cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p90ohvic
title: Compilation of Public-Supply Well Construction Depths in California

description: This data release is a compilation of construction depth information for 12,383 active and inactive public-supply wells (PSWs) in California from various data sources. Construction data from multiple sources were indexed by the California State Water Resources Control Board Division of Drinking Water (DDW) primary station code (PS Code). Five different data sources were compared with the following priority order: 1, Local sources from select municipalities and water purveyors (Local); 2, Local DDW district data (DDW); 3, The United States Geological Survey (USGS) National Water Information System (NWIS); 4, The California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment Groundwater Information System (SWRCB); and 5, USGS attribution of California Department of Water Resources well completion report data (WCR). For all data sources, the uppermost depth to the well's open or perforated interval was attributed as depth to top of perforations (ToP). The composite depth to bottom of well (Composite BOT) field was attributed from available construction data in the following priority order: 1, Depth to bottom of perforations (BoP); 2, Depth of completed well (Well Depth); 3; Borehole depth (Hole Depth). PSW ToPs and Composite BOTs from each of the five data sources were then compared and summary construction depths for both fields were selected for wells with multiple data sources according to the data-source priority order listed above. Case-by-case modifications to the final selected summary construction depths were made after priority order-based selection to ensure internal logical consistency (for example, ToP must not exceed Composite BOT). This data release contains eight tab-delimited text files. WellConstructionSourceData_Local.txt contains well construction-depth data, Composite BOT data-source attribution, and local agency data-source attribution for the Local data. WellConstructionSourceData_DDW.txt contains well construction-depth data and Composite BOT data-source attribution for the DDW data. WellConstructionSourceData_NWIS.txt contains well construction-depth data, Composite BOT data-source attribution, and USGS site identifiers for the NWIS data. WellConstructionSourceData_SWRCB.txt contains well construction-depth data and Composite BOT data-source attribution for the SWRCB data. WellConstructionSourceData_WCR.txt contains contains well construction depth data and Composite BOT data-source attribution for the WCR data. WellConstructionCompilation_ToP.txt contains all ToP data listed by data source. WellConstructionCompilation_BOT.txt contains all Composite BOT data listed by data source. WellConstructionCompilation_Summary.txt contains summary ToP and Composite BOT values for each well with data-source attribution for both construction fields. All construction depths are in units of feet below land surface and are reported to the nearest foot.
DOI: 10.25625/rzz9vm/oyvc2e
title: 2022-12-RZZ9VM_LIBYA.csv

description: :unav
DOI: 10.25625/rzz9vm/jikeqc
title: 2022-12-RZZ9VM_NAGSSUGTOQIDIAN_OROGEN_BELT.csv

description: :unav
DOI: 10.25625/rzz9vm/bowgte
title: 2022-12-RZZ9VM_MAKKOVIK-KETILIDIAN_OROGENIC_BELT.csv

description: :unav
DOI: 10.25625/rzz9vm/hawwcq
title: 2022-12-RZZ9VM_NEW_ZEALAND.csv

description: :unav
DOI: 10.25625/rzz9vm/2xrxs1
title: 2022-12-RZZ9VM_ANTARCTICA_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/tkoiec
title: 2022-12-RZZ9VM_ANATOLIA-IRAN_BELT_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/8svbez
title: 2022-12-RZZ9VM_SUL_RIO_GRANDENSE_SHIELD_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/kajgly
title: 2022-12-RZZ9VM_ILLINOIS-KENTUCKY_FLUORSPAR_DISTRICT.csv

description: :unav
DOI: 10.25625/rzz9vm/katjj4
title: 2022-12-RZZ9VM_AMAZONIAN_CRATON_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/lijate
title: 2022-12-RZZ9VM_CALEDONIDES.csv

