cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7267/t148fq49f
title: Southern Sierra Nevada Fractional Land Cover 2014 - 2018

description: This dataset is designed to capture landscape changes brought on by a severe tree mortality episode in California's southern Sierra Nevada range, by mapping multiple land cover types in a continuous way at annual time-steps throughout the mortality event. By mapping multiple land cover types we can see not only the occurrence of tree mortality explicitly, but also associated landscape changes, such as reduced live forest cover and increased bare ground cover. Fractional cover maps provide a continuous measure of the percentage of a land cover type present within each pixel across a landscape. We modeled fractional land cover in the southern Sierra Nevada at annual time steps (2014-2018) using a two-step approach. First, we created high resolution (5x5-meter pixel) categorical models of land cover for a ~2000 km2 area by training random forest classification models on RapidEye (Planet Labs Inc.) satellite spectral data for each year (McGregor et al, 2021 https://doi.org/10.7267/j9602723w) . Training data for this step were created by visually interpreting random plots over high resolution NAIP aerial imagery. Land cover classes included live forest, tree mortality, shrub, herbaceous, and bare ground. Class definitions generally follow California Wildlife Habitat Relationships (CWHR) categories. CWHR tree dominated habitats were combined to represent a single “live forest” category where trees were alive and “tree mortality” where dead. Shrub dominated habitats, namely Mixed and Montane Chaparral, were included in the “shrub” class. Perennial grasslands and wet meadow habitats were included in the “herbaceous” class, and non-vegetated habitats along with annual grasslands made up the “bare ground” class. We interpreted 75-125 samples per land cover class, per year. Model accuracy was evaluated using a 30% holdout of training data per class. To construct fractional cover models, we created new training data representing land cover proportions by overlaying 30x30-meter square plots (matching Landsat pixel resolution) with the 5-meter categorical maps. Proportional values for each class (excluding herbaceous) were calculated based on the 36 categorical pixels nested within each 30m plot. These new training data were then used to model the relationship between proportional cover values and Landsat 8 derived spectral covariates using a random forest regression model. See metadata file for accuracy assessment details. The output pixel values represent estimates of the percentage of a land cover type within each pixel. Pixel values should be multiplied by 0.0001 to convert from integer to decimal values representing proportions (0.0 - 1.0). Tree mortality was not modeled in 2014 as it was scarce in the study area at that time, and no fractional cover maps were created for herbaceous cover, leaving a total of 19 individual fractional cover maps. Tree mortality estimates represent the proportion of a pixel covered by dead trees, and includes all standing dead trees regardless of time since mortality.
DOI: 10.5281/zenodo.5530142
title: Wave Parameters - Azores - Period 1995-2004 - MODEL: Wavewatch III - Global Driver: ACCESS

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> ACCESS (Australian Community Climate and Earth System Simulator) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.05° x 0.05° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 36.5° northernmost latitude = 39.5° westernmost longitude = 330.5° easternmost longitude = 335.8° <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.5281/zenodo.5530141
title: Wave Parameters - Azores - Period 1995-2004 - MODEL: Wavewatch III - Global Driver: ACCESS

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> ACCESS (Australian Community Climate and Earth System Simulator) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.05° x 0.05° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 36.5° northernmost latitude = 39.5° westernmost longitude = 330.5° easternmost longitude = 335.8° <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.5281/zenodo.5530276
title: Wave Parameters - Azores - Period 1996-2005 - MODEL: Wavewatch III - Global Driver: HadGEM

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> HadGEM (Hadley Centre Global Environmental Model) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.05° x 0.05° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 36.6° northernmost latitude = 39.5° westernmost longitude = 330.5° easternmost longitude = 335.8° <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.5281/zenodo.5530275
title: Wave Parameters - Azores - Period 1996-2005 - MODEL: Wavewatch III - Global Driver: HadGEM

description: <strong>Wave Model:</strong> WAVEWATCH_III - version number 5.16 <strong>Global driver: </strong> HadGEM (Hadley Centre Global Environmental Model) <strong>Variables:</strong> Significant Wave Height Mean period, peak frequency Mean wave direction 0.05° x 0.05° horizontal resolution - 3h time resolution <strong>Region: </strong> southernmost latitude = 36.6° northernmost latitude = 39.5° westernmost longitude = 330.5° easternmost longitude = 335.8° <strong>360-day calendar</strong> <strong>NetCDF format</strong>
description: The data can also be downloaded from the SOCLIMPACT FTP Repository hosted at https://giotto.casaccia.enea.it/soclimpact/index.php/
DOI: 10.6084/m9.figshare.16688782.v1
title: Hyperpolarized [1‐13C]pyruvate combined with the hyperinsulinemic euglycemic and hypoglycemic clamp technique in skeletal muscle in a large animal model

