cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.5sttky
title: Occurrence Download

description: A dataset containing 239347 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Holcus mollis L."
 ]
}

 The dataset includes 239347 records from 583 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0267162-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zygv6v
title: Occurrence Download

description: A dataset containing 231 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Cuba",
 "OccurrenceStatus is Present",
 "TaxonKey is Anolis angusticeps Hallowell, 1856"
 ]
}

 The dataset includes 231 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029014-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tfxrqn
title: Occurrence Download

description: A dataset containing 73035 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-26 34,66 34,66 73,-26 73,-26 34))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Prunus domestica L."
 ]
}

 The dataset includes 73035 records from 711 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029010-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18144/db30ab1c-6edf-4452-a6ac-d6f2a35a0230
title: SisFrance-Nouvelle-Calédonie database
title: SisFrance New-Caledonia Wallis-Futuna database

description: La base de données SisFrance Nouvelle-Calédonie, Wallis et Futuna se présente sous la forme de 3 fichiers : - EPICENTRES : contient les informations concernant la date et l’heure de l’évènement, sa nature et la localisation épicentrale (pour les évènements de type séisme). Chaque champ est associé à un indicateur d’incertitude (un facteur de qualité). Cette table répertorie 97 évènements dont 92 vrais séismes. - OBSERVATION : contient les observations macrosismiques, leur localisation et l'intensité associée évaluée à partir de l'échelle MSK 1964. Chaque champ est associé à un indicateur d’incertitude (indice de fiabilité). Cette table compte 245 observations. - DOCUMENTATION : inclus les références bibliographiques des observations. Un indice de fiabilité est associé à chaque référence. La table DOCUMENTATION compte 182 références bibliographiques. Les clés des évènements (numevt) permettent de connecter les trois tables de la base de données. Chaque fichier de données est associé à un fichier texte nommé README qui décrit chaque champ.
description: The SisFrance New-Caledonia Wallis-Futuna database is represented by 3 text files: - EPICENTRES: contains the information about the time of the event, its nature and the epicentre location (for real earthquakes). Accuracy is fill out for each field (thanks to a fiability factor). This table contains 97 events from which 92 are true earthquakes. - OBSERVATION: contains the macroseismic observations, their location and intensity in the MSK 1964 scale. Accuracy is fill out for each field thanks to a fiability factor. OBSERVATION table counts 245 observations. - DOCUMENTATION: contains the bibliographic sources of the observations. Fiability is fill out for the bibliographic source. The DOCUMENTATION table contains 182 bibliographic sources. The event ID (numevt) field links the three data tables of the database. Each data file is associated to a README text file that describes in detail each field of the data file.
DOI: 10.5281/zenodo.12772940
title: InSAR stack of San Francisco Bay, California from Sentinel-1 descending track 42 processed with GMTSAR

description: A stack of unwrapped interferograms in the San Francisco Bay area, California, USA

Sensor: Sentinel-1 descending track 42

Processor: GMTSAR

This is an input dataset for the time series analysis with MintPy.

Version 0.x (~290 MB; for fast testing of code development)Time: 2020.01.04 - 2021.07.15 (70 acquisitions, 184 interferograms)
DOI: 10.5281/zenodo.11049257
title: InSAR stack of the 2019 Ridgecrest, California earthquake sequence from Sentinel-1 descending track 71 processed with ASF HyP3

description: A stack of unwrapped interferograms on Owens Valley, California for the 2019 Ridgecrest earthquake sequence.

Sensor: Sentinel-1 descending track 71

Time: 2019.06.10 - 2019.08.15, 7 acquisitions, 11 interferograms

Processor: ASF HyP3 (GAMMA)

Tropospheric delay estimated from ERA-5 using PyAPS is attached.

This is an input dataset for the time series analysis with MintPy.
DOI: 10.5281/zenodo.5502402
title: InSAR stack of the 2019 Ridgecrest, California earthquake sequence from Sentinel-1 descending track 71 processed with ASF HyP3

description: A stack of unwrapped interferograms on Owens Valley, California for the 2019 Ridgecrest earthquake sequence.

Sensor: Sentinel-1 descending track 71

Time: 2019.06.10 - 2019.08.15, 7 acquisitions, 11 interferograms

Processor: ASF HyP3 (GAMMA)

Tropospheric delay estimated from ERA-5 using PyAPS is attached.

