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/

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 `

This dataset was developed by NREL's distributed energy systems integration group as part of a study on high penetrations of distributed solar PV [1]. It consists of hourly load data in CSV format for use with the PNNL taxonomy of distribution feeders [2]. These feeders were developed in the open source GridLAB-D modelling language [3]. In this dataset each of the load points in the taxonomy feeders is populated with hourly averaged load data from a utility in the feeder’s geographical region, scaled and randomized to emulate real load profiles. For more information on the scaling and randomization process, see [1].

The taxonomy feeders are statistically representative of the various types of distribution feeders found in five geographical regions of the U.S. Efforts are underway (possibly complete) to translate these feeders into the OpenDSS modelling language.

This data set consists of one large CSV file for each feeder. Within each CSV, each column represents one load bus on the feeder. The header row lists the name of the load bus. The subsequent 8760 rows represent the loads for each hour of the year. The loads were scaled and randomized using a Python script, so each load series represents only one of many possible randomizations. In the header row, "rl" = residential load and "cl" = commercial load. Commercial loads are followed by a phase letter (A, B, or C). For regions 1-3, the data is from 2009. For regions 4-5, the data is from 2000.

For use in GridLAB-D, each column will need to be separated into its own CSV file without a header. The load value goes in the second column, and corresponding datetime values go in the first column, as shown in the sample file, sample_individual_load_file.csv. Only the first value in the time column needs to written as an absolute time; subsequent times may be written in relative format (i.e. "+1h", as in the sample). The load should be written in P+Qj format, as seen in the sample CSV, in units of Watts (W) and Volt-amps reactive (VAr). This dataset was derived from metered load data and hence includes only real power; reactive power can be generated by assuming an appropriate power factor. These loads were used with GridLAB-D version 2.2.

Browse files in this dataset, accessible as individual files and as a single ZIP file. This dataset is approximately 242MB compressed or 475MB uncompressed.

For questions about this dataset, contact andy.hoke@nrel.gov.

If you find this dataset useful, please mention NREL and cite [1] in your work.

References:

[1] A. Hoke, R. Butler, J. Hambrick, and B. Kroposki, “Steady-State Analysis of Maximum Photovoltaic Penetration Levels on Typical Distribution Feeders,” IEEE Transactions on Sustainable Energy, April 2013, available at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6357275 .

[2] K. Schneider, D. P. Chassin, R. Pratt, D. Engel, and S. Thompson, “Modern Grid Initiative Distribution Taxonomy Final Report”, PNNL, Nov. 2008. Accessed April 27, 2012: http://www.gridlabd.org/models/feeders/taxonomy of prototypical feeders.pdf

[3] K. Schneider, D. Chassin, Y. Pratt, and J. C. Fuller, “Distribution power flow for smart grid technologies”, IEEE/PES Power Systems Conference and Exposition, Seattle, WA, Mar. 2009, pp. 1-7, 15-18.

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.

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.

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

Data Pre-processing

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

https://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png"> https://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">

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

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

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

This dataset provides a collection of German question-answer pairs along with their corresponding context [1]. It is designed to enhance and facilitate natural language processing (NLP) tasks in the German language [1]. The dataset includes two main files, train.csv and test.csv, each containing numerous entries of various contexts with associated questions and answers in German [1]. The contextual information can range from paragraphs to concise sentences, offering a well-rounded representation of different scenarios [1]. It serves as a valuable resource for training machine learning models to improve question-answering systems or other NLP applications specific to the German language [1].

Columns

The dataset consists of the following columns [1, 2]: * id: An identifier for each entry [2]. * context: This column contains the context in which the question is being asked. It can be a paragraph, a sentence, or any other relevant information [1]. * question: The question related to the given context [2]. * answers: This column contains the answer or answers to the given question within the corresponding context [1]. The answers could be single or multiple [1]. * Label Count: Numerical ranges with corresponding counts [2].

Distribution

The dataset is provided in CSV format [1, 3], comprising two main files: train.csv and test.csv [1]. Both files contain a significant number of question-answer pairs and their respective contexts [1]. While specific total row or record counts are not explicitly stated, the source material indicates substantial amounts of data [1]. For instance, certain label counts range from 36,419.00 to 45,662.00, with varying numbers of entries within those ranges, such as 529, 508, or 29 unique values for specific segments [2].