description: Raw data from the study: Hyperpolarized [1‐13C]pyruvate combined with the hyperinsulinemic euglycemic and hypoglycemic clamp technique in skeletal muscle in a large animal model
DOI: 10.6084/m9.figshare.16688782
title: Hyperpolarized [1‐13C]pyruvate combined with the hyperinsulinemic euglycemic and hypoglycemic clamp technique in skeletal muscle in a large animal model

description: Raw data from the study: Hyperpolarized [1‐13C]pyruvate combined with the hyperinsulinemic euglycemic and hypoglycemic clamp technique in skeletal muscle in a large animal model
DOI: 10.6084/m9.figshare.16679800
title: P23

description: CCEP data for patient 23
DOI: 10.6084/m9.figshare.16679800.v1
title: P23

description: CCEP data for patient 23
DOI: 10.6084/m9.figshare.16688791
title: Supplementary Material – Chemical profile of Stachytarpheta schottiana by LC-HRMS/MS dereplication and molecular networking

description: <b>Supplemental File 1 – </b>NMR elucidation tables of compounds 9 and 13; Structures of the compounds suggested by dereplication, molecular networking, and NMR elucidation; Molecular networkings of the <i>Stachytarpheta schottiana </i>polar extract in negative mode.
DOI: 10.5281/zenodo.5532746
title: LANL electron flux and ephemeris data for two magnetospheric substorms

description: These are the data that accompany a paper titled "Impact angle control of local intense dB/dt variations during shock-induced substorms" by the same authors listed here. The paper has been submitted submitted to the journal Space Weather. There are two txt data file for two dates: 5 April 2010 and 20 September 2015. Each file contains LANL ephemeris and electron flux data (channels 86 keV, 176 keV, 254 keV, 586 keV). The first file contains LANL-1989-046 data, whereas the second file contains LANL-01A data. These data were used to make Figure 6 (LANL positions) and Figure 9 (electron flux) in the above mentioned paper.
DOI: 10.5281/zenodo.5532745
title: LANL electron flux and ephemeris data for two magnetospheric substorms

description: These are the data that accompany a paper titled "Impact angle control of local intense dB/dt variations during shock-induced substorms" by the same authors listed here. The paper has been submitted submitted to the journal Space Weather. There are two txt data file for two dates: 5 April 2010 and 20 September 2015. Each file contains LANL ephemeris and electron flux data (channels 86 keV, 176 keV, 254 keV, 586 keV). The first file contains LANL-1989-046 data, whereas the second file contains LANL-01A data. These data were used to make Figure 6 (LANL positions) and Figure 9 (electron flux) in the above mentioned paper.
DOI: 10.5281/zenodo.5532569
title: HydroDART Research Data

description: Research data obtained as part of the peer-reviewed HESS paper: "Ensemble streamflow data assimilation using WRF-Hydro and Data Assimilation Research Testbed (DART): novel localization and inflation techniques applied to Hurricane Florence flooding" (2021)
DOI: 10.5281/zenodo.5532568
title: HydroDART Research Data

description: Research data obtained as part of the peer-reviewed HESS paper: "Ensemble streamflow data assimilation using WRF-Hydro and Data Assimilation Research Testbed (DART): novel localization and inflation techniques applied to Hurricane Florence flooding" (2021)
DOI: 10.5063/8g8j46
title: Temperature data, Balbina Reservoir, Brazil, 15-20 July 2013, 10 second data.

description: Time series temperature measurements were obtained on moorings in Balbina Reservoir, Brazil, as part of a physical limnology experiment in July 2013. Temperature arrays, consisting of newly calibrated fast response temperature sensors (RBR Solos with accuracy of 0.002oC and resolution 0.0002oC) sampling at 10 second intervals, were positioned in the upper 20 m offshore (01°54'33.3'' S; 59°28'10.7'' W) at the southern end of the reservoir. The array consisted of a taut line mooring with an upper line connected to a thin float which moved freely on the water surface. One sensor was attached horizontally to the base of the float, which shielded it from direct solar radiation, allowing temperature measurements ~0.05 m below the water surface. Two other loggers, facing downwards, were suspended from the same line and obtained measurements at 0.5 and 0.95 m. The remaining loggers were below the subsurface float and measured temperatures at 1.15 m, 1.75 m, 3.25 m, 4.75 m, 6.25 m, 7.75 m, 9.25 m, 10.75 m, 12.25 m, 13.75m, 15.25 m, 16.75 m, 18.25 m, and 19.75 m below the water surface.
DOI: 10.6084/m9.figshare.16688800.v1
title: P24