This is an input dataset for the time series analysis with MintPy.
DOI: 10.15468/dl.jw8bz3
title: Occurrence Download

description: A dataset containing 575 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Cuba",
 "OccurrenceStatus is Present",
 "TaxonKey is Anolis argenteolus Cope, 1861"
 ]
}

 The dataset includes 575 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029015-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.4320005
title: InSAR stack of San Francisco Bay in California from Sentinel-1 descending track 42

description: A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42 Processor: ARIA (processed using ISCE/topsStack and prepared using ARIA-tools as shown below) Tropospheric delay estimated from ERA-5 using PyAPS is attached. This is an input dataset for the time series analysis with MintPy. <strong>Version 1+ (~2.7 GB):</strong><br> Time: 2015.05.12 - 2020.03.10, 114 acquisitions, 505 interferograms<br> Used ARIA-tools commands (access date Nov 9th, 2020): <pre><code>ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42 --end 20200315 ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download</code></pre> <strong>Version 0.* (~410 MB; for fast testing of code development)</strong><br> Time: 2015.05.12 - 2017.05.10, 32 acquisitions, 121 interferograms<br> Used ARIA-tools commands (access date Nov 9th, 2020): <pre><code>ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --end 20170510 ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download</code></pre>
DOI: 10.5281/zenodo.5152543
title: InSAR stack of San Francisco Bay in California from Sentinel-1 descending track 42

description: A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42 Processor: ARIA (processed using ISCE and prepared using ARIA-tools as shown below) Tropospheric delay estimated from ERA-5 using PyAPS is attached. This is an input dataset for the time series analysis with MintPy. <strong>Version 1.1 (~3.1 GB):</strong><br> Time: 2015.05.12 - 2021.07.03, 154 acquisitions, 596 interferograms<br> Used ARIA-tools commands (access date Aug 1st, 2021): <pre><code>ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42 ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download</code></pre> <strong>Version 0.1 (~410 MB; for fast testing of code development)</strong><br> Time: 2015.05.12 - 2017.05.10, 32 acquisitions, 121 interferograms<br> Used ARIA-tools commands (access date Nov 9th, 2020): <pre><code>ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --end 20170510 ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download</code></pre>
DOI: 10.5281/zenodo.4265413
title: InSAR Stack of San Francisco Bay in California from Sentinel-1

description: A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42 Processor: ARIA (processed using ISCE/topsStack and prepared using ARIA-tools as shown below) Tropospheric delay estimated from ERA-5 using PyAPS is attached. This is an input dataset for the time series analysis with MintPy. Version 1+ (~2.7 GB):<br> Time: 2015.05.12 - 2020.03.10, 114 acquisitions, 505 interferograms<br> Used ARIA-tools commands (access date Nov 9th, 2020):<br> &gt;&gt;&gt; ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42 --end 20200315<br> &gt;&gt;&gt; ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download Version 0.* (~410 MB; for fast testing of code development)<br> Time: 2015.05.12 - 2017.05.10, 32 acquisitions, 121 interferograms<br> Used ARIA-tools commands (access date Nov 9th, 2020):<br> &gt;&gt;&gt; ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --end 20170510<br> &gt;&gt;&gt; ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download
DOI: 10.5281/zenodo.6662079
title: InSAR stack of San Francisco Bay in California from Sentinel-1 descending track 42

description: A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42 Processor: ARIA (processed using ISCE and prepared using ARIA-tools as shown below) Tropospheric delay estimated from ERA-5 using PyAPS is attached. This is an input dataset for the time series analysis with MintPy. <strong>Version 1.1 (~3.1 GB):</strong><br> Time: 2015.05.12 - 2021.07.03, 154 acquisitions, 596 interferograms<br> Used ARIA-tools commands (access date Aug 1st, 2021): <pre><code>ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42 ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download</code></pre> <strong>Version 0.2 (~280 MB; for fast testing of code development)</strong><br> Time: 2016.01.31 - 2017.05.10, 23 acquisitions, 91 interferograms<br> Used ARIA-tools commands (access date Jun 18th, 2022): <pre><code>ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --start 20160101 --end 20170510 ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download</code></pre>
DOI: 10.5281/zenodo.4265412
title: InSAR stack of San Francisco Bay in California from Sentinel-1 descending track 42 processed with ARIA

description: A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42


Processor: ARIA (processed using ISCE and prepared using ARIA-tools as shown below)


Tropospheric delay estimated from ERA5 using PyAPS is attached.


This is an input dataset for the time series analysis with MintPy.


