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 `

Complete dataset of “Film Circulation on the International Film Festival Network and the Impact on Global Film Culture”

A peer-reviewed data paper for this dataset is in review to be published in NECSUS_European Journal of Media Studies - an open access journal aiming at enhancing data transparency and reusability, and will be available from https://necsus-ejms.org/ and https://mediarep.org

Please cite this when using the dataset.

Detailed description of the dataset:

1 Film Dataset: Festival Programs

The Film Dataset consists a data scheme image file, a codebook and two dataset tables in csv format.

The codebook (csv file “1_codebook_film-dataset_festival-program”) offers a detailed description of all variables within the Film Dataset. Along with the definition of variables it lists explanations for the units of measurement, data sources, coding and information on missing data.

The csv file “1_film-dataset_festival-program_long” comprises a dataset of all films and the festivals, festival sections, and the year of the festival edition that they were sampled from. The dataset is structured in the long format, i.e. the same film can appear in several rows when it appeared in more than one sample festival. However, films are identifiable via their unique ID.

The csv file “1_film-dataset_festival-program_wide” consists of the dataset listing only unique films (n=9,348). The dataset is in the wide format, i.e. each row corresponds to a unique film, identifiable via its unique ID. For easy analysis, and since the overlap is only six percent, in this dataset the variable sample festival (fest) corresponds to the first sample festival where the film appeared. For instance, if a film was first shown at Berlinale (in February) and then at Frameline (in June of the same year), the sample festival will list “Berlinale”. This file includes information on unique and IMDb IDs, the film title, production year, length, categorization in length, production countries, regional attribution, director names, genre attribution, the festival, festival section and festival edition the film was sampled from, and information whether there is festival run information available through the IMDb data.

2 Survey Dataset

The Survey Dataset consists of a data scheme image file, a codebook and two dataset tables in csv format.

The codebook “2_codebook_survey-dataset” includes coding information for both survey datasets. It lists the definition of the variables or survey questions (corresponding to Samoilova/Loist 2019), units of measurement, data source, variable type, range and coding, and information on missing data.

The csv file “2_survey-dataset_long-festivals_shared-consent” consists of a subset (n=161) of the original survey dataset (n=454), where respondents provided festival run data for films (n=206) and gave consent to share their data for research purposes. This dataset consists of the festival data in a long format, so that each row corresponds to the festival appearance of a film.

The csv file “2_survey-dataset_wide-no-festivals_shared-consent” consists of a subset (n=372) of the original dataset (n=454) of survey responses corresponding to sample films. It includes data only for those films for which respondents provided consent to share their data for research purposes. This dataset is shown in wide format of the survey data, i.e. information for each response corresponding to a film is listed in one row. This includes data on film IDs, film title, survey questions regarding completeness and availability of provided information, information on number of festival screenings, screening fees, budgets, marketing costs, market screenings, and distribution. As the file name suggests, no data on festival screenings is included in the wide format dataset.

3 IMDb & Scripts

The IMDb dataset consists of a data scheme image file, one codebook and eight datasets, all in csv format. It also includes the R scripts that we used for scraping and matching.

The codebook “3_codebook_imdb-dataset” includes information for all IMDb datasets. This includes ID information and their data source, coding and value ranges, and information on missing data.

The csv file “3_imdb-dataset_aka-titles_long” contains film title data in different languages scraped from IMDb in a long format, i.e. each row corresponds to a title in a given language.

The csv file “3_imdb-dataset_awards_long” contains film award data in a long format, i.e. each row corresponds to an award of a given film.

The csv file “3_imdb-dataset_companies_long” contains data on production and distribution companies of films. The dataset is in a long format, so that each row corresponds to a particular company of a particular film.

The csv file “3_imdb-dataset_crew_long” contains data on names and roles of crew members in a long format, i.e. each row corresponds to each crew member. The file also contains binary gender assigned to directors based on their first names using the GenderizeR application.

The csv file “3_imdb-dataset_festival-runs_long” contains festival run data scraped from IMDb in a long format, i.e. each row corresponds to the festival appearance of a given film. The dataset does not include each film screening, but the first screening of a film at a festival within a given year. The data includes festival runs up to 2019.

