Articles | Volume 13, issue 4
https://doi.org/10.5194/essd-13-1653-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-1653-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 Apr 2021
Data description paper |
| 21 Apr 2021
| 21 Apr 2021
Open access to regional geoid models: the International Service for the Geoid
Mirko Reguzzoni
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Daniela Carrion
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Alberta Albertella
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Lorenzo Rossi
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Giovanna Sona
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Khulan Batsukh
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Juan Fernando Toro Herrera
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Kirsten Elger
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Riccardo Barzaghi
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
Fernando Sansó
International Service for the Geoid, Department of Civil and
Environmental Engineering, Politecnico di Milano, Milan, 20133, Italy
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L. Rossi, C. I. De Gaetani, D. Pagliari, E. Realini, M. Reguzzoni, and L. Pinto
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J. F. Toro Herrera, D. Carrion, and M. A. Brovelli
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L. Rossi, F. Ioli, E. Capizzi, L. Pinto, and M. Reguzzoni
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Friederike Koerting, Nicole Koellner, Agnieszka Kuras, Nina Kristin Boesche, Christian Rogass, Christian Mielke, Kirsten Elger, and Uwe Altenberger
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Mineral resource exploration and mining is an essential part of today's high-tech industry. Modern remote-sensing exploration techniques from multiple platforms (e.g., satellite) to detect the spectral characteristics of the surface require spectral libraries as an essential reference. To enable remote mapping, the spectral libraries for rare-earth-bearing minerals, copper-bearing minerals and surface samples from a copper mine are presented here with their corresponding geochemical validation.
V. Yordanov, M. A. Brovelli, D. Carrion, L. Barazzetti, L. J. A. Francisco, H. R. Comia, and M. I. Caravela
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J. F. Toro, D. Carrion, M. A. Brovelli, and M. Percoco
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C. A. Biraghi, E. Pessina, D. Carrion, and M. A. Brovelli
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D. Carrion, E. Pessina, C. A. Biraghi, and G. Bratic
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 245–251, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-245-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-245-2020, 2020
K. Spasenovic, D. Carrion, and F. Migliaccio
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S. Jovanovic, D. Carrion, and M. A. Brovelli
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K. Spasenovic, D. Carrion, F. Migliaccio, and B. Pernici
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J. F. Toro, D. Carrion, A. Albertella, and M. A. Brovelli
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W14, 233–238, https://doi.org/10.5194/isprs-archives-XLII-4-W14-233-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W14-233-2019, 2019
K. Spasenovic and D. Carrion
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F. Migliaccio, D. Carrion, and F. Ferrario
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1321–1326, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1321-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1321-2019, 2019
E. Sinem Ince, Franz Barthelmes, Sven Reißland, Kirsten Elger, Christoph Förste, Frank Flechtner, and Harald Schuh
Earth Syst. Sci. Data, 11, 647–674, https://doi.org/10.5194/essd-11-647-2019, https://doi.org/10.5194/essd-11-647-2019, 2019
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R. Barzaghi, M. Reguzzoni, C. I. De Gaetani, S. Caldera, and L. Rossi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 209–216, https://doi.org/10.5194/isprs-archives-XLII-2-W11-209-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W11-209-2019, 2019
C. Cappelletti, M. Boniardi, A. Casaroli, C. I. De Gaetani, D. Passoni, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W9, 227–234, https://doi.org/10.5194/isprs-archives-XLII-2-W9-227-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W9-227-2019, 2019
G. Ronchetti, D. Pagliari, and G. Sona
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 983–989, https://doi.org/10.5194/isprs-archives-XLII-2-983-2018, https://doi.org/10.5194/isprs-archives-XLII-2-983-2018, 2018
L. Rossi, C. I. De Gaetani, D. Pagliari, E. Realini, M. Reguzzoni, and L. Pinto
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A. Albertella, M. A. Brovelli, and D. Gonzalez Ferreiro
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W2, 11–17, https://doi.org/10.5194/isprs-archives-XLII-4-W2-11-2017, https://doi.org/10.5194/isprs-archives-XLII-4-W2-11-2017, 2017
Francesco Avanzi, Alberto Bianchi, Alberto Cina, Carlo De Michele, Paolo Maschio, Diana Pagliari, Daniele Passoni, Livio Pinto, Marco Piras, and Lorenzo Rossi
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-57, https://doi.org/10.5194/tc-2017-57, 2017
Revised manuscript not accepted
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We compare three different instruments used to collect snow depth, i.e., photogrammetric surveys using Unmanned Aerial Systems (UAS), a 3D laser scanning, and manual probing. The relatively high density of manual data (135 pt over 6700 m2, i.e., 2 pt/100 m2) enables to assess the performance of UAS in capturing the marked spatial variability of snow. Results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.
