The Data Visualization Corpus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1430847%2F29f7950c3b7daf11175aab404725542c%2FGettyImages-1187621904-600x360.jpg?generation=1601115151722854&alt=media" alt="">

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions

The Data Visualizaion Copus

The Data Visualization corpus consists:

• 32 cheat sheets: This includes A-Z about the techniques and tricks that can be used for visualization, Python and R visualization cheat sheets, Types of charts, and their significance, Storytelling with data, etc..

• 32 Charts: The corpus also consists of a significant amount of data visualization charts information along with their python code, d3.js codes, and presentations relation to the respective charts explaining in a clear manner!

• Some recommended books for data visualization every data scientist's should read:

  1. Beautiful Visualization by Julie Steele and Noah Iliinsky
  2. Information Dashboard Design by Stephen Few
  3. Knowledge is beautiful by David McCandless (Short abstract)
  4. The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo
  5. The Visual Display of Quantitative Information by Edward R. Tufte
  6. storytelling with data: a data visualization guide for business professionals by cole Nussbaumer knaflic
  7. Research paper - Cheat Sheets for Data Visualization Techniques by Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach

Suggestions:

In case, if you find any books, cheat sheets, or charts missing and if you would like to suggest some new documents please let me know in the discussion sections!

Resources:

• Charts: I personally recommend data viz catalogue, it's easy to understand with their explanation!
• Python codes: Plotly for python and Python graph gallery
• R codes for charts:Plotly for R
• d3 codes: Visualization codes using d3

Request to kaggle users:

• A kind request to kaggle users to create notebooks on different visualization charts as per their interest by choosing a dataset of their own as many beginners and other experts could find it useful!

• To create interactive EDA using animation with a combination of data visualization charts to give an idea about how to tackle data and extract the insights from the data

Suggestion and queries:

Feel free to use the discussion platform of this data set to ask questions or any queries related to the data visualization corpus and data visualization techniques

Kindly upvote the dataset if you find it useful or if you wish to appreciate the effort taken to gather this corpus! Thank you and have a great day!

• Automated parametric analysis workflow built using R Studio.
• Demonstrates core statistical analysis methods on numerical datasets.
• Includes step-by-step R scripts for performing t-tests, ANOVA, and summary statistics.
• Provides visual outputs such as boxplots and distribution plots for better interpretation.
• Designed for students, researchers, and data analysts learning statistical automation in R.
• Useful for understanding reproducible research workflows in data analysis.
• Dataset helps in teaching how to automate statistical pipelines using R programming.

1. Recent technical and methodological advances have led to a dramatic increase in the use of spectrometry to quantify reflectance properties of biological materials, as well as models to determine how these colours are perceived by animals, providing important insights into ecological and evolutionary aspects of animal visual communication. 2. Despite this growing interest, a unified cross-platform framework for analyzing and visualizing spectral data has not been available. We introduce pavo, an R package that facilitates the organization, visualization, and analysis of spectral data in a cohesive framework. pavo is highly flexible, allowing users to (a) organize and manipulate data from a variety of sources, (b) visualize data using R's state-of-the-art graphics capabilities, and (c) analyze data using spectral curve shape properties and visual system modeling for a broad range of taxa. 3. In this paper, we present a summary of the functions implemented in pavo and how they integrate in a workflow to explore and analyze spectral data. We also present an exact solution for the calculation of colour volume overlap in colourspace, thus expanding previously published methodologies. 4. As an example of pavo's capabilities, we compare the colour patterns of three African Glossy Starling species, two of which have diverged very recently. We demonstrate how both colour vision models and direct spectral measurement analysis can be used to describe colour attributes and differences between these species. Different approaches to visual models and several plotting capabilities exemplify the package's versatility and streamlined workflow. 5. pavo provides a cohesive environment for handling spectral data and addressing complex sensory ecology questions, while integrating with R's modular core for a broader and comprehensive analytical framework, automated management of spectral data, and reproducible workflows for colour analysis.

