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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

- create a folder `

Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies. Methods This serves as an overview of the analysis performed on PacBio sequence data that is summarized in Analysis Flowchart.pdf and was used as primary data for the paper by Westfall et al. "Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations – application to HIV-1 quasispecies" Five different PacBio sequencing datasets were used for this analysis: M027, M2199, M1567, M004, and M005 For the datasets which were indexed (M027, M2199), CCS reads from PacBio sequencing files and the chunked_demux_config files were used as input for the chunked_demux pipeline. Each config file lists the different Index primers added during PCR to each sample. The pipeline produces one fastq file for each Index primer combination in the config. For example, in dataset M027 there were 3–4 samples using each Index combination. The fastq files from each demultiplexed read set were moved to the sUMI_dUMI_comparison pipeline fastq folder for further demultiplexing by sample and consensus generation with that pipeline. More information about the chunked_demux pipeline can be found in the README.md file on GitHub. The demultiplexed read collections from the chunked_demux pipeline or CCS read files from datasets which were not indexed (M1567, M004, M005) were each used as input for the sUMI_dUMI_comparison pipeline along with each dataset's config file. Each config file contains the primer sequences for each sample (including the sample ID block in the cDNA primer) and further demultiplexes the reads to prepare data tables summarizing all of the UMI sequences and counts for each family (tagged.tar.gz) as well as consensus sequences from each sUMI and rank 1 dUMI family (consensus.tar.gz). More information about the sUMI_dUMI_comparison pipeline can be found in the paper and the README.md file on GitHub. The consensus.tar.gz and tagged.tar.gz files were moved from sUMI_dUMI_comparison pipeline directory on the server to the Pipeline_Outputs folder in this analysis directory for each dataset and appended with the dataset name (e.g. consensus_M027.tar.gz). Also in this analysis directory is a Sample_Info_Table.csv containing information about how each of the samples was prepared, such as purification methods and number of PCRs. There are also three other folders: Sequence_Analysis, Indentifying_Recombinant_Reads, and Figures. Each has an .Rmd file with the same name inside which is used to collect, summarize, and analyze the data. All of these collections of code were written and executed in RStudio to track notes and summarize results. Sequence_Analysis.Rmd has instructions to decompress all of the consensus.tar.gz files, combine them, and create two fasta files, one with all sUMI and one with all dUMI sequences. Using these as input, two data tables were created, that summarize all sequences and read counts for each sample that pass various criteria. These are used to help create Table 2 and as input for Indentifying_Recombinant_Reads.Rmd and Figures.Rmd. Next, 2 fasta files containing all of the rank 1 dUMI sequences and the matching sUMI sequences were created. These were used as input for the python script compare_seqs.py which identifies any matched sequences that are different between sUMI and dUMI read collections. This information was also used to help create Table 2. Finally, to populate the table with the number of sequences and bases in each sequence subset of interest, different sequence collections were saved and viewed in the Geneious program. To investigate the cause of sequences where the sUMI and dUMI sequences do not match, tagged.tar.gz was decompressed and for each family with discordant sUMI and dUMI sequences the reads from the UMI1_keeping directory were aligned using geneious. Reads from dUMI families failing the 0.7 filter were also aligned in Genious. The uncompressed tagged folder was then removed to save space. These read collections contain all of the reads in a UMI1 family and still include the UMI2 sequence. By examining the alignment and specifically the UMI2 sequences, the site of the discordance and its case were identified for each family as described in the paper. These alignments were saved as "Sequence Alignments.geneious". The counts of how many families were the result of PCR recombination were used in the body of the paper. Using Identifying_Recombinant_Reads.Rmd, the dUMI_ranked.csv file from each sample was extracted from all of the tagged.tar.gz files, combined and used as input to create a single dataset containing all UMI information from all samples. This file dUMI_df.csv was used as input for Figures.Rmd. Figures.Rmd used dUMI_df.csv, sequence_counts.csv, and read_counts.csv as input to create draft figures and then individual datasets for eachFigure. These were copied into Prism software to create the final figures for the paper.

The data represent web-scraping of hyperlinks from a selection of environmental stewardship organizations that were identified in the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017). There are two data sets: 1) the original scrape containing all hyperlinks within the websites and associated attribute values (see "README" file); 2) a cleaned and reduced dataset formatted for network analysis. For dataset 1: Organizations were selected from from the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017), a publicly available, spatial data set about environmental stewardship organizations working in New York City, USA (N = 719). To create a smaller and more manageable sample to analyze, all organizations that intersected (i.e., worked entirely within or overlapped) the NYC borough of Staten Island were selected for a geographically bounded sample. Only organizations with working websites and that the web scraper could access were retained for the study (n = 78). The websites were scraped between 09 and 17 June 2020 to a maximum search depth of ten using the snaWeb package (version 1.0.1, Stockton 2020) in the R computational language environment (R Core Team 2020). For dataset 2: The complete scrape results were cleaned, reduced, and formatted as a standard edge-array (node1, node2, edge attribute) for network analysis. See "READ ME" file for further details. References: R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Version 4.0.3. Stockton, T. (2020). snaWeb Package: An R package for finding and building social networks for a website, version 1.0.1. USDA Forest Service. (2017). Stewardship Mapping and Assessment Project (STEW-MAP). New York City Data Set. Available online at https://www.nrs.fs.fed.us/STEW-MAP/data/. This dataset is associated with the following publication: Sayles, J., R. Furey, and M. Ten Brink. How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations. Applied Network Science. Springer Nature, New York, NY, 7: 36, (2022).

