This Python script (Shape2DJI_Pilot_KML.py) will scan a directory, find all the ESRI shapefiles (.shp), reproject to EPSG 4326 (geographic coordinate system WGS84 ellipsoid), create an output directory and make a new Keyhole Markup Language (.kml) file for every line or polygon found in the files. These new *.kml files are compatible with DJI Pilot 2 on the Smart Controller (e.g., for M300 RTK). The *.kml files created directly by ArcGIS or QGIS are not currently compatible with DJI Pilot.

Dataset Card for Dataset Name

Reformatted from stanfordnlp/SHP dataset. To make it consistent with other preference dsets, we:

convert upvotes to scores in a [1, 10] scale. This is achieved by 1) convert the better response's upvotes to score of [5.0, 10.0] by:def shp_map_score(score, threshold=78): # 78 is chosen because about the best 10% data has score > 78 if score > threshold: return 10.0 # linearly map the rest # start with 5.0 because we assume that any… See the full description on the dataset page: https://huggingface.co/datasets/when2rl/SHP_reformatted.

Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

Parcels and property data maintained and provided by Lee County Property Appraiser are converted to points. Property attribute data joined to parcel GIS layer by Lee County Government GIS. This dataset is generally used in spatial analysis.Process description: Parcel polygons, condominium points and property data provided by the Lee County Property Appraiser are processed by Lee County's GIS Department using the following steps:Join property data to parcel polygons Join property data to condo pointsConvert parcel polygons to points using ESRI's ArcGIS tool "Feature to Point" and designate the "Source" field "P".Load Condominium points into this layer and designate the "Source" field "C". Add X/Y coordinates in Florida State Plane West, NAD 83, feet using the "Add X/Y" tool.Projected coordinate system name: NAD_1983_StatePlane_Florida_West_FIPS_0902_FeetGeographic coordinate system name: GCS_North_American_1983

 Name
 Type
 Length
 Description


 STRAP
 String
 25
 17-digit Property ID (Section, Township, Range, Area, Block, Lot)


 BLOCK
 String
 10
 5-digit portion of STRAP (positions 9-13)


 LOT
 String
 8
 Last 4-digits of STRAP


 FOLIOID
 Double
 8
 Unique Property ID


 MAINTDATE
 Date
 8
 Date LeePA staff updated record


 MAINTWHO
 String
 20
 LeePA staff who updated record


 UPDATED
 Date
 8
 Data compilation date


 HIDE_STRAP
 String
 1
 Confidential parcel ownership


 TRSPARCEL
 String
 17
 Parcel ID sorted by Township, Range & Section


 DORCODE
 String
 2
 Department of Revenue. See https://leepa.org/Docs/Codes/DOR_Code_List.pdf


 CONDOTYPE
 String
 1
 Type of condominium: C (commercial) or R (residential)


 UNITOFMEAS
 String
 2
 Type of Unit of Measure (ex: AC=acre, LT=lot, FF=frontage in feet)


 NUMUNITS
 Double
 8
 Number of Land Units (units defined in UNITOFMEAS)