The csv file “3_imdb-dataset_general-info_wide” contains general information about films such as genre as defined by IMDb, languages in which a film was shown, ratings, and budget. The dataset is in wide format, so that each row corresponds to a unique film.

The csv file “3_imdb-dataset_release-info_long” contains data about non-festival release (e.g., theatrical, digital, tv, dvd/blueray). The dataset is in a long format, so that each row corresponds to a particular release of a particular film.

The csv file “3_imdb-dataset_websites_long” contains data on available websites (official websites, miscellaneous, photos, video clips). The dataset is in a long format, so that each row corresponds to a website of a particular film.

The dataset includes 8 text files containing the script for webscraping. They were written using the R-3.6.3 version for Windows.

The R script “r_1_unite_data” demonstrates the structure of the dataset, that we use in the following steps to identify, scrape, and match the film data.

The R script “r_2_scrape_matches” reads in the dataset with the film characteristics described in the “r_1_unite_data” and uses various R packages to create a search URL for each film from the core dataset on the IMDb website. The script attempts to match each film from the core dataset to IMDb records by first conducting an advanced search based on the movie title and year, and then potentially using an alternative title and a basic search if no matches are found in the advanced search. The script scrapes the title, release year, directors, running time, genre, and IMDb film URL from the first page of the suggested records from the IMDb website. The script then defines a loop that matches (including matching scores) each film in the core dataset with suggested films on the IMDb search page. Matching was done using data on directors, production year (+/- one year), and title, a fuzzy matching approach with two methods: “cosine” and “osa.” where the cosine similarity is used to match titles with a high degree of similarity, and the OSA algorithm is used to match titles that may have typos or minor variations.

The script “r_3_matching” creates a dataset with the matches for a manual check. Each pair of films (original film from the core dataset and the suggested match from the IMDb website was categorized in the following five categories: a) 100% match: perfect match on title, year, and director; b) likely good match; c) maybe match; d) unlikely match; and e) no match). The script also checks for possible doubles in the dataset and identifies them for a manual check.

The script “r_4_scraping_functions” creates a function for scraping the data from the identified matches (based on the scripts described above and manually checked). These functions are used for scraping the data in the next script.

The script “r_5a_extracting_info_sample” uses the function defined in the “r_4_scraping_functions”, in order to scrape the IMDb data for the identified matches. This script does that for the first 100 films, to check, if everything works. Scraping for the entire dataset took a few hours. Therefore, a test with a subsample of 100 films is advisable.

The script “r_5b_extracting_info_all” extracts the data for the entire dataset of the identified matches.

The script “r_5c_extracting_info_skipped” checks the films with missing data (where data was not scraped) and tried to extract data one more time to make sure that the errors were not caused by disruptions in the internet connection or other technical issues.

The script “r_check_logs” is used for troubleshooting and tracking the progress of all of the R scripts used. It gives information on the amount of missing values and errors.

4 Festival Library Dataset

The Festival Library Dataset consists of a data scheme image file, one codebook and one dataset, all in csv format.

The codebook (csv file “4_codebook_festival-library_dataset”) offers a detailed description of all variables within the Library Dataset. It lists the definition of variables, such as location and festival name, and festival categories, units of measurement, data sources and coding and missing data.

The csv file “4_festival-library_dataset_imdb-and-survey” contains data on all unique festivals collected from both IMDb and survey sources. This dataset appears in wide format, all information for each festival is listed in one row. This

The Meta-Corpus of Datasets: The Reddit Dataset

The Complete Collection of Datasets Posted on Reddit

By SocialGrep [source]

About this dataset

A subreddit dataset is a collection of posts and comments made on Reddit's /r/datasets board. This dataset contains all the posts and comments made on the /r/datasets subreddit from its inception to March 1, 2022. The dataset was procured using SocialGrep. The data does not include usernames to preserve users' anonymity and to prevent targeted harassment

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

In order to use this dataset, you will need to have a text editor such as Microsoft Word or LibreOffice installed on your computer. You will also need a web browser such as Google Chrome or Mozilla Firefox.

Once you have the necessary software installed, open the The Reddit Dataset folder and double-click on the the-reddit-dataset-dataset-posts.csv file to open it in your preferred text editor.

In the document, you will see a list of posts with the following information for each one: title, sentiment, score, URL, created UTC, permalink, subreddit NSFW status, and subreddit name.

