cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7647358
title: Dataset for "Parametrization of energetic ion and electron fluxes in the near-Earth magnetotail" draft

description: This archive contains four dataset of electron and ion fluxes measured by MMS, THEMIS, and GOES mission in the near-Earth plasma sheet. We used three THEMIS inner probes measurements from 2008-2020, GOES-13 measurements from 2011-2017, GOES-14 measurements from 2018-2019, GOES-15 measurements from 2011-2012, and MMS-1 measurements from 2017-2019. We implemented the following selection criteria to identify near-Earth plasma sheet observations: (1) X &lt; -6RE, |Y| &lt; |X|/2, Bxy &lt; 20nT for THEMIS; (2) X&lt;0, |Y| &lt; |X|, Bxy &lt; 2Bz for GOES; (3) X &lt; -6RE, |Y| &lt; |X|/2, Bxy &lt; 2Bz, Rxy &lt; 12RE for MMS. Fluxes are given in units of energy flux, keV/cm^2/s/sr/keV. *THEMIS_Electron_DATA.dat: Bz, AE, fluxes of 75keV, 20keV, 150keV, and 300keV electrons *THEMIS_Ion_DATA.dat: fluxes of 75keV, 20keV, 150keV, and 300keV ions *MMS_Electron_DATA.dat: Bz, AE, fluxes of 75keV electrons *GOES_Electron_DATA.dat: Bz, AE, fluxes of 75keV electrons
DOI: 10.5517/ccdc.csd.cc27zgtj
title: CCDC 2084868: Experimental Crystal Structure Determination

description: Related Article: Francois Jacobs, Alice Brink|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28l2fd
title: CCDC 2102743: Experimental Crystal Structure Determination

description: Related Article: David McMorran, Aidan MacKay, Nicholas Smith|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28l2gf
title: CCDC 2102744: Experimental Crystal Structure Determination

description: Related Article: David McMorran, Aidan MacKay, Nicholas Smith|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28l2hg
title: CCDC 2102745: Experimental Crystal Structure Determination

description: Related Article: David McMorran, Aidan MacKay, Nicholas Smith|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cq2y4
title: CCDC 2195976: Experimental Crystal Structure Determination

description: Related Article: Tajrian Chowdhury, Matthew J. Evans, Martyn P. Coles, Anna G. Bailey, William J. Peveler, Claire Wilson, Joy H. Farnaby|2023|Chem.Commun.|59|2134|doi:10.1039/D2CC03189B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxl52
title: CCDC 2202214: Experimental Crystal Structure Determination

description: Related Article: Ewelina Jach, Dorota A. Kowalska, Marek A. Gusowski, Monika Trzebiatowska, Michał Krupiński, Wojciech Medycki, Jarosław Jędryka, Piotr Staniorowski, Agnieszka Ciżman|2023|J.Phys.Chem.C|127|2589|doi:10.1021/acs.jpcc.2c07218
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxl63
title: CCDC 2202215: Experimental Crystal Structure Determination

description: Related Article: Ewelina Jach, Dorota A. Kowalska, Marek A. Gusowski, Monika Trzebiatowska, Michał Krupiński, Wojciech Medycki, Jarosław Jędryka, Piotr Staniorowski, Agnieszka Ciżman|2023|J.Phys.Chem.C|127|2589|doi:10.1021/acs.jpcc.2c07218
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxl74
title: CCDC 2202216: Experimental Crystal Structure Determination

description: Related Article: Ewelina Jach, Dorota A. Kowalska, Marek A. Gusowski, Monika Trzebiatowska, Michał Krupiński, Wojciech Medycki, Jarosław Jędryka, Piotr Staniorowski, Agnieszka Ciżman|2023|J.Phys.Chem.C|127|2589|doi:10.1021/acs.jpcc.2c07218
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxl85
title: CCDC 2202217: Experimental Crystal Structure Determination

description: Related Article: Ewelina Jach, Dorota A. Kowalska, Marek A. Gusowski, Monika Trzebiatowska, Michał Krupiński, Wojciech Medycki, Jarosław Jędryka, Piotr Staniorowski, Agnieszka Ciżman|2023|J.Phys.Chem.C|127|2589|doi:10.1021/acs.jpcc.2c07218
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxlb7
title: CCDC 2202219: Experimental Crystal Structure Determination