 FRONTAGE
 Integer
 4
 Road Frontage in Feet


 DEPTH
 Integer
 4
 Property Depth in Feet


 GISACRES
 Double
 8
 Total Computed Acres from GIS


 TAXINGDIST
 String
 3
 Taxing District of Property


 TAXDISTDES
 String
 60
 Taxing District Description


 FIREDIST
 String
 3
 Fire District of Property


 FIREDISTDE
 String
 60
 Fire District Description


 ZONING
 String
 10
 Zoning of Property


 ZONINGAREA
 String
 3
 Governing Area for Zoning


 LANDUSECOD
 SmallInteger
 2
 Land Use Code


 LANDUSEDES
 String
 60
 Land Use Description


 LANDISON
 String
 5
 BAY,CANAL,CREEK,GULF,LAKE,RIVER & GOLF


 SITEADDR
 String
 55
 Lee County Addressing/E911


 SITENUMBER
 String
 10
 Property Location - Street Number


 SITESTREET
 String
 40
 Street Name


 SITEUNIT
 String
 5
 Unit Number


 SITECITY
 String
 20
 City


 SITEZIP
 String
 5
 Zip Code


 JUST
 Double
 8
 Market Value


 ASSESSED
 Double
 8
 Building Value + Land Value


 TAXABLE
 Double
 8
 Taxable Value


 LAND
 Double
 8
 Land Value


 BUILDING
 Double
 8
 Building Value


 LXFV
 Double
 8
 Land Extra Feature Value


 BXFV
 Double
 8
 Building Extra Feature value


 NEWBUILT
 Double
 8
 New Construction Value


 AGAMOUNT
 Double
 8
 Agriculture Exemption Value


 DISAMOUNT
 Double
 8
 Disability Exemption Value


 HISTAMOUNT
 Double
 8
 Historical Exemption Value


 HSTDAMOUNT
 Double
 8
 Homestead Exemption Value


 SNRAMOUNT
 Double
 8
 Senior Exemption Value


 WHLYAMOUNT
 Double
 8
 Wholly Exemption Value


 WIDAMOUNT
 Double
 8
 Widow Exemption Value


 WIDRAMOUNT
 Double
 8
 Widower Exemption Value


 BLDGCOUNT
 SmallInteger
 2
 Total Number of Buildings on Parcel


 MINBUILTY
 SmallInteger
 2
 Oldest Building Built


 MAXBUILTY
 SmallInteger
 2
 Newest Building Built


 TOTALAREA
 Double
 8
 Total Building Area


 HEATEDAREA
 Double
 8
 Total Heated Area


 MAXSTORIES
 Double
 8
 Tallest Building on Parcel


 BEDROOMS
 Integer
 4
 Total Number of Bedrooms


 BATHROOMS
 Double
 8
 Total Number of Bathrooms / Not For Comm


 GARAGE
 String
 1
 Garage on Property 'Y'


 CARPORT
 String
 1
 Carport on Property 'Y'


 POOL
 String
 1
 Pool on Property 'Y'


 BOATDOCK
 String
 1
 Boat Dock on Property 'Y'


 SEAWALL
 String
 1
 Sea Wall on Property 'Y'


 NBLDGCOUNT
 SmallInteger
 2
 Total Number of New Buildings on ParcelTotal Number of New Buildings on Parcel


 NMINBUILTY
 SmallInteger
 2
 Oldest New Building Built


 NMAXBUILTY
 SmallInteger
 2
 Newest New Building Built


 NTOTALAREA
 Double
 8
 Total New Building Area


 NHEATEDARE
 Double
 8
 Total New Heated Area


 NMAXSTORIE
 Double
 8
 Tallest New Building on Parcel


 NBEDROOMS
 Integer
 4
 Total Number of New Bedrooms


 NBATHROOMS
 Double
 8
 Total Number of New Bathrooms/Not For Comm


 NGARAGE
 String
 1
 New Garage on Property 'Y'


 NCARPORT
 String
 1
 New Carport on Property 'Y'


 NPOOL
 String
 1
 New Pool on Property 'Y'


 NBOATDOCK
 String
 1
 New Boat Dock on Property 'Y'


 NSEAWALL
 String
 1
 New Sea Wall on Property 'Y'


 O_NAME
 String
 30
 Owner Name


 O_OTHERS
 String
 120
 Other Owners


 O_CAREOF
 String
 30
 In Care Of Line


 O_ADDR1
 String
 30
 Owner Mailing Address Line 1


 O_ADDR2
 String
 30
 Owner Mailing Address Line 2


 O_CITY
 String
 30
 Owner Mailing City


 O_STATE
 String
 2
 Owner Mailing State


 O_ZIP
 String
 9
 Owner Mailing Zip


 O_COUNTRY
 String
 30
 Owner Mailing Country


 S_1DATE
 Date
 8
 Most Current Sale Date > $100.00


 S_1AMOUNT
 Double
 8
 Sale Amount


 S_1VI
 String
 1
 Sale Vacant or Improved


 S_1TC
 String
 2
 Sale Transaction Code


 S_1TOC
 String
 2
 Sale Transaction Override Code


 S_1OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_2DATE
 Date
 8
 Previous Sale Date > $100.00