In 2012, GreyNet published a page on its website and made accessible the first edition of IDGL, International Directory of Organizations in Grey Literature . The latest update of this PDF publication was in August 2016, providing a list of some 280 organizations in 40 countries worldwide that have contact with the Grey Literature Network Service. The listing appears by country followed by the names of the organizations in alphabetical order, which are then linked to a URL.This year GreyNet International marks its Twenty Fifth Anniversary and seeks to more fully showcase organizations, whose involvement in grey literature is in one or more ways linked to GreyNet.org. Examples of which include: members, partners, conference hosts, sponsors, authors, service providers, committee members, associate editors, etc.This revised and updated edition of IDGL will benefit from the use of visualization software mapping the cities in which GreyNet’s contacts are located. Behind each point of contact are a number of fields that can be grouped and cross-tabulated for further data analysis. Such fields include the source, name of organization, acronym, affiliate’s job title, sector of information, subject/discipline, city, state, country, ISO code, continent, and URL. Eight of the twelve fields require input, while the other four fields do not.The population of the study was derived by extracting records from GreyNet’s in-house, administrative file. Only recipients on GreyNet’s Distribution List as of February 2017 were included. The records were then further filtered and only those that allowed for completion of the required fields remained. This set of records was then converted to Excel format, duplications were removed, and further normalization of field entries took place. In fine, 510 records form the corpus of this study. In the coming months, an in-depth analysis of the data will be carried out - the results of which will be recorded and made visually accessible.The expected outcome of the project will not only produce a revised, expanded, and updated publication of IDGL, but will also provide a visual overview of GreyNet as an international organization serving diverse communities with shared interests in grey literature. It will be a demonstration of GreyNet’s commitment to research, publication, open access, education, and public awareness in this field of library and information science. Finally, this study will serve to pinpoint geographic and subject based areas currently within as well as outside of GreyNet’s catchment.

Evidence about the relationship between lighting and crime is mixed. Although a review of evidence found that improved road / street lighting was associated with reductions in crime, these reductions occurred in daylight as well as after dark, suggesting any effect was not due only to changes in visual conditions. One limitation of previous studies is that crime data are reported in aggregate and thus previous analyses were required to make simplifications concerning types of crimes or locations. We will overcome that by working with a UK police force to access records of individual crimes. We will use these data to determine whether the risk of crime at a specific time of day is greater after dark than during daylight. If no difference is found, this would suggest improvements to visual conditions after dark through lighting would have no effect. If however the risk of crime occurring after dark was greater than during daylight, quantifying this effect would provide a measure to assess the potential effectiveness of lighting in reducing crime risk after dark. We will use a case and control approach to analyse ten years of crime data. We will compare counts of crimes in ‘case’ hours, that are in daylight and darkness at different times of the year, and ‘control’ hours, that are in daylight throughout the year. From these counts we will calculate odds ratios as a measure of the effect of darkness on risk of crime, using these to answer three questions: 1) Is the risk of overall crime occurring greater after dark than during daylight? 2) Does the risk of crime occurring after dark vary depending on the category of crime? 3) Does the risk of crime occurring after dark vary depending on the geographical area?

This repository contains per-trial data and R analysis code reported in Stauch, B., Peter, A., Ehrlich, I., Nolte, Z., and Fries, P. (2022), Human visual gamma for color stimuli. eLife 11:e75897. doi: 10.7554/eLife.75897. If you want to have a look at the full analysis outcomes, start with analysis_notebook.html. The underlying code is in analysis_notebook.rmd.

Additionally, Matlab code that was used to extract per-trial data from the raw data is provided as preprocessingCode.zip.

When students learn linear regression, they must learn to use diagnostics to check and improve their models. Model-building is an expert skill requiring the interpretation of diagnostic plots, an understanding of model assumptions, the selection of appropriate changes to remedy problems, and an intuition for how potential problems may affect results. Simulation offers opportunities to practice these skills, and is already widely used to teach important concepts in sampling, probability, and statistical inference. Visual inference, which uses simulation, has also recently been applied to regression instruction. This article presents the regressinator, an R package designed to facilitate simulation and visual inference in regression settings. Simulated regression problems can be easily defined with minimal programming, using the same modeling and plotting code students may already learn. The simulated data can then be used for model diagnostics, visual inference, and other activities, with the package providing functions to facilitate common tasks with a minimum of programming. Example activities covering model diagnostics, statistical power, and model selection are shown for both advanced undergraduate and Ph.D.-level regression courses.

The Medical Data Visualization market has rapidly evolved, fueled by the increasing complexity of healthcare data and the need for efficient management and presentation of this information. By transforming raw data into intuitive visual representations, medical data visualization tools enable healthcare providers, r

Exploratory Data Analysis for the Physical Properties of Lakes

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on the physical properties of lakes.