Version 1.2 (~5 GB):
Time: 2015.03.01 - 2022.06.04, 189 acquisitions, 961 interferograms
Used ARIA-tools and MintPy commands:


ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42
ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download
prep_aria.py -s ../stack/ -d ../DEM/SRTM_3arcsec.dem -i ../incidenceAngle/*.vrt -a ../azimuthAngle/*.vrt -w ../mask/watermask.msk


Version 0.2 (~280 MB; for fast testing of code development)
Time: 2016.01.31 - 2017.05.10, 23 acquisitions, 91 interferograms
Used ARIA-tools commands (access date Jun 18th, 2022):


ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --start 20160101 --end 20170510
ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download
DOI: 10.5281/zenodo.6990323
title: InSAR stack of San Francisco Bay in California from Sentinel-1 descending track 42 processed with ARIA

description: A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42


Processor: ARIA (processed using ISCE and prepared using ARIA-tools as shown below)


Tropospheric delay estimated from ERA5 using PyAPS is attached.


This is an input dataset for the time series analysis with MintPy.


Version 1.2 (~5 GB):
Time: 2015.03.01 - 2022.06.04, 189 acquisitions, 961 interferograms
Used ARIA-tools and MintPy commands:


ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42
ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download
prep_aria.py -s ../stack/ -d ../DEM/SRTM_3arcsec.dem -i ../incidenceAngle/*.vrt -a ../azimuthAngle/*.vrt -w ../mask/watermask.msk


Version 0.2 (~280 MB; for fast testing of code development)
Time: 2016.01.31 - 2017.05.10, 23 acquisitions, 91 interferograms
Used ARIA-tools commands (access date Jun 18th, 2022):


ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --start 20160101 --end 20170510
ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download
DOI: 10.5281/zenodo.4127335
title: InSAR Stack of Western Cape, South Africa from Sentinel-1

description: A stack of unwrapped interferograms on Western Cape, South Africa. Sensor: Sentinel-1ascending track 29 Time: 2019.03.03 - 2019.06.19, 10 acquisitions, 21 interferograms Processor: SNAP (accessed on 14 July 2019) Tropospheric delay estimated from ERA-5 using PyAPS is attached. This is an input dataset for the time series analysis with MintPy.
DOI: 10.5281/zenodo.4127334
title: InSAR stack of Western Cape, South Africa from Sentinel-1 ascending track 29 processed with SNAP

description: A stack of unwrapped interferograms on Western Cape, South Africa.


Sensor: Sentinel-1ascending track 29


Time: 2019.03.03 - 2019.05.14, 7 acquisitions, 15 interferograms


Processor: SNAP (accessed on 14 July 2019)


Tropospheric delay estimated from ERA-5 using PyAPS is attached.


This is an input dataset for the time series analysis with MintPy.
DOI: 10.5281/zenodo.4318134
title: InSAR stack of Western Cape, South Africa from Sentinel-1 ascending track 29

description: A stack of unwrapped interferograms on Western Cape, South Africa. Sensor: Sentinel-1ascending track 29 Time: 2019.03.03 - 2019.06.19, 10 acquisitions, 21 interferograms Processor: SNAP (accessed on 14 July 2019) Tropospheric delay estimated from ERA-5 using PyAPS is attached. This is an input dataset for the time series analysis with MintPy.
DOI: 10.5281/zenodo.6661536
title: InSAR stack of Western Cape, South Africa from Sentinel-1 ascending track 29 processed with SNAP

description: A stack of unwrapped interferograms on Western Cape, South Africa.


Sensor: Sentinel-1ascending track 29


Time: 2019.03.03 - 2019.05.14, 7 acquisitions, 15 interferograms


Processor: SNAP (accessed on 14 July 2019)


Tropospheric delay estimated from ERA-5 using PyAPS is attached.


This is an input dataset for the time series analysis with MintPy.
DOI: 10.18112/openneuro.ds005258.v1.0.4
title: Full-Body Mobility Data to Validate Inertial Measurement Unit Algorithms in Healthy and Neurological Cohorts
DOI: 10.5281/zenodo.2603396
title: InSAR Stack of the 2008 Wells, Nevada earthquake from Envisat

