Processing of soil data from datasets published in the Brazilian Soil Data Repository (FEBR, https://www.pedometria.org/febr/; SoilData, https://soildata.mapbiomas.org/) until the end of 2019. The data undergoes cleaning, standardization and, when possible, harmonization. The resulting dataset is made available in a single TXT file for reuse, respecting the original data use licenses.

TerraDS

The TerraDS dataset provides a comprehensive collection of Terraform programs written in the HashiCorp Configuration Language (HCL). As Infrastructure as Code (IaC) gains popularity for managing cloud infrastructure, Terraform has become one of the leading tools due to its declarative nature and widespread adoption. However, a lack of publicly available, large-scale datasets has hindered systematic research on Terraform practices. TerraDS addresses this gap by compiling metadata and source code from 62,406 open-source repositories with valid licenses. This dataset aims to foster research on best practices, vulnerabilities, and improvements in IaC methodologies.

Structure of the Database

The TerraDS dataset is organized into two main components: a SQLite database containing metadata and an archive of source code (~335 MB). The metadata, captured in a structured format, includes information about repositories, modules, and resources:

1. Repository Data:

• Contains 62,406 repositories with fields such as repository name, creation date, star count, and permissive license details.
• Provides cloneable URLs for access and analysis.
• Tracks additional metrics like repository size and the latest commit details.

2. Module Data:

• Consists of 279,344 modules identified within the repositories.
• Each module includes its relative path, referenced providers, and external module calls stored as JSON objects.

3. Resource Data:

• Encompasses 1,773,991 resources, split into managed (1,484,185) and data (289,806) resources.
• Each resource entry details its type, provider, and whether it is managed or read-only.

Structure of the Archive

The provided archive contains the source code of the 62,406 repositories to allow further analysis based on the actual source instead of the metadata only. As such, researcher can access the permissive repositories and conduct studies on the executable HCL code.

Tools

The "HCL Dataset Tools" file contains a snapshot of the https://github.com/prg-grp/hcl-dataset-tools repository - for long term archival reasons. The tools in this repository can be used to reproduce this dataset.

One of the tools - "RepositorySearcher" - can be used to fetch metadata for various other GitHub API queries, not only Terraform code. While the RepositorySearcher allows usage for other types of repository search, the other tools provided are focused on Terraform repositories.

Citation: If using this dataset please cite the following in your work: @misc{VotDasNemSri2010 , author = "Petr Votava and Kamalika Das and Rama Nemani and Ashok N. Srivastava", year = "2010", title = "MODIS surface reflectance data repository", url = "https://c3.ndc.nasa.gov/dashlink/resources/331/", institution = "NASA Ames Research Center" } Petr Votava, Kamalika Das, Rama Nemani, Ashok N. Srivastava. (2010). MODIS surface reflectance data repository. NASA Ames Research Center. Data Description: The California satellite dataset using the MODerate-resolution Imaging Spectroradiometer (MODIS) product MCD43A4 provides reflectance data adjusted using a bidirectional reflectance distribution function (BRDF) to model the values as if they were taken from nadir view. Both Terra and Aqua data are used in the generation of this product, providing the highest probability for quality input data. More information at: https://lpdaac.usgs.gov/lpdaac/products/modis_products_table/nadir_brdf_adjusted_reflectance/16_day_l3_global_500m/v5/combined Data Organization: The nine data folders correspond to three years of data.Under this top level directory structure are separate files for each band (1 - 7) and each 8-day period of the particular year. Within the period the best observations were selected for each location. File Naming Conventions: Each of the files represent a 2D dataset with the naming conventions as follows: MCD43A4.CA_1KM.005.. .flt32 where is the beginning year-day of the period that where YYYY = year and DDD = day of year (001 - 366) represents the observations in particular (spectral) band (band 1 - band 7) - since the indexing is 0-based, the range of indexes on the files is from 0 - 6 (where 0 = band 1, and 6 = band 7) The spectral band frequencies for the MODIS acquisitions are as follows: BAND1 620 - 670 nm BAND2 841 - 876 nm BAND3 459 - 479 nm BAND4 545 - 565 nm BAND5 1230 - 1250 nm BAND6 1628 - 1652 nm BAND7 2105 - 2155 nm File Specifications: Each file is a single 2D dataset. DATA TYPE: 32-bit floating point (IEEE754) with little-Endian byte ordering NUMBER OF ROWS: 1203 NUMBER OF COLUMNS: 738 FILL VALUES (observations that are either not valid or not on land, such as ocean etc.): -999.0 Overview: DATASET: MODIS 8-day Surface Reflectance BRDF-adjusted from Terra and Aqua COLLECTION: 5 DATA TYPE: IEEE754 float (32-bit float) BYTE ORDER: LITTLE ENDIAN (Intel) DIMS: 1203 rows x 738 columns FILL VALUE: -999.0 SPATIAL RESOLUTION: 1km PROJECTION: Lambert Azimuthal Equal Area