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

R scripts in this fileset are those used in the PLOS ONE publication "A snapshot of translational research funded by the National Institutes of Health (NIH): A case study using behavioral and social science research awards and Clinical and Translational Science Awards funded publications." The article can be accessed here: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196545This consists of all R scripts used for data cleaning, data manipulation, and statistical analysis used in the publication.There are eleven files in total:1. "Step1a.bBSSR.format.grants.and.publications.data.R" combines all bBSSR 2008-2014 grant award data and associated publications downloaded from NIH Reporter. 2. "Step1b.BSSR.format.grants.and.publications.data.R" combines all BSSR-only 2008-2014 grant award data and associated publications downloaded from NIH Reporter. 3. "Step2a.bBSSR.get.pubdates.transl.and.all.grants.R" queries PubMed and downloads associated bBSSR publication data.4. "Step2b.BSSR.get.pubdates.transl.and.all.grants.R" queries PubMed and downloads associated BSSR-only publication data.5. "Step3.summary.stats.R" performs summary statistics6. "Step4.time.to.first.publication.R" performs time to first publication analysis.7. "Step5.time.to.citation.analysis.R" performs time to first citation and time to overall citation analyses.8. "Step6.combine.NIH.iCite.data.R" combines NIH iCite citation data.9. "Step7.iCite.data.analysis.R" performs citation analysis on combined iCite data.10. "Step8.MeSH.descriptors.R" queries PubMed and pulls down all MeSH descriptors for all publications11. "Step9.CTSA.publications.R" compares the percent of translational publications among bBSSR, BSSR-only, and CTSA publications.

Time-Series Matrix (TSMx): A visualization tool for plotting multiscale temporal trends TSMx is an R script that was developed to facilitate multi-temporal-scale visualizations of time-series data. The script requires only a two-column CSV of years and values to plot the slope of the linear regression line for all possible year combinations from the supplied temporal range. The outputs include a time-series matrix showing slope direction based on the linear regression, slope values plotted with colors indicating magnitude, and results of a Mann-Kendall test. The start year is indicated on the y-axis and the end year is indicated on the x-axis. In the example below, the cell in the top-right corner is the direction of the slope for the temporal range 2001–2019. The red line corresponds with the temporal range 2010–2019 and an arrow is drawn from the cell that represents that range. One cell is highlighted with a black border to demonstrate how to read the chart—that cell represents the slope for the temporal range 2004–2014. This publication entry also includes an excel template that produces the same visualizations without a need to interact with any code, though minor modifications will need to be made to accommodate year ranges other than what is provided. TSMx for R was developed by Georgios Boumis; TSMx was originally conceptualized and created by Brad G. Peter in Microsoft Excel. Please refer to the associated publication: Peter, B.G., Messina, J.P., Breeze, V., Fung, C.Y., Kapoor, A. and Fan, P., 2024. Perspectives on modifiable spatiotemporal unit problems in remote sensing of agriculture: evaluating rice production in Vietnam and tools for analysis. Frontiers in Remote Sensing, 5, p.1042624. https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624 TSMx sample chart from the supplied Excel template. Data represent the productivity of rice agriculture in Vietnam as measured via EVI (enhanced vegetation index) from the NASA MODIS data product (MOD13Q1.V006). TSMx R script: # import packages library(dplyr) library(readr) library(ggplot2) library(tibble) library(tidyr) library(forcats) library(Kendall) options(warn = -1) # disable warnings # read data (.csv file with "Year" and "Value" columns) data <- read_csv("EVI.csv") # prepare row/column names for output matrices years <- data %>% pull("Year") r.names <- years[-length(years)] c.names <- years[-1] years <- years[-length(years)] # initialize output matrices sign.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) pval.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) slope.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) # function to return remaining years given a start year getRemain <- function(start.year) { years <- data %>% pull("Year") start.ind <- which(data[["Year"]] == start.year) + 1 remain <- years[start.ind:length(years)] return (remain) } # function to subset data for a start/end year combination splitData <- function(end.year, start.year) { keep <- which(data[['Year']] >= start.year & data[['Year']] <= end.year) batch <- data[keep,] return(batch) } # function to fit linear regression and return slope direction fitReg <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(sign(slope)) } # function to fit linear regression and return slope magnitude fitRegv2 <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(slope) } # function to implement Mann-Kendall (MK) trend test and return significance # the test is implemented only for n>=8 getMann <- function(batch) { if (nrow(batch) >= 8) { mk <- MannKendall(batch[['Value']]) pval <- mk[['sl']] } else { pval <- NA } return(pval) } # function to return slope direction for all combinations given a start year getSign <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) signs <- lapply(combs, fitReg) return(signs) } # function to return MK significance for all combinations given a start year getPval <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) pvals <- lapply(combs, getMann) return(pvals) } # function to return slope magnitude for all combinations given a start year getMagn <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) magns <- lapply(combs, fitRegv2) return(magns) } # retrieve slope direction, MK significance, and slope magnitude signs <- lapply(years, getSign) pvals <- lapply(years, getPval) magns <- lapply(years, getMagn) # fill-in output matrices dimension <- nrow(sign.matrix) for (i in 1:dimension) { sign.matrix[i, i:dimension] <- unlist(signs[i]) pval.matrix[i, i:dimension] <- unlist(pvals[i]) slope.matrix[i, i:dimension] <- unlist(magns[i]) } sign.matrix <-...

README

Authors

César E. Corona-González, Claudia Rebeca De Stefano-Ramos, Juan Pablo Rosado-Aíza, Fabiola R Gómez-Velázquez, David I. Ibarra-Zarate, Luz María Alonso-Valerdi

Contact person

César E. Corona-González

https://orcid.org/0000-0002-7680-2953

a00833959@tec.mx

Project name

Psychophysiological data from Mexican children with learning difficulties who strengthen reading and math skills by assistive technology

Year that the project ran

2023

Brief overview of the tasks in the experiment

The current dataset consists of psychometric and electrophysiological data from children with reading or math learning difficulties. These data were collected to evaluate improvements in reading or math skills resulting from using an online learning method called Smartick.

The psychometric evaluations from children with reading difficulties encompassed: spelling tests, where 1) orthographic and 2) phonological errors were considered, 3) reading speed, expressed in words read per minute, and 4) reading comprehension, where multiple-choice questions were given to the children. The last 2 parameters were determined according to the standards from the Ministry of Public Education (Secretaría de Educación Pública in Spanish) in Mexico. On the other hand, group 2 assessments embraced: 1) an assessment of general mathematical knowledge, as well as 2) the hits percentage, and 3) reaction time from an arithmetical task. Additionally, selective attention and intelligence quotient (IQ) were also evaluated.