 S_2AMOUNT
 Double
 8
 Sale Amount


 S_2VI
 String
 1
 Sale Vacant or Improved


 S_2TC
 String
 2
 Sale Transaction Code


 S_2TOC
 String
 2
 Sale Transaction Override Code


 S_2OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_3DATE
 Date
 8
 Next Previous Sale Date > $100.00


 S_3AMOUNT
 Double
 8
 Sale Amount


 S_3VI
 String
 1
 Sale Vacant or Improved


 S_3TC
 String
 2
 Sale Transaction Code


 S_3TOC
 String
 2
 Sale Transaction Override Code


 S_3OR_NUM
 String
 13
 Original Record (Lee County Clerk)


 S_4DATE
 Date
 8
 Next Previous Sale Date > $100.00


 S_4AMOUNT
 Double
 8
 Sale Amount


 S_4VI
 String
 1
 Sale Vacant or Improved


 S_4TC
 String
 2
 Sale Transaction Code


 S_4TOC
 String
 2
 Sale Transaction Override Code


 S_4OR_NUM
 String
 13

Hydrographic sheets (H-sheets) and nautical charts produced by the National Ocean Service (NOS) during the 1800s provide historic sounding (water depth) measurements of coastal areas. The data can be vectorized into a geographic information system (GIS), adjusted to a modern vertical datum, and converted into a digital elevation model to provide an interpretation of the historic seafloor elevation. These data were produced to provide an estimate of historical bathymetry for the Mississippi-Alabama coastal region to aid geologic and coastal hazards studies. This data release includes georeferenced H-sheets, depth soundings, and a bathymetric grid derived from the 1847 and 1895 soundings. The original NOS H-sheets and nautical charts were scanned by the National Oceanic and Atmospheric Administration (NOAA) and are available through the National Geophysical Data Center (NGDC) website (NOAA, 2021) as non-georeferenced digital raster files. U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) staff performed the following procedures: H-sheets were georeferenced, georeferenced raster images were projected to a modern datum, and historical bathymetric sounding measurements were digitized to create a vector point shapefile. Sounding data were converted from feet (ft) and fathoms (fm) to meters (m), projected to modern mean low water (MLW), and converted to the North American Vertical Datum of 1988 (NAVD88) GEOID12A using NOAA's datum transformation software, VDatum. Please read the full metadata for details on data collection, digitized data, dataset variables, and data quality.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

VDatum is designed to vertically transform geospatial data among a variety of tidal, orthometric and ellipsoidal vertical datums - allowing users to convert their data from different horizontal/vertical references into a common system and enabling the fusion of diverse geospatial data in desired reference levels.This particular layer allows you to convert from NAVD 88 to MHHW.Units: metersThese data are a derived product of the NOAA VDatum tool and they extend the tool's Mean Higher High Water (MHHW) tidal datum conversion inland beyond its original extent.VDatum was designed to vertically transform geospatial data among a variety of tidal, orthometric and ellipsoidal vertical datums - allowing users to convert their data from different horizontal/vertical references into a common system and enabling the fusion of diverse geospatial data in desired reference levels (https://vdatum.noaa.gov/). However, VDatum's conversion extent does not completely cover tidally-influenced areas along the coast. For more information on why VDatum does not provide tidal datums inland, see https://vdatum.noaa.gov/docs/faqs.html.Because of the extent limitation and since most inundation mapping activities use a tidal datum as the reference zero (i.e., 1 meter of sea level rise on top of Mean Higher High Water), the NOAA Office for Coastal Management created this dataset for the purpose of extending the MHHW tidal datum beyond the areas covered by VDatum. The data do not replace VDatum, nor do they supersede the valid datum transformations VDatum provides. However, the data are based on VDatum's underlying transformation data and do provide an approximation of MHHW where VDatum does not provide one. In addition, the data are in a GIS-friendly format and represent MHHW in NAVD88, which is the vertical datum by which most topographic data are referenced.Data are in the UTM NAD83 projection. Horizontal resolution varies by VDatum region, but is either 50m or 100m. Data are vertically referenced to NAVD88 meters.More information about the NOAA VDatum transformation and associated tools can be found here.