A total of 12 software defect data sets from NASA were used in this study, where five data sets (part I) including CM1, JM1, KC1, KC2, and PC1 are obtained from PROMISE software engineering repository (http://promise.site.uottawa.ca/SERepository/), the other seven data sets (part II) are obtained from tera-PROMISE Repository (http://openscience.us/repo/defect/mccabehalsted/).

This dataset is a compilation of several single-beam bathymetry surveys of the Caribbean ocean displaying a wide range of tectonic activity, uneven distribution, and even clear systematic errors in some of the survey lines.

Note: This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the Fatiando a Terra project. Please cite the original authors when using this dataset.

Changes made: Convert from MGD77 to a simpler compressed CSV format. Retain only the survey ID, coordinates, and depth. Cut the data to a slightly smaller region.

Source: NOAA NCEI

Source license: public domain

Repository: https://github.com/fatiando-data/caribbean-bathymetry

This data repository includes a 1:1,000,000 scale ArcGIS 10.5 map of geologic units, craters, other structures, and valleys in a study area in Terra Sabaea, Mars, bounded by 19–22°S, 40.4–45.4°E. Base image mosaics are included. The study area has wind-eroded crater floors and rims with a variety of mineralogical compositions, as described in the paper.

This is a public domain compilation of ground measurements of gravity from Southern Africa. The observations are the absolute gravity values in mGal. The horizontal datum is not specified and heights are referenced to "sea level", which we will interpret as the geoid (which realization is likely not relevant since the uncertainty in the height is probably larger than geoid model differences).

Note: This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the Fatiando a Terra project. Please cite the original authors when using this dataset.

Changes made: Keep only coordinates, absolute gravity, and the (sea-level) observation height. Remove some points below sea-level (a bit suspicious and are potentially flawed heights from shipborne measurements). Convert from a custom text format to compressed CSV.

Source: NOAA NCEI

Source license: public domain

Repository: https://github.com/fatiando-data/southern-africa-gravity

Binary Engineering production data for August 2014. Used to demonstrate the techno-economic feasibility of utilizing the available unused heat to generate additional electric power from a binary power plant.

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

[Adapted from "About the GSA Data Repository", "http://www.geosociety.org/pubs/drpint.htm#aboutdrp"]

The above data set may be dowloaded from the Geological Society of America (GSA) Data Repository, which is located at the following URL:"http://www.geosociety.org/pubs/drpint.htm".

The GSA Data Repository is an open file where authors of articles in GSA journals can place information that supplements and expands on their article, will not appear in print, but may be obtained from GSA.

The abstract for the corresponding article, Multistage, multidirectional Tertiary shortening and compression in north-central New Mexico, may be viewed at the URL below.

[Adapted from "About the GSA Data Repository", "http://www.geosociety.org/pubs/drpint.htm#aboutdrp"]

The above data set may be dowloaded from the Geological Society of America (GSA) Data Repository, which is located at the following URL:"http://www.geosociety.org/pubs/drpint.htm".

The GSA Data Repository is an open file where authors of articles in GSA journals can place information that supplements and expands on their article, will not appear in print, but may be obtained from GSA.

The abstract for the corresponding article, "Tectonic setting of the plutonic belts of Yakutia, northeast Russia, based on 40Ar/39Ar geochronology and trace element geochemistry", may be viewed at the URL below.

Global 10 arc-minute resolution grids of the amplitude of the gravity acceleration (gravitational + centrifugal) of the Moon at a constant height. Generated from the spherical harmonic model GRGM1200B (Goossens et al., 2019).