Introduction

Lakes are dynamic, nonuniform bodies of water in which the physical, biological, and chemical properties interact. Lakes also contain the majority of Earth's fresh water supply. This lesson introduces exploratory data analysis using R statistical software in the context of the physical properties of lakes.

Learning Objectives

After successfully completing this exercise, you will be able to:

1. Apply exploratory data analytics skills to applied questions about physical properties of lakes
2. Communicate findings with peers through oral, visual, and written modes

The main results file are saved separately:

• ASSR2.html: R output of the main analyses (N = 33)
• ASSR2_subset.html: R output of the main analyses for the smaller sample (N = 25)

FIGSHARE METADATA

Categories

• Biological psychology
• Neuroscience and physiological psychology
• Sensory processes, perception, and performance

Keywords

• crossmodal attention
• electroencephalography (EEG)
• early-filter theory
• task difficulty
• envelope following response

References

• https://doi.org/10.17605/OSF.IO/6FHR8
• https://github.com/stamnosslin/mn
• https://doi.org/10.17045/sthlmuni.4981154.v3
• https://biosemi.com/
• https://www.python.org/
• https://mne.tools/stable/index.html#- https://www.r-project.org/- https://rstudio.com/products/rstudio/

GENERAL INFORMATION

1. Title of Dataset: Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones

2. Author Information A. Principal Investigator Contact Information Name: Stefan Wiens Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.su.se/profiles/swiens-1.184142 Email: sws@psychology.su.se

   B. Associate or Co-investigator Contact Information Name: Malina Szychowska Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.researchgate.net/profile/Malina_Szychowska Email: malina.szychowska@psychology.su.se

3. Date of data collection: Subjects (N = 33) were tested between 2019-11-15 and 2020-03-12.

4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden

5. Information about funding sources that supported the collection of the data: Swedish Research Council (Vetenskapsrådet) 2015-01181

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: CC BY 4.0

2. Links to publications that cite or use the data: Szychowska M., & Wiens S. (2020). Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Submitted manuscript.

The study was preregistered: https://doi.org/10.17605/OSF.IO/6FHR8

1. Links to other publicly accessible locations of the data: N/A

2. Links/relationships to ancillary data sets: N/A

3. Was data derived from another source? No

4. Recommended citation for this dataset: Wiens, S., & Szychowska M. (2020). Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.12582002

DATA & FILE OVERVIEW

File List: The files contain the raw data, scripts, and results of main and supplementary analyses of an electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.

ASSR2_experiment_scripts.zip: contains the Python files to run the experiment.

ASSR2_rawdata.zip: contains raw datafiles for each subject

• data_EEG: EEG data in bdf format (generated by Biosemi)
• data_log: logfiles of the EEG session (generated by Python)

ASSR2_EEG_scripts.zip: Python-MNE scripts to process the EEG data

ASSR2_EEG_preprocessed_data.zip: EEG data in fif format after preprocessing with Python-MNE scripts

ASSR2_R_scripts.zip: R scripts to analyze the data together with the main datafiles. The main files in the folder are:

• ASSR2.html: R output of the main analyses
• ASSR2_subset.html: R output of the main analyses but after excluding eight subjects who were recorded as pilots before preregistering the study

ASSR2_results.zip: contains all figures and tables that are created by Python-MNE and R.

METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation of data: The auditory stimuli were amplitude-modulated tones with a carrier frequency (fc) of 500 Hz and modulation frequencies (fm) of 20.48 Hz, 40.96 Hz, or 81.92 Hz. The experiment was programmed in python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mn

The EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and saved in .bdf format. For more information, see linked publication.

1. Methods for processing the data: We conducted frequency analyses and computed event-related potentials. See linked publication

2. Instrument- or software-specific information needed to interpret the data: MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html# Rstudio used with R (R Core Team, 2020): https://rstudio.com/products/rstudio/ Wiens, S. (2017). Aladins Bayes Factor in R (Version 3). https://www.doi.org/10.17045/sthlmuni.4981154.v3

3. Standards and calibration information, if appropriate: For information, see linked publication.

4. Environmental/experimental conditions: For information, see linked publication.

5. Describe any quality-assurance procedures performed on the data: For information, see linked publication.

6. People involved with sample collection, processing, analysis and/or submission:

• Data collection: Malina Szychowska with assistance from Jenny Arctaedius.
• Data processing, analysis, and submission: Malina Szychowska and Stefan Wiens

DATA-SPECIFIC INFORMATION: All relevant information can be found in the MNE-Python and R scripts (in EEG_scripts and analysis_scripts folders) that process the raw data. For example, we added notes to explain what different variables mean.