description: Related Article: Ewelina Jach, Dorota A. Kowalska, Marek A. Gusowski, Monika Trzebiatowska, Michał Krupiński, Wojciech Medycki, Jarosław Jędryka, Piotr Staniorowski, Agnieszka Ciżman|2023|J.Phys.Chem.C|127|2589|doi:10.1021/acs.jpcc.2c07218
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxlc8
title: CCDC 2202220: Experimental Crystal Structure Determination

description: Related Article: Ewelina Jach, Dorota A. Kowalska, Marek A. Gusowski, Monika Trzebiatowska, Michał Krupiński, Wojciech Medycki, Jarosław Jędryka, Piotr Staniorowski, Agnieszka Ciżman|2023|J.Phys.Chem.C|127|2589|doi:10.1021/acs.jpcc.2c07218
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d2sy7
title: CCDC 2207229: Experimental Crystal Structure Determination

description: Related Article: Chengshan Ji, Jian Zhang, Ruiqing Fan, Yinan Chen, Yifan Zhang, Tiancheng Sun, Yulin Yang|2023|J.Mater.Chem.C|11|2514|doi:10.1039/D2TC04972D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d2t0b
title: CCDC 2207231: Experimental Crystal Structure Determination

description: Related Article: Chengshan Ji, Jian Zhang, Ruiqing Fan, Yinan Chen, Yifan Zhang, Tiancheng Sun, Yulin Yang|2023|J.Mater.Chem.C|11|2514|doi:10.1039/D2TC04972D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.34740/kaggle/dsv/4349412
title: Estudo de Caso ATDBank
DOI: 10.6084/m9.figshare.22114298
title: Recall Transcripts for Event Schema Experiment

description: Transcripts of subject recalls for the experiment reported in these two publications: https://doi.org/10.1523/JNEUROSCI.0251-18.2018 https://doi.org/10.7554/eLife.70445 <br> Timestamps indicate MM:SS since the start of each of the two recall runs. Lines of the form "PROMPT: X" indicate when the name of the next story to recall was presented to the subject.
DOI: 10.5281/zenodo.7647320
title: Laser cooling a membrane-in-the-middle system close to the quantum ground state from room temperature

description: This dataset contains processed data corresponding to the figures in the main text of our paper "Laser cooling a membrane-in-the-middle system close to the quantum ground state from room temperature" The dataset consists of 10 .csv files and a jupyter notebook for generating the figures in the main text.
DOI: 10.5066/p9koxce5
title: Drilling, construction, geophysical data, and lithologic log for borehole USGS 151

description: In 2020, the U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy (DOE) for Naval Reactors Laboratory Field Office or Naval Reactors Facility (NRF), drilled and constructed borehole USGS 151 (433846112540701) for stratigraphic framework analyses at the Idaho National Laboratory (INL) near the NRF, located in southeastern Idaho. Borehole USGS 151 was continuously cored from approximately 48 to 1,070 feet below land surface (BLS) and rotary drilled from approximately 1,070 to 1,720 feet BLS. Core drilling was discontinued after 1,070 feet BLS and changed over to straight rotary drilling due to funding constraints and deadlines. The USGS collected select geophysical data, daily drilling notes, and prepared detailed core descriptions for core collected to 1,070 ft BLS, which are included as part of this data release. The USGS Research Drilling Program (RDP) performed the core drilling operations and well construction between June 4, 2020 to September 21, 2020; additionally, the USGS RDP collected geophysical data, single caliper log, that was collected on August 15, 2020. The USGS INL Project Office collected multiple geophysical logs on September 15, 2020. Select geophysical data include natural gamma, caliper, neutron, and gamma-gamma density logs which were examined synergistically with available core material to identify contacts between basalt flows and location and thickness of sediment layers. Additionally, a gyroscopic deviation survey was performed and analyzed to reflect the projected well bore path. Geophysical data were collected using Century? multi-parameter logging probes and select logs are displayed in well log figures. Geophysical data shown in figure displays include the natural gamma and neutron probe (9055C), caliper probe (9065A), gamma-gamma density probe (0024C), and gyroscopic deviation probe (9095C). With the exception of the caliper log, all geophysical data were collected through drill pipe after reaching total depth. Geophysical log data not represented in figure displays can be obtained by downloading attached LAS files. Drill core was taken to the USGS Lithologic Core Storage Library for storage and permanent archive located at Central Facilities Area on the INL. Drill core was photographed and described using standardized methods (Johnson and others, 2005). These standardized methods make use of commercially available software that include using a procedure developed by the USGS INL Project Office. The standardized method maximizes description and minimizes interpretation.
DOI: 10.5683/sp2/vagk02
title: Okotoks Erratic -- Okotoks -- Laser Scanning -- Metadata -- 2016