description: :unav
DOI: 10.25625/rzz9vm/sczaig
title: 2022-12-RZZ9VM_NORTH_AMERICAN_CORDILLERA_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/kayezc
title: 2022-12-RZZ9VM_ARAVALLI_CRATON_MESOZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/qkj0md
title: 2022-12-RZZ9VM_CENTRAL_ASIAN_FOLDBELT_CENOZOIC_QUATERNARY_part1.csv

description: :unav
DOI: 10.25625/rzz9vm/m2bjnb
title: 2022-12-RZZ9VM_PARAGUA_CRATON.csv

description: :unav
DOI: 10.25625/rzz9vm/5ecdfv
title: 2022-12-RZZ9VM_ANTARCTICA_PALEOZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/6ajvyt
title: 2022-12-RZZ9VM_NAMAQUALAND_NATAL_BELT_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/8frh1v
title: 2022-12-RZZ9VM_BUNDELKHAND_CRATON_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/laplqf
title: 2022-12-RZZ9VM_ARCTIC_ALASKA-CHUKOTKA_MICROPLATE.csv

description: :unav
DOI: 10.25625/rzz9vm/ub3b7d
title: 2022-12-RZZ9VM_ARABIAN-NUBIAN_SHIELD_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/25mjsj
title: 2022-12-RZZ9VM_CENTRAL_ASIAN_FOLDBELT_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/epsmxv
title: 2022-12-RZZ9VM_MIDCONTINENT_US_KIMBERLITE-CARBONATITE_PROVINCE.csv

description: :unav
DOI: 10.25625/rzz9vm/ipbpms
title: 2022-12-RZZ9VM_SAO_FRANCISCO_CRATON_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/haddsl
title: 2022-12-RZZ9VM_BASTAR_CRATON_MESOZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/unntpz
title: 2022-12-RZZ9VM_MADAGASCAR_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/wwyeyh
title: 2022-12-RZZ9VM_DAMARA_OROGEN.csv

description: :unav
DOI: 10.25625/rzz9vm/g8lhnk
title: 2022-12-RZZ9VM_CENTRAL_ASIAN_FOLDBELT_PALEOZOIC_part1.csv

description: :unav
DOI: 10.25625/rzz9vm/utz3qk
title: 2022-12-RZZ9VM_PATAGONIA.csv

description: :unav
DOI: 10.25625/rzz9vm/yimevl
title: 2022-12-RZZ9VM_DHARWAR_CRATON_MESOZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/hmphec
title: 2022-12-RZZ9VM_KLAMATH_MOUNTAINS.csv

description: :unav
DOI: 10.25625/rzz9vm/efkmws
title: 2022-12-RZZ9VM_MADAGASCAR_PALEOZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/lsemem
title: 2022-12-RZZ9VM_CANADIAN_SHIELD_PALEOZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/gst0rd
title: 2022-12-RZZ9VM_CANADIAN_SHIELD_CENOZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/gnclxt
title: 2022-12-RZZ9VM_CENTRAL_ASIAN_FOLDBELT_MESOZOIC_part1.csv

description: :unav
DOI: 10.25625/rzz9vm/adariv
title: 2022-12-RZZ9VM_EAST_EUROPEAN_CRATON_PROTEROZOIC.csv

description: :unav
DOI: 10.25625/rzz9vm/lr6uj3
title: 2022-12-RZZ9VM_MADAGASCAR_CENOZOIC_QUATERNARY.csv

description: :unav
DOI: 10.25625/rzz9vm/ous01x
title: 2022-12-RZZ9VM_GREAT_METEOR_OR_NEW_ENGLAND_HOTSPOT_TRACK_OR_CONT.csv

description: :unav
DOI: 10.34740/kaggle/dsv/4717874
title: Mahanagar Yatayat Dataset
DOI: 10.57745/q6ntuz
title: Supplementary_Chapt1_TableS1.ods

description: :unav
DOI: 10.5281/zenodo.7433326
title: LU_Data_Table_V1.0_20221213