Then, individuals underwent an EEG experimental paradigm where two conditions were recorded: 1) a 3-minute eyes-open resting state and 2) performing either reading or mathematical activities. EEG recordings from the reading experiment consisted of reading a text aloud and then answering questions about the text. Alternatively, EEG recordings from the math experiment involved the solution of two blocks with 20 arithmetic operations (addition and subtraction). Subsequently, each child was randomly subcategorized as 1) the experimental group, who were asked to engage with Smartick for three months, and 2) the control group, who were not involved with the intervention. Once the 3-month period was over, every child was reassessed as described before.

Description of the contents of the dataset

The dataset contains a total of 76 subjects (sub-), where two study groups were assessed: 1) reading difficulties (R) and 2) math difficulties (M). Then, each individual was subcategorized as experimental subgroup (e), where children were compromised to engage with Smartick, or control subgroup (c), where they did not get involved with any intervention.

Every subject was followed up on for three months. During this period, each subject underwent two EEG sessions, representing the PRE-intervention (ses-1) and the POST-intervention (ses-2).

The EEG recordings from the reading difficulties group consisted of a resting state condition (run-1) and while performing active reading and reading comprehension activities (run-2). On the other hand, EEG data from the math difficulties group was collected from a resting state condition (run-1) and when solving two blocks of 20 arithmetic operations (run-2 and run-3). All EEG files were stored in .set format. The nomenclature and description from filenames are shown below:

Example: the file sub-Rc11_ses-1_task-SmartickDataset_run-2_eeg.set is related to: - The 11th subject from the reading difficulties group, control subgroup (sub-Rc11). - EEG recording from the PRE-intervention (ses-1) while performing the reading activity (run-2)

Independent variables

• Study groups:
  • Reading difficulties
    • Control: children did not follow any intervention
    • Experimental: Children used the reading program of Smartick for 3 months
  • Math difficulties
    • Control: children did not follow any intervention
    • Experimental: Children used the math program of Smartick for 3 months
• Condition:
  • PRE-intervention: first psychological and electroencephalographic evaluation
  • POST-intervention: second psychological and electroencephalographic evaluation

Dependent variables

• Psychometric data from the reading difficulties group:

  • Orthographic_ERR: number of orthographic errors.
  • Phonological_ERR: number of phonological errors.
  • Selective_Attention: score from the selective attention test.
  • Reading_Speed: reading speed in words per minute.
  • Comprehension: score on a reading comprehension task.
  • GROUP: C for the control group, E for the experimental group.
  • GENDER: M for male, F for Female.
  • AGE: age at the beginning of the study.
  • IQ: intelligence quotient.

• Psychometric data from the math difficulties group:

  • WRAT4: score from the WRAT-4 test.
  • hits: hits during the EEG acquisition [%].
  • RT: reaction time during the EEG acquisition [s].
  • Selective_Attention: score from the selective attention test.
  • GROUP: C for the control Group, E for the experimental group.
  • GENDER: M for male, F for female.
  • AGE: age at the beginning of the study.
  • IQ: intelligence quotient.

Psychometric data can be found in the 01_Psychometric_Data.xlsx file

• Engagement percentage within Smartick (only for experimental group)
  • These values represent the engagement percentage through Smartick.
  • Students were asked to get involved with the online method for learning for 3 months, 5 days a week.
  • Greater values than 100% denote participants who regularly logged in more than 5 days weekly.

Engagement percentage be found in the 05_SessionEngagement.xlsx file

Methods

Subjects

Seventy-six Mexican children between 7 and 13 years old were enrolled in this study.

Information about the recruitment procedure

The sample was recruited through non-profit foundations that support learning and foster care programs.

Apparatus

g.USBamp RESEARCH amplifier

Initial setup

1. Explain the task to the participant.
2. Sign informed consent.
3. Set up electrodes.

Task details

The stimuli nested folder contains all stimuli employed in the EEG experiments.

Level 1 - Math: Images used in the math experiment.​​​​​​​ - Reading: Images used in the reading experiment.

Level 2 - Math * POST_Operations: arithmetic operations from the POST-intervention.
* PRE_Operations: arithmetic operations from the PRE-intervention. - Reading * POST_Reading1: text 1 and text-related comprehension questions from the POST-intervention. * POST_Reading2: text 2 and text-related comprehension questions from the POST-intervention. * POST_Reading3: text 3 and text-related comprehension questions from the POST-intervention. * PRE_Reading1: text 1 and text-related comprehension questions from the PRE-intervention. * PRE_Reading2: text 2 and text-related comprehension questions from the PRE-intervention. * PRE_Reading3: text 3 and text-related comprehension questions from the PRE-intervention.

Level 3 - Math * Operation01.jpg to Operation20.jpg: arithmetical operations solved during the first block of the math

Dataset

This dataset was created by Medou Neine

Contents

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.

https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

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.

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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.

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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 ...

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 3 release notes:Adds data in the following formats: Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 2 release notes:Adds data for 2017.Adds a "number_of_months_reported" variable which says how many months of the year the agency reported data.Property Stolen and Recovered is a Uniform Crime Reporting (UCR) Program data set with information on the number of offenses (crimes included are murder, rape, robbery, burglary, theft/larceny, and motor vehicle theft), the value of the offense, and subcategories of the offense (e.g. for robbery it is broken down into subcategories including highway robbery, bank robbery, gas station robbery). The majority of the data relates to theft. Theft is divided into subcategories of theft such as shoplifting, theft of bicycle, theft from building, and purse snatching. For a number of items stolen (e.g. money, jewelry and previous metals, guns), the value of property stolen and and the value for property recovered is provided. This data set is also referred to as the Supplement to Return A (Offenses Known and Reported). All the data was received directly from the FBI as text or .DTA files. I created a setup file based on the documentation provided by the FBI and read the data into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here: https://github.com/jacobkap/crime_data. The Word document file available for download is the guidebook the FBI provided with the raw data which I used to create the setup file to read in data.There may be inaccuracies in the data, particularly in the group of columns starting with "auto." To reduce (but certainly not eliminate) data errors, I replaced the following values with NA for the group of columns beginning with "offenses" or "auto" as they are common data entry error values (e.g. are larger than the agency's population, are much larger than other crimes or months in same agency): 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99942. This cleaning was NOT done on the columns starting with "value."For every numeric column I replaced negative indicator values (e.g. "j" for -1) with the negative number they are supposed to be. These negative number indicators are not included in the FBI's codebook for this data but are present in the data. I used the values in the FBI's codebook for the Offenses Known and Clearances by Arrest data.To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. If an agency has used a different FIPS code in the past, check to make sure the FIPS code is the same as in this data.