SFT Format Dataset

  Overview

This dataset is converted to SFT (Supervised Fine-Tuning) format. It was created by transforming OpenMathInstruct and Stanford Human Preferences (SHP) datasets.

  Dataset Structure

Each entry follows this format: Instruction: [Problem, question, or conversation history] Response: [Solution, answer, or response]

  Usage Guide





  Loading the Dataset

from datasets import load_dataset

Load datasets from Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/Seono/sft-shp-train.

The thickness contours were created using biostratigraphic data in the Paleo-Data, Inc., Tenroc Regional Geologic Database. The depths of the microfossil locations were associated with the wells data provided by the Louisiana Department of Natural Resources. Because of the proprietary nature of the Tenroc database, no actual data can be shown and only those data points contained in the Louisiana State wells database are included in the control points layer. Contouring was accomplished in Dynamic Graphics, Inc., EarthVision modeling software (v.5) using minimum tension gridding. Three custom programs were used to convert contour lines generated from grids in EarthVision to Arc/Info coverages and then to shapefiles. The data are provided as both lines and polygons (umthicklg.shp and umthickpg.shp), and the public wells that penetrate the entire thickness of the Upper Miocene sequence are provided in a point shapefile (umthickptg.shp). These datasets contain basic data and inter ...

Seychelles Ecosystem Services: Seagrass and Mangrove Blue CarbonMangroves and seagrasses represent rich sources of blue carbon, that is carbon stored and sequestered by coastal and marine ecosystems. In mangroves, carbon is stored and sequestered in living aboveground biomass and in the soil.Model OutputsMangrove carbon: Estimates of mangrove carbon have been calculated for Seychelles using the global mangrove map developed by Global Mangrove Watch (GMW) 2016. Unfortunately, the GMW misses several key mangrove areas in the Seychelles, most notably in the Aldabra group. While these errors are being amended in newer versions of the global mangrove map, the GMW base-map is relatively low resolution while the mangrove layer created for the Seychelles MSP (Klaus 2015) provides higher resolution and an estimate of 30.7 km2 of total mangroves. Existing estimates of mangrove carbon for the Seychelles from the global extents are thus major undestimates. To improve estimates of carbon stored in Seychelles’ mangroves, we used the Global Mangrove Watch models of aboveground biomass (AGB) (derived from Simard et al. 2019) and soil organic carbon (SOC) (derived from Sanderman et al. 2018) and applied them to the locally-derived mangrove map layer (Klaus 2015). For areas of the local-scale layer that overlapped with the global carbon estimates, we used zonal statistics to find the mean AGB and SOC values (expressed in MgC per ha) per mangrove polygon. We then multiplied this value by the area (in ha) for each polygon to get the total values AGB and SOC values per polygon. For local-scale mangrove polygons that did not overlap with the global carbon estimates, we used a spatial join to assign the nearest AGB and SOC values to each polygon, and converted the values from MgC per ha by multiplying the value by the polygon area to obtain total AGB and SOC. To convert AGB to aboveground carbon (AGC), we used a conversion factor of 0.451 (Simard et al. 2019); AGC and SOC values were summed to get total carbon values. Seagrass carbon: As no known global or local-scale estimates of seagrass carbon exist for Seychelles, we provide an estimate based on maps of seagrass derived for the MSP (Klaus 2015). These maps assign a density class (high, medium, low) to each mangrove polygon. To estimate the above and belowground biomass for each seagrass polygon, we used aboveground and belowground