Giovanna Sona, Daniele Passoni, Livio Pinto, Diana Pagliari, Daniele Masseroni, Bianca Ortuani, and Arianna Facchi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 1023–1029, https://doi.org/10.5194/isprs-archives-XLI-B1-1023-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-1023-2016, 2016
Carlo De Michele, Francesco Avanzi, Daniele Passoni, Riccardo Barzaghi, Livio Pinto, Paolo Dosso, Antonio Ghezzi, Roberto Gianatti, and Giacomo Della Vedova
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Short summary
We investigate snow depth distribution at peak accumulation over a small Alpine area using photogrammetry-based surveys with a fixed wing unmanned aerial system. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to -0.073 m. Moreover, in this case study snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m.
B. K. Biskaborn, J.-P. Lanckman, H. Lantuit, K. Elger, D. A. Streletskiy, W. L. Cable, and V. E. Romanovsky
Earth Syst. Sci. Data, 7, 245–259, https://doi.org/10.5194/essd-7-245-2015, https://doi.org/10.5194/essd-7-245-2015, 2015
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This paper introduces the new database of the Global Terrestrial Network for Permafrost (GTN-P) on permafrost temperature and active layer thickness data. It describes the operability of the Data Management System and the data quality. By applying statistics on GTN-P metadata, we analyze the spatial sample representation of permafrost monitoring sites. Comparison with environmental variables and climate projection data enable identification of potential future research locations.
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Simon Deggim, Annette Eicker, Lennart Schawohl, Helena Gerdener, Kerstin Schulze, Olga Engels, Jürgen Kusche, Anita T. Saraswati, Tonie van Dam, Laura Ellenbeck, Denise Dettmering, Christian Schwatke, Stefan Mayr, Igor Klein, and Laurent Longuevergne
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Andreas Kvas, Jan Martin Brockmann, Sandro Krauss, Till Schubert, Thomas Gruber, Ulrich Meyer, Torsten Mayer-Gürr, Wolf-Dieter Schuh, Adrian Jäggi, and Roland Pail
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João Teixeira da Encarnação, Pieter Visser, Daniel Arnold, Aleš Bezdek, Eelco Doornbos, Matthias Ellmer, Junyi Guo, Jose van den IJssel, Elisabetta Iorfida, Adrian Jäggi, Jaroslav Klokocník, Sandro Krauss, Xinyuan Mao, Torsten Mayer-Gürr, Ulrich Meyer, Josef Sebera, C. K. Shum, Chaoyang Zhang, Yu Zhang, and Christoph Dahle
Earth Syst. Sci. Data, 12, 1385–1417, https://doi.org/10.5194/essd-12-1385-2020, https://doi.org/10.5194/essd-12-1385-2020, 2020
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E. Sinem Ince, Franz Barthelmes, Sven Reißland, Kirsten Elger, Christoph Förste, Frank Flechtner, and Harald Schuh
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Short summary
Short summary
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Cited articles
Avalos Naranjo, D., Hernández Navarro, A., Muñoz Abundes, R., and
Sosa Gaytán, M.: The Mexican gravimetric geoid: GGM04, GFZ Data
Services, https://doi.org/10.5880/ISG.2004.001, 2004.