This dataset contains files reconstructing single-cell data presented in 'Reference transcriptomics of porcine peripheral immune cells created through bulk and single-cell RNA sequencing' by Herrera-Uribe & Wiarda et al. 2021. Samples of peripheral blood mononuclear cells (PBMCs) were collected from seven pigs and processed for single-cell RNA sequencing (scRNA-seq) in order to provide a reference annotation of porcine immune cell transcriptomics at enhanced, single-cell resolution. Analysis of single-cell data allowed identification of 36 cell clusters that were further classified into 13 cell types, including monocytes, dendritic cells, B cells, antibody-secreting cells, numerous populations of T cells, NK cells, and erythrocytes. Files may be used to reconstruct the data as presented in the manuscript, allowing for individual query by other users. Scripts for original data analysis are available at https://github.com/USDA-FSEPRU/PorcinePBMCs_bulkRNAseq_scRNAseq. Raw data are available at https://www.ebi.ac.uk/ena/browser/view/PRJEB43826. Funding for this dataset was also provided by NRSP8: National Animal Genome Research Program (https://www.nimss.org/projects/view/mrp/outline/18464). Resources in this dataset:Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells 10X Format. File Name: PBMC7_AllCells.zipResource Description: Zipped folder containing PBMC counts matrix, gene names, and cell IDs. Files are as follows: matrix of gene counts* (matrix.mtx.gx) gene names (features.tsv.gz) cell IDs (barcodes.tsv.gz) *The ‘raw’ count matrix is actually gene counts obtained following ambient RNA removal. During ambient RNA removal, we specified to calculate non-integer count estimations, so most gene counts are actually non-integer values in this matrix but should still be treated as raw/unnormalized data that requires further normalization/transformation. Data can be read into R using the function Read10X().Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells Metadata. File Name: PBMC7_AllCells_meta.csvResource Description: .csv file containing metadata for cells included in the final dataset. Metadata columns include: nCount_RNA = the number of transcripts detected in a cell nFeature_RNA = the number of genes detected in a cell Loupe = cell barcodes; correspond to the cell IDs found in the .h5Seurat and 10X formatted objects for all cells prcntMito = percent mitochondrial reads in a cell Scrublet = doublet probability score assigned to a cell seurat_clusters = cluster ID assigned to a cell PaperIDs = sample ID for a cell celltypes = cell type ID assigned to a cellResource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells PCA Coordinates. File Name: PBMC7_AllCells_PCAcoord.csvResource Description: .csv file containing first 100 PCA coordinates for cells. Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells t-SNE Coordinates. File Name: PBMC7_AllCells_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells UMAP Coordinates. File Name: PBMC7_AllCells_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells t-SNE Coordinates. File Name: PBMC7_CD4only_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells UMAP Coordinates. File Name: PBMC7_CD4only_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells UMAP Coordinates. File Name: PBMC7_GDonly_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells t-SNE Coordinates. File Name: PBMC7_GDonly_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gene Annotation Information. File Name: UnfilteredGeneInfo.txtResource Description: .txt file containing gene nomenclature information used to assign gene names in the dataset. 'Name' column corresponds to the name assigned to a feature in the dataset.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells H5Seurat. File Name: PBMC7.tarResource Description: .h5Seurat object of all cells in PBMC dataset. File needs to be untarred, then read into R using function LoadH5Seurat().

The dataset contains raw and intermediated files, and scripts required to reproduce the results associated with the manuscript "Assigning transcriptomic subtypes to CLL samples using nanopore RNA-sequencing and self-organizing maps". Here, we demonstrate that integrating publicly available short-read data with in-house generated ONT data, along with the application of machine learning approaches, enables the characterization of the CLL transcriptome landscape, the identification of clinically relevant molecular subtypes, and the assignment of these subtypes to nanopore-sequenced samples.

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ONT_Projection_paper.zip archive contains scripts and data used to generate the results for the initial submission of the paper.

The content of the data archive is following:

Scripts

Projection_CML_CLL_ONT.Rproj - project workspace and metadata about available files and datasets.

CLL_ONT_4_pub.Rmd - R Markdown file with complete analysis workflow. It includes scripts for data conversion and analysis.

test_SVM_ONT.r - R script for supervised projection of ONT sequencing data on CLL map SOM landscape and assigning transcriptome subtypes.

phenomap.R - R script for generation of phenotype maps.

SOM2jpeg.R - R script for saving SOM portrait image.

assign_SOM_class.R - R script for assignment transcriptome subtypes to nanopore sequencing samples.

Raw Data

ONT_exp_matrix_w_samplenames.csv - raw count matrix of nanopore sequencing samples

Sample_metadata.csv - nanopore sequencing sample metadata

cllmap_rnaseq_tpms_full.csv - tmp value matrix of CLL Map Project [R1]

cllmap_participants.csv - metadata of CLL Map Project

Intermediate files

CLL_MAP_Knisbacher_2022.Rdata - Rdata object with CLL Map tmp matrix and metadata

CLL_MAP_Knisbacher_2022_adj.Rdata - Rdata object with CLL Map batch corrected tmp matrix and metadata

ont_merged_counts.Rdata - Rdata object with raw count matrix of nanopore sequencing samples

bmTable.Rdata - Rdata with ENSEMBL to Gene Official Symbol conversion table

CLL-ONT.Rdata - Rdata object with tpm value matrix (Gene Symbols as row names) of nanopore sequencing samples

metadata.pred - Folder with supSOM image of ONT samples

mean.m.tr.pred - Folder with group SOM images of CLL map transcriptomic subtypes.

