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SuperDARN Grid data in netCDF format (2019-Jun)

2019-Jun SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN Grid data in netCDF format (2019-Feb)

2019-Feb SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN Grid data in netCDF format (2019-Dec)

2019-Dec SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN Grid data in netCDF format (2019-Jul)

2019-Jul SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN Grid data in netCDF format (2019-Aug)

2019-Aug SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN Grid data in netCDF format (2019-Oct)

2019-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN Grid data in netCDF format (2019-Jan)

2019-Jan SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

opencc-zeroMay 2023View details →

SAMI3 data in netCDF format (2019-Mar)

This SAMI3 monthly record contains references to the daily records for March 2019.

opencc-by-4.0May 2023View details →

Le Morne drone survey 2019 (Coral bleaching event)

Drone images collected by DJI Mavic Pro of the Morne lagoon (Mauritius) and reef during a coral bleaching event in 2019 Transects flights, could be use in photogrammetry software.

Figure 8 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia

Figure 8. Examined and verified occurrence records of (A) P. annabelli and (B) P. morti from the Australian Museum dry collection. Main maps showing records in NSW, Queensland and Victoria. Insets showing all of Australia. Source of base map: Hans Braxmeier, maps-for-free.com.

Figure 6 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia

Figure 6. Boxplots showing comparisons between Paralaoma annabelli and Paralaoma morti in shape metrics: (A) SW/SH, (B) SW/ NOW, (C) SH/NOW, (D) BWH/SH, (E) AW/SW, (F) AH/SH, (G) AH/BWH, (H) AH/AW, and (I) UW/SW. Asterisk above pairwise comparisons of the two species indicate significant difference (p ≤ 0.003) with a Wilcoxon Rank Sum Test with Bonferroni correction.

Figure 5 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia

Figure 5. Boxplots showing comparisons between Paralaoma annabelli and Paralaoma morti in size and shape metrics: (A) Umbilicus width, (B) Shell width, (C) Shell height, (D) Protoconch width, (E) Body whorl height, (F) Aperture height, (G) Aperture width, (H) Number of whorls, and (I) UW/NOW. Asterisk above pairwise comparisons of the two species indicate significant difference (p ≤ 0.003) with a Wilcoxon Rank Sum Test with Bonferroni correction.

Figure 4 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia

Figure 4. Best Maximum Likelihood tree based on analysis of ELAV-I8 sequences. Numbers on branches indicate nodal support based on 10,000 ultra-fast bootstrap replicates. Scale bar indicating 2% of modelled sequence divergence. Note that the species for the immature specimens were misidentified (L638 = Iotula microcosmos misidentified as P. morti; L645 = Pseudiotula eurysiana misidentified as P. annabelli).

Figure 3 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia

Figure 3. Best Maximum Likelihood tree based on analysis of the concatenated dataset of 16S and COI genes. Numbers on branches indicate nodal support by 10,000 ultra-fast bootstrap replicates. Scale bar indicating 10% of modelled sequence divergence. Note that the species for the immature specimens were misidentified (L638 = Iotula microcosmos misidentified as P. morti; L645 = Pseudiotula eurysiana misidentified as P. annabelli).

Figure 2 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia

Figure 2. Shell dimensions measured. Abbreviations: SW, shell width; AW, aperture width; SH, shell height; BWH, body whorl height; AH, aperture height; UW, umbilicus width; and PW, protoconch width.

Figure 1 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia

Figure 1. Shells of (A–B) Paralaoma morti and (C–D) Paralaoma annabelli: (A) lectotype, Darling Point (AM C.031181); (B) Woollahra, 1 km from lectotype locality of P. morti (AM C.347565); (C) holotype, Wombeyan Caves (AM C.353384); (D) Jenolan Caves (AM C.466715). Main scale bar = 1 mm. Inset scale bar = 100 µm.

Figure 7 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia

Figure 7. Scatterplots of shell shape metrics for Paralaoma annabelli and Paralaoma morti: (A) AH/AW, (B) UW/SW, (C) SW/SH, (D) SW/NOW, (E) UW/NOW, (F) BWH/SH, (G) AW/SW, (H) AH/SH, (I) AH/BWH, and (J) SH/NOW.

opencc-by-4.0May 2023View details →

FABLE Scenathon database 2019

This database contains key parameters and variables from the 2019 Scenathon run by the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Consortium. A scenathon - a scenario marathon - is a multi-objective challenge that allows a decentralized global modeling approach with multiple models developed by different teams in the world at national and regional scales and a methodology to link them, ensuring international trade consistency and tracking collective progress towards the achievement of global sustainability targets.  The Scenathon 2019 database includes results at the global, country, and rest of the world region levels for indicators related to food and nutrition security, land and biodiversity, GHG emissions from agriculture and land use change, and agricultural input use. It also includes key parameters that can be used to explain the results, such as the evolution of productivity and all supply and use balance items at the commodity level. It is possible to visualise some of the key results on the Scenathon dashboard. Scope of the 2019 database: <ul> <li>Pathways: The Current Trends (CT) pathway reflects a low-ambition future shaped by existing policies.</li> <li>Countries and regions: Argentina, Australia, Brazil, Canada, China, Colombia, Ethiopia, Finland, Germany, India, Indonesia, Malaysia, Mexico, Russia, Rwanda, Sweden, South Africa, the UK, and the United States and Rest of America, Rest of Asia, Rest of Central Asia, Rest of European Union, Rest of Middle East, Rest of non-European Union, Rest of Pacific.</li> <li>Time: 2000-2050, results are provided for each five-year-time step.</li> <li>Trade adjustment: results are provided before and after the trade adjustment; the total imports are balanced.</li> </ul> The readme worksheet provides all the relevant information on the indicators and definitions of acronyms used in the database.

opencc-by-4.0Jul 2023View details →

Backward and forward in time particle-tracking simulation in the Kuroshio Extension re-circulation gyre in 2019

Tracers were released at the targeted mesoscale eddy in September 01 2019, and their surface transport was modeled for the previous 26 days and the forward 72 days. Units are expressed as the number of tracer particles in each glid of 1/12° horizontal resolution. The color shades indicate the number of particles.