You can use this information to analyze trends in data sets posted on /r/datasets over time. For example, you could calculate the average score for all posts and compare it to the average score for posts in specific subReddits. Additionally, sentiment analysis could be performed on the titles of posts to see if there is a correlation between positive/negative sentiment and upvotes/downvotes

Research Ideas

• Finding correlations between different types of datasets
• Determining which datasets are most popular on Reddit
• Analyzing the sentiments of post and comments on Reddit's /r/datasets board

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: the-reddit-dataset-dataset-comments.csv | Column name | Description | |:-------------------|:---------------------------------------------------| | type | The type of post. (String) | | subreddit.name | The name of the subreddit. (String) | | subreddit.nsfw | Whether or not the subreddit is NSFW. (Boolean) | | created_utc | The time the post was created, in UTC. (Timestamp) | | permalink | The permalink for the post. (String) | | body | The body of the post. (String) | | sentiment | The sentiment of the post. (String) | | score | The score of the post. (Integer) |

File: the-reddit-dataset-dataset-posts.csv | Column name | Description | |:-------------------|:---------------------------------------------------| | type | The type of post. (String) | | subreddit.name | The name of the subreddit. (String) | | subreddit.nsfw | Whether or not the subreddit is NSFW. (Boolean) | | created_utc | The time the post was created, in UTC. (Timestamp) | | permalink | The permalink for the post. (String) | | score | The score of the post. (Integer) | | domain | The domain of the post. (String) | | url | The URL of the post. (String) | | selftext | The self-text of the post. (String) | | title | The title of the post. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit SocialGrep.

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

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.

Context

I created these files and analysis as part of working on a case study for the Google Data Analyst certificate.

Question investigated: Do annual members and casual riders use Cyclistic bikes differently? Why do we want to know?: Knowing bike usage/behavior by rider type will allow the Marketing, Analytics, and Executive team stakeholders to design, assess, and approve appropriate strategies that drive profitability.

Content

I used the script noted below to clean the files and then added some additional steps to create the visualizations to complete my analysis. The additional steps are noted in corresponding R Markdown file for this data set.

Acknowledgements

Files: most recent 1 year of data available, Divvy_Trips_2019_Q2.csv, Divvy_Trips_2019_Q3.csv, Divvy_Trips_2019_Q4.csv, Divvy_Trips_2020_Q1.csv Source: Downloaded from https://divvy-tripdata.s3.amazonaws.com/index.html

Data cleaning script: followed this script to clean and merge files https://docs.google.com/document/d/1gUs7-pu4iCHH3PTtkC1pMvHfmyQGu0hQBG5wvZOzZkA/copy

Note: Combined data set has 3,876,042 rows, so you will likely need to run R analysis on your computer (e.g., R Console) rather than in the cloud (e.g., RStudio Cloud)

Inspiration

This was my first attempt to conduct an analysis in R and create the R Markdown file. As you might guess, it was an eye-opening experience, with both exciting discoveries and aggravating moments.

One thing I have not yet been able to figure out is how to add a legend to the map. I was able to get a legend to appear on a separate (empty) map, but not on the map you will see here.

I am also interested to see what others did with this analysis - what were the findings and insights you found?

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

# Annotated 12 lead ECG dataset

Contain 827 ECG tracings from different patients, annotated by several cardiologists, residents and medical students.
It is used as test set on the paper:
"Automatic Diagnosis of the Short-Duration12-Lead ECG using a Deep Neural Network".

It contain annotations about 6 different ECGs abnormalities:
- 1st degree AV block (1dAVb);
- right bundle branch block (RBBB);
- left bundle branch block (LBBB);
- sinus bradycardia (SB);
- atrial fibrillation (AF); and,
- sinus tachycardia (ST).

## Folder content:

- `ecg_tracings.hdf5`: HDF5 file containing a single dataset named `tracings`. This dataset is a 
`(827, 4096, 12)` tensor. The first dimension correspond to the 827 different exams from different 
patients; the second dimension correspond to the 4096 signal samples; the third dimension to the 12
different leads of the ECG exam. 

