Context

This dataset is part of a series that contains three other datasets:

• UN SDG - EU 27 sample datamart (subset for 7 KPIs)
• Selected Indicators from World Bank 2000-2019: 33 indicators from World Bank website for a data project
• EU 27 Energy sources - consumption 1990-2018 / energy production, import, export by source - consumption by sector

Content

Dataset components

Sources

Processing done

Source: A

Converted .shp files (same name as the one under "dataset components") into CSV by using GDAL 3.0.4, released 2020/01/28, offline, under Windows 10, using the following command line: ogr2ogr -f CSV

Source: B

The data from this source for the time being are not uploaded due to errors in processing the sources (i.e. formatting errors in both .shp files and, when available, the .csv conversion provided by the source).

Anyway, if interested: the list of all the location as of 2011 is within the ZIP file Localita_2011_Point.csv from "Località italiane (shp)"

Selected the ZIP file containing the set of files WGS 84 UTM Zona 32n, latest available as of 2020-12-06: 2011

Release date and timeframe coverage

The collated dataset was released on 2020-12-06.

No timeframe coverage information available (the "localita" file is stated by ISTAT as updated at 2011).

Acknowledgements

Thanks to EEA and ISTAT for publishing the data

Inspiration

Connecting different data points to identify potential correlations, as part of my knowledge update/learning process (and to complement my other publication activities).

As part of a long-term publishing project (started in 2015 at Expo2015 in Milan), routinely share data that collect along my writing journey- generally via articles on my website on business and social change.

We present a flora and fauna dataset for the Mira-Mataje binational basins. This is an area shared between southwestern Colombia and northwestern Ecuador, where both the Chocó and Tropical Andes biodiversity hotspots converge. Information from 120 sources was systematized in the Darwin Core Archive (DwC-A) standard and geospatial vector data format for geographic information systems (GIS) (shapefiles). Sources included natural history museums, published literature, and citizen science repositories across 18 countries. The resulting database has 33,460 records from 5,281 species, of which 1,083 are endemic and 680 threatened. The diversity represented in the dataset is equivalent to 10\% of the total plant species and 26\% of the total terrestrial vertebrate species in the hotspots. It corresponds to 0.07\% of their total area. The dataset can be used to estimate and compare biodiversity patterns with environmental parameters and provide value to ecosystems, ecoregions, and protected areas. The dataset is a baseline for future assessments of biodiversity in the face of environmental degradation, climate change, and accelerated extinction processes. The data has been formally presented in the manuscript entitled "The Tropical Andes Biodiversity Hotspot: A Comprehensive Dataset for the Mira-Mataje Binational Basins" in the journal "Scientific Data". To maintain DOI integrity, this version will not change after publication of the manuscript and therefore we cannot provide further references on volume, issue, and DOI of manuscript publication. - Data format 1: The .rds file extension saves a single object to be read in R and provides better compression, serialization, and integration within the R environment, than simple .csv files. The description of file names is in the original manuscript. -- m_m_flora_2021_voucher_ecuador.rds -- m_m_flora_2021_observation_ecuador.rds -- m_m_flora_2021_total_ecuador.rds -- m_m_fauna_2021_ecuador.rds - Data format 2: The .csv file has been encoded in UTF-8, and is an ASCII file with text separated by commas. The description of file names is in the original manuscript. -- m_m_flora_fauna_2021_all.zip. This file includes all biodiversity datasets. -- m_m_flora_2021_voucher_ecuador.csv -- m_m_flora_2021_observation_ecuador.csv -- m_m_flora_2021_total_ecuador.csv -- m_m_fauna_2021_ecuador.csv - Data format 3: We consolidated a shapefile for the basin containing layers for vegetation ecosystems and the total number of occurrences, species, and endemic and threatened species for each ecosystem. -- biodiversity_measures_mira_mataje.zip. This file includes the .shp file and accessory geomatic files. - A set of 3D shaded-relief map representations of the data in the shapefile can be found at https://doi.org/10.6084/m9.figshare.23499180.v4 Three taxonomic data tables were used in our technical validation of the presented dataset. These three files are: 1) the_catalog_of_life.tsv (Source: Bánki, O. et al. Catalogue of life checklist (version 2024-03-26). https://doi.org/10.48580/dfz8d (2024)) 2) world_checklist_of_vascular_plants_names.csv (we are also including ancillary tables "world_checklist_of_vascular_plants_distribution.csv", and "README_world_checklist_of_vascular_plants_.xlsx") (Source: Govaerts, R., Lughadha, E. N., Black, N., Turner, R. & Paton, A. The World Checklist of Vascular Plants is a continuously updated resource for exploring global plant diversity. Sci. Data 8, 215, 10.1038/s41597-021-00997-6 (2021).) 3) world_flora_online.csv (Source: The World Flora Online Consortium et al. World flora online plant list December 2023, 10.5281/zenodo.10425161 (2023).)