description: CCEP data for patient 24
DOI: 10.6084/m9.figshare.16688800
title: P24

description: CCEP data for patient 24
DOI: 10.5281/zenodo.5528213
title: An ensemble deep learning framework to refine large deletions in linked-reads

description: An ensemble deep learning framework to refine large deletions in linked-reads
DOI: 10.5281/zenodo.5528212
title: An ensemble deep learning framework to refine large deletions in linked-reads

description: An ensemble deep learning framework to refine large deletions in linked-reads
DOI: 10.5517/ccdc.csd.cc1pjbq8
title: CCDC 1564840: Experimental Crystal Structure Determination

description: Related Article: Manoj Trivedi, Santosh Kumar Dubey, Gurmeet Kaur, Nigam P. Rath|2021|New J.Chem.|45|17339|doi:10.1039/D1NJ03041H
DOI: 10.5517/ccdc.csd.cc23203b
title: CCDC 1938340: Experimental Crystal Structure Determination

description: Related Article: Veselin Nasufović,  Florian Küllmer,  Johanna Bößneck,  Hans‐Martin Dahse,  Helmar Görls,  Peter Bellstedt,  Pierre Stallforth,  Hans‐Dieter Arndt|2021|Chem.-Eur.J.|27|11633|doi:10.1002/chem.202100989
DOI: 10.5517/ccdc.csd.cc23204c
title: CCDC 1938341: Experimental Crystal Structure Determination

description: Related Article: Veselin Nasufović,  Florian Küllmer,  Johanna Bößneck,  Hans‐Martin Dahse,  Helmar Görls,  Peter Bellstedt,  Pierre Stallforth,  Hans‐Dieter Arndt|2021|Chem.-Eur.J.|27|11633|doi:10.1002/chem.202100989
DOI: 10.5517/ccdc.csd.cc23hv9s
title: CCDC 1951645: Experimental Crystal Structure Determination

description: Related Article: Prachi Kour, Mallu Chenna Reddy, Shiv Pal, Siraj Sidhik, Tisita Das, Padmini Pandey, Shatabdi Porel Mukherjee, Sudip Chakraborty, Aditya Mohite, Satishchandra B. Ogale|2021|Angew.Chem.,Int.Ed.|60|18750|doi:10.1002/anie.202105918
DOI: 10.5517/ccdc.csd.cc23hvbt
title: CCDC 1951646: Experimental Crystal Structure Determination

description: Related Article: Prachi Kour, Mallu Chenna Reddy, Shiv Pal, Siraj Sidhik, Tisita Das, Padmini Pandey, Shatabdi Porel Mukherjee, Sudip Chakraborty, Aditya Mohite, Satishchandra B. Ogale|2021|Angew.Chem.,Int.Ed.|60|18750|doi:10.1002/anie.202105918
DOI: 10.25505/fiz.icsd.cc20lc55
title: CSD 1864686: Experimental Crystal Structure Determination

description: Related Article: Lisa Gamperl, Lukas Neudert, Peter Schultz, Dajana Durach, Wolfgang Schnick, Oliver Oeckler|2021|Chem.-Eur.J.|27|12835|doi:10.1002/chem.202101665
DOI: 10.5517/ccdc.csd.cc24gd78
title: CCDC 1980039: Experimental Crystal Structure Determination

description: Related Article: Yongwei Zhao, Haoran Zhu, Siyoung Sung, Donald J. Wink, Joseph M. Zadrozny, and Tom G. Driver|2021|Angew.Chem.,Int.Ed.|60|19207|doi:10.1002/anie.202104319
DOI: 10.5517/ccdc.csd.cc25504j
title: CCDC 2000806: Experimental Crystal Structure Determination

description: Related Article: Mojtaba Khanpour,  Wen‐Zhou Deng,  Zhi‐Bin Fang,  Yu‐Lin Li,  Qi Yin,  An‐An Zhang,  Farzaneh Rouhani,  Ali Morsali,  Tian‐Fu Liu|2021|Chem.-Eur.J.|27|10957|doi:10.1002/chem.202101061
DOI: 10.5517/ccdc.csd.cc25516m
title: CCDC 2000839: Experimental Crystal Structure Determination

description: Related Article: Mojtaba Khanpour,  Wen‐Zhou Deng,  Zhi‐Bin Fang,  Yu‐Lin Li,  Qi Yin,  An‐An Zhang,  Farzaneh Rouhani,  Ali Morsali,  Tian‐Fu Liu|2021|Chem.-Eur.J.|27|10957|doi:10.1002/chem.202101061
DOI: 10.5517/ccdc.csd.cc25jkw5
title: CCDC 2011927: Experimental Crystal Structure Determination