The signals are sampled at 400 Hz. Some signals originally have a duration of 
10 seconds (10 * 400 = 4000 samples) and others of 7 seconds (7 * 400 = 2800 samples).
In order to make them all have the same size (4096 samples) we fill them with zeros
on both sizes. For instance, for a 7 seconds ECG signal with 2800 samples we include 648
samples at the beginning and 648 samples at the end, yielding 4096 samples that are them saved
in the hdf5 dataset. All signal are represented as floating point numbers at the scale 1e-4V: so it should
be multiplied by 1000 in order to obtain the signals in V.

In python, one can read this file using the following sequence:
```python
import h5py
with h5py.File(args.tracings, "r") as f:
  x = np.array(f['tracings'])
```

- The file `attributes.csv` contain basic patient attributes: sex (M or F) and age. It
contain 827 lines (plus the header). The i-th tracing in `ecg_tracings.hdf5` correspond to the i-th line.
- `annotations/`: folder containing annotations csv format. Each csv file contain 827 lines (plus the header).
The i-th line correspond to the i-th tracing in `ecg_tracings.hdf5` correspond to the in all csv files.
The csv files all have 6 columns `1dAVb, RBBB, LBBB, SB, AF, ST`
corresponding to weather the annotator have detect the abnormality in the ECG (`=1`) or not (`=0`).
 1. `cardiologist[1,2].csv` contain annotations from two different cardiologist.
 2. `gold_standard.csv` gold standard annotation for this test dataset. When the cardiologist 1 and cardiologist 2
 agree, the common diagnosis was considered as gold standard. In cases where there was any disagreement, a 
 third senior specialist, aware of the annotations from the other two, decided the diagnosis. 
 3. `dnn.csv` prediction from the deep neural network described in 
 "Automatic Diagnosis of the Short-Duration 12-Lead ECG using a Deep Neural Network". The threshold is set in such way 
 it maximizes the F1 score.
 4. `cardiology_residents.csv` annotations from two 4th year cardiology residents (each annotated half of the dataset).
 5. `emergency_residents.csv` annotations from two 3rd year emergency residents (each annotated half of the dataset).
 6. `medical_students.csv` annotations from two 5th year medical students (each annotated half of the dataset).

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?

Welcome to my Kickstarter case study! In this project I’m trying to understand what the success’s factors for a Kickstarter campaign are, analyzing an available public dataset from Web Robots. The process of analysis will follow the data analysis roadmap: ASK, PREPARE, PROCESS, ANALYZE, SHARE and ACT.

ASK

Different questions will guide my analysis: 1. Is the campaign duration influencing the success of the project? 2. Is it the chosen funding budget? 3. Which category of campaign is the most likely to be successful?

PREPARE

I’m using the Kickstarter Datasets publicly available on Web Robots. Data are scraped using a bot which collects the data in CSV format once a month and all the data are divided into CSV files. Each table contains: - backers_count : number of people that contributed to the campaign - blurb : a captivating text description of the project - category : the label categorizing the campaign (technology, art, etc) - country - created_at : day and time of campaign creation - deadline : day and time of campaign max end - goal : amount to be collected - launched_at : date and time of campaign launch - name : name of campaign - pledged : amount of money collected - state : success or failure of the campaign

Each month scraping produce a huge amount of CSVs, so for an initial analysis I decided to focus on three months: November and December 2023, and January 2024. I’ve downloaded zipped files which once unzipped contained respectively: 7 CSVs (November 2023), 8 CSVs (December 2023), 8 CSVs (January 2024). Each month was divided into a specific folder.

Having a first look at the spreadsheets, it’s clear that there is some need for cleaning and modification: for example, dates and times are shown in Unix code, there are multiple columns that are not helpful for the scope of my analysis, currencies need to be uniformed (some are US$, some GB£, etc). In general, I have all the data that I need to answer my initial questions, identify trends, and make predictions.