description: Data table for digital scholarship module at Faculty of Arts of KU Leuven.
DOI: 10.5281/zenodo.7433327
title: LU_Data_Table_V1.0_20221213

description: Data table for digital scholarship module at Faculty of Arts of KU Leuven.
DOI: 10.6084/m9.figshare.21710975.v1
title: Raw Data Pantelleria

description: The dataset contains raw files concerning Differential Scanning Calorimetry and Paleomagnetism measurements together with Thermal modeling simulations output and videos.
DOI: 10.5517/ccdc.csd.cc2cmpmb
title: CCDC 2193672: Experimental Crystal Structure Determination

description: Related Article: Swetha Jos, Christine Tan, Pierre Thilmany, Alaâ Saadane, Carla Slebodnick, Gwilherm Evano, Webster L. Santos|2022|Chem.Commun.|58|13751|doi:10.1039/D2CC04543E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cmpnc
title: CCDC 2193673: Experimental Crystal Structure Determination

description: Related Article: Swetha Jos, Christine Tan, Pierre Thilmany, Alaâ Saadane, Carla Slebodnick, Gwilherm Evano, Webster L. Santos|2022|Chem.Commun.|58|13751|doi:10.1039/D2CC04543E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cmppd
title: CCDC 2193674: Experimental Crystal Structure Determination

description: Related Article: Swetha Jos, Christine Tan, Pierre Thilmany, Alaâ Saadane, Carla Slebodnick, Gwilherm Evano, Webster L. Santos|2022|Chem.Commun.|58|13751|doi:10.1039/D2CC04543E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.48577/jpl.pw4kjh
title: Accuracy of using metastable state measurements in laser-induced fluorescence diagnostics of xenon ion velocity in Hall thrusters

description: Laser-induced fluorescence (LIF) measurements of singly-charged xenon ion velocities in Hall thrusters typically target metastable states due to lack of available laser technology for exciting the ground state. The measured velocity distribution of these metastable ions are assumed to reflect the ground state ion behavior. However, this assumption has not been experimentally verified. To investigate the accuracy of this assumption, a recently developed xenon ion (Xe II) collisional-radiative (CR) model is combined with a 1D fluid model for ions, using plasma parameters from higher fidelity simulations of each thruster, to calculate the metastable and ground state ion velocities as a function of position along the channel centerline. For the HERMeS and SPT-100 thruster channel centerlines, differences up to 0.2 km/s were observed between the metastable and ground state ion velocities. The differences between the metastable ion velocity and ground state ion velocity can be explained by excitations from the slower ground state neutral atoms. While both the ground state ions and metastable state ions experience the same acceleration by the electric field, these small velocity differences arise because ionization directly into these states from the slower neutral ground state can reduce their mean velocities by different amounts. Therefore, the velocity discrepancy may be larger for thrusters with lower propellant utilization efficiency and higher neutral density. For example, differences up to 1.5 km/s were calculated on the HET-P70 thruster channel centerline. Note that although the creation of slow ions can influence the mean velocity, the most probable velocity should be unaffected by these processes.
DOI: 10.6084/m9.figshare.21711320.v3
title: CODEX mouse spleen

description: Processed dataset from Goltsev et al., 2018 of CODEX spatial proteomics measurement of 9 spleen sections from healthy mice and mice with systemic lupus erythematosus.
DOI: 10.35078/yuonlx/nuuzqe
title: MTCI Experiências e reflexões sobre medicinas tradicionais, complementares e integrativas em sistemas de saúde das Américas.pdf

description: MTCI - Experiências e reflexões sobre medicinas tradicionais, complementares e integrativas em sistemas de saúde das Américas (arquivo digital)
DOI: 10.6084/m9.figshare.21711320.v4
title: CODEX mouse spleen

description: Processed dataset from Goltsev et al., 2018 of CODEX spatial proteomics measurement of 9 spleen sections from healthy mice and mice with systemic lupus erythematosus.
DOI: 10.5281/zenodo.32025
title: Nearly optimal coverings of orientation space