Result files

results.CLLMAPadj_overExp_2 - Results - Folder with the results of CLL map transcriptomic portrayal using oposSOM pipeline [R2].

all_significant_GS.csv - Functional annotation of SOM gene modules (spots). This file contains significant (FDR-adjusted) gene sets.

specific_GS.csv - Functional annotation of SOM gene modules (spots). This file contains significant (FDR-adjusted) gene sets specific for a given spot.

References

R1. CLL-map Portal. https://cllmap.org/. Last accessed December 20, 2024

R2. Löffler-Wirth H, Kalcher M, Binder H. oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor. Bioinformatics. 2015 Oct 1;31(19):3225-7. doi: 10.1093/bioinformatics/btv342. Epub 2015 Jun 10. PMID: 26063839.

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ONT_Projection_paper_revision.zip folder contains additional scripts created in response to the Reviewers' comments during the first round of revisions.

CLL_ONT_revision.Rmd - An R Markdown file containing revision-related scripts.

hr_table_ffs.csv and hr_table_os.csv- Hazard ratio tables from the multivariable Cox regression model for failure-free survival and overall survival, with PAT types, gender, and spot I as independent variables.

Context

To make this a seamless process, I cleaned the data and delete many variables that I thought were not important to our dataset. I then uploaded all of those files to Kaggle for each of you to download. The rideshare_data has both lyft and uber but it is still a cleaned version from the dataset we downloaded from Kaggle.

Use of Data Files

You can easily subset the data into the car types that you will be modeling by first loading the csv into R, here is the code for how you do this:

This loads the file into R

df<-read.csv('uber.csv')

The next codes is to subset the data into specific car types. The example below only has Uber 'Black' car types.

df_black<-subset(uber_df, uber_df$name == 'Black')

This next portion of code will be to load it into R. First, we must write this dataframe into a csv file on our computer in order to load it into R.

write.csv(df_black, "nameofthefileyouwanttosaveas.csv")

The file will appear in you working directory. If you are not familiar with your working directory. Run this code:

getwd()

The output will be the file path to your working directory. You will find the file you just created in that folder.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This dataset includes all the data and R code needed to reproduce the analyses in a forthcoming manuscript:Copes, W. E., Q. D. Read, and B. J. Smith. Environmental influences on drying rate of spray applied disinfestants from horticultural production services. PhytoFrontiers, DOI pending.Study description: Instructions for disinfestants typically specify a dose and a contact time to kill plant pathogens on production surfaces. A problem occurs when disinfestants are applied to large production areas where the evaporation rate is affected by weather conditions. The common contact time recommendation of 10 min may not be achieved under hot, sunny conditions that promote fast drying. This study is an investigation into how the evaporation rates of six commercial disinfestants vary when applied to six types of substrate materials under cool to hot and cloudy to sunny weather conditions. Initially, disinfestants with low surface tension spread out to provide 100% coverage and disinfestants with high surface tension beaded up to provide about 60% coverage when applied to hard smooth surfaces. Disinfestants applied to porous materials were quickly absorbed into the body of the material, such as wood and concrete. Even though disinfestants evaporated faster under hot sunny conditions than under cool cloudy conditions, coverage was reduced considerably in the first 2.5 min under most weather conditions and reduced to less than or equal to 50% coverage by 5 min. Dataset contents: This dataset includes R code to import the data and fit Bayesian statistical models using the model fitting software CmdStan, interfaced with R using the packages brms and cmdstanr. The models (one for 2022 and one for 2023) compare how quickly different spray-applied disinfestants dry, depending on what chemical was sprayed, what surface material it was sprayed onto, and what the weather conditions were at the time. Next, the statistical models are used to generate predictions and compare mean drying rates between the disinfestants, surface materials, and weather conditions. Finally, tables and figures are created. These files are included:Drying2022.csv: drying rate data for the 2022 experimental runWeather2022.csv: weather data for the 2022 experimental runDrying2023.csv: drying rate data for the 2023 experimental runWeather2023.csv: weather data for the 2023 experimental rundisinfestant_drying_analysis.Rmd: RMarkdown notebook with all data processing, analysis, and table creation codedisinfestant_drying_analysis.html: rendered output of notebookMS_figures.R: additional R code to create figures formatted for journal requirementsfit2022_discretetime_weather_solar.rds: fitted brms model object for 2022. This will allow users to reproduce the model prediction results without having to refit the model, which was originally fit on a high-performance computing clusterfit2023_discretetime_weather_solar.rds: fitted brms model object for 2023data_dictionary.xlsx: descriptions of each column in the CSV data files