Note: This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the Fatiando a Terra project. Please cite the original authors when using this dataset.

Changes made:

• Expanded the spherical harmonic model into a grid.
• Replaced the GM, radius, and angular velocity of the model the those from Wieczorek (2015).
• Generate the grid at a constant height of 11 km above the reference lunar sphere to guarantee the data are outside the topographic masses.

Source: Goossens, S., Sabaka, T. J., Wieczorek, M. A., Neumann, G. A., Mazarico, E., Lemoine, F. G., et al. (2020). High‐Resolution Gravity Field Models from GRAIL Data and Implications for Models of the Density Structure of the Moon's Crust. Journal of Geophysical Research: Planets. doi:10.1029/2019je006086 ; NASA's Planetary Geology, Geophysics and Geochemistry Laboratory

Source license: public domain

Repository: https://github.com/fatiando-data/moon-gravity-10arcmin

This U.S. Geological Survey (USGS) metadata release consists of 17 different spatial layers in GeoTIFF format. They are: 1) average water capacity (AWC.zip), 2) percent sand (Sand.zip), 3) percent silt (Silt.zip), 4) percent clay (Clay.zip), 5) soil texture (TEXT_PRMS.zip), 6) land use/land cover (LULC.zip), 7) snow values (Snow.zip), 8) summer rain values (SRain.zip), 9) winter rain values (WRain.zip), 10) leaf presence values (keep.zip), 11) leaf loss values (loss.zip), 12) percent tree canopy (CNPY.zip), 13) percent impervious surface (Imperv.zip), 14) snow depletion curve numbers (Snow.zip), 15) rooting depth (RootDepth.zip), 16) permeability values (Lithology_exp_Konly_Project.zip), and 17) water bodies. All data cover the National Hydrologic Model's (NHM) version 1.1 domain. The NHM is a modeling infrastructure consisting of three main parts: 1) an underlying geospatial fabric of modeling units (hydrologic response units and stream segments) with an associated parameter database, 2) a model input data archive, and 3) a repository of the physical model simulation code bases (Regan and others, 2014). The NHM has been used for a variety of applications since its initial development.The 250-meter (m) raster data sets for soils are derived from the OpenGeoHub's LandGIS data (Hengl, 2018). The 30-meter raster of land use and land cover data are a simplified re-classification version of the North American Land-Change Monitoring System (NALCMS, Latifovic and others, 2012) data following the guidance in Viger and Leavesley (2007). This layer was used to derive rasters representing dominant vegetative cover type, snow, summer and winter rain interception values, leaf cover and loss, and rooting depth. The impervious data was compiled from the Global Man-made Impervious Surface (GMIS) Dataset from Landsat, v1 (NASA, 2010). The tree canopy data was compiled from MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006, (Carroll and others, 2017). The snow depletion data was compiled from data by Liston and others (2009) and further processed using methods by Sexstone and others (2020). All file formats are in GeoTIFF (Geograhpic Tagged Imaged Format).

Dixie Valley production data for January 2014, for a DOE Report. Used to demonstrate the techno-economic feasibility of utilizing the available unused heat to generate additional electric power from a binary power plant. *Note - This data is incomplete. See link below "Monthly Production Data September 2014" for more complete data set.

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/

This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

This is a topography point cloud of the 2018 lava flows of the Sierra Negra volcano, located on the Galápagos islands, Ecuador. The data are generated using structure from motion (SFM) and shows nice topographic features and different roughness of the lava flows. Good to show examples of calculating slope and other terrain properties from the point cloud or gridded data.

Note: This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the Fatiando a Terra project. Please cite the original authors when using this dataset.

Changes made: Data were cropped to smaller region to align with previously published studies of the data and make file sizes under 10 Mb. Coordinates converted from UTM to WGS84 geographic. Export to a compressed CSV for easier loading with Pandas.

Source: Carr, B. (2020). Sierra Negra Volcano (TIR Flight 3): Galápagos, Ecuador, October 22 2018. Distributed by OpenTopography. https://doi.org/10.5069/G957196P

Additional reference: Carr, B. B., Lev, E., Sawi, T., Bennett, K. A., Edwards, C. S., Soule, S. A., et al. (2021). Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2021.112581

Source license: CC-BY

Repository: https://github.com/fatiando-data/sierra-negra-topography

This is a digitized version of an airborne magnetic survey of Britain. Data are sampled where flight lines crossed contours on the archive maps. Contains only the total field magnetic anomaly, not the magnetic field intensity measurements or corrections.

