cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4496923
title: HYCOM Outputs Used in the paper

description: The Hybrid Coordinate Ocean Model (HYCOM) is conducted in this paper to characterize intraseasonal features of equatorial currents and to explore the dynamics process in the Indian Ocean. The model has a horizontal resolution of 0.25°´0.25° and 26 vertical layers in the Indian Ocean Basin (50°S-30°N, 30°-122.5°N). The model is spun up from a state of rest for 30 years using monthly climatological forcing, and then is integrated forward by cross-calibrated Multi-Platform (CCMP) satellite ocean surface winds (Atlas et al., 2008) from 1 March 2000 to 31 December 2010 and by the daily 0.25 ° × 0.25° gridded Advanced Scatterometer (ASCAT) satellite ocean surface winds (Bentamy and Fillon, 2012) from 1 January 2011 to 31 December 2012. This experiment is named the main run (MR). We performed two additional experiments besides MR, named NoISO and NoSTRESS. In the NoISO, the forcing is overall atmospheric ISOs that fully filtered with a 105-day low-pass Lanczos digital filter. The outputs of the three experiments are daily data, we used the records of 2001-2012 in our analysis. MR-- Controlled experiment NoISO -- There is no intraseasonal oscillation (ISO) in the driving atmospheric field in this experiment, so it can measure the effect of oceanic interannual instability, and MR-NoISO measure the effect of atmospheric ISOs) NoStress -- There is no intraseasonal wind stress in the driving atmospheric field in this experiment, so MR-NoStress measures the effect of intraseasonal wind stress
DOI: 10.5281/zenodo.4496922
title: HYCOM Outputs Used in the paper

description: The Hybrid Coordinate Ocean Model (HYCOM) is conducted in this paper to characterize intraseasonal features of equatorial currents and to explore the dynamics process in the Indian Ocean. The model has a horizontal resolution of 0.25°´0.25° and 26 vertical layers in the Indian Ocean Basin (50°S-30°N, 30°-122.5°N). The model is spun up from a state of rest for 30 years using monthly climatological forcing, and then is integrated forward by cross-calibrated Multi-Platform (CCMP) satellite ocean surface winds (Atlas et al., 2008) from 1 March 2000 to 31 December 2010 and by the daily 0.25 ° × 0.25° gridded Advanced Scatterometer (ASCAT) satellite ocean surface winds (Bentamy and Fillon, 2012) from 1 January 2011 to 31 December 2012. This experiment is named the main run (MR). We performed two additional experiments besides MR, named NoISO and NoSTRESS. In the NoISO, the forcing is overall atmospheric ISOs that fully filtered with a 105-day low-pass Lanczos digital filter. The outputs of the three experiments are daily data, we used the records of 2001-2012 in our analysis. MR-- Controlled experiment NoISO -- There is no intraseasonal oscillation (ISO) in the driving atmospheric field in this experiment, so it can measure the effect of oceanic interannual instability, and MR-NoISO measure the effect of atmospheric ISOs) NoStress -- There is no intraseasonal wind stress in the driving atmospheric field in this experiment, so MR-NoStress measures the effect of intraseasonal wind stress
DOI: 10.5281/zenodo.4497369
title: Chemoreceptor hyperactivity in heart failure: is lactate the culprit?

description: we propose that in patients with HF, the blunted cardiac output during exercise results in inadequate oxygen delivery to working muscle, and earlier anaerobic metabolism which increase both lactate production and H+ and both are stimulus that augment the ventilatory drive and induce an early reduction of PaCO2 tensions. The link between lactate and hyperventilation is further supported by the demonstration that a reduction of arterial lactate concentration throughout exercise is associated with a reduction of ventilatory response to exercise in patients with HF after 2 months of exercise training.10 Also, reduction of blood lactate concentrations by dichloroacetate administration is associated with an improvement of myocardial function in patients with HF. We recognize that the documentation of the correlation between blood lactate concentration and VE/VCO2 slope during exercise in HF does not prove a cause-effect relationship. Accordingly, the present report should be considered only as hypothesis generating but this fascinating hypothesis has a strong physiological background but needs to be confirmed by dedicated studies.
DOI: 10.5281/zenodo.4497368
title: Chemoreceptor hyperactivity in heart failure: is lactate the culprit?