Avalos Naranjo, D., Hernández Navarro, A., Muñoz Abundes, R., and
Sosa Gaytán, M.: The Mexican gravimetric geoid: GGM05, GFZ Data
Services, https://doi.org/10.5880/ISG.2005.001, 2005.
Avalos Naranjo, D., Hernández Navarro, A., Muñoz Abundes, R., and
Sosa Gaytán, M.: The Mexican gravimetric geoid: GGM06, GFZ Data
Services, https://doi.org/10.5880/ISG.2006.001, 2006.
Avalos Naranjo, D., Sosa Gaytán, M., and Muñoz Abundes, R.: The
Mexican gravimetric geoid: GGM10, GFZ Data Services,
https://doi.org/10.5880/ISG.2010.001, 2010.
Ayres-Sampaio, D., Deurloo, R., Bos, M., Magalhães, A., and Bastos, L.:
The gravimetric geoid of Madeira: GEOMAD, GFZ Data Services,
https://doi.org/10.5880/ISG.2015.002, 2015.
Barzaghi, R., Migliaccio, F., Reguzzoni, M., and Albertella, A.: The Earth
gravity field in the time of satellites, Rendiconti Lincei, 26, 13–23,
https://doi.org/10.1007/s12210-015-0382-9, 2015a.
Barzaghi, R., Carrion, D., Reguzzoni, M., and Venuti, G.: A feasibility
study on the unification of the Italian height systems using GNSS-leveling
data and global satellite gravity models, in: IAG 150 Years, edited by: Rizos, C. and Willis, P., International Association of Geodesy Symposia, 143, 281–288,
Springer, Cham, https://doi.org/10.1007/1345_2015_35, 2015b.
Barzaghi, R., Carrion, D., and Koç, Ö.: The PoliMI quasi-geoid based
on windowed Least-Squares Collocation for the Colorado Experiment:
ColWLSC2020, GFZ Data Services,
https://doi.org/10.5880/isg.2020.001, 2020a.
Barzaghi, R., Carrion, D., and Koç, Ö.: The PoliMI geoid based on
windowed Least-Squares Collocation for the Colorado Experiment: ColWLSC2020, GFZ Data Services, https://doi.org/10.5880/isg.2020.002, 2020b.
Bingham, R. J., Haines K., and Hughes C. W.: Calculating the ocean's mean
dynamic topography from a mean sea surface and a geoid, J.
Atmos. Ocean. Tech., 25, 1808–1822,
https://doi.org/10.1175/2008JTECHO568.1, 2008.
Blitzkow, D., de Matos, A. C. O. C., Machado, W. C., Nunes, M. A.,
Lengruber, N. V., Xavier, E. M. L., and Fortes, L. P. S.: The Brazilian
gravimetric geoid: MAPGEO2015, GFZ Data Services,
https://doi.org/10.5880/ISG.2015.001, 2015.
Cerri, D. and Fuggetta, A.: Open standards, open formats, and open source,
J. Syst. Softw., 80, 11, 1930–1937,
https://doi.org/10.1016/j.jss.2007.01.048, 2007.
Chan, L. M. and Zeng, M. L.: Metadata interoperability and standardization
– A study of methodology Part 1, D-Lib Magazine, 12, 6, https://doi.org/10.1045/june2006-zeng, 2006.
Data Citation Synthesis Group: Joint declaration of data citation
principles, FORCE11, https://doi.org/10.25490/a97f-egyk, 2014.
de Matos, A. C. O. C., Blitzkow, D., Guimarães, G. do N., Lobianco, M.
C. B., and Costa, S. M. A.: The Brazilian gravimetric geoid: MAPGEO2010, GFZ
Data Services, https://doi.org/10.5880/ISG.2010.002, 2010.