Note: This is a processed and formatted version of the source dataset below. It's mean for use in documentation and tutorials of the Fatiando a Terra project. Please cite the original authors when using this dataset.

Changes made: Datum was changed to WGS8; Year was separated from the survey name; Some fields were dropped; Exported to compressed CSV format.

Source: British Geological Survey

Source license: Open Government Licence

Repository: https://github.com/fatiando-data/britain-magnetic

Contains British Geological Survey materials © UKRI 2021.

Dataset for: Bedding scale correlation on Mars in western Arabia Terra

A.M. Annex et al.

Data Product Overview

This repository contains all source data for the publication. Below is a description of each general data product type, software that can load the data, and a list of the file names along with the short description of the data product.

HiRISE Digital Elevation Models (DEMs).

HiRISE DEMs produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*X_0_DEM-adj.tif’, the “X” prefix denotes the spatial resolution of the data product in meters. Geotiff files are able to be read by free GIS software like QGIS.

HiRISE map-projected imagery (DRGs).

Map-projected HiRISE images produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*0_Y_DRG-cog.tif’, the “Y” prefix denotes the spatial resolution of the data product in centimeters. Geotiff files are able to be read by free GIS software like QGIS. The DRG files are formatted as COG-geotiffs for enhanced compression and ease of use.

3D Topography files (.ply).

Traingular Mesh versions of the HiRISE/CTX topography data used for 3D figures in “.ply” format. Meshes are greatly geometrically simplified from source files. Topography files can be loaded in a variety of open source tools like ParaView and Meshlab. Textures can be applied using embedded texture coordinates.

3D Geological Model outputs (.vtk)

VTK 3D file format files of model output over the spatial domain of each study site. VTK files can be loaded by ParaView open source software. The “block” files contain the model evaluation over a regular grid over the model extent. The “surfaces” files contain just the bedding surfaces as interpolated from the “block” files using the marching cubes algorithm.

Geological Model geologic maps (geologic_map.tif).

Geologic maps from geological models are standard geotiffs readable by conventional GIS software. The maximum value for each geologic map is the “no-data” value for the map. Geologic maps are calculated at a lower resolution than the topography data for storage efficiency.

Beds Geopackage File (.gpkg).

Geopackage vector data file containing all mapped layers and associated metadata including dip corrected bed thickness as well as WKB encoded 3D linestrings representing the sampled topography data to which the bedding orientations were fit. Geopackage files can be read using GIS software like QGIS and ArcGIS as well as the OGR/GDAL suite. A full description of each column in the file is provided below.