dry weight biomass estimates per unit area (m2) for low, medium, an high density seagrass from Mallombasi et al. (2020). These biomass values were then converted to carbon using a conversion factor of 0.35 (from Fourqueran et al. 2012) and then converted to total carbon by multiplying by the area of the seagrass polygon. Model Output Datasets Seagrass Blue Carbon Dataset name: Seychelles_Seagrass_Blue_Carbon.shp Dataset type: ESRI File Geodatabase, polygon feature class Values: Estimated seagrass blue carbon (summed by polygon) Field ValuesAGgDWm2 Above-ground dry weight biomass estimates per unit area (meters squared) for low, medium and high density seagrassBGDWm2 Below-ground dry weight biomass estimates per unit area (meters squared) for low, medium and high density seagrassAGMgCha AGgDWm2 converted to aboveground carbon (MgC) per unit area (hectare). Biomass converted to carbon using 0.35 carbon conc from Fourqueran et al (2012)BGMgCha BGgDWm2 converted to aboveground carbon (MgC) per unit area (hectare). Biomass converted to carbon using 0.35 carbon concentration from Fourqueran et al (2012)socMgCha Mean soil organic carbon (Mg) per unit area (hectare) TotMgCha AGC + BGC + SOC per unit area (hectares)TotMgC TotMgCha Mean soil organic carbon (Mg) per unit area (hectare) multiplied by the area estimate of each unique polygon (MgCha * ha)TotTgC TotMgC converted to teragrams (TgC) Mangrove Blue Carbon Dataset name: Seychelles_Mangrove_Blue_Carbon.shp Dataset type: ESRI File Geodatabase, polygon feature class Values: Estimated mangrove blue carbon (summed by polygon) Field ValuesAGC_ton Aboveground carbon, metric tonnesSOC_ton Soil organic carbon, metric tonnesTotal_ton AGC_ton + SOC_tonHa Unique polygon areal estimate in hectares References: Fourqurean, J. W., Duarte, C. M., Kennedy, H., Marbà, N., Holmer, M., Mateo, M. A., ... & Serrano, O. (2012). Seagrass ecosystems as a globally significant carbon stock. Nature geoscience, 5(7), 505-509. Klaus, R. (2015). Strengthening Seychelles ’ protected area system through NGO management modalities. Mallombasi, A., Mashoreng, S., & La Nafie, Y. A. (2020). The relationship between seagrass Thalassia hemprichii percentage cover and their biomass. Jurnal Ilmu Kelautan SPERMONDE, 6(1), 7-10. Palacios, M. M., Waryszak, P., de Paula Costa, M. D., Wartman, M., Ebrahim, A., & Macreadie, P. I. (2021). Literature Review: Blue Carbon research in the Tropical Western Indian Ocean.Simard, M., Fatoyinbo, T., Smetanka, C., Rivera-Monroy, V. H., CASTANEDA, E., Thomas, N., & Van der Stocken, T. (2019). Global Mangrove Distribution, Aboveground Biomass, and Canopy Height. ORNL DAAC. Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C.M. (2018). The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10(10): 1669. doi: 10.3390/rs1010669.

Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A, Simard M. (2017). Distribution and drivers of global mangrove forest change, 1996-2010. PLOS ONE 12: e0179302. doi: 10.1371/journal.pone.0179302

The Office of the Geographer’s Global Large Scale International Boundary Detailed Polygons file combines two datasets, the Office of the Geographer’s Large Scale International Boundary Lines and NGA shoreline data. The LSIB is believed to be the most accurate worldwide (non- W. Europe) international boundary vector line file available. The lines reflect U.S. government (USG) policy and thus not necessarily de facto control. The 1:250,000 scale World Vector Shoreline (WVS) coastline data was used in places and is generally shifted by several hundred meters to over a km. There are no restrictions on use of this public domain data. The Tesla Government PiX team performed topology checks and other GIS processing while merging data sets, created more accurate island shoreline in numerous cases, and worked closely with the US Dept. of State Office of the Geographer on quality control checks.

