Images metadata (file)Image_metadata.xlsx is a file which specifies the experiment, days of growth, stress/media, and stressor concentration associated with each image file included in this project.Images (zipped folder)This folder contains all of the phenotyping images obtained in this study.Sequenced mutants dataset (zipped folder)Includes two items:1) Sulfite phenotyping of haploid mutants of S. cerevisiae and S. paradoxus chosen as candidates for sequencing.2) Copper phenotyping of haploid mutants of S. cerevisiae and S. paradoxus chosen as candidates for sequencing.For sulfite the files provided contain the following info: Raw_data_positions_sulfite.txt = colony sizes at each position for each plate. Raw_data_strains_sulfite.csv = The raw data processed to link the colony size measurements with a technical replicate of a particular strain. Sulfite concentrations of each plate can also be found in the rightmost column. ANC_key_triplicate_sulfite.csv = Link the numeric designations of the mutants to their ancestors. positions_key_triplicate_sulfite.csv = links the positions on the plates to the numeric designations of the mutants. YJF_key_triplicate_sulfite.csv = YJF designations for the mutants that were chosen for sequencing linked to their numeric id in this experiment.For copper, two files contain all of the information. 4_13_21_seqed_coppermutsreplicatedphenod3_ColonyData_AllPlates.txt contains all of the colony sizes for each position in the images. Copper_design_YJFdesignations.csv specifies the YJF designations of each strain in each position.Diploid dataset (zipped folder)This dataset includes images and colony size measurements from several phenotyping experiments: Copper phenotyping of diploid mutants of S. cerevisiae and S. paradoxus with elevated resistance.Sulfite phenotyping of diploid mutants of S. cerevisiae and S. paradoxus with elevated resistance.Phenotyping these mutants in permissive conditions.The file diploid_colony_size_dataset.csv contains colony size measurements derived from the images in this item along with the collection metadata associated with each sample (relative size, color, recovery concentration, circularity, spontaneous/induced).Note the column "mutnumericid_techreps" in this file, which defines the positions that are technical replicates of the same mutant/strain.Haploid dataset (zipped folder)This dataset includes images and colony size measurements from several phenotyping experiments: Copper phenotyping of haploid mutants of S. cerevisiae and S. paradoxus with elevated resistance.Sulfite phenotyping of haploid mutants of S. cerevisiae and S. paradoxus with elevated resistance.Phenotyping these mutants in permissive conditions.The file haploid_colony_size_dataset.csv contains colony size measurements derived from the images in this item along with the collection metadata associated with each sample (relative size, color, recovery concentration, circularity, spontaneous/induced).Processed data used to generate figures (zipped folder)The following files contain the data used to generate the figures in the associated publication:canavanine2.csv = mutation rates and standard deviations of those rates for the three concentrations of canavanine used for both species for each treatment (mutagenized and mock mutagenized)copper2.csv = mutation rates and standard deviations for each copper concentration for both species for both treatments. Columns are added that were used to specify line connections and horizontal point offset in ggplot2.copper3.csv = Total mutation rates for copper for both species for both treatments. Includes a column used for horizontal offset in ggplot2.hapcop.csv, dipcop,csv, hapsul.csv, dipsul.csv contain effect size data for all the nonescapee strains that were phenotyped for both species.hapcopc.csv, dipcopc,csv, hapsulc.csv, dipsulc.csv contain costs data for all the nonescapee strains that were phenotyped for both species.rc_da_cop.csv and rc_da_sul.csv contain delta AUC values and costs measurements for the sequenced mutants and contain columns to split the mutants by category.Incidence.csv contains the incidence of the major mutant classes recovered in this study split between species.KSP1_muts.csv, PMA1_muts.csv, RTS1_muts.csv, REG1_muts.csv, encodes the position and identity of mutants recovered in this study such that they can be visualized as bar charts. Negative values are used for S. paradoxus counts.YJF4464_CUP1.csv contained coverage data at the CUP1 locus for S. paradoxus copper mutant YJF4464

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.

This code analyses behavioural data from a group of 13 Parkinson patients and 15 healthy control participants performing a moving dots paradigm with Speed vs. Accuracy instructions. The main behavioural outcomes are reaction times, movement times, peak force and response accuracy (folder 1, used for figure 1 in the published article), as well as computational analysis of behaviour with hierarchical drift diffusion modelling (folder 2, used for figure 2). In patients local field potentials were recorded during the task and corresponding code is stored in folder 3-5 (figure 3). In 10 patients burst deep brain stimulation was applied during a second session. Its effects on behaviour and local field potentials are analysed with code from folder 6 and 7 (figures 4-6). The results have been published in a paper termed ‘Dynamic control of decision and movement speed in the human basal ganglia’ by Herz et al.