    Column
    Type
    Description




    uuid
    String
    unique identifier


    stratum_order
    Real
    0-indexed bed order


    section
    Real
    section number


    layer_id
    Real
    bed number/index


    layer_id_bk
    Real
    unused backup bed number/index


    source_raster
    String
    dem file path used


    raster
    String
    dem file name


    gsd
    Real
    ground sampling distant for dem


    wkn
    String
    well known name for dem


    rtype
    String
    raster type


    minx
    Real
    minimum x position of trace in dem crs


    miny
    Real
    minimum y position of trace in dem crs


    maxx
    Real
    maximum x position of trace in dem crs


    maxy
    Real
    maximum y position of trace in dem crs


    method
    String
    internal interpolation method


    sl
    Real
    slope in degrees


    az
    Real
    azimuth in degrees


    error
    Real
    maximum error ellipse angle


    stdr
    Real
    standard deviation of the residuals


    semr
    Real
    standard error of the residuals


    X
    Real
    mean x position in CRS


    Y
    Real
    mean y position in CRS


    Z
    Real
    mean z position in CRS


    b1
    Real
    plane coefficient 1


    b2
    Real
    plane coefficient 2


    b3
    Real
    plane coefficient 3


    b1_se
    Real
    standard error plane coefficient 1


    b2_se
    Real
    standard error plane coefficient 2


    b3_se
    Real
    standard error plane coefficient 3


    b1_ci_low
    Real
    plane coefficient 1 95% confidence interval low


    b1_ci_high
    Real
    plane coefficient 1 95% confidence interval high


    b2_ci_low
    Real
    plane coefficient 2 95% confidence interval low


    b2_ci_high
    Real
    plane coefficient 2 95% confidence interval high


    b3_ci_low
    Real
    plane coefficient 3 95% confidence interval low


    b3_ci_high
    Real
    plane coefficient 3 95% confidence interval high


    pca_ev_1
    Real
    pca explained variance ratio pc 1


    pca_ev_2
    Real
    pca explained variance ratio pc 2


    pca_ev_3
    Real
    pca explained variance ratio pc 3


    condition_number
    Real
    condition number for regression


    n
    Integer64
    number of data points used in regression


    rls
    Integer(Boolean)
    unused flag


    demeaned_regressions
    Integer(Boolean)
    centering indicator


    meansl
    Real
    mean section slope


    meanaz
    Real
    mean section azimuth


    angular_error
    Real
    angular error for section


    mB_1
    Real
    mean plane coefficient 1 for section


    mB_2
    Real
    mean plane coefficient 2 for section


    mB_3
    Real
    mean plane coefficient 3 for section


    R
    Real
    mean plane normal orientation vector magnitude


    num_valid
    Integer64
    number of valid planes in section


    meanc
    Real
    mean stratigraphic position


    medianc
    Real
    median stratigraphic position


    stdc
    Real
    standard deviation of stratigraphic index


    stec
    Real
    standard error of stratigraphic index


    was_monotonic_increasing_layer_id
    Integer(Boolean)
    monotonic layer_id after projection to stratigraphic index


    was_monotonic_increasing_meanc
    Integer(Boolean)
    monotonic meanc after projection to stratigraphic index


    was_monotonic_increasing_z
    Integer(Boolean)
    monotonic z increasing after projection to stratigraphic index


    meanc_l3sigma_std
    Real
    lower 3-sigma meanc standard deviation


    meanc_u3sigma_std
    Real
    upper 3-sigma meanc standard deviation


    meanc_l2sigma_sem
    Real
    lower 3-sigma meanc standard error


    meanc_u2sigma_sem
    Real
    upper 3-sigma meanc standard error


    thickness
    Real
    difference in meanc


    thickness_fromz
    Real
    difference in Z value


    dip_cor
    Real
    dip correction


    dc_thick
    Real
    thickness after dip correction


    dc_thick_fromz
    Real
    z thickness after dip correction


    dc_thick_dev
    Integer(Boolean)
    dc_thick <= total mean dc_thick


    dc_thick_fromz_dev
    Integer(Boolean)
    dc_thick <= total mean dc_thick_fromz


    thickness_fromz_dev
    Integer(Boolean)
    dc_thick <= total mean thickness_fromz


    dc_thick_dev_bg
    Integer(Boolean)
    dc_thick <= section mean dc_thick


    dc_thick_fromz_dev_bg
    Integer(Boolean)
    dc_thick <= section mean dc_thick_fromz


    thickness_fromz_dev_bg
    Integer(Boolean)
    dc_thick <= section mean thickness_fromz


    slr
    Real
    slope in radians


    azr
    Real
    azimuth in radians


    meanslr
    Real
    mean slope in radians


    meanazr
    Real
    mean azimuth in radians


    angular_error_r
    Real
    angular error of section in radians


    pca_ev_1_ok
    Integer(Boolean)
    pca_ev_1 < 99.5%


    pca_ev_2_3_ratio
    Real
    pca_ev_2/pca_ev_3


    pca_ev_2_3_ratio_ok
    Integer(Boolean)
    pca_ev_2_3_ratio > 15


    xyz_wkb_hex
    String
    hex encoded wkb geometry for all points used in regression

Geological Model input files (.gpkg).

Four geopackage (.gpkg) files represent the input dataset for the geological models, one per study site as specified in the name of the file. The files contain most of the columns described above in the Beds geopackage file, with the following additional columns. The final seven columns (azimuth, dip, polarity, formation, X, Y, Z) constituting the actual parameters used by the geological model (GemPy).