Fenner, M., Crosas, M., Grethe, J. S., Kennedy, D., Hermjakob, H.,
Rocca-Serra, P., Durand, G., Berjon, R., Karcher, S., Martone, M., and
Clark, T.: A data citation roadmap for scholarly data repositories,
Sci. Data, 6, 28, https://doi.org/10.1038/s41597-019-0031-8, 2019.
Förste, C., Bruinsma, Sean. L., Abrikosov, O., Lemoine, J.-M., Marty, J.
C., Flechtner, F., Balmino, G., Barthelmes, F., and Biancale, R.: EIGEN-6C4
The latest combined global gravity field model including GOCE data up to
degree and order 2190 of GFZ Potsdam and GRGS Toulouse, GFZ Data Services, https://doi.org/10.5880/ICGEM.2015.1, 2014.
Forsberg, R. and Tscherning, C. C.: Overview manual for the GRAVSOFT
Geodetic Gravity Field Modelling Programs, 2nd Edn. Technical report,
DTU-Space, 2008.
Fukuda, Y., Kuroda, J., Takabatake, Y., Itoh, J., and Murakami, M.:
Improvement of JGEOID93 by the geoidal heights derived from GPS/leveling
survey, in: Gravity, Geoid and Marine Geodesy, edited by: Segawa, J., Fujimoto, H., and Okubo, S., International Association of Geodesy Symposia, 117, 589–596,
Springer Verlag, https://doi.org/10.1007/978-3-662-03482-8_78, 1997.
Grigoriadis, V. N. and Vergos, G. S.: The AUTh quasi-geoid based on 1D FFT
with Wong-Gore modification of the Stokes kernel for the Colorado
Experiment: ColFFTWG2020, GFZ Data Services,
https://doi.org/10.5880/ISG.2020.003, 2020a.
Grigoriadis, V. N. and Vergos, G. S.: The AUTh geoid based on 1D FFT with
Wong-Gore modification of the Stokes kernel for the Colorado Experiment:
ColFFTWG2020, GFZ Data Services, https://doi.org/10.5880/ISG.2020.004, 2020b.
Heiskanen, W. A. and Moritz, H.: Physical Geodesy, W.H. Freeman and Co.,
USA, 364 pp., 1967.
Hodson, S., Jones, S., Collins, S., Genova, F., Harrower, N., Laaksonen, L.,
Mietchen, D., Petrauskaité, R., and Wittenburg, P.: Turning FAIR into
reality: Final Report and Action Plan from the European Commission Expert
Group on FAIR Data, https://doi.org/10.2777/1524, 2018.
Hwang, C., Hsu, H., Featherstone, W. E., Cheng, C., Yang, M., Huang, W.,
Wang, C., Huang, J., Chen, K., Huang, C., Chen, H., and Su, W.: The
gravimetric geoid of Taiwan: TWGEOID18g, GFZ Data Services,
https://doi.org/10.5880/ISG.2018.001, 2018a.
Hwang, C., Hsu, H., Featherstone, W. E., Cheng, C., Yang, M., Huang, W.,
Wang, C., Huang, J., Chen, K., Huang, C., Chen, H., and Su, W.: The hybrid
geoid of Taiwan: TWGEOID18h, GFZ Data Services,
https://doi.org/10.5880/ISG.2018.002, 2018b.
Ince, E. S., Barthelmes, F., Reißland, S., Elger, K., Förste, C., Flechtner, F., and Schuh, H.: ICGEM – 15 years of successful collection and distribution of global gravitational models, associated services, and future plans, Earth Syst. Sci. Data, 11, 647–674, https://doi.org/10.5194/essd-11-647-2019, 2019.
Knudsen, P., Bingham, R. J., Andersen, O., and Rio M. H.: A global mean
dynamic topography and ocean circulation estimation using a preliminary GOCE
gravity model, J. Geodesy, 85, 861–879,
https://doi.org/10.1007/s00190-011-0485-8, 2011.
Kuroishi, Y.: A new geoid model for Japan, JGEOID2000, in:
Gravity, Geoid and Geodynamics, edited by: Sideris M. G., International Association of Geodesy
Symposia, 123, 329–333, Springer Verlag,
https://doi.org/10.1007/978-3-662-04827-6_55, 2000.