Contains the raw data and analysis scripts for this data set: Wiens, S., Szychowska, M., Eklund, R., & Nilsson, M. E. (2017). Data on the auditory duration mismatch negativity for different sound pressure levels and visual perceptual loads. Data in Brief, 11, 159-164. https://doi.org/10.1016/j.dib.2017.02.007

Wiens et al. (2017) contained only aggregated data for these studies:

Szychowska, M., Eklund, R., Nilsson, M. E., & Wiens, S. (2017). Effects of sound pressure level and visual perceptual load on the auditory mismatch negativity. Neuroscience Letters, 640, 37-41. https://doi.org/10.1016/j.neulet.2017.01.001

Wiens, S., Szychowska, M., & Nilsson, M. E. (2016). Visual task demands and the auditory mismatch negativity: An empirical study and a meta-analysis. PLoS ONE, 11(1), e0146567. https://doi.org/10.1371/journal.pone.0146567

Content: rawdata_EEG_bdf_2017.zip contains the raw eeg data files that were recorded with a biosemi system (www.biosemi.com). The files can be opened in matlab with the fieldtrip toolbox. https://www.mathworks.com/products/matlab.html http://www.fieldtriptoolbox.org/ fieldtrip_mat*.zip contain the final, preprocessed individual data files. They can be opened with matlab.

fieldtrip_analysis*.zip contain all the matlab scripts that were used to process the erp data with the toolbox fieldtrip. http://www.fieldtriptoolbox.org/

Supplementary_Table_1.csv is the datafile in Wiens et al. (2017).

Dataset and full R script used in the data analysis of the paper "Artificial light changes visual perception by pollinators in a hawkmoth-plant interaction system".

Summary: Night-flying pollinators, such as hawkmoths, are particularly vulnerable to the global spread of urban artificial lighting which is changing nighttime environments worldwide, impacting organisms and their interactions. Incident light quality can alter flower and leaf color perception by insects, depending on the emission spectra of light sources and the spectral sensitivity of insects. We asked, using Manduca sexta visual models, whether color contrast against natural backgrounds is altered by artificial lights for flowers and leaves of 16 plant species with an estimated long history of coevolution with hawkmoth pollinators. Specifically, we compared the perception of flowers and leaves by hawkmoths under artificial lights, including light-emitting diodes (5000 K LED), mercury vapor (MV), and high-pressure sodium (HPS) artificial lights, with the perception under natural illuminations. The models we implemented estimate that LED and HPS lighting change hawkmoth perception of flowers and leaves, with color loci appearing nearer to each other in hawkmoths perceptual space than they would be under natural nighttime conditions. Receptor Noise Limited models show that under the different lighting conditions hawkmoths would still discriminate flowers from their leaves in most but not all species. Consequently, artificial lights likely alter perception by hawkmoths of floral and leaf signals possibly affecting interactions and fitness of plants and pollinators. Our results emphasize the intricate and insidious ways in which human-made environments impact species interactions. Further studies should confirm whether light pollution represents a novel selective force to nocturnal interacting partners as emerging evidence suggests. Addressing the effects of artificial lighting is crucial for designing infrastructure development strategies that minimize these far-reaching effects on ecosystem functioning.

This archive contains the stimulus materials, anonymized behavioral data, cleaned datasets, and R analysis script associated with the article "Thematic knowledge survives visual crowding and influences object identification" (Slaski, Sayim & Kalénine). It enables full transparency and reproducibility of the reported results.

Animal spatial behaviour is often presumed to reflect responses to visual cues. However, inference of behaviour in relation to the environment is challenged by the lack of objective methods to identify the information that effectively is available to an animal from a given location. In general, animals are assumed to have unconstrained information on the environment within a detection circle of a certain radius (the perceptual range; PR). However, visual cues are only available up to the first physical obstruction within an animal’s PR, making information availability a function of an animal’s location within the physical environment (the effective visual perceptual range; EVPR). By using LiDAR data and viewshed analysis, we model forest birds’ EVPRs at each step along a movement path. We found that the EVPR was on average 0.063% that of an unconstrained PR and, by applying a step-selection analysis, that individuals are 1.57 times more likely to move to a tree within their EVPR than to...