description: The metadata for the subsampled point cloud created of Okotoks Erratic from twenty three (23) 3D laser scans.
DOI: 10.5281/zenodo.7647534
title: Korn—Software Verification with Horn clauses (Post-Competition Evaluation)

description: Korn is a software verifier that infers correctness certificates automatically using state-of-the-art Horn-clause solvers, such as Z3 and Eldarica, which are used in a portfolio together with cheap random sampling. The tool is available at https://github.com/gernst/korn. This data documents the post SV-COMP 2023 experiments of Korn.
DOI: 10.5281/zenodo.7647533
title: Korn—Software Verification with Horn clauses (Post-Competition Evaluation)

description: Korn is a software verifier that infers correctness certificates automatically using state-of-the-art Horn-clause solvers, such as Z3 and Eldarica, which are used in a portfolio together with cheap random sampling. The tool is available at https://github.com/gernst/korn. This data documents the post SV-COMP 2023 experiments of Korn.
DOI: 10.5066/p9rsxq2u
title: Sentinel-1 and Sentinel-2 based frequency of open and vegetated water across the United States (2017-2021)

description: High-frequency observations of surface water at fine spatial scales are critical to effectively manage aquatic habitat, flood risk and water quality. We developed inundation algorithms for Sentinel-1 and Sentinel-2 across 12 sites within the conterminous United States (CONUS) covering greater than 536,000 km2 and representing diverse hydrologic and vegetation landscapes. These algorithms were trained on data from 13,412 points spread throughout the 12 sites. Each scene in the 5-year (2017-2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables not only from Sentinel-1 and Sentinel-2, but also variables derived from topographic and weather datasets. The Sentinel-1 model was developed distinct from the Sentinel-2 model to enable the two time series to be integrated into a single high-frequency time series, while open water and vegetated water were both mapped to retain mixed pixel inundation. Results were validated against 7,200 visually inspected points derived from WorldView and PlanetScope imagery. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for Sentinel-1 and 3.1% and 0.5% for Sentinel-2, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. Sentinel-2 showed higher accuracy (10.7% omission and 7.9% commission error) relative to Sentinel-1 (28.4% omission and 16.0% commission error). Our results demonstrated that Sentinel-1 and Sentinel-2 time series can be integrated to improve the temporal resolution when mapping open and vegetated waters, although sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for subpixel, vegetated water compared with open water.
DOI: 10.21429/skh5-y723
title: 1993 and 2019 Case Studies of Mississippi River Basin Extreme Precipitation Events and Floodings

description: In 1993 and 2019, the Mississippi River Basin (MRB) experienced unprecedentedly persistent extreme precipitation events that resulted in a notable number of catastrophic flooding episodes. These floods caused disruption to almost every area of human endeavor and brought widespread damage to agricultural lands, homes, businesses, and infrastructure. These large flood events were associated with extreme rainfall events over an extended period and encompassed a number of U.S. states. 

We conducted  two case studies: the MRB flooding events of 1993 and 2019. For each case study, we conducted a literature review and examined the sequence of extreme precipitation events and the evolution of flooding. We identified synoptic-scale atmospheric patterns by examining meteorological variables such as precipitation, geopotenial heights and precipitable water.  The global teleconnection patterns that were conducive and precursors to the heavy precipitation were investigated as well through climate indices such as Arctic oscillation, North Atlantic Oscillation, Pacific-North American pattern, and El Niño Oscillation (through Southern Oscillation Index and Sea Surface Temperature Anomalies). We also verified the weather maps based on actual observations during the occurrence of extreme precipitation events.

