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Figures 22–24 in Abrolophus balkanicus sp. nov. from Montenegro, with redescriptions of A. stanislavae (Haitlinger, 1986) and A. wratislaviensis (Haitlinger, 1986) and notes on A. podorasensis (Haitlinger, 2007) (Acari: Erythraeidae)

Figures 22–24. Abrolophus wratislaviensis (Haitlinger) (larva). 22. Leg I; 23. Leg II; 24. Leg III.

Figures 14–16 in Abrolophus balkanicus sp. nov. from Montenegro, with redescriptions of A. stanislavae (Haitlinger, 1986) and A. wratislaviensis (Haitlinger, 1986) and notes on A. podorasensis (Haitlinger, 2007) (Acari: Erythraeidae)

Figures 14–16. Abrolophus stanislavae (Haitlinger) (larva). 14. Leg I; 15. Leg II; 16. Leg III.

Figures 1–2 in Abrolophus balkanicus sp. nov. from Montenegro, with redescriptions of A. stanislavae (Haitlinger, 1986) and A. wratislaviensis (Haitlinger, 1986) and notes on A. podorasensis (Haitlinger, 2007) (Acari: Erythraeidae)

Figures 1–2. Abrolophus balkanicus sp. nov. (larva). 1. Dorsal view of idiosoma; 2. Scutum.

Figures 9–10 in Abrolophus balkanicus sp. nov. from Montenegro, with redescriptions of A. stanislavae (Haitlinger, 1986) and A. wratislaviensis (Haitlinger, 1986) and notes on A. podorasensis (Haitlinger, 2007) (Acari: Erythraeidae)

Figures 9–10. Abrolophus stanislavae (Haitlinger) (larva). 9. Dorsal view of idiosoma; 10. Scutum.

Figures 6–8 in Abrolophus balkanicus sp. nov. from Montenegro, with redescriptions of A. stanislavae (Haitlinger, 1986) and A. wratislaviensis (Haitlinger, 1986) and notes on A. podorasensis (Haitlinger, 2007) (Acari: Erythraeidae)

Figures 6–8. Abrolophus balkanicus sp. nov. (larva). 6. Leg I; 7. Leg II; 8 Leg III.

opencc-by-4.0Oct 2021View details →

Environmental watering volumes (ML) used (held and planned environmental water) on the Murray-Darling Basin between 2007-08 and 2020-21

An assessment of the volumes of environmental water released in the Murray-Darling Basin, Australia, by catchment, between 2007-08 and 2020-21

opencc-by-4.0Dec 2021View details →

Rome (ITALY) - Urban Agriculture spatial dataset (years 2007 and 2013)

Motivation The data in this dataset is a spatial inventory of urban agriculture (UA) carried out in the city of Rome (Italy) (Grande Raccordo Anulare (GRA)). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps. License Creative Commons CC-BY Disclaimer Despite our best efforts to validate the data, some information may be incorrect. Description of the dataset Typologies of UA <ul> <li>Residential garden: Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.</li> <li>Community garden: A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.</li> <li>Urban farm: Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus exclusively on horticulture, vineyard, olive groves and orchard.</li> <li>Institutional garden: Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).</li> <li>Illegal garden: Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.</li> </ul> Land use typologies <ul> <li>Horticulture: annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).</li> <li>Vineyard: grape vines grown in order to produce wine or table grape.</li> <li>Olive groves: olive trees grown in order to produce olive oil or table olives.</li> <li>Orchards: mixed trees such as orange, stone fruit, pome fruit, olive trees.</li> <li>Mixed crops: an area grown with a mix of horticulture crops and fruit trees, not divisible.</li> </ul> Credit Pulighe G., Lupia F. (2016) Mapping spatial patterns of urban agriculture in Rome (Italy) using Google Earth and web-mapping services. Land Use Policy 59(2016) 49-58. <a href="http://www.sciencedirect.com/science/article/pii/S0264837716300059">www.sciencedirect.com/science/article/pii/S0264837716300059</a>

opencc-by-4.0Apr 2022View details →

Grain size distribution of Amazon river sediment samples collected over the period 2005-2008; and ADCP water velocity profiles collected on the major tributaries of the Amazon in Bolivia and Peru, 2007-2008

This dataset contains two items: - The grain size distribution of river sediment samples collected along the Amazon River and its tributaries during four sampling campaigns performed in June 2005 (lower Amazon, Brazil), March 2006 (lower Amazon, Brazil), May 2007 (Upper Madeira, Bolivia), and April 2008 (Upper Solimões-Amazonas, Peru). [spreadsheet "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_data.xlsx"]. -  River water velocity profiles derived from Acoustic Doppler Current Profiler (ADCP) measurements performed on the major tributaries of the Amazon in May 2007 (Upper Madeira, Bolivia) and April 2008 (Upper Solimões-Amazonas, Peru) [folder "ADCP_dataset_Amazon_2007-2008_Bouchez_data"]. The dataset description and the relevant references are provided in the text files "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_description.docx" and "ADCP_dataset_Amazon_2007-2008_Bouchez_description.docx" . These data were acquired thanks to the support of the French National Service for Observation "HYBAM" ("Hydrogeochemistry of the Amazon Basin"), part of the CNRS National Infrastructure "OZCAR" ("Critical Zone Observatories: Applications and Research").

SuperDARN data in netCDF format (2007-May)

2007-May SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, 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 data in netCDF format (2007-Aug)

2007-Aug SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, 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 data in netCDF format (2007-Sep)

2007-Sep SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, 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 data in netCDF format (2007-Nov)

2007-Nov SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, 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 data in netCDF format (2007-Jul)

2007-Jul SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, 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 data in netCDF format (2007-Dec)

2007-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, 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 data in netCDF format (2007-Feb)

2007-Feb SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, 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 data in netCDF format (2007-Jun)

2007-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, 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 data in netCDF format (2007-Mar)

2007-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, 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 data in netCDF format (2007-Oct)

2007-Oct SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, 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.'

openmit-licenseJul 2022View details →

Seismic Noise from Gornergletscher, Switzerland (Summer 2007)

This is a subset of open access data from the following reference; please cite accordingly:  Walter, F. (2009). Seismic activity on gornergletscher during gornersee outburst floods. Dissertation, 31(1-2), 14–27. https://doi.org/10 .1007/BF03322148 Curated by T. Sawi for the manuscript (in submission), "An Unsupervised Machine-Learning Approach to Understanding Seismicity at an Alpine Glacier" (2022) by T. Sawi; B. Holtzman; M. Nettles; F. Walter; J. Paisley; submitted to JGR: Earth Surface in August 2022. This data set contains sixty-second samples of glacial seismic noise (N=1,478) from the vertical channel of a Geospace GS-11D geophone operating in shallow borehole at Gornergletscher between June 14 and July 22, 2007. Sampling rate is 1000 samples per second, and the natural frequency of the instrument is around 8 Hz. An STA/LTA filter has been applied to remove records with prominent icequakes in them, although many smaller icequakes remain in the records. Waveform files are named after their event origin time in format 7{MDhms}. For example, waveform "70614004000.sac" occurred on June 14 00:40:00, 2007.