Methodology: Tesla Government’s Protected Internet Exchange (PiX) GIS team converted the LSIB linework and the island data provided by the State Department to polygons. The LSIB Admin 0 world polygons (Admin 0 polygons) were created by conflating the following datasets: Eurasia_Oceania_LSIB7a_gen_polygons, Africa_Americas_LSIB7a_gen_polygons, Africa_Americas_LSIB7a, Eurasia_LSIB7a, additional updates from LSIB8, WVS shoreline data, and other shoreline data from United States Government (USG) sources. The two simplified polygon shapefiles were merged, dissolved, and converted to lines to create a single global coastline dataset. The two detailed line shapefiles (Eurasia_LSIB7a and Africa_Americas_LSIB7a) were merged with each other and the coastlines to create an international boundary shapefile with coastlines. The dataset was reviewed for the following topological errors: must not self overlap, must not overlap, and must not have dangles. Once all topological errors were fixed, the lines were converted to polygons. Attribution was assigned by exploding the simplified polygons into multipart features, converting to centroids, and spatially joining with the newly created dataset. The polygons were then dissolved by country name. Another round of QC was performed on the dataset through the data reviewer tool to ensure that the conversion worked correctly. Additional errors identified during this process consisted of islands shifted from their true locations and not representing their true shape; these were adjusted using high resolution imagery whereupon a second round of QC was applied with SRTM digital elevation model data downloaded from USGS. The same procedure was performed for every individual island contained in the islands from other USG sources.
After the island dataset went through another round of QC, it was then merged with the Admin 0 polygon shapefile to form a comprehensive world dataset. The entire dataset was then evaluated, including for proper attribution for all of the islands, by the Office of the Geographer.

This layer features special areas of interest (AOIs) that have been contributed to Esri Community Maps using the new Community Maps Editor app. The data that is accepted by Esri will be included in selected Esri basemaps, including our suite of Esri Vector Basemaps, and made available through this layer to export and use offline. Export DataThe contributed data is also available for contributors and other users to export (or extract) and re-use for their own purposes. Users can export the full layer from the ArcGIS Online item details page by clicking the Export Data button and selecting one of the supported formats (e.g. shapefile, or file geodatabase (FGDB)). User can extract selected layers for an area of interest by opening in Map Viewer, clicking the Analysis button, viewing the Manage Data tools, and using the Extract Data tool. To display this data with proper symbology and metadata in ArcGIS Pro, you can download and use this layer file.Data UsageThe data contributed through the Community Maps Editor app is primarily intended for use in the Esri Basemaps. Esri staff will periodically (e.g. weekly) review the contents of the contributed data and either accept or reject the data for use in the basemaps. Accepted features will be added to the Esri basemaps in a subsequent update and will remain in the app for the contributor or others to edit over time. Rejected features will be removed from the app.Esri Community Maps Contributors and other ArcGIS Online users can download accepted features from this layer for their internal use or map publishing, subject to the terms of use below.

This is an exercise on the use of Postal Code Conversion Files (PCCF) with GIS. (Note: Data associated with this exercise is available on the DLI FTP site under folder 1873-299.)

Dataset for provision of cadastral map in digital form in SHP format. Data comes from ISKN (Information System of Cadastre of Real Estates). Cadastral map includes planimetric and descriptive component. Planimetry contains boundaries of parcels, cadastral and administrative units, perimeters of buildings and geodetic control points. Descriptive elements contain lettering (parcel numbers, geographical names etc.), map symbols (symbols of nature of land use etc.) and lines (boundaries of protected zones etc.). Some information of digital map is omitted during the data conversion into SHP format (information on point number, quality code linked to planimetry points, line symbols etc.). Dataset is provided as Open Data (licence CC-BY 4.0). Data is based on ISKN (Information System of the Cadastre of Real Estates). Cadastral map is provided via cadastral units using JTSK coordinate system (EPSG:5514). Data is available for cadastral units with digital form of cadastral map only - (to the 2025-07-07 it is 99.34% of the territory of the Czech Republic, i.e. 78 342.59km2). Data is provided in SHP format (Windows-1250 character encoding). Dataset is compressed (ZIP) for downloading. More in the Cadastral Act No. 256/2013 Coll., Cadastral Decree No. 357/2013 Coll., Cadastral Decree on Data Provision No. 357/2013 Coll., as amended.