Replication Package

This repository contains data and source files needed to replicate our work described in the paper "Unboxing Default Argument Breaking Changes in Scikit Learn".

Requirements

We recommend the following requirements to replicate our study:

1. Internet access
2. At least 100GB of space
3. Docker installed
4. Git installed

Package Structure

We relied on Docker containers to provide a working environment that is easier to replicate. Specifically, we configure the following containers:

• data-analysis, an R-based Container we used to run our data analysis.
• data-collection, a Python Container we used to collect Scikit's default arguments and detect them in client applications.
• database, a Postgres Container we used to store clients' data, obtainer from Grotov et al.
• storage, a directory used to store the data processed in data-analysis and data-collection. This directory is shared in both containers.
• docker-compose.yml, the Docker file that configures all containers used in the package.

In the remainder of this document, we describe how to set up each container properly.

Using VSCode to Setup the Package

We selected VSCode as the IDE of choice because its extensions allow us to implement our scripts directly inside the containers. In this package, we provide configuration parameters for both data-analysis and data-collection containers. This way you can directly access and run each container inside it without any specific configuration.

You first need to set up the containers

$ cd /replication/package/folder
$ docker-compose build
$ docker-compose up
# Wait docker creating and running all containers

Then, you can open them in Visual Studio Code:

1. Open VSCode in project root folder
2. Access the command palette and select "Dev Container: Reopen in Container"
   1. Select either Data Collection or Data Analysis.
3. Start working

If you want/need a more customized organization, the remainder of this file describes it in detail.

Longest Road: Manual Package Setup

Database Setup

The database container will automatically restore the dump in dump_matroskin.tar in its first launch. To set up and run the container, you should:

Build an image:

$ cd ./database
$ docker build --tag 'dabc-database' .
$ docker image ls
REPOSITORY  TAG    IMAGE ID    CREATED     SIZE
dabc-database latest  b6f8af99c90d  50 minutes ago  18.5GB

Create and enter inside the container:

$ docker run -it --name dabc-database-1 dabc-database
$ docker exec -it dabc-database-1 /bin/bash
root# psql -U postgres -h localhost -d jupyter-notebooks
jupyter-notebooks=# \dt
       List of relations
 Schema |    Name    | Type | Owner
--------+-------------------+-------+-------
 public | Cell       | table | root
 public | Code_cell     | table | root
 public | Md_cell      | table | root
 public | Notebook     | table | root
 public | Notebook_features | table | root
 public | Notebook_metadata | table | root
 public | repository    | table | root

If you got the tables list as above, your database is properly setup.

It is important to mention that this database is extended from the one provided by Grotov et al.. Basically, we added three columns in the table Notebook_features (API_functions_calls, defined_functions_calls, andother_functions_calls) containing the function calls performed by each client in the database.

Data Collection Setup

This container is responsible for collecting the data to answer our research questions. It has the following structure:

• dabcs.py, extract DABCs from Scikit Learn source code, and export them to a CSV file.
• dabcs-clients.py, extract function calls from clients and export them to a CSV file. We rely on a modified version of Matroskin to leverage the function calls. You can find the tool's source code in the `matroskin`` directory.
• Makefile, commands to set up and run both dabcs.py and dabcs-clients.py
• matroskin, the directory containing the modified version of matroskin tool. We extended the library to collect the function calls performed on the client notebooks of Grotov's dataset.
• storage, a docker volume where the data-collection should save the exported data. This data will be used later in Data Analysis.
• requirements.txt, Python dependencies adopted in this module.

Note that the container will automatically configure this module for you, e.g., install dependencies, configure matroskin, download scikit learn source code, etc. For this, you must run the following commands:

$ cd ./data-collection
$ docker build --tag "data-collection" .
$ docker run -it -d --name data-collection-1 -v $(pwd)/:/data-collection -v $(pwd)/../storage/:/data-collection/storage/ data-collection
$ docker exec -it data-collection-1 /bin/bash
$ ls
Dockerfile Makefile config.yml dabcs-clients.py dabcs.py matroskin storage requirements.txt utils.py

If you see project files, it means the container is configured accordingly.