Usage

This dataset is ideal for a variety of applications and use cases, including [1]: * Building question-answering systems in German. * Training models for German language understanding and translation tasks. * Developing information retrieval systems that can process German user queries and return relevant information from provided contexts. * Enhancing NLP models for accuracy and robustness in German. * Exploring state-of-the-art methodologies or developing novel approaches for natural language understanding in German [1].

Coverage

The dataset's linguistic scope is specifically the German language [1]. Geographically, it is intended for global use [4]. There are no specific notes on time range or demographic availability within the provided sources.

License

CC0

Who Can Use It

The dataset is intended for [1]: * Researchers working on advancements in machine learning techniques applied to natural language understanding in German. * Developers building and refining NLP applications for the German language. * Enthusiasts exploring and implementing machine learning models for language processing.

Dataset Name Suggestions

• German QA Context Dataset
• German NLP Question-Answer Pairs
• Contextual German Questions & Answers
• German Language Understanding Dataset

Attributes

Original Data Source: German Question-Answer Context Dataset

Database of Uniaxial Cyclic and Tensile Coupon Tests for Structural Metallic Materials

Background

This dataset contains data from monotonic and cyclic loading experiments on structural metallic materials. The materials are primarily structural steels and one iron-based shape memory alloy is also included. Summary files are included that provide an overview of the database and data from the individual experiments is also included.

The files included in the database are outlined below and the format of the files is briefly described. Additional information regarding the formatting can be found through the post-processing library (https://github.com/ahartloper/rlmtp/tree/master/protocols).

Usage

• The data is licensed through the Creative Commons Attribution 4.0 International.
• If you have used our data and are publishing your work, we ask that you please reference both:
  1. this database through its DOI, and
  2. any publication that is associated with the experiments. See the Overall_Summary and Database_References files for the associated publication references.

Included Files

• Overall_Summary_2022-08-25_v1-0-0.csv: summarises the specimen information for all experiments in the database.
• Summarized_Mechanical_Props_Campaign_2022-08-25_v1-0-0.csv: summarises the average initial yield stress and average initial elastic modulus per campaign.
• Unreduced_Data-#_v1-0-0.zip: contain the original (not downsampled) data
  • Where # is one of: 1, 2, 3, 4, 5, 6. The unreduced data is broken into separate archives because of upload limitations to Zenodo. Together they provide all the experimental data.
  • We recommend you un-zip all the folders and place them in one "Unreduced_Data" directory similar to the "Clean_Data"
  • The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.
  • There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the unreduced data.
  • The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.
• Clean_Data_v1-0-0.zip: contains all the downsampled data
  • The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.
  • There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the clean data.
  • The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.
• Database_References_v1-0-0.bib
  • Contains a bibtex reference for many of the experiments in the database. Corresponds to the "citekey" entry in the summary files.

File Format: Downsampled Data

These are the "LP_

• The header of the first column is empty: the first column corresponds to the index of the sample point in the original (unreduced) data
• Time[s]: time in seconds since the start of the test
• e_true: true strain
• Sigma_true: true stress in MPa
• (optional) Temperature[C]: the surface temperature in degC

These data files can be easily loaded using the pandas library in Python through:

import pandas
data = pandas.read_csv(data_file, index_col=0)

The data is formatted so it can be used directly in RESSPyLab (https://github.com/AlbanoCastroSousa/RESSPyLab). Note that the column names "e_true" and "Sigma_true" were kept for backwards compatibility reasons with RESSPyLab.

File Format: Unreduced Data

These are the "LP_

• The first column is the index of each data point
• S/No: sample number recorded by the DAQ
• System Date: Date and time of sample
• Time[s]: time in seconds since the start of the test
• C_1_Force[kN]: load cell force
• C_1_Déform1[mm]: extensometer displacement
• C_1_Déplacement[mm]: cross-head displacement
• Eng_Stress[MPa]: engineering stress
• Eng_Strain[]: engineering strain
• e_true: true strain
• Sigma_true: true stress in MPa
• (optional) Temperature[C]: specimen surface temperature in degC

The data can be loaded and used similarly to the downsampled data.