description: Related Article: Cheng Zeng, Yujian Liu, Ning Xue, Wei Jiang, Shouke Yan, Zhaohui Wang|2021|Angew.Chem.,Int.Ed.|60|19018|doi:10.1002/anie.202105044
DOI: 10.5517/ccdc.csd.cc25yt87
title: CCDC 2024649: Experimental Crystal Structure Determination

description: Related Article: Timothée Cadart,  David Nečas,  Reinhard P. Kaiser,  Ludovic Favereau,  Ivana Císařová,  Róbert Gyepes,  Jana Hodačová,  Květa Kalíková,  Lucie Bednárová,  Jeanne Crassous,  Martin Kotora|2021|Chem.-Eur.J.|27|11279|doi:10.1002/chem.202100759
DOI: 10.5517/ccdc.csd.cc25yt98
title: CCDC 2024650: Experimental Crystal Structure Determination

description: Related Article: Timothée Cadart,  David Nečas,  Reinhard P. Kaiser,  Ludovic Favereau,  Ivana Císařová,  Róbert Gyepes,  Jana Hodačová,  Květa Kalíková,  Lucie Bednárová,  Jeanne Crassous,  Martin Kotora|2021|Chem.-Eur.J.|27|11279|doi:10.1002/chem.202100759
DOI: 10.5517/ccdc.csd.cc25ytb9
title: CCDC 2024651: Experimental Crystal Structure Determination

description: Related Article: Timothée Cadart,  David Nečas,  Reinhard P. Kaiser,  Ludovic Favereau,  Ivana Císařová,  Róbert Gyepes,  Jana Hodačová,  Květa Kalíková,  Lucie Bednárová,  Jeanne Crassous,  Martin Kotora|2021|Chem.-Eur.J.|27|11279|doi:10.1002/chem.202100759
DOI: 10.5517/ccdc.csd.cc25ytcb
title: CCDC 2024652: Experimental Crystal Structure Determination

description: Related Article: Timothée Cadart,  David Nečas,  Reinhard P. Kaiser,  Ludovic Favereau,  Ivana Císařová,  Róbert Gyepes,  Jana Hodačová,  Květa Kalíková,  Lucie Bednárová,  Jeanne Crassous,  Martin Kotora|2021|Chem.-Eur.J.|27|11279|doi:10.1002/chem.202100759
DOI: 10.5517/ccdc.csd.cc267cqk
title: CCDC 2032878: Experimental Crystal Structure Determination

description: Related Article: Eyad A. Younes, Maram J. Issa, Maryam F. Abdollahi, Yuan-Fu Ding, Anas J. Rasras, Greta S. P. Mok, Jian-Bin Lin, Yuming Zhao|2021|New J.Chem.|45|17366|doi:10.1039/D1NJ03044B
DOI: 10.25422/azu.data.15070800
title: CESA (Corpus del Español en el Sur de Arizona)

description: CESA (Corpus del Español en el Sur de Arizona) aims at documenting and disseminating Spanish varieties spoken in Arizona. Graduate and undergraduate students, under PI’s supervision, collect, transcribe and analyze interviews with local Spanish speakers. This digital oral corpus provides material for multiple linguistic analyses of local bilingual Spanish and subsequent comparisons with other varieties.<br><br>Access is restricted to approved users. Request an account<br>The Corpus Bilingüe del Valle (CoBiVa) which documents the Spanish and English spoken in the Rio Grande Valley of South Texas is a sister corpus to CESA.<br>Cite as: Carvalho, Ana M. (2012-). <i>Corpus del Español en el Sur de Arizona (CESA)</i>. University of Arizona. cesa.arizona.edu. https://doi.org/10.25422/azu.data.15070800<br><br><i>For all inquiries regarding this dataset, please contact CESA. The data is subject to the CESA's terms of use.</i>
DOI: 10.25422/azu.data.15070800.v1
title: CESA (Corpus del Español en el Sur de Arizona)

description: CESA (Corpus del Español en el Sur de Arizona) aims at documenting and disseminating Spanish varieties spoken in Arizona. Graduate and undergraduate students, under PI’s supervision, collect, transcribe and analyze interviews with local Spanish speakers. This digital oral corpus provides material for multiple linguistic analyses of local bilingual Spanish and subsequent comparisons with other varieties.<br><br>Access is restricted to approved users. Request an account<br>The Corpus Bilingüe del Valle (CoBiVa) which documents the Spanish and English spoken in the Rio Grande Valley of South Texas is a sister corpus to CESA.<br>Cite as: Carvalho, Ana M. (2012-). <i>Corpus del Español en el Sur de Arizona (CESA)</i>. University of Arizona. cesa.arizona.edu. https://doi.org/10.25422/azu.data.15070800<br><br><i>For all inquiries regarding this dataset, please contact CESA. The data is subject to the CESA's terms of use.</i>
DOI: 10.5063/4q7sdg
title: Meteorological Data: Balbina R. 15 - 20 July 2013 (5 minute averaged)