PROCESS

I decided to use R to clean and process the data. For each month I started setting a new working environment in its own folder. After loading the necessary libraries: R library(tidyverse) library(lubridate) library(ggplot2) library(dplyr) library(tidyr) I scripted a general R code that searches for CSVs files in the folder, open them as separate variable and into a single data frame:

csv_files <- list.files(pattern = "\\.csv$")
data_frames <- list()

for (file in csv_files) {
 variable_name <- sub("\\.csv$", "", file)
 assign(variable_name, read.csv(file))
 data_frames[[variable_name]] <- get(variable_name)
}

Next, I converted some columns in numeric values because I was running into types error when trying to merge all the CSVs into a single comprehensive file.

data_frames <- lapply(data_frames, function(df) {
 df$converted_pledged_amount <- as.numeric(df$converted_pledged_amount)
 return(df)
})
data_frames <- lapply(data_frames, function(df) {
 df$usd_exchange_rate <- as.numeric(df$usd_exchange_rate)
 return(df)
})
data_frames <- lapply(data_frames, function(df) {
 df$usd_pledged <- as.numeric(df$usd_pledged)
 return(df)
})

In each folder I then ran a command to merge the CSVs in a single file (one for November 2023, one for December 2023 and one for January 2024):

all_nov_2023 = bind_rows(data_frames)
all_dec_2023 = bind_rows(data_frames)
all_jan_2024 = bind_rows(data_frames)`

After merging I converted the UNIX code datestamp into a readable datetime for the columns “created”, “launched”, “deadline” and deleted all the columns that had these data set to 0. I also filtered the values into the “slug” columns to show only the category of the campaign, without unnecessary information for the scope of my analysis. The final table was then saved.

filtered_dec_2023 <- all_dec_2023 %>% #this was modified according to the considered month
 select(blurb, backers_count, category, country, created_at, launched_at, deadline,currency, usd_exchange_rate, goal, pledged, state) %>%
 filter(created_at != 0 & deadline != 0 & launched_at != 0) %>% 
 mutate(category_slug = sub('.*?"slug":"(.*?)".*', '\\1', category)) %>% 
 mutate(created = as.POSIXct(created_at, origin = "1970-01-01")) %>% 
 mutate(launched = as.POSIXct(launched_at, origin = "1970-01-01")) %>% 
 mutate(setted_deadline = as.POSIXct(deadline, origin = "1970-01-01")) %>% 
 select(-category, -deadline, -launched_at, -created_at) %>% 
 relocate(created, launched, setted_deadline, .before = goal)

write.csv(filtered_dec_2023, "filtered_dec_2023.csv", row.names = FALSE)

The three generated files were then merged into one comprehensive CSV called "kickstarter_cleaned" which was further modified, converting a...

This module series covers how to import, manipulate, format and plot time series data stored in .csv format in R. Originally designed to teach researchers to use NEON plant phenology and air temperature data; has been used in undergraduate classrooms.

The DIAMAS project investigates Institutional Publishing Service Providers (IPSP) in the broadest sense, with a special focus on those publishing initiatives that do not charge fees to authors or readers. To collect information on Institutional Publishing in the ERA, a survey was conducted among IPSPs between March-May 2024. This dataset contains aggregated data from the 685 valid responses to the DIAMAS survey on Institutional Publishing.

The dataset supplements D2.3 Final IPSP landscape Report Institutional Publishing in the ERA: results from the DIAMAS survey.

The data

Basic aggregate tabular data

Full individual survey responses are not being shared to prevent the easy identification of respondents (in line with conditions set out in the survey questionnaire). This dataset contains full tables with aggregate data for all questions from the survey, with the exception of free-text responses, from all 685 survey respondents. This includes, per question, overall totals and percentages for the answers given as well the breakdown by both IPSP-types: institutional publishers (IPs) and service providers (SPs). Tables at country level have not been shared, as cell values often turned out to be too low to prevent potential identification of respondents. The data is available in csv and docx formats, with csv files grouped and packaged into ZIP files. Metadata describing data type, question type, as well as question response rate, is available in csv format. The R code used to generate the aggregate tables is made available as well.

Files included in this dataset

survey_questions_data_description.csv - metadata describing data type, question type, as well as question response rate per survey question.

tables_raw_all.zip - raw tables (csv format) with aggregated data per question for all respondents, with the exception of free-text responses. Questions with multiple answers have a table for each answer option. Zip file contains 180 csv files.

tables_raw_IP.zip - as tables_raw_all.zip, for responses from institutional publishers (IP) only. Zip file contains 180 csv files.

tables_raw_SP.zip - as tables_raw_all.zip, for responses from service providers (SP) only. Zip file contains 170 csv files.

tables_formatted_all.docx - formatted tables (docx format) with aggregated data per question for all respondents, with the exception of free-text responses. Questions with multiple answers have a table for each answer option.

tables_formatted_IP.docx - as tables_formatted_all.docx, for responses from institutional publishers (IP) only.

tables_formatted_SP.docx - as tables_formatted_all.docx, for responses from service providers (SP) only.