analyze the survey of income and program participation (sipp) with r if the census bureau's budget was gutted and only one complex sample survey survived, pray it's the survey of income and program participation (sipp). it's giant. it's rich with variables. it's monthly. it follows households over three, four, now five year panels. the congressional budget office uses it for their health insurance simulation . analysts read that sipp has person-month files, get scurred, and retreat to inferior options. the american community survey may be the mount everest of survey data, but sipp is most certainly the amazon. questions swing wild and free through the jungle canopy i mean core data dictionary. legend has it that there are still species of topical module variables that scientists like you have yet to analyze. ponce de león would've loved it here. ponce. what a name. what a guy. the sipp 2008 panel data started from a sample of 105,663 individuals in 42,030 households. once the sample gets drawn, the census bureau surveys one-fourth of the respondents every four months, over f our or five years (panel durations vary). you absolutely must read and understand pdf pages 3, 4, and 5 of this document before starting any analysis (start at the header 'waves and rotation groups'). if you don't comprehend what's going on, try their survey design tutorial. since sipp collects information from respondents regarding every month over the duration of the panel, you'll need to be hyper-aware of whether you want your results to be point-in-time, annualized, or specific to some other period. the analysis scripts below provide examples of each. at every four-month interview point, every respondent answers every core question for the previous four months. after that, wave-specific addenda (called topical modules) get asked, but generally only regarding a single prior month. to repeat: core wave files contain four records per person, topical modules contain one. if you stacked every core wave, you would have one record per person per month for the duration o f the panel. mmmassive. ~100,000 respondents x 12 months x ~4 years. have an analysis plan before you start writing code so you extract exactly what you need, nothing more. better yet, modify something of mine. cool? this new github repository contains eight, you read me, eight scripts: 1996 panel - download and create database.R 2001 panel - download and create database.R 2004 panel - download and create database.R 2008 panel - download and create database.R since some variables are character strings in one file and integers in anoth er, initiate an r function to harmonize variable class inconsistencies in the sas importation scripts properly handle the parentheses seen in a few of the sas importation scripts, because the SAScii package currently does not create an rsqlite database, initiate a variant of the read.SAScii function that imports ascii data directly into a sql database (.db) download each microdata file - weights, topical modules, everything - then read 'em into sql 2008 panel - full year analysis examples.R< br /> define which waves and specific variables to pull into ram, based on the year chosen loop through each of twelve months, constructing a single-year temporary table inside the database read that twelve-month file into working memory, then save it for faster loading later if you like read the main and replicate weights columns into working memory too, merge everything construct a few annualized and demographic columns using all twelve months' worth of information construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half, again save it for faster loading later, only if you're so inclined reproduce census-publish ed statistics, not precisely (due to topcoding described here on pdf page 19) 2008 panel - point-in-time analysis examples.R define which wave(s) and specific variables to pull into ram, based on the calendar month chosen read that interview point (srefmon)- or calendar month (rhcalmn)-based file into working memory read the topical module and replicate weights files into working memory too, merge it like you mean it construct a few new, exciting variables using both core and topical module questions construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half reproduce census-published statistics, not exactly cuz the authors of this brief used the generalized variance formula (gvf) to calculate the margin of error - see pdf page 4 for more detail - the friendly statisticians at census recommend using the replicate weights whenever possible. oh hayy, now it is. 2008 panel - median value of household assets.R define which wave(s) and spe cific variables to pull into ram, based on the topical module chosen read the topical module and replicate weights files into working memory too, merge once again construct a replicate-weighted complex sample design with a...

If using this dataset, please cite the following paper above and the current Zenodo repository:A. Mundanad Narayanan, R. Zink, and A. Bertrand, "EEG miniaturization limits for stimulus decoding with EEG sensor networks", Journal of Neural Engineering, vol. 18, 2021, doi: 10.1088/1741-2552/ac2629

Experiment*************

This dataset contains 255-channel electroencephalography (EEG) data collected during an auditory attention decoding experiment (AAD). The EEG was recorded using a SynAmps RT device (Compumedics, Australia) at a sampling rate of 1 kHz and using active Ag/Cl electrodes. The electrodes were placed on the head according to the international 10-5 (5%) system. 30 normal hearing male subjects between 22 and 35 years old participated in the experiment. All of them signed an informed consent form approved by the KU Leuven ethical committee.

Two Dutch stories narrated by different male speakers divided into two parts of 6 minutes each were used as the stimuli in the experiment [1]. A single trial of the experiment involved the presentation of these two parts (one of both stories) to the subject through insert phones (Etymotic ER3A) at 60dBA. These speech stimuli were filtered using a head-related transfer function (HRTF) such that the stories seemed to arrive from two distinct spatial locations, namely left and right with respect to the subject with 180 degrees separation. In each trial, the subjects were asked to attend to only one ear while ignoring the other. Four trials of 6 minutes each were carried out, in which each story part is used twice. The order of presentations was randomized and balanced over different subjects. Thus approximately 24 minutes of EEG data was recorded per subject.

File organization and details********************************

The EEG data of each of the 30 subjects are uploaded as a ZIP file with the name Sx.tar.gzip here x=0,1,2,..,29. When a zip file is extracted, the EEG data are in their original raw format as recorded by the CURRY software [2]. The data files of each recording consist of four files with the same name but different extensions, namely, .dat, ,dap, .rs3 and .ceo. The name of each file follows the following convention: Sx_AAD_P. With P taking one of the following values for each file:1. 1L2. 1R3. 2L4. 2R

The letter 'L' or 'R' in P indicates the attended direction of each subject in a recording: left and right respectively. A MATLAB function to read the software is provided in the directory called scripts. A python function to read the file is available in this Github repository [3].The original version of stimuli presented to subjects, i.e. without the HRTF filtering, can be found after extracting the stimuli.zip file in WAV format. There are 4 WAV files corresponding to the two parts of each of the two stories. These files have been sampled at 44.1 kHz. The order of presentation of these WAV files is given in the table below: Stimuli presentation and attention information of files

Trial (P) Stimuli: Left-ear Stimuli: Right-ear Attention

1L part1_track1_dry part1_track2_dry Left

1R part1_track1_dry part1_track2_dry Right

2L part2_track2_dry part2_track1_dry Left

2R part2_track2_dry part2_track1_dry Right

Additional files (after extracting scripts.zip and misc.zip):

scripts/sample_script.m: Demonstrates reading an EEG-AAD recording and extracting the start and end of the experiment.

misc/channel-layout.jpeg: The 255-channel EEG cap layout

misc/eeg255ch_locs.csv: The channel names, numbers and their spherical (theta and phi) scalp coordinates.