description: we propose that in patients with HF, the blunted cardiac output during exercise results in inadequate oxygen delivery to working muscle, and earlier anaerobic metabolism which increase both lactate production and H+ and both are stimulus that augment the ventilatory drive and induce an early reduction of PaCO2 tensions. The link between lactate and hyperventilation is further supported by the demonstration that a reduction of arterial lactate concentration throughout exercise is associated with a reduction of ventilatory response to exercise in patients with HF after 2 months of exercise training.10 Also, reduction of blood lactate concentrations by dichloroacetate administration is associated with an improvement of myocardial function in patients with HF. We recognize that the documentation of the correlation between blood lactate concentration and VE/VCO2 slope during exercise in HF does not prove a cause-effect relationship. Accordingly, the present report should be considered only as hypothesis generating but this fascinating hypothesis has a strong physiological background but needs to be confirmed by dedicated studies.
DOI: 10.5066/p9owrtw8
title: Data for Beach Mice Bayesian Network Model

description: This U.S. Geological Survey (USGS) data release represents tabular and geospatial data for the Biological Objectives for the Gulf Coast Projects Beach Mice Bayesian network model. The data release was produced in compliance with 'open data' requirements as a way to make the scientific products associated with USGS research efforts and publications available to the public. The release consists of six items: 1. Bayesian network model that predicts the annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Tabular datasets) 2. Bayesian network model beach mice casefile (Tabular dataset) 3. Bayesian network model detection casefile (Tabular dataset) 4. Bayesian network model output of the 2009 annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Raster datasets) 5. Bayesian network model output of the 2010 annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Raster datasets) 6. Bayesian network model output of the 2011 annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Raster datasets) The USGS partnered with the U.S. Fish and Wildlife Service (USFWS), the Florida Fish and Wildlife Conservation Commission, and their conservation partners to develop a Bayesian Network model that predicts the annual probability of beach mouse presence at a local (30-m) scale. The model was used to predict the annual probability of presence across a portion of the USFWS's Central Gulf and Florida Panhandle Coast Biological Planning Unit. This spatial extent included critical habitat for three endangered subspecies of beach mice (Peromyscus polionotus ssp.). The annual probability of beach mouse presence is predicted from both local and neighborhood habitat characteristics that could be influenced by management actions. The model was created using a combination of expert elicitation, simplifying assumptions, literature-derived empirical values, and a beach mouse detection and nondetection survey. When coupled with established population objectives, this study can provide insight into how much habitat is available, how much more is needed, and where conservation or restoration efforts can most efficiently achieve established objectives. The results could be used to help guide strategic habitat conservation and adaptive management of beach mice.
DOI: 10.15468/gszea6
title: A review of Solierella Spinola, 1851 (Hymenoptera, Crabronidae) from Iran with description of four new species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Schmid-Egger, Christian, Fallahzadeh, Majid, Sadeghi, Maryam (2021): A review of Solierella Spinola, 1851 (Hymenoptera, Crabronidae) from Iran with description of four new species. Zootaxa 4920 (1): 101-113, DOI: https://doi.org/10.11646/zootaxa.4920.1.4
DOI: 10.5258/soton/d1212
title: Dataset for: The possibilities of using Ultrasonically Activated Streams to reduce the risk of foodborne infection from salad

description: Dataset supports: Chong, W.Y., Secker, T., Dolder, C., Keevil, C., & Leighton, T. (2021). The possibilities of using Ultrasonically Activated Streams to reduce the risk of foodborne infection from salad. Ultrasound in Medicine & Biology (in press)
DOI: 10.15468/gjpxrh
title: Three new species of oribatid mites of the family Galumnidae (Acari, Oribatida) from South Africa

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ermilov, Sergey G., Hugo-Coetzee, Elizabeth A., Khaustov, Alexander A. (2021): Three new species of oribatid mites of the family Galumnidae (Acari, Oribatida) from South Africa. Zootaxa 4920 (1): 91-100, DOI: https://doi.org/10.11646/zootaxa.4920.1.3
DOI: 10.5517/ccdc.csd.cc25rh1h
title: CCDC 2018566: Experimental Crystal Structure Determination

description: Related Article: Jinhua Wang, Barbara Wicher, Victor Maurizot, Ivan Huc|2020|Chem.-Eur.J.|27|1031|doi:10.1002/chem.202003559
DOI: 10.5517/ccdc.csd.cc25rh2j
title: CCDC 2018567: Experimental Crystal Structure Determination

description: Related Article: Jinhua Wang, Barbara Wicher, Victor Maurizot, Ivan Huc|2020|Chem.-Eur.J.|27|1031|doi:10.1002/chem.202003559
DOI: 10.5517/ccdc.csd.cc25rh3k
title: CCDC 2018568: Experimental Crystal Structure Determination