DIAMAS_Tables_single.R - R script used to generate raw tables with aggregated data for all single response questions

DIAMAS_Tables_multiple.R - R script used to generate raw tables with aggregated data for all multiple response questions

DIAMAS_Tables_layout.R - R script used to generate document with formatted tables from raw tables with aggregated data

DIAMAS Survey on Instititutional Publishing - data availability statement (pdf)

All data are made available under a CC0 license.

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().

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

This is the supplementary material accompanying the manuscript "Daily life in the Open Biologist’s second job, as a Data Curator", published in Wellcome Open Research.

It contains:

- Python_scripts.zip: Python scripts used for data cleaning and organization:

-add_headers.py: adds specified headers automatically to a list of csv files, creating new output files containing a "_with_headers" suffix.

-count_NaN_values.py: counts the total number of rows containing null values in a csv file and prints the location of null values in the (row, column) format.

-remove_rowsNaN_file.py: removes rows containing null values in a single csv file and saves the modified file with a "_dropNaN" suffix.

-remove_rowsNaN_list.py: removes rows containing null values in list of csv files and saves the modified files with a "_dropNaN" suffix.

- README_template.txt: a template for a README file to be used to describe and accompany a dataset.

- template_for_source_data_information.xlsx: a spreadsheet to help manuscript authors to keep track of data used for each figure (e.g., information about data location and links to dataset description).

- Supplementary_Figure_1.tif: Example of a dataset shared by us on Zenodo. The elements that make the dataset FAIR are indicated by the respective letters. Findability (F) is achieved by the dataset unique and persistent identifier (DOI), as well as by the related identifiers for the publication and dataset on GitHub. Additionally, the dataset is described with rich metadata, (e.g., keywords). Accessibility (A) is achieved by the ease of visualization and downloading using a standardised communications protocol (https). Also, the metadata are publicly accessible and licensed under the public domain. Interoperability (I) is achieved by the open formats used (CSV; R), and metadata are harvestable using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), a low-barrier mechanism for repository interoperability. Reusability (R) is achieved by the complete description of the data with metadata in README files and links to the related publication (which contains more detailed information, as well as links to protocols on protocols.io). The dataset has a clear and accessible data usage license (CC-BY 4.0).

Data and code files used for analyses in Indigenous caretaking of beargrass and the social and ecological consequences of adaptations to maintain beargrass weaving practices

Hart-Fredeluces, G. M., M. Burnham, M. Blaich Vaughan, G. Hart, J. A. Hart, E. St. Martin, J. Ward, and T. Ticktin. 2022. Indigenous caretaking of beargrass and the social and ecological consequences of adaptations to maintain beargrass weaving practices. Ecology and Society 27(4):22. https://doi.org/10.5751/ES-13588-270422

The specific .csv file used for analysis and the accompanying R code are available through the Open Ecology and Society 27(4): 22 https://www.ecologyandsociety.org/vol27/iss4/art22/ Science Framework at https://osf.io/edfrh/.

Data Use
License
Creative Commons Attribution 4.0 International (CC-BY 4.0)
Recommended Citation
Hart-Fredeluces G. 2021. Code and data for structural equation model of beargrass growth [Dataset]. OSF. https://doi.org/10.17605/OSF.IO/EDFRH

Funding
US National Science Foundation and Idaho EPSCoR: OIA-1757324

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

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.

Online Retail Transaction Records

Online Retail Sales: Product Transactions and Customer Details

By Ali Prasla [source]

About this dataset

The Online Retail Sales Dataset, often referred to as the Online Retail.csv file, is an extensive and comprehensive collection of data points relating to e-commerce transactions. This dataset provides a detailed view of sales activities within the online retail sector, covering numerous essential attributes necessary for a quantitative understanding of consumer behavior and the overall business performance.

One of the key elements covered in this dataset is 'InvoiceNo', which is a unique identifier for each transaction taking place in this retail environment. Given its uniqueness, it serves as a primary key for distinguishing individual transactions. It's worthwhile to note that these Invoice Numbers are numerical values.