During 1980, a seismic-reflection survey utilizing Uniboom seismics was conducted by the U.S. Geological Survey (USGS) in western Rhode Island Sound aboard the Research Vessel Neecho. This cruise consisted of 2 legs totalling 8 survey days. Data from this survey were recorded in analog form and archived at the USGS. As a result of recent interest in the geology of Rhode Island Sound and in an effort to make the data more readily accessible while preserving the original paper records, the seismic data from this cruise were scanned and converted to TIFF images and SEG-Y data files. Navigation data were converted from LORAN-C time delays to latitudes and longitudes, which are available in ESRI shapefile format and as eastings and northings in space-delimited text format.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset contains 4 different scale GEODATA TOPO series, Geoscience Australia topographic datasets. 1M, 2.5M, 5M and 10M with age ranges from 2001 to 2004.

1:1 Million - Global Map Australia 1M 2001 is a digital dataset covering the Australian landmass and island territories, at a 1:1 million scale. Product Specifications -Themes: It consists of eight layers of information: Vector layers - administrative boundaries, drainage, transportation and population centres Raster layers - elevation, vegetation, land use and land cover -Coverage: Australia -Currency: Variable, based on GEODATA TOPO 250K Series 1 -Coordinates: Geographical -Datum: GDA94, AHD -Medium: Free online -Format: -Vector: ArcInfo Export, ESRI Shapefile, MapInfo mid/mif and Vector Product Format (VPF) -Raster: Band Interleaved by Line (BIL)

1:2.5 Million - GEODATA TOPO 2.5M 2003 is a national seamless data product aimed at regional or national applications. It is a vector representation of the Australian landscape as represented on the Geoscience Australia 2.5 million general reference map and is suitable for GIS applications. The product consists of the following layers: built-up areas; contours; drainage; framework; localities; offshore; rail transport; road transport; sand ridges; Spot heights; and waterbodies. It is a vector representation of the Australian landscape as represented on the Geoscience Australia 1:2.5 million scale general reference maps. This data supersedes the TOPO 2.5M 1998 product through the following characteristics: developed according to GEODATA specifications derived from GEODATA TOPO 250K Series 2 data where available. Product Specifications Themes: GEODATA TOPO 2.5M 2003 consists of eleven layers: built-up areas; contours; drainage; framework; localities; offshore; rail transport; road transport; sand ridges; spot heights; and waterbodies Coverage: Australia Currency: 2003 Coordinates: Geographical Datum: GDA94, AHD Format: ArcInfo Export, ArcView Shapefile and MapInfo mid/mif Medium: Free online - Available in ArcInfo Export, ArcView Shapefile and MapInfo mid/mif

1:5 Million - GEODATA TOPO 5M 2004 is a national seamless data product aimed at regional or national applications. It is a vector representation of the Australian landscape as represented on the Geoscience Australia 5 million general reference map and is suitable for GIS applications. Offshore and sand ridge layers were sourced from scanning of the original 1:5 million map production material. The remaining nine layers were derived from the GEODATA TOPO 2.5M 2003 dataset. Free online. Available in ArcInfo Export, ArcView Shapefile and MapInfo mid/mif. Product Specifications: Themes: consists of eleven layers: built-up areas; contours; drainage; framework; localities; offshore; rail transport; road transport; sand ridges, spot heights and waterbodies Coverage: Australia Currency: 2004 Coordinates: Geographical Datum: GDA94, AHD Format: ArcInfo Export, ArcView Shapefile and MapInfo mid/mif Medium: Free online

1:10 Million - The GEODATA TOPO 10M 2002 version of this product has been completely revised, including the source information. The data is derived primarily from GEODATA TOPO 250K Series 1 data. In October 2003, the data was released in double precision coordinates. It provides a fundamental base layer of geographic information on which you can build a wide range of applications and is particularly suited to State-wide and national applications. The data consists of ten layers: built-up areas, contours, drainage, Spot heights, framework, localities, offshore, rail transport, road transport, and waterbodies. Coverage: Australia Currency: 2002 Coordinates: Geographical Datum: GDA94, AHD Format: ArcInfo Export, Arcview Shapefile and MapInfo mid/mif Medium: Free online