description: We give various sets of orientations which cover orientation space nearly optimally. These are suitable for searching orientation space and for integrating over orientation (together with the provided weights). The data is identical to that provided in March, 2006. The documentation has been updated for this release; this is available at https://github.com/cffk/orientation/tree/v1.1
description: {"references": ["Charles F. F. Karney, Quaternions in molecular modeling, J. Mol. Graph. Mod. 25(5), 595-604 (Jan. 2007)."]}
DOI: 10.6084/m9.figshare.21187030
title: LUMIERE dataset - Pyradiomics features based on HD-GLIO-AUTO segmentations

description: This CSV contains the radiomic features extracted for all study dates where all four MRI sequences and automated segmentation from HD-GLIO-AUTO are available. The features were extracted from the images resampled to atlas space. Please note that features could not be extracted for studies where a given segmentation label was not found (or too small, see the minimum ROI size in the settings column). See our GitHub repository for a script to customize the extraction.
DOI: 10.6084/m9.figshare.21187030.v1
title: LUMIERE dataset - Pyradiomics features based on HD-GLIO-AUTO segmentations

description: This CSV contains the radiomic features extracted for all study dates where all four MRI sequences and automated segmentation from HD-GLIO-AUTO are available. The features were extracted from the images resampled to atlas space. Please note that features could not be extracted for studies where a given segmentation label was not found (or too small, see the minimum ROI size in the settings column). See our GitHub repository for a script to customize the extraction.
DOI: 10.6084/m9.figshare.21187033
title: LUMIERE dataset - Pyradiomics features based on DeepBraTumIA segmentations

description: This CSV contains the radiomic features extracted for all study dates where all four MRI sequences and automated segmentation from DeepBraTumIA are available. The features were extracted from the images resampled to atlas space. Please note that features could not be extracted for studies where a given segmentation label was not found (or too small, see the minimum ROI size in the settings column). See our GitHub repository for a script to customize the extraction.
DOI: 10.6084/m9.figshare.21195430
title: LUMIERE dataset - Information on completeness of individual MR studies

description: This file summarizes which MRI sequences are available for which study, and indicates if CoLIAGe features were successfully extracted. For all entries, "x" stands for available, empty for not available.
DOI: 10.6084/m9.figshare.21195556
title: LUMIERE dataset - Expert RANO rating

description: This CSV file contains the expert rating according to the Response Assessment in Neuro-Oncology (RANO) criteria. The rating also includes the dimensions of measurable lesions and the rationale behind the rating decisions.
DOI: 10.6084/m9.figshare.21195559
title: LUMIERE dataset - Demographics and pathology information

description: This CSV file gives information on MGMT status, IDH1, overall survival, age, and sex of the patients included in this dataset.
DOI: 10.6084/m9.figshare.21195562
title: LUMIERE dataset - MRI parameters

description: This CSV file details MRI acquisition information for all studies in this dataset. The information was extracted from the DICOM headers. Please note that the slice count might not correspond to the NIfTI file slice count in some cases, due to (a) original DICOM sequences being "interleaved" and separated before conversion, or (b) we had to crop out orientation markers burned into the files.
DOI: 10.6084/m9.figshare.21249516
title: LUMIERE dataset - MRI data and automated segmentations

description: This zip files contains the anonymized MRI data for 91 Glioblastoma patients. 599 of a total of 638 studies include the complete set of four MRI sequences (pre- and post-contrast T1-weighted, T2-weighted and fluid-attenuated inversion recovery). The remaining studies consist of three of fewer MRI images. The dataset contains a total of 2487 MRI images. For the time points with all four MRI sequences, we additionally provide automated tumor segmentation by DeepBraTumIA and HD-GLIO-AUTO.
DOI: 10.6084/m9.figshare.21251643
title: LUMIERE dataset - CoLIAGe radiomic features