description: Related Article: Jinhua Wang, Barbara Wicher, Victor Maurizot, Ivan Huc|2020|Chem.-Eur.J.|27|1031|doi:10.1002/chem.202003559
DOI: 10.5517/ccdc.csd.cc25rh4l
title: CCDC 2018569: Experimental Crystal Structure Determination

description: Related Article: Jinhua Wang, Barbara Wicher, Victor Maurizot, Ivan Huc|2020|Chem.-Eur.J.|27|1031|doi:10.1002/chem.202003559
DOI: 10.5517/ccdc.csd.cc254qmp
title: CCDC 2000542: Experimental Crystal Structure Determination

description: Related Article: Jesús A. Lara-Cerón, Víctor M. Jiménez-Pérez, Areli A. Molina-Paredes, Mario Sánchez, H. V. Rasika Dias, Pedro I. Ramírez-Montes, Blanca M. Muñoz-Flores|2021|New J.Chem.|45|1727|doi:10.1039/D0NJ04720A
DOI: 10.25403/upresearchdata.13302191
title: Investigating how the black middle class cope with the black tax obligation in South Africa

description: Interviews for a qualitative study on investigating how black middle class cope with their obligations on black tax.
DOI: 10.25403/upresearchdata.13302191.v1
title: Investigating how the black middle class cope with the black tax obligation in South Africa

description: Interviews for a qualitative study on investigating how black middle class cope with their obligations on black tax.
DOI: 10.25377/sussex.12912699
title: Dataset for paper "Larger GPU-accelerated brain simulations with procedural connectivity"

description: <b>Dataset for paper published in Nat Comput Sci Feb 2021</b><b><br></b>Dataset contains raw spiking data from full-scale multi-area model simulation run using GeNN 4.3.3. Each tar.gz archive contains the configuration files for each simulation and, in the recording directory, binary numpy files contains the spike trains from each population.<br>Archives with filenames starting with <b>82d3c0816b0ad1c07ea27e61eb981f7a </b>contain spike data from three 10.5 second "ground state" simulations of the model's "ground state" (chi=1.0)<br>Archives with filenames starting with <b>b03fdaa1fd47a0e4a10483bc3901f1e5 </b>contain spike data from three 100.5 second "ground state" simulations of the model's "resting state" (chi=1.9)<br><br><b>Abstract</b><br>"Simulations are an important tool for investigating brain function but large models are needed to faithfully reproduce the statistics and dynamics of brain activity.Simulating large spiking neural network models has, until now, needed so much memory for storing synaptic connections that it required high performance computer systems. Here, we present an alternative simulation method we call `procedural connectivity' where connectivity and synaptic weights are generated `on the fly' instead of stored and retrieved from memory. This method is particularly well-suited for use on Graphical Processing Units (GPUs) - which are a common fixture in many workstations. Extending our GeNN software with procedural connectivity and a second technical innovation for GPU code generation, we can simulate a recent model of the Macaque visual cortex with 4.13<sup>6</sup> neurons and 24.2<sup>9 </sup>synapses on a single GPU - a significant step forward in making large-scale brain modelling accessible to more researchers."<br>FundingBrains on Board grant number EP/P006094/1<br><br>
DOI: 10.25377/sussex.12912699.v1
title: Dataset for paper "Larger GPU-accelerated brain simulations with procedural connectivity"

description: <b>Dataset for paper published in Nat Comput Sci Feb 2021</b><b><br></b>Dataset contains raw spiking data from full-scale multi-area model simulation run using GeNN 4.3.3. Each tar.gz archive contains the configuration files for each simulation and, in the recording directory, binary numpy files contains the spike trains from each population.<br>Archives with filenames starting with <b>82d3c0816b0ad1c07ea27e61eb981f7a </b>contain spike data from three 10.5 second "ground state" simulations of the model's "ground state" (chi=1.0)<br>Archives with filenames starting with <b>b03fdaa1fd47a0e4a10483bc3901f1e5 </b>contain spike data from three 100.5 second "ground state" simulations of the model's "resting state" (chi=1.9)<br><br><b>Abstract</b><br>"Simulations are an important tool for investigating brain function but large models are needed to faithfully reproduce the statistics and dynamics of brain activity.Simulating large spiking neural network models has, until now, needed so much memory for storing synaptic connections that it required high performance computer systems. Here, we present an alternative simulation method we call `procedural connectivity' where connectivity and synaptic weights are generated `on the fly' instead of stored and retrieved from memory. This method is particularly well-suited for use on Graphical Processing Units (GPUs) - which are a common fixture in many workstations. Extending our GeNN software with procedural connectivity and a second technical innovation for GPU code generation, we can simulate a recent model of the Macaque visual cortex with 4.13<sup>6</sup> neurons and 24.2<sup>9 </sup>synapses on a single GPU - a significant step forward in making large-scale brain modelling accessible to more researchers."<br>FundingBrains on Board grant number EP/P006094/1<br><br>
DOI: 10.6084/m9.figshare.13701889
title: Apertium RDF v2.1 example queries