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.

(9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis.

Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael

Abstract

This dataset was derived by the Bioregional Assessment Programme. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset converts the original Digital Atlas of Australian Soils (GUID: 9e7d2f5b-ff51-4f0f-898a-a55be8837828) shapefile into the Australian soil classification, as per data from Conversion of the Atlas of Australian Soils to the Australian Soil Classification (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662). A Layer file is also supplied using the RGB colour reference table, also found in the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset.

Purpose

Provides a spatial and cartographic representation of the Digital Atlas of Australian Soils shapefile into the new Australian soil classification.

Dataset History

From the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662) the file asclut.txt was converted to .csv format and field headings added (MAP_UNIT, SOIL_CODE, SOIL_SYMBOL, SOIL).

This csv file (asclut.csv) was joined to the Digital Atlas of Australian Soils (GUID: 9e7d2f5b-ff51-4f0f-898a-a55be8837828), soilAtlas2M shapefile on the common 'MAP_UNIT' field. The resulting join was saved as 'soilAtlas2M_ASC_Conversion.shp'

The symbology of this shapefile was updated by matching the RGB values provided in the 'asc_colours.xls' spreadsheet from the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662) to the 'SOIL' field. A Layer File was created 'soilAtlas2M_ASC_Conversion.lyr'

Dataset Citation

Bioregional Assessment Programme (2015) Spatial Data Conversion of the Atlas of Australian Soils to the Australian Soil Classification v01. Bioregional Assessment Derived Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/6f804e8b-2de9-4c88-adfa-918ec327c32f.

Dataset Ancestors

• Derived From Digital Atlas of Australian Soils

• Derived From Conversion of the Atlas of Australian Soils to the Australian Soil Classification

The Randolph Glacier Inventory (RGI) is a global set of glacier outlines

This repository contains three datasets resulting from the reconstruction of global wetland loss over 1700-2020. The three datasets are listed here and described in more detail below: A. National and subnational statistics of drained or converted areas B. Regional wetland percentage loss estimates and geospatial polygons C. Gridded reconstruction The scripts used to process input data, model and calibrate the wetland loss reconstruction, and produce the figures are publicly available at https://github.com/etiennefluetchouinard/wetland-loss-reconstruction. A. National and subnational statistics of drained or converted areas This tabular database containing national and subnational statistics of wetland area drained and peat mass extracted. The database includes four land use types: cropland, forestry, peat extraction and wetland cultivation. These data are used as input to the mapped wetland loss reconstruction. Column descriptions of drainage_db_v10.csv: unit: Scale of administrative unit ("national" or "subnational"). type: Land use type ("Cropland", "Forestry", "Peat Extraction" or "Wetland Cultivation") iso_a3: 3-letter code of each country. region: Name of subnational unit. Blank if data is national scale. HASC_1: Hierarchical Administrative Subdivisions Codes for the subnational units. Blank if data is national scale. year: Year of data. drained_area_1000ha: Cumulative area drained by the year specified, in thousands of hectares. drained_weight_1000tonsyr: Annual peat extraction rate for the year, in thousand tons per year. peatland_only: Label indicating whether the drained area applies to all wetlands or peatlands specifically ("Peatland only" or blank). Comment: Additional description from original data source, or unit conversion, or data corrections. Source: Reference of data source and/or compilers. B. Regional wetland percentage loss estimates and geospatial polygons A shapefile of 151 polygons projected in WGS84. Columns description for polygon shapefile of the regional wetland loss percentage: regional_loss_poly.shp: id: Numerical identifier. name: Name of administrative unit, region or water feature the polygon area covers. country: Name of country. continent: Name of continent. wet_categ: Broad category of wetlands included in the estimate (“Peatlands”, “Inland natural wetlands”, “Coastal natural wetlands”, “Unspecified natural type(s)” or “All wetlands”). yr_start: Start year of the period over which wetland loss is estimated. yr_end: End year of the period over which wetland loss is estimated. area_mkm2: Surface area of the polygon, in million square kilometers (Mkm2). perc_loss: Numerical value of percentage wetland loss (positive value represent loss of wetland area between start and end year. comment: Additional description of estimate used or estimation method. source: Citation of original data source. compiler: Citation of intermediary data compiler. C. Gridded reconstruction Gridded outputs are stored in a separate NetCDF file for each of the 12 reconstructions of simulated wetland and present-day wetland maps. An ensemble average was also computed from the 12 reconstructions (only individual reconstructions were discussed in the manuscript). These data consist of global maps generated from the drainage reconstruction methodology for 33 decadal intervals (1700-2020 inclusive) for 9 variables: The filenames of ensemble members are labelled to with the name of the input present-day and simulated wetland maps: “wetland_loss_1700-2020_” + simulated input + “_” + present-day input + “_v10.nc” The 4 simulated wetland map inputs are: LPJwsl, SDGVM, ORCHIDEE, DLEM. The 3 present-day wetland map inputs are: GIEMSv2, GLWD3, WAD2M. Description of the 9 variables in each NCDF file: wetland_loss: Cumulative wetland area lost (km2 per grid cell). This variable is equivalent to the sum of area drained for the seven land uses drained nat_wetland: Remaining natural wetland area (km2 per grid cell) cropland: Cropland area drained (km2 per grid cell) leading to wetland loss forestry: Forestry area drained (km2 per grid cell) leading to wetland loss peatextr: Peat harvest area drained (km2 per grid cell) leading to wetland loss wetcultiv: Wetland cultivation area (km2 per grid cell) leading to wetland loss ir_rice: Irrigated rice area leading to wetland loss (km2 per grid cell) pasture: Pasture area drained leading to wetland loss (km2 per grid cell) urban: Urban area drained leading to wetland loss (km2 per grid cell) All layers were capped below the land pixel area grid (from HYDE 3.2, excl. open water). Time: 33 slices; numerical years spread at decadal intervals, ranging between 1700-2020 (inclusive) Extent: Longitude: -180° to 180°. Latitude: -56° to 84°. See the README file for a more detailed description of this dataset. Anyone wishing to use this dataset should cite Fluet-Chouinard et al. 2023. Please contact Etienne Fluet-Chouinard at etienne.fluet@gmail.com with any questions or comments with regards to the best usage of our dataset. Fluet-Chouinard E., Stocker B., Zhang Z., Malhotra A., Melton J.R., Poulter B., Kaplan J., Goldewijk K.K., Siebert S., Minayeva T., Hugelius G., Prigent C., Aires F., Hoyt A., Davidson N., Finlayson C.M., Lehner B., Jackson R.B., McIntyre P.B. Nature. Extensive global wetland loss over the last three centuries