The code has been tested on a MacBook Pro, macOS Mojave 10.14.6. The required software is listed in each part of the analysis including where installation information can be found. Installation times vary between the different softwares. Run times of code is longest for Bayesian analysis (~ 5-10 minutes per analysis) and drift diffusion modelling (up to several hours). The remaining code in R and Matlab is much faster.

Example data is provided as HDDM.csv (example behavioural data of Parkinson’s disease (PD) patients), HDDM_HC.csv (example behavioural data of healthy control (HC) participants) and HDDM_LFP (combined example behavioural and local field potential data from patients). This is not the actual data from the study, and thus the results will not match the study results, but the structure is the same and therefore they allow testing all scripts if applicable. Note that for this data the log transform should be omitted, since the distribution is normal, not tailed.

(i) Behavioral data:

Software requirements: R v4.0.5, RStan v2.21.2, rethinking package v2.13, rstanarm package v2.21.1, bayesplot v1.8.0, ggplot2 v3.4.0, lme4 v.1.1.30, MuMIn, afex.

Installation guides can be found on: https://cran.r-project.org/, https://mc-stan.org/users/interfaces/rstan, https://github.com/rmcelreath/rethinking, https://cran.r-project.org/web/packages/rstanarm/index.html, https://mc-stan.org/bayesplot/, https://ggplot2.tidyverse.org/, https://cran.r-project.org/web/packages/lme4/index.html, https://cran.r-project.org/web/packages/MuMIn/index.html, https://cran.r-project.org/web/packages/afex/index.html.

Scripts:

‘Script_Behavior_Bayesian’: Loads behaviour from PD patients (‘HDDM.csv’) and from healthy controls (‘HDDM_HC.csv’), and computes Bayesian hierarchical regression models of reaction times, movement times, peak force, accuracy and regression between reaction times and movement times. It also plots the relevant posteriors.

‘Script_Behavior_NonBayesian’: Conducts the non-Bayesian equivalents of the above mentioned analyses.

(ii) Drift Diffusion Modelling:

Software requirements: Python v3.6, Hierarchical drift diffusion modelling (HDDM v0.8.0), arviz.

Installation guides can be found on: https://www.python.org/downloads/, http://ski.clps.brown.edu/hddm_docs/, https://arviz-devs.github.io/arviz/

Scripts:

‘Scripts_HDDM_Behaviour.py’: Loads behaviour from PD patients (‘HDDM.csv’) and from healthy controls (‘HDDM_HC.csv’), conducts drift diffusion modelling with all possible effects (i), a reduced model with only Instruction effects on decision thresholds (ii) for model convergence checks and posterior predictive checks, and a regression model with single trial movement times as predictor (iii).

(iii) Local field potential data:

Software requirements: Matlab (2019a, requires a software license), FieldTrip v20201126.

Installation guides can be found on: https://matlab.mathworks.com/, https://www.fieldtriptoolbox.org/download/.

Scripts:

‘Script_LFP_FirstLevel.m’: Loads data and applies preprocessing, time-frequency analysis and re-aligning of data using FieldTrip. It uses the custom-written functions ‘ScriptFunction_MakeMontage_AllBipolar’ (which creates a bipolar montage from the monopolar data) and ‘ScriptFunction_EpochData’ (which epochs the continuous data aligned to the moving dots cue and movement onset). The epoched spectra of contralateral and ipsilateral subthalamic nucleus are saved.

‘Script_LFP_SecondLevel_TrialAverages.m’: Loads the spectra from first level analysis and plots the grand average as well as group-averaged theta, beta and gamma traces for all trials, Accuracy trials and Speed trials, for cue- and movement-aligned data.

‘Script_LFP_SecondLevel_SingleTrials.m’: Loads the spectra from first level analysis and computes single trial values of cue-aligned theta, cue-aligned beta, movement-aligned beta and movement-aligned gamma based on time windows of interest. It saves the single trial values together with behavioural data (accuracy, reaction times and movement times) separately for cue-aligned beta, movement-aligned beta and movement-aligned gamma in one csv file and for cue-aligned theta in a separate csv file. These files are used for regression analyses and HDDM. It also saves a csv file with reaction times < 0.4 s excluded for control analyses of cue-aligned beta.

