcalibration\v01\HUN\postprocessing\')

   in files calval_results_XXXXXX_v5.00.csv

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN AWRA-LR Model v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/670de516-30c5-4724-bd76-8ff4a42ca7a5.

Dataset Ancestors

• Derived From Hunter River Salinity Scheme Discharge NSW EPA 2006-2012

• Derived From River Styles Spatial Layer for New South Wales

• Derived From HUN AWRA-L simulation nodes_v01

• Derived From HUN AWRA-R River Reaches Simulation v01

• Derived From HUN AWRA-R simulation nodes v01

• Derived From [Bioregional Assessment

This dataset contains the results reported for the Trends in Forest Tree Seed Use indicator published on Environmental Reporting BC. The “Methods” section in the link above provides a summary of: - The source data and methods used to develop these results; and - The R code used to create the indicator graphs. The two datasets published here are linked to the two measures investigated in the indicator: - Forest Regeneration by Tree Seed Source in B.C. - bcregen.csv - Select Seed Use by Natural Resource District in B.C. - districtregen.csv

This thesis examines how support-recipients influence social support outcomes through the way they talk about their problems, employing a multi-method approach combining social media analysis, surveys, experiments, and computational methods. In chapter 2, we used reddit data to examine how narrative features of posts (e.g., agency, coherence, affective tone, etc...) influence the quantity and quality of support they receive from other users. In chapters 3 and 4, we use an experimental vignette design to examine how variations in narrative features causally influence participants' desire to support the narrator and the type of support they provide them. Finally in chapter 5, we utilise GPT to examine how narrators feel about the support they receive.The first two datasets consists of posts and comments extracted from Reddit in 2021 from r/Anxiety and r/Depression which have been coded by trained human coders for narrative features and social support. The third and fourth dataset are from two experimental vignette studies conducted on Qualtrics between 2023-2024. The last dataset consists of an experimental study utilising GPT on Qualtrics collected in 2025.