description: This zip file contains the CoLIAGe radiomic features for the LUMIERE GBM MRI dataset. The features are extracted for each study with the full set of four MRI sequences and for both automated segmentation tools (DeepBraTumIA and HD-GLIO-AUTO). Please note that the feature extraction was only possible for studies where tumor/edema area was detected, and it was of sufficient size. The features are provided per MRI sequence, segmentation label, and segmentation tool. The files are provided as vector NIfTI images, split into primary and secondary feature components. The folder structure is the same as with the imaging data. The code used for feature extraction is provided through our GitHub repository.
DOI: 10.6084/m9.figshare.21266241
title: LUMIERE dataset - Readme file

description: This read me file contains descriptions of the files contained in the LUMIERE dataset.
DOI: 10.5066/p9cn4uw6
title: SedCam Model Calibration Imagery Acquired June 2020 to September 2021 at East Branch Brandywine Creek (USGS 01480870)

description: Two empirical simple linear regression models were developed from SedCam imagery and concurrent physical sediment samples over a 20-month period at the East Branch Brandywine Creek gage (USGS 01480870). The image files included here are a subset, used in the calibration dataset for these regression models. Models relate the explanatory variable, Rmax (maximum digital number of the red band, which peaks at a wavelength of 590 nanometers) to point and cross-sectional mean sample response variables (milligrams per liter). These regression models predict suspended-sediment concentration from a non-contact sediment surrogate technique to improve surface water monitoring operations. Files include Nikon Electronic Format (.NEF) raw files, Adobe Photoshop sidecar (.xmp) files, and post-processed (.tif) files for ten frames.
DOI: 10.5281/zenodo.32156
title: Test set for geodesics

description: This is a set of 500000 geodesics for the WGS84 ellipsoid; this is an ellipsoid of revolution with equatorial radius <em>a</em> = 6378137 m and flattening <em>f</em> = 1/298.257223563. Each line of the test set gives 10 space delimited numbers latitude at point 1, φ<sub>1</sub> (degrees, exact) longitude at point 1, λ<sub>1</sub> (degrees, always 0) azimuth at point 1, α<sub>1</sub> (clockwise from north in degrees, exact) latitude at point 2, φ<sub>2</sub> (degrees, accurate to 10<sup>−18</sup> deg) longitude at point 2, λ<sub>2</sub> (degrees, accurate to 10<sup>−18</sup> deg) azimuth at point 2, α<sub>2</sub> (degrees, accurate to 10<sup>−18</sup> deg) geodesic distance from point 1 to point 2, <em>s</em><sub>12</sub> (meters, exact) arc distance on the auxiliary sphere, σ<sub>12</sub> (degrees, accurate to 10<sup>−18</sup> deg) reduced length of the geodesic, <em>m</em><sub>12</sub> (meters, accurate to 0.1 pm) the area between the geodesic and the equator, <em>S</em><sub>12</sub> (m<sup>2</sup>, accurate to 1 mm<sup>2</sup>) These are computed using high-precision direct geodesic calculations with the given φ<sub>1</sub>, λ<sub>1</sub>, α<sub>1</sub>, and <em>s</em><sub>12</sub>. The distance <em>s</em><sub>12</sub> always corresponds to an arc length σ<sub>12</sub> ≤ 180°, so the given geodesics give the shortest paths from point 1 to point 2. For simplicity and without loss of generality, φ<sub>1</sub> is chosen in [0°, 90°], λ<sub>1</sub> is taken to be zero, α<sub>1</sub> is chosen in [0°, 180°]. Furthermore, φ<sub>1</sub> and α<sub>1</sub> are taken to be multiples of 10<sup>−12</sup> deg and <em>s</em><sub>12</sub> is a multiple of 0.1 μm in [0 m, 20003931.4586254 m]. This results in λ<sub>2</sub> in [0°, 180°] and α<sub>2</sub> in [0°, 180°]. The contents of the file are as follows: 100000 entries randomly distributed 50000 entries which are nearly antipodal 50000 entries with short distances 50000 entries with one end near a pole 50000 entries with both ends near opposite poles 50000 entries which are nearly meridional 50000 entries which are nearly equatorial 50000 entries running between vertices (α<sub>1</sub> = α<sub>2</sub> = 90°) 50000 entries ending close to vertices The values for <em>s</em><sub>12</sub> for the geodesics running between vertices are truncated to a multiple of 0.1 pm and this is used to determine point 2.
description: {"references": ["C. F. F. Karney, Geodesics on an ellipsoid of revolution (2011). http://arxiv.org/abs/1102.1215", "C. F. F. Karney, Algorithms for geodesics, J. Geodesy 87(1), 43-55 (2013)."]}
DOI: 10.5281/zenodo.32470
title: Test data for the transverse Mercator projection