File Format: Overall_Summary

The overall summary file provides data on all the test specimens in the database. The columns include:

• hidden_index: internal reference ID
• grade: material grade
• spec: specifications for the material
• source: base material for the test specimen
• id: internal name for the specimen
• lp: load protocol
• size: type of specimen (M8, M12, M20)
• gage_length_mm_: unreduced section length in mm
• avg_reduced_dia_mm_: average measured diameter for the reduced section in mm
• avg_fractured_dia_top_mm_: average measured diameter of the top fracture surface in mm
• avg_fractured_dia_bot_mm_: average measured diameter of the bottom fracture surface in mm
• fy_n_mpa_: nominal yield stress
• fu_n_mpa_: nominal ultimate stress
• t_a_deg_c_: ambient temperature in degC
• date: date of test
• investigator: person(s) who conducted the test
• location: laboratory where test was conducted
• machine: setup used to conduct test
• pid_force_k_p, pid_force_t_i, pid_force_t_d: PID parameters for force control
• pid_disp_k_p, pid_disp_t_i, pid_disp_t_d: PID parameters for displacement control
• pid_extenso_k_p, pid_extenso_t_i, pid_extenso_t_d: PID parameters for extensometer control
• citekey: reference corresponding to the Database_References.bib file
• yield_stress_mpa_: computed yield stress in MPa
• elastic_modulus_mpa_: computed elastic modulus in MPa
• fracture_strain: computed average true strain across the fracture surface
• c,si,mn,p,s,n,cu,mo,ni,cr,v,nb,ti,al,b,zr,sn,ca,h,fe: chemical compositions in units of %mass
• file: file name of corresponding clean (downsampled) stress-strain data

File Format: Summarized_Mechanical_Props_Campaign

Meant to be loaded in Python as a pandas DataFrame with multi-indexing, e.g.,

tab1 = pd.read_csv('Summarized_Mechanical_Props_Campaign_' + date + version + '.csv',
          index_col=[0, 1, 2, 3], skipinitialspace=True, header=[0, 1],
          keep_default_na=False, na_values='')

• citekey: reference in "Campaign_References.bib".
• Grade: material grade.
• Spec.: specifications (e.g., J2+N).
• Yield Stress [MPa]: initial yield stress in MPa
  • size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign
• Elastic Modulus [MPa]: initial elastic modulus in MPa
  • size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign

Caveats

• The files in the following directories were tested before the protocol was established. Therefore, only the true stress-strain is available for each:
  • A500
  • A992_Gr50
  • BCP325
  • BCR295
  • HYP400
  • S460NL
  • S690QL/25mm
  • S355J2_Plates/S355J2_N_25mm and S355J2_N_50mm