description: Some example queries, to be run on the Apertum RDF v2.1 SPARQL endpoint, which illustrate how to obtain information related to lexical entries, as well as direct and indirect translations across languages.<br>Temporarily, such queries can be run in this testing SPARQL endpoint by University of Frankfurt:http://dbserver.acoli.cs.uni-frankfurt.de:5005/dataset.html?tab=query&ds=/apertium<br>If you are accessing it programmatically, you can use this URL instead: http://dbserver.acoli.cs.uni-frankfurt.de:5005/apertium/query
DOI: 10.6084/m9.figshare.13701889.v2
title: Apertium RDF v2.1 example queries

description: Some example queries, to be run on the Apertum RDF v2.1 SPARQL endpoint, which illustrate how to obtain information related to lexical entries, as well as direct and indirect translations across languages.<br>Temporarily, such queries can be run in this testing SPARQL endpoint by University of Frankfurt:http://dbserver.acoli.cs.uni-frankfurt.de:5005/dataset.html?tab=query&ds=/apertium<br>If you are accessing it programmatically, you can use this URL instead: http://dbserver.acoli.cs.uni-frankfurt.de:5005/apertium/query
DOI: 10.6084/m9.figshare.13701889.v1
title: Apertium RDF v2.1 example queries

description: Some example queries, to be run on the Apertum RDF v2.1 SPARQL endpoint, which illustrate how to obtain information related to lexical entries, as well as direct and indirect translations across languages.<br>Temporarily, such queries can be run in this testing SPARQL endpoint by University of Frankfurt:http://dbserver.acoli.cs.uni-frankfurt.de:5005/dataset.html?tab=query&ds=/apertium<br>If you are accessing it programmatically, you can use this URL instead: http://dbserver.acoli.cs.uni-frankfurt.de:5005/apertium/query
DOI: 10.15468/yyfb4w
title: Late Triassic to Early Jurassic radiolarian, conodont and ammonite assemblages from the Tavuscayiri block, Mersin Mélange, southern Turkey: Time constraints for the T / J boundary and sedimentary evolution of the southern margin of the northern Neotethys

description: This dataset contains the digitized treatments in Plazi based on the original journal article Tekin, Kagan, Krystyn, Leopold, Okuyucu, Cengiz, Bedi, Yavuz, Sayit, Kaan (2020): Late Triassic to Early Jurassic radiolarian, conodont and ammonite assemblages from the Tavuscayiri block, Mersin Mélange, southern Turkey: Time constraints for the T / J boundary and sedimentary evolution of the southern margin of the northern Neotethys. Geodiversitas 42 (27): 493-537, DOI: 10.5252/geodiversitas2020v42a27
DOI: 10.21942/uva.13693702.v8
title: Geomorphology model, input datasets and legend symbology files for use in ArcGIS Pro

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results:Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.21942/uva.13643189.v8
title: Geomorphology model, input datasets and legend symbology files for use in ArcMap

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results: Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.21942/uva.13693702.v9
title: Geomorphology model, input datasets and legend symbology files for use in ArcGIS Pro

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results:Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.21942/uva.13693702.v10
title: Geomorphology model, input datasets and legend symbology files for use in ArcGIS Pro

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results:Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.21942/uva.13643189.v9
title: Geomorphology model, input datasets and legend symbology files for use in ArcMap

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results: Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.1285/i17201632vxxiiin41-42pi
title: Copertina
DOI: 10.1285/i17201632vxxiiin41-42p1
title: Pagine iniziali e sommario
DOI: 10.1285/i17201632vxxiiin41-42p7
title: Indice
DOI: 10.1285/10.1285/i17201632vxxiiin41-42p9
title: Editoriale: Mass-media e realtà "suggerita"