description: A stack of unwrapped interferograms on Wells, Nevada for the 2008 Wells earthquake Sensor: Envisat ASAR descending track 399 frame 2871 Time: 2007.07.09 - 2008.09.01, 11 acquisitions, 51 interferograms Processor: GAMMA A minimum coherence of 0.3 is chosen during phase unwrapping, thus the low coherent pixels are assigned the zero phase value (green in the unwrapPhase_wrap.png). Tropospheric delay estimated from ERA-Interim using PyAPS is attached. This is a demo dataset prepared for the time series analysis using PySAR (https://yunjunz.github.io/PySAR/).
DOI: 10.5281/zenodo.2613771
title: InSAR Stack of the 2008 Wells, Nevada earthquake from Envisat

description: A stack of unwrapped interferograms on Wells, Nevada for the 2008 Wells earthquake Sensor: Envisat ASAR descending track 399 frame 2871 Time: 2007.07.09 - 2008.09.01, 11 acquisitions, 51 interferograms Processor: GAMMA A minimum coherence of 0.3 is chosen during phase unwrapping, thus the low coherent pixels are assigned the zero phase value (green in the unwrapPhase_wrap.png). Tropospheric delay estimated from ERA-Interim using PyAPS is attached. This is a demo dataset prepared for the time series analysis using PySAR (https://yunjunz.github.io/PySAR/).
DOI: 10.5281/zenodo.2748560
title: InSAR Stack of the 2008 Wells, Nevada earthquake from Envisat

description: A stack of unwrapped interferograms on Wells, Nevada for the 2008 Wells earthquake Sensor: Envisat ASAR descending track 399 frame 2871 Time: 2007.07.09 - 2008.09.01, 11 acquisitions, 51 interferograms Processor: GAMMA A minimum coherence of 0.3 is chosen during phase unwrapping, thus the low coherent pixels are assigned the zero phase value (green in the unwrapPhase_wrap.png). Tropospheric delay estimated from ERA-Interim using PyAPS is attached. This is an input dataset for the time series analysis with MintPy.
DOI: 10.5281/zenodo.2603395
title: InSAR stack of the 2008 Wells, Nevada earthquake from Envisat descending track 399 processed with Gamma

description: A stack of unwrapped interferograms on Wells, Nevada for the 2008 Wells earthquake.


Sensor: Envisat ASAR descending track 399 frame 2871


Time: 2007.07.09 - 2008.09.01, 11 acquisitions, 51 interferograms


Processor: GAMMA


A minimum coherence of 0.3 is chosen during phase unwrapping, thus the low coherent pixels are assigned the zero phase value (green in the unwrapPhase_wrap.png).


Tropospheric delay estimated from ERA-5 using PyAPS is attached.


This is an input dataset for the time series analysis with MintPy.
DOI: 10.5281/zenodo.3635258
title: InSAR Stack of the 2008 Wells, Nevada earthquake from Envisat

description: A stack of unwrapped interferograms on Wells, Nevada for the 2008 Wells earthquake Sensor: Envisat ASAR descending track 399 frame 2871 Time: 2007.07.09 - 2008.09.01, 11 acquisitions, 51 interferograms Processor: GAMMA A minimum coherence of 0.3 is chosen during phase unwrapping, thus the low coherent pixels are assigned the zero phase value (green in the unwrapPhase_wrap.png). Tropospheric delay estimated from ERA-5 using PyAPS is attached. This is an input dataset for the time series analysis with MintPy.
DOI: 10.5281/zenodo.3952950
title: InSAR stack of the 2008 Wells, Nevada earthquake from Envisat descending track 399 processed with Gamma

description: A stack of unwrapped interferograms on Wells, Nevada for the 2008 Wells earthquake.


Sensor: Envisat ASAR descending track 399 frame 2871


Time: 2007.07.09 - 2008.09.01, 11 acquisitions, 51 interferograms


Processor: GAMMA


A minimum coherence of 0.3 is chosen during phase unwrapping, thus the low coherent pixels are assigned the zero phase value (green in the unwrapPhase_wrap.png).


Tropospheric delay estimated from ERA-5 using PyAPS is attached.