Overview

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

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

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

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

Description/abstract

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

Folder structure

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

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

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

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

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

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

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

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

Code structure

1_MODIS_NDVI_hdf_file_extraction.R

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

2_MERGE_MODIS_tiles.R

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

3_CROP_MODIS_merged_tiles.R

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

4_TREND_analysis_NDVI.R

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

5_BUILT_UP_change_raster.R

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

6_POPULATION_numbers_plot.R

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

7_YIELD_plot.R

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

8_GLDAS_read_extract_trend

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

Cyclistic: Google Data Analytics Capstone Project

Cyclistic - Google Data Analytics Certification Capstone Project Moirangthem Arup Singh How Does a Bike-Share Navigate Speedy Success? Background: This project is for the Google Data Analytics Certification capstone project. I am wearing the hat of a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. Cyclistic is a bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore,my team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, my team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve the recommendations, so they must be backed up with compelling data insights and professional data visualizations. This project will be completed by using the 6 Data Analytics stages: Ask: Identify the business task and determine the key stakeholders. Prepare: Collect the data, identify how it’s organized, determine the credibility of the data. Process: Select the tool for data cleaning, check for errors and document the cleaning process. Analyze: Organize and format the data, aggregate the data so that it’s useful, perform calculations and identify trends and relationships. Share: Use design thinking principles and data-driven storytelling approach, present the findings with effective visualization. Ensure the analysis has answered the business task. Act: Share the final conclusion and the recommendations. Ask: Business Task: Recommend marketing strategies aimed at converting casual riders into annual members by better understanding how annual members and casual riders use Cyclistic bikes differently. Stakeholders: Lily Moreno: The director of marketing and my manager. Cyclistic executive team: A detail-oriented executive team who will decide whether to approve the recommended marketing program. Cyclistic marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Cyclistic’s marketing strategy. Prepare: For this project, I will use the public data of Cyclistic’s historical trip data to analyze and identify trends. The data has been made available by Motivate International Inc. under the license. I downloaded the ZIP files containing the csv files from the above link but while uploading the files in kaggle (as I am using kaggle notebook), it gave me a warning that the dataset is already available in kaggle. So I will be using the dataset cyclictic-bike-share dataset from kaggle. The dataset has 13 csv files from April 2020 to April 2021. For the purpose of my analysis I will use the csv files from April 2020 to March 2021. The source csv files are in Kaggle so I can rely on it's integrity. I am using Microsoft Excel to get a glimpse of the data. There is one csv file for each month and has information about the bike ride which contain details of the ride id, rideable type, start and end time, start and end station, latitude and longitude of the start and end stations. Process: I will use R as language in kaggle to import the dataset to check how it’s organized, whether all the columns have appropriate data type, find outliers and if any of these data have sampling bias. I will be using below R libraries

Load the tidyverse, lubridate, ggplot2, sqldf and psych libraries

library(tidyverse) library(lubridate) library(ggplot2) library(plotrix) ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

✔ ggplot2 3.3.5 ✔ purrr 0.3.4 ✔ tibble 3.1.4 ✔ dplyr 1.0.7 ✔ tidyr 1.1.3 ✔ stringr 1.4.0 ✔ readr 2.0.1 ✔ forcats 0.5.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ✖ dplyr::filter() masks stats::filter() ✖ dplyr::lag() masks stats::lag()

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

date, intersect, setdiff, union

Set the working directory

setwd("/kaggle/input/cyclistic-bike-share")

Import the csv files

r_202004 <- read.csv("202004-divvy-tripdata.csv") r_202005 <- read.csv("20...

This dataset was created to allow researchers to gain an in-depth understanding of the inner workings of human-generated movie reviews. With these train, test, and validation sets, researchers can explore different aspects of movie reviews, such as sentiment labels or rationales behind them. By analyzing this information and finding patterns and correlations, insightful ideas can be discovered that can lead to developing models powerful enough to uncover importance of the unique human perspectives when interpreting movie reviews. Any data scientist or researcher interested in AI applications is encouraged to take advantage of this dataset which may potentially provide useful insights into better understanding user intent when reviewing movies

More Datasets For more datasets, click here.

Featured Notebooks 🚨 Your notebook can be here! 🚨! How to use the dataset This dataset is intended to enable researchers and developers to uncover the rationales behind movie reviews. To use it effectively, you must understand the data format and how each column in the dataset works.

What does each column mean? review: The text of the movie review. (String)

label: The sentiment label of the review (Positive, Negative, or Neutral). (String)

validation.csv: The validation set which contains reviews, labels, and evidence which can be used to validate models developed for understanding human perspective on movie reviews.

train.csv: The train set which contains reviews, labels as well as evidence used for training a model based on human annotations of movie reviews.

test.csv: The test set which contains reviews, labels and evidence that can be used to evaluate models on unseen data related to understanding perspectives of humans when it comes to movie reviews..

How do I use this dataset? To get started with this dataset you need a working environment such as Python or R where you have access library’s needed for natural language processing(NLP). After setting up an environment with libraries that support NLP tasks execute following steps :

Import csv files into your workspace using appropriate functions provided by specified language libraries e,.g., for Python use pandas read_csv() method .

Preprocess your text data in 'review' & 'label' columns by standardizing them like removing stopwords from sentences & converting words into lowercase etc .Following link link provides best possible preprocessing libraries available in Python .