Data Analysis Setup

We use this container to conduct the analysis over the data produced by the Data Collection container. It has the following structure:

• dependencies.R, an R script containing the dependencies used in our data analysis.
• data-analysis.Rmd, the R notebook we used to perform our data analysis
• datasets, a docker volume pointing to the storage directory.

Execute the following commands to run this container:

$ cd ./data-analysis
$ docker build --tag "data-analysis" .
$ docker run -it -d --name data-analysis-1 -v $(pwd)/:/data-analysis -v $(pwd)/../storage/:/data-collection/datasets/ data-analysis
$ docker exec -it data-analysis-1 /bin/bash
$ ls
data-analysis.Rmd datasets dependencies.R Dockerfile figures Makefile

If you see project files, it means the container is configured accordingly.

A note on storage shared folder

As mentioned, the storage folder is mounted as a volume and shared between data-collection and data-analysis containers. We compressed the content of this folder due to space constraints. Therefore, before starting working on Data Collection or Data Analysis, make sure you extracted the compressed files. You can do this by running the Makefile inside storage folder.

$ make unzip # extract files
$ ls
clients-dabcs.csv clients-validation.csv dabcs.csv Makefile scikit-learn-versions.csv versions.csv
$ make zip # compress files
$ ls
csv-files.tar.gz Makefile

This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication Liming effects on microbial carbon use efficiency and its potential consequences for soil organic carbon stocks Julia Schroeder1, Claudia Dǎmǎtîrcǎ2,6, Tobias Bölscher3, Claire Chenu3, Lars Elsgaard4, Christoph C. Tebbe5, Laura Skadell1, Christopher Poeplau1 1 Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, 38116 Braunschweig, Germany 2 University of Turin, Department of Agricultural, Forest and Food Sciences, Largo Paolo Braccini 2, 10095 Grugliasco TO, Italy 3 Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, 22 place de l'Agronomie, 91120 Palaiseau, France 4 Aarhus University, Department of Agroecology, Blichers Allé 20, 8830 Tjele, Denmark 5 Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany 6 current address: Euro-Mediterranean Center on Climate Change (CMCC) Foundation, Division on Climate Change Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Via Igino Garbini 51, 01100 Viterbo, Italy DOI: 10.1016/j.soilbio.2024.109342 In this study, we set out to test the potential of liming as means to control the microbial carobn use efficiency (CUE). We assessed CUE using the 18O-labelling method for soils from three European long-term liming field trials (i.e. Jyndevad, Versailles, and Dürnast). Additionally, the immediate response of CUE to liming in the lab was tested accounting for lime-derived CO2 emission. The lime-induced pH shift was a strong determinant of CUE. However, the relationship between CUE and soil pH followed a U-shaped (i.e. quadratic) curve, suggesting that CUE may be lowest at near neutral soil pH and therefore to interfere with agronomic interests (i.e. high crop yield). To assess the potential contribution of CUE on the net liming effect on SOC stocks, we calculated OC inputs and SOC stocks. Liming had a positive effect on SOC stocks, regardless of the change in CUE. Our results suggest that CUE added to the net liming effect on SOC stocks. Statistical analyses and data visualisation were conducted in R v4.1.2 (2021-11-01) (R Core Team, 2020) using RStudio v2022.12.0 (Posit team, 2022). The repository includes the following files: liming_sample_data_R.csv - 18O-CUE data and measured pH for DK, DA, VB and DL (n=43) site_info_R.csv - C, N, bulk density and pH data shared by co-authors for DK, DA and VB (n=32) yield_R.csv - yield data shared by co-authors for DK, DA and VB (n=236) CO2sources_R.csv - long-formatted data for CO2 source differentiation in the direct liming experiment (n=66) C_input_allocation_factors_R.csv - allocation factors to crop types (Jacobs et al. 2020, https://doi.org/10.1007/s10705-020-10087-5 ) Schroeder_et_al._liming_effect_on_CUE.Rproj - Rproject (load project to work on provided scripts and data) load_data.R - loads required data liming_on_soil_pH.R - statistical analysis liming effect on soil pH, creates output for Table 1 (additional figure effect liming on soil pH) liming_on_CUE.R - statistical analysis liming effect on CUE, creates output for Tables 2, S1 and S2 liming_on_CmicCorg.R - statistical analysis liming effect on Cmic/Corg (laboratory liming excluded), creates output for Table 3 liming_on_microbial_params.R - statistical analysis liming effect on Cmic, Cgrowth, Crespiration (all treatments), creates output for Tables S1 and S2 liming_on_abundances.R - statistical analysis liming effect on microbial abundances (fungi, bacteria, archaea), creates output for Tables S1 and S2 liming_on_K2SO4extrC.R - statistical analysis liming effect on K2SO4 extractable C as proxy for DOC, creates output for Table S3 and Figure S1 z-tranformation_best_fit.R - tests different models to find best fit of z-transformed data over pH calculation_C_stocks.R - test on treatment differences in bulk density, calculation of SOC stocks, creates output for Table S4 and Figure 7 calculation_C_input.R - calculation of C inputs based on yield_R.csv data and C_input_allocation_factors_R.csv, output Figure S3 and Table S5 calculation_SOC_formation_efficiency.R - calculation of SOC formation efficiency based on estimated marginal mean difference of C stocks and inputs, script requires calculation_C_stocks.R and calculation_C_inputs.R to be run beforehand plot_figures.R - plots Figures 2, 3, 4, 5 ,6, and Figures S2 and S4 plot_Figure8_radar_chart.R - plots Figure 8 calculation_maximum_relative_error_respiration_rate_estimates.xlsx - Output data from Visual MINTEQ secnarios plus calculation for error estimation