    Column
    Type
    Description




    azimuth_mean
    String
    Mean section dip azimuth 


    azimuth_indi
    Real
    Individual bed azimuth


    azimuth
    Real
    Azimuth of trace used by the geological model


    dip
    Real
    Dip for the trace used by the geological mode


    polarity
    Real
    Polarity of the dip vector normal vector 


    formation
    String
    String representation of layer_id required for GemPy models


    X
    Real
    X position in the CRS of the sampled point on the trace


    Y
    Real
    Y position in the CRS of the sampled point on the trace


    Z
    Real
    Z position in the CRS of the sampled point on the trace

Stratigraphic Column Files (.gpkg).

Stratigraphic columns computed from the Geological Models come in three kinds of Geopackage vector files indicated by the postfixes _sc, rbsc, and rbssc. File names include the wkn site name.

sc (_sc.gpkg).

Geopackage vector data file containing measured bed thicknesses from Geological Model joined with corresponding Beds Geopackage file, subsetted partially. The columns largely overlap with the the list above for the Beds Geopackage but with the following additions

    Column
    Type
    Description




    X
    Real
    X position of thickness measurement


    Y
    Real
    Y position of thickness measurement


    Z
    Real
    Z position of thickness measurement


    formation
    String
    Model required string representation of bed index


    bed thickness (m)
    Real
    difference of bed elevations


    azimuths
    Real
    azimuth as measured from model in degrees


    dip_degrees
    Real
    dip as measured from model in

[Adapted from "About the GSA Data Repository", "http://www.geosociety.org/pubs/drpint.htm#aboutdrp"]

The above data set may be dowloaded from the Geological Society of America (GSA) Data Repository, which is located at the following URL:"http://www.geosociety.org/pubs/drpint.htm".

The GSA Data Repository is an open file where authors of articles in GSA journals can place information that supplements and expands on their article, will not appear in print, but may be obtained from GSA.

The abstract for the corresponding article, "Origin of late Quaternary dune fields on the Southern High Plains of Texas and New Mexico", may be viewed at the URL below.

Scientists working within the Hawaiian Ocean Time-series (HOT) project, https://hahana.soest.hawaii.edu/, have been making repeated observations of the hydrography, chemistry and biology at a station north of Hawaii since October 1988. The objective of this research is to provide a comprehensive description of the ocean at a site representative of the central North Pacific Ocean. Cruises are made approximately once a month to Station ALOHA, the HOT deep-water station (22 45'N, 158W) located about 100 km north of Oahu, Hawaii. Measurements of the thermohaline structure, water column chemistry, currents, primary production and particle sedimentation rates are made over a 72-hour period on each cruise.

        Thermosalinograph data have been obtained since cruise HOT-63 using a
        SBE-21 Seacat thermosalinograph system. Data are recorded
        every 10 seconds from water collected by a continuous seawater system aboard
        R/V Moana Wave from a depth of about 3 meters. These data are processed and
        quality controlled. Details of the thermosalinograph processing can be
        found in HOT Data Report #7 (see the file Readme.first for information
        on how to obtain the Data Report).

        Data consist of time, temperature and salinity for
        every sample interval recorded. Error flags for temperature and salinity were
        incorporated to each data record beginning with cruise HOT-72. Details
        of this format are given in the file `Readme.ts.format'. The data
        records are written so that they can be read with a simple FORTRAN
        read statement.

        Navigation data are also included in the thermosalinograph data file.
        The navigation data are recorded every minute from the Global
        Positioning System (GPS) aboard R/V Moana Wave and linearly interpolated at the
        same times of the thermosalinograph record. These data include latitude and
        longitude. Data exist in ASCII files which can be read by all
        users. There is one file per cruise containing the thermosalinograph
        and navigation data. The file name is based on cruise name and
        number. Thus, hot63ts.dat contains the data from HOT-63. Data file names
        from ALOHA-Climax (AC) cruises have the prefix ac,e.g. ac1ts.dat
        contains data from AC-1 cruise.

        The continuous flow of these data depends on funding, and that depends
        in part on the credits that we get from the data users. If
        you use these data in your project, please contact Dr. Roger Lukas to
        avoid possible duplication of efforts. Please consider the
        benefits of possible scientific collaborations in the analysis of
        these data. If you use our data in your project, we would appreciate your
        acknowledgement of the HOT project.The following NSF grant number
        should be cited in publications: OCE-9303094. A preprint and reprint
        of the publications utilizing HOT data would be appreciated.