description: This is a set of 287000 geographic points together with their coordinates in the transverse Mercator projection. The WGS84 ellipsoid (equatorial radius <em>a</em> = 6378137 m, flattening <em>f</em> = 1/298.257223563) is used, with central meridian 0°, central scale factor 0.9996 (the UTM value), false easting = false northing = 0 m. Each line of the test set gives 6 space delimited numbers latitude, φ (degrees, exact) longitude, λ (degrees, exact — see below) easting (meters, accurate to 0.1 pm) northing (meters, accurate to 0.1 pm) meridian convergence (degrees, accurate to 10<sup>−18</sup> deg) scale (accurate to 10<sup>−20</sup>) These are computed using high-precision calculations using the exact formulas for the projection, see Lee (1976). The latitude and longitude are all multiples of 10<sup>−12</sup> deg and should be regarded as exact, except that λ = 82.63627282416406551° should be interpreted as exactly (1 − <em>e</em>) 90°, where <em>e</em> is the eccentricity given by <em>e</em><sup>2</sup> = <em>f</em> (2 − <em>f</em> ). The contents of the file are as follows: 250000 entries randomly distributed in φ ∈ [0°, 90°], λ ∈ [0°, 90°] 1000 entries randomly distributed on φ ∈ [0°, 90°], λ = 0° 1000 entries randomly distributed on φ = 0°, λ ∈ [0°, 90°] 1000 entries randomly distributed on φ ∈ [0°, 90°], λ = 90° 1000 entries close to φ = 90° with λ ∈ [0°, 90°] 1000 entries close to φ = 0°, λ = 0° with φ ≥ 0°, λ ≥ 0° 1000 entries close to φ = 0°, λ = 90° with φ ≥ 0°, λ ≤ 90° 2000 entries close to φ = 0°, λ = (1 − <em>e</em>) 90° with φ ≥ 0° 25000 entries randomly distributed in φ ∈ [−89°, 0°], λ ∈ [(1 − <em>e</em>) 90°, 90°] 1000 entries randomly distributed on φ ∈ [−89°, 0°], λ = 90° 1000 entries randomly distributed on φ ∈ [−89°, 0°], λ = (1 − <em>e</em>) 90° 1000 entries close to φ = 0°, λ = 90° (φ &lt; 0°, λ ≤ 90°) 1000 entries close to φ = 0°, λ = (1 − <em>e</em>) 90° (φ &lt; 0°, λ ≤ (1 − <em>e</em>) 90°) The entries for φ &lt; 0° and λ ∈ [(1 − <em>e</em>) 90°, 90°] use the “extended” domain for the transverse Mercator projection explained in Sec. 5 of Karney (2011). The first 258000 entries have φ ≥ 0° and are suitable for testing implementations following the standard convention.
description: {"references": ["L. P. Lee, Conformal Projections Based on Elliptic Functions, (B. V. Gutsell, Toronto, 1976).", "C. F. F. Karney, Transverse Mercator with an accuracy of a few nanometers, J. Geodesy 85(8), 475-485 (2011)."]}