[1] Radioboeken voor kinderen, http://radioboeken.eu/kinderradioboeken.php?lang=NL, 2007 (Accessed: 8 Feb 2021)

[2] CURRY 8 X – Data Acquisition and Online Processing, https://compumedicsneuroscan.com/product/curry-data-acquisition-online-processing-x/ (Accessed: 8, Feb, 2021)

[3] Abhijith Mundanad Narayanan, "EEG analysis in python", 2021. https://github.com/mabhijithn/eeg-analyse , (Accessed: 8 Feb, 2021)

This data release presents the data, JAGS models, and R code used to manipulate data and to produce results and figures presented in the USGS Open File Report, "Decision-Support Framework for Linking Regional-Scale Management Actions to Continental-Scale Conservation of Wide-Ranging Species, (https://res1doid-o-torg.vcapture.xyz/10.5066/P93YTR3X). The zip folder is provided so that other can reproduce results from the integrated population model, inspect model structure and posterior simulations, conduct analyses not present in the report, and use and modify the code. Raw source data can be sourced from the USGS Bird Banding Laboratory, USFWS Surveys and Monitoring Branch, National Oceanic and Atmospheric administration, and Ducks Unlimited Canada. The zip file contains the following objects when extracted: Readme.txt: A plain text file describing each file in this directory. Figures-Pintail-IPM.r: R code that generates report figures in png, pdf, and eps format. Generates Figures 2-11 and calls source code for figures 12 and 13 found in other files. * get pintail IPM data.r: R source code that must be run to format data for the IPM code file. * getbandrecovs.r: R code that takes Bird Banding Lab data for pintail band releases and recoveries and formats for analysis. This file is called by 'get pintail IPM data.r'. File was originally written by Scott Boomer (USFWS) and modified by Erik Osnas for use for the IPM. * Model_1_post.txt: Text representation of the posterior simulations from Model 1. This file can be read by the R function dget() to produce an R list object that contain posterior draws from Model 1. The list is the BUGSoutput$sims.list object from a call to rjags::jags. * Model_2_post.txt: As above but for Model 2. * Model_S1_post.txt: As above but for Model S1. * Pintail IPM.r: This is the main file that defines the IPM models in JAGS, structures the data for JAGS, defines initial values, and calls runs the models. Outputs are text files that contains JAGS model files, R work spaces that contains all data models, and results, include the output from the jags() function. From this the BUGSoutput$sims.list object was written to text for each model. * MSY_metrics.txt: Summary of results produced from running code in source_figure_12.R. This table is a text representation of a summary of the maximum sustained yield analysis at various mean rainfall levels, used for Table 1 of report and can be reproduced by running the code in source_figure_12.R. To understand the structure of this file, you must consult the code file and understand the structure of the R objects created from that code. Otherwise, consult Figure 12 and Table 1 in report. * source_figure_12.R: R code to produce Figure 12. Code is written to work with Rworkspace output from Model 1, but can be modified to use the Model_1_post.txt file without re-running the model. This would allow use of the same posterior realizations as used in the report. * source_figure_13.R: This is the code used to product the results for Figure 13. Required here is the posterior from Model 1 and data for the Prairie Parkland Model based on Jim Devries/Ducks Unlimited data. These are described in the report text. * Data: A directory that contains the raw data used for this report. * Data/2015_LCC_Networks_shapefile: A directory that contain ESRI shapefiles for used in Figure 1 and to define the boundaries of the Landscape Conservation Cooperatives. Found at (https://res1wwwd-o-tsciencebased-o-tgov.vcapture.xyz/catalog/item/55b943ade4b09a3b01b65d78) * Data/bndg_1430_yr1960up_DBISC_03042014.csv: A comma delimited file for banded pintail from 1960 to 2014. Obtained from the USGS Bird Banding Lab. This file is used by 'getbandrecovs.r' to produce and 'm-array' used in the Integrated Population Model (IPM). A data dictionary describing the codes for each field can be found here, https://res1wwwd-o-tpwrcd-o-tusgsd-o-tgov.vcapture.xyz/BBL/manual/summary.cfm * Data/cponds.csv: A comma delimited file of estimated Canadian ponds based on counts from the North American Breeding Waterfowl and Habitat Survey, 1955-2014. Given is the year, point estimate, and estimated standard error. * Data/enc_1430_yr1960up_DBISC_03042014.csv: A comma delimited file for encounters of banded pintail. Obtained from the USGS Bird Banding Lab. This file is use by 'getbandrecovs.r' to produce and 'm-array' used in the Integrated Population Model (IPM). A data dictionary describing the codes for each field can be found here, (https://res1wwwd-o-tpwrcd-o-tusgsd-o-tgov.vcapture.xyz/BBL/manual/enc.cfm) * Data/nopiBPOP19552014.csv: A comma delimited file of estimated northern pintail based on counts from the North American Breeding Waterfowl and Habitat Survey, 1955-2014. Given is the year, pintail point estimate (bpop), and pintail estimated standard error (bpopSE), mean latitude of the pintail population (lat), latitude variance of the pintail population (latVAR), mean longitude of the pintail population (lon), and the variance in longitude of the pintail population (lonVAR). * Data/Summary Climate Data California CV 2.csv: Rainfall data for the California central valley downloaded from National Climate Data Center (www.ncdc.noaa.gov/cdo-web/) as described in report text (https://res1doid-o-torg.vcapture.xyz/10.5066/P93YTR3X) and publication found at https://res1doid-o-torg.vcapture.xyz/10.1002/jwmg.21124 . Used in 'get pintail IPM data.r' for IPM. * Data/Summary data MAV.csv: Rainfall data for the Mississippi Aluvial valley downloaded from National Climate Data Center (www.ncdc.noaa.gov/cdo-web/) as described in report text (https://res1doid-o-torg.vcapture.xyz/10.5066/P93YTR3X) and publication found at https://res1doid-o-torg.vcapture.xyz/10.1002/jwmg.21124 . Used in 'get pintail IPM data.r' for IPM. * Data/Wing data 1961 2011 NOPI.txt: Comma delimited text file for pintail wing age data for 1961 to 2011 from the Parts Collection Survey. Each row is an individual wing with sex cohorts 4 = male, 5 = female and age cohorts 1 = After Hatch Year and 2 = Hatch Year. Wt is a weighting factor that determines how many harvested pintails this wing represent. See USFWS documentation for the Part Collection survey for descriptions. Summing Wt for each age, sex, and year gives an estimate of the number of pintail harvested. Used in 'get pintail IPM data.r' for IPM. * Data/Wing data 2012 2013 NOPI.csv: Same as 'Wing data 1961 2011 NOPI.txt' but for years 2012 and 2013.