GIS shapefiles, python script, and .csv files for Evidence of Topographic Change Recorded by Lava Flows at Atete and Aruru Coronae on Venus. Submitted to JGR: Planets

Rockfish Conservation Areas (RCAs) are closed areas for west coast groundfish fisheries and for some fisheries that may incidentally take groundfish as bycatch. The RCA boundary line is a connection of a series of GPS coordinates published in federal regulations (See 50 CFR 660.71-660.74) that are intended to approximate underwater depth contours. RCA boundaries are used in groundfish regulations to avoid interactions with certain groundfish species of concern and may change between seasons and Recreational Fishing Management Areas. The process of digitizing these boundary lines is as follows: 30, 40, 50, 100, and 150fm waypoint .csv files were downloaded from NOAA’s West Coast Groundfish Closed Areas website https://www.fisheries.noaa.gov/west-coast/sustainable-fisheries/west-coast-groundfish-closed-areas and imported into ArcGIS Pro. Each point feature was clipped to ocean waters offshore of California and merged together. “Fathom” was added as a field to each shapefile and populated with the corresponding depth in fathoms. Boundary lines for each shapefile (30, 40, 50, 100, and 150 fm) were created using the “Points to line” tool. Line Field: “area_name”. Attribute Source: Start Point. Transfer Fields: FID, area_name, Fathom. Attributes: area_name: Unique name field displaying depth and location. Fathom: Approximate depth in fathoms of contour line. Region: Describes which of the five groundfish management zones the section of the contour line is in.

Tiff files: Maps of Above Ground Biomass change (2019-2020) over the study region near Iñapari, Peru, derived from the texture of the NIR band for SPOT-7 (SPOT_DeltaAGB_Map), PlanetScope (PlanetScope_DeltaAGB_Map.tif) and Sentinel-2 (Sentinel2_DeltaAGB_Map.tif) data for a 1-ha resolution.QML file contains the style for the biomass change maps. Shapefile contains location of four selectively logged plots.CSV file contains data on observed changes in these four plots, obtained by TLS and manual inventory.

This dataset contains the results of Food Standards Agency inspections of food outlets in England and Wales. The FSA inspect and rate food outlets, scores of 0-5 are returned with 0/1 indicating urgent and immediate action is required and 5 being exemplary. To pass, an outlet must score 3 or above. Even 0 and 1 scores are allowed to remain open, only outlets that are deemed to pose an immediate risk to health anre closed down. 91% of outlets pass, but that leaves 9% that fail. The dataset contains over 300,000 records. Data source from the Guardian website () who in turn sourced it from the Food Standards Agency. It was downloaded from the Guardian as a CSV and converted to a shp file in QGIS. Data was cleaned and converted from Txt to float using MMQGIS. The original data contains 340,000 records but the shp file has 310,000. The lost records did not contain lat/lon. The most probable reason for this is that they were outside catering or mobile catering and had no fixed address for their kitchens. This data is free to use, but acknowledging The FSA and the Guardian would be courteous. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2013-08-09 and migrated to Edinburgh DataShare on 2017-02-22.