Dataset History

1:1Million - Vector data was produced by generalising Geoscience Australia's GEODATA TOPO 250K Series 1 data and updated using Series 2 data where available in January 2001. Raster data was sourced from USGS and updated using GEODATA 9 Second DEM Series 2, 1:5 million, Vegetation - Present (1988) and National Land and Water Resources data. However, updates have not been subjected to thorough vetting. A more detailed land use classification for Australia is available at www.nlwra.gov.au.

Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_48006

1:2.5Million - Data for the Contours, Offshore, and Sand ridge layers was captured from 1:2.5 million scale mapping by scanning stable base photographic film positives of the original map production material. The key source material for Built-up areas, Drainage, Spot heights, Framework, Localities, Rail transport, Road transport and Waterbodies layers was GEODATA TOPO 2.5M 2003

Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_60804

1:5Million - Offshore and Sand Ridge layers have been derived from 1:5M scale mapping by scanning stable base photographic film positives of the various layers of the original map production material. The remaining layers were sourced from the GEODATA TOPO 2.5M 2003 product.

Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_61114

1:10Million - The key source for production of the Builtup Areas, Drainage, Framework, Localities, Rail Transport, Road Transport and Waterbodies layers was the GEODATA TOPO 250K Series 1 product. Some revision of the Builtup Areas, Road Transport, Rail Transport and Waterbodies layers was carried out using the latest available satelite imagery. The primary source for the Spot Heights, Contours and Offshore layers was the GEODATA TOPO 10M Version 1 product. A further element to the production of GEODATA TOPO 10M 2002 has been the datum shift from the Australian Geodetic Datum 1966 (AGD66) to the Geocentric Datum of Australia 1994 (GDA94).

Full Metadata - http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_60803

Dataset Citation

Geoscience Australia (2001) Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale. Bioregional Assessment Source Dataset. Viewed 09 October 2018, http://data.bioregionalassessments.gov.au/dataset/310c5d07-5a56-4cf7-a5c8-63bdb001cd1a.

SFT Format Dataset

  Overview

This dataset is converted to SFT (Supervised Fine-Tuning) format. It was created by transforming OpenMathInstruct and Stanford Human Preferences (SHP) datasets.

  Dataset Structure

Each entry follows this format: Instruction: [Problem, question, or conversation history] Response: [Solution, answer, or response]

  Usage Guide





  Loading the Dataset

from datasets import load_dataset

Load datasets from Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/Seono/sft-shp-eval.

GIS2DJI is a Python 3 program created to exports GIS files to a simple kml compatible with DJI pilot. The software is provided with a GUI. GIS2DJI has been tested with the following file formats: gpkg, shp, mif, tab, geojson, gml, kml and kmz. GIS_2_DJI will scan every file, every layer and every geometry collection (ie: MultiPoints) and create one output kml or kmz for each object found. It will import points, lines and polygons, and converted each object into a compatible DJI kml file. Lines and polygons will be exported as kml files. Points will be converted as PseudoPoints.kml. A PseudoPoints fools DJI to import a point as it thinks it's a line with 0 length. This allows you to import points in mapping missions. Points will also be exported as Point.kmz because PseudoPoints are not visible in a GIS or in Google Earth. The .kmz file format should make points compatible with some DJI mission software.

This dataset contains the location of 32,000 airports around the world. The data is shapefile format (.shp). The dataset contains the name of the airport, its lat/lon, elevation, country and timezone. The 3 letter codes are available in the alternative name field but are mixed in with other information. Data was extracted from the Geonames database and converted to shapefile using GDAL commands in Postgis. All data from GeoNames is distributed under the creative commons attribution 3.0 agreement. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-04-18 and migrated to Edinburgh DataShare on 2017-02-22.