Train&Test ML algorithms using appropriate feature extraction techniques related to NLP( Bag Of Words , TF-IDF , Word2Vec ) eines are some examples in many more are available Refer link

Measure performance accuracy after running experiments on datasets provided validation & test sets we have also included precision recall curves along famous metrics like F1 score & accuracy score so you could easily analyze hyperparameter tuning & algorithm efficiency according their outputs values you get while testing your ML algorithm

Recommendation systems are always fun! build a simple machine learning reccomendation system by collecting user visits logs post hand writting new featuers might

Research Ideas Developing an automated movie review summarizer based on user ratings, that can accurately capture the salient points of a review and summarize it for moviegoers. Training a model to predict the sentiment of a review, by combining machine learning models with human-annotated rationales from this dataset. Building an AI system that can detect linguistic markers of deception in reviews (e.g., 'fake news', thin reviews etc) and issue warnings on possible fraudulent purchases or online reviews

Columns File: validation.csv

Column name Description review Text from the movie review. (String) label Indicates whether a particular review’s sentiment can be classified as Positive (1), Negative (-1) or Neutral (0). (Integer) File: train.csv

Column name Description review Text from the movie review. (String) label Indicates whether a particular review’s sentiment can be classified as Positive (1), Negative (-1) or Neutral (0). (Integer) File: test.csv

Column name Description review Text from the movie review. (String) label Indicates whether a particular review’s sentiment can be classified as Positive (1), Negative (-1) or Neutral (0). (Integer) Acknowledgements If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

License

CC0

Original Data Source: Movie Rationales (Rationales For Movie Reviews)

Dataset Overview: This dataset pertains to the examination results of students who participated in a series of academic assessments at a fictitious educational institution named "University of Exampleville." The assessments were administered across various courses and academic levels, with a focus on evaluating students' performance in general management and domain-specific topics.

Columns: The dataset comprises 12 columns, each representing specific attributes and performance indicators of the students. These columns encompass information such as the students' names (which have been anonymized), their respective universities, academic program names (including BBA and MBA), specializations, the semester of the assessment, the type of examination domain (general management or domain-specific), general management scores (out of 50), domain-specific scores (out of 50), total scores (out of 100), student ranks, and percentiles.

Data Collection: The examination data was collected during a standardized assessment process conducted by the University of Exampleville. The exams were designed to assess students' knowledge and skills in general management and their chosen domain-specific subjects. It involved students from both BBA and MBA programs who were in their final year of study.

Data Format: The dataset is available in a structured format, typically as a CSV file. Each row represents a unique student's performance in the examination, while columns contain specific information about their results and academic details.

Data Usage: This dataset is valuable for analyzing and gaining insights into the academic performance of students pursuing BBA and MBA degrees. It can be used for various purposes, including statistical analysis, performance trend identification, program assessment, and comparison of scores across domains and specializations. Furthermore, it can be employed in predictive modeling or decision-making related to curriculum development and student support.

Data Quality: The dataset has undergone preprocessing and anonymization to protect the privacy of individual students. Nevertheless, it is essential to use the data responsibly and in compliance with relevant data protection regulations when conducting any analysis or research.

Data Format: The exam data is typically provided in a structured format, commonly as a CSV (Comma-Separated Values) file. Each row in the dataset represents a unique student's examination performance, and each column contains specific attributes and scores related to the examination. The CSV format allows for easy import and analysis using various data analysis tools and programming languages like Python, R, or spreadsheet software like Microsoft Excel.

Here's a column-wise description of the dataset:

Name OF THE STUDENT: The full name of the student who took the exam. (Anonymized)

UNIVERSITY: The university where the student is enrolled.

PROGRAM NAME: The name of the academic program in which the student is enrolled (BBA or MBA).

Specialization: If applicable, the specific area of specialization or major that the student has chosen within their program.

Semester: The semester or academic term in which the student took the exam.

Domain: Indicates whether the exam was divided into two parts: general management and domain-specific.

GENERAL MANAGEMENT SCORE (OUT of 50): The score obtained by the student in the general management part of the exam, out of a maximum possible score of 50.

Domain-Specific Score (Out of 50): The score obtained by the student in the domain-specific part of the exam, also out of a maximum possible score of 50.