Data and R code to reproduce the figures in our EJN technical spotlight:Modern graphical methods to compare two groups of observationsGuillaume A. Rousselet, Cyril R. Pernet, Rand R. WilcoxEuropean Journal of Neuroscience (submitted)Also contains a pdf version of the submitted article, all the figures in tif format, and Matlab code implementing the main R functions.

Data of image calculation averages, coefficient of variations, and experimental measurements that were presented in the manuscript, Visualizing Plant Responses: Novel Insights Possible through Affordable Imaging Techniques in the Greenhouse, is provided.Abstract: Global climatic pressures and increased human demands create a modern necessity for efficient and affordable plant phenotyping unencumbered by arduous technical requirements. The analysis and archival of imagery have become easier as modern camera technology and computers are leveraged. This facilitates the detection of vegetation status and changes over time. Using a custom lightbox, an inexpensive camera, and common software, turfgrass pots were photographed in a greenhouse environment over an 8-week experiment period. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results indicate that Red Green Blue-based (RGB) imagery with simple controls is sufficient to measure the effects of plant treatments. Notable correlations were observed for corrected imagery, including between a percent yellow color area classification segment (%Y) with human visual quality ratings (VQ) (R = -0.89), the dark green color index (DGCI) with clipping productivity in mg d-1 (mg) (R = 0.61), and an index combination term (COMB2) with water use in mm d-1 (mm) (R = -0.60). The calculation of green cover area (%G) correlated with Normalized Difference Vegetation Index (NDVI) (R = 0.91) and its RED reflectance spectra (R = -0.87). A CIELAB b/a chromatic ratio (BA) correlated with Normalized Difference Red-Edge index (NDRE) (R = 0.90), and its Red-Edge (RE) (R = -0.74) reflectance spectra, while a new calculation termed HSVi correlated strongest to the Near-Infrared (NIR) (R = 0.90) reflectance spectra. Additionally, COMB2 significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analysis of typical image file formats and corrections that included JPEG (JPG), TIFF (TIF), geometric lens correction (LC), and color correction (CC) were conducted. Results underscore the need for further research to support image corrections standardization and better connect image data to biological processes. This study demonstrates the potential of consumer-grade photography to capture plant phenotypic traits.

This dataset was used for the study titled "Pterin-based color predicts the outcome of intrasexual competition for males in Guinan toad-headed lizard", which include fours parts:(1) The spectral data were obtained using a Jaz optic spectrophotometer on the ventrolateral region of lizards.(2) The sand substrate reflection and irradiance data were measured in the study site.(3) Morphological traits were measured according to the standard methods on lizards.(4) A R-script for data analysis.

This is the official data repository to reproduce all figures and statistical analysis for project: "Assessing cognitive flexibility in humans and rhesus macaques with visual motion and neutral distractors" by Yurt P, Calapai A., Mundry R., Treue S. September 2022. Matlab (2020a) is required, as well as the gramm package for data visualization (https://github.com/piermorel/gramm)