description: As part of a physical limnology experiment in July 2013, an Onset meteorological station was deployed on a floating platform at an offshore site in Balbina Reservoir, Brazil (01°54'38.5'' S; 59°28'08.5'' W) using Onset D-WSET-B to measure wind speed, maximal wind speed, wind direction and standard deviation of wind direction (set to 0), using Onset S-THB-MOO2 to measure air temperature and relative humidity, and an Onset pyranometer to measure downwelling shortwave radiation. The anemometer height was 2.8 m. Downwelling and upwelling longwave radiation were measured at an inshore site (1o 54’ 33.3” S; 59o 27’42.21S W) using a net radiometer (Kipp and Zonen CGR3). These data were corrected for temperature in post-processing based on the mean of the temperature measured by the upwelling and downwelling sensors. Sampling of all sensors was once per second, and 5-minute averages were stored. Attenuation of photosynthetically available radiation was computed following Beer’s Law from irradiance measurements with a Li Cor 2 π quantum sensor. The value was 0.5 m-1.
DOI: 10.15468/bvxzyg
title: Onosma satensis sp. nov. (Boraginaceae: Lithospermeae), a new species from Hakkari (eastern Anatolia, Turkey)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Firat, Mehmet, Binzet, Rıza (2021): Onosma satensis sp. nov. (Boraginaceae: Lithospermeae), a new species from Hakkari (eastern Anatolia, Turkey). Adansonia (3) 43 (16): 185-195, DOI: 10.5252/adansonia2021v43a16
DOI: 10.7267/wm117w947
title: Phytoplankton in Upper Willamette Reservoirs Dataset

description: Phytoplankton samples were collected from 5 upper Willamette Basin reservoirs in variable months during years 2013-2016 and 2019. Samples were collected from Blue River Reservoir, Cougar Reservoir, Fall Creek Reservoir, Lookout Point Reservoir, and Hills Creek Reservoir. The phytoplankton were identified and enumerated by Jim Sweet at Aquatic Analysts. Density and biovolume were calculated for each phytoplankton species identified per sample collected. This dataset also includes environmental parameters corresponding to sample collection date or the week preceding sample collection.
Research context: This research was conducted to determine spatial and temporal phytoplankton diversity and bloom timing in the upper Willamette Reservoirs. Cyanobacteria are of particular concern as some species can produce toxins that are harmful to wildlife and humans. Thus, understanding the phytoplankton species present in these reservoirs and their general bloom times is of critical importance.
DOI: 10.5281/zenodo.4362495
title: Flow through a submerged canopy partially covering the bed: spatial flow pattern, multi-dimensional vortices and junction momentum exchange

description: The dataset (including videos) are uploaded to support the research study <strong>Flow through a submerged canopy partially covering the bed: spatial flow pattern, multi-dimensional vortices and junction momentum exchange (submitted to Water Resources Research) </strong>
DOI: 10.5281/zenodo.4362494
title: Flow through a submerged canopy partially covering the bed: spatial flow pattern, multi-dimensional vortices and junction momentum exchange

description: The dataset (including videos) are uploaded to support the research study <strong>Flow through a submerged canopy partially covering the bed: spatial flow pattern, multi-dimensional vortices and junction momentum exchange (submitted to Water Resources Research) </strong>
DOI: 10.5281/zenodo.5532709
title: A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields -- Datasets and results

description: Data archives for test experiments (Section 3) and Pine Island Glacier application (Section 4) from the manuscript "Kevin Bulthuis and Eric Larour, A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-Level System Model v4.19 using Gaussian Markov random fields" Source code is available at https://doi.org/10.5281/zenodo.5532775.
description: {"references": ["Kevin Bulthuis and Eric Larour (2021), A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields"]}
DOI: 10.5281/zenodo.5532788
title: Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"

description: The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration. Unit: Euro/Megawatthour <strong>Abbreviations:</strong> BEV - Battery Electric Vehicles GER - Germany FRA - France OCGT - Open Cycle Gas Turbine PHEV - Plug-In Hybrid Vehicles RES - Renewable energy sources (here: wind and solar power) th. - thermal