TOTAL SCORE (OUT of 100): The total score obtained by adding the scores from the general management and domain-specific parts, out of a maximum possible score of 100.

This dataset contains all the spectra used in the paper "Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, set-up and cross validate the PCA-LDA model and generate the figures shown in the paper.

Data are available in 2 different formats:

• 1 compressed archive ("dataset.zip") containing all the 144 TXT files (1 file = 1 spectrum)

• 1 single CSV file (“dataset.csv”) with all the 144 spectra in the form of a table. The data are structured as follow, with each row being 1 spectrum, preceded by metadata: "acquisition_date", "substrate_batch", "class", "sample_code".

The code for R is available as a single file "Rcode.R".

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.

[Note 2023-08-14 - Supersedes version 1, https://doi.org/10.15482/USDA.ADC/1528086 ] This dataset contains all code and data necessary to reproduce the analyses in the manuscript: Mengistu, A., Read, Q. D., Sykes, V. R., Kelly, H. M., Kharel, T., & Bellaloui, N. (2023). Cover crop and crop rotation effects on tissue and soil population dynamics of Macrophomina phaseolina and yield under no-till system. Plant Disease. https://doi.org/10.1094/pdis-03-23-0443-re The .zip archive cropping-systems-1.0.zip contains data and code files. Data stem_soil_CFU_by_plant.csv: Soil disease load (SoilCFUg) and stem tissue disease load (StemCFUg) for individual plants in CFU per gram, with columns indicating year, plot ID, replicate, row, plant ID, previous crop treatment, cover crop treatment, and comments. Missing data are indicated with . yield_CFU_by_plot.csv: Yield data (YldKgHa) at the plot level in units of kg/ha, with columns indicating year, plot ID, replicate, and treatments, as well as means of soil and stem disease load at the plot level. Code cropping_system_analysis_v3.0.Rmd: RMarkdown notebook with all data processing, analysis, and visualization code equations.Rmd: RMarkdown notebook with formatted equations formatted_figs_revision.R: R script to produce figures formatted exactly as they appear in the manuscript The Rproject file cropping-systems.Rproj is used to organize the RStudio project. Scripts and notebooks used in older versions of the analysis are found in the testing/ subdirectory. Excel spreadsheets containing raw data from which the cleaned CSV files were created are found in the raw_data subdirectory.

# 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).

The LSC (Leicester Scientific Corpus)

April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk) Supervised by Prof Alexander Gorban and Dr Evgeny MirkesThe data are extracted from the Web of Science [1]. You may not copy or distribute these data in whole or in part without the written consent of Clarivate Analytics.[Version 2] A further cleaning is applied in Data Processing for LSC Abstracts in Version 1*. Details of cleaning procedure are explained in Step 6.* Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v1.Getting StartedThis text provides the information on the LSC (Leicester Scientific Corpus) and pre-processing steps on abstracts, and describes the structure of files to organise the corpus. This corpus is created to be used in future work on the quantification of the meaning of research texts and make it available for use in Natural Language Processing projects.LSC is a collection of abstracts of articles and proceeding papers published in 2014, and indexed by the Web of Science (WoS) database [1]. The corpus contains only documents in English. Each document in the corpus contains the following parts:1. Authors: The list of authors of the paper2. Title: The title of the paper 3. Abstract: The abstract of the paper 4. Categories: One or more category from the list of categories [2]. Full list of categories is presented in file ‘List_of _Categories.txt’. 5. Research Areas: One or more research area from the list of research areas [3]. Full list of research areas is presented in file ‘List_of_Research_Areas.txt’. 6. Total Times cited: The number of times the paper was cited by other items from all databases within Web of Science platform [4] 7. Times cited in Core Collection: The total number of times the paper was cited by other papers within the WoS Core Collection [4]The corpus was collected in July 2018 online and contains the number of citations from publication date to July 2018. We describe a document as the collection of information (about a paper) listed above. The total number of documents in LSC is 1,673,350.Data ProcessingStep 1: Downloading of the Data Online

The dataset is collected manually by exporting documents as Tab-delimitated files online. All documents are available online.Step 2: Importing the Dataset to R