This is an input dataset for the time series analysis with MintPy.
DOI: 10.5281/zenodo.1059007
title: InSAR Stack of Kuju volcano in Kyushu, Japan from ALOS-1

description: Stack of Radar Interferograms on Kuju volcano, Kyushu, Japan Data: ALOS-1 ascending track 422 frame 650 Time: 2007.01.06 - 2011.01.17, 24 acquisitions InSAR processor: ROI_PAC
description: {"references": ["Yunjun, Z., Amelung F., Aoki Y., (2016). Poster: A time series InSAR survey of volcanic deformation in Kyushu, SW Japan with JERS and ALOS data (G51B-1113). AGU Fall Meeting 2016, Dec 12-16, 2016, San Francisco, CA, USA."]}
DOI: 10.5281/zenodo.2557863
title: InSAR Stack of Kuju volcano in Kyushu, Japan from ALOS-1

description: A stack of unwrapped interferograms on Kuju volcano, Kyushu, Japan Sensor: ALOS-1 PALSAR ascending track 422 frame 650 Time: 2007.01.06 - 2011.01.17, 24 acquisitions, 167 interferograms Processor: ROI_PAC Tropospheric delay estimated from ERA-Interim using PyAPS is attached. This is a demo dataset prepared for the time series analysis using PySAR (https://yunjunz.github.io/PySAR/).
description: {"references": ["Yunjun, Z., Amelung F., Aoki Y., (2016). Poster: A time series InSAR survey of volcanic deformation in Kyushu, SW Japan with JERS and ALOS data (G51B-1113). AGU Fall Meeting 2016, Dec 12-16, 2016, San Francisco, CA, USA."]}
DOI: 10.5281/zenodo.1059006
title: InSAR stack of Kuju volcano in Kyushu, Japan from ALOS ascending track 422 processed with ROI_PAC

description: A stack of unwrapped interferograms on Kuju volcano, Kyushu, Japan


Sensor: ALOS PALSAR ascending track 422 frame 650


Time: 2007.01.06 - 2011.01.17, 24 acquisitions, 167 interferograms


Processor: ROI_PAC


Tropospheric delay estimated from ERA-5 using PyAPS is attached.


This is an input dataset for the time series analysis with MintPy.
DOI: 10.5281/zenodo.2748170
title: InSAR Stack of Kuju volcano in Kyushu, Japan from ALOS-1

description: A stack of unwrapped interferograms on Kuju volcano, Kyushu, Japan Sensor: ALOS-1 PALSAR ascending track 422 frame 650 Time: 2007.01.06 - 2011.01.17, 24 acquisitions, 167 interferograms Processor: ROI_PAC Tropospheric delay estimated from ERA-Interim using PyAPS is attached. This is an input dataset for the time series analysis with MintPy.
description: {"references": ["Yunjun, Z., Amelung F., Aoki Y., (2016). Poster: A time series InSAR survey of volcanic deformation in Kyushu, SW Japan with JERS and ALOS data (G51B-1113). AGU Fall Meeting 2016, Dec 12-16, 2016, San Francisco, CA, USA."]}
DOI: 10.5281/zenodo.3635262
title: InSAR Stack of Kuju volcano in Kyushu, Japan from ALOS-1

description: A stack of unwrapped interferograms on Kuju volcano, Kyushu, Japan Sensor: ALOS-1 PALSAR ascending track 422 frame 650 Time: 2007.01.06 - 2011.01.17, 24 acquisitions, 167 interferograms Processor: ROI_PAC Tropospheric delay estimated from ERA-5 using PyAPS is attached. This is an input dataset for the time series analysis with MintPy.
description: {"references": ["Yunjun, Z., Amelung F., Aoki Y., (2016). Poster: A time series InSAR survey of volcanic deformation in Kyushu, SW Japan with JERS and ALOS data (G51B-1113). AGU Fall Meeting 2016, Dec 12-16, 2016, San Francisco, CA, USA."]}
DOI: 10.5281/zenodo.3952917
title: InSAR stack of Kuju volcano in Kyushu, Japan from ALOS ascending track 422 processed with ROI_PAC

description: A stack of unwrapped interferograms on Kuju volcano, Kyushu, Japan


Sensor: ALOS PALSAR ascending track 422 frame 650


Time: 2007.01.06 - 2011.01.17, 24 acquisitions, 167 interferograms


Processor: ROI_PAC


Tropospheric delay estimated from ERA-5 using PyAPS is attached.


This is an input dataset for the time series analysis with MintPy.
DOI: 10.15468/dl.thqphg
title: Occurrence Download

description: A dataset containing 737168 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pteridium aquilinum (L.) Kuhn"
 ]
}

 The dataset includes 737168 records from 2983 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029005-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dhngug
title: Occurrence Download

description: A dataset containing 377 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Carex perakensis C.B.Clarke"
 ]
}

 The dataset includes 377 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082173-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2m353t
title: Occurrence Download

description: A dataset containing 11880738 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Ecuador"
 ]
}

 The dataset includes 11880738 records from 2165 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038740-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.