Another important attribute included here is 'StockCode'. Each product listed or sold on this online retail platform has been assigned with its unique identification code or StockCode. These codes are also numerical values that offer another layer to clearly classify items and distinguish one from another.

For further understanding, every product comes with a basic description noted under the 'Description' column. In textual form, these descriptions provide insights into what exactly each product item entails. Aside from aiding identification efforts, they can potentially open avenues for text-based analysis such as sentiment analysis or keyword flagging based on product trends.

'Moving onto details about transactions themselves', we have two crucial columns: 'Quantity' and 'UnitPrice'. As their names suggest, these show respectively how many particular units of an item were sold per transaction and at what price per unit was sold at.

Further adding detail to our transactions information comes 'InvoiceDate', which records when each separate purchase occurred down to accurate date & time records. This data can be pivotal in recognizing sales patterns throughout different periods or predicting future trends based on historical timing behavior.

Finally yet importantly comes our global indicator - The ‘Country’ column specifies various countries where customers reside who interacts with this particular online platform regularly by making purchases. This application allows us insights into the geographical dispersion of user base across various countries, potentially providing us insights into regional preferences or global market segmentation.

Ith such a wealth of detailed transaction records and customer information, the Online Retail.csv dataset stands as an invaluable tool for those looking to delve deep into online retail sales data analysis. The possibilities with this dataset are vast, ranging from shaping efficient marketing strategies based on geographical data to predicting sales & growth metrics using historical behavior and much more

How to use the dataset

Here's how to make best use of this dataset:

Getting Started Before you start analyzing your data – you'll have to load it into statistical software such as Python (using pandas library) or R. The dataset is saved in .csv file format which supports easy reading into most data manipulation software.

Understand The Fields

• InvoiceNo: Each transaction made has an associated unique numerical identifier called InvoiceNo. Consider it like a receipt code - these allow for tracking individual transactions.

• StockCode: To identify each product uniquely during analysis, refer to each StockCode value which is essentially a product identification code.

• Description: A brief textual description about each product that can be invaluable when dealing with categories for market-basket type analysis.

• Quantity: Each row lists out how many units of a particular item were involved in a single transaction - watch out for very large values as they might represent bulk orders.

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This child page contains a zipped folder which contains all items necessary to run trend models and produce results published in U.S. Geological Scientific Investigations Report 2021–XXXX [Tatge, W.S., Nustad, R.A., and Galloway, J.M., 2021, Evaluation of Salinity and Nutrient Conditions in the Heart River Basin, North Dakota, 1970-2020: U.S. Geological Survey Scientific Investigations Report 2021-XXXX, XX p.]. To run the R-QWTREND program in R 6 files are required and each is included in this child page: prepQWdataV4.txt, runQWmodelV4XXUEP.txt, plotQWtrendV4XXUEP.txt, qwtrend2018v4.exe, salflibc.dll, and StartQWTrendV4.R (Vecchia and Nustad, 2020). The folder contains: six items required to run the R–QWTREND trend analysis tool; a readme.txt file; a flowtrendData.RData file; an allsiteinfo.table.csv file, a folder called "scripts", and a folder called "waterqualitydata". The "scripts" folder contains the scripts that can be used to reproduce the results found in the USGS Scientific Investigations Report referenced above. The "waterqualitydata" folder contains .csv files with the naming convention of site_ions or site_nuts for major ions and nutrients constituents and contains machine readable files with the water-quality data used for the trend analysis at each site. R–QWTREND is a software package for analyzing trends in stream-water quality. The package is a collection of functions written in R (R Development Core Team, 2019), an open source language and a general environment for statistical computing and graphics. The following system requirements are necessary for using R–QWTREND: • Windows 10 operating system • R (version 3.4 or later; 64 bit recommended) • RStudio (version 1.1.456 or later). An accompanying report (Vecchia and Nustad, 2020) serves as the formal documentation for R–QWTREND. Vecchia, A.V., and Nustad, R.A., 2020, Time-series model, statistical methods, and software documentation for R–QWTREND—An R package for analyzing trends in stream-water quality: U.S. Geological Survey Open-File Report 2020–1014, 51 p., https://doi.org/10.3133/ofr20201014 R Development Core Team, 2019, R—A language and environment for statistical computing: Vienna, Austria, R Foundation for Statistical Computing, accessed December 7, 2020, at https://www.r-project.org.