The LSC was collected as TXT files. All documents are extracted to R.Step 3: Cleaning the Data from Documents with Empty Abstract or without CategoryAs our research is based on the analysis of abstracts and categories, all documents with empty abstracts and documents without categories are removed.Step 4: Identification and Correction of Concatenate Words in AbstractsEspecially medicine-related publications use ‘structured abstracts’. Such type of abstracts are divided into sections with distinct headings such as introduction, aim, objective, method, result, conclusion etc. Used tool for extracting abstracts leads concatenate words of section headings with the first word of the section. For instance, we observe words such as ConclusionHigher and ConclusionsRT etc. The detection and identification of such words is done by sampling of medicine-related publications with human intervention. Detected concatenate words are split into two words. For instance, the word ‘ConclusionHigher’ is split into ‘Conclusion’ and ‘Higher’.The section headings in such abstracts are listed below:

Background Method(s) Design Theoretical Measurement(s) Location Aim(s) Methodology Process Abstract Population Approach Objective(s) Purpose(s) Subject(s) Introduction Implication(s) Patient(s) Procedure(s) Hypothesis Measure(s) Setting(s) Limitation(s) Discussion Conclusion(s) Result(s) Finding(s) Material (s) Rationale(s) Implications for health and nursing policyStep 5: Extracting (Sub-setting) the Data Based on Lengths of AbstractsAfter correction, the lengths of abstracts are calculated. ‘Length’ indicates the total number of words in the text, calculated by the same rule as for Microsoft Word ‘word count’ [5].According to APA style manual [6], an abstract should contain between 150 to 250 words. In LSC, we decided to limit length of abstracts from 30 to 500 words in order to study documents with abstracts of typical length ranges and to avoid the effect of the length to the analysis.

Step 6: [Version 2] Cleaning Copyright Notices, Permission polices, Journal Names and Conference Names from LSC Abstracts in Version 1Publications can include a footer of copyright notice, permission policy, journal name, licence, author’s right or conference name below the text of abstract by conferences and journals. Used tool for extracting and processing abstracts in WoS database leads to attached such footers to the text. For example, our casual observation yields that copyright notices such as ‘Published by Elsevier ltd.’ is placed in many texts. To avoid abnormal appearances of words in further analysis of words such as bias in frequency calculation, we performed a cleaning procedure on such sentences and phrases in abstracts of LSC version 1. We removed copyright notices, names of conferences, names of journals, authors’ rights, licenses and permission policies identified by sampling of abstracts.Step 7: [Version 2] Re-extracting (Sub-setting) the Data Based on Lengths of AbstractsThe cleaning procedure described in previous step leaded to some abstracts having less than our minimum length criteria (30 words). 474 texts were removed.Step 8: Saving the Dataset into CSV FormatDocuments are saved into 34 CSV files. In CSV files, the information is organised with one record on each line and parts of abstract, title, list of authors, list of categories, list of research areas, and times cited is recorded in fields.To access the LSC for research purposes, please email to ns433@le.ac.uk.References[1]Web of Science. (15 July). Available: https://apps.webofknowledge.com/ [2]WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html [3]Research Areas in WoS. Available: https://images.webofknowledge.com/images/help/WOS/hp_research_areas_easca.html [4]Times Cited in WoS Core Collection. (15 July). Available: https://support.clarivate.com/ScientificandAcademicResearch/s/article/Web-of-Science-Times-Cited-accessibility-and-variation?language=en_US [5]Word Count. Available: https://support.office.com/en-us/article/show-word-count-3c9e6a11-a04d-43b4-977c-563a0e0d5da3 [6]A. P. Association, Publication manual. American Psychological Association Washington, DC, 1983.

To get the consumption model from Section 3.1, one needs load execute the file consumption_data.R. Load the data for the 3 Phases ./data/CONSUMPTION/PL1.csv, PL2.csv, PL3.csv, transform the data and build the model (starting line 225). The final consumption data can be found in one file for each year in ./data/CONSUMPTION/MEGA_CONS_list.Rdata

To get the results for the optimization problem, one needs to execute the file analyze_data.R. It provides the functions to compare production and consumption data, and to optimize for the different values (PV, MBC,).

To reproduce the figures one needs to execute the file visualize_results.R. It provides the functions to reproduce the figures.