Lastly, we identified the impacts of these extreme precipitation events on biotic communities. Our project has provided a foundation to identify risks and impacts of extreme precipitation on the specific aquatic species, wildlife, and agroecosystems supported by the rivers and streams of the MRB. We consider our research as an early step to understand the impacts of climate change on the greater MRB. The identification in our case studies of synoptic-scale atmospheric patterns and global teleconnection patterns that are conducive to heavy precipitation events within the MRB will help identify what patterns to examine in the future for changes in amount, frequency, extent, and timing as a result of climate change.
DOI: 10.21429/491y-sk69
title: Evaluation Responses for Research Symposium: Culturally Significant Plants and Climate Change

description: THE CHICKASAW NATION HOSTED THIS RESEARCH SYMPOSIUM ON MARCH 22-24, 2022, HELD AT THE CHICKASAW RETREAT AND CONFERENCE CENTER IN SULPHUR, OK.

After the Climate Workshop for The Chickasaw Nation in 2019, the Tribal managers discussed the need to include culture in conservation efforts and decided to seek a grant for culturally significant plants. This event was organized and led by the Chickasaw Nation and was funded by the U.S. Geological Survey through the South Central Climate Adaptation Science Center.

There were 106 attendees with 75 Native representatives from 21 different tribes. There were 11 student attendees. Attendees came from all across the United States including Maine, New York, Michigan, Montana, California, Alabama, and North Carolina. One of the speakers even presented via livestream from Hawaii.

The event brought tribes and researchers together on equal levels to learn about what they are doing related to plants and climate change and discuss collaboration opportunities. This dataset contains evaluation responses from participants who attended the research symposium. The purpose of the evaluation responses is to determine what was successful about the symposium and what could be improved upon.
DOI: 10.25345/c5td9nj4g
title: MassIVE MSV000091300 - Identification and validation of plasma protein biomarkers that accurately identify an ectopic pregnancy
title: An in-depth proteomics discovery analysis to identify significant changes in plasma from women with different early pregnancy outcomes. Validation was performed for candidate biomarkers in an independent cohort using targeted parallel reaction monitoring mass spectrometry (PRM-MS)
DOI: 10.6084/m9.figshare.21817923
title: Revisiting Convolutional Neural Networks from the Viewpoint of Kernel-Based Methods

description: Convolutional neural networks, as most artificial neural networks, are frequently viewed as methods different in essence from kernel-based methods. In this work we translate several classical convolutional neural networks into kernel-based counterparts. Each kernel-based counterpart is a statistical model called a convolutional kernel network with parameters that can be learned from data. We provide an alternating minimization algorithm with mini-batch sampling and implicit partial differentiation to learn from data the parameters of each convolutional kernel network. We also show how to obtain inexact derivatives with respect to the parameters using an algorithm based on two inter-twined Newton iterations. The models and the algorithms are illustrated on benchmark datasets in image classification. We find that the convolutional neural networks and their kernel counterparts often perform similarly. Supplemental appendices and code for the article are available online.
DOI: 10.6084/m9.figshare.21817923.v2
title: Revisiting Convolutional Neural Networks from the Viewpoint of Kernel-Based Methods

description: Convolutional neural networks, as most artificial neural networks, are frequently viewed as methods different in essence from kernel-based methods. In this work we translate several classical convolutional neural networks into kernel-based counterparts. Each kernel-based counterpart is a statistical model called a convolutional kernel network with parameters that can be learned from data. We provide an alternating minimization algorithm with mini-batch sampling and implicit partial differentiation to learn from data the parameters of each convolutional kernel network. We also show how to obtain inexact derivatives with respect to the parameters using an algorithm based on two inter-twined Newton iterations. The models and the algorithms are illustrated on benchmark datasets in image classification. We find that the convolutional neural networks and their kernel counterparts often perform similarly. Supplemental appendices and code for the article are available online.
DOI: 10.6084/m9.figshare.21817923.v1
title: Revisiting Convolutional Neural Networks from the Viewpoint of Kernel-Based Methods

description: Convolutional neural networks, as most artificial neural networks, are frequently viewed as methods different in essence from kernel-based methods. In this work we translate several classical convolutional neural networks into kernel-based counterparts. Each kernel-based counterpart is a statistical model called a convolutional kernel network with parameters that can be learned from data. We provide an alternating minimization algorithm with mini-batch sampling and implicit partial differentiation to learn from data the parameters of each convolutional kernel network. We also show how to obtain inexact derivatives with respect to the parameters using an algorithm based on two inter-twined Newton iterations. The models and the algorithms are illustrated on benchmark datasets in image classification. We find that the convolutional neural networks and their kernel counterparts often perform similarly. Supplemental appendices and code for the article are available online.