Lobianco, M. C. B., Blitzkow, D., and de Matos, A. C. O. C.: The Brazilian
gravimetric geoid: MAPGEO2004, GFZ Data Services,
https://doi.org/10.5880/ISG.2004.002, 2004.
Longhorn, R.: Geospatial standards, interoperability, metadata semantics and
spatial data infrastructure, background paper for NIEeS Workshop on
Activating Metadata, Cambridge, UK, 6–7 July 2005.
Merson, R. and King-Hele, D.: Use of artificial satellites to explore the
Earth's gravitational field: Results from Sputnik 2, Nature, 182, 640–641,
https://doi.org/10.1038/182640a0, 1958.
Miyahara, B., Kodama, T., and Kuroishi, Y.: Development of new hybrid geoid
model for Japan, “GSIGEO2011”, Bulletin of the Geographical Information
Authority of Japan, 62, 11–20, 2014.
Pail, R., Goiginger, H., Schuh, W.-D., Höck, E., Brockmann, J. M.,
Fecher, T., Gruber, T., Mayer-Gürr, T., Kusche, J., Jäggi, A., and
Rieser, D.: Combined satellite gravity field model GOCO01S derived from GOCE
and GRACE, Geophys. Res. Lett., 37, L20314,
https://doi.org/10.1029/2010GL044906, 2010.
Pail, R., Bruinsma, S., Migliaccio, F., Förste, C., Goiginger, H.,
Schuh, W.-D., Höck, E., Reguzzoni, M., Brockmann, J. M., Abrikosov, O.,
Veicherts, M., Fecher, T., Mayrhofer, R., Krasbutter, I., Sansò, F., and
Tscherning, C. C.: First GOCE gravity field models derived by three
different approaches, J. Geodesy, 85, 819–843,
https://doi.org/10.1007/s00190-011-0467-x, 2011.
Pail, R., Fecher, T., Barnes, D., Factor, J., Holmes, S., Gruber, T., and
Zingerle, P.: The experimental gravity field model XGM2016, GFZ Data
Services, https://doi.org/10.5880/icgem.2017.003, 2017.
Pavlis, N. K., Holmes, S. A., Kenyon, S. C., and Factor, J. K.: The
development and evaluation of the Earth Gravitational Model 2008 (EGM2008),
J. Geophys. Res., 117, B04406,
https://doi.org/10.1029/2011JB008916, 2012.
Rummel, R. and Teunissen, P.: Height datum definition, height datum
connection and the role of the geodetic boundary value problem, B.
Geod., 62, 477–498, https://doi.org/10.1007/BF02520239, 1988.
Sansò, F. and Usai, S.: Height datum and local geodetic datum in the
theory of geodetic boundary value problems, Allgemeine
Vermessungsnachrichten, Wichmann, 8–9, 343– 385, 1995.
Sansò, F. and Sideris, M. G.: Geoid Determination: Theory and Methods,
Springer Nature, Switzerland, https://doi.org/10.1007/978-3-540-74700-0,
2013.
Sansò, F., Reguzzoni, M., and Barzaghi, R.: Geodetic Heights, Springer
Nature, Switzerland, https://doi.org/10.1007/978-3-030-10454-2, 2019.
Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Growth, P., Goble, C., Grethe, J. S., Heringa, J., Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S. A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR Guiding Principles for
scientific data management and stewardship, Sci. Data, 3, 160018 1, https://doi.org/10.1038/sdata.2016.18, 2016.
Short summary
The International Service for the Geoid provides free access to a repository of geoid models. The most important ones are freely available to perform analyses on the evolution of the geoid computation research field. Furthermore, the ISG performs research taking advantage of its archive and organizes specific training courses on geoid determination. This paper aims at describing the service and showing the added value of the archive of geoid models for the scientific community and technicians.
The International Service for the Geoid provides free access to a repository of geoid models....
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