The uploaded files are:

1) Excel file containing 6 sheets in respective Order: "Data Extraction" (summarized final data extractions from the three reviewers involved), "Comparison Data" (data related to the comparisons investigated), "Paper level data" (summaries at paper level), "Outcome Event Data" (information with respect to number of events for every outcome investigated within a paper), "Tuning Classification" (data related to the manner of hyperparameter tuning of Machine Learning Algorithms).

2) R script used for the Analysis (In order to read the data, please: Save "Comparison Data", "Paper level data", "Outcome Event Data" Excel sheets as txt files. In the R script srpap: Refers to the "Paper level data" sheet, srevents: Refers to the "Outcome Event Data" sheet and srcompx: Refers to " Comparison data Sheet".

3) Supplementary Material: Including Search String, Tables of data, Figures

4) PRISMA checklist items

R script for reading hdf5 files

Cover crops provide many agroecosystem services, including weed suppression, which is partially exerted through release of allelopathic benzoxazinoid (BX) compounds. This research characterizes (1) changes in concentrations of BX compounds in shoots, roots, and soil at three growth stages (GS) of cereal rye (Secale cereale L.), and (2) their degradation over time following termination. Concentrations of shoot dominant BX compounds, DIBOA-glc and DIBOA, were least at GS 83 (boot). The root dominant BX compound, HMBOA-glc, concentration was least at GS 54 (elongation). Rhizosphere soil BX concentrations were 1000 times smaller than in root tissues. Dominant compounds in soil were HMBOA-glc and HMBOA. Concentrations of BX compounds were similar for soil near root crowns and between-rows. Soil BX concentrations following cereal rye termination declined exponentially over time in three of four treatments: incorporated shoots (S) and roots (R), no-till S+R (cereal rye rolled flat), and no-till R (shoots removed), but not in no-till S. On the day following cereal rye termination, soil concentrations of HMBOA-glc and HMBOA in these three treatments increased above initial concentrations. Concentrations of these two compounds decreased the fastest while DIBOA-glc declined the slowest (half-life of 4 d in no-till S+R soil). Placement of shoots on the surface of an area where cereal rye had not grown (no-till S) did not increase soil concentrations of BX compounds. The short duration and complex dynamics of BX compounds in soil prior to and following termination illustrate the limited window for enhancing weed suppression by cereal rye allelochemicals; valuable information for programs breeding for enhanced weed suppression. In addition to the data analyzed for this article, we also include the R code. Resources in this dataset:Resource Title: BX data following termination. File Name: FinalBXsForMatt-20200908.csvResource Description: For each sample, gives the time, depth, location, and plot treatment, and then the compound concentrations. This is the principal data set analyzed with the R (anal2-cleaned.r) code, see that code for use.Resource Title: BX compounds from 3rd sampling time before termination. File Name: soil2-20201123.csvResource Description: These data are for comparison with the post termination data. They were taken at the 3rd sampling time (pre-termination), a day prior to termination. Each sample is identified with a treatment, date, and plot location, in addition to the BX concentrations. See R code (anal2-cleaned.r) for how this file is used.Resource Title: Soil location (within row versus between row) values of BX compounds. File Name: s2b.csvResource Description: Each row gives the average BX compound for each soil location (within row versus between row) for the second sample for each plot. These data are combined with bx3 (the data set read in from the file , "FinalBXsForMatt-20200908.csv"). See R (anal2-cleaned.r) code for use.Resource Title: R code for analysis of the decay (post-termination) BX data.. File Name: anal2-cleaned.rResource Description: This is the R code used to analyze the termination data. It also creates and writes out some data subsets (used for analysis and plots) that are later read in.Resource Software Recommended: R version 3.6.3,url: https://www.R-project.org/ Resource Title: Tissue BX compounds. File Name: tissues20210728b.csvResource Description: Data file holding results from a tissue analysis for BX compounds, in ug, from shoots and roots, and at various sampling times. Read into the R file, anal1-cleaned.r where it is used in a statistical analysis and to create figures.Resource Title: BX compounds from soil with a live rye cover crop. File Name: soil2-20201214.csvResource Description: BX compounds (in ng/g dry wt), by treatment, sampling time, date, and plot ID. These are data are read into the R program, anal1-cleaned.r, for analysis and to create figures. These are soil samples taken from locations with a live rye plant cover crop.Resource Title: R code for BX analyses of soil under rye and plant tissues. File Name: anal1-cleaned.rResource Description: R code for analysis of the soil BX compounds under a live rye cover crop at different growing stages, and for the analysis of tissue BX compounds. In addition to statistical analyses, code in this file creates figures, also some statistical output that is used to create a file that is later read in for figure creation (s2-CLD20220730-Stage.csv).Resource Software Recommended: R version 3.6.3,url: https://www.R-project.org/ Resource Title: Description of data files for anal2-cleaned.r. File Name: readme2.txtResource Description: Describes the input files used in the R code in anal2-cleaned.r, including descriptions and formats for each field. The file also describes some output (results) files that were uploaded to this site. This is a plain ASCII text file.Resource Title: Estimates produced by anal2-cleaned.r from statistical modeling.. File Name: Estimates20201110.csvResource Description: Estimates produced by anal2-cleaned.r from statistical modeling (see readme2.txt)Resource Title: Summary statistics from anal2-cleaned.r. File Name: CV20210412.csvResource Description: Summary statistics from anal2-cleaned.r, used for plotsResource Title: Data summaries (same as CV20210412.csv), rescaled. File Name: RESCALE-20210412.csvResource Description: Same as "CV20210412.csv" except log of data have been rescaled to minimum at least zero and maximum one, see readme2.txtResource Title: Statistical summaries for different stages. File Name: s2-CLD20220730-Stage.csvResource Description: Statistical summaries used for creating a figure (not used in paper), used in anal1-cleaned.r; data for soil BX under living rye.Resource Title: Description of data files for anal1-cleaned.r. File Name: readme1.txtResource Description: Contains general descriptions of data imported into anal1-cleaned.r, and a description of each field. Also contains some descriptions of files output by anal1-cleaned.r, used to create tables or figures.