To calculate the solar radiation that is needed in the Section Production Data, follow file calculate_total_radiation.R.

To reproduce the radiation data from from ERA5, that can be found in data.zip, do the following steps: 1. ERA5 - download the reanalysis datasets as GRIB file. For FDIR select "Total sky direct solar radiation at surface", for GHI select "Surface solar radiation downwards", and for ALBEDO select "Forecast albedo". 2. convert GRIB to csv with the file era5toGRID.sh 3. convert the csv file to the data that is used in this paper with the file convert_year_to_grid.R

This child page contains a zipped folder which contains all of the items necessary to run load estimation using R-LOADEST to produce results that are published in U.S. Geological Survey 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]. The folder contains an allsiteinfo.table.csv file, a "datain" folder, and a "scripts" folder. The allsiteinfo.table.csv file can be used to cross reference the sites with the main report (Tatge and others, 2021). The "datain" folder contains all the input data necessary to reproduce the load estimation results. The naming convention in the "datain" folder is site_MI_rloadest or site_NUT_rloadest for either the major ion loads or the nutrient loads. The .Rdata files are used in the scripts to run the estimations and the .csv files can be used to look at the data. The "scripts" folder contains the written R scripts to produce the results of the load estimation from the main report. R-LOADEST is a software package for analyzing loads in streams and an accompanying report (Runkel and others, 2004) serves as the formal documentation for R-LOADEST. 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 producing results: Windows 10 operating system R (version 3.4 or later; 64-bit recommended) RStudio (version 1.1.456 or later) R-LOADEST program (available at https://github.com/USGS-R/rloadest). Runkel, R.L., Crawford, C.G., and Cohn, T.A., 2004, Load Estimator (LOADEST): A FORTRAN Program for Estimating Constituent Loads in Streams and Rivers: U.S. Geological Survey Techniques and Methods Book 4, Chapter A5, 69 p., [Also available at https://pubs.usgs.gov/tm/2005/tm4A5/pdf/508final.pdf.] 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.

The purpose of this dataset is to provide a detailed picture of the characteristics of Syrian towns in the years preceding the 2011 Syrian uprising and ensuing civil war. It incorporates the 2004 national census, the last before the uprising, and a newly collected set of data on ethnic identity. The level of analysis is the town (the Syrian Census Bureau’s fourth administrative level). TECHNICAL NOTE: The .csv files in this data package contain both Arabic and English, so are encoded in UTF-8. The Arabic script should render if opened directly in Open Office, Numbers, Google Drive, or R statistical software. To read the Arabic in Excel, you can open the .csv file in any of these applications and save it as an .xlsx file, or open it through Excel using the following steps: (1) open a blank excel document (2) import the data using “Data -> Get External Data -> Import text file” (3) select “File Origin: Unicode (UTF-8)” (4) select “Delimiters: comma” (5) select the top left cell to place the data See the following post for further details: https://stackoverflow.com/questions/6002256/is-it-possible-to-force-excel-recognize-utf-8-csv-files-automatically

To get the consumption model from Section 3.1, one needs load execute the file consumption_data.R. Load the data for the 3 Phases ./data/CONSUMPTION/PL1.csv, PL2.csv, PL3.csv, transform the data and build the model (starting line 225). The final consumption data can be found in one file for each year in ./data/CONSUMPTION/MEGA_CONS_list.Rdata To get the results for the optimization problem, one needs to execute the file analyze_data.R. It provides the functions to compare production and consumption data, and to optimize for the different values (PV, MBC,). To reproduce the figures one needs to execute the file visualize_results.R. It provides the functions to reproduce the figures. To calculate the solar radiation that is needed in the Section Production Data, follow file calculate_total_radiation.R. To reproduce the radiation data from from ERA5, that can be found in data.zip, do the following steps: 1. ERA5 - download the reanalysis datasets as GRIB file. For FDIR select "Total sky direct solar radiation at surface", for GHI select "Surface solar radiation downwards", and for ALBEDO select "Forecast albedo". 2. convert GRIB to csv with the file era5toGRID.sh 3. convert the csv file to the data that is used in this paper with the file convert_year_to_grid.R