(iv) Regression between local field potential and behavioural data:

Software requirements: R v4.0.5, RStan v2.21.2, rethinking package v2.13, rstanarm package v2.21.1, bayesplot v1.8.0, ggplot2 v3.4.0, lme4 v.1.1.30, MuMIn, afex.

Installation guides: see above.

Scripts:

‘Script_LFP_Bayesian’: Loads behaviour and LFP data from PD patients (‘HDDM_LFP.csv’), and computes Bayesian hierarchical regression models using reaction times and movement times as dependent variables including putative post-hoc tests. It also plots the relevant posteriors. For theta a separate file is loaded (‘HDDM_LFP_Theta.csv’).

‘Script_LFP_NonBayesian’: Conducts the non-Bayesian equivalents of the above mentioned analyses.

(v) Drift Diffusion modelling with local field potential data:

Software requirements: Python v3.6, Hierarchical drift diffusion modelling (HDDM v0.8.0), arviz.

Installation guides: see above.

Scripts:

‘Scripts_HDDM_LFP.py’: Loads behaviour and LFP data from PD patients (‘HDDM_LFP.csv’) and conducts drift diffusion regression modelling with cue-aligned beta and cue- aligned theta (for theta the file ‘HDDM_LFP_Theta.csv’ is loaded).

(vi) DBS effects on behaviour:

Software requirements: Matlab (2019a, requires a software license)

Installation guides: see above.

Scripts:

‘Script_StimulationTime’ loads a file with the stimulation trace during the task, calls the function ‘ScriptFunction_DownsampleBinaryRemoveRamp’ (which downsamples the data to 1000Hz, makes stimulation binary (1 for ON, 0 for OFF) and removes the ramping so that only stimulation at effective intensities counts as stimulation) and loads the behavioural data (reaction time and movement time). It then calls the functions ‘ScriptFunction_WindowedStim’ (which computes for each trial whether or not stimulation was given in any 100 ms moving windows for cue- and movement aligned data), ‘ScriptFunction_WindowedRT’ (which computes reaction times for windows in which stimulation was applied vs. was not applied, also for Accuracy and Speed trials) and ‘ScriptFunction_WindowedMT’ (which computes the analogous measures for movement times). The results are saved separately for reaction times and movement times.

‘Script_StimulationEffects’ loads this data, plots effects of stimulation on movement times and reaction times and provides statistics using cluster-based permutation tests (‘ScriptFunction_PermTest’). It also saves single trial behavioural data with a column stating whether DBS was applied in the critical time window (which is used for the DBS effects on local field potentials analysis).

(vii) DBS effects on local field potentials:

Software requirements: Matlab (2019a, requires a software license), FieldTrip v20201126.

Installation guides: see above.

Scripts:

‘Script_LFP_FirstLevel_Stim.m’: Loads data and applies preprocessing, time-frequency analysis and re-aligning of data using FieldTrip analogously to the script described under (iii) except that it also detrends and demeans the data, applies a low-pass filter at 100 Hz and excludes noisy data points, which are then interpolated. The epoched spectra of contralateral and ipsilateral subthalamic nucleus are saved.

‘Script_LFP_SecondLevel_Stim.m’: Loads data from the previous analysis, loads single trial data with info whether DBS was applied at critical time windows and plots these beta traces together with beta power off stimulation. To exclude that the DBS effect on reaction times might explain any changes, single trial beta power is capped at the single trial reaction times so that no movements fall into the plotted beta power.

Of note, the scripts for unilateral DBS are identical to the scripts described in (vi) and (vii) with the exception that stimulation is analysed for responses with the contralateral and ipsilateral hand.

The dataset was collected by reviewing abstracts in the journal Freshwater Science (formerly the Journal of North American Benthological Society [JNABS]) from 1997 to 2019 as well as searching abstracts from oral presentations at the SFS Annual Meeting (available online for 1997–2012 and 2015–2019 at https://sfsannualmeeting.org/SearchAll.cfm) for key words (public engagement, science communication, education, outreach) related to PES. The dataset was processed by inputting the data collected from our search (i.e., year, type of work, keyword, and number of times the keyword appeared in that type of work during the specified year) into a .csv file using Microsoft Excel. R was used (https://www.r-project.org/) and its accompanying package ggplot2 (https://ggplot